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Bhatia K, Sandhu V, Wong MH, Iyer P, Bhatt S. Therapeutic biomarkers in acute myeloid leukemia: functional and genomic approaches. Front Oncol 2024; 14:1275251. [PMID: 38410111 PMCID: PMC10894932 DOI: 10.3389/fonc.2024.1275251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 01/17/2024] [Indexed: 02/28/2024] Open
Abstract
Acute myeloid leukemia (AML) is clinically and genetically a heterogeneous disease characterized by clonal expansion of abnormal hematopoietic progenitors. Genomic approaches to precision medicine have been implemented to direct targeted therapy for subgroups of AML patients, for instance, IDH inhibitors for IDH1/2 mutated patients, and FLT3 inhibitors with FLT3 mutated patients. While next generation sequencing for genetic mutations has improved treatment outcomes, only a fraction of AML patients benefit due to the low prevalence of actionable targets. In recent years, the adoption of newer functional technologies for quantitative phenotypic analysis and patient-derived avatar models has strengthened the potential for generalized functional precision medicine approach. However, functional approach requires robust standardization for multiple variables such as functional parameters, time of drug exposure and drug concentration for making in vitro predictions. In this review, we first summarize genomic and functional therapeutic biomarkers adopted for AML therapy, followed by challenges associated with these approaches, and finally, the future strategies to enhance the implementation of precision medicine.
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Affiliation(s)
- Karanpreet Bhatia
- Department of Pharmacy, National University of Singapore, Singapore, Singapore
| | - Vedant Sandhu
- Department of Pharmacy, National University of Singapore, Singapore, Singapore
| | - Mei Hsuan Wong
- Department of Pharmacy, National University of Singapore, Singapore, Singapore
| | - Prasad Iyer
- Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore
- Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
| | - Shruti Bhatt
- Department of Pharmacy, National University of Singapore, Singapore, Singapore
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2
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Allayee H, Farber CR, Seldin MM, Williams EG, James DE, Lusis AJ. Systems genetics approaches for understanding complex traits with relevance for human disease. eLife 2023; 12:e91004. [PMID: 37962168 PMCID: PMC10645424 DOI: 10.7554/elife.91004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/16/2023] [Indexed: 11/15/2023] Open
Abstract
Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.
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Affiliation(s)
- Hooman Allayee
- Departments of Population & Public Health Sciences, University of Southern CaliforniaLos AngelesUnited States
- Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Charles R Farber
- Center for Public Health Genomics, University of Virginia School of MedicineCharlottesvilleUnited States
- Departments of Biochemistry & Molecular Genetics, University of Virginia School of MedicineCharlottesvilleUnited States
- Public Health Sciences, University of Virginia School of MedicineCharlottesvilleUnited States
| | - Marcus M Seldin
- Department of Biological Chemistry, University of California, IrvineIrvineUnited States
| | - Evan Graehl Williams
- Luxembourg Centre for Systems Biomedicine, University of LuxembourgLuxembourgLuxembourg
| | - David E James
- School of Life and Environmental Sciences, University of SydneyCamperdownAustralia
- Faculty of Medicine and Health, University of SydneyCamperdownAustralia
- Charles Perkins Centre, University of SydneyCamperdownAustralia
| | - Aldons J Lusis
- Departments of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Medicine, University of California, Los AngelesLos AngelesUnited States
- Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLALos AngelesUnited States
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3
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Jacques M, Landen S, Romero JA, Hiam D, Schittenhelm RB, Hanchapola I, Shah AD, Voisin S, Eynon N. Methylome and proteome integration in human skeletal muscle uncover group and individual responses to high-intensity interval training. FASEB J 2023; 37:e23184. [PMID: 37698381 DOI: 10.1096/fj.202300840rr] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/17/2023] [Accepted: 08/24/2023] [Indexed: 09/13/2023]
Abstract
Exercise is a major beneficial contributor to muscle metabolism, and health benefits acquired by exercise are a result of molecular shifts occurring across multiple molecular layers (i.e., epigenome, transcriptome, and proteome). Identifying robust, across-molecular level targets associated with exercise response, at both group and individual levels, is paramount to develop health guidelines and targeted health interventions. Sixteen, apparently healthy, moderately trained (VO2 max = 51.0 ± 10.6 mL min-1 kg-1 ) males (age range = 18-45 years) from the Gene SMART (Skeletal Muscle Adaptive Responses to Training) study completed a longitudinal study composed of 12-week high-intensity interval training (HIIT) intervention. Vastus lateralis muscle biopsies were collected at baseline and after 4, 8, and 12 weeks of HIIT. DNA methylation (~850 CpG sites) and proteomic (~3000 proteins) analyses were conducted at all time points. Mixed models were applied to estimate group and individual changes, and methylome and proteome integration was conducted using a holistic multilevel approach with the mixOmics package. A total of 461 proteins significantly changed over time (at 4, 8, and 12 weeks), whilst methylome overall shifted with training only one differentially methylated position (DMP) was significant (adj.p-value < .05). K-means analysis revealed cumulative protein changes by clusters of proteins that presented similar changes over time. Individual responses to training were observed in 101 proteins. Seven proteins had large effect-sizes >0.5, among them are two novel exercise-related proteins, LYRM7 and EPN1. Integration analysis showed bidirectional relationships between the methylome and proteome. We showed a significant influence of HIIT on the epigenome and more so on the proteome in human muscle, and uncovered groups of proteins clustering according to similar patterns across the exercise intervention. Individual responses to exercise were observed in the proteome with novel mitochondrial and metabolic proteins consistently changed across individuals. Future work is required to elucidate the role of these proteins in response to exercise.
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Affiliation(s)
- Macsue Jacques
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Victoria, Australia
| | - Shanie Landen
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Victoria, Australia
| | - Javier Alvarez Romero
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Victoria, Australia
| | - Danielle Hiam
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Victoria, Australia
- Institute of Nutrition and Health Sciences, Deakin University, Melbourne, Victoria, Australia
| | - Ralf B Schittenhelm
- Monash Proteomics & Metabolomics Facility, Monash University, Melbourne, Victoria, Australia
| | - Iresha Hanchapola
- Monash Proteomics & Metabolomics Facility, Monash University, Melbourne, Victoria, Australia
| | - Anup D Shah
- Monash Proteomics & Metabolomics Facility, Monash University, Melbourne, Victoria, Australia
| | - Sarah Voisin
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Victoria, Australia
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nir Eynon
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Victoria, Australia
- Australian Regenerative Medicine Institute, Monash University, Melbourne, Victoria, Australia
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4
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Hiam D, Jones P, Pitsiladis Y, Eynon N. Genomics and Biology of Exercise, Where Are We Now? Clin J Sport Med 2023; 33:e112-e114. [PMID: 37656977 DOI: 10.1097/jsm.0000000000001012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Affiliation(s)
- Danielle Hiam
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Australia
- School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition (IPAN), Deakin University, Geelong, Australia; and
| | - Patrice Jones
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Australia
| | - Yannis Pitsiladis
- School of Sport and Health Sciences, University of Brighton, Eastbourne, United Kingdom
| | - Nir Eynon
- Institute for Health and Sport (iHeS), Victoria University, Melbourne, Australia
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5
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Tello J, Ibáñez J. Review: Status and prospects of association mapping in grapevine. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 327:111539. [PMID: 36410567 DOI: 10.1016/j.plantsci.2022.111539] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 06/16/2023]
Abstract
Thanks to current advances in sequencing technologies, novel bioinformatics tools, and efficient modeling solutions, association mapping has become a widely accepted approach to unravel the link between genotype and phenotype diversity in numerous crops. In grapevine, this strategy has been used in the last decades to understand the genetic basis of traits of agronomic interest (fruit quality, crop yield, biotic and abiotic resistance), of special relevance nowadays to improve crop resilience to cope with future climate scenarios. Genome-wide association studies have identified many putative causative loci for different traits, some of them overlapping well-known causal genes identified by conventional quantitative trait loci studies in biparental progenies, and/or validated by functional approaches. In addition, candidate-gene association studies have been useful to pinpoint the causal mutation underlying phenotypic variation for several traits of high interest in breeding programs (like berry color, seedlessness, and muscat flavor), information that has been used to develop highly informative and useful markers already in use in marker-assisted selection processes. Thus, association mapping has proved to represent a valuable step towards high quality and sustainable grape production. This review summarizes current applications of association mapping in grapevine research and discusses future prospects in view of current viticulture challenges.
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Affiliation(s)
- Javier Tello
- Instituto de Ciencias de la Vid y del Vino (CSIC, UR, Gobierno de La Rioja), Logroño 26007, Spain.
| | - Javier Ibáñez
- Instituto de Ciencias de la Vid y del Vino (CSIC, UR, Gobierno de La Rioja), Logroño 26007, Spain
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Boahen CK, Oelen R, Le K, Netea MG, Franke L, van der Wijst MGP, Kumar V. Integration of Candida albicans-induced single-cell gene expression data and secretory protein concentrations reveal genetic regulators of inflammation. Front Immunol 2023; 14:1069379. [PMID: 36865558 PMCID: PMC9972217 DOI: 10.3389/fimmu.2023.1069379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 01/23/2023] [Indexed: 02/16/2023] Open
Abstract
Both gene expression and protein concentrations are regulated by genetic variants. Exploring the regulation of both eQTLs and pQTLs simultaneously in a context- and cell-type dependent manner may help to unravel mechanistic basis for genetic regulation of pQTLs. Here, we performed meta-analysis of Candida albicans-induced pQTLs from two population-based cohorts and intersected the results with Candida-induced cell-type specific expression association data (eQTL). This revealed systematic differences between the pQTLs and eQTL, where only 35% of the pQTLs significantly correlated with mRNA expressions at single cell level, indicating the limitation of eQTLs use as a proxy for pQTLs. By taking advantage of the tightly co-regulated pattern of the proteins, we also identified SNPs affecting protein network upon Candida stimulations. Colocalization of pQTLs and eQTLs signals implicated several genomic loci including MMP-1 and AMZ1. Analysis of Candida-induced single cell gene expression data implicated specific cell types that exhibit significant expression QTLs upon stimulation. By highlighting the role of trans-regulatory networks in determining the abundance of secretory proteins, our study serve as a framework to gain insights into the mechanisms of genetic regulation of protein levels in a context-dependent manner.
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Affiliation(s)
- Collins K Boahen
- Department of Internal Medicine and Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands.,Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
| | - Roy Oelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Kieu Le
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Mihai G Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands.,Department for Immunology and Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Monique G P van der Wijst
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Vinod Kumar
- Department of Internal Medicine and Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands.,Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands.,Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.,Nitte University Centre for Science Education and Research (NUCSER), Nitte (Deemed to be University), Mangalore, India
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7
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Vincent D, Bui A, Ezernieks V, Shahinfar S, Luke T, Ram D, Rigas N, Panozzo J, Rochfort S, Daetwyler H, Hayden M. A community resource to mass explore the wheat grain proteome and its application to the late-maturity alpha-amylase (LMA) problem. Gigascience 2022; 12:giad084. [PMID: 37919977 PMCID: PMC10627334 DOI: 10.1093/gigascience/giad084] [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: 04/24/2023] [Revised: 08/02/2023] [Accepted: 09/19/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND Late-maturity alpha-amylase (LMA) is a wheat genetic defect causing the synthesis of high isoelectric point alpha-amylase following a temperature shock during mid-grain development or prolonged cold throughout grain development, both leading to starch degradation. While the physiology is well understood, the biochemical mechanisms involved in grain LMA response remain unclear. We have applied high-throughput proteomics to 4,061 wheat flours displaying a range of LMA activities. Using an array of statistical analyses to select LMA-responsive biomarkers, we have mined them using a suite of tools applicable to wheat proteins. RESULTS We observed that LMA-affected grains activated their primary metabolisms such as glycolysis and gluconeogenesis; TCA cycle, along with DNA- and RNA- binding mechanisms; and protein translation. This logically transitioned to protein folding activities driven by chaperones and protein disulfide isomerase, as well as protein assembly via dimerisation and complexing. The secondary metabolism was also mobilized with the upregulation of phytohormones and chemical and defence responses. LMA further invoked cellular structures, including ribosomes, microtubules, and chromatin. Finally, and unsurprisingly, LMA expression greatly impacted grain storage proteins, as well as starch and other carbohydrates, with the upregulation of alpha-gliadins and starch metabolism, whereas LMW glutenin, stachyose, sucrose, UDP-galactose, and UDP-glucose were downregulated. CONCLUSIONS To our knowledge, this is not only the first proteomics study tackling the wheat LMA issue but also the largest plant-based proteomics study published to date. Logistics, technicalities, requirements, and bottlenecks of such an ambitious large-scale high-throughput proteomics experiment along with the challenges associated with big data analyses are discussed.
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Affiliation(s)
- Delphine Vincent
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - AnhDuyen Bui
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Vilnis Ezernieks
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Saleh Shahinfar
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Timothy Luke
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Doris Ram
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
| | - Nicholas Rigas
- Agriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, Australia
| | - Joe Panozzo
- Agriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, Australia
- Centre for Agricultural Innovation, University of Melbourne, Parkville, VIC 3010, Australia
| | - Simone Rochfort
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Hans Daetwyler
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Matthew Hayden
- Agriculture Victoria Research, AgriBio, Center Centre for AgriBioscience, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
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8
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Mendelian randomization and genetic colocalization infer the effects of the multi-tissue proteome on 211 complex disease-related phenotypes. Genome Med 2022; 14:140. [PMID: 36510323 PMCID: PMC9746220 DOI: 10.1186/s13073-022-01140-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 11/10/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Human proteins are widely used as drug targets. Integration of large-scale protein-level genome-wide association studies (GWAS) and disease-related GWAS has thus connected genetic variation to disease mechanisms via protein. Previous proteome-by-phenome-wide Mendelian randomization (MR) studies have been mainly focused on plasma proteomes. Previous MR studies using the brain proteome only reported protein effects on a set of pre-selected tissue-specific diseases. No studies, however, have used high-throughput proteomics from multiple tissues to perform MR on hundreds of phenotypes. METHODS Here, we performed MR and colocalization analysis using multi-tissue (cerebrospinal fluid (CSF), plasma, and brain from pre- and post-meta-analysis of several disease-focus cohorts including Alzheimer disease (AD)) protein quantitative trait loci (pQTLs) as instrumental variables to infer protein effects on 211 phenotypes, covering seven broad categories: biological traits, blood traits, cancer types, neurological diseases, other diseases, personality traits, and other risk factors. We first implemented these analyses with cis pQTLs, as cis pQTLs are known for being less prone to horizontal pleiotropy. Next, we included both cis and trans conditionally independent pQTLs that passed the genome-wide significance threshold keeping only variants associated with fewer than five proteins to minimize pleiotropic effects. We compared the tissue-specific protein effects on phenotypes across different categories. Finally, we integrated the MR-prioritized proteins with the druggable genome to identify new potential targets. RESULTS In the MR and colocalization analysis including study-wide significant cis pQTLs as instrumental variables, we identified 33 CSF, 13 plasma, and five brain proteins to be putative causal for 37, 18, and eight phenotypes, respectively. After expanding the instrumental variables by including genome-wide significant cis and trans pQTLs, we identified a total of 58 CSF, 32 plasma, and nine brain proteins associated with 58, 44, and 16 phenotypes, respectively. For those protein-phenotype associations that were found in more than one tissue, the directions of the associations for 13 (87%) pairs were consistent across tissues. As we were unable to use methods correcting for horizontal pleiotropy given most of the proteins were only associated with one valid instrumental variable after clumping, we found that the observations of protein-phenotype associations were consistent with a causal role or horizontal pleiotropy. Between 66.7 and 86.3% of the disease-causing proteins overlapped with the druggable genome. Finally, between one and three proteins, depending on the tissue, were connected with at least one drug compound for one phenotype from both DrugBank and ChEMBL databases. CONCLUSIONS Integrating multi-tissue pQTLs with MR and the druggable genome may open doors to pinpoint novel interventions for complex traits with no effective treatments, such as ovarian and lung cancers.
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9
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Safder I, Shao G, Sheng Z, Hu P, Tang S. Genome-wide identification studies - A primer to explore new genes in plant species. PLANT BIOLOGY (STUTTGART, GERMANY) 2022; 24:9-22. [PMID: 34558163 DOI: 10.1111/plb.13340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 08/18/2021] [Indexed: 06/13/2023]
Abstract
Genome data have accumulated rapidly in recent years, doubling roughly after every 6 months due to the influx of next-generation sequencing technologies. A plethora of plant genomes are available in comprehensive public databases. This easy access to data provides an opportunity to explore genome datasets and recruit new genes in various plant species not possible a decade ago. In the past few years, many gene families have been published using these public datasets. These genome-wide studies identify and characterize gene members, gene structures, evolutionary relationships, expression patterns, protein interactions and gene ontologies, and predict putative gene functions using various computational tools. Such studies provide meaningful information and an initial framework for further functional elucidation. This review provides a concise layout of approaches used in these gene family studies and demonstrates an outline for employing various plant genome datasets in future studies.
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Affiliation(s)
- I Safder
- State Key Laboratory of Rice Biology and China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, China
| | - G Shao
- State Key Laboratory of Rice Biology and China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, China
| | - Z Sheng
- State Key Laboratory of Rice Biology and China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, China
| | - P Hu
- State Key Laboratory of Rice Biology and China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, China
| | - S Tang
- State Key Laboratory of Rice Biology and China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, China
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10
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Molendijk J, Blazev R, Mills RJ, Ng YK, Watt KI, Chau D, Gregorevic P, Crouch PJ, Hilton JBW, Lisowski L, Zhang P, Reue K, Lusis AJ, Hudson JE, James DE, Seldin MM, Parker BL. Proteome-wide systems genetics identifies UFMylation as a regulator of skeletal muscle function. eLife 2022; 11:82951. [PMID: 36472367 PMCID: PMC9833826 DOI: 10.7554/elife.82951] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022] Open
Abstract
Improving muscle function has great potential to improve the quality of life. To identify novel regulators of skeletal muscle metabolism and function, we performed a proteomic analysis of gastrocnemius muscle from 73 genetically distinct inbred mouse strains, and integrated the data with previously acquired genomics and >300 molecular/phenotypic traits via quantitative trait loci mapping and correlation network analysis. These data identified thousands of associations between protein abundance and phenotypes and can be accessed online (https://muscle.coffeeprot.com/) to identify regulators of muscle function. We used this resource to prioritize targets for a functional genomic screen in human bioengineered skeletal muscle. This identified several negative regulators of muscle function including UFC1, an E2 ligase for protein UFMylation. We show UFMylation is up-regulated in a mouse model of amyotrophic lateral sclerosis, a disease that involves muscle atrophy. Furthermore, in vivo knockdown of UFMylation increased contraction force, implicating its role as a negative regulator of skeletal muscle function.
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Affiliation(s)
- Jeffrey Molendijk
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
| | - Ronnie Blazev
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
| | | | - Yaan-Kit Ng
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
| | - Kevin I Watt
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
| | - Daryn Chau
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, IrvineIrvineUnited States
| | - Paul Gregorevic
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
| | - Peter J Crouch
- Department of Biochemistry and Pharmacology, University of MelbourneMelbourneAustralia
| | - James BW Hilton
- Department of Biochemistry and Pharmacology, University of MelbourneMelbourneAustralia
| | - Leszek Lisowski
- Children's Medical Research Institute, University of SydneySydneyAustralia,Military Institute of MedicineWarszawaPoland
| | - Peixiang Zhang
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Karen Reue
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Aldons J Lusis
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States,Department of Microbiology, Immunology and Molecular Genetics, University of California, Los AngelesLos AngelesUnited States
| | - James E Hudson
- QIMR Berghofer Medical Research InstituteBrisbaneAustralia
| | - David E James
- Charles Perkins Centre, School of Life and Environmental Science, School of Medical Science, University of SydneySydneyAustralia
| | - Marcus M Seldin
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, IrvineIrvineUnited States
| | - Benjamin L Parker
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia,Centre for Muscle Research, University of MelbourneMelbourneAustralia
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11
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Zhu F, Fernie AR, Scossa F. Preparation and Curation of Omics Data for Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:127-150. [PMID: 35641762 DOI: 10.1007/978-1-0716-2237-7_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
With the development of large-scale molecular phenotyping platforms, genome-wide association studies have greatly developed, being no longer limited to the analysis of classical agronomic traits, such as yield or flowering time, but also embracing the dissection of the genetic basis of molecular traits. Data generated by omics platforms, however, pose some technical and statistical challenges to the classical methodology and assumptions of an association study. Although genotyping data are subject to strict filtering procedures, and several advanced statistical approaches are now available to adjust for population structure, less attention has been instead devoted to the preparation of omics data prior to GWAS. In the present chapter, we briefly present the methods to acquire profiling data from transcripts, proteins, and small molecules, and discuss the tools and possibilities to clean, normalize, and remove the unwanted variation from large datasets of molecular phenotypic traits prior to their use in GWAS.
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Affiliation(s)
- Feng Zhu
- National R&D Center for Citrus Preservation, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, China
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Federico Scossa
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
- Council for Agricultural Research and Economics (CREA), Research Centre for Genomics and Bioinformatics (CREA-GB), Rome, Italy.
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12
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Deutsch EW, Omenn GS, Sun Z, Maes M, Pernemalm M, Palaniappan KK, Letunica N, Vandenbrouck Y, Brun V, Tao SC, Yu X, Geyer PE, Ignjatovic V, Moritz RL, Schwenk JM. Advances and Utility of the Human Plasma Proteome. J Proteome Res 2021; 20:5241-5263. [PMID: 34672606 PMCID: PMC9469506 DOI: 10.1021/acs.jproteome.1c00657] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Gilbert S Omenn
- Institute for Systems Biology, Seattle, Washington 98109, United States.,Departments of Computational Medicine & Bioinformatics, Internal Medicine, and Human Genetics and School of Public Health, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
| | - Zhi Sun
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Michal Maes
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Maria Pernemalm
- Department of Oncology and Pathology/Science for Life Laboratory, Karolinska Institutet, 171 65 Stockholm, Sweden
| | | | - Natasha Letunica
- Murdoch Children's Research Institute, 50 Flemington Road, Parkville 3052, Victoria, Australia
| | - Yves Vandenbrouck
- Université Grenoble Alpes, CEA, Inserm U1292, Grenoble 38000, France
| | - Virginie Brun
- Université Grenoble Alpes, CEA, Inserm U1292, Grenoble 38000, France
| | - Sheng-Ce Tao
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, B207 SCSB Building, 800 Dongchuan Road, Shanghai 200240, China
| | - Xiaobo Yu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Philipp E Geyer
- OmicEra Diagnostics GmbH, Behringstr. 6, 82152 Planegg, Germany
| | - Vera Ignjatovic
- Murdoch Children's Research Institute, 50 Flemington Road, Parkville 3052, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, 50 Flemington Road, Parkville 3052, Victoria, Australia
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Tomtebodavägen 23, SE-171 65 Solna, Sweden
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13
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Correa Rojo A, Heylen D, Aerts J, Thas O, Hooyberghs J, Ertaylan G, Valkenborg D. Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review. Front Physiol 2021; 12:723510. [PMID: 34512391 PMCID: PMC8427610 DOI: 10.3389/fphys.2021.723510] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/05/2021] [Indexed: 12/26/2022] Open
Abstract
Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.
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Affiliation(s)
- Alejandro Correa Rojo
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Dries Heylen
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Jan Aerts
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium
| | - Olivier Thas
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Department of Applied Mathematics, Computer Science and Statistics, Faculty of Sciences, Ghent University, Ghent, Belgium.,National Institute for Applied Statistics Research Australia (NIASRA), Wollongong, NSW, Australia
| | - Jef Hooyberghs
- Flemish Institute for Technological Research (VITO), Mol, Belgium.,Theoretical Physics, Data Science Institute, Hasselt University, Diepenbeek, Belgium
| | - Gökhan Ertaylan
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Dirk Valkenborg
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium
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14
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De Paoli-Iseppi R, Gleeson J, Clark MB. Isoform Age - Splice Isoform Profiling Using Long-Read Technologies. Front Mol Biosci 2021; 8:711733. [PMID: 34409069 PMCID: PMC8364947 DOI: 10.3389/fmolb.2021.711733] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 07/19/2021] [Indexed: 01/12/2023] Open
Abstract
Alternative splicing (AS) of RNA is a key mechanism that results in the expression of multiple transcript isoforms from single genes and leads to an increase in the complexity of both the transcriptome and proteome. Regulation of AS is critical for the correct functioning of many biological pathways, while disruption of AS can be directly pathogenic in diseases such as cancer or cause risk for complex disorders. Current short-read sequencing technologies achieve high read depth but are limited in their ability to resolve complex isoforms. In this review we examine how long-read sequencing (LRS) technologies can address this challenge by covering the entire RNA sequence in a single read and thereby distinguