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Deuis JR, Klasfauseweh T, Walker L, Vetter I. The 'dispanins' and related proteins in physiology and neurological disease. Trends Neurosci 2024:S0166-2236(24)00118-8. [PMID: 39025729 DOI: 10.1016/j.tins.2024.06.004] [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: 02/21/2024] [Revised: 05/15/2024] [Accepted: 06/21/2024] [Indexed: 07/20/2024]
Abstract
The dispanins are a family of 15 transmembrane proteins that have diverse and often unclear physiological functions. Many dispanins, including synapse differentiation induced gene 1 (SynDIG1), proline-rich transmembrane protein 1 (PRRT1)/SynDIG4, and PRRT2, are expressed in the central nervous system (CNS), where they are involved in the development of synapses, regulation of neurotransmitter release, and interactions with ion channels, including AMPA receptors (AMPARs). Others, including transmembrane protein 233 (TMEM233) and trafficking regulator of GLUT4-1 (TRARG1), are expressed in the peripheral nervous system (PNS); however, the function of these dispanins is less clear. Recently, a family of neurotoxins isolated from the giant Australian stinging tree was shown to target TMEM233 to modulate the function of voltage-gated sodium (NaV) channels, suggesting that the dispanins are inherently druggable. Here, we review current knowledge about the structure and function of the dispanins, in particular TMEM233 and its two most closely related homologs PRRT2 and TRARG1, which may be drug targets involved in neurological disease.
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Affiliation(s)
- Jennifer R Deuis
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia
| | - Tabea Klasfauseweh
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia
| | - Lucinda Walker
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia
| | - Irina Vetter
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia; School of Pharmacy, The University of Queensland, Woolloongabba, QLD, Australia.
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2
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Alvarado K, Tang WJ, Watson CJ, Ahmed AR, Gomez AE, Donaka R, Amemiya C, Karasik D, Hsu YH, Kwon RY. Loss of cped1 does not affect bone and lean mass in zebrafish. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.10.601974. [PMID: 39026892 PMCID: PMC11257572 DOI: 10.1101/2024.07.10.601974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Human genetic studies have nominated Cadherin-like and PC-esterase Domain-containing 1 ( CPED1 ) as a candidate target gene mediating bone mineral density (BMD) and fracture risk heritability. Recent efforts to define the role of CPED1 in bone in mouse and human models have revealed complex alternative splicing and inconsistent results arising from gene targeting, making its function difficult to interpret. To better understand the role of CPED1 in adult bone mass and morphology, we turned to zebrafish, an emerging model for orthopaedic research. We analyzed two different cped1 mutant lines and performed deep phenotyping to characterize more than 200 measures of adult vertebral, craniofacial, and lean tissue morphology. We also examined alternative splicing of zebrafish cped1 and gene expression in various cell/tissue types. Our studies fail to support an essential role of cped1 in adult zebrafish bone. Specifically, homozygous mutants for both cped1 mutant alleles, which are expected to result in loss-of-function and impact all cped1 isoforms, exhibited no significant differences in the measures examined when compared to their respective wildtype controls, suggesting that cped1 does not significantly contribute to these traits. We identified sequence differences in critical residues of the catalytic triad between the zebrafish and mouse orthologs of CPED1, and discuss how these differences, as well as distinct alternative splicing, could underlie different functions of CPED1 orthologs in the two species. Our studies demonstrate that cped1 is not required for normal adult zebrafish bone mass, lean mass, or bone and lean mass morphology, adding to evidence that variants at 7q31.31 can act independently of CPED1 to influence BMD and fracture risk.
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3
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Sonti S, Littleton SH, Pahl MC, Zimmerman AJ, Chesi A, Palermo J, Lasconi C, Brown EB, Pippin JA, Wells AD, Doldur-Balli F, Pack AI, Gehrman PR, Keene AC, Grant SFA. Perturbation of the insomnia WDR90 genome-wide association studies locus pinpoints rs3752495 as a causal variant influencing distal expression of neighboring gene, PIG-Q. Sleep 2024; 47:zsae085. [PMID: 38571402 PMCID: PMC11236950 DOI: 10.1093/sleep/zsae085] [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: 08/15/2023] [Revised: 01/28/2024] [Indexed: 04/05/2024] Open
Abstract
Although genome-wide association studies (GWAS) have identified loci for sleep-related traits, they do not directly uncover the underlying causal variants and corresponding effector genes. The majority of such variants reside in non-coding regions and are therefore presumed to impact cis-regulatory elements. Our previously reported 'variant-to-gene mapping' effort in human induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPCs), combined with validation in both Drosophila and zebrafish, implicated phosphatidyl inositol glycan (PIG)-Q as a functionally relevant gene at the insomnia "WDR90" GWAS locus. However, importantly that effort did not characterize the corresponding underlying causal variant. Specifically, our previous 3D genomic datasets nominated a shortlist of three neighboring single nucleotide polymorphisms (SNPs) in strong linkage disequilibrium within an intronic enhancer region of WDR90 that contacted the open PIG-Q promoter. We sought to investigate the influence of these SNPs collectively and then individually on PIG-Q modulation to pinpoint the causal "regulatory" variant. Starting with gross level perturbation, deletion of the entire region in NPCs via CRISPR-Cas9 editing and subsequent RNA sequencing revealed expression changes in specific PIG-Q transcripts. Results from individual luciferase reporter assays for each SNP in iPSCs revealed that the region with the rs3752495 risk allele (RA) induced a ~2.5-fold increase in luciferase expression. Importantly, rs3752495 also exhibited an allele-specific effect, with the RA increasing the luciferase expression by ~2-fold versus the non-RA. In conclusion, our variant-to-function approach and in vitro validation implicate rs3752495 as a causal insomnia variant embedded within WDR90 while modulating the expression of the distally located PIG-Q.
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Affiliation(s)
- Shilpa Sonti
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sheridan H Littleton
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew C Pahl
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Amber J Zimmerman
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory, Medicine University of Pennsylvania Perelman School of Medicine, Philadelphia PA, USA
| | - Justin Palermo
- Department of Biology, Texas A&M University, College Station, TX, USA
| | - Chiara Lasconi
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Elizabeth B Brown
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - James A Pippin
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Fusun Doldur-Balli
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allan I Pack
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Phillip R Gehrman
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alex C Keene
- Department of Biology, Texas A&M University, College Station, TX, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Divisions of Human Genetics and Endocrinology & Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
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4
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Venkatesh SS, Ganjgahi H, Palmer DS, Coley K, Linchangco GV, Hui Q, Wilson P, Ho YL, Cho K, Arumäe K, Wittemans LBL, Nellåker C, Vainik U, Sun YV, Holmes C, Lindgren CM, Nicholson G. Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records. Nat Commun 2024; 15:5801. [PMID: 38987242 PMCID: PMC11237142 DOI: 10.1038/s41467-024-49998-0] [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: 01/25/2023] [Accepted: 06/25/2024] [Indexed: 07/12/2024] Open
Abstract
Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.
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Affiliation(s)
- Samvida S Venkatesh
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Habib Ganjgahi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Gregorio V Linchangco
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Qin Hui
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Peter Wilson
- Atlanta VA Health Care System, Decatur, GA, USA
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kadri Arumäe
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Christoffer Nellåker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Uku Vainik
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
- Estonian Genome Centre, Institute of Genomics, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
- Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, University of McGill, Montreal, Canada
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Chris Holmes
- Department of Statistics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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5
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Zeng T, Spence JP, Mostafavi H, Pritchard JK. Bayesian estimation of gene constraint from an evolutionary model with gene features. Nat Genet 2024:10.1038/s41588-024-01820-9. [PMID: 38977852 DOI: 10.1038/s41588-024-01820-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 05/29/2024] [Indexed: 07/10/2024]
Abstract
Measures of selective constraint on genes have been used for many applications, including clinical interpretation of rare coding variants, disease gene discovery and studies of genome evolution. However, widely used metrics are severely underpowered at detecting constraints for the shortest ~25% of genes, potentially causing important pathogenic mutations to be overlooked. Here we developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric, shet. Our estimates outperform existing metrics for prioritizing genes important for cell essentiality, human disease and other phenotypes, especially for short genes. Our estimates of selective constraint should have wide utility for characterizing genes relevant to human disease. Finally, our inference framework, GeneBayes, provides a flexible platform that can improve the estimation of many gene-level properties, such as rare variant burden or gene expression differences.
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Affiliation(s)
- Tony Zeng
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | | | - Hakhamanesh Mostafavi
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Population Health, New York University, New York, NY, USA
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Biology, Stanford University, Stanford, CA, USA.
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6
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Smal N, Majdoub F, Janssens K, Reyniers E, Meuwissen MEC, Ceulemans B, Northrup H, Hill JB, Liu L, Errichiello E, Gana S, Strong A, Rohena L, Franciskovich R, Murali CN, Huybrechs A, Sulem T, Fridriksdottir R, Sulem P, Stefansson K, Bai Y, Rosenfeld JA, Lalani SR, Streff H, Kooy RF, Weckhuysen S. Burden re-analysis of neurodevelopmental disorder cohorts for prioritization of candidate genes. Eur J Hum Genet 2024:10.1038/s41431-024-01661-4. [PMID: 38965372 DOI: 10.1038/s41431-024-01661-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 04/12/2024] [Accepted: 06/25/2024] [Indexed: 07/06/2024] Open
Abstract
This study aimed to uncover novel genes associated with neurodevelopmental disorders (NDD) by leveraging recent large-scale de novo burden analysis studies to enhance a virtual gene panel used in a diagnostic setting. We re-analyzed historical trio-exome sequencing data from 745 individuals with NDD according to the most recent diagnostic standards, resulting in a cohort of 567 unsolved individuals. Next, we designed a virtual gene panel containing candidate genes from three large de novo burden analysis studies in NDD and prioritized candidate genes by stringent filtering for ultra-rare de novo variants with high pathogenicity scores. Our analysis revealed an increased burden of de novo variants in our selected candidate genes within the unsolved NDD cohort and identified qualifying de novo variants in seven candidate genes: RIF1, CAMK2D, RAB11FIP4, AGO3, PCBP2, LEO1, and VCP. Clinical data were collected from six new individuals with de novo or inherited LEO1 variants and three new individuals with de novo PCBP2 variants. Our findings add additional evidence for LEO1 as a risk gene for autism and intellectual disability. Furthermore, we prioritize PCBP2 as a candidate gene for NDD associated with motor and language delay. In summary, by leveraging de novo burden analysis studies, employing a stringent variant filtering pipeline, and engaging in targeted patient recruitment, our study contributes to the identification of novel genes implicated in NDDs.
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Affiliation(s)
- Noor Smal
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Applied and Translational Neurogenomics Group, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Fatma Majdoub
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Applied and Translational Neurogenomics Group, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Medical Genetics Department, University Hedi Chaker Hospital of Sfax, University of Sfax, Sfax, Tunisia
| | - Katrien Janssens
- Department of Medical Genetics, University of Antwerp, Antwerp, Belgium
- Center of Medical Genetics, University Hospital Antwerp, Drie Eikenstraat 655, Edegem, 2650, Belgium
| | - Edwin Reyniers
- Department of Medical Genetics, University of Antwerp, Antwerp, Belgium
- Center of Medical Genetics, University Hospital Antwerp, Drie Eikenstraat 655, Edegem, 2650, Belgium
| | - Marije E C Meuwissen
- Department of Medical Genetics, University of Antwerp, Antwerp, Belgium
- Center of Medical Genetics, University Hospital Antwerp, Drie Eikenstraat 655, Edegem, 2650, Belgium
| | - Berten Ceulemans
- Translational Neurosciences, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
- Department of Pediatric Neurology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School at the University of Texas Health Science Center at Houston (UTHealth) and Children's Memorial Hermann Hospital, Houston, TX, USA
| | - Jeremy B Hill
- Department of Pediatrics, McGovern Medical School at the University of Texas Health Science Center at Houston (UTHealth) and Children's Memorial Hermann Hospital, Houston, TX, USA
| | - Lingying Liu
- Department of Pediatrics, McGovern Medical School at the University of Texas Health Science Center at Houston (UTHealth) and Children's Memorial Hermann Hospital, Houston, TX, USA
| | - Edoardo Errichiello
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
- Neurogenetics Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Simone Gana
- Neurogenetics Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Alanna Strong
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Luis Rohena
- Division of Medical Genetics, Department of Pediatrics, San Antonio Military Medical Center, San Antonio, TX, USA
- Department of Pediatrics, Long School of Medicine-UT Health San Antonio, San Antonio, TX, USA
| | - Rachel Franciskovich
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
| | - Chaya N Murali
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
| | - An Huybrechs
- Department of Pediatrics, Heilig Hart Ziekenhuis, Lier, Belgium
| | - Telma Sulem
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | | | | | | | - Yan Bai
- GeneDx, Gaithersburg, MD, USA
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Seema R Lalani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
| | - Haley Streff
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
| | - R Frank Kooy
- Department of Medical Genetics, University of Antwerp, Antwerp, Belgium
| | - Sarah Weckhuysen
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium.
- Translational Neurosciences, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium.
- Department of Neurology, University Hospital Antwerp, Antwerp, Belgium.
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
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7
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Roston RA, Whikehart SM, Rolfe SM, Maga M. Morphological simulation tests the limits on phenotype discovery in 3D image analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.30.601430. [PMID: 39005442 PMCID: PMC11244899 DOI: 10.1101/2024.06.30.601430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
In the past few decades, advances in 3D imaging have created new opportunities for reverse genetic screens. Rapidly growing datasets of 3D images of genetic knockouts require high-throughput, automated computational approaches for identifying and characterizing new phenotypes. However, exploratory, discovery-oriented image analysis pipelines used to discover these phenotypes can be difficult to validate because, by their nature, the expected outcome is not known a priori . Introducing known morphological variation through simulation can help distinguish between real phenotypic differences and random variation; elucidate the effects of sample size; and test the sensitivity and reproducibility of morphometric analyses. Here we present a novel approach for 3D morphological simulation that uses open-source, open-access tools available in 3D Slicer, SlicerMorph, and Advanced Normalization Tools in R (ANTsR). While we focus on diffusible-iodine contrast-enhanced micro-CT (diceCT) images, this approach can be used on any volumetric image. We then use our simulated datasets to test whether tensor-based morphometry (TBM) can recover our introduced differences; to test how effect size and sample size affect detectability; and to determine the reproducibility of our results. In our approach to morphological simulation, we first generate a simulated deformation based on a reference image and then propagate this deformation to subjects using inverse transforms obtained from the registration of subjects to the reference. This produces a new dataset with a shifted population mean while retaining individual variability because each sample deforms more or less based on how different or similar it is from the reference. TBM is a widely-used technique that statistically compares local volume differences associated with local deformations. Our results showed that TBM recovered our introduced morphological differences, but that detectability was dependent on the effect size, the sample size, and the region of interest (ROI) included in the analysis. Detectability of subtle phenotypes can be improved both by increasing the sample size and by limiting analyses to specific body regions. However, it is not always feasible to increase sample sizes in screens of essential genes. Therefore, methodical use of ROIs is a promising way to increase the power of TBM to detect subtle phenotypes. Generating known morphological variation through simulation has broad applicability in developmental, evolutionary, and biomedical morphometrics and is a useful way to distinguish between a failure to detect morphological difference and a true lack of morphological difference. Morphological simulation can also be applied to AI-based supervised learning to augment datasets and overcome dataset limitations.
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8
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Reed JN, Huang J, Li Y, Ma L, Banka D, Wabitsch M, Wang T, Ding W, Björkegren JL, Civelek M. Systems genetics analysis of human body fat distribution genes identifies adipocyte processes. Life Sci Alliance 2024; 7:e202402603. [PMID: 38702075 PMCID: PMC11068934 DOI: 10.26508/lsa.202402603] [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: 01/18/2024] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/06/2024] Open
Abstract
Excess abdominal fat is a sexually dimorphic risk factor for cardio-metabolic disease and is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Whereas this trait is highly heritable, few causal genes are known. We aimed to identify novel drivers of WHRadjBMI using systems genetics. We used two independent cohorts of adipose tissue gene expression and constructed sex- and depot-specific Bayesian networks to model gene-gene interactions from 8,492 genes. Using key driver analysis, we identified genes that, in silico and putatively in vitro, regulate many others. 51-119 key drivers in each network were replicated in both cohorts. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We overexpressed or down-regulated seven key driver genes in human subcutaneous pre-adipocytes. Key driver genes ANAPC2 and RSPO1 inhibited adipogenesis, whereas PSME3 increased adipogenesis. RSPO1 increased Wnt signaling activity. In differentiated adipocytes, MIGA1 and UBR1 down-regulation led to mitochondrial dysfunction. These five genes regulate adipocyte function, and we hypothesize that they regulate fat distribution.
