1
|
Sun Y, Zheng H, Wang M, Gu R, Wu X, Yang Q, Zhao H, Bi Y, Zheng J. The effect of histo-blood group ABO system transferase (BGAT) on pregnancy related outcomes:A Mendelian randomization study. Comput Struct Biotechnol J 2024; 23:2067-2075. [PMID: 38800635 PMCID: PMC11126538 DOI: 10.1016/j.csbj.2024.04.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 04/14/2024] [Accepted: 04/15/2024] [Indexed: 05/29/2024] Open
Abstract
Protein level of Histo-Blood Group ABO System Transferase (BGAT) has been reported to be associated with cardiometabolic diseases. But its effect on pregnancy related outcomes still remains unclear. Here we conducted a two-sample Mendelian randomization (MR) study to ascertain the putative causal roles of protein levels of BGAT in pregnancy related outcomes. Cis-acting protein quantitative trait loci (pQTLs) robustly associated with protein level of BGAT (P < 5 ×10-8) were used as instruments to proxy the BGAT protein level (N = 35,559, data from deCODE), with two additional pQTL datasets from Fenland (N = 10,708) and INTERVAL (N = 3301) used as validation exposures. Ten pregnancy related diseases and complications were selected as outcomes. We observed that a higher protein level of BGAT showed a putative causal effect on venous complications and haemorrhoids in pregnancy (VH) (odds ratio [OR]=1.19, 95% confidence interval [95% CI]=1.12-1.27, colocalization probability=91%), which was validated by using pQTLs from Fenland and INTERVAL. The Mendelian randomization results further showed effects of the BGAT protein on gestational hypertension (GH) (OR=0.97, 95% CI=0.96-0.99), despite little colocalization evidence to support it. Sensitivity analyses, including proteome-wide Mendelian randomization of the cis-acting BGAT pQTLs, showed little evidence of horizontal pleiotropy. Correctively, our study prioritised BGAT as a putative causal protein for venous complications and haemorrhoids in pregnancy. Future epidemiology and clinical studies are needed to investigate whether BGAT can be considered as a drug target to prevent adverse pregnancy outcomes.
Collapse
Affiliation(s)
- Yuqi Sun
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology,Shanghai Jiao Tong University School of Medicine, China
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haonan Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Basic Medical Science,Shanghai Jiao Tong University School of Medicine, China
| | - Manqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology,Shanghai Jiao Tong University School of Medicine, China
| | - Rongrong Gu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology,Shanghai Jiao Tong University School of Medicine, China
| | - Xueyan Wu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qian Yang
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, United Kingdom
| | - Huiling Zhao
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, United Kingdom
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, United Kingdom
| |
Collapse
|
2
|
Saluja S, Darlay R, Lennon R, Keavney BD, Cordell HJ. Whole -genome survival analysis of 144 286 people from the UK Biobank identifies novel loci associated with blood pressure. J Hypertens 2024; 42:1647-1652. [PMID: 39011893 PMCID: PMC11296269 DOI: 10.1097/hjh.0000000000003801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 06/03/2024] [Accepted: 06/11/2024] [Indexed: 07/17/2024]
Abstract
This study utilized UK Biobank data from 144 286 participants and employed whole-genome sequencing (WGS) data and time-to-event data over a 12-year follow-up period to identify susceptibility in genetic variants associated with hypertension. Following genotype quality control, 6 319 822 single nucleotide polymorphisms underwent analysis, revealing 31 significant variant-level associations. Among these, 29 were novel - 15 in Fibrillin-2 ( FBN2 ) and 4 in Junctophilin-2 ( JPH2 ). Mendelian randomization utilizing two identified variants (rs17677724 and rs1014754) suggested that a genetically induced decrease in heart FBN2 expression and an increase in adrenal gland JPH2 expression were causally linked to hypertension. Phenome-wide association (PheWAS) analysis using the FinnGen dataset confirmed positive associations of rs17677724 and rs1014754 with hypertension, assessed across 2727 traits in 377 277 individuals. Lastly, rs1014754 positively associated with kallistatin, whereas rs17677724 negatively associated with renin in the Fenland study, suggesting a counterregulatory response to high blood pressure. This study, employing WGS data, identified novel genetic loci and potential therapeutic targets for hypertension.
Collapse
Affiliation(s)
- Sushant Saluja
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester
- Division of Medicine and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust Manchester, Manchester
| | - Rebecca Darlay
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne
| | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, division of Cell-Matrix biology and regenerative Medicine, School of biological Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Bernard D. Keavney
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester
- Division of Medicine and Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust Manchester, Manchester
| | - Heather J. Cordell
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne
| |
Collapse
|
3
|
Vacik Díaz R, Munsch G, Iglesias MJ, Pallares Robles A, Ibrahim-Kosta M, Nourse J, Khan E, Castoldi E, Saut N, Boland A, Germain M, Deleuze JF, Odeberg J, Morange PE, Danckwardt S, Tregouët DA, Goumidi L. Plasma levels of complement components C5 and C9 are associated with thrombin generation. J Thromb Haemost 2024; 22:2531-2542. [PMID: 38838952 DOI: 10.1016/j.jtha.2024.04.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/30/2024] [Accepted: 04/24/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND The thrombin generation assay (TGA) evaluates the potential of plasma to generate thrombin over time, providing a global picture of an individual's hemostatic balance. OBJECTIVES This study aimed to identify novel biological determinants of thrombin generation using a multiomics approach. METHODS Associations between TGA parameters and plasma levels of 377 antibodies targeting 236 candidate proteins for cardiovascular risk were tested using multiple linear regression analysis in 770 individuals with venous thrombosis from the Marseille Thrombosis Association (MARTHA) study. Proteins associated with at least 3 TGA parameters were selected for validation in an independent population of 536 healthy individuals (Etablissement Français du Sang Alpes-Méditerranée [EFS-AM]). Proteins with strongest associations in both groups underwent additional genetic analyses and in vitro experiments. RESULTS Eighteen proteins were associated (P < 1.33 × 10⁻4) with at least 3 TGA parameters in MARTHA, among which 13 demonstrated a similar pattern of associations in EFS-AM. Complement proteins C5 and C9 had the strongest associations in both groups. Ex vivo supplementation of platelet-poor plasma with purified C9 protein had a significant dose-dependent effect on TGA parameters. No effect was observed with purified C5. Several single nucleotide polymorphisms associated with C5 and C9 plasma levels were identified, with the strongest association for the C5 missense variant rs17611, which was associated with a decrease in C5 levels, endogenous thrombin potential, and peak in MARTHA. No association of this variant with TGA parameters was observed in EFS-AM. CONCLUSION This study identified complement proteins C5 and C9 as potential determinants of thrombin generation. Further studies are warranted to establish causality and elucidate the underlying mechanisms.
Collapse
Affiliation(s)
- Rocío Vacik Díaz
- Cardiovascular and Nutrition Research Center Centre de recherche en CardioVasculaire et Nutrition (C2VN), Aix-Marseille University, Institut national de la santé et de la recherche médicale 1263, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement 1260, Marseille, France; Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg, Mainz, Germany. https://twitter.com/RocioVacik
| | - Gaëlle Munsch
- Institut national de la santé et de la recherche médicale Unité Mixte de Recherche_S 1219, Bordeaux Population Health Center, University of Bordeaux, Bordeaux, France
| | - Maria Jesus Iglesias
- Science for Life Laboratory, Kungliga Tekniska högskolan-Royal Institute of Technology, Stockholm, Sweden
| | - Alejandro Pallares Robles
- Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg, Mainz, Germany
| | - Manal Ibrahim-Kosta
- Cardiovascular and Nutrition Research Center Centre de recherche en CardioVasculaire et Nutrition (C2VN), Aix-Marseille University, Institut national de la santé et de la recherche médicale 1263, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement 1260, Marseille, France; Department of Hematology, Centre Hospitalier Universitaire Timone, Marseille, France
| | - Jamie Nourse
- Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg, Mainz, Germany
| | - Essak Khan
- Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg, Mainz, Germany
| | - Elisabetta Castoldi
- Department of Biochemistry, Cell Biochemistry of Thrombosis and Haemostasis, Maastricht University, Maastricht, the Netherlands
| | - Noémie Saut
- Department of Hematology, Centre Hospitalier Universitaire Timone, Marseille, France
| | - Anne Boland
- Commissariat à l'énergie atomique et aux énergies alternatives, Centre National de Recherche en Génomique Humaine, Université Paris-Saclay, Evry, France
| | - Marine Germain
- Institut national de la santé et de la recherche médicale Unité Mixte de Recherche_S 1219, Bordeaux Population Health Center, University of Bordeaux, Bordeaux, France
| | - Jean-François Deleuze
- Commissariat à l'énergie atomique et aux énergies alternatives, Centre National de Recherche en Génomique Humaine, Université Paris-Saclay, Evry, France
| | - Jacob Odeberg
- Science for Life Laboratory, Kungliga Tekniska högskolan-Royal Institute of Technology, Stockholm, Sweden
| | - Pierre-Emmanuel Morange
- Cardiovascular and Nutrition Research Center Centre de recherche en CardioVasculaire et Nutrition (C2VN), Aix-Marseille University, Institut national de la santé et de la recherche médicale 1263, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement 1260, Marseille, France; Department of Hematology, Centre Hospitalier Universitaire Timone, Marseille, France
| | - Sven Danckwardt
- Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg, Mainz, Germany
| | - David-Alexandre Tregouët
- Institut national de la santé et de la recherche médicale Unité Mixte de Recherche_S 1219, Bordeaux Population Health Center, University of Bordeaux, Bordeaux, France
| | - Louisa Goumidi
- Cardiovascular and Nutrition Research Center Centre de recherche en CardioVasculaire et Nutrition (C2VN), Aix-Marseille University, Institut national de la santé et de la recherche médicale 1263, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement 1260, Marseille, France.
| |
Collapse
|
4
|
Erdman V, Tuktarova I, Nasibullin T, Timasheva Y, Petintseva A, Korytina G. Polygenic markers of survival and longevity in the antioxidant genes PON1, PON2, MTHFR, MSRA, SOD1, NQO1, and CAT in a 20-year follow-up study in the population from the Volga-Ural region. Gene 2024; 919:148510. [PMID: 38679184 DOI: 10.1016/j.gene.2024.148510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/16/2024] [Accepted: 04/24/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Genetic background of healthy or pathological styles of aging and human lifespan is determined by joint gene interactions. Lucky combinations of antioxidant gene polymorphisms can result in a highly adaptive phenotype, providing a successful way to interact with external triggers. Our purpose was to identify the polygenic markers of survival and longevity in the antioxidant genes among elderly people with physiological and pathological aging. METHODS In a 20-year follow-up study of 2350 individuals aged 18-114 years residing in the Volga-Ural region of Russia, sex-adjusted association analyses of MTHFR rs1801133, MSRA rs10098474, PON1 rs662, PON2 rs7493, SOD1 rs2070424, NQO1 rs1131341 and CAT rs1001179 polymorphic loci with longevity were carried out. Survival analysis was subsequently performed using the established single genes and gene-gene combinations as cofactors. RESULTS The PON1 rs662*G allele was defined as the main longevity marker in women (OR = 1.44, p = 3E-04 in the log-additive model; HR = 0.77, p = 1.9E-04 in the Cox-survival model). The polymorphisms in the MTHFR, MSRA, PON2, SOD1, and CAT genes had an additive effect on longevity. A strong protective effect of combined MTHFR rs1801133*C, MSRA rs10098474*T, PON1 rs662*G, and PON2 rs7493*C alleles against mortality was obtained in women (HR = 0.81, p = 5E-03). The PON1 rs662*A allele had a meaningful impact on mortality for both long-lived men with cerebrovascular accidents (HR = 1.76, p = 0.027 for the PON1 rs662*AG genotype) and women with cardiovascular diseases (HR = 1.43, p = 0.002 for PON1 rs662*AA genotype). The MTHFR rs1801133*TT (HR = 1.91, p = 0.036), CAT rs1001179*TT (HR = 2.83, p = 0.031) and SOD1 rs2070424*AG (HR = 1.58, p = 0.018) genotypes were associated with the cancer mortality. CONCLUSION In our longitudinal 20-year study, we found the combinations of functional polymorphisms in antioxidant genes involved in longevity and survival in certain clinical phenotypes in the advanced age.
Collapse
Affiliation(s)
- Vera Erdman
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa 450054, Russia.
| | - Ilsia Tuktarova
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa 450054, Russia
| | - Timur Nasibullin
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa 450054, Russia
| | - Yanina Timasheva
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa 450054, Russia; Bashkir State Medical University, Ufa 450008, Russia
| | - Anna Petintseva
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa 450054, Russia
| | - Gulnaz Korytina
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa 450054, Russia; Bashkir State Medical University, Ufa 450008, Russia
| |
Collapse
|
5
|
Oslund RC, Holland PM, Lesley SA, Fadeyi OO. Therapeutic potential of cis-targeting bispecific antibodies. Cell Chem Biol 2024; 31:1473-1489. [PMID: 39111317 DOI: 10.1016/j.chembiol.2024.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 05/13/2024] [Accepted: 07/12/2024] [Indexed: 08/18/2024]
Abstract
The growing clinical success of bispecific antibodies (bsAbs) has led to rapid interest in leveraging dual targeting in order to generate novel modes of therapeutic action beyond mono-targeting approaches. While bsAbs that bind targets on two different cells (trans-targeting) are showing promise in the clinic, the co-targeting of two proteins on the same cell surface through cis-targeting bsAbs (cis-bsAbs) is an emerging strategy to elicit new functionalities. This includes the ability to induce proximity, enhance binding to a target, increase target/cell selectivity, and/or co-modulate function on the cell surface with the goal of altering, reversing, or eradicating abnormal cellular activity that contributes to disease. In this review, we focus on the impact of cis-bsAbs in the clinic, their emerging applications, and untangle the intricacies of improving bsAb discovery and development.
Collapse
|
6
|
Yang N, Shi L, Xu P, Ren F, Li C, Qi X. Identification of potential drug targets for amyotrophic lateral sclerosis by Mendelian randomization analysis based on brain and plasma proteomics. Exp Gerontol 2024; 195:112538. [PMID: 39116956 DOI: 10.1016/j.exger.2024.112538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 07/18/2024] [Accepted: 08/05/2024] [Indexed: 08/10/2024]
Abstract
Amyotrophic lateral sclerosis as a fatal neurodegenerative disease currently lacks effective therapeutic agents. Thus, finding new therapeutic targets to drive disease treatment is necessary. In this study, we utilized brain and plasma proteins as genetic instruments obtained from genome-wide association studies to conduct a Mendelian randomization analysis to identify potential drug targets for amyotrophic lateral sclerosis. Additionally, we validated our results externally using other datasets. We also used Bayesian co-localization analysis and phenotype scanning. Furthermore, we constructed a protein-protein interaction network to elucidate potential correlations between the identified proteins and existing targets. Mendelian randomization analysis indicated that elevated levels of ANO5 (OR = 1.30; 95 % CI, 1.14-1.49; P = 1.52E-04), SCFD1 (OR = 3.82; 95 % CI, 2.39-6.10; P = 2.19E-08), and SIGLEC9 (OR = 1.05; 95% CI, 1.03-1.07; P = 4.71E-05) are associated with an increased risk of amyotrophic lateral sclerosis, with external validation supporting these findings. Co-localization analysis confirmed that ANO5, SCFD1, and SIGLEC9 (coloc.abf-PPH4 = 0.848, 0.984, and 0.945, respectively) shared the same variant with amyotrophic lateral sclerosis, further substantiating potential role of these proteins as a therapeutic target. There are interactive relationships between the potential proteins and existing targets of amyotrophic lateral sclerosis. Our findings suggested that elevated levels of ANO5, SCFD1, and SIGLEC9 are connected with an increased risk of amyotrophic lateral sclerosis and might be promising therapeutic targets. However, further exploration is necessary to fully understand the underlying mechanisms involved.
Collapse
Affiliation(s)
- Ni Yang
- Department of First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Liangyuan Shi
- Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, China.
| | - Pengfei Xu
- Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao, China
| | - Fang Ren
- Department of Laboratory, Jimo District Qingdao Hospital of Traditional Chinese Medicine, Qingdao, China
| | - Chunlin Li
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xianghua Qi
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| |
Collapse
|
7
|
Jiang MZ, Gaynor SM, Li X, Van Buren E, Stilp A, Buth E, Wang FF, Manansala R, Gogarten SM, Li Z, Polfus LM, Salimi S, Bis JC, Pankratz N, Yanek LR, Durda P, Tracy RP, Rich SS, Rotter JI, Mitchell BD, Lewis JP, Psaty BM, Pratte KA, Silverman EK, Kaplan RC, Avery C, North KE, Mathias RA, Faraday N, Lin H, Wang B, Carson AP, Norwood AF, Gibbs RA, Kooperberg C, Lundin J, Peters U, Dupuis J, Hou L, Fornage M, Benjamin EJ, Reiner AP, Bowler RP, Lin X, Auer PL, Raffield LM. Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium. Hum Mol Genet 2024; 33:1429-1441. [PMID: 38747556 PMCID: PMC11305684 DOI: 10.1093/hmg/ddae050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 01/31/2024] [Accepted: 03/11/2024] [Indexed: 05/28/2024] Open
Abstract
Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.
Collapse
Affiliation(s)
- Min-Zhi Jiang
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Sheila M Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, United States
- Regeneron Genetics Center, 777 Old Saw Mill River Road, Tarrytown, NY 10591, United States
| | - Xihao Li
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
- Department of Biostatistics, 135 Dauer Drive, 4115D McGavran-Greenberg Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Eric Van Buren
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, United States
| | - Adrienne Stilp
- Department of Biostatistics, 4333 Brooklyn Ave NE, University of Washington, Seattle, WA 98105, United States
| | - Erin Buth
- Department of Biostatistics, 4333 Brooklyn Ave NE, University of Washington, Seattle, WA 98105, United States
| | - Fei Fei Wang
- Department of Biostatistics, 4333 Brooklyn Ave NE, University of Washington, Seattle, WA 98105, United States
| | - Regina Manansala
- Centre for Health Economics Research & Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO) WHO Collaborating Centre, University of Antwerp, Campus Drie Eiken - Building S; Universiteitsplein 1 2610 Antwerpen, Belgium
| | - Stephanie M Gogarten
- Department of Biostatistics, 4333 Brooklyn Ave NE, University of Washington, Seattle, WA 98105, United States
| | - Zilin Li
- School of Mathematics and Statistics, Northeast Normal University, 5268 Renmin Street, Changchun, JL 130024, China
| | - Linda M Polfus
- Advanced Analytics, Ambry Genetics, 1 Enterprise, Aliso Viejo, CA 92656, United States
| | - Shabnam Salimi
- Department of Epidemiology and Public Health, Division of Gerontology, University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD 21201, United States
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave NE, Box 359458, Seattle, WA 98195, United States
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, 420 Delaware Street SE, Minneapolis, MN 55455, United States
| | - Lisa R Yanek
- Department of Medicine, General Internal Medicine, Johns Hopkins University School of Medicine, 1830 E Monument St Rm 8024, Baltimore, MD 21287, United States
| | - Peter Durda
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, 360 South Park Drive, Colchester, VT 05446, United States
| | - Russell P Tracy
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, 360 South Park Drive, Colchester, VT 05446, United States
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, 200 Jeanette Lancaster Way, Charlottesville, VA 22903, United States
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA 90502, United States
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, 670 W. Baltimore St., Baltimore, MD 21201, United States
| | - Joshua P Lewis
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, 670 W. Baltimore St., Baltimore, MD 21201, United States
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave NE, Box 359458, Seattle, WA 98195, United States
- Departments of Epidemiology and Health Systems and Population Health, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA 98101, United States
| | - Katherine A Pratte
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, 1400 Jackson Street, Denver, CO 80206, United States
| | - Edwin K Silverman
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, United States
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, United States
| | - Christy Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, 137 East Franklin Street, Chapel Hill, NC 27599, United States
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, 137 East Franklin Street, Chapel Hill, NC 27599, United States
| | - Rasika A Mathias
- Department of Medicine, Allergy and Clinical Immunology, Johns Hopkins University School of Medicine, 5501 Hopkins Bayview Cir JHAAC Room 3B53, Baltimore, MD 21287, United States
| | - Nauder Faraday
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287, United States
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Ave North, Worcester, MA 01655, United States
| | - Biqi Wang
- Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Ave North, Worcester, MA 01655, United States
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, 350 W. Woodrow Wilson Avenue, Suite 701, Jackson, MS 39213, United States
| | - Arnita F Norwood
- Department of Medicine, University of Mississippi Medical Center, 350 W. Woodrow Wilson Avenue, Suite 701, Jackson, MS 39213, United States
| | - Richard A Gibbs
- Department of Molecular and Human Genetics, Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, United States
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Avenue N, Seattle, WA 98109, United States
| | - Jessica Lundin
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Avenue N, Seattle, WA 98109, United States
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Avenue N, Seattle, WA 98109, United States
| | - Josée Dupuis
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 2001 McGill College Avenue, Montreal, QC H3A 1G1, Canada
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA 02118, United States
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N Lake Shore Drive, Chicago, IL 60611, United States
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, 1825 Pressler Street, Houston, TX 77030, United States
| | - Emelia J Benjamin
- Department of Medicine, Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, 72 East Newton Street, Boston, MA 02118, United States
- Department of Epidemiology, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA 02118, United States
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, 73 Mount Wayte Ave #2, Framingham, MA 01702, United States
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA 98105, United States
| | - Russell P Bowler
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, 1400 Jackson Street, Denver, CO 80206, United States
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, United States
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, United States
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
| | | |
Collapse
|
8
|
Nam Y, Kim J, Jung SH, Woerner J, Suh EH, Lee DG, Shivakumar M, Lee ME, Kim D. Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine. Annu Rev Biomed Data Sci 2024; 7:225-250. [PMID: 38768397 DOI: 10.1146/annurev-biodatasci-102523-103801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.
Collapse
Affiliation(s)
- Yonghyun Nam
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Jaesik Kim
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Jakob Woerner
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Erica H Suh
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Dong-Gi Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Matthew E Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Dokyoon Kim
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| |
Collapse
|
9
|
Rao P, Keyes MJ, Mi MY, Barber JL, Tahir UA, Deng S, Clish CB, Shen D, Farrell LA, Wilson JG, Gao Y, Yimer WK, Ekunwe L, Hall ME, Muntner PM, Guo X, Taylor KD, Tracy RP, Rich SS, Rotter JI, Xanthakis V, Vasan RS, Bouchard C, Sarzynski MA, Gerszten RE, Robbins JM. Plasma Proteomics of Exercise Blood Pressure and Incident Hypertension. JAMA Cardiol 2024; 9:713-722. [PMID: 38865108 PMCID: PMC11170454 DOI: 10.1001/jamacardio.2024.1397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 04/10/2024] [Indexed: 06/13/2024]
Abstract
Importance Blood pressure response during acute exercise (exercise blood pressure [EBP]) is associated with the future risk of hypertension and cardiovascular disease (CVD). Biochemical characterization of EBP could inform disease biology and identify novel biomarkers of future hypertension. Objective To identify protein markers associated with EBP and test their association with incident hypertension. Design, Setting, and Participants This study assayed 4977 plasma proteins in 681 healthy participants (from 763 assessed) of the Health, Risk Factors, Exercise Training and Genetics (HERITAGE; data collection from January 1993 to December 1997 and plasma proteomics from January 2019 to January 2020) Family Study at rest who underwent 2 cardiopulmonary exercise tests. Individuals were free of CVD at the time of recruitment. Individuals with resting SBP ≥160 mm Hg or DBP ≥100 mm Hg or taking antihypertensive drug therapy were excluded from the study. The association between resting plasma protein levels to both resting BP and EBP was evaluated. Proteins associated with EBP were analyzed for their association with incident hypertension in the Framingham Heart Study (FHS; n = 1177) and validated in the Jackson Heart Study (JHS; n = 772) and Multi-Ethnic Study of Atherosclerosis (MESA; n = 1367). Proteins associated with incident hypertension were tested for putative causal links in approximately 700 000 individuals using cis-protein quantitative loci mendelian randomization (cis-MR). Data were analyzed from January 2023 to January 2024. Exposures Plasma proteins. Main Outcomes and Measures EBP was defined as the BP response during a fixed workload (50 W) on a cycle ergometer. Hypertension was defined as BP ≥140/90 mm Hg or taking antihypertensive medication. Results Among the 681 participants in the HERITAGE Family Study, the mean (SD) age was 34 (13) years; 366 participants (54%) were female; 238 (35%) were self-reported Black and 443 (65%) were self-reported White. Proteomic profiling of EBP revealed 34 proteins that would not have otherwise been identified through profiling of resting BP alone. Transforming growth factor β receptor 3 (TGFBR3) and prostaglandin D2 synthase (PTGDS) had the strongest association with exercise systolic BP (SBP) and diastolic BP (DBP), respectively (TGFBR3: exercise SBP, β estimate, -3.39; 95% CI, -4.79 to -2.00; P = 2.33 × 10-6; PTGDS: exercise DBP β estimate, -2.50; 95% CI, -3.29 to -1.70; P = 1.18 × 10-9). In fully adjusted models, TGFBR3 was inversely associated with incident hypertension in FHS, JHS, and MESA (hazard ratio [HR]: FHS, 0.86; 95% CI, 0.75-0.97; P = .01; JHS, 0.87; 95% CI, 0.77-0.97; P = .02; MESA, 0.84; 95% CI, 0.71-0.98; P = .03; pooled cohort, 0.86; 95% CI, 0.79-0.92; P = 6 × 10-5). Using cis-MR, genetically predicted levels of TGFBR3 were associated with SBP, hypertension, and CVD events (SBP: β, -0.38; 95% CI, -0.64 to -0.11; P = .006; hypertension: odds ratio [OR], 0.99; 95% CI, 0.98-0.99; P < .001; heart failure with hypertension: OR, 0.86; 95% CI, 0.77-0.97; P = .01; CVD: OR, 0.84; 95% CI, 0.77-0.92; P = 8 × 10-5; cerebrovascular events: OR, 0.77; 95% CI, 0.70-0.85; P = 5 × 10-7). Conclusions and Relevance Plasma proteomic profiling of EBP identified a novel protein, TGFBR3, which may protect against elevated BP and long-term CVD outcomes.
Collapse
Affiliation(s)
- Prashant Rao
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Michelle. J. Keyes
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Michael Y. Mi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Jacob L. Barber
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia
| | - Usman A. Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Shuliang Deng
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Clary B. Clish
- Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge
| | - Dongxiao Shen
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Laurie. A. Farrell
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - James G. Wilson
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Yan Gao
- Department of Data Sciences, University of Mississippi Medical Center, Jackson
| | - Wondwosen K. Yimer
- Department of Data Sciences, University of Mississippi Medical Center, Jackson
| | - Lynette Ekunwe
- Jackson Heart Study Field Center, University of Mississippi Medical Center, Jackson
| | - Michael E. Hall
- Department of Medicine, Division of Cardiology, University of Mississippi Medical Center, Jackson
| | - Paul M. Muntner
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor–University of California, Los Angeles Medical Center, Torrance
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor–University of California, Los Angeles Medical Center, Torrance
| | - Russell P. Tracy
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor–University of California, Los Angeles Medical Center, Torrance
| | - Vanessa Xanthakis
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Ramachandran S. Vasan
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana
| | - Mark A. Sarzynski
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge
| | - Jeremy M. Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| |
Collapse
|
10
|
Chen H, Chen Q, Chen J, Mao Y, Duan L, Ye D, Cheng W, Chen J, Gao X, Lin R, Lin W, Zhang M, Qi Y. Deciphering the Effects of the PYCR Family on Cell Function, Prognostic Value, Immune Infiltration in ccRCC and Pan-Cancer. Int J Mol Sci 2024; 25:8096. [PMID: 39125668 PMCID: PMC11311831 DOI: 10.3390/ijms25158096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 07/17/2024] [Accepted: 07/19/2024] [Indexed: 08/12/2024] Open
Abstract
Pyrroline-5-carboxylate reductase (PYCR) is pivotal in converting pyrroline-5-carboxylate (P5C) to proline, the final step in proline synthesis. Three isoforms, PYCR1, PYCR2, and PYCR3, existed and played significant regulatory roles in tumor initiation and progression. In this study, we first assessed the molecular and immune characteristics of PYCRs by a pan-cancer analysis, especially focusing on their prognostic relevance. Then, a kidney renal clear cell carcinoma (KIRC)-specific prognostic model was established, incorporating pathomics features to enhance predictive capabilities. The biological functions and regulatory mechanisms of PYCR1 and PYCR2 were investigated by in vitro experiments in renal cancer cells. The PYCRs' expressions were elevated in diverse tumors, correlating with unfavorable clinical outcomes. PYCRs were enriched in cancer signaling pathways, significantly correlating with immune cell infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI). In KIRC, a prognostic model based on PYCR1 and PYCR2 was independently validated statistically. Leveraging features from H&E-stained images, a pathomics feature model reliably predicted patient prognosis. In vitro experiments demonstrated that PYCR1 and PYCR2 enhanced the proliferation and migration of renal carcinoma cells by activating the mTOR pathway, at least in part. This study underscores PYCRs' pivotal role in various tumors, positioning them as potential prognostic biomarkers and therapeutic targets, particularly in malignancies like KIRC. The findings emphasize the need for a broader exploration of PYCRs' implications in pan-cancer contexts.
Collapse
MESH Headings
- Humans
- Pyrroline Carboxylate Reductases/metabolism
- Pyrroline Carboxylate Reductases/genetics
- Carcinoma, Renal Cell/immunology
- Carcinoma, Renal Cell/pathology
- Carcinoma, Renal Cell/genetics
- Carcinoma, Renal Cell/metabolism
- Prognosis
- Kidney Neoplasms/immunology
- Kidney Neoplasms/pathology
- Kidney Neoplasms/genetics
- Kidney Neoplasms/metabolism
- Biomarkers, Tumor/metabolism
- Biomarkers, Tumor/genetics
- Cell Line, Tumor
- Gene Expression Regulation, Neoplastic
- delta-1-Pyrroline-5-Carboxylate Reductase
- Cell Proliferation
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/metabolism
- Signal Transduction
Collapse
Affiliation(s)
- Hongquan Chen
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Qing Chen
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Jinyang Chen
- College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350009, China;
| | - Yazhen Mao
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Lidi Duan
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Dongjie Ye
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Wenxiu Cheng
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Jiaxi Chen
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Xinrong Gao
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Renxi Lin
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Weibin Lin
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Mingfang Zhang
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Yuanlin Qi
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| |
Collapse
|
11
|
Kraemer S, Schneider DJ, Paterson C, Perry D, Westacott MJ, Hagar Y, Katilius E, Lynch S, Russell TM, Johnson T, Astling DP, DeLisle RK, Cleveland J, Gold L, Drolet DW, Janjic N. Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome. J Proteome Res 2024. [PMID: 39038188 DOI: 10.1021/acs.jproteome.4c00411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.
Collapse
Affiliation(s)
- Stephan Kraemer
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Daniel J Schneider
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Clare Paterson
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Darryl Perry
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Matthew J Westacott
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Yolanda Hagar
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Evaldas Katilius
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Sean Lynch
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Theresa M Russell
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Ted Johnson
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - David P Astling
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Robert Kirk DeLisle
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Jason Cleveland
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Larry Gold
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Daniel W Drolet
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Nebojsa Janjic
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| |
Collapse
|
12
|
Ye C, Xia L, Gong R, Chang J, Sun Q, Xu J, Li F. Integrating plasma proteome with genome reveals novel protein biomarkers in colorectal cancer. Clin Transl Oncol 2024:10.1007/s12094-024-03616-z. [PMID: 39017955 DOI: 10.1007/s12094-024-03616-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Accepted: 07/09/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND Biomarkers for colorectal cancer (CRC) can complement population screening methods, but so far, few plasma proteins have been identified as biomarkers for CRC. This study aims to identify potential protein biomarkers and therapeutic targets for CRC within the proteome range. METHODS We extracted summary-level data of circulating protein from 7 published genome-wide association studies (GWASs) of plasma proteome for Mendelian randomization (MR), summary-data-based MR (SMR), and co-localization analyses to screen and validate proteins with causal effects in CRC. In addition, we further conducted druggability evaluation, prognosis analysis at the transcriptional level, and enrichment expression at the single-cell level, highlighting the important role of these plasma protein biomarkers in CRC. RESULTS We identified 117 plasma protein biomarkers associated with CRC risk, with 9 proteins showing stronger genetic correlations in Bayesian co-localization (PP.H4 > 0.70). Further, we found 26 protein-coding genes already used in targeted drug development and may potentially become therapeutic targets for CRC. In prognosis analysis, the encoding genes of plasma proteins exhibited consistent effects with MR analysis and can serve as prognostic biomarkers for CRC. Additionally, we also found that the differentially expressed proteins are mainly expressed in fibroblasts, endothelial cells, macrophages, and T cells. CONCLUSION Our study has identified plasma protein biomarkers associated with CRC risk, which may complement population screening methods for CRC and achieve more precise treatment for patients.
Collapse
Affiliation(s)
- Changchun Ye
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Leizhou Xia
- Department of General Surgery, Affiliated People's Hospital, Jiangsu University, Zhenjiang, China
| | - Ruimin Gong
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jingbo Chang
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Qi Sun
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jiaxi Xu
- Department of Physiology and Pathophysiology, Xi'an Jiaotong University Health Science Center, Xi'an, China.
| | - Fanni Li
- Department of Talent Highland, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| |
Collapse
|
13
|
Wang Q, Antone J, Alsop E, Reiman R, Funk C, Bendl J, Dudley JT, Liang WS, Karr TL, Roussos P, Bennett DA, De Jager PL, Serrano GE, Beach TG, Van Keuren-Jensen K, Mastroeni D, Reiman EM, Readhead BP. Single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease. Nat Commun 2024; 15:5815. [PMID: 38987616 PMCID: PMC11237088 DOI: 10.1038/s41467-024-49790-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 06/13/2024] [Indexed: 07/12/2024] Open
Abstract
The emergence of single nucleus RNA sequencing (snRNA-seq) offers to revolutionize the study of Alzheimer's disease (AD). Integration with complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link cell subpopulations and molecular networks with a broader disease-relevant context. We report snRNA-seq profiles from superior frontal gyrus samples from 101 well characterized subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in hematological parameters. We observed an AD-associated CD83(+) microglial subtype with unique molecular networks and which is associated with immunoglobulin IgG4 production in the transverse colon. Our major observations were replicated in two additional, independent snRNA-seq data sets. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal disease biology.
Collapse
Affiliation(s)
- Qi Wang
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Jerry Antone
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Eric Alsop
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Rebecca Reiman
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Cory Funk
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Jaroslav Bendl
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Joel T Dudley
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Winnie S Liang
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Timothy L Karr
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Panos Roussos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Philip L De Jager
- Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Geidy E Serrano
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | - Thomas G Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | | | - Diego Mastroeni
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, 85006, USA
| | - Benjamin P Readhead
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA.
| |
Collapse
|
14
|
Guo Y, Chen SD, You J, Huang SY, Chen YL, Zhang Y, Wang LB, He XY, Deng YT, Zhang YR, Huang YY, Dong Q, Feng JF, Cheng W, Yu JT. Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer's disease. Nat Hum Behav 2024:10.1038/s41562-024-01924-6. [PMID: 38987357 DOI: 10.1038/s41562-024-01924-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 06/10/2024] [Indexed: 07/12/2024]
Abstract
Recent expansion of proteomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer's disease (AD). Among 6,361 cerebrospinal fluid (CSF) proteins analysed from the ADNI database, YWHAG performed best in diagnosing both biologically (AUC = 0.969) and clinically (AUC = 0.857) defined AD. Four- (YWHAG, SMOC1, PIGR and TMOD2) and five- (ACHE, YWHAG, PCSK1, MMP10 and IRF1) protein panels greatly improved the accuracy to 0.987 and 0.975, respectively. Their superior performance was validated in an independent external cohort and in discriminating autopsy-confirmed AD versus non-AD, rivalling even canonical CSF ATN biomarkers. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Synaptic, neurogenic and infectious pathways were enriched in distinct AD stages. Mendelian randomization did not support the significant genetic link between CSF proteins and AD. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction, with implications for clinical trials targeting different pathomechanisms.
Collapse
Affiliation(s)
- Yu Guo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jia You
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shu-Yi Huang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Lin Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lin-Bo Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| |
Collapse
|
15
|
Han QJ, Zhu YP, Sun J, Ding XY, Wang X, Zhang QZ. PTGES2 and RNASET2 identified as novel potential biomarkers and therapeutic targets for basal cell carcinoma: insights from proteome-wide mendelian randomization, colocalization, and MR-PheWAS analyses. Front Pharmacol 2024; 15:1418560. [PMID: 39035989 PMCID: PMC11257982 DOI: 10.3389/fphar.2024.1418560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 06/12/2024] [Indexed: 07/23/2024] Open
Abstract
Introduction Basal cell carcinoma (BCC) is the most common skin cancer, lacking reliable biomarkers or therapeutic targets for effective treatment. Genome-wide association studies (GWAS) can aid in identifying drug targets, repurposing existing drugs, predicting clinical trial side effects, and reclassifying patients in clinical utility. Hence, the present study investigates the association between plasma proteins and skin cancer to identify effective biomarkers and therapeutic targets for BCC. Methods Proteome-wide mendelian randomization was performed using inverse-variance-weight and Wald Ratio methods, leveraging 1 Mb cis protein quantitative trait loci (cis-pQTLs) in the UK Biobank Pharma Proteomics Project (UKB-PPP) and the deCODE Health Study, to determine the causal relationship between plasma proteins and skin cancer and its subtypes in the FinnGen R10 study and the SAIGE database of Lee lab. Significant association with skin cancer and its subtypes was defined as a false discovery rate (FDR) < 0.05. pQTL to GWAS colocalization analysis was executed using a Bayesian model to evaluate five exclusive hypotheses. Strong colocalization evidence was defined as a posterior probability for shared causal variants (PP.H4) of ≥0.85. Mendelian randomization-Phenome-wide association studies (MR-PheWAS) were used to evaluate potential biomarkers and therapeutic targets for skin cancer and its subtypes within a phenome-wide human disease category. Results PTGES2, RNASET2, SF3B4, STX8, ENO2, and HS3ST3B1 (besides RNASET2, five other plasma proteins were previously unknown in expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL)) were significantly associated with BCC after FDR correction in the UKB-PPP and deCODE studies. Reverse MR showed no association between BCC and these proteins. PTGES2 and RNASET2 exhibited strong evidence of colocalization with BCC based on a posterior probability PP.H4 >0.92. Furthermore, MR-PheWAS analysis showed that BCC was the most significant phenotype associated with PTGES2 and RNASET2 among 2,408 phenotypes in the FinnGen R10 study. Therefore, PTGES2 and RNASET2 are highlighted as effective biomarkers and therapeutic targets for BCC within the phenome-wide human disease category. Conclusion The study identifies PTGES2 and RNASET2 plasma proteins as novel, reliable biomarkers and therapeutic targets for BCC, suggesting more effective clinical application strategies for patients.
Collapse
Affiliation(s)
- Qiu-Ju Han
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, and the Haihe Laboratory of Cell Ecosystem, Tianjin, China
| | - Yi-Pan Zhu
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, and the Haihe Laboratory of Cell Ecosystem, Tianjin, China
| | - Jing Sun
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, and the Haihe Laboratory of Cell Ecosystem, Tianjin, China
| | - Xin-Yu Ding
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, and the Haihe Laboratory of Cell Ecosystem, Tianjin, China
| | - Xiuyu Wang
- Department of Neurosurgery, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Qiang-Zhe Zhang
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, and the Haihe Laboratory of Cell Ecosystem, Tianjin, China
| |
Collapse
|
16
|
Ma X, Lu Y, Xu S. Adaptive Evolution of Two Distinct Adaptive Haplotypes of Neanderthal Origin at the Immunoglobulin Heavy-chain Locus in East Asian and European Populations. Mol Biol Evol 2024; 41:msae147. [PMID: 39011558 DOI: 10.1093/molbev/msae147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 07/05/2024] [Accepted: 07/11/2024] [Indexed: 07/17/2024] Open
Abstract
Immunoglobulins (Igs) have a crucial role in humoral immunity. Two recent studies have reported a high-frequency Neanderthal-introgressed haplotype throughout Eurasia and a high-frequency Neanderthal-introgressed haplotype specific to southern East Asia at the immunoglobulin heavy-chain (IGH) gene locus on chromosome 14q32.33. Surprisingly, we found the previously reported high-frequency Neanderthal-introgressed haplotype does not exist throughout Eurasia. Instead, our study identified two distinct high-frequency haplotypes of putative Neanderthal origin in East Asia and Europe, although they shared introgressed alleles. Notably, the alleles of putative Neanderthal origin reduced the expression of IGHG1 and increased the expression of IGHG2 and IGHG3 in various tissues. These putatively introgressed alleles also affected the production of IgG1 upon antigen stimulation and increased the risk of systemic lupus erythematosus. Additionally, the greatest genetic differentiation across the whole genome between southern and northern East Asians was observed for the East Asian haplotype of putative Neanderthal origin. The frequency decreased from southern to northern East Asia and correlated positively with the genome-wide proportion of southern East Asian ancestry, indicating that this putative positive selection likely occurred in the common ancestor of southern East Asian populations before the admixture with northern East Asian populations.
Collapse
Affiliation(s)
- Xixian Ma
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
17
|
Zhao H, Liu Y, Zhang X, Liao Y, Zhang H, Han X, Guo L, Fan B, Wang W, Lu C. Identifying novel proteins for suicide attempt by integrating proteomes from brain and blood with genome-wide association data. Neuropsychopharmacology 2024; 49:1255-1265. [PMID: 38317018 PMCID: PMC11224332 DOI: 10.1038/s41386-024-01807-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/28/2023] [Accepted: 01/16/2024] [Indexed: 02/07/2024]
Abstract
Genome-wide association studies (GWASs) have identified risk loci for suicide attempt (SA), but deciphering how they confer risk for SA remains largely unknown. This study aims to identify the key proteins and gain insights into SA pathogenesis. We integrated data from the brain proteome (N = 376) and blood proteome (N = 35,559) and combined it with the largest SA GWAS summary statistics to date (N = 518,612). A comprehensive set of methods was employed, including Mendelian randomization (MR), Steiger filtering, Bayesian colocalization, proteome‑wide association studies (PWAS), transcript-levels, cell-type specificity, correlation, and protein-protein interaction (PPI) network analysis. Validation was performed using other protein datasets and the SA dataset from FinnGen study. We identified ten proteins (GLRX5, GMPPB, B3GALTL, FUCA2, TTLL12, ADCK1, MMAA, HIBADH, ACP1, DOC2A) associated with SA in brain proteomics. GLRX5, GMPPB, and FUCA2 showed strong colocalization evidence and were supported by PWAS and transcript-level analysis, and were predominantly expressed in glutamatergic neuronal cells. In blood proteomics, one significant protein (PEAR1) and three near-significant proteins (NDE1, EVA1C, B4GALT2) were identified, but lacked colocalization evidence. Moreover, despite the limited correlation between the same protein in brain and blood, the PPI network analysis provided new insights into the interaction between brain and blood in SA. Furthermore, GLRX5 was associated with the GSTP1, the target of Clozapine. The comprehensive analysis provides strong evidence supporting a causal association between three genetically determined brain proteins (GLRX5, GMPPB, and FUCA2) with SA. These findings offer valuable insights into SA's underlying mechanisms and potential therapeutic approaches.
Collapse
Affiliation(s)
- Hao Zhao
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China
| | - Yifeng Liu
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Xuening Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuhua Liao
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Huimin Zhang
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Xue Han
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Lan Guo
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China
| | - Beifang Fan
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China.
| | - Wanxin Wang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China.
| | - Ciyong Lu
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
18
|
Sun BB, Suhre K, Gibson BW. Promises and Challenges of populational Proteomics in Health and Disease. Mol Cell Proteomics 2024; 23:100786. [PMID: 38761890 PMCID: PMC11193116 DOI: 10.1016/j.mcpro.2024.100786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.
Collapse
Affiliation(s)
- Benjamin B Sun
- Human Genetics, Informatics and Predictive Sciences, Bristol-Myers Squibb, Cambridge, Massachusetts, USA.
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Bradford W Gibson
- Pharmaceutical Chemistry, University of California, San Francisco, California, USA
| |
Collapse
|
19
|
Ma S, Xu F, Fu Y, Zheng JS. Genetic mapping of plasma proteome on brain structure. J Genet Genomics 2024; 51:774-777. [PMID: 38608937 DOI: 10.1016/j.jgg.2024.03.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/28/2024] [Accepted: 03/31/2024] [Indexed: 04/14/2024]
Affiliation(s)
- Shengyi Ma
- Fudan University, Shanghai 200433, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310030, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310030, China
| | - Fengzhe Xu
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310030, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310030, China
| | - Yuanqing Fu
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310030, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310030, China.
| | - Ju-Sheng Zheng
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310030, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310030, China.
| |
Collapse
|
20
|
Mao R, Li J, Xiao W. Identification of prospective aging drug targets via Mendelian randomization analysis. Aging Cell 2024; 23:e14171. [PMID: 38572516 PMCID: PMC11258487 DOI: 10.1111/acel.14171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/26/2024] [Accepted: 03/13/2024] [Indexed: 04/05/2024] Open
Abstract
Aging represents a multifaceted process culminating in the deterioration of biological functions. Despite the introduction of numerous anti-aging strategies, their therapeutic outcomes have often been less than optimal. Consequently, discovering new targets to mitigate aging effects is of critical importance. We applied Mendelian randomization (MR) to identify potential pharmacological targets against aging, drawing upon summary statistics from both the Decode and FinnGen cohorts, with further validation in an additional cohort. To address potential reverse causality, bidirectional MR analysis with Steiger filtering was utilized. Additionally, Bayesian co-localization and phenotype scanning were implemented to investigate previous associations between genetic variants and traits. Summary-data-based Mendelian randomization (SMR) analysis was conducted to assess the impact of genetic variants on aging via their effects on protein expression. Additionally, mediation analysis was orchestrated to uncover potential intermediaries in these associations. Finally, we probed the systemic implications of drug-target protein expression across diverse indications by MR-PheWas analysis. Utilizing a Bonferroni-corrected threshold, our MR examination identified 10 protein-aging associations. Within this cohort of proteins, MST1, LCT, GMPR2, PSMB4, ECM1, EFEMP1, and ISLR2 appear to exacerbate aging risks, while MAX, B3GNT8, and USP8 may exert protective influences. None of these proteins displayed reverse causality except EFEMP1. Bayesian co-localization inferred shared variants between aging and proteins such as B3GNT8 (rs11670143), ECM1 (rs61819393), and others listed. Mediator analysis pinpointed 1,5-anhydroglucitol as a partial intermediary in the influence LCT exhibits on telomere length. Circulating proteins play a pivotal role in influencing the aging process, making them promising candidates for therapeutic intervention. The implications of these proteins in aging warrant further investigation in future clinical research.
Collapse
Affiliation(s)
- Rui Mao
- Department of Dermatology, Xiangya HospitalCentral South UniversityChangshaChina
- Hunan Key Laboratory of Aging Biology, Xiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaChina
| | - Ji Li
- Department of Dermatology, Xiangya HospitalCentral South UniversityChangshaChina
- Hunan Key Laboratory of Aging Biology, Xiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaChina
| | - Wenqin Xiao
- Department of Dermatology, Xiangya HospitalCentral South UniversityChangshaChina
- Hunan Key Laboratory of Aging Biology, Xiangya HospitalCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaChina
| |
Collapse
|
21
|
Kentistou KA, Kaisinger LR, Stankovic S, Vaudel M, Mendes de Oliveira E, Messina A, Walters RG, Liu X, Busch AS, Helgason H, Thompson DJ, Santoni F, Petricek KM, Zouaghi Y, Huang-Doran I, Gudbjartsson DF, Bratland E, Lin K, Gardner EJ, Zhao Y, Jia RY, Terao C, Riggan MJ, Bolla MK, Yazdanpanah M, Yazdanpanah N, Bradfield JP, Broer L, Campbell A, Chasman DI, Cousminer DL, Franceschini N, Franke LH, Girotto G, He C, Järvelin MR, Joshi PK, Kamatani Y, Karlsson R, Luan J, Lunetta KL, Mägi R, Mangino M, Medland SE, Meisinger C, Noordam R, Nutile T, Concas MP, Polašek O, Porcu E, Ring SM, Sala C, Smith AV, Tanaka T, van der Most PJ, Vitart V, Wang CA, Willemsen G, Zygmunt M, Ahearn TU, Andrulis IL, Anton-Culver H, Antoniou AC, Auer PL, Barnes CLK, Beckmann MW, Berrington de Gonzalez A, Bogdanova NV, Bojesen SE, Brenner H, Buring JE, Canzian F, Chang-Claude J, Couch FJ, Cox A, Crisponi L, Czene K, Daly MB, Demerath EW, Dennis J, Devilee P, De Vivo I, Dörk T, Dunning AM, Dwek M, Eriksson JG, Fasching PA, Fernandez-Rhodes L, Ferreli L, Fletcher O, Gago-Dominguez M, García-Closas M, García-Sáenz JA, González-Neira A, Grallert H, Guénel P, Haiman CA, Hall P, Hamann U, Hakonarson H, Hart RJ, Hickey M, Hooning MJ, Hoppe R, Hopper JL, Hottenga JJ, Hu FB, Huebner H, Hunter DJ, Jernström H, John EM, Karasik D, Khusnutdinova EK, Kristensen VN, Lacey JV, Lambrechts D, Launer LJ, Lind PA, Lindblom A, Magnusson PKE, Mannermaa A, McCarthy MI, Meitinger T, Menni C, Michailidou K, Millwood IY, Milne RL, Montgomery GW, Nevanlinna H, Nolte IM, Nyholt DR, Obi N, O'Brien KM, Offit K, Oldehinkel AJ, Ostrowski SR, Palotie A, Pedersen OB, Peters A, Pianigiani G, Plaseska-Karanfilska D, Pouta A, Pozarickij A, Radice P, Rennert G, Rosendaal FR, Ruggiero D, Saloustros E, Sandler DP, Schipf S, Schmidt CO, Schmidt MK, Small K, Spedicati B, Stampfer M, Stone J, Tamimi RM, Teras LR, Tikkanen E, Turman C, Vachon CM, Wang Q, Winqvist R, Wolk A, Zemel BS, Zheng W, van Dijk KW, Alizadeh BZ, Bandinelli S, Boerwinkle E, Boomsma DI, Ciullo M, Chenevix-Trench G, Cucca F, Esko T, Gieger C, Grant SFA, Gudnason V, Hayward C, Kolčić I, Kraft P, Lawlor DA, Martin NG, Nøhr EA, Pedersen NL, Pennell CE, Ridker PM, Robino A, Snieder H, Sovio U, Spector TD, Stöckl D, Sudlow C, Timpson NJ, Toniolo D, Uitterlinden A, Ulivi S, Völzke H, Wareham NJ, Widen E, Wilson JF, Pharoah PDP, Li L, Easton DF, Njølstad PR, Sulem P, Murabito JM, Murray A, Manousaki D, Juul A, Erikstrup C, Stefansson K, Horikoshi M, Chen Z, Farooqi IS, Pitteloud N, Johansson S, Day FR, Perry JRB, Ong KK. Understanding the genetic complexity of puberty timing across the allele frequency spectrum. Nat Genet 2024; 56:1397-1411. [PMID: 38951643 PMCID: PMC11250262 DOI: 10.1038/s41588-024-01798-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 05/13/2024] [Indexed: 07/03/2024]
Abstract
Pubertal timing varies considerably and is associated with later health outcomes. We performed multi-ancestry genetic analyses on ~800,000 women, identifying 1,080 signals for age at menarche. Collectively, these explained 11% of trait variance in an independent sample. Women at the top and bottom 1% of polygenic risk exhibited ~11 and ~14-fold higher risks of delayed and precocious puberty, respectively. We identified several genes harboring rare loss-of-function variants in ~200,000 women, including variants in ZNF483, which abolished the impact of polygenic risk. Variant-to-gene mapping approaches and mouse gonadotropin-releasing hormone neuron RNA sequencing implicated 665 genes, including an uncharacterized G-protein-coupled receptor, GPR83, which amplified the signaling of MC3R, a key nutritional sensor. Shared signals with menopause timing at genes involved in DNA damage response suggest that the ovarian reserve might signal centrally to trigger puberty. We also highlight body size-dependent and independent mechanisms that potentially link reproductive timing to later life disease.
Collapse
Affiliation(s)
- Katherine A Kentistou
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Lena R Kaisinger
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Stasa Stankovic
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Marc Vaudel
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Edson Mendes de Oliveira
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - Andrea Messina
- Division of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Alexander S Busch
- Department of General Pediatrics, University of Münster, Münster, Germany
- Department of Growth and Reproduction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Hannes Helgason
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Deborah J Thompson
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Federico Santoni
- Division of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Konstantin M Petricek
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pharmacology, Berlin, Germany
| | - Yassine Zouaghi
- Division of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Isabel Huang-Doran
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Eirik Bratland
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Eugene J Gardner
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Yajie Zhao
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Raina Y Jia
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Marjorie J Riggan
- Department of Gynecology, Duke University Medical Center, Durham, NC, USA
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Mojgan Yazdanpanah
- Research Center of the Sainte-Justine University Hospital, University of Montreal, Montreal, Quebec, Canada
| | - Nahid Yazdanpanah
- Research Center of the Sainte-Justine University Hospital, University of Montreal, Montreal, Quebec, Canada
| | - Jonathan P Bradfield
- Quantinuum Research, Wayne, PA, USA
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Linda Broer
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Diana L Cousminer
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Lude H Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Giorgia Girotto
- Institute for Maternal and Child Health-IRCCS 'Burlo Garofolo', Trieste, Italy
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Chunyan He
- Department of Internal Medicine, Division of Medical Oncology, University of Kentucky College of Medicine, Lexington, KY, USA
- Cancer Prevention and Control Research Program, Markey Cancer Center, University of Kentucky, Lexington, KY, USA
| | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPA) Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
- Department of Children and Young People and Families, National Institute for Health and Welfare, Oulu, Finland
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- NHLBI's and Boston University's Framingham Heart Study, Framingham, MA, USA
| | - Reedik Mägi
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St. Thomas' Foundation Trust, London, UK
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Christa Meisinger
- Epidemiology, Medical Faculty, University of Augsburg, University Hospital of Augsburg, Augsburg, Germany
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Teresa Nutile
- Institute of Genetics and Biophysics 'A. Buzzati-Traverso', CNR, Naples, Italy
| | - Maria Pina Concas
- Institute for Maternal and Child Health-IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Ozren Polašek
- University of Split School of Medicine, Split, Croatia
- Algebra University College, Zagreb, Croatia
| | - Eleonora Porcu
- Institute of Genetics and Biomedical Research, National Research Council, Sardinia, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Susan M Ring
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Cinzia Sala
- Division of Genetics and Cell Biology, San Raffele Hospital, Milano, Italy
| | - Albert V Smith
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Toshiko Tanaka
- National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Carol A Wang
- School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam; Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - Marek Zygmunt
- Clinic of Gynaecology and Obstetrics, University Medicine Greifswald, Greifswald, Germany
| | - Thomas U Ahearn
- Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health, Department of Health and Human Services Bethesda, Bethesda, MD, USA
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Hoda Anton-Culver
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Catriona L K Barnes
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | | | - Natalia V Bogdanova
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
- N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital Copenhagen University Hospital, Herlev, Denmark
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Julie E Buring
- Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, Rochester, MN, USA
| | - Angela Cox
- Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Laura Crisponi
- Institute of Genetics and Biomedical Research, National Research Council, Sardinia, Italy
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Ellen W Demerath
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Immaculata De Vivo
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Miriam Dwek
- School of Life Sciences, University of Westminster, London, UK
| | - Johan G Eriksson
- Department of General Practice and Primary Healthcare, University of Helsinki, Helsinki University Hospital, Helsinki, Finland
- Yong Loo Lin School of Medicine, Department of Obstetrics and Gynecology and Human Potential Translational Research Programme, National University Singapore, Singapore City, Singapore
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore City, Singapore
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | | | - Liana Ferreli
- Institute of Genetics and Biomedical Research, National Research Council, Sardinia, Italy
| | - Olivia Fletcher
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, International Cancer Genetics and Epidemiology Group Fundación Pública Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS Santiago de Compostela, Coruña, Spain
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health, Department of Health and Human Services Bethesda, Bethesda, MD, USA
| | - José A García-Sáenz
- Medical Oncology Department, Hospital Clínico San Carlos Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Anna González-Neira
- Human Genotyping Unit-CeGen, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Pascal Guénel
- Team 'Exposome and Heredity', CESP, Gustave Roussy INSERM, University Paris-Saclay, UVSQ, Orsay, France
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Roger J Hart
- Division of Obstetrics and Gynaecology, University of Western Australia, Crawley, Western Australia, Australia
| | - Martha Hickey
- Department of Obstetrics and Gynaecology, University of Melbourne and The Royal Women's Hospital, Parkville, Victoria, Australia
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Reiner Hoppe
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam; Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - Frank B Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health School of Public Health, Boston, MA, USA
| | - Hanna Huebner
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - David J Hunter
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Helena Jernström
- Oncology, Department of Clinical Sciences in Lund, Lund University, Lund, Sweden
| | - Esther M John
- Department of Epidemiology and Population Health, Stanford University School of Medicine Stanford, Stanford, CA, USA
- Department of Medicine, Division of Oncology Stanford Cancer Institute, Stanford University School of Medicine Stanford, Stanford, CA, USA
| | - David Karasik
- Hebrew SeniorLife Institute for Aging Research, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Elza K Khusnutdinova
- Institute of Biochemistry and Genetics of the Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia
| | - Vessela N Kristensen
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - James V Lacey
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA
- City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Penelope A Lind
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arto Mannermaa
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Thomas Meitinger
- Institute of Human Genetics, Klinikum rechts der Isar, Technical University of Munich, School of Medicine, Munich, Germany
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Dale R Nyholt
- School of Biomedical Sciences, Faculty of Health, Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Nadia Obi
- Institute for Occupational Medicine and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katie M O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH Research Triangle Park, Durham, NC, USA
| | - Kenneth Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Albertine J Oldehinkel
- Interdisciplinary Center Psychopathology and Emotion Regulation, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet-University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Aarno Palotie
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Ole B Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology-IBE, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Giulia Pianigiani
- Institute for Maternal and Child Health-IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Dijana Plaseska-Karanfilska
- Research Centre for Genetic Engineering and Biotechnology 'Georgi D. Efremov', MASA, Skopje, Republic of North Macedonia
| | - Anneli Pouta
- National Institute for Health and Welfare, Helsinki, Finland
| | - Alfred Pozarickij
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Paolo Radice
- Unit of Preventive Medicine: Molecular Bases of Genetic Risk, Department of Experimental Oncology, Fondazione IRCCS, Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Gad Rennert
- Faculty of Medicine, Clalit National Cancer Control Center, Carmel Medical Center and Technion, Haifa, Israel
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics 'A. Buzzati-Traverso', CNR, Naples, Italy
- IRCCS Neuromed, Isernia, Italy
| | - Emmanouil Saloustros
- Division of Oncology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH Research Triangle Park, Durham, NC, USA
| | - Sabine Schipf
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Carsten O Schmidt
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Kerrin Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Beatrice Spedicati
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Meir Stampfer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia Perth, Perth, Western Australia, Australia
| | - Rulla M Tamimi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York City, NY, USA
| | - Lauren R Teras
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Emmi Tikkanen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Celine M Vachon
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic Rochester, Rochester, MN, USA
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre, Oulu, Finland
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Babette S Zemel
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ko W van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Behrooz Z Alizadeh
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam; Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Marina Ciullo
- Institute of Genetics and Biophysics 'A. Buzzati-Traverso', CNR, Naples, Italy
- IRCCS Neuromed, Isernia, Italy
| | | | - Francesco Cucca
- Institute of Genetics and Biomedical Research, National Research Council, Sardinia, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Tõnu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Struan F A Grant
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ivana Kolčić
- University of Split School of Medicine, Split, Croatia
- Algebra University College, Zagreb, Croatia
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ellen A Nøhr
- Institute of Clinical Research, University of Southern Denmark, Department of Obstetrics and Gynecology, Odense University Hospital, Odense, Denmark
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Craig E Pennell
- School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
- Department of Maternity and Gynaecology, John Hunter Hospital, Newcastle, New South Wales, Australia
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Antonietta Robino
- Institute for Maternal and Child Health-IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ulla Sovio
- Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPA) Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Doris Stöckl
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- State Institute of Health, Bavarian Health and Food Safety Authority (LGL), Oberschleissheim, Germany
| | - Cathie Sudlow
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Nic J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Daniela Toniolo
- Division of Genetics and Cell Biology, San Raffele Hospital, Milano, Italy
| | - André Uitterlinden
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Sheila Ulivi
- Institute for Maternal and Child Health-IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Elisabeth Widen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Pål R Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Adolescent Clinic, Haukeland University Hospital, Bergen, Norway
| | | | - Joanne M Murabito
- NHLBI's and Boston University's Framingham Heart Study, Framingham, MA, USA
- Boston University Chobanian and Avedisian School of Medicine, Department of Medicine, Section of General Internal Medicine, Boston, MA, USA
| | - Anna Murray
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, RILD Level 3, Royal Devon and Exeter Hospital, Exeter, UK
| | - Despoina Manousaki
- Centre Hospitalier Universitaire (CHU) Sainte-Justine Research Center, University of Montreal, Montreal, Quebec, Canada
- Department of Pediatrics, University of Montreal, Montreal, Quebec, Canada
- Department of Biochemistry and Molecular Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Anders Juul
- Department of Growth and Reproduction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Kari Stefansson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - I Sadaf Farooqi
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - Nelly Pitteloud
- Division of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Stefan Johansson
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Felix R Day
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK.
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Ken K Ong
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| |
Collapse
|
22
|
Gadd DA, Hillary RF, Kuncheva Z, Mangelis T, Cheng Y, Dissanayake M, Admanit R, Gagnon J, Lin T, Ferber KL, Runz H, Foley CN, Marioni RE, Sun BB. Blood protein assessment of leading incident diseases and mortality in the UK Biobank. NATURE AGING 2024; 4:939-948. [PMID: 38987645 PMCID: PMC11257969 DOI: 10.1038/s43587-024-00655-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/22/2024] [Indexed: 07/12/2024]
Abstract
The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (n = 47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalized Cox regression. When applied to test sets, six ProteinScores improve the area under the curve estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c-a clinical marker used to monitor and diagnose type 2 diabetes. The performance of scores using metabolomic and proteomic features is also compared. These data characterize early proteomic contributions to major age-related diseases, demonstrating the value of the plasma proteome for risk stratification.
Collapse
Affiliation(s)
- Danni A Gadd
- Optima Partners, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Optima Partners, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Zhana Kuncheva
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Tasos Mangelis
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Manju Dissanayake
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Romi Admanit
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Jake Gagnon
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Tinchi Lin
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Kyle L Ferber
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Heiko Runz
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Christopher N Foley
- Optima Partners, Edinburgh, UK.
- Bayes Centre, University of Edinburgh, Edinburgh, UK.
| | - Riccardo E Marioni
- Optima Partners, Edinburgh, UK.
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Benjamin B Sun
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA.
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| |
Collapse
|
23
|
Kim T, Surapaneni AL, Schmidt IM, Eadon MT, Kalim S, Srivastava A, Palsson R, Stillman IE, Hodgin JB, Menon R, Otto EA, Coresh J, Grams ME, Waikar SS, Rhee EP. Plasma Proteins Associated with Chronic Histopathologic Lesions on Kidney Biopsy. J Am Soc Nephrol 2024; 35:910-922. [PMID: 38656806 PMCID: PMC11230715 DOI: 10.1681/asn.0000000000000358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 04/17/2024] [Indexed: 04/26/2024] Open
Abstract
Key Points Proteomic profiling identified 35 blood proteins associated with chronic histopathologic lesions in the kidney. Testican-2 was expressed in the glomerulus, released by the kidney into circulation, and inversely associated with glomerulosclerosis severity. NELL1 was expressed in tubular epithelial cells, released by the kidney into circulation, and inversely associated with interstitial fibrosis and tubular atrophy severity. Background The severity of chronic histopathologic lesions on kidney biopsy is independently associated with higher risk of progressive CKD. Because kidney biopsies are invasive, identification of blood markers that report on underlying kidney histopathology has the potential to enhance CKD care. Methods We examined the association between 6592 plasma protein levels measured by aptamers and the severity of interstitial fibrosis and tubular atrophy (IFTA), glomerulosclerosis, arteriolar sclerosis, and arterial sclerosis among 434 participants of the Boston Kidney Biopsy Cohort. For proteins significantly associated with at least one histologic lesion, we assessed renal arteriovenous protein gradients among 21 individuals who had undergone invasive catheterization and assessed the expression of the cognate gene among 47 individuals with single-cell RNA sequencing data in the Kidney Precision Medicine Project. Results In models adjusted for eGFR, proteinuria, and demographic factors, we identified 35 proteins associated with one or more chronic histologic lesions, including 20 specific for IFTA, eight specific for glomerulosclerosis, and one specific for arteriolar sclerosis. In general, higher levels of these proteins were associated with more severe histologic score and lower eGFR. Exceptions included testican-2 and NELL1, which were associated with less glomerulosclerosis and IFTA, respectively, and higher eGFR; notably, both of these proteins demonstrated significantly higher levels from artery to renal vein, demonstrating net kidney release. In the Kidney Precision Medicine Project, 13 of the 35 protein hits had cognate gene expression enriched in one or more cell types in the kidney, including podocyte expression of select glomerulosclerosis markers (including testican-2) and tubular expression of several IFTA markers (including NELL1). Conclusions Proteomic analysis identified circulating proteins associated with chronic histopathologic lesions, some of which had concordant site-specific expression within the kidney.
Collapse
Affiliation(s)
- Taesoo Kim
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Aditya L. Surapaneni
- Department of Medicine, New York University Grossman School of Medicine, New York, New York
| | - Insa M. Schmidt
- Section of Nephrology, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine and Boston Medical Center, Boston, Massachusetts
| | - Michael T. Eadon
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Sahir Kalim
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Anand Srivastava
- Division of Nephrology, University of Illinois Chicago, Chicago, Illinois
| | - Ragnar Palsson
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Isaac E. Stillman
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jeffrey B. Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | - Rajasree Menon
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Edgar A. Otto
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Josef Coresh
- Departments of Population Health and Medicine, New York University Grossman School of Medicine, New York, New York
| | - Morgan E. Grams
- Department of Medicine, New York University Grossman School of Medicine, New York, New York
| | - Sushrut S. Waikar
- Section of Nephrology, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine and Boston Medical Center, Boston, Massachusetts
| | - Eugene P. Rhee
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Endocrine Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| |
Collapse
|
24
|
Desai TA, Hedman ÅK, Dimitriou M, Koprulu M, Figiel S, Yin W, Johansson M, Watts EL, Atkins JR, Sokolov AV, Schiöth HB, Gunter MJ, Tsilidis KK, Martin RM, Pietzner M, Langenberg C, Mills IG, Lamb AD, Mälarstig A, Key TJ, Travis RC, Smith-Byrne K. Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics. EBioMedicine 2024; 105:105168. [PMID: 38878676 PMCID: PMC11233900 DOI: 10.1016/j.ebiom.2024.105168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 06/25/2024] Open
Abstract
BACKGROUND Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. METHODS We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. FINDINGS We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions. INTERPRETATION Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer. FUNDING This work was supported by Cancer Research UK (grant no. C8221/A29017).
Collapse
Affiliation(s)
- Trishna A Desai
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom.
| | - Åsa K Hedman
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Marios Dimitriou
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge, United Kingdom
| | - Sandy Figiel
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Wencheng Yin
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Eleanor L Watts
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Joshua R Atkins
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Aleksandr V Sokolov
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124, Uppsala, Sweden
| | - Helgi B Schiöth
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124, Uppsala, Sweden
| | - Marc J Gunter
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Richard M Martin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; NIHR Bristol Biomedical Research Centre, Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, United Kingdom
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, United Kingdom; Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, United Kingdom; Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom
| | - Ian G Mills
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Alastair D Lamb
- University of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom
| | - Anders Mälarstig
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tim J Key
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Ruth C Travis
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
25
|
Liu A, Genovese G, Zhao Y, Pirinen M, Zekavat SM, Kentistou KA, Yang Z, Yu K, Vlasschaert C, Liu X, Brown DW, Hudjashov G, Gorman BR, Dennis J, Zhou W, Momozawa Y, Pyarajan S, Tuzov V, Pajuste FD, Aavikko M, Sipilä TP, Ghazal A, Huang WY, Freedman ND, Song L, Gardner EJ, Sankaran VG, Palotie A, Ollila HM, Tukiainen T, Chanock SJ, Mägi R, Natarajan P, Daly MJ, Bick A, McCarroll SA, Terao C, Loh PR, Ganna A, Perry JRB, Machiela MJ. Genetic drivers and cellular selection of female mosaic X chromosome loss. Nature 2024; 631:134-141. [PMID: 38867047 DOI: 10.1038/s41586-024-07533-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/07/2024] [Indexed: 06/14/2024]
Abstract
Mosaic loss of the X chromosome (mLOX) is the most common clonal somatic alteration in leukocytes of female individuals1,2, but little is known about its genetic determinants or phenotypic consequences. Here, to address this, we used data from 883,574 female participants across 8 biobanks; 12% of participants exhibited detectable mLOX in approximately 2% of leukocytes. Female participants with mLOX had an increased risk of myeloid and lymphoid leukaemias. Genetic analyses identified 56 common variants associated with mLOX, implicating genes with roles in chromosomal missegregation, cancer predisposition and autoimmune diseases. Exome-sequence analyses identified rare missense variants in FBXO10 that confer a twofold increased risk of mLOX. Only a small fraction of associations was shared with mosaic Y chromosome loss, suggesting that distinct biological processes drive formation and clonal expansion of sex chromosome missegregation. Allelic shift analyses identified X chromosome alleles that are preferentially retained in mLOX, demonstrating variation at many loci under cellular selection. A polygenic score including 44 allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Our results support a model in which germline variants predispose female individuals to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of clonal expansion.
Collapse
Affiliation(s)
- Aoxing Liu
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Giulio Genovese
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - Yajie Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Seyedeh M Zekavat
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Katherine A Kentistou
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Zhiyu Yang
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | | | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Derek W Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Bryan R Gorman
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Booz Allen Hamilton, McLean, VA, USA
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Weiyin Zhou
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saiju Pyarajan
- Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Valdislav Tuzov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Mervi Aavikko
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Timo P Sipilä
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Awaisa Ghazal
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Eugene J Gardner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Vijay G Sankaran
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Taru Tukiainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pradeep Natarajan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexander Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Steven A McCarroll
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
| |
Collapse
|
26
|
Zhang X, Xu J. Fibroblast growth factor 23-mediated regulation of osteoporosis: Assessed via Mendelian randomization and in vitro study. J Cell Mol Med 2024; 28:e18551. [PMID: 39054573 PMCID: PMC11272609 DOI: 10.1111/jcmm.18551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/17/2024] [Accepted: 07/12/2024] [Indexed: 07/27/2024] Open
Abstract
Despite numerous investigations on the influence of fibroblast growth factor 23 (FGF23), α-Klotho and FGF receptor-1 (FGFR1) on osteoporosis (OP), there is no clear consensus. Mendelian randomization (MR) analysis was conducted on genome-wide association studies (GWASs)-based datasets to evaluate the causal relationship between FGF23, α-Klotho, FGFR1 and OP. The primary endpoint was the odds ratio (OR) of the inverse-variance weighted (IVW) approach. Furthermore, we stably transfected FGF23-mimic or siRNA-FGF23 into human bone marrow mesenchymal stem cells (hBMSCs) in culture and determined its cell proliferation and the effects on osteogenic differentiation. Using MR analysis, we demonstrated a strong correlation between serum FGF23 levels and Heel- and femoral neck-BMDs, with subsequent ORs of 0.919 (95% CI: 0.860-0.983, p = 0.014) and 0.751 (95% CI: 0.587-0.962; p = 0.023), respectively. The expression levels of FGF23 were significantly increased in femoral neck of patients with OP than in the control cohort (p < 0.0001). Based on our in vitro investigation, after overexpression of FGF23, compared to the control group, the BMSC's proliferation ability decreased, the expression level of key osteogenic differentiation genes (RUNX2, OCN and OSX) significantly reduced, mineralized nodules and ALP activity significantly decreased. After silencing FGF23, it showed a completely opposite trend. Augmented FGF23 levels are causally associated with increased risk of OP. Similarly, FGF23 overexpression strongly inhibits the osteogenic differentiation of hBMSCs, thereby potentially aggravating the pathological process of OP.
Collapse
Affiliation(s)
- Xiang Zhang
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of EndocrinologyShandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanShandongChina
- Shandong Key Laboratory of Endocrinology and Lipid MetabolismJinanShandongChina
- Shandong Institute of Endocrine and Metabolic DiseasesJinanShandongChina
| | - Jin Xu
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of EndocrinologyShandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanShandongChina
- Shandong Key Laboratory of Endocrinology and Lipid MetabolismJinanShandongChina
- Shandong Institute of Endocrine and Metabolic DiseasesJinanShandongChina
- “Chuangxin China” Innovation Base of stem cell and Gene Therapy for endocrine Metabolic diseasesJinanShandongChina
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic DiseasesJinanShandongChina
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic DiseasesJinanShandongChina
| |
Collapse
|
27
|
Si S, Liu H, Xu L, Zhan S. Identification of novel therapeutic targets for chronic kidney disease and kidney function by integrating multi-omics proteome with transcriptome. Genome Med 2024; 16:84. [PMID: 38898508 PMCID: PMC11186236 DOI: 10.1186/s13073-024-01356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 06/05/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) is a progressive disease for which there is no effective cure. We aimed to identify potential drug targets for CKD and kidney function by integrating plasma proteome and transcriptome. METHODS We designed a comprehensive analysis pipeline involving two-sample Mendelian randomization (MR) (for proteins), summary-based MR (SMR) (for mRNA), and colocalization (for coding genes) to identify potential multi-omics biomarkers for CKD and combined the protein-protein interaction, Gene Ontology (GO), and single-cell annotation to explore the potential biological roles. The outcomes included CKD, extensive kidney function phenotypes, and different CKD clinical types (IgA nephropathy, chronic glomerulonephritis, chronic tubulointerstitial nephritis, membranous nephropathy, nephrotic syndrome, and diabetic nephropathy). RESULTS Leveraging pQTLs of 3032 proteins from 3 large-scale GWASs and corresponding blood- and tissue-specific eQTLs, we identified 32 proteins associated with CKD, which were validated across diverse CKD datasets, kidney function indicators, and clinical types. Notably, 12 proteins with prior MR support, including fibroblast growth factor 5 (FGF5), isopentenyl-diphosphate delta-isomerase 2 (IDI2), inhibin beta C chain (INHBC), butyrophilin subfamily 3 member A2 (BTN3A2), BTN3A3, uromodulin (UMOD), complement component 4A (C4a), C4b, centrosomal protein of 170 kDa (CEP170), serologically defined colon cancer antigen 8 (SDCCAG8), MHC class I polypeptide-related sequence B (MICB), and liver-expressed antimicrobial peptide 2 (LEAP2), were confirmed. To our knowledge, 20 novel causal proteins have not been previously reported. Five novel proteins, namely, GCKR (OR 1.17, 95% CI 1.10-1.24), IGFBP-5 (OR 0.43, 95% CI 0.29-0.62), sRAGE (OR 1.14, 95% CI 1.07-1.22), GNPTG (OR 0.90, 95% CI 0.86-0.95), and YOD1 (OR 1.39, 95% CI 1.18-1.64,) passed the MR, SMR, and colocalization analysis. The other 15 proteins were also candidate targets (GATM, AIF1L, DQA2, PFKFB2, NFATC1, activin AC, Apo A-IV, MFAP4, DJC10, C2CD2L, TCEA2, HLA-E, PLD3, AIF1, and GMPR1). These proteins interact with each other, and their coding genes were mainly enrichment in immunity-related pathways or presented specificity across tissues, kidney-related tissue cells, and kidney single cells. CONCLUSIONS Our integrated analysis of plasma proteome and transcriptome data identifies 32 potential therapeutic targets for CKD, kidney function, and specific CKD clinical types, offering potential targets for the development of novel immunotherapies, combination therapies, or targeted interventions.
Collapse
Affiliation(s)
- Shucheng Si
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China
- Peking University Health Science Center, Beijing, 100191, China
| | - Hongyan Liu
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China
- Peking University Health Science Center, Beijing, 100191, China
| | - Lu Xu
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China
- Peking University Health Science Center, Beijing, 100191, China
| | - Siyan Zhan
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China.
- Peking University Health Science Center, Beijing, 100191, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 38 Xueyuan Road, Haidian District, Beijing, 100191, China.
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
- Institute for Artificial Intelligence, Peking University, Beijing, 100871, China.
| |
Collapse
|
28
|
Sikirzhytskaya A, Tyagin I, Sutton SS, Wyatt MD, Safro I, Shtutman M. AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597745. [PMID: 38895485 PMCID: PMC11185689 DOI: 10.1101/2024.06.06.597745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing. Author Summary This manuscript outlines our project involving the application of AGATHA, an AI-based literature mining tool, to discover drugs with the potential for repurposing in the context of neurocognitive disorders. The primary objective is to identify connections between approved medications and specific health conditions through advanced statistical analysis, including techniques like Partial Least Squares Discriminant Analysis (PLSDA) and unsupervised clustering. The methodology involves grouping scientific terms related to different health conditions and genes, followed by building discrimination models to extract lists of disease-specific genes. These genes are then analyzed through pathway analysis to select candidates for drug repurposing.
Collapse
|
29
|
Wang J, Gu R, Kong X, Luan S, Luo YLL. Genome-wide association studies (GWAS) and post-GWAS analyses of impulsivity: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110986. [PMID: 38430953 DOI: 10.1016/j.pnpbp.2024.110986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/30/2024] [Accepted: 02/28/2024] [Indexed: 03/05/2024]
Abstract
Impulsivity is related to a host of mental and behavioral problems. It is a complex construct with many different manifestations, most of which are heritable. The genetic compositions of these impulsivity manifestations, however, remain unclear. A number of genome-wide association studies (GWAS) and post-GWAS analyses have tried to address this issue. We conducted a systematic review of all GWAS and post-GWAS analyses of impulsivity published up to December 2023. Available data suggest that single nucleotide polymorphisms (SNPs) in more than a dozen of genes (e.g., CADM2, CTNNA2, GPM6B) are associated with different measures of impulsivity at genome-wide significant levels. Post-GWAS analyses further show that different measures of impulsivity are subject to different degrees of genetic influence, share few genetic variants, and have divergent genetic overlap with basic personality traits such as extroversion and neuroticism, cognitive ability, psychiatric disorders, substance use, and obesity. These findings shed light on controversies in the conceptualization and measurement of impulsivity, while providing new insights on the underlying mechanisms that yoke impulsivity to psychopathology.
Collapse
Affiliation(s)
- Jiaqi Wang
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China
| | - Ruolei Gu
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; Department of Psychiatry of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 Qingchundong Road, Hangzhou 310016, China
| | - Shenghua Luan
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China
| | - Yu L L Luo
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China.
| |
Collapse
|
30
|
Ghosh S, Bouchard C. Considerations on efforts needed to improve our understanding of the genetics of obesity. Int J Obes (Lond) 2024:10.1038/s41366-024-01528-0. [PMID: 38849463 DOI: 10.1038/s41366-024-01528-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/18/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Affiliation(s)
- Sujoy Ghosh
- Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA.
| | - Claude Bouchard
- Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA.
| |
Collapse
|
31
|
Gong J, Williams DM, Scholes S, Assaad S, Bu F, Hayat S, Zaninotto P, Steptoe A. Unraveling the role of plasma proteins in dementia: insights from two cohort studies in the UK, with causal evidence from Mendelian randomization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.04.24308415. [PMID: 38883777 PMCID: PMC11177911 DOI: 10.1101/2024.06.04.24308415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Population-based proteomics offer a groundbreaking avenue to predict dementia onset. This study employed a proteome-wide, data-driven approach to investigate protein-dementia associations in 229 incident all-cause dementia (ACD) among 3,249 participants from the English Longitudinal Study of Ageing (ELSA) over a median 9.8-year follow-up, then validated in 1,506 incident ACD among 52,745 individuals from the UK Biobank (UKB) over median 13.7 years. NEFL and RPS6KB1 were robustly associated with incident ACD; MMP12 was associated with vascular dementia in ELSA. Additional markers EDA2R and KIM1 (HAVCR1) were identified from sensitivity analyses. Combining NEFL and RPS6KB1 with other factors yielded high predictive accuracy (area under the curve (AUC)=0.871) for incident ACD. Replication in the UKB confirmed associations between identified proteins with various dementia subtypes. Results from reverse Mendelian Randomization also supported the role of several proteins as early dementia biomarkers. These findings underscore proteomics' potential in identifying novel risk screening targets for dementia.
Collapse
|
32
|
Suhre K, Chen Q, Halama A, Mendez K, Dahlin A, Stephan N, Thareja G, Sarwath H, Guturu H, Dwaraka VB, Batzoglou S, Schmidt F, Lasky-Su JA. A genome-wide association study of mass spectrometry proteomics using the Seer Proteograph platform. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.27.596028. [PMID: 38853852 PMCID: PMC11160678 DOI: 10.1101/2024.05.27.596028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Genome-wide association studies (GWAS) with proteomics are essential tools for drug discovery. To date, most studies have used affinity proteomics platforms, which have limited discovery to protein panels covered by the available affinity binders. Furthermore, it is not clear to which extent protein epitope changing variants interfere with the detection of protein quantitative trait loci (pQTLs). Mass spectrometry-based (MS) proteomics can overcome some of these limitations. Here we report a GWAS using the MS-based Seer Proteograph™ platform with blood samples from a discovery cohort of 1,260 American participants and a replication in 325 individuals from Asia, with diverse ethnic backgrounds. We analysed 1,980 proteins quantified in at least 80% of the samples, out of 5,753 proteins quantified across the discovery cohort. We identified 252 and replicated 90 pQTLs, where 30 of the replicated pQTLs have not been reported before. We further investigated 200 of the strongest associated cis-pQTLs previously identified using the SOMAscan and the Olink platforms and found that up to one third of the affinity proteomics pQTLs may be affected by epitope effects, while another third were confirmed by MS proteomics to be consistent with the hypothesis that genetic variants induce changes in protein expression. The present study demonstrates the complementarity of the different proteomics approaches and reports pQTLs not accessible to affinity proteomics, suggesting that many more pQTLs remain to be discovered using MS-based platforms.
Collapse
Affiliation(s)
- Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, 24144 Doha, Qatar
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Qingwen Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, U.S.A
| | - Anna Halama
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, 24144 Doha, Qatar
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Kevin Mendez
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, U.S.A
| | - Amber Dahlin
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, U.S.A
| | - Nisha Stephan
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, 24144 Doha, Qatar
| | - Gaurav Thareja
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, 24144 Doha, Qatar
| | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, 24144 Doha, Qatar
| | | | | | | | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, 24144 Doha, Qatar
| | - Jessica A. Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, U.S.A
| |
Collapse
|
33
|
Gholami M. Novel genetic association between obesity, colorectal cancer, and inflammatory bowel disease. J Diabetes Metab Disord 2024; 23:739-744. [PMID: 38932827 PMCID: PMC11196566 DOI: 10.1007/s40200-023-01343-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 11/06/2023] [Indexed: 06/28/2024]
Abstract
Purpose Obesity/overweight is an important risk factor for CRC and IBD. The aim of this study was to investigate the role of common genetic factors and haplotypes associated with obesity, CRC and IBD. Methods Significant GWAS variants associated with CRC, IBD or obesity were extracted from the GWAS catalog. The common variants between CRC-IBD, CRC-obesity or IBD-obesity were identified. Finally, the haplotypic structure between these diseases was identified, and SNP function analysis, gene-gene expression, protein-protein interactions, gene survival analysis and pathway analysis were performed with the results. Results While the results showed several common variants between CRC and IBD, IBD and obesity, and CRC and obesity identified in previous GWAS, rs3184504 was the only common variant for CRC-IBD-obesity (P ≤ 5E-8). The result also identified a haplotypic block AGCAGT (r2 ≥ 0.8 and D'≥0.08) associated with the common variants of CRC-IBD-obesity. These variants are located on the SH2B3 gene, whose expression level decreases in both colon and rectal cancers (P ≤ 1E-3) and which has protein-protein interaction with inflammation- and cancer-associated genes. Conclusion The rs3184504 variant and the novel haplotype AGCAGT co-occurred in CRC, IBD, obesity, and inflammation. This novel haplotype could potentially be used in genetic panels to identify CRC/IBD susceptibility in obese patients.
Collapse
Affiliation(s)
- Morteza Gholami
- North Research Center, Pasteur Institute of Iran, Amol, Iran
- Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
34
|
Wang L, Zhang W, Fang Z, Lu T, Gu Z, Sun T, Han D, Wang Y, Cao F. Association between Human Blood Proteome and the Risk of Myocardial Infarction. Rev Cardiovasc Med 2024; 25:199. [PMID: 39076342 PMCID: PMC11270110 DOI: 10.31083/j.rcm2506199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/25/2023] [Accepted: 01/10/2024] [Indexed: 07/31/2024] Open
Abstract
Background The objective of this study is to estimate the causal relationship between plasma proteins and myocardial infarction (MI) through Mendelian randomization (MR), predict potential target-mediated side effects associated with protein interventions, and ensure a comprehensive assessment of clinical safety. Methods From 3 proteome genome-wide association studies (GWASs) involving 9775 European participants, 331 unique blood proteins were screened and chosed. The summary data related to MI were derived from a GWAS meta-analysis, incorporating approximately 61,000 cases and 577,000 controls. The assessment of associations between blood proteins and MI was conducted through MR analyses. A phenome-wide MR (Phe-MR) analysis was subsequently employed to determine the potential on-target side effects of protein interventions. Results Causal mediators for MI were identified, encompassing cardiotrophin-1 (CT-1) (odds ratio [OR] per SD increase: 1.16; 95% confidence interval [CI]: 1.13-1.18; p = 1.29 × 10 - 31 ), Selenoprotein S (SELENOS) (OR: 1.16; 95% CI: 1.13-1.20; p = 4.73 × 10 - 24 ), killer cell immunoglobulin-like receptor 2DS2 (KIR2DS2) (OR: 0.93; 95% CI: 0.90-0.96; p = 1.08 × 10 - 5 ), vacuolar protein sorting-associated protein 29 (VPS29) (OR: 0.92; 95% CI: 0.90-0.94; p = 8.05 × 10 - 13 ), and histo-blood group ABO system transferase (NAGAT) (OR: 1.05; 95% CI: 1.03-1.07; p = 1.41 × 10 - 5 ). In the Phe-MR analysis, memory loss risk was mediated by CT-1, VPS29 exhibited favorable effects on the risk of 5 diseases, and KIR2DS2 showed no predicted detrimental side effects. Conclusions Elevated genetic predictions of KIR2DS2 and VPS29 appear to be linked to a reduced risk of MI, whereas an increased risk is associated with CT-1, SELENOS, and NAGAT. The characterization of side effect profiles aids in the prioritization of drug targets. Notably, KIR2DS2 emerges as a potentially promising target for preventing and treating MI, devoid of predicted detrimental side effects.
Collapse
Affiliation(s)
- Linghuan Wang
- Department of Medicine School, Nankai University, 300071 Tianjin, China
- Department of Cardiology, National Research Centre for Geriatric Diseases & National Key Lab for Chronic Kidney Disease & Second Medical Centre of Chinese PLA General Hospital, 100853 Beijing, China
| | - Weiwei Zhang
- Department of Medicine School, Nankai University, 300071 Tianjin, China
- Department of Cardiology, National Research Centre for Geriatric Diseases & National Key Lab for Chronic Kidney Disease & Second Medical Centre of Chinese PLA General Hospital, 100853 Beijing, China
| | - Zhiyi Fang
- Department of Medicine School, Nankai University, 300071 Tianjin, China
- Department of Cardiology, National Research Centre for Geriatric Diseases & National Key Lab for Chronic Kidney Disease & Second Medical Centre of Chinese PLA General Hospital, 100853 Beijing, China
| | - Tingting Lu
- Department of Medicine School, Nankai University, 300071 Tianjin, China
- Department of Cardiology, National Research Centre for Geriatric Diseases & National Key Lab for Chronic Kidney Disease & Second Medical Centre of Chinese PLA General Hospital, 100853 Beijing, China
| | - Zhenghui Gu
- Chinese PLA Medical College & Department of Cardiology, National Clinic Research Center Geriatric Disease, 2nd Medical Center of Chinese PLA General Hospital, 100853 Beijing, China
| | - Ting Sun
- Department of Cardiology, National Research Centre for Geriatric Diseases & National Key Lab for Chronic Kidney Disease & Second Medical Centre of Chinese PLA General Hospital, 100853 Beijing, China
| | - Dong Han
- Department of Cardiology, National Research Centre for Geriatric Diseases & National Key Lab for Chronic Kidney Disease & Second Medical Centre of Chinese PLA General Hospital, 100853 Beijing, China
| | - Yabin Wang
- Department of Cardiology, National Research Centre for Geriatric Diseases & National Key Lab for Chronic Kidney Disease & Second Medical Centre of Chinese PLA General Hospital, 100853 Beijing, China
| | - Feng Cao
- Department of Medicine School, Nankai University, 300071 Tianjin, China
- Department of Cardiology, National Research Centre for Geriatric Diseases & National Key Lab for Chronic Kidney Disease & Second Medical Centre of Chinese PLA General Hospital, 100853 Beijing, China
| |
Collapse
|
35
|
Yoo L, Mendoza D, Richard AJ, Stephens JM. KAT8 beyond Acetylation: A Survey of Its Epigenetic Regulation, Genetic Variability, and Implications for Human Health. Genes (Basel) 2024; 15:639. [PMID: 38790268 PMCID: PMC11121512 DOI: 10.3390/genes15050639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/07/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024] Open
Abstract
Lysine acetyltransferase 8, also known as KAT8, is an enzyme involved in epigenetic regulation, primarily recognized for its ability to modulate histone acetylation. This review presents an overview of KAT8, emphasizing its biological functions, which impact many cellular processes and range from chromatin remodeling to genetic and epigenetic regulation. In many model systems, KAT8's acetylation of histone H4 lysine 16 (H4K16) is critical for chromatin structure modification, which influences gene expression, cell proliferation, differentiation, and apoptosis. Furthermore, this review summarizes the observed genetic variability within the KAT8 gene, underscoring the implications of various single nucleotide polymorphisms (SNPs) that affect its functional efficacy and are linked to diverse phenotypic outcomes, ranging from metabolic traits to neurological disorders. Advanced insights into the structural biology of KAT8 reveal its interaction with multiprotein assemblies, such as the male-specific lethal (MSL) and non-specific lethal (NSL) complexes, which regulate a wide range of transcriptional activities and developmental functions. Additionally, this review focuses on KAT8's roles in cellular homeostasis, stem cell identity, DNA damage repair, and immune response, highlighting its potential as a therapeutic target. The implications of KAT8 in health and disease, as evidenced by recent studies, affirm its importance in cellular physiology and human pathology.
Collapse
Affiliation(s)
- Lindsey Yoo
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - David Mendoza
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Allison J. Richard
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
| | - Jacqueline M. Stephens
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| |
Collapse
|
36
|
Feng T, Jie M, Deng K, Yang J, Jiang H. Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer. Clin Chim Acta 2024; 558:119675. [PMID: 38631604 DOI: 10.1016/j.cca.2024.119675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/30/2024] [Accepted: 04/14/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis. METHODS We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models. RESULTS In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively. CONCLUSIONS This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.
Collapse
Affiliation(s)
- Tong Feng
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Minwen Jie
- Laboratory for Aging and Cancer Research, Frontiers Science Center Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Kai Deng
- Department of Gastroenterology & Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Jinlin Yang
- Department of Gastroenterology & Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
| | - Hao Jiang
- Laboratory for Aging and Cancer Research, Frontiers Science Center Disease-related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
| |
Collapse
|
37
|
Assary E, Coleman J, Hemani G, van Der Veijer M, Howe L, Palviainen T, Grasby K, Ahlskog R, Nygaard M, Cheesman R, Lim K, Reynolds C, Ordoñana J, Colodro-Conde L, Gordon S, Madrid-Valero J, Thalamuthu A, Hottenga JJ, Mengel-From J, Armstrong NJ, Sachdev P, Lee T, Brodaty H, Trollor J, Wright M, Ames D, Catts V, Latvala A, Vuoksimaa E, Mallard T, Harden K, Tucker-Drob E, Oskarsson S, Hammond C, Christensen K, Taylor M, Lundström S, Larsson H, Karlsson R, Pedersen N, Mather K, Medland S, Boomsma D, Martin N, Plomin R, Bartels M, Lichtenstein P, Kaprio J, Eley T, Davies N, Munroe P, Keers R. Genetics of environmental sensitivity to psychiatric and neurodevelopmental phenotypes: evidence from GWAS of monozygotic twins. RESEARCH SQUARE 2024:rs.3.rs-4333635. [PMID: 38746362 PMCID: PMC11092831 DOI: 10.21203/rs.3.rs-4333635/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Individual sensitivity to environmental exposures may be genetically influenced. This genotype-by-environment interplay implies differences in phenotypic variance across genotypes. However, environmental sensitivity genetic variants have proven challenging to detect. GWAS of monozygotic twin differences is a family-based variance analysis method, which is more robust to systemic biases that impact population-based methods. We combined data from up to 21,792 monozygotic twins (10,896 pairs) from 11 studies to conduct the largest GWAS meta-analysis of monozygotic phenotypic differences in children and adolescents/adults for seven psychiatric and neurodevelopmental phenotypes: attention deficit hyperactivity disorder (ADHD) symptoms, autistic traits, anxiety and depression symptoms, psychotic-like experiences, neuroticism, and wellbeing. The SNP-heritability of variance in these phenotypes were estimated (h2: 0% to 18%), but were imprecise. We identified a total of 13 genome-wide significant associations (SNP, gene, and gene-set), including genes related to stress-reactivity for depression, growth factor-related genes for autistic traits and catecholamine uptake-related genes for psychotic-like experiences. Monozygotic twins are an important new source of evidence about the genetics of environmental sensitivity.
Collapse
Affiliation(s)
| | - Jonathan Coleman
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London
| | | | | | | | - Teemu Palviainen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Karen Mather
- Centre for Healthy Brain Ageing, Psychiatry, University of New South Wales (UNSW)
| | | | - D Boomsma
- Vrije Universiteit Amsterdam, The Netherlands
| | | | - Robert Plomin
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London
| | | | | | | | | | | | | | | |
Collapse
|
38
|
Zhang W, Lu T, Sladek R, Li Y, Najafabadi H, Dupuis J. SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals. Bioinformatics 2024; 40:btae295. [PMID: 38688586 PMCID: PMC11105950 DOI: 10.1093/bioinformatics/btae295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 04/11/2024] [Accepted: 04/29/2024] [Indexed: 05/02/2024] Open
Abstract
MOTIVATION Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus. RESULTS We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals. AVAILABILITY AND IMPLEMENTATION SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc.
Collapse
Affiliation(s)
- Wenmin Zhang
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec H3A 1E3, Canada
- Montreal Heart Institute, Université de Montréal, Montreal, Quebec H1T 1C8, Canada
| | - Tianyuan Lu
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Robert Sladek
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec H3A 1E3, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0C7, Canada
- Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec H3A 0G1, Canada
| | - Yue Li
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec H3A 1E3, Canada
- School of Computer Science, McGill University, Montreal, Quebec H3A 2A7, Canada
| | - Hamed Najafabadi
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec H3A 1E3, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0C7, Canada
- Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec H3A 0G1, Canada
| | - Josée Dupuis
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec H3A 1E3, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, McGill College, QC H3A 1Y7, Canada
| |
Collapse
|
39
|
Lin R, Zhu Y, Chen W, Wang Z, Wang Y, Du J. Identification of Circulating Inflammatory Proteins Associated with Calcific Aortic Valve Stenosis by Multiplex Analysis. Cardiovasc Toxicol 2024; 24:499-512. [PMID: 38589550 DOI: 10.1007/s12012-024-09854-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 03/29/2024] [Indexed: 04/10/2024]
Abstract
Calcific aortic valve stenosis (CAVS) is characterized by increasing inflammation and progressive calcification in the aortic valve leaflets and is a major cause of death in the aging population. This study aimed to identify the inflammatory proteins involved in CAVS and provide potential therapeutic targets. We investigated the observational and causal associations of 92 inflammatory proteins, which were measured using affinity-based proteomic assays. Firstly, the case-control cohort identified differential proteins associated with the occurrence and progression of CAVS. Subsequently, we delved into exploring the causal impacts of these associated proteins through Mendelian randomization. This involved utilizing genetic instruments derived from cis-protein quantitative loci identified in genome-wide association studies, encompassing a cohort of over 400,000 individuals. Finally, we investigated the gene transcription and protein expression levels of inflammatory proteins by single-cell and immunohistochemistry analysis. Multivariate logistic regression and spearman's correlation analysis showed that five proteins showed a significant positive correlation with disease severity. Mendelian randomization showed that elevated levels of two proteins, namely, matrix metallopeptidase-1 (MMP1) and sirtuin 2 (SIRT2), were associated with an increased risk of CAVS. Immunohistochemistry and single-cell transcriptomes showed that expression levels of MMP1 and SIRT2 at the tissue and cell levels were significantly higher in calcified valves than in non-calcified control valves. These findings indicate that MMP1 and SIRT2 are causally related to CAVS and open up the possibility for identifying novel therapeutic targets.
Collapse
Affiliation(s)
- Rui Lin
- The Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Yuexin Zhu
- The Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Weiyao Chen
- The Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Zhiao Wang
- The Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Yuan Wang
- The Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road, Chaoyang District, Beijing, 100029, China.
| | - Jie Du
- The Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road, Chaoyang District, Beijing, 100029, China.
| |
Collapse
|
40
|
Kurgan N, Kjærgaard Larsen J, Deshmukh AS. Harnessing the power of proteomics in precision diabetes medicine. Diabetologia 2024; 67:783-797. [PMID: 38345659 DOI: 10.1007/s00125-024-06097-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/20/2023] [Indexed: 03/21/2024]
Abstract
Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.
Collapse
Affiliation(s)
- Nigel Kurgan
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Jeppe Kjærgaard Larsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Atul S Deshmukh
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
| |
Collapse
|
41
|
Gagnon E, Girard A, Bourgault J, Abner E, Gill D, Thériault S, Vohl MC, Tchernof A, Esko T, Mathieu P, Arsenault BJ. Genetic assessment of efficacy and safety profiles of coagulation cascade proteins identifies Factors II and XI as actionable anticoagulant targets. EUROPEAN HEART JOURNAL OPEN 2024; 4:oeae043. [PMID: 38933427 PMCID: PMC11200102 DOI: 10.1093/ehjopen/oeae043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/29/2024] [Accepted: 03/05/2024] [Indexed: 06/28/2024]
Abstract
Aims Anticoagulants are routinely used by millions of patients worldwide to prevent blood clots. Yet, problems with anticoagulant therapy remain, including a persistent and cumulative bleeding risk in patients undergoing prolonged anticoagulation. New safer anticoagulant targets are needed. Methods and results To prioritize anticoagulant targets with the strongest efficacy [venous thromboembolism (VTE) prevention] and safety (low bleeding risk) profiles, we performed two-sample Mendelian randomization and genetic colocalization. We leveraged three large-scale plasma protein data sets (deCODE as discovery data set and Fenland and Atherosclerosis Risk in Communities as replication data sets] and one liver gene expression data set (Institut Universitaire de Cardiologie et de Pneumologie de Québec bariatric biobank) to evaluate evidence for a causal effect of 26 coagulation cascade proteins on VTE from a new genome-wide association meta-analysis of 44 232 VTE cases and 847 152 controls, stroke subtypes, bleeding outcomes, and parental lifespan as an overall measure of efficacy/safety ratio. A 1 SD genetically predicted reduction in F2 blood levels was associated with lower risk of VTE [odds ratio (OR) = 0.44, 95% confidence interval (CI) = 0.38-0.51, P = 2.6e-28] and cardioembolic stroke risk (OR = 0.55, 95% CI = 0.39-0.76, P = 4.2e-04) but not with bleeding (OR = 1.13, 95% CI = 0.93-1.36, P = 2.2e-01). Genetically predicted F11 reduction was associated with lower risk of VTE (OR = 0.61, 95% CI = 0.58-0.64, P = 4.1e-85) and cardioembolic stroke (OR = 0.77, 95% CI = 0.69-0.86, P = 4.1e-06) but not with bleeding (OR = 1.01, 95% CI = 0.95-1.08, P = 7.5e-01). These Mendelian randomization associations were concordant across the three blood protein data sets and the hepatic gene expression data set as well as colocalization analyses. Conclusion These results provide strong genetic evidence that F2 and F11 may represent safe and efficacious therapeutic targets to prevent VTE and cardioembolic strokes without substantially increasing bleeding risk.
Collapse
Affiliation(s)
- Eloi Gagnon
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec, Y-3106, Pavillon Marguerite D'Youville, 2725 chemin Ste-Foy, Quebec, QC, Canada, G1V 4G5
| | - Arnaud Girard
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec, Y-3106, Pavillon Marguerite D'Youville, 2725 chemin Ste-Foy, Quebec, QC, Canada, G1V 4G5
| | - Jérôme Bourgault
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec, Y-3106, Pavillon Marguerite D'Youville, 2725 chemin Ste-Foy, Quebec, QC, Canada, G1V 4G5
| | - Erik Abner
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec, Y-3106, Pavillon Marguerite D'Youville, 2725 chemin Ste-Foy, Quebec, QC, Canada, G1V 4G5
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Sébastien Thériault
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec, Y-3106, Pavillon Marguerite D'Youville, 2725 chemin Ste-Foy, Quebec, QC, Canada, G1V 4G5
- Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Université Laval, Quebec, QC, Canada
| | - Marie-Claude Vohl
- School of Nutrition, Université Laval, Quebec, QC, Canada
- Centre Nutrition, Santé et société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Quebec, QC, Canada
| | - André Tchernof
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec, Y-3106, Pavillon Marguerite D'Youville, 2725 chemin Ste-Foy, Quebec, QC, Canada, G1V 4G5
- School of Nutrition, Université Laval, Quebec, QC, Canada
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Patrick Mathieu
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec, Y-3106, Pavillon Marguerite D'Youville, 2725 chemin Ste-Foy, Quebec, QC, Canada, G1V 4G5
- Department of Surgery, Faculty of Medicine, Université Laval, Quebec, QC, Canada
| | - Benoit J Arsenault
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec, Y-3106, Pavillon Marguerite D'Youville, 2725 chemin Ste-Foy, Quebec, QC, Canada, G1V 4G5
- Department of Medicine, Faculty of Medicine, 1050 Av. de la Médecine, Québec City, Quebec G1V 0A6, Canada
| |
Collapse
|
42
|
Sun Z, Yun Z, Lin J, Sun X, Wang Q, Duan J, Li C, Zhang X, Xu S, Wang Z, Xiong X, Yao K. Comprehensive mendelian randomization analysis of plasma proteomics to identify new therapeutic targets for the treatment of coronary heart disease and myocardial infarction. J Transl Med 2024; 22:404. [PMID: 38689297 PMCID: PMC11061979 DOI: 10.1186/s12967-024-05178-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 04/05/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Ischemic heart disease is one of the leading causes of mortality worldwide, and thus calls for development of more effective therapeutic strategies. This study aimed to identify potential therapeutic targets for coronary heart disease (CHD) and myocardial infarction (MI) by investigating the causal relationship between plasma proteins and these conditions. METHODS A two-sample Mendelian randomization (MR) study was performed to evaluate more than 1600 plasma proteins for their causal associations with CHD and MI. The MR findings were further confirmed through Bayesian colocalization, Summary-data-based Mendelian Randomization (SMR), and Transcriptome-Wide Association Studies (TWAS) analyses. Further analyses, including enrichment analysis, single-cell analysis, MR analysis of cardiovascular risk factors, phenome-wide Mendelian Randomization (Phe-MR), and protein-protein interaction (PPI) network construction were conducted to verify the roles of selected causal proteins. RESULTS Thirteen proteins were causally associated with CHD, seven of which were also causal for MI. Among them, FES and PCSK9 were causal proteins for both diseases as determined by several analytical methods. PCSK9 was a risk factor of CHD (OR = 1.25, 95% CI: 1.13-1.38, P = 7.47E-06) and MI (OR = 1.36, 95% CI: 1.21-1.54, P = 2.30E-07), whereas FES was protective against CHD (OR = 0.68, 95% CI: 0.59-0.79, P = 6.40E-07) and MI (OR = 0.65, 95% CI: 0.54-0.77, P = 5.38E-07). Further validation through enrichment and single-cell analysis confirmed the causal effects of these proteins. Moreover, MR analysis of cardiovascular risk factors, Phe-MR, and PPI network provided insights into the potential drug development based on the proteins. CONCLUSIONS This study investigated the causal pathways associated with CHD and MI, highlighting the protective and risk roles of FES and PCSK9, respectively. FES. Specifically, the results showed that these proteins are promising therapeutic targets for future drug development.
Collapse
Affiliation(s)
- Ziyi Sun
- Department of Cardiovascular, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China
- Graduate School, Beijing University of Chinese Medicine, Beijing, 10029, China
| | - Zhangjun Yun
- Graduate School, Beijing University of Chinese Medicine, Beijing, 10029, China
- Department of Oncology and Hematology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 10070, China
| | - Jianguo Lin
- Department of Cardiovascular, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China
- Graduate School, China Academy of Chinese Medical Sciences, Beijing, 10070, China
| | - Xiaoning Sun
- Department of Cardiovascular, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China
- Graduate School, China Academy of Chinese Medical Sciences, Beijing, 10070, China
| | - Qingqing Wang
- Department of Cardiovascular, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China
| | - Jinlong Duan
- Department of Andrology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China
| | - Cheng Li
- Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, 10040, China
| | - Xiaoxiao Zhang
- Department of Cardiovascular, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China
- Graduate School, China Academy of Chinese Medical Sciences, Beijing, 10070, China
| | - Siyu Xu
- Department of Cardiovascular, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China
- Graduate School, Beijing University of Chinese Medicine, Beijing, 10029, China
| | - Zeqi Wang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 10070, China
| | - Xingjiang Xiong
- Department of Cardiovascular, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China.
| | - Kuiwu Yao
- Department of Cardiovascular, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China.
- Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, 10040, China.
| |
Collapse
|
43
|
Smith-Byrne K, Hedman Å, Dimitriou M, Desai T, Sokolov AV, Schioth HB, Koprulu M, Pietzner M, Langenberg C, Atkins J, Penha RC, McKay J, Brennan P, Zhou S, Richards BJ, Yarmolinsky J, Martin RM, Borlido J, Mu XJ, Butterworth A, Shen X, Wilson J, Assimes TL, Hung RJ, Amos C, Purdue M, Rothman N, Chanock S, Travis RC, Johansson M, Mälarstig A. Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers. Nat Commun 2024; 15:3621. [PMID: 38684708 PMCID: PMC11059161 DOI: 10.1038/s41467-024-46834-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 03/05/2024] [Indexed: 05/02/2024] Open
Abstract
Circulating proteins can reveal key pathways to cancer and identify therapeutic targets for cancer prevention. We investigate 2,074 circulating proteins and risk of nine common cancers (bladder, breast, endometrium, head and neck, lung, ovary, pancreas, kidney, and malignant non-melanoma) using cis protein Mendelian randomisation and colocalization. We conduct additional analyses to identify adverse side-effects of altering risk proteins and map cancer risk proteins to drug targets. Here we find 40 proteins associated with common cancers, such as PLAUR and risk of breast cancer [odds ratio per standard deviation increment: 2.27, 1.88-2.74], and with high-mortality cancers, such as CTRB1 and pancreatic cancer [0.79, 0.73-0.85]. We also identify potential adverse effects of protein-altering interventions to reduce cancer risk, such as hypertension. Additionally, we report 18 proteins associated with cancer risk that map to existing drugs and 15 that are not currently under clinical investigation. In sum, we identify protein-cancer links that improve our understanding of cancer aetiology. We also demonstrate that the wider consequence of any protein-altering intervention on well-being and morbidity is required to interpret any utility of proteins as potential future targets for therapeutic prevention.
Collapse
Affiliation(s)
- Karl Smith-Byrne
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK.
| | - Åsa Hedman
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
- Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - Marios Dimitriou
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
- Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - Trishna Desai
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK
| | - Alexandr V Sokolov
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Helgi B Schioth
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare Institute, Queen Mary University of London, London, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare Institute, Queen Mary University of London, London, UK
| | - Joshua Atkins
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK
| | - Ricardo Cortez Penha
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - James McKay
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Paul Brennan
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Sirui Zhou
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Brent J Richards
- Departments of Medicine (Endocrinology), Human Genetics, Epidemiology and Biostatistics, McGill University, Montréal, QC, Canada
| | - James Yarmolinsky
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Richard M Martin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UK
| | - Joana Borlido
- Cancer Immunology Discovery, Pfizer Worldwide Research and Development Medicine, Pfizer Inc, San Diego, USA
| | - Xinmeng J Mu
- Oncology Research Unit, Pfizer Worldwide Research and Development Medicine, Pfizer Inc, San Diego, USA
| | - Adam Butterworth
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Xia Shen
- Usher Institute, MRC Human Genetics Unit, University of Edinburgh, Edinburgh, UK
| | - Jim Wilson
- Usher Institute, MRC Human Genetics Unit, University of Edinburgh, Edinburgh, UK
| | - Themistocles L Assimes
- Division of Cardiovascular Medicine and the Cardiovascular Institute, School of Medicine, Stanford University, Stanford, USA
| | - Rayjean J Hung
- Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System and University of Toronto, Toronto, Canada
| | - Christopher Amos
- Department of Medicine, Epidemiology Section, Institute for Clinical and Translational Research, Baylor Medical College, Houston, USA
| | - Mark Purdue
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, USA
| | - Nathaniel Rothman
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, USA
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, USA
| | - Ruth C Travis
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Anders Mälarstig
- External Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
- Department of Medicine, Karolinska Institute, Stockholm, Sweden
| |
Collapse
|
44
|
Yang N, Shi L, Xu P, Ren F, Lv S, Li C, Qi X. Identification of potential drug targets for insomnia by Mendelian randomization analysis based on plasma proteomics. Front Neurol 2024; 15:1380321. [PMID: 38725646 PMCID: PMC11079244 DOI: 10.3389/fneur.2024.1380321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/12/2024] [Indexed: 05/12/2024] Open
Abstract
Introduction Insomnia, a common clinical disorder, significantly impacts the physical and mental well-being of patients. Currently, available hypnotic medications are unsatisfactory due to adverse reactions and dependency, necessitating the identification of new drug targets for the treatment of insomnia. Methods In this study, we utilized 734 plasma proteins as genetic instruments obtained from genome-wide association studies to conduct a Mendelian randomization analysis, with insomnia as the outcome variable, to identify potential drug targets for insomnia. Additionally, we validated our results externally using other datasets. Sensitivity analyses entailed reverse Mendelian randomization analysis, Bayesian co-localization analysis, and phenotype scanning. Furthermore, we constructed a protein-protein interaction network to elucidate potential correlations between the identified proteins and existing targets. Results Mendelian randomization analysis indicated that elevated levels of TGFBI (OR = 1.01; 95% CI, 1.01-1.02) and PAM ((OR = 1.01; 95% CI, 1.01-1.02) in plasma are associated with an increased risk of insomnia, with external validation supporting these findings. Moreover, there was no evidence of reverse causality for these two proteins. Co-localization analysis confirmed that PAM (coloc.abf-PPH4 = 0.823) shared the same variant with insomnia, further substantiating its potential role as a therapeutic target. There are interactive relationships between the potential proteins and existing targets of insomnia. Conclusion Overall, our findings suggested that elevated plasma levels of TGFBI and PAM are connected with an increased risk of insomnia and might be promising therapeutic targets, particularly PAM. However, further exploration is necessary to fully understand the underlying mechanisms involved.
Collapse
Affiliation(s)
- Ni Yang
- Department of First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Liangyuan Shi
- Qingdao Traditional Chinese Medicine Hospital (Qingdao Hiser Hospital) Qingdao Hiser Hospital Affiliated of Qingdao University, Qingdao, China
| | - Pengfei Xu
- Qingdao Traditional Chinese Medicine Hospital (Qingdao Hiser Hospital) Qingdao Hiser Hospital Affiliated of Qingdao University, Qingdao, China
| | - Fang Ren
- Department of Laboratory, Jimo District Qingdao Hospital of Traditional Chinese Medicine, Qingdao, China
| | - Shimeng Lv
- Department of First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Chunlin Li
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xianghua Qi
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| |
Collapse
|
45
|
Yi H, Yang Q, Repaci C, Lee CM, Heo G, Timsina J, Gorijala P, Yang C, Budde J, Wang L, Cruchaga C, Sung YJ. TOPMed imputed genomics enhances genomic atlas of the human proteome in brain, cerebrospinal fluid, and plasma. Sci Data 2024; 11:387. [PMID: 38627416 PMCID: PMC11021418 DOI: 10.1038/s41597-024-03140-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 03/14/2024] [Indexed: 04/19/2024] Open
Abstract
Comprehensive expression quantitative trait loci studies have been instrumental for understanding tissue-specific gene regulation and pinpointing functional genes for disease-associated loci in a tissue-specific manner. Compared to gene expressions, proteins more directly affect various biological processes, often dysregulated in disease, and are important drug targets. We previously performed and identified tissue-specific protein quantitative trait loci in brain, cerebrospinal fluid, and plasma. We now enhance this work by analyzing more proteins (1,300 versus 1,079) and an almost twofold increase in high quality imputed genetic variants (8.4 million versus 4.4 million) by using TOPMed reference panel. We identified 38 genomic regions associated with 43 proteins in brain, 150 regions associated with 247 proteins in cerebrospinal fluid, and 95 regions associated with 145 proteins in plasma. Compared to our previous study, this study newly identified 12 loci in brain, 30 loci in cerebrospinal fluid, and 22 loci in plasma. Our improved genomic atlas uncovers the genetic control of protein regulation across multiple tissues. These resources are accessible through the Online Neurodegenerative Trait Integrative Multi-Omics Explorer for use by the scientific community.
Collapse
Affiliation(s)
- Heng Yi
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Qijun Yang
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Charlie Repaci
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Cheolmin Matthew Lee
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Institute for Informatics, Washington University School of Medicine, St. Louis, MO, USA
| | - Gyujin Heo
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Chengran Yang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John Budde
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurologic Diseases, Washington University, St. Louis, MO, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA.
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA.
| |
Collapse
|
46
|
Rich AL, Lin P, Gamazon ER, Zinkel SS. The broad impact of cell death genes on the human disease phenome. Cell Death Dis 2024; 15:251. [PMID: 38589365 PMCID: PMC11002008 DOI: 10.1038/s41419-024-06632-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/10/2024]
Abstract
Cell death mediated by genetically defined signaling pathways influences the health and dynamics of all tissues, however the tissue specificity of cell death pathways and the relationships between these pathways and human disease are not well understood. We analyzed the expression profiles of an array of 44 cell death genes involved in apoptosis, necroptosis, and pyroptosis cell death pathways across 49 human tissues from GTEx, to elucidate the landscape of cell death gene expression across human tissues, and the relationship between tissue-specific genetically determined expression and the human phenome. We uncovered unique cell death gene expression profiles across tissue types, suggesting there are physiologically distinct cell death programs in different tissues. Using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank (n ~ 500,000), we evaluated 513 traits encompassing ICD-10 defined diagnoses and laboratory-derived traits. Our analysis revealed hundreds of significant (FDR < 0.05) associations between genetically regulated cell death gene expression and an array of human phenotypes encompassing both clinical diagnoses and hematologic parameters, which were independently validated in another large-scale DNA biobank (BioVU) at Vanderbilt University Medical Center (n = 94,474) with matching phenotypes. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits. Our findings are also concordant with independently published studies (e.g. associations between BCL2L11/BIM expression and platelet & lymphocyte counts). Overall, these results suggest that cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.
Collapse
Affiliation(s)
- Abigail L Rich
- Department of Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Phillip Lin
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Sandra S Zinkel
- Department of Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Cell and Developmental Biology, Vanderbilt University Medical Center, Nashville, TN, USA.
| |
Collapse
|
47
|
Kalnapenkis A, Jõeloo M, Lepik K, Kukuškina V, Kals M, Alasoo K, Mägi R, Esko T, Võsa U. Genetic determinants of plasma protein levels in the Estonian population. Sci Rep 2024; 14:7694. [PMID: 38565889 PMCID: PMC10987560 DOI: 10.1038/s41598-024-57966-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 03/23/2024] [Indexed: 04/04/2024] Open
Abstract
The proteome holds great potential as an intermediate layer between the genome and phenome. Previous protein quantitative trait locus studies have focused mainly on describing the effects of common genetic variations on the proteome. Here, we assessed the impact of the common and rare genetic variations as well as the copy number variants (CNVs) on 326 plasma proteins measured in up to 500 individuals. We identified 184 cis and 94 trans signals for 157 protein traits, which were further fine-mapped to credible sets for 101 cis and 87 trans signals for 151 proteins. Rare genetic variation contributed to the levels of 7 proteins, with 5 cis and 14 trans associations. CNVs were associated with the levels of 11 proteins (7 cis and 5 trans), examples including a 3q12.1 deletion acting as a hub for multiple trans associations; and a CNV overlapping NAIP, a sensor component of the NAIP-NLRC4 inflammasome which is affecting pro-inflammatory cytokine interleukin 18 levels. In summary, this work presents a comprehensive resource of genetic variation affecting the plasma protein levels and provides the interpretation of identified effects.
Collapse
Affiliation(s)
- Anette Kalnapenkis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.
| | - Maarja Jõeloo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Kaido Lepik
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Viktorija Kukuškina
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| |
Collapse
|
48
|
Gagnon E, Arsenault BJ. Drug target Mendelian randomization supports apolipoprotein C3-lowering for lipoprotein-lipid levels reductions and cardiovascular diseases prevention. Atherosclerosis 2024; 391:117501. [PMID: 38547584 DOI: 10.1016/j.atherosclerosis.2024.117501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 04/12/2024]
Abstract
BACKGROUND AND AIMS Inhibitors of apolipoprotein C-III (apoC3) are currently approved for the reduction of triglyceride levels in patients with Familial Chylomicronemia Syndrome. We used drug target Mendelian randomization (MR) to assess the effect of genetically predicted decrease in apoC3 blood protein levels on cardiometabolic traits and diseases. METHODS We quantified lifelong reductions in apoC3 blood levels by selecting all genome wide significant and independent (r2<0.1) single nucleotide polymorphisms (SNPs) in the APOC3 gene region ±1 Mb, from three genome-wide association studies (GWAS) of apoC3 blood protein levels (deCODE, n = 35,378, Fenland, n = 10,708 and ARIC, n = 7213). We included the largest GWASes on 18 cardiometabolic traits and 9 cardiometabolic diseases as study outcomes. RESULTS A one standard deviation lowering in apoC3 blood protein levels was associated with lower triglycerides, apolipoprotein B, low-density lipoprotein cholesterol, alanine aminotransferase, and glomerular filtration rate as well as higher high-density lipoprotein cholesterol levels. ApoC3 lowering was also associated with lower risk of acute pancreatitis (odds ratio [OR] = 0.91 95% CI = 0.82 to 1.00), aortic stenosis (OR = 0.82 95% CI = 0.73 to 0.93), and coronary artery disease (OR = 0.86 95% CI = 0.80 to 0.93), and was associated with increased parental lifespan (0.06 95% CI = 0.03-0.09 years). These results were concordant across robust MR methods, the three protein datasets and upon adjustment for APOA1, APOA4 and APOA5 using a multivariable MR framework. CONCLUSIONS These results provide evidence that apoC3 lowering could result in widespread benefits for cardiometabolic health and encourage the launch of trials on apoC3 inhibition for coronary artery disease prevention.
Collapse
Affiliation(s)
- Eloi Gagnon
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada
| | - Benoit J Arsenault
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada; Department of Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada.
| |
Collapse
|
49
|
Pietzner M, Uluvar B, Kolnes KJ, Jeppesen PB, Frivold SV, Skattebo Ø, Johansen EI, Skålhegg BS, Wojtaszewski JFP, Kolnes AJ, Yeo GSH, O'Rahilly S, Jensen J, Langenberg C. Systemic proteome adaptions to 7-day complete caloric restriction in humans. Nat Metab 2024; 6:764-777. [PMID: 38429390 DOI: 10.1038/s42255-024-01008-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/01/2024] [Indexed: 03/03/2024]
Abstract
Surviving long periods without food has shaped human evolution. In ancient and modern societies, prolonged fasting was/is practiced by billions of people globally for religious purposes, used to treat diseases such as epilepsy, and recently gained popularity as weight loss intervention, but we still have a very limited understanding of the systemic adaptions in humans to extreme caloric restriction of different durations. Here we show that a 7-day water-only fast leads to an average weight loss of 5.7 kg (±0.8 kg) among 12 volunteers (5 women, 7 men). We demonstrate nine distinct proteomic response profiles, with systemic changes evident only after 3 days of complete calorie restriction based on in-depth characterization of the temporal trajectories of ~3,000 plasma proteins measured before, daily during, and after fasting. The multi-organ response to complete caloric restriction shows distinct effects of fasting duration and weight loss and is remarkably conserved across volunteers with >1,000 significantly responding proteins. The fasting signature is strongly enriched for extracellular matrix proteins from various body sites, demonstrating profound non-metabolic adaptions, including extreme changes in the brain-specific extracellular matrix protein tenascin-R. Using proteogenomic approaches, we estimate the health consequences for 212 proteins that change during fasting across ~500 outcomes and identified putative beneficial (SWAP70 and rheumatoid arthritis or HYOU1 and heart disease), as well as adverse effects. Our results advance our understanding of prolonged fasting in humans beyond a merely energy-centric adaptions towards a systemic response that can inform targeted therapeutic modulation.
Collapse
Affiliation(s)
- Maik Pietzner
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
| | - Burulça Uluvar
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Kristoffer J Kolnes
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
| | - Per B Jeppesen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - S Victoria Frivold
- Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Øyvind Skattebo
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Egil I Johansen
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Bjørn S Skålhegg
- Department of Nutrition, Division for Molecular Nutrition, University of Oslo, Oslo, Norway
| | - Jørgen F P Wojtaszewski
- August Krogh Section for Molecular Physiology, Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
| | - Anders J Kolnes
- Section of Specialized Endocrinology, Department of Endocrinology, Oslo University Hospital, Oslo, Norway
| | - Giles S H Yeo
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Stephen O'Rahilly
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Jørgen Jensen
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Claudia Langenberg
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
| |
Collapse
|
50
|
Kresge HA, Blostein F, Goleva S, Albiñana C, Revez JA, Wray NR, Vilhjálmsson BJ, Zhu Z, McGrath JJ, Davis LK. Phenomewide Association Study of Health Outcomes Associated With the Genetic Correlates of 25 Hydroxyvitamin D Concentration and Vitamin D Binding Protein Concentration. Twin Res Hum Genet 2024; 27:69-79. [PMID: 38644690 PMCID: PMC11138239 DOI: 10.1017/thg.2024.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
While it is known that vitamin D deficiency is associated with adverse bone outcomes, it remains unclear whether low vitamin D status may increase the risk of a wider range of health outcomes. We had the opportunity to explore the association between common genetic variants associated with both 25 hydroxyvitamin D (25OHD) and the vitamin D binding protein (DBP, encoded by the GC gene) with a comprehensive range of health disorders and laboratory tests in a large academic medical center. We used summary statistics for 25OHD and DBP to generate polygenic scores (PGS) for 66,482 participants with primarily European ancestry and 13,285 participants with primarily African ancestry from the Vanderbilt University Medical Center Biobank (BioVU). We examined the predictive properties of PGS25OHD, and two scores related to DBP concentration with respect to 1322 health-related phenotypes and 315 laboratory-measured phenotypes from electronic health records. In those with European ancestry: (a) the PGS25OHD and PGSDBP scores, and individual SNPs rs4588 and rs7041 were associated with both 25OHD concentration and 1,25 dihydroxyvitamin D concentrations; (b) higher PGS25OHD was associated with decreased concentrations of triglycerides and cholesterol, and reduced risks of vitamin D deficiency, disorders of lipid metabolism, and diabetes. In general, the findings for the African ancestry group were consistent with findings from the European ancestry analyses. Our study confirms the utility of PGS and two key variants within the GC gene (rs4588 and rs7041) to predict the risk of vitamin D deficiency in clinical settings and highlights the shared biology between vitamin D-related genetic pathways a range of health outcomes.
Collapse
Affiliation(s)
- Hailey A. Kresge
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Freida Blostein
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Slavina Goleva
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Clara Albiñana
- National Centre for Register-Based Research, Aarhus University, Aarhus V, Denmark
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Joana A. Revez
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Naomi R. Wray
- Department of Psychiatry, University of Oxford, Oxford, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Bjarni J. Vilhjálmsson
- National Centre for Register-Based Research, Aarhus University, Aarhus V, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus C, Denmark
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
| | - Zhihong Zhu
- National Centre for Register-Based Research, Aarhus University, Aarhus V, Denmark
| | - John J. McGrath
- National Centre for Register-Based Research, Aarhus University, Aarhus V, Denmark
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
| | - Lea K. Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Neurology, Pharmacology and Special Education, Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|