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Affiliation(s)
- Jordan N Reed
- https://ror.org/0153tk833 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
- https://ror.org/0153tk833 Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jiansheng Huang
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Yong Li
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Lijiang Ma
- https://ror.org/04a9tmd77 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dhanush Banka
- https://ror.org/0153tk833 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Martin Wabitsch
- Division of Paediatric Endocrinology and Diabetes, Department of Paediatrics and Adolescent Medicine, Ulm University Medical Centre, Ulm, Germany
| | - Tianfang Wang
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Wen Ding
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Johan Lm Björkegren
- https://ror.org/04a9tmd77 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Karolinska Institutet, Huddinge, Stockholm, Sweden
| | - Mete Civelek
- https://ror.org/0153tk833 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
- https://ror.org/0153tk833 Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
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9
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Cacheiro P, Lawson S, Van den Veyver IB, Marengo G, Zocche D, Murray SA, Duyzend M, Robinson PN, Smedley D. Lethal phenotypes in Mendelian disorders. Genet Med 2024; 26:101141. [PMID: 38629401 PMCID: PMC11232373 DOI: 10.1016/j.gim.2024.101141] [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: 02/05/2024] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 04/26/2024] Open
Abstract
PURPOSE Existing resources that characterize the essentiality status of genes are based on either proliferation assessment in human cell lines, viability evaluation in mouse knockouts, or constraint metrics derived from human population sequencing studies. Several repositories document phenotypic annotations for rare disorders; however, there is a lack of comprehensive reporting on lethal phenotypes. METHODS We queried Online Mendelian Inheritance in Man for terms related to lethality and classified all Mendelian genes according to the earliest age of death recorded for the associated disorders, from prenatal death to no reports of premature death. We characterized the genes across these lethality categories, examined the evidence on viability from mouse models and explored how this information could be used for novel gene discovery. RESULTS We developed the Lethal Phenotypes Portal to showcase this curated catalog of human essential genes. Differences in the mode of inheritance, physiological systems affected, and disease class were found for genes in different lethality categories, as well as discrepancies between the lethal phenotypes observed in mouse and human. CONCLUSION We anticipate that this resource will aid clinicians in the diagnosis of early lethal conditions and assist researchers in investigating the properties that make these genes essential for human development.
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Affiliation(s)
- Pilar Cacheiro
- William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Samantha Lawson
- ITS Research, Queen Mary University of London, London, United Kingdom
| | - Ignatia B Van den Veyver
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX; Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX
| | - Gabriel Marengo
- William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - David Zocche
- North West Thames Regional Genetics Service, Northwick Park and St Mark's Hospitals, London, United Kingdom
| | | | - Michael Duyzend
- Massachusetts General Hospital, Boston, MA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA; Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Peter N Robinson
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Damian Smedley
- William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.
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10
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Peterson L, Yacoub MH, Ayares D, Yamada K, Eisenson D, Griffith BP, Mohiuddin MM, Eyestone W, Venter JC, Smolenski RT, Rothblatt M. Physiological basis for xenotransplantation from genetically modified pigs to humans. Physiol Rev 2024; 104:1409-1459. [PMID: 38517040 DOI: 10.1152/physrev.00041.2023] [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: 10/26/2023] [Revised: 03/06/2024] [Accepted: 03/14/2024] [Indexed: 03/23/2024] Open
Abstract
The collective efforts of scientists over multiple decades have led to advancements in molecular and cellular biology-based technologies including genetic engineering and animal cloning that are now being harnessed to enhance the suitability of pig organs for xenotransplantation into humans. Using organs sourced from pigs with multiple gene deletions and human transgene insertions, investigators have overcome formidable immunological and physiological barriers in pig-to-nonhuman primate (NHP) xenotransplantation and achieved prolonged pig xenograft survival. These studies informed the design of Revivicor's (Revivicor Inc, Blacksburg, VA) genetically engineered pigs with 10 genetic modifications (10 GE) (including the inactivation of 4 endogenous porcine genes and insertion of 6 human transgenes), whose hearts and kidneys have now been studied in preclinical human xenotransplantation models with brain-dead recipients. Additionally, the first two clinical cases of pig-to-human heart xenotransplantation were recently performed with hearts from this 10 GE pig at the University of Maryland. Although this review focuses on xenotransplantation of hearts and kidneys, multiple organs, tissues, and cell types from genetically engineered pigs will provide much-needed therapeutic interventions in the future.
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Affiliation(s)
- Leigh Peterson
- United Therapeutics Corporation, Silver Spring, Maryland, United States
| | | | - David Ayares
- United Therapeutics Corporation, Silver Spring, Maryland, United States
| | - Kazuhiko Yamada
- Department of Surgery, Division of Transplantation, Johns Hopkins Medicine, Baltimore, Maryland, United States
| | - Daniel Eisenson
- Department of Surgery, Division of Transplantation, Johns Hopkins Medicine, Baltimore, Maryland, United States
| | - Bartley P Griffith
- University of Maryland Medical Center, Baltimore, Maryland, United States
| | | | - Willard Eyestone
- United Therapeutics Corporation, Silver Spring, Maryland, United States
| | - J Craig Venter
- J. Craig Venter Institute, Rockville, Maryland, United States
| | | | - Martine Rothblatt
- United Therapeutics Corporation, Silver Spring, Maryland, United States
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11
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Li X, Morel JD, Sulc J, De Masi A, Lalou A, Benegiamo G, Poisson J, Liu Y, Von Alvensleben GVG, Gao AW, Bou Sleiman M, Auwerx J. Systems genetics of metabolic health in the BXD mouse genetic reference population. Cell Syst 2024; 15:497-509.e3. [PMID: 38866010 DOI: 10.1016/j.cels.2024.05.006] [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: 10/11/2023] [Revised: 02/29/2024] [Accepted: 05/20/2024] [Indexed: 06/14/2024]
Abstract
Susceptibility to metabolic syndrome (MetS) is dependent on genetics, environment, and gene-by-environment interactions, rendering the study of underlying mechanisms challenging. The majority of experiments in model organisms do not incorporate genetic variation and lack specific evaluation criteria for MetS. Here, we derived a continuous metric, the metabolic health score (MHS), based on standard clinical parameters and defined its molecular signatures in the liver and circulation. In human UK Biobank, the MHS associated with MetS status and was predictive of future disease incidence, even in individuals without MetS. Using quantitative trait locus analyses in mice, we found two MHS-associated genetic loci and replicated them in unrelated mouse populations. Through a prioritization scheme in mice and human genetic data, we identified TNKS and MCPH1 as candidates mediating differences in the MHS. Our findings provide insights into the molecular mechanisms sustaining metabolic health across species and uncover likely regulators. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Xiaoxu Li
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Jean-David Morel
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Jonathan Sulc
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Alessia De Masi
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Amélia Lalou
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Giorgia Benegiamo
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Johanne Poisson
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Yasmine Liu
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Giacomo V G Von Alvensleben
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Arwen W Gao
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Maroun Bou Sleiman
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Johan Auwerx
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
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12
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Rad A, Bartsch O, Bakhtiari S, Zhu C, Xu Y, Monteiro FP, Kok F, Vulto-van Silfhout AT, Kruer MC, Bowl MR, Vona B. Expanding the spectrum of phenotypes for MPDZ: Report of four unrelated families and review of the literature. Clin Genet 2024. [PMID: 38857973 DOI: 10.1111/cge.14563] [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: 02/22/2024] [Revised: 04/24/2024] [Accepted: 05/13/2024] [Indexed: 06/12/2024]
Abstract
MPDZ, a gene with diverse functions mediating cell-cell junction interactions, receptor signaling, and binding multivalent scaffold proteins, is associated with a spectrum of clinically heterogeneous phenotypes with biallelic perturbation. Despite its clinical relevance, the mechanistic underpinnings of these variants remain elusive, underscoring the need for extensive case series and functional investigations. In this study, we conducted a systematic review of cases in the literature through two electronic databases following the PRISMA guidelines. We selected nine studies, including 18 patients, with homozygous or compound heterozygous variants in MPDZ and added five patients from four unrelated families with novel MPDZ variants. To evaluate the role of Mpdz on hearing, we analyzed available auditory electrophysiology data from a knockout murine model (Mpdzem1(IMPC)J/em1(IMPC)J) generated by the International Mouse Phenotyping Consortium. Using exome and genome sequencing, we identified three families with compound heterozygous variants, and one family with a homozygous frameshift variant. MPDZ-related disease is clinically heterogenous with hydrocephaly, vision impairment, hearing impairment and cardiovascular disease occurring most frequently. Additionally, we describe two unrelated patients with spasticity, expanding the phenotypic spectrum. Our murine analysis of the Mpdzem1(IMPC)J/em1(IMPC)J allele showed severe hearing impairment. Overall, we expand understanding of MPDZ-related phenotypes and highlight hearing impairment and spasticity among the heterogeneous phenotypes.
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Affiliation(s)
- Aboulfazl Rad
- Department of Otolaryngology - Head and Neck Surgery, Tübingen Hearing Research Centre, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Oliver Bartsch
- Medical Care Centre Section Human Genetics and Institute of Human Genetics, University Medical Centre of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Somayeh Bakhtiari
- Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, Arizona, USA
- Department of Child Health, Cellular and Molecular Medicine, Genetics, and Neurology, University of Arizona College of Medicine-Phoenix, Phoenix, Arizona, USA
| | - Changlian Zhu
- Center for Brain Repair and Rehabilitation, Institute of Neuroscience and Physiology, University of Gothenburg, Göteborg, Sweden
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yiran Xu
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | | | - Fernando Kok
- Medical Department, Mendelics Genomic Analysis, Sao Paulo, Brazil
- Neurogenetics, Neurology Department, Hospital das Clínicas da Universidade de São Paulo, São Paulo, Brazil
| | - Anneke T Vulto-van Silfhout
- Department of Human Genetics, Radboud University Medical Centre, Nijmegen, the Netherlands
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Michael C Kruer
- Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, Arizona, USA
- Department of Child Health, Cellular and Molecular Medicine, Genetics, and Neurology, University of Arizona College of Medicine-Phoenix, Phoenix, Arizona, USA
| | - Michael R Bowl
- UCL Ear Institute, University College London, London, UK
| | - Barbara Vona
- Department of Otolaryngology - Head and Neck Surgery, Tübingen Hearing Research Centre, Eberhard Karls University Tübingen, Tübingen, Germany
- Institute of Human Genetics, University Medical Center Göttingen, Göttingen, Germany
- Institute for Auditory Neuroscience and InnerEarLab, University Medical Center Göttingen, Göttingen, Germany
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13
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Federspiel JD, Catlin NR, Nowland WS, Stethem CM, Mathialagan N, Fernandez Ocaña M, Bowman CJ. Differential Analysis of Cereblon Neosubstrates in Rabbit Embryos Using Targeted Proteomics. Mol Cell Proteomics 2024; 23:100797. [PMID: 38866076 DOI: 10.1016/j.mcpro.2024.100797] [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: 03/22/2024] [Revised: 05/31/2024] [Accepted: 06/06/2024] [Indexed: 06/14/2024] Open
Abstract
Targeted protein degradation is the selective removal of a protein of interest through hijacking intracellular protein cleanup machinery. This rapidly growing field currently relies heavily on the use of the E3 ligase cereblon (CRBN) to target proteins for degradation, including the immunomodulatory drugs (IMiDs) thalidomide, lenalidomide, and pomalidomide which work through a molecular glue mechanism of action with CRBN. While CRBN recruitment can result in degradation of a specific protein of interest (e.g., efficacy), degradation of other proteins (called CRBN neosubstrates) also occurs. Degradation of one or more of these CRBN neosubstrates is believed to play an important role in thalidomide-related developmental toxicity observed in rabbits and primates. We identified a set of 25 proteins of interest associated with CRBN-related protein homeostasis and/or embryo/fetal development. We developed a targeted assay for these proteins combining peptide immunoaffinity enrichment and high-resolution mass spectrometry and successfully applied this assay to rabbit embryo samples from pregnant rabbits dosed with three IMiDs. We confirmed previously reported in vivo decreases in neosubstrates like SALL4, as well as provided evidence of neosubstrate changes for proteins only examined in vitro previously. While there were many proteins that were similarly decreased by all three IMiDs, no compound had the exact same neosubstrate degradation profile as another. We compared our data to previous literature reports of IMiD-induced degradation and known developmental biology associations. Based on our observations, we recommend monitoring at least a major subset of these neosubstrates in a developmental test system to improve CRBN-binding compound-specific risk assessment. A strength of our assay is that it is configurable, and the target list can be readily adapted to focus on only a subset of proteins of interest or expanded to incorporate new findings as additional information about CRBN biology is discovered.
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Affiliation(s)
- Joel D Federspiel
- Drug Safety Research & Development, Pfizer, Inc, Andover, Massachusetts, USA
| | - Natasha R Catlin
- Drug Safety Research & Development, Pfizer, Inc, Groton, Connecticut, USA
| | - William S Nowland
- Drug Safety Research & Development, Pfizer, Inc, Groton, Connecticut, USA
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14
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Lecca M, Mauri L, Gana S, Del Longo A, Morelli F, Nicotra R, Plumari M, Galli J, Sirchia F, Valente EM, Cavallari U, Mazza M, Signorini S, Errichiello E. Novel molecular, structural and clinical findings in an Italian cohort of congenital cataract. Clin Genet 2024. [PMID: 38840272 DOI: 10.1111/cge.14568] [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: 01/12/2024] [Revised: 05/16/2024] [Accepted: 05/24/2024] [Indexed: 06/07/2024]
Abstract
The current genetic diagnostic workup of congenital cataract (CC) is mainly based on NGS panels, whereas exome sequencing (ES) has occasionally been employed. In this multicentre study, we investigated by ES the detection yield, mutational spectrum and genotype-phenotype correlations in a CC cohort recruited between 2020 and mid-2022. The cohort consisted of 67 affected individuals from 51 unrelated families and included both non-syndromic (75%) and syndromic (25%) phenotypes, with extra-CC ocular/visual features present in both groups (48% and 76%, respectively). The functional effect of variants was predicted by 3D modelling and hydropathy properties changes. Variant clustering was used for the in-depth assessment of genotype-phenotype correlations. A diagnostic (pathogenic or likely pathogenic) variant was identified in 19 out of 51 probands/families (~37%). In a further 14 probands/families a candidate variant was identified: in 12 families a VUS was detected, of which 9 were considered plausibly pathogenic (i.e., 4 or 5 points according to ACMG criteria), while in 2 probands ES identified a single variant in an autosomal recessive gene associated with CC. Eighteen probands/families, manifesting primarily non-syndromic CC (15/18, 83%), remained unsolved. The identified variants (8 P, 12 LP, 10 VUS-PP, and 5 VUS), half of which were unreported in the literature, affected five functional categories of genes involved in transcription/splicing, lens formation/homeostasis (i.e., crystallin genes), membrane signalling, cell-cell interaction, and immune response. A phenotype-specific variant clustering was observed in four genes (KIF1A, MAF, PAX6, SPTAN1), whereas variable expressivity and potential phenotypic expansion in two (BCOR, NHS) and five genes (CWC27, KIF1A, IFIH1, PAX6, SPTAN1), respectively. Finally, ES allowed to detect variants in six genes not commonly included in commercial CC panels. These findings broaden the genotype-phenotype correlations in one of the largest CC cohorts tested by ES, providing novel insights into the underlying pathogenetic mechanisms and emphasising the power of ES as first-tier test.
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Affiliation(s)
- Mauro Lecca
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Lucia Mauri
- Medical Genetics Unit, Department of Laboratory Medicine, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Simone Gana
- Medical Genetics Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Alessandra Del Longo
- Pediatric Ophthalmology Unit, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- European Reference Network on Eye Diseases (ERN-EYE), ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Federica Morelli
- Developmental Neuro-ophthalmology Unit, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Roberta Nicotra
- Developmental Neuro-ophthalmology Unit, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Massimo Plumari
- Medical Genetics Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Jessica Galli
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Unit of Child Neurology and Psychiatry, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Fabio Sirchia
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
- Medical Genetics Unit, IRCCS San Matteo Foundation, Pavia, Italy
| | - Enza Maria Valente
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
- Medical Genetics Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Ugo Cavallari
- Medical Genetics Unit, Department of Laboratory Medicine, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Marco Mazza
- Pediatric Ophthalmology Unit, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- European Reference Network on Eye Diseases (ERN-EYE), ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Sabrina Signorini
- Developmental Neuro-ophthalmology Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Edoardo Errichiello
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
- Medical Genetics Unit, IRCCS Mondino Foundation, Pavia, Italy
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15
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Zhong X, Peddada N, Moresco JJ, Wang J, Jiang Y, Rios JJ, Moresco EMY, Choi JH, Beutler B. Viable mutations of mouse midnolin suppress B cell malignancies. J Exp Med 2024; 221:e20232132. [PMID: 38625151 PMCID: PMC11022886 DOI: 10.1084/jem.20232132] [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: 11/18/2023] [Revised: 02/20/2024] [Accepted: 03/28/2024] [Indexed: 04/17/2024] Open
Abstract
In a genetic screen, we identified two viable missense alleles of the essential gene Midnolin (Midn) that were associated with reductions in peripheral B cells. Causation was confirmed in mice with targeted deletion of four of six MIDN protein isoforms. MIDN was expressed predominantly in lymphocytes where it augmented proteasome activity. We showed that purified MIDN directly stimulated 26S proteasome activity in vitro in a manner dependent on the ubiquitin-like domain and a C-terminal region. MIDN-deficient B cells displayed aberrant activation of the IRE-1/XBP-1 pathway of the unfolded protein response. Partial or complete MIDN deficiency strongly suppressed Eμ-Myc-driven B cell leukemia and the antiapoptotic effects of Eμ-BCL2 on B cells in vivo and induced death of Sp2/0 hybridoma cells in vitro, but only partially impaired normal lymphocyte development. Thus, MIDN is required for proteasome activity in support of normal lymphopoiesis and is essential for malignant B cell proliferation over a broad range of differentiation states.
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Affiliation(s)
- Xue Zhong
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Nagesh Peddada
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James J. Moresco
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jianhui Wang
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yiao Jiang
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jonathan J. Rios
- Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children, Dallas, TX, USA
- McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Orthopaedic Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Eva Marie Y. Moresco
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jin Huk Choi
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bruce Beutler
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA
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16
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Cacheiro P, Pava D, Parkinson H, VanZanten M, Wilson R, Gunes O, The International Mouse Phenotyping Consortium, Smedley D. Computational identification of disease models through cross-species phenotype comparison. Dis Model Mech 2024; 17:dmm050604. [PMID: 38881316 PMCID: PMC11247498 DOI: 10.1242/dmm.050604] [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: 11/13/2023] [Accepted: 06/11/2024] [Indexed: 06/18/2024] Open
Abstract
The use of standardised phenotyping screens to identify abnormal phenotypes in mouse knockouts, together with the use of ontologies to describe such phenotypic features, allows the implementation of an automated and unbiased pipeline to identify new models of disease by performing phenotype comparisons across species. Using data from the International Mouse Phenotyping Consortium (IMPC), approximately half of mouse mutants are able to mimic, at least partially, the human ortholog disease phenotypes as computed by the PhenoDigm algorithm. We found the number of phenotypic abnormalities in the mouse and the corresponding Mendelian disorder, the pleiotropy and severity of the disease, and the viability and zygosity status of the mouse knockout to be associated with the ability of mouse models to recapitulate the human disorder. An analysis of the IMPC impact on disease gene discovery through a publication-tracking system revealed that the resource has been implicated in at least 109 validated rare disease-gene associations over the last decade.
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Affiliation(s)
- Pilar Cacheiro
- William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Diego Pava
- William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Helen Parkinson
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Maya VanZanten
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Robert Wilson
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Osman Gunes
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | | | - Damian Smedley
- William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
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17
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Pedro MP, Lund K, Kang SWS, Chen T, Stuelten CH, Porat-Shliom N, Iglesias-Bartolome R. GPCR Screening Reveals that the Metabolite Receptor HCAR3 Regulates Epithelial Proliferation, Migration, and Cellular Respiration. J Invest Dermatol 2024; 144:1311-1321.e7. [PMID: 38103827 PMCID: PMC11116076 DOI: 10.1016/j.jid.2023.12.002] [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: 10/12/2023] [Revised: 11/24/2023] [Accepted: 12/04/2023] [Indexed: 12/19/2023]
Abstract
Epithelial cells in the skin and other tissues rely on signals from their environment to maintain homeostasis and respond to injury, and GPCRs play a critical role in this communication. A better understanding of the GPCRs expressed in epithelial cells will contribute to understanding the relationship between cells and their niche and could lead to developing new therapies to modulate cell fate. This study used human primary keratinocytes as a model to investigate the specific GPCRs regulating epithelial cell proliferation and differentiation. We identified 3 key receptors-HCAR3, LTB4R, and GPR137-and found that knockdown of these receptors led to changes in numerous gene networks that are important for maintaining cell identity and promoting proliferation while inhibiting differentiation. Our study also revealed that the metabolite receptor HCAR3 regulates keratinocyte migration and cellular metabolism. Knockdown of HCAR3 led to reduced keratinocyte migration and respiration, which could be attributed to altered metabolite use and aberrant mitochondrial morphology caused by the absence of the receptor. This study contributes to understanding the complex interplay between GPCR signaling and epithelial cell fate decisions.
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Affiliation(s)
- M Pilar Pedro
- Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Katherine Lund
- Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Sun Woo Sophie Kang
- Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Ting Chen
- Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Christina H Stuelten
- Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Natalie Porat-Shliom
- Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Ramiro Iglesias-Bartolome
- Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
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18
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Tomar A, Gomez-Velazquez M, Gerlini R, Comas-Armangué G, Makharadze L, Kolbe T, Boersma A, Dahlhoff M, Burgstaller JP, Lassi M, Darr J, Toppari J, Virtanen H, Kühnapfel A, Scholz M, Landgraf K, Kiess W, Vogel M, Gailus-Durner V, Fuchs H, Marschall S, Hrabě de Angelis M, Kotaja N, Körner A, Teperino R. Epigenetic inheritance of diet-induced and sperm-borne mitochondrial RNAs. Nature 2024; 630:720-727. [PMID: 38839949 PMCID: PMC11186758 DOI: 10.1038/s41586-024-07472-3] [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: 06/30/2023] [Accepted: 04/26/2024] [Indexed: 06/07/2024]
Abstract
Spermatozoa harbour a complex and environment-sensitive pool of small non-coding RNAs (sncRNAs)1, which influences offspring development and adult phenotypes1-7. Whether spermatozoa in the epididymis are directly susceptible to environmental cues is not fully understood8. Here we used two distinct paradigms of preconception acute high-fat diet to dissect epididymal versus testicular contributions to the sperm sncRNA pool and offspring health. We show that epididymal spermatozoa, but not developing germ cells, are sensitive to the environment and identify mitochondrial tRNAs (mt-tRNAs) and their fragments (mt-tsRNAs) as sperm-borne factors. In humans, mt-tsRNAs in spermatozoa correlate with body mass index, and paternal overweight at conception doubles offspring obesity risk and compromises metabolic health. Sperm sncRNA sequencing of mice mutant for genes involved in mitochondrial function, and metabolic phenotyping of their wild-type offspring, suggest that the upregulation of mt-tsRNAs is downstream of mitochondrial dysfunction. Single-embryo transcriptomics of genetically hybrid two-cell embryos demonstrated sperm-to-oocyte transfer of mt-tRNAs at fertilization and suggested their involvement in the control of early-embryo transcription. Our study supports the importance of paternal health at conception for offspring metabolism, shows that mt-tRNAs are diet-induced and sperm-borne and demonstrates, in a physiological setting, father-to-offspring transfer of sperm mitochondrial RNAs at fertilization.
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MESH Headings
- Animals
- Female
- Humans
- Male
- Mice
- Body Mass Index
- Diet, High-Fat/adverse effects
- Embryo, Mammalian/cytology
- Embryo, Mammalian/embryology
- Embryo, Mammalian/metabolism
- Epididymis/cytology
- Epigenesis, Genetic/genetics
- Fertilization/genetics
- Gene Expression Profiling
- Gene Expression Regulation, Developmental
- Mice, Inbred C57BL
- Mitochondria/genetics
- Mitochondria/metabolism
- Mitochondria/pathology
- Obesity/genetics
- Obesity/metabolism
- Obesity/etiology
- Oocytes/metabolism
- Overweight/genetics
- Overweight/metabolism
- Paternal Inheritance/genetics
- RNA, Mitochondrial/genetics
- RNA, Mitochondrial/metabolism
- RNA, Small Untranslated/genetics
- RNA, Small Untranslated/metabolism
- RNA, Transfer/genetics
- RNA, Transfer/metabolism
- Spermatozoa/metabolism
- Testis/cytology
- Transcription, Genetic
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Affiliation(s)
- A Tomar
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - M Gomez-Velazquez
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - R Gerlini
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - G Comas-Armangué
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - L Makharadze
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - T Kolbe
- Unit of in vivo and in vitro Models, Center for Biological Sciences, Department of Biological Sciences and Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
- IFA-Tulln, University of Natural Resources and Life Sciences, Vienna, Austria
| | - A Boersma
- Unit of in vivo and in vitro Models, Center for Biological Sciences, Department of Biological Sciences and Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - M Dahlhoff
- Unit of in vivo and in vitro Models, Center for Biological Sciences, Department of Biological Sciences and Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - J P Burgstaller
- Institute of Animal Breeding and Genetics, Department of Biological Sciences and Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
- Group Molecular Reproduction, IFA-Tulln, Tulln, Austria
| | - M Lassi
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - J Darr
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - J Toppari
- Institute of Biomedicine, Integrative Physiology and Pharmacology Unit, University of Turku, Turku, Finland
- Center for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - H Virtanen
- Institute of Biomedicine, Integrative Physiology and Pharmacology Unit, University of Turku, Turku, Finland
- Center for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - A Kühnapfel
- University of Leipzig, Medical Faculty, Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany
| | - M Scholz
- University of Leipzig, Medical Faculty, Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany
| | - K Landgraf
- Center for Pediatric Research Leipzig (CPL), Hospital for Children & Adolescents, University of Leipzig, Leipzig, Germany
| | - W Kiess
- Center for Pediatric Research Leipzig (CPL), Hospital for Children & Adolescents, University of Leipzig, Leipzig, Germany
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - M Vogel
- Center for Pediatric Research Leipzig (CPL), Hospital for Children & Adolescents, University of Leipzig, Leipzig, Germany
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - V Gailus-Durner
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - H Fuchs
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - S Marschall
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - M Hrabě de Angelis
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Chair of Experimental Genetics, TUM School of Life Sciences, Technische Universität München, Freising, Germany
| | - N Kotaja
- Institute of Biomedicine, Integrative Physiology and Pharmacology Unit, University of Turku, Turku, Finland
| | - A Körner
- Center for Pediatric Research Leipzig (CPL), Hospital for Children & Adolescents, University of Leipzig, Leipzig, Germany
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Helmholtz Institute for Metabolic Obesity and Vascular Research (HI-MAG), Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - R Teperino
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
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19
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Kasprzyk-Pawelec A, Tan M, Rahhal R, McIntosh A, Fernandez H, Mosaoa R, Jiang L, Pearson GW, Glasgow E, Vockley J, Albanese C, Avantaggiati ML. Loss of the mitochondrial carrier, SLC25A1, during embryogenesis induces a unique senescence program controlled by p53. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.18.549409. [PMID: 37503155 PMCID: PMC10370133 DOI: 10.1101/2023.07.18.549409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Germline inactivating mutations of the SLC25A1 gene contribute to various human developmental disorders, including combined D/L-2-hydroxyglutaric aciduria (D/L-2HGA), a severe systemic syndrome characterized by the accumulation of both enantiomers of 2-hydroxyglutaric acid (2HG). The mechanisms by which SLC25A1 deficiency leads to this disease and the role of 2HG are unclear and no therapies exist. We now show that mice lacking both Slc25a1 alleles display a spectrum of alterations that resemble human D/L-2HGA. Mechanistically, SLC25A1 loss results in a proliferation defect and activates two distinct senescence pathways, oncogene-induced senescence (OIS) and mitochondrial dysfunction-induced senescence (MiDAS), both involving the p53 tumor suppressor and driven by two discernible signals: the accumulation of 2HG, inducing OIS, and mitochondrial dysfunction, triggering MiDAS. Inhibiting these senescence programs or blocking p53 activity reverses the growth defect caused by SLC25A1 dysfunction and restores proliferation. These findings reveal novel pathogenic roles of senescence in human disorders and suggest potential strategies to correct the molecular alterations caused by SLC25A1 loss.
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20
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Martinez-Mayer J, Brinkmeier ML, O'Connell SP, Ukagwu A, Marti MA, Miras M, Forclaz MV, Benzrihen MG, Cheung LYM, Camper SA, Ellsworth BS, Raetzman LT, Pérez-Millán MI, Davis SW. Knockout mice with pituitary malformations help identify human cases of hypopituitarism. Genome Med 2024; 16:75. [PMID: 38822427 PMCID: PMC11140907 DOI: 10.1186/s13073-024-01347-y] [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: 10/09/2023] [Accepted: 05/20/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND Congenital hypopituitarism (CH) and its associated syndromes, septo-optic dysplasia (SOD) and holoprosencephaly (HPE), are midline defects that cause significant morbidity for affected people. Variants in 67 genes are associated with CH, but a vast majority of CH cases lack a genetic diagnosis. Whole exome and whole genome sequencing of CH patients identifies sequence variants in genes known to cause CH, and in new candidate genes, but many of these are variants of uncertain significance (VUS). METHODS The International Mouse Phenotyping Consortium (IMPC) is an effort to establish gene function by knocking-out all genes in the mouse genome and generating corresponding phenotype data. We used mouse embryonic imaging data generated by the Deciphering Mechanisms of Developmental Disorders (DMDD) project to screen 209 embryonic lethal and sub-viable knockout mouse lines for pituitary malformations. RESULTS Of the 209 knockout mouse lines, we identified 51 that have embryonic pituitary malformations. These genes not only represent new candidates for CH, but also reveal new molecular pathways not previously associated with pituitary organogenesis. We used this list of candidate genes to mine whole exome sequencing data of a cohort of patients with CH, and we identified variants in two unrelated cases for two genes, MORC2 and SETD5, with CH and other syndromic features. CONCLUSIONS The screening and analysis of IMPC phenotyping data provide proof-of-principle that recessive lethal mouse mutants generated by the knockout mouse project are an excellent source of candidate genes for congenital hypopituitarism in children.
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Affiliation(s)
- Julian Martinez-Mayer
- Institute of Biosciences, Biotechnology and Translational Biology (iB3), University of Buenos Aires, Intendente Güiraldes 2160, Ciudad Universitaria, C1428EGA, Buenos Aires, Argentina
| | - Michelle L Brinkmeier
- Department of Human Genetics, University of Michigan, 1241 Catherine St., Ann Arbor, MI, 48109-5618, USA
| | - Sean P O'Connell
- Department of Biological Sciences, University of South Carolina, 715 Sumter St., Columbia, SC, 29208, USA
| | - Arnold Ukagwu
- Department of Physiology, Southern Illinois University, 1135 Lincoln Dr, Carbondale, IL, 62901, USA
| | - Marcelo A Marti
- Instituto de Química Biológica de La Facultad de Ciencias Exactas y Naturales (IQUIBICEN), Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Mirta Miras
- Hospital De Niños de La Santísima Trinidad, Córdoba, Argentina
| | - Maria V Forclaz
- Servicio de Endocrinología, Hospital Posadas, Buenos Aires, Argentina
| | - Maria G Benzrihen
- Servicio de Endocrinología, Hospital Posadas, Buenos Aires, Argentina
| | - Leonard Y M Cheung
- Department of Human Genetics, University of Michigan, 1241 Catherine St., Ann Arbor, MI, 48109-5618, USA
- Department of Physiology and Biophyscis, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Sally A Camper
- Department of Human Genetics, University of Michigan, 1241 Catherine St., Ann Arbor, MI, 48109-5618, USA
| | - Buffy S Ellsworth
- Department of Physiology, Southern Illinois University, 1135 Lincoln Dr, Carbondale, IL, 62901, USA
| | - Lori T Raetzman
- Department of Molecular and Integrative Physiology, University of Illinois, Champaign-Urbana, Urbana, IL, 61801, USA
| | - Maria I Pérez-Millán
- Institute of Biosciences, Biotechnology and Translational Biology (iB3), University of Buenos Aires, Intendente Güiraldes 2160, Ciudad Universitaria, C1428EGA, Buenos Aires, Argentina.
| | - Shannon W Davis
- Department of Biological Sciences, University of South Carolina, 715 Sumter St., Columbia, SC, 29208, USA.
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21
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Cox RM, Papoulas O, Shril S, Lee C, Gardner T, Battenhouse AM, Lee M, Drew K, McWhite CD, Yang D, Leggere JC, Durand D, Hildebrandt F, Wallingford JB, Marcotte EM. Ancient eukaryotic protein interactions illuminate modern genetic traits and disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.26.595818. [PMID: 38853926 PMCID: PMC11160598 DOI: 10.1101/2024.05.26.595818] [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
All eukaryotes share a common ancestor from roughly 1.5 - 1.8 billion years ago, a single-celled, swimming microbe known as LECA, the Last Eukaryotic Common Ancestor. Nearly half of the genes in modern eukaryotes were present in LECA, and many current genetic diseases and traits stem from these ancient molecular systems. To better understand these systems, we compared genes across modern organisms and identified a core set of 10,092 shared protein-coding gene families likely present in LECA, a quarter of which are uncharacterized. We then integrated >26,000 mass spectrometry proteomics analyses from 31 species to infer how these proteins interact in higher-order complexes. The resulting interactome describes the biochemical organization of LECA, revealing both known and new assemblies. We analyzed these ancient protein interactions to find new human gene-disease relationships for bone density and congenital birth defects, demonstrating the value of ancestral protein interactions for guiding functional genetics today.
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Affiliation(s)
- Rachael M Cox
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ophelia Papoulas
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Shirlee Shril
- Division of Nephrology, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Chanjae Lee
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Tynan Gardner
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Anna M Battenhouse
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Muyoung Lee
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Kevin Drew
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Claire D McWhite
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - David Yang
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Janelle C Leggere
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Dannie Durand
- Department of Biological Sciences, Carnegie Mellon University, 4400 5th Avenue Pittsburgh, PA 15213, USA
| | - Friedhelm Hildebrandt
- Division of Nephrology, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - John B Wallingford
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Edward M Marcotte
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
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22
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Bower G, Hollingsworth EW, Jacinto S, Clock B, Cao K, Liu M, Dziulko A, Alcaina-Caro A, Xu Q, Skowronska-Krawczyk D, Lopez-Rios J, Dickel DE, Bardet AF, Pennacchio LA, Visel A, Kvon EZ. Conserved Cis-Acting Range Extender Element Mediates Extreme Long-Range Enhancer Activity in Mammals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.26.595809. [PMID: 38826394 PMCID: PMC11142232 DOI: 10.1101/2024.05.26.595809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
While most mammalian enhancers regulate their cognate promoters over moderate distances of tens of kilobases (kb), some enhancers act over distances in the megabase range. The sequence features enabling such extreme-distance enhancer-promoter interactions remain elusive. Here, we used in vivo enhancer replacement experiments in mice to show that short- and medium-range enhancers cannot initiate gene expression at extreme-distance range. We uncover a novel conserved cis-acting element, Range EXtender (REX), that confers extreme-distance regulatory activity and is located next to a long-range enhancer of Sall1. The REX element itself has no endogenous enhancer activity. However, addition of the REX to other short- and mid-range enhancers substantially increases their genomic interaction range. In the most extreme example observed, addition of the REX increased the range of an enhancer by an order of magnitude, from its native 71kb to 840kb. The REX element contains highly conserved [C/T]AATTA homeodomain motifs. These motifs are enriched around long-range limb enhancers genome-wide, including the ZRS, a benchmark long-range limb enhancer of Shh. Mutating the [C/T]AATTA motifs within the ZRS does not affect its limb-specific enhancer activity at short range, but selectively abolishes its long-range activity, resulting in severe limb reduction in knock-in mice. In summary, we identify a sequence signature globally associated with long-range enhancer-promoter interactions and describe a prototypical REX element that is necessary and sufficient to confer extreme-distance gene activation by remote enhancers.
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Affiliation(s)
- Grace Bower
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
| | - Ethan W. Hollingsworth
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
- Medical Scientist Training Program, University of California, Irvine, CA 92967, USA
| | - Sandra Jacinto
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
| | - Benjamin Clock
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
| | - Kaitlyn Cao
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
| | - Mandy Liu
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
| | - Adam Dziulko
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Ana Alcaina-Caro
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, 41013, Spain
| | - Qianlan Xu
- Department of Physiology and Biophysics, Department of Ophthalmology, Center for Translational Vision Research, School of Medicine, University of California, Irvine, CA, USA
| | - Dorota Skowronska-Krawczyk
- Department of Physiology and Biophysics, Department of Ophthalmology, Center for Translational Vision Research, School of Medicine, University of California, Irvine, CA, USA
| | - Javier Lopez-Rios
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, 41013, Spain
| | - Diane E. Dickel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Anaïs F. Bardet
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg, CNRS UMR7104, INSERM U1258, 67400 Illkirch, France
| | - Len A. Pennacchio
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA
- Comparative Biochemistry Program, University of California, Berkeley, CA 94720, USA
| | - Axel Visel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA
- School of Natural Sciences, University of California, Merced, CA 95343, USA
| | - Evgeny Z. Kvon
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
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23
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Zhao Y, Ansarullah, Kumar P, Mahoney JM, He H, Baker C, George J, Li S. Causal network perturbation analysis identifies known and novel type-2 diabetes driver genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595431. [PMID: 38826370 PMCID: PMC11142180 DOI: 10.1101/2024.05.22.595431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The molecular pathogenesis of diabetes is multifactorial, involving genetic predisposition and environmental factors that are not yet fully understood. However, pancreatic β-cell failure remains among the primary reasons underlying the progression of type-2 diabetes (T2D) making targeting β-cell dysfunction an attractive pathway for diabetes treatment. To identify genetic contributors to β-cell dysfunction, we investigated single-cell gene expression changes in β-cells from healthy (C57BL/6J) and diabetic (NZO/HlLtJ) mice fed with normal or high-fat, high-sugar diet (HFHS). Our study presents an innovative integration of the causal network perturbation assessment (ssNPA) framework with meta-cell transcriptome analysis to explore the genetic underpinnings of type-2 diabetes (T2D). By generating a reference causal network and in silico perturbation, we identified novel genes implicated in T2D and validated our candidates using the Knockout Mouse Phenotyping (KOMP) Project database.
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Affiliation(s)
- Yue Zhao
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Ansarullah
- Center for Biometric Analysis, The Jackson Laboratory, Bar Harbor, ME, USA
| | - Parveen Kumar
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | - Hao He
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Candice Baker
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Joshy George
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Sheng Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut School of Medicine, Farmington, CT, USA
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24
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Dorans E, Jagadeesh K, Dey K, Price AL. Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.24.24307813. [PMID: 38826240 PMCID: PMC11142273 DOI: 10.1101/2024.05.24.24307813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Methods that analyze single-cell paired RNA-seq and ATAC-seq multiome data have shown great promise in linking regulatory elements to genes. However, existing methods differ in their modeling assumptions and approaches to account for biological and technical noise-leading to low concordance in their linking scores-and do not capture the effects of genomic distance. We propose pgBoost, an integrative modeling framework that trains a non-linear combination of existing linking strategies (including genomic distance) on fine-mapped eQTL data to assign a probabilistic score to each candidate SNP-gene link. We applied pgBoost to single-cell multiome data from 85k cells representing 6 major immune/blood cell types. pgBoost attained higher enrichment for fine-mapped eSNP-eGene pairs (e.g. 21x at distance >10kb) than existing methods (1.2-10x; p-value for difference = 5e-13 vs. distance-based method and < 4e-35 for each other method), with larger improvements at larger distances (e.g. 35x vs. 0.89-6.6x at distance >100kb; p-value for difference < 0.002 vs. each other method). pgBoost also outperformed existing methods in enrichment for CRISPR-validated links (e.g. 4.8x vs. 1.6-4.1x at distance >10kb; p-value for difference = 0.25 vs. distance-based method and < 2e-5 for each other method), with larger improvements at larger distances (e.g. 15x vs. 1.6-2.5x at distance >100kb; p-value for difference < 0.009 for each other method). Similar improvements in enrichment were observed for links derived from Activity-By-Contact (ABC) scores and GWAS data. We further determined that restricting pgBoost to features from a focal cell type improved the identification of SNP-gene links relevant to that cell type. We highlight several examples where pgBoost linked fine-mapped GWAS variants to experimentally validated or biologically plausible target genes that were not implicated by other methods. In conclusion, a non-linear combination of linking strategies, including genomic distance, improves power to identify target genes underlying GWAS associations.
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25
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Li G, Liu Y, Feng X, Diao S, Zhong Z, Li B, Teng J, Zhang W, Zeng H, Cai X, Gao Y, Liu X, Yuan X, Li J, Zhang Z. Integrating Multiple Database Resources to Elucidate the Gene Flow in Southeast Asian Pig Populations. Int J Mol Sci 2024; 25:5689. [PMID: 38891877 PMCID: PMC11171535 DOI: 10.3390/ijms25115689] [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: 04/17/2024] [Revised: 05/16/2024] [Accepted: 05/18/2024] [Indexed: 06/21/2024] Open
Abstract
The domestic pig (Sus scrofa) and its subfamilies have experienced long-term and extensive gene flow, particularly in Southeast Asia. Here, we analyzed 236 pigs, focusing on Yunnan indigenous, European commercial, East Asian, and Southeast Asian breeds, using the Pig Genomics Reference Panel (PGRP v1) of Pig Genotype-Tissue Expression (PigGTEx) to investigate gene flow and associated complex traits by integrating multiple database resources. In this study, we discovered evidence of admixtures from European pigs into the genome of Yunnan indigenous pigs. Additionally, we hypothesized that a potential conceptual gene flow route that may have contributed to the genetic composition of the Diannan small-ear pig is a gene exchange from the Vietnamese pig. Based on the most stringent gene introgression scan using the fd statistic, we identified three specific loci on chromosome 8, ranging from 51.65 to 52.45 Mb, which exhibited strong signatures of selection and harbored the NAF1, NPY1R, and NPY5R genes. These genes are associated with complex traits, such as fat mass, immunity, and litter weight, in pigs, as supported by multiple bio-functionalization databases. We utilized multiple databases to explore the potential dynamics of genetic exchange in Southeast Asian pig populations and elucidated specific gene functionalities.
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Affiliation(s)
- Guangzhen Li
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Yuqiang Liu
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Xueyan Feng
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Shuqi Diao
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Zhanming Zhong
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Bolang Li
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Jinyan Teng
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Wenjing Zhang
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Haonan Zeng
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Xiaodian Cai
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Yahui Gao
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Xiaohong Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China;
| | - Xiaolong Yuan
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Jiaqi Li
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
| | - Zhe Zhang
- National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (G.L.); (Y.L.); (X.F.); (S.D.); (Z.Z.); (B.L.); (J.T.); (W.Z.); (H.Z.); (X.C.); (Y.G.); (X.Y.)
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26
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Dillard LJ, Calabrese GM, Mesner LD, Farber CR. Cell type-specific network analysis in Diversity Outbred mice identifies genes potentially responsible for human bone mineral density GWAS associations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.20.594981. [PMID: 38826475 PMCID: PMC11142079 DOI: 10.1101/2024.05.20.594981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Genome-wide association studies (GWASs) have identified many sources of genetic variation associated with bone mineral density (BMD), a clinical predictor of fracture risk and osteoporosis. Aside from the identification of causal genes, other difficult challenges to informing GWAS include characterizing the roles of predicted causal genes in disease and providing additional functional context, such as the cell type predictions or biological pathways in which causal genes operate. Leveraging single-cell transcriptomics (scRNA-seq) can assist in informing BMD GWAS by linking disease-associated variants to genes and providing a cell type context for which these causal genes drive disease. Here, we use large-scale scRNA-seq data from bone marrow-derived stromal cells cultured under osteogenic conditions (BMSC-OBs) from Diversity Outbred (DO) mice to generate cell type-specific networks and contextualize BMD GWAS-implicated genes. Using trajectories inferred from the scRNA-seq data, we identify networks enriched with genes that exhibit the most dynamic changes in expression across trajectories. We discover 21 network driver genes, which are likely to be causal for human BMD GWAS associations that colocalize with expression/splicing quantitative trait loci (eQTL/sQTL). These driver genes, including Fgfrl1 and Tpx2, along with their associated networks, are predicted to be novel regulators of BMD via their roles in the differentiation of mesenchymal lineage cells. In this work, we showcase the use of single-cell transcriptomics from mouse bone-relevant cells to inform human BMD GWAS and prioritize genetic targets with potential causal roles in the development of osteoporosis.
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Affiliation(s)
- Luke J Dillard
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908
| | - Gina M Calabrese
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908
| | - Larry D Mesner
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908
| | - Charles R Farber
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908
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27
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Karpov DS. CRISPR-Cas Systems and Genome Editing: Beginning the Era of CRISPR/Cas Therapies for Humans. Int J Mol Sci 2024; 25:5292. [PMID: 38791336 PMCID: PMC11121477 DOI: 10.3390/ijms25105292] [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: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024] Open
Abstract
Harnessing of CRISPR/Cas (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated genes) systems for detection, chemical modification, and sequence editing of nucleic acids dramatically changed many fields of fundamental science, biotechnology, and biomedicine [...].
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Affiliation(s)
- Dmitry S Karpov
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilov Str. 32, Moscow 119991, Russia
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28
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McIlwraith EK, Loganathan N, Mak KWY, He W, Belsham DD. Phoenixin knockout mice show no impairment in fertility or differences in metabolic response to a high-fat diet, but exhibit behavioral differences in an open field test. J Neuroendocrinol 2024:e13398. [PMID: 38733120 DOI: 10.1111/jne.13398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 03/27/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024]
Abstract
Phoenixin (PNX) is a conserved secreted peptide that was identified 10 years ago with numerous studies published on its pleiotropic functions. PNX is associated with estrous cycle length, protection from a high-fat diet, and reduction of anxiety behavior. However, no study had yet evaluated the impact of deleting PNX in the whole animal. We sought to evaluate a mouse model lacking the PNX parent gene, small integral membrane protein 20 (Smim20), and the resulting effect on reproduction, energy homeostasis, and anxiety. We found that the Smim20 knockout mice had normal fertility and estrous cycle lengths. Consistent with normal fertility, the hypothalamii of the knockout mice showed no changes in the levels of reproduction-related genes, but the male mice had some changes in energy homeostasis-related genes, such as melanocortin receptor 4 (Mc4r). When placed on a high-fat diet, the wildtype and knockout mice responded similarly, but the male heterozygous mice gained slightly less weight. When placed in an open field test box, the female knockout mice traveled less distance in the outer zone, indicating alterations in anxiety or locomotor behavior. In summary, the homozygous knockout of PNX did not alter fertility and modestly alters a few neuroendocrine genes in response to a high-fat diet, especially in the female mice. However, it altered the behavior of mice in an open field test. PNX therefore may not be crucial for reproductive function or weight, however, we cannot rule out possible compensatory mechanisms in the knockout model. Understanding the role of PNX in physiology may ultimately lead to an enhanced understanding of neuroendocrine mechanisms involving this enigmatic peptide.
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Affiliation(s)
- Emma K McIlwraith
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Neruja Loganathan
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Kimberly W Y Mak
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Wenyuan He
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Denise D Belsham
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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29
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Baldarelli RM, Smith CL, Ringwald M, Richardson JE, Bult CJ. Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse. Genetics 2024; 227:iyae031. [PMID: 38531069 PMCID: PMC11075557 DOI: 10.1093/genetics/iyae031] [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: 12/02/2023] [Accepted: 02/13/2024] [Indexed: 03/28/2024] Open
Abstract
Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD.
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Affiliation(s)
| | | | | | | | - Carol J Bult
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
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30
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Hadar N, Dolgin V, Oustinov K, Yogev Y, Poleg T, Safran A, Freund O, Agam N, Jean MM, Proskorovski-Ohayon R, Wormser O, Drabkin M, Halperin D, Eskin-Schwartz M, Narkis G, Sued-Hendrickson S, Aminov I, Gombosh M, Aharoni S, Birk OS. VARista: a free web platform for streamlined whole-genome variant analysis across T2T, hg38, and hg19. Hum Genet 2024; 143:695-701. [PMID: 38607411 DOI: 10.1007/s00439-024-02671-4] [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: 10/30/2023] [Accepted: 03/24/2024] [Indexed: 04/13/2024]
Abstract
With the increasing importance of genomic data in understanding genetic diseases, there is an essential need for efficient and user-friendly tools that simplify variant analysis. Although multiple tools exist, many present barriers such as steep learning curves, limited reference genome compatibility, or costs. We developed VARista, a free web-based tool, to address these challenges and provide a streamlined solution for researchers, particularly those focusing on rare monogenic diseases. VARista offers a user-centric interface that eliminates much of the technical complexity typically associated with variant analysis. The tool directly supports VCF files generated using reference genomes hg19, hg38, and the emerging T2T, with seamless remapping capabilities between them. Features such as gene summaries and links, tissue and cell-specific gene expression data for both adults and fetuses, as well as automated PCR design and integration with tools such as SpliceAI and AlphaMissense, enable users to focus on the biology and the case itself. As we demonstrate, VARista proved effective in narrowing down potential disease-causing variants, prioritizing them effectively, and providing meaningful biological context, facilitating rapid decision-making. VARista stands out as a freely available and comprehensive tool that consolidates various aspects of variant analysis into a single platform that embraces the forefront of genomic advancements. Its design inherently supports a shift in focus from technicalities to critical thinking, thereby promoting better-informed decisions in genetic disease research. Given its unique capabilities and user-centric design, VARista has the potential to become an essential asset for the genomic research community. https://VARista.link.
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Affiliation(s)
- Noam Hadar
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Vadim Dolgin
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Katya Oustinov
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Yuval Yogev
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Tomer Poleg
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Amit Safran
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Ofek Freund
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Nadav Agam
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Matan M Jean
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Regina Proskorovski-Ohayon
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Ohad Wormser
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Max Drabkin
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Daniel Halperin
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Marina Eskin-Schwartz
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
- Genetics Institute, Soroka University Medical Center, Beer-Sheva, Israel
| | - Ginat Narkis
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
- Genetics Institute, Soroka University Medical Center, Beer-Sheva, Israel
| | - Sufa Sued-Hendrickson
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Ilana Aminov
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Maya Gombosh
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Sarit Aharoni
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Ohad S Birk
- The Morris Kahn Laboratory of Human Genetics at the National Institute of Biotechnology in the Negev and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.
- Genetics Institute, Soroka University Medical Center, Beer-Sheva, Israel.
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31
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Lyu TJ, Ma J, Zhang XY, Xie GG, Liu C, Du J, Xu YN, Yang DC, Cen C, Wang MY, Lyu NY, Wang Y, Zhang HQ. Deficiency of FRMD5 results in neurodevelopmental dysfunction and autistic-like behavior in mice. Mol Psychiatry 2024; 29:1253-1264. [PMID: 38228891 DOI: 10.1038/s41380-024-02407-w] [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: 07/25/2023] [Revised: 12/17/2023] [Accepted: 01/02/2024] [Indexed: 01/18/2024]
Abstract
The pathophysiology of autism spectrum disorders (ASDs) is causally linked to postsynaptic scaffolding proteins, as evidenced by numerous large-scale genomic studies [1, 2] and in vitro and in vivo neurobiological studies of mutations in animal models [3, 4]. However, due to the distinct phenotypic and genetic heterogeneity observed in ASD patients, individual mutation genes account for only a small proportion (<2%) of cases [1, 5]. Recently, a human genetic study revealed a correlation between de novo variants in FERM domain-containing-5 (FRMD5) and neurodevelopmental abnormalities [6]. In this study, we demonstrate that deficiency of the scaffolding protein FRMD5 leads to neurodevelopmental dysfunction and ASD-like behavior in mice. FRMD5 deficiency results in morphological abnormalities in neurons and synaptic dysfunction in mice. Frmd5-deficient mice display learning and memory dysfunction, impaired social function, and increased repetitive stereotyped behavior. Mechanistically, tandem mass tag (TMT)-labeled quantitative proteomics revealed that FRMD5 deletion affects the distribution of synaptic proteins involved in the pathological process of ASD. Collectively, our findings delineate the critical role of FRMD5 in neurodevelopment and ASD pathophysiology, suggesting potential therapeutic implications for the treatment of ASD.
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Affiliation(s)
- Tian-Jie Lyu
- Neuroscience Research Institute and Department of Neurobiology, School of Basic Medical Sciences, Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, National Health Commission and State Key Laboratory of Natural and Biomimetic Drugs, Peking University, 100191, Beijing, China
| | - Ji Ma
- Department of Human Anatomy, Histology and Embryology, School of Basic Medical Sciences, State Key Laboratory of Molecular Oncology and International Cancer Institute, Peking University Health Science Center, 100191, Beijing, China
| | - Xi-Yin Zhang
- Neuroscience Research Institute and Department of Neurobiology, School of Basic Medical Sciences, Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, National Health Commission and State Key Laboratory of Natural and Biomimetic Drugs, Peking University, 100191, Beijing, China
| | - Guo-Guang Xie
- Neuroscience Research Institute and Department of Neurobiology, School of Basic Medical Sciences, Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, National Health Commission and State Key Laboratory of Natural and Biomimetic Drugs, Peking University, 100191, Beijing, China
| | - Cheng Liu
- Department of Human Anatomy, Histology and Embryology, School of Basic Medical Sciences, State Key Laboratory of Molecular Oncology and International Cancer Institute, Peking University Health Science Center, 100191, Beijing, China
| | - Juan Du
- Department of Human Anatomy, Histology and Embryology, School of Basic Medical Sciences, State Key Laboratory of Molecular Oncology and International Cancer Institute, Peking University Health Science Center, 100191, Beijing, China
| | - Yi-Nuo Xu
- Neuroscience Research Institute and Department of Neurobiology, School of Basic Medical Sciences, Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, National Health Commission and State Key Laboratory of Natural and Biomimetic Drugs, Peking University, 100191, Beijing, China
| | - De-Cao Yang
- Department of Human Anatomy, Histology and Embryology, School of Basic Medical Sciences, State Key Laboratory of Molecular Oncology and International Cancer Institute, Peking University Health Science Center, 100191, Beijing, China
| | - Cheng Cen
- Neuroscience Research Institute and Department of Neurobiology, School of Basic Medical Sciences, Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, National Health Commission and State Key Laboratory of Natural and Biomimetic Drugs, Peking University, 100191, Beijing, China
| | - Meng-Yuan Wang
- Department of Human Anatomy, Histology and Embryology, School of Basic Medical Sciences, State Key Laboratory of Molecular Oncology and International Cancer Institute, Peking University Health Science Center, 100191, Beijing, China
| | - Na-Yun Lyu
- Neuroscience Research Institute and Department of Neurobiology, School of Basic Medical Sciences, Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, National Health Commission and State Key Laboratory of Natural and Biomimetic Drugs, Peking University, 100191, Beijing, China
| | - Yun Wang
- Neuroscience Research Institute and Department of Neurobiology, School of Basic Medical Sciences, Key Laboratory for Neuroscience, Ministry of Education/National Health Commission, National Health Commission and State Key Laboratory of Natural and Biomimetic Drugs, Peking University, 100191, Beijing, China.
- PKU-IDG/McGovern Institute for Brain Research, Peking University, 100871, Beijing, China.
| | - Hong-Quan Zhang
- Department of Human Anatomy, Histology and Embryology, School of Basic Medical Sciences, State Key Laboratory of Molecular Oncology and International Cancer Institute, Peking University Health Science Center, 100191, Beijing, China.
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32
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Parolek J, Burd CG. Bridge-like lipid transfer protein family member 2 suppresses ciliogenesis. Mol Biol Cell 2024; 35:br11. [PMID: 38536441 PMCID: PMC11151097 DOI: 10.1091/mbc.e24-02-0065] [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: 02/09/2024] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 04/12/2024] Open
Abstract
Bridge-like lipid transfer protein family member 2 (BLTP2) is an evolutionary conserved protein with unknown function(s). The absence of BLTP2 in Drosophila melanogaster results in impaired cellular secretion and larval death, while in mice (Mus musculus), it causes preweaning lethality. Structural predictions propose that BLTP2 belongs to the repeating β-groove domain-containing (also called the VPS13) protein family, forming a long tube with a hydrophobic core, suggesting that it operates as a lipid transfer protein (LTP). We establish BLTP2 as a negative regulator of ciliogenesis in RPE-1 cells based on a strong genetic interaction with WDR44, a gene that also suppresses ciliogenesis. Like WDR44, BLTP2 localizes to membrane contact sites involving the endoplasmic reticulum and the tubular endosome network in HeLa cells and that BLTP2 depletion enhanced ciliogenesis in RPE-1 cells grown in serum-containing medium, a condition where ciliogenesis is normally suppressed. This study establishes human BLTP2 as a putative LTP acting between tubular endosomes and ER that regulates primary cilium biogenesis.
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Affiliation(s)
- Jan Parolek
- Department of Cell Biology, Yale School of Medicine, New Haven, CT 06520
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33
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Radaszkiewicz KA, Sulcova M, Kohoutkova E, Harnos J. The role of prickle proteins in vertebrate development and pathology. Mol Cell Biochem 2024; 479:1199-1221. [PMID: 37358815 PMCID: PMC11116189 DOI: 10.1007/s11010-023-04787-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/09/2023] [Indexed: 06/27/2023]
Abstract
Prickle is an evolutionarily conserved family of proteins exclusively associated with planar cell polarity (PCP) signalling. This signalling pathway provides directional and positional cues to eukaryotic cells along the plane of an epithelial sheet, orthogonal to both apicobasal and left-right axes. Through studies in the fruit fly Drosophila, we have learned that PCP signalling is manifested by the spatial segregation of two protein complexes, namely Prickle/Vangl and Frizzled/Dishevelled. While Vangl, Frizzled, and Dishevelled proteins have been extensively studied, Prickle has been largely neglected. This is likely because its role in vertebrate development and pathologies is still being explored and is not yet fully understood. The current review aims to address this gap by summarizing our current knowledge on vertebrate Prickle proteins and to cover their broad versatility. Accumulating evidence suggests that Prickle is involved in many developmental events, contributes to homeostasis, and can cause diseases when its expression and signalling properties are deregulated. This review highlights the importance of Prickle in vertebrate development, discusses the implications of Prickle-dependent signalling in pathology, and points out the blind spots or potential links regarding Prickle, which could be studied further.
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Affiliation(s)
- K A Radaszkiewicz
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, 62500, Czechia
| | - M Sulcova
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, 62500, Czechia
| | - E Kohoutkova
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, 62500, Czechia
| | - J Harnos
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, 62500, Czechia.
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34
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Atasu B, Simón-Sánchez J, Hanagasi H, Bilgic B, Hauser AK, Guven G, Heutink P, Gasser T, Lohmann E. Dissecting genetic architecture of rare dystonia: genetic, molecular and clinical insights. J Med Genet 2024; 61:443-451. [PMID: 38458754 DOI: 10.1136/jmg-2022-109099] [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: 12/08/2022] [Accepted: 12/24/2023] [Indexed: 03/10/2024]
Abstract
BACKGROUND Dystonia is one of the most common movement disorders. To date, the genetic causes of dystonia in populations of European descent have been extensively studied. However, other populations, particularly those from the Middle East, have not been adequately studied. The purpose of this study is to discover the genetic basis of dystonia in a clinically and genetically well-characterised dystonia cohort from Turkey, which harbours poorly studied populations. METHODS Exome sequencing analysis was performed in 42 Turkish dystonia families. Using co-expression network (CEN) analysis, identified candidate genes were interrogated for the networks including known dystonia-associated genes and genes further associated with the protein-protein interaction, animal model-based characteristics and clinical findings. RESULTS We identified potentially disease-causing variants in the established dystonia genes (PRKRA, SGCE, KMT2B, SLC2A1, GCH1, THAP1, HPCA, TSPOAP1, AOPEP; n=11 families (26%)), in the uncommon forms of dystonia-associated genes (PCCB, CACNA1A, ALDH5A1, PRKN; n=4 families (10%)) and in the candidate genes prioritised based on the pathogenicity of the variants and CEN-based analyses (n=11 families (21%)). The diagnostic yield was found to be 36%. Several pathways and gene ontologies implicated in immune system, transcription, metabolic pathways, endosomal-lysosomal and neurodevelopmental mechanisms were over-represented in our CEN analysis. CONCLUSIONS Here, using a structured approach, we have characterised a clinically and genetically well-defined dystonia cohort from Turkey, where dystonia has not been widely studied, and provided an uncovered genetic basis, which will facilitate diagnostic dystonia research.
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Affiliation(s)
- Burcu Atasu
- Eberhard Karls Universität Tübingen Hertie Institut für klinische Hirnforschung Allgemeine Neurologie, Tubingen, Germany
| | - Javier Simón-Sánchez
- Eberhard Karls Universität Tübingen Hertie Institut für klinische Hirnforschung Allgemeine Neurologie, Tubingen, Germany
| | - Hasmet Hanagasi
- Department of Neurology, Istanbul University Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Basar Bilgic
- Department of Neurology, Istanbul University Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Ann-Kathrin Hauser
- Eberhard Karls Universität Tübingen Hertie Institut für klinische Hirnforschung Allgemeine Neurologie, Tubingen, Germany
| | - Gamze Guven
- Genetics Department, Aziz Sancar Institute of Experimental Medicine, Istanbul, Turkey
| | | | - Thomas Gasser
- Eberhard Karls Universität Tübingen Hertie Institut für klinische Hirnforschung Allgemeine Neurologie, Tubingen, Germany
| | - Ebba Lohmann
- Eberhard Karls Universität Tübingen Hertie Institut für klinische Hirnforschung Allgemeine Neurologie, Tubingen, Germany
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35
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Liu X, Koyama S, Tomizuka K, Takata S, Ishikawa Y, Ito S, Kosugi S, Suzuki K, Hikino K, Koido M, Koike Y, Horikoshi M, Gakuhari T, Ikegawa S, Matsuda K, Momozawa Y, Ito K, Kamatani Y, Terao C. Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing. SCIENCE ADVANCES 2024; 10:eadi8419. [PMID: 38630824 PMCID: PMC11023554 DOI: 10.1126/sciadv.adi8419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 03/07/2024] [Indexed: 04/19/2024]
Abstract
We generated Japanese Encyclopedia of Whole-Genome/Exome Sequencing Library (JEWEL), a high-depth whole-genome sequencing dataset comprising 3256 individuals from across Japan. Analysis of JEWEL revealed genetic characteristics of the Japanese population that were not discernible using microarray data. First, rare variant-based analysis revealed an unprecedented fine-scale genetic structure. Together with population genetics analysis, the present-day Japanese can be decomposed into three ancestral components. Second, we identified unreported loss-of-function (LoF) variants and observed that for specific genes, LoF variants appeared to be restricted to a more limited set of transcripts than would be expected by chance, with PTPRD as a notable example. Third, we identified 44 archaic segments linked to complex traits, including a Denisovan-derived segment at NKX6-1 associated with type 2 diabetes. Most of these segments are specific to East Asians. Fourth, we identified candidate genetic loci under recent natural selection. Overall, our work provided insights into genetic characteristics of the Japanese population.
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Affiliation(s)
- Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
| | - Satoshi Koyama
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Sadaaki Takata
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yuki Ishikawa
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shuji Ito
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
- Department of Orthopedic Surgery, Faculty of Medicine, Shimane University, Izumo, Japan
| | - Shunichi Kosugi
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kunihiko Suzuki
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Keiko Hikino
- Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masaru Koido
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yoshinao Koike
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
- Department of Orthopedic Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Takashi Gakuhari
- Institute for the Study of Ancient Civilizations and Cultural Resources, College of Human and Social Sciences, Kanazawa University, Kanazawa, Japan
| | - Shiro Ikegawa
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
| | - Kochi Matsuda
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kaoru Ito
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
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36
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Sultanakhmetov G, Limlingan SJM, Fukuchi A, Tsuda K, Suzuki H, Kato I, Saito T, Weitemier AZ, Ando K. Mark4 ablation attenuates pathological phenotypes in a mouse model of tauopathy. Brain Commun 2024; 6:fcae136. [PMID: 38712317 PMCID: PMC11073748 DOI: 10.1093/braincomms/fcae136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 02/20/2024] [Accepted: 04/16/2024] [Indexed: 05/08/2024] Open
Abstract
Accumulation of abnormally phosphorylated tau proteins is linked to various neurodegenerative diseases, including Alzheimer's disease and frontotemporal dementia. Microtubule affinity-regulating kinase 4 (MARK4) has been genetically and pathologically associated with Alzheimer's disease and reported to enhance tau phosphorylation and toxicity in Drosophila and mouse traumatic brain-injury models but not in mammalian tauopathy models. To investigate the role of MARK4 in tau-mediated neuropathology, we crossed P301S tauopathy model (PS19) and Mark4 knockout mice. We performed behaviour, biochemical and histology analyses to evaluate changes in PS19 pathological phenotype with and without Mark4. Here, we demonstrated that Mark4 deletion ameliorated the tau pathology in a mouse model of tauopathy. In particular, we found that PS19 with Mark4 knockout showed improved mortality and memory compared with those bearing an intact Mark4 gene. These phenotypes were accompanied by reduced neurodegeneration and astrogliosis in response to the reduction of pathological forms of tau, such as those phosphorylated at Ser356, AT8-positive tau and thioflavin S-positive tau. Our data indicate that MARK4 critically contributes to tau-mediated neuropathology, suggesting that MARK4 inhibition may serve as a therapeutic avenue for tauopathies.
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Affiliation(s)
- Grigorii Sultanakhmetov
- Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
| | - Sophia Jobien M Limlingan
- Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
| | - Aoi Fukuchi
- Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
| | - Keisuke Tsuda
- Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
| | - Hirokazu Suzuki
- Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
| | - Iori Kato
- Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
| | - Taro Saito
- Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
- Department of Biological Sciences, School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
| | - Adam Z Weitemier
- Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
- Department of Biological Sciences, School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
| | - Kanae Ando
- Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
- Department of Biological Sciences, School of Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan
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37
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Carvalho S, Zea-Redondo L, Tang TCC, Stachel-Braum P, Miller D, Caldas P, Kukalev A, Diecke S, Grosswendt S, Grosso AR, Pombo A. SRRM2 splicing factor modulates cell fate in early development. Biol Open 2024; 13:bio060415. [PMID: 38656788 PMCID: PMC11070786 DOI: 10.1242/bio.060415] [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: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024] Open
Abstract
Embryo development is an orchestrated process that relies on tight regulation of gene expression to guide cell differentiation and fate decisions. The Srrm2 splicing factor has recently been implicated in developmental disorders and diseases, but its role in early mammalian development remains unexplored. Here, we show that Srrm2 dosage is critical for maintaining embryonic stem cell pluripotency and cell identity. Srrm2 heterozygosity promotes loss of stemness, characterised by the coexistence of cells expressing naive and formative pluripotency markers, together with extensive changes in gene expression, including genes regulated by serum-response transcription factor (SRF) and differentiation-related genes. Depletion of Srrm2 by RNA interference in embryonic stem cells shows that the earliest effects of Srrm2 heterozygosity are specific alternative splicing events on a small number of genes, followed by expression changes in metabolism and differentiation-related genes. Our findings unveil molecular and cellular roles of Srrm2 in stemness and lineage commitment, shedding light on the roles of splicing regulators in early embryogenesis, developmental diseases and tumorigenesis.
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Affiliation(s)
- Silvia Carvalho
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Epigenetic Regulation and Chromatin Structure Group, 10115 Berlin, Germany
- Associate Laboratory i4HB – Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- UCIBIO – Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Instituto de Ciências Biomédicas Abel Salazar (ICBAS), Universidade do Porto, 4050-313 Porto, Portugal
- Graduate Program in Areas of Basic and Applied Biology (GABBA), ICBAS, University of Porto, 4050-313 Porto, Portugal
| | - Luna Zea-Redondo
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Epigenetic Regulation and Chromatin Structure Group, 10115 Berlin, Germany
- Humboldt-Universität zu Berlin, Institute of Biology, 10115 Berlin, Germany
| | - Tsz Ching Chloe Tang
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Epigenetic Regulation and Chromatin Structure Group, 10115 Berlin, Germany
| | - Philipp Stachel-Braum
- Humboldt-Universität zu Berlin, Institute of Biology, 10115 Berlin, Germany
- Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, Exploratory Diagnostic Sciences (EDS) 10178 Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), From Cell State to Function Group, 10115 Berlin, Germany
| | - Duncan Miller
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Pluripotent Stem Cells Platform, 13125 Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, 10785 Berlin, Germany
| | - Paulo Caldas
- Associate Laboratory i4HB – Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- UCIBIO – Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Alexander Kukalev
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Epigenetic Regulation and Chromatin Structure Group, 10115 Berlin, Germany
| | - Sebastian Diecke
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Pluripotent Stem Cells Platform, 13125 Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, 10785 Berlin, Germany
| | - Stefanie Grosswendt
- Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, Exploratory Diagnostic Sciences (EDS) 10178 Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), From Cell State to Function Group, 10115 Berlin, Germany
| | - Ana Rita Grosso
- Associate Laboratory i4HB – Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- UCIBIO – Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Ana Pombo
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Epigenetic Regulation and Chromatin Structure Group, 10115 Berlin, Germany
- Humboldt-Universität zu Berlin, Institute of Biology, 10115 Berlin, Germany
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38
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González JT, Thrush K, Meer M, Levine ME, Higgins-Chen AT. Age-Invariant Genes: Multi-Tissue Identification and Characterization of Murine Reference Genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.09.588721. [PMID: 38645168 PMCID: PMC11030416 DOI: 10.1101/2024.04.09.588721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Studies of the aging transcriptome focus on genes that change with age. But what can we learn from age-invariant genes-those that remain unchanged throughout the aging process? These genes also have a practical application: they serve as reference genes (often called housekeeping genes) in expression studies. Reference genes have mostly been identified and validated in young organisms, and no systematic investigation has been done across the lifespan. Here, we build upon a common pipeline for identifying reference genes in RNA-seq datasets to identify age-invariant genes across seventeen C57BL/6 mouse tissues (brain, lung, bone marrow, muscle, white blood cells, heart, small intestine, kidney, liver, pancreas, skin, brown, gonadal, marrow, and subcutaneous adipose tissue) spanning 1 to 21+ months of age. We identify 9 pan-tissue age-invariant genes and many tissue-specific age-invariant genes. These genes are stable across the lifespan and are validated in independent bulk RNA-seq datasets and RT-qPCR. We find age-invariant genes have shorter transcripts on average and are enriched for CpG islands. Interestingly, pathway enrichment analysis for age-invariant genes identifies an overrepresentation of molecular functions associated with some, but not all, hallmarks of aging. Thus, though hallmarks of aging typically involve changes in cell maintenance mechanisms, select genes associated with these hallmarks resist fluctuations in expression with age. Finally, our analysis concludes no classical reference gene is appropriate for aging studies in all tissues. Instead, we provide tissue-specific and pan-tissue genes for assays utilizing reference gene normalization (i.e., RT-qPCR) that can be applied to animals across the lifespan.
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Affiliation(s)
- John T. González
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Kyra Thrush
- Altos Labs, San Diego Institute of Sciences, San Diego, CA, USA
| | - Margarita Meer
- Altos Labs, San Diego Institute of Sciences, San Diego, CA, USA
| | - Morgan E. Levine
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Altos Labs, San Diego Institute of Sciences, San Diego, CA, USA
| | - Albert T. Higgins-Chen
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven CT, USA
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39
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Zeng T, Spence JP, Mostafavi H, Pritchard JK. Bayesian estimation of gene constraint from an evolutionary model with gene features. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.19.541520. [PMID: 37292653 PMCID: PMC10245655 DOI: 10.1101/2023.05.19.541520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Measures of selective constraint on genes have been used for many applications including clinical interpretation of rare coding variants, disease gene discovery, and studies of genome evolution. However, widely-used metrics are severely underpowered at detecting constraint for the shortest ∼25% of genes, potentially causing important pathogenic mutations to be overlooked. We developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric, shet. Our estimates outperform existing metrics for prioritizing genes important for cell essentiality, human disease, and other phenotypes, especially for short genes. Our new estimates of selective constraint should have wide utility for characterizing genes relevant to human disease. Finally, our inference framework, GeneBayes, provides a flexible platform that can improve estimation of many gene-level properties, such as rare variant burden or gene expression differences.
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Affiliation(s)
- Tony Zeng
- Department of Genetics, Stanford University, Stanford CA
| | | | | | - Jonathan K. Pritchard
- Department of Genetics, Stanford University, Stanford CA
- Department of Biology, Stanford University, Stanford CA
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40
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Mackay TFC, Anholt RRH. Pleiotropy, epistasis and the genetic architecture of quantitative traits. Nat Rev Genet 2024:10.1038/s41576-024-00711-3. [PMID: 38565962 DOI: 10.1038/s41576-024-00711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2024] [Indexed: 04/04/2024]
Abstract
Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.
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Affiliation(s)
- Trudy F C Mackay
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
| | - Robert R H Anholt
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
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41
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Jäger R, Geyer SH, Kavirayani A, Kiss MG, Waltenberger E, Rülicke T, Binder CJ, Weninger WJ, Kralovics R. Effects of Tulp4 deficiency on murine embryonic development and adult phenotype. Microsc Res Tech 2024; 87:854-866. [PMID: 38115643 DOI: 10.1002/jemt.24476] [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: 09/26/2023] [Accepted: 12/08/2023] [Indexed: 12/21/2023]
Abstract
Genetically engineered mouse models have the potential to unravel fundamental biological processes and provide mechanistic insights into the pathogenesis of human diseases. We have previously observed that germline genetic variation at the TULP4 locus influences clinical characteristics in patients with myeloproliferative neoplasms. To elucidate the role of TULP4 in pathological and physiological processes in vivo, we generated a Tulp4 knockout mouse model. Systemic Tulp4 deficiency exerted a strong impact on embryonic development in both Tulp4 homozygous null (Tulp4-/-) and heterozygous (Tulp4+/-) knockout mice, the former exhibiting perinatal lethality. High-resolution episcopic microscopy (HREM) of day 14.5 embryos allowed for the identification of multiple developmental defects in Tulp4-/- mice, including severe heart defects. Moreover, in Tulp4+/- embryos HREM revealed abnormalities of several organ systems, which per se do not affect prenatal or postnatal survival. In adult Tulp4+/- mice, extensive examinations of hematopoietic and cardiovascular features, involving histopathological surveys of multiple tissues as well as blood counts and immunophenotyping, did not provide evidence for anomalies as observed in corresponding embryos. Finally, evaluating a potential obesity-related phenotype as reported for other TULP family members revealed a trend for increased body weight of Tulp4+/- mice. RESEARCH HIGHLIGHTS: To study the role of the TULP4 gene in vivo, we generated a Tulp4 knockout mouse model. Correlative analyses involving HREM revealed a strong impact of Tulp4 deficiency on murine embryonic development.
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Affiliation(s)
- Roland Jäger
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Stefan H Geyer
- Division of Anatomy, Center for Anatomy and Cell Biology, Medical Imaging Cluster, Medical University of Vienna, Vienna, Austria
| | - Anoop Kavirayani
- Vienna BioCenter Core Facilities GmbH, Austrian BioImaging/CMI, Vienna, Austria
| | - Máté G Kiss
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Elisabeth Waltenberger
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Thomas Rülicke
- Department of Biomedical Sciences, University of Veterinary Medicine Vienna, Vienna, Austria
- Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Vienna, Austria
| | - Christoph J Binder
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Wolfgang J Weninger
- Division of Anatomy, Center for Anatomy and Cell Biology, Medical Imaging Cluster, Medical University of Vienna, Vienna, Austria
| | - Robert Kralovics
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
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42
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Duyzend MH, Cacheiro P, Jacobsen JO, Giordano J, Brand H, Wapner RJ, Talkowski ME, Robinson PN, Smedley D. Improving prenatal diagnosis through standards and aggregation. Prenat Diagn 2024; 44:454-464. [PMID: 38242839 PMCID: PMC11006584 DOI: 10.1002/pd.6522] [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: 10/26/2023] [Revised: 12/17/2023] [Accepted: 12/22/2023] [Indexed: 01/21/2024]
Abstract
Advances in sequencing and imaging technologies enable enhanced assessment in the prenatal space, with a goal to diagnose and predict the natural history of disease, to direct targeted therapies, and to implement clinical management, including transfer of care, election of supportive care, and selection of surgical interventions. The current lack of standardization and aggregation stymies variant interpretation and gene discovery, which hinders the provision of prenatal precision medicine, leaving clinicians and patients without an accurate diagnosis. With large amounts of data generated, it is imperative to establish standards for data collection, processing, and aggregation. Aggregated and homogeneously processed genetic and phenotypic data permits dissection of the genomic architecture of prenatal presentations of disease and provides a dataset on which data analysis algorithms can be tuned to the prenatal space. Here we discuss the importance of generating aggregate data sets and how the prenatal space is driving the development of interoperable standards and phenotype-driven tools.
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Affiliation(s)
- Michael H. Duyzend
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics and Genomics, Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Pilar Cacheiro
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Julius O.B. Jacobsen
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Jessica Giordano
- Department of Obstetrics & Gynecology, Columbia University Medical Center, New York, NY, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Ronald J. Wapner
- Department of Obstetrics & Gynecology, Columbia University Medical Center, New York, NY, USA
| | - Michael E. Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
- Program in Bioinformatics and Integrative Genomics, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
| | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
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Jurgens JA, Barry BJ, Chan WM, MacKinnon S, Whitman MC, Matos Ruiz PM, Pratt BM, England EM, Pais L, Lemire G, Groopman E, Glaze C, Russell KA, Singer-Berk M, Di Gioia SA, Lee AS, Andrews C, Shaaban S, Wirth MM, Bekele S, Toffoloni M, Bradford VR, Foster EE, Berube L, Rivera-Quiles C, Mensching FM, Sanchis-Juan A, Fu JM, Wong I, Zhao X, Wilson MW, Weisburd B, Lek M, Brand H, Talkowski ME, MacArthur DG, O’Donnell-Luria A, Robson CD, Hunter DG, Engle EC. Expanding the genetics and phenotypes of ocular congenital cranial dysinnervation disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.22.24304594. [PMID: 38585811 PMCID: PMC10996726 DOI: 10.1101/2024.03.22.24304594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Purpose To identify genetic etiologies and genotype/phenotype associations for unsolved ocular congenital cranial dysinnervation disorders (oCCDDs). Methods We coupled phenotyping with exome or genome sequencing of 467 pedigrees with genetically unsolved oCCDDs, integrating analyses of pedigrees, human and animal model phenotypes, and de novo variants to identify rare candidate single nucleotide variants, insertion/deletions, and structural variants disrupting protein-coding regions. Prioritized variants were classified for pathogenicity and evaluated for genotype/phenotype correlations. Results Analyses elucidated phenotypic subgroups, identified pathogenic/likely pathogenic variant(s) in 43/467 probands (9.2%), and prioritized variants of uncertain significance in 70/467 additional probands (15.0%). These included known and novel variants in established oCCDD genes, genes associated with syndromes that sometimes include oCCDDs (e.g., MYH10, KIF21B, TGFBR2, TUBB6), genes that fit the syndromic component of the phenotype but had no prior oCCDD association (e.g., CDK13, TGFB2), genes with no reported association with oCCDDs or the syndromic phenotypes (e.g., TUBA4A, KIF5C, CTNNA1, KLB, FGF21), and genes associated with oCCDD phenocopies that had resulted in misdiagnoses. Conclusion This study suggests that unsolved oCCDDs are clinically and genetically heterogeneous disorders often overlapping other Mendelian conditions and nominates many candidates for future replication and functional studies.
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Affiliation(s)
- Julie A. Jurgens
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Brenda J. Barry
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Wai-Man Chan
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Sarah MacKinnon
- Department of Ophthalmology, Boston Children’s Hospital, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Mary C. Whitman
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Boston, MA, USA
- Department of Ophthalmology, Boston Children’s Hospital, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | | | - Brandon M. Pratt
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
| | - Eleina M. England
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Lynn Pais
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Gabrielle Lemire
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Emily Groopman
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Carmen Glaze
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn A. Russell
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Moriel Singer-Berk
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Silvio Alessandro Di Gioia
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Regeneron Pharmaceuticals, Tarrytown, NY, 10591, USA
| | - Arthur S. Lee
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Caroline Andrews
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
| | - Sherin Shaaban
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Megan M. Wirth
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
| | - Sarah Bekele
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
| | - Melissa Toffoloni
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
| | | | - Emma E. Foster
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
| | - Lindsay Berube
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
| | | | | | - Alba Sanchis-Juan
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jack M. Fu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Isaac Wong
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Xuefang Zhao
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Michael W. Wilson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ben Weisburd
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Monkol Lek
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Harrison Brand
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Pediatric Surgical Research Laboratories, Massachusetts General Hospital, Boston, MA, USA
| | - Michael E. Talkowski
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel G. MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anne O’Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Caroline D. Robson
- Division of Neuroradiology, Department of Radiology, Boston Children’s Hospital, Boston, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - David G. Hunter
- Department of Ophthalmology, Boston Children’s Hospital, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Elizabeth C. Engle
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Boston Children’s Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Ophthalmology, Boston Children’s Hospital, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
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Guleray Lafci N, van Goor M, Cetinkaya S, van der Wijst J, Acun M, Kurt Colak F, Cetinkaya A, Hoenderop J. Decreased calcium permeability caused by biallelic TRPV5 mutation leads to autosomal recessive renal calcium-wasting hypercalciuria. Eur J Hum Genet 2024:10.1038/s41431-024-01589-9. [PMID: 38528055 DOI: 10.1038/s41431-024-01589-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/10/2024] [Accepted: 03/04/2024] [Indexed: 03/27/2024] Open
Abstract
Hypercalciuria is the most common metabolic risk factor in people with kidney stone disease. Its etiology is mostly multifactorial, although monogenetic causes of hypercalciuria have also been described. Despite the increased availability of genetic diagnostic tests, the vast majority of individuals with familial hypercalciuria remain unsolved. In this study, we investigated a consanguineous pedigree with idiopathic hypercalciuria. The proband additionally exhibited severe skeletal deformities and hyperparathyroidism. Whole-exome sequencing of the proband revealed a homozygous ultra-rare variant in TRPV5 (NM_019841.7:c.1792G>A; p.(Val598Met)), which encodes for a renal Ca2+-selective ion channel. The variant segregates with the three individuals with hypercalciuria. The skeletal phenotype unique to the proband was due to an additional pathogenic somatic mutation in GNAS (NM_000516.7:c.601C>T; p.(Arg201Cys)), which leads to polyostotic fibrous dysplasia. The variant in TRPV5 is located in the TRP helix, a characteristic amphipathic helix that is indispensable for the gating movements of TRP channels. Biochemical characterization of the TRPV5 p.(Val598Met) channel revealed a complete loss of Ca2+ transport capability. This defect is caused by reduced expression of the mutant channel, due to misfolding and preferential targeting to the proteasome for degradation. Based on these findings, we conclude that biallelic loss of TRPV5 function causes a novel form of monogenic autosomal recessive hypercalciuria, which we name renal Ca2+-wasting hypercalciuria (RCWH). The recessive inheritance pattern explains the rarity of RCWH and underscores the potential prevalence of RCWH in highly consanguineous populations, emphasizing the importance of exploration of this disorder within such communities.
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Affiliation(s)
- Naz Guleray Lafci
- Hacettepe University, Medical Faculty, Department of Medical Genetics, Ankara, Turkey
- Health Science University, Dr. Sami Ulus Obstetrics and Gynecology, Children Health and Disease Training and Research Hospital, Department of Medical Genetics, Ankara, Turkey
| | - Mark van Goor
- Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Semra Cetinkaya
- Health Science University, Dr. Sami Ulus Obstetrics and Gynecology, Children Health and Disease Training and Research Hospital, Department of Pediatric Endocrinology, Ankara, Turkey
| | - Jenny van der Wijst
- Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Melisa Acun
- Hacettepe University, Institute of Health Sciences, Department of Bioinformatics, Ankara, Turkey
| | - Fatma Kurt Colak
- Health Science University, Dr. Sami Ulus Obstetrics and Gynecology, Children Health and Disease Training and Research Hospital, Department of Medical Genetics, Ankara, Turkey
| | - Arda Cetinkaya
- Hacettepe University, Medical Faculty, Department of Medical Genetics, Ankara, Turkey.
- Hacettepe University, Institute of Health Sciences, Department of Bioinformatics, Ankara, Turkey.
| | - Joost Hoenderop
- Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, The Netherlands.
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45
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Venkatesh SS, Wittemans LBL, Palmer DS, Baya NA, Ferreira T, Hill B, Lassen FH, Parker MJ, Reibe S, Elhakeem A, Banasik K, Bruun MT, Erikstrup C, Jensen BA, Juul A, Mikkelsen C, Nielsen HS, Ostrowski SR, Pedersen OB, Rohde PD, Sorensen E, Ullum H, Westergaard D, Haraldsson A, Holm H, Jonsdottir I, Olafsson I, Steingrimsdottir T, Steinthorsdottir V, Thorleifsson G, Figueredo J, Karjalainen MK, Pasanen A, Jacobs BM, Hubers N, Lippincott M, Fraser A, Lawlor DA, Timpson NJ, Nyegaard M, Stefansson K, Magi R, Laivuori H, van Heel DA, Boomsma DI, Balasubramanian R, Seminara SB, Chan YM, Laisk T, Lindgren CM. Genome-wide analyses identify 21 infertility loci and over 400 reproductive hormone loci across the allele frequency spectrum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304530. [PMID: 38562841 PMCID: PMC10984039 DOI: 10.1101/2024.03.19.24304530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Genome-wide association studies (GWASs) may help inform treatments for infertility, whose causes remain unknown in many cases. Here we present GWAS meta-analyses across six cohorts for male and female infertility in up to 41,200 cases and 687,005 controls. We identified 21 genetic risk loci for infertility (P≤5E-08), of which 12 have not been reported for any reproductive condition. We found positive genetic correlations between endometriosis and all-cause female infertility (rg=0.585, P=8.98E-14), and between polycystic ovary syndrome and anovulatory infertility (rg=0.403, P=2.16E-03). The evolutionary persistence of female infertility-risk alleles in EBAG9 may be explained by recent directional selection. We additionally identified up to 269 genetic loci associated with follicle-stimulating hormone (FSH), luteinising hormone, oestradiol, and testosterone through sex-specific GWAS meta-analyses (N=6,095-246,862). While hormone-associated variants near FSHB and ARL14EP colocalised with signals for anovulatory infertility, we found no rg between female infertility and reproductive hormones (P>0.05). Exome sequencing analyses in the UK Biobank (N=197,340) revealed that women carrying testosterone-lowering rare variants in GPC2 were at higher risk of infertility (OR=2.63, P=1.25E-03). Taken together, our results suggest that while individual genes associated with hormone regulation may be relevant for fertility, there is limited genetic evidence for correlation between reproductive hormones and infertility at the population level. We provide the first comprehensive view of the genetic architecture of infertility across multiple diagnostic criteria in men and women, and characterise its relationship to other health conditions.
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Affiliation(s)
- Samvida S Venkatesh
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Nikolas A Baya
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Teresa Ferreira
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Barney Hill
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Frederik Heymann Lassen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Melody J Parker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Saskia Reibe
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Ahmed Elhakeem
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Mie T Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - Bitten A Jensen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Anders Juul
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen; Copenhagen, Denmark
- Department of Growth and Reproduction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Christina Mikkelsen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, Copenhagen University, Copenhagen, Denmark
| | - Henriette S Nielsen
- Department of Obstetrics and Gynecology, The Fertility Clinic, Hvidovre University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - 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, Kge, Denmark
| | - Palle D Rohde
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Erik Sorensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Asgeir Haraldsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Children's Hospital Iceland, Landspitali University Hospital, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Ingileif Jonsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Thora Steingrimsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Obstetrics and Gynecology, Landspitali University Hospital, Reykjavik, Iceland
| | | | | | - Jessica Figueredo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Minna K Karjalainen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Finland
- Northern Finland Birth Cohorts, Arctic Biobank, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Anu Pasanen
- Research Unit of Clinical Medicine, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Benjamin M Jacobs
- Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University London, London, EC1M 6BQ, United Kingdom
| | - Nikki Hubers
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Margaret Lippincott
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Mette Nyegaard
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Kari Stefansson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Reedik Magi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Hannele Laivuori
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics and Gynecology, Tampere University Hospital, Finland
- Center for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Finland
| | - David A van Heel
- Blizard Institute, Queen Mary University London, London, E1 2AT, United Kingdom
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Ravikumar Balasubramanian
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Stephanie B Seminara
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yee-Ming Chan
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Endocrinology, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, United States of America
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
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Nagarajan P, Winkler TW, Bentley AR, Miller CL, Kraja AT, Schwander K, Lee S, Wang W, Brown MR, Morrison JL, Giri A, O’Connell JR, Bartz TM, de las Fuentes L, Gudmundsdottir V, Guo X, Harris SE, Huang Z, Kals M, Kho M, Lefevre C, Luan J, Lyytikäinen LP, Mangino M, Milaneschi Y, Palmer ND, Rao V, Rauramaa R, Shen B, Stadler S, Sun Q, Tang J, Thériault S, van der Graaf A, van der Most PJ, Wang Y, Weiss S, Westerman KE, Yang Q, Yasuharu T, Zhao W, Zhu W, Altschul D, Ansari MAY, Anugu P, Argoty-Pantoja AD, Arzt M, Aschard H, Attia JR, Bazzanno L, Breyer MA, Brody JA, Cade BE, Chen HH, Ida Chen YD, Chen Z, de Vries PS, Dimitrov LM, Do A, Du J, Dupont CT, Edwards TL, Evans MK, Faquih T, Felix SB, Fisher-Hoch SP, Floyd JS, Graff M, Gu C, Gu D, Hairston KG, Hanley AJ, Heid IM, Heikkinen S, Highland HM, Hood MM, Kähönen M, Karvonen-Gutierrez CA, Kawaguchi T, Kazuya S, Kelly TN, Komulainen P, Levy D, Lin HJ, Liu PY, Marques-Vidal P, McCormick JB, Mei H, Meigs JB, Menni C, Nam K, Nolte IM, Pacheco NL, Petty LE, Polikowsky HG, Province MA, Psaty BM, Raffield LM, Raitakari OT, Rich SS, Riha RL, Risch L, Risch M, Ruiz-Narvaez EA, Scott RJ, Sitlani CM, Smith JA, Sofer T, Teder-Laving M, Völker U, Vollenweider P, Wang G, van Dijk KW, Wilson OD, Xia R, Yao J, Young KL, Zhang R, Zhu X, Below JE, Böger CA, Conen D, Cox SR, Dörr M, Feitosa MF, Fox ER, Franceschini N, Gharib SA, Gudnason V, Harlow SD, He J, Holliday EG, Kutalik Z, Lakka TA, Lawlor DA, Lee S, Lehtimäki T, Li C, Liu CT, Mägi R, Matsuda F, Morrison AC, Penninx BWJH, Peyser PA, Rotter JI, Snieder H, Spector TD, Wagenknecht LE, Wareham NJ, Zonderman AB, North KE, Fornage M, Hung AM, Manning AK, Gauderman J, Chen H, Munroe PB, Rao DC, van Heemst D, Redline S, Noordam R, Wang H. A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.07.24303870. [PMID: 38496537 PMCID: PMC10942520 DOI: 10.1101/2024.03.07.24303870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to genes involved in neurological, thyroidal, bone metabolism, and hematopoietic pathways. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausibility of distinct influences of both sleep duration extremes in cardiovascular health. With several of our loci reflecting specificity towards population background or sex, our discovery sheds light on the importance of embracing granularity when addressing heterogeneity entangled in gene-environment interactions, and in therapeutic design approaches for blood pressure management.
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Affiliation(s)
- Pavithra Nagarajan
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, US National Institutes of Health, Bethesda, MD, USA
| | - Clint L Miller
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesvil le, VA, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville ,VA, USA
| | - Aldi T Kraja
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Karen Schwander
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Songmi Lee
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
| | - Wenyi Wang
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - John L Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Ayush Giri
- Division of Quantitative Sciences, Department of Obstetrics & Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626), Department of Veterans Affairs/ Nashville, TN, USA
| | - Jeffrey R O’Connell
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Lisa de las Fuentes
- Cardiovascular Division, Department of Medicine, Washington University School of Medicine in St. Louis, MO, USA
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
| | - Valborg Gudmundsdottir
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, Department of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Sarah E Harris
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, US
| | - Mart Kals
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Minjung Kho
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Christophe Lefevre
- Department of Data Sciences, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Jian’an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Massimo Mangino
- Department of Twin Research, King’s College London, London, UK
- National Heart & Lung Institute, Cardiovascular Genomics and Precision Medicine, Imperial College London, London, UK
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam UMC/Vrije universiteit, Amsterdam, Netherlands
- GGZ inGeest, Amsterdam, Netherlands
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Varun Rao
- Division of Nephrology, Department of Medicine, University of Illinois Chicago, Chicago, USA
| | - Rainer Rauramaa
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Botong Shen
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Stefan Stadler
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jingxian Tang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Sébastien Thériault
- Department of Molecular Biology, Medical Biochemistry and Pathology, Université Laval, Quebec City, Qc, Canada
| | - Adriaan van der Graaf
- Statistical Genetics Group, Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Yujie Wang
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stefan Weiss
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Kenneth E Westerman
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
| | - Qian Yang
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tabara Yasuharu
- Graduate School of Public Health, Shizuoka Graduate University of Public Health, Shizuoka, Japan
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Wei Zhao
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Wanying Zhu
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Drew Altschul
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Md Abu Yusuf Ansari
- Department of Data Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Pramod Anugu
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, USA
| | - Anna D Argoty-Pantoja
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Michael Arzt
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Hugues Aschard
- Department of Computational Biology, F-75015 Paris, France Institut Pasteur, Université Paris Cité, Paris, France
- Department of Epidemiology, Harvard TH School of Public Health, Boston, MA, USA
| | - John R Attia
- School of Medicine and Public Health, College of Health Medicine and Wellbeing, University of Newcastle, New Lambton Heights, NSW, Australia
| | - Lydia Bazzanno
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, US
| | - Max A Breyer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Hung-hsin Chen
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Zekai Chen
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Latchezar M Dimitrov
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Anh Do
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
| | - Jiawen Du
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Charles T Dupont
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Todd L Edwards
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626), Department of Veterans Affairs/ Nashville, TN, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, US A
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Tariq Faquih
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Stephan B Felix
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine, Department of Internal Medicine B, Un iversity Medicine Greifswald, Greifswald, Germany
| | - Susan P Fisher-Hoch
- School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Brownsville, TX, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Mariaelisa Graff
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Charles Gu
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
| | - Dongfeng Gu
- Shenzhen Key Laboratory of Cardiovascular Health and Precision Medicine, Southern University of Science an d Technology, Shenzhen, China
| | - Kristen G Hairston
- Department of Endocrinology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Anthony J Hanley
- Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Sami Heikkinen
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Kuopio
| | - Heather M Highland
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michelle M Hood
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Mika Kähönen
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
| | | | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Setoh Kazuya
- Graduate School of Public Health, Shizuoka Graduate University of Public Health, Shizuoka, Japan
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, University of Illinois Chicago, Chicago, USA
| | | | - Daniel Levy
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Henry J Lin
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Peter Y Liu
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Joseph B McCormick
- School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Brownsville, TX, USA
| | - Hao Mei
- Department of Data Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - James B Meigs
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Cristina Menni
- Department of Twin Research, King’s College London, London, UK
| | - Kisung Nam
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Natasha L Pacheco
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Lauren E Petty
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hannah G Polikowsky
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, and Department of Clinical Physiology and Nuclear Medicine, University of Turku, and Turku University Hospital, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Renata L Riha
- Department of Sleep Medicine, The University of Edinburgh, Edinburgh, UK
| | - Lorenz Risch
- Faculty of Medical Sciences , Institute for Laboratory Medicine, Private University in the Principality of Liecht enstein, Vaduz, Liechtenstein
- Center of Laboratory Medicine, Institute of Clinical Chemistry, University of Bern and Inselspital, Bern, Switze rland
| | - Martin Risch
- Central Laboratory, Cantonal Hospital Graubünden, Chur, Switzerland
- Medical Laboratory, Dr. Risch Anstalt, Vaduz, Liechtenstein
| | | | - Rodney J Scott
- School of Biomedical Sciences and Pharmacy, College of Health Medicine and Wellbeing, University of Newcastle, New Lambton Heights, NSW, Australia
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Uwe Völker
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Guanchao Wang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden, Netherlands
| | - Otis D Wilson
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626), Department of Veterans Affairs/ Nashville, TN, USA
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rui Xia
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kristin L Young
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, US
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jennifer E Below
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Carsten A Böger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Nephrology and Rheumatology, Kliniken Südostbayern, Traunstein, Germany
- KfH Kidney Centre Traunstein, Traunstein, Germany
| | - David Conen
- Population Health Research Institute, Medicine, McMaster University, Hamilton, On, Canada
| | - Simon R Cox
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Marcus Dörr
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine, Department of Internal Medicine B, Un iversity Medicine Greifswald, Greifswald, Germany
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Ervin R Fox
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Nora Franceschini
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sina A Gharib
- Pulmonary, Critical Care and Sleep Medicine, Medicine, University of Washington, Seattle, WA, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, Department of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Sioban D Harlow
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, US
- Tulane University Translational Sciences Institute, New Orleans, LA , USA
| | - Elizabeth G Holliday
- School of Medicine and Public Health, College of Health Medicine and Wellbeing, University of Newcastle, New Lambton Heights, NSW, Australia
| | - Zoltan Kutalik
- Statistical Genetics Group, Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Timo A Lakka
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Kuopio
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, US
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Brenda WJH Penninx
- Department of Psychiatry, Amsterdam UMC/Vrije universiteit, Amsterdam, Netherlands
- GGZ inGeest, Amsterdam, Netherlands
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Tim D Spector
- Department of Twin Research, King’s College London, London, UK
| | - Lynne E Wagenknecht
- Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | | | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Kari E North
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
- Human Genetics Center, Department of Epidemiology, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | | | - Adriana M Hung
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626), Department of Veterans Affairs/ Nashville, TN, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Alisa K Manning
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Patricia B Munroe
- Clinical Pharmacology and Precision Medicine, Queen Mary University of London, London, UK
| | - Dabeeru C Rao
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Lei den, Netherlands
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Lei den, Netherlands
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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Salgado Rezende de Mendonça L, Senar S, Moreira LL, Silva Júnior JA, Nader M, Campos LA, Baltatu OC. Evidence for the druggability of aldosterone targets in heart failure: A bioinformatics and data science-driven decision-making approach. Comput Biol Med 2024; 171:108124. [PMID: 38412691 DOI: 10.1016/j.compbiomed.2024.108124] [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: 10/25/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND Aldosterone plays a key role in the neurohormonal drive of heart failure. Systematic prioritization of drug targets using bioinformatics and database-driven decision-making can provide a competitive advantage in therapeutic R&D. This study investigated the evidence on the druggability of these aldosterone targets in heart failure. METHODS The target disease predictability of mineralocorticoid receptors (MR) and aldosterone synthase (AS) in cardiac failure was evaluated using Open Targets target-disease association scores. The Open Targets database collections were downloaded to MongoDB and queried according to the desired aggregation level, and the results were retrieved from the Europe PMC (data type: text mining), ChEMBL (data type: drugs), Open Targets Genetics Portal (data type: genetic associations), and IMPC (data type: genetic associations) databases. The target tractability of MR and AS in the cardiovascular system was investigated by computing activity scores in a curated ChEMBL database using supervised machine learning. RESULTS The medians of the association scores of the MR and AS groups were similar, indicating a comparable predictability of the target disease. The median of the MR activity scores group was significantly lower than that of AS, indicating that AS has higher target tractability than MR [Hodges-Lehmann difference 0.62 (95%CI 0.53-0.70, p < 0.0001]. The cumulative distributions of the overall multiplatform association scores of cardiac diseases with MR were considerably higher than with AS, indicating more advanced investigations on a wider range of disorders evaluated for MR (Kolmogorov-Smirnov D = 0.36, p = 0.0009). In curated ChEMBL, MR had a higher cumulative distribution of activity scores in experimental cardiovascular assays than AS (Kolmogorov-Smirnov D = 0.23, p < 0.0001). Documented clinical trials for MR in heart failures surfaced in database searches, none for AS. CONCLUSIONS Although its clinical development has lagged behind that of MR, our findings indicate that AS is a promising therapeutic target for the treatment of cardiac failure. The multiplatform-integrated identification used in this study allowed us to comprehensively explore the available scientific evidence on MR and AS for heart failure therapy.
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Affiliation(s)
- Lucas Salgado Rezende de Mendonça
- Center of Innovation, Technology, and Education (CITE) at Anhembi Morumbi University, Anima Institute, Sao Jose dos Campos Technology Park, Sao Jose dos Campos, Brazil
| | | | - Luana Lorena Moreira
- Center of Innovation, Technology, and Education (CITE) at Anhembi Morumbi University, Anima Institute, Sao Jose dos Campos Technology Park, Sao Jose dos Campos, Brazil
| | | | - Moni Nader
- College of Medicine & Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Luciana Aparecida Campos
- Center of Innovation, Technology, and Education (CITE) at Anhembi Morumbi University, Anima Institute, Sao Jose dos Campos Technology Park, Sao Jose dos Campos, Brazil.
| | - Ovidiu Constantin Baltatu
- Center of Innovation, Technology, and Education (CITE) at Anhembi Morumbi University, Anima Institute, Sao Jose dos Campos Technology Park, Sao Jose dos Campos, Brazil.
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48
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San Gil R, Pascovici D, Venturato J, Brown-Wright H, Mehta P, Madrid San Martin L, Wu J, Luan W, Chui YK, Bademosi AT, Swaminathan S, Naidoo S, Berning BA, Wright AL, Keating SS, Curtis MA, Faull RLM, Lee JD, Ngo ST, Lee A, Morsch M, Chung RS, Scotter E, Lisowski L, Mirzaei M, Walker AK. A transient protein folding response targets aggregation in the early phase of TDP-43-mediated neurodegeneration. Nat Commun 2024; 15:1508. [PMID: 38374041 PMCID: PMC10876645 DOI: 10.1038/s41467-024-45646-9] [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: 09/26/2023] [Accepted: 01/31/2024] [Indexed: 02/21/2024] Open
Abstract
Understanding the mechanisms that drive TDP-43 pathology is integral to combating amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD) and other neurodegenerative diseases. Here we generated a longitudinal quantitative proteomic map of the cortex from the cytoplasmic TDP-43 rNLS8 mouse model of ALS and FTLD, and developed a complementary open-access webtool, TDP-map ( https://shiny.rcc.uq.edu.au/TDP-map/ ). We identified distinct protein subsets enriched for diverse biological pathways with temporal alterations in protein abundance, including increases in protein folding factors prior to disease onset. This included increased levels of DnaJ homolog subfamily B member 5, DNAJB5, which also co-localized with TDP-43 pathology in diseased human motor cortex. DNAJB5 over-expression decreased TDP-43 aggregation in cell and cortical neuron cultures, and knockout of Dnajb5 exacerbated motor impairments caused by AAV-mediated cytoplasmic TDP-43 expression in mice. Together, these findings reveal molecular mechanisms at distinct stages of ALS and FTLD progression and suggest that protein folding factors could be protective in neurodegenerative diseases.
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Affiliation(s)
- Rebecca San Gil
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Dana Pascovici
- Insight Stats, Croydon Park, NSW, Australia
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, North Ryde Sydney, NSW, Australia
| | - Juliana Venturato
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Heledd Brown-Wright
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Prachi Mehta
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Motor Neuron Disease Research Centre, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Lidia Madrid San Martin
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Jemma Wu
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, North Ryde Sydney, NSW, Australia
| | - Wei Luan
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Yi Kit Chui
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Adekunle T Bademosi
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Shilpa Swaminathan
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Serey Naidoo
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
- Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Britt A Berning
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Amanda L Wright
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Sean S Keating
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Maurice A Curtis
- Centre for Brain Research, University of Auckland, Auckland, New Zealand
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Richard L M Faull
- Centre for Brain Research, University of Auckland, Auckland, New Zealand
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - John D Lee
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, St Lucia, Brisbane, QLD, Australia
| | - Shyuan T Ngo
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
| | - Albert Lee
- Motor Neuron Disease Research Centre, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Marco Morsch
- Motor Neuron Disease Research Centre, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Roger S Chung
- Motor Neuron Disease Research Centre, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Emma Scotter
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
- Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Leszek Lisowski
- Vector and Genome Engineering Facility, Children's Medical Research Institute, Westmead, NSW, Australia
- Laboratory of Molecular Oncology and Innovative Therapies, Military Institute of Medicine - National Research Institute, Warsaw, Poland
- Translational Vectorology Research Unit, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Mehdi Mirzaei
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, North Ryde Sydney, NSW, Australia
| | - Adam K Walker
- Neurodegeneration Pathobiology Laboratory, Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.
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49
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Szot JO, Cuny H, Martin EM, Sheng DZ, Iyer K, Portelli S, Nguyen V, Gereis JM, Alankarage D, Chitayat D, Chong K, Wentzensen IM, Vincent-Delormé C, Lermine A, Burkitt-Wright E, Ji W, Jeffries L, Pais LS, Tan TY, Pitt J, Wise CA, Wright H, Andrews ID, Pruniski B, Grebe TA, Corsten-Janssen N, Bouman K, Poulton C, Prakash S, Keren B, Brown NJ, Hunter MF, Heath O, Lakhani SA, McDermott JH, Ascher DB, Chapman G, Bozon K, Dunwoodie SL. A metabolic signature for NADSYN1-dependent congenital NAD deficiency disorder. J Clin Invest 2024; 134:e174824. [PMID: 38357931 PMCID: PMC10866660 DOI: 10.1172/jci174824] [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: 09/06/2023] [Accepted: 12/20/2023] [Indexed: 02/16/2024] Open
Abstract
Nicotinamide adenine dinucleotide (NAD) is essential for embryonic development. To date, biallelic loss-of-function variants in 3 genes encoding nonredundant enzymes of the NAD de novo synthesis pathway - KYNU, HAAO, and NADSYN1 - have been identified in humans with congenital malformations defined as congenital NAD deficiency disorder (CNDD). Here, we identified 13 further individuals with biallelic NADSYN1 variants predicted to be damaging, and phenotypes ranging from multiple severe malformations to the complete absence of malformation. Enzymatic assessment of variant deleteriousness in vitro revealed protein domain-specific perturbation, complemented by protein structure modeling in silico. We reproduced NADSYN1-dependent CNDD in mice and assessed various maternal NAD precursor supplementation strategies to prevent adverse pregnancy outcomes. While for Nadsyn1+/- mothers, any B3 vitamer was suitable to raise NAD, preventing embryo loss and malformation, Nadsyn1-/- mothers required supplementation with amidated NAD precursors (nicotinamide or nicotinamide mononucleotide) bypassing their metabolic block. The circulatory NAD metabolome in mice and humans before and after NAD precursor supplementation revealed a consistent metabolic signature with utility for patient identification. Our data collectively improve clinical diagnostics of NADSYN1-dependent CNDD, provide guidance for the therapeutic prevention of CNDD, and suggest an ongoing need to maintain NAD levels via amidated NAD precursor supplementation after birth.
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Affiliation(s)
- Justin O. Szot
- Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, New South Wales, Australia
| | - Hartmut Cuny
- Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, New South Wales, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, Sydney, New South Wales, Australia
| | - Ella M.M.A. Martin
- Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, New South Wales, Australia
| | - Delicia Z. Sheng
- Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, New South Wales, Australia
| | - Kavitha Iyer
- Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, New South Wales, Australia
| | - Stephanie Portelli
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Vivien Nguyen
- Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, New South Wales, Australia
| | - Jessica M. Gereis
- Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, New South Wales, Australia
| | - Dimuthu Alankarage
- Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, New South Wales, Australia
| | - David Chitayat
- Department of Pediatrics, Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, and
- Prenatal Diagnosis and Medical Genetics Program, Department of Obstetrics and Gynecology, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Karen Chong
- Prenatal Diagnosis and Medical Genetics Program, Department of Obstetrics and Gynecology, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Alban Lermine
- Laboratoire de Biologie Médicale Multisites SeqOIA, FMG2025, Paris, France
| | - Emma Burkitt-Wright
- Manchester Centre for Genomic Medicine, St. Mary’s Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
| | - Weizhen Ji
- Yale University School of Medicine, Pediatric Genomics Discovery Program, New Haven, Connecticut, USA
| | - Lauren Jeffries
- Yale University School of Medicine, Pediatric Genomics Discovery Program, New Haven, Connecticut, USA
| | - Lynn S. Pais
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Tiong Y. Tan
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia
| | - James Pitt
- Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia
- Metabolic Laboratory, Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
| | - Cheryl A. Wise
- Department of Diagnostic Genomics, PathWest Laboratory Medicine Western Australia, Nedlands, Perth, Western Australia, Australia
| | - Helen Wright
- General Paediatric Department, Perth Children’s Hospital, Perth, Western Australia, Australia
- Rural Clinical School, University of Western Australia, Perth, Western Australia, Australia
| | | | - Brianna Pruniski
- Division of Genetics and Metabolism, Phoenix Children’s Hospital, Phoenix, Arizona, USA
| | - Theresa A. Grebe
- Division of Genetics and Metabolism, Phoenix Children’s Hospital, Phoenix, Arizona, USA
| | - Nicole Corsten-Janssen
- Department of Genetics, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Katelijne Bouman
- Department of Genetics, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Cathryn Poulton
- Genetic Services of Western Australia, King Edward Memorial Hospital, Perth, Western Australia, Australia
| | - Supraja Prakash
- Division of Genetics and Metabolism, Phoenix Children’s Hospital, Phoenix, Arizona, USA
| | - Boris Keren
- Département de Génétique, Groupe Hospitalier Pitié-Salpêtrière, Assistance Publique – Hôpitaux de Paris, Sorbonne Université, Paris, France
| | - Natasha J. Brown
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia
| | - Matthew F. Hunter
- Monash Genetics, Monash Health, Clayton, Victoria, Australia
- Department of Paediatrics, Monash University, Clayton, Victoria, Australia
| | - Oliver Heath
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
- Department of Metabolic Medicine, The Royal Children’s Hospital, Melbourne, Victoria, Australia
| | - Saquib A. Lakhani
- Yale University School of Medicine, Pediatric Genomics Discovery Program, New Haven, Connecticut, USA
| | - John H. McDermott
- Manchester Centre for Genomic Medicine, St. Mary’s Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
- Division of Evolution, Infection and Genomics, School of Biological Sciences, University of Manchester, Manchester, United Kingdom
| | - David B. Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Gavin Chapman
- Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, New South Wales, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, Sydney, New South Wales, Australia
| | - Kayleigh Bozon
- Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, New South Wales, Australia
| | - Sally L. Dunwoodie
- Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, New South Wales, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, Sydney, New South Wales, Australia
- Faculty of Science, University of New South Wales, Sydney, New South Wales, Australia
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50
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Vogt CC, Zipple MN, Sprockett DD, Miller CH, Hardy SX, Arthur MK, Greenstein AM, Colvin MS, Michel LM, Moeller AH, Sheehan MJ. Female behavior drives the formation of distinct social structures in C57BL/6J versus wild-derived outbred mice in field enclosures. BMC Biol 2024; 22:35. [PMID: 38355587 PMCID: PMC10865716 DOI: 10.1186/s12915-024-01809-0] [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: 11/09/2023] [Accepted: 01/02/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Social behavior and social organization have major influences on individual health and fitness. Yet, biomedical research focuses on studying a few genotypes under impoverished social conditions. Understanding how lab conditions have modified social organizations of model organisms, such as lab mice, relative to natural populations is a missing link between socioecology and biomedical science. RESULTS Using a common garden design, we describe the formation of social structure in the well-studied laboratory mouse strain, C57BL/6J, in replicated mixed-sex populations over 10-day trials compared to control trials with wild-derived outbred house mice in outdoor field enclosures. We focus on three key features of mouse social systems: (i) territory establishment in males, (ii) female social relationships, and (iii) the social networks formed by the populations. Male territorial behaviors were similar but muted in C57 compared to wild-derived mice. Female C57 sharply differed from wild-derived females, showing little social bias toward cage mates and exploring substantially more of the enclosures compared to all other groups. Female behavior consistently generated denser social networks in C57 than in wild-derived mice. CONCLUSIONS C57 and wild-derived mice individually vary in their social and spatial behaviors which scale to shape overall social organization. The repeatable societies formed under field conditions highlights opportunities to experimentally study the interplay between society and individual biology using model organisms.
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Affiliation(s)
- Caleb C Vogt
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, USA.
| | - Matthew N Zipple
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, USA
| | - Daniel D Sprockett
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Caitlin H Miller
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, USA
| | - Summer X Hardy
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, USA
| | - Matthew K Arthur
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, USA
| | - Adam M Greenstein
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, USA
| | - Melanie S Colvin
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, USA
| | - Lucie M Michel
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, USA
| | - Andrew H Moeller
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Michael J Sheehan
- Laboratory for Animal Social Evolution and Recognition, Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, USA.
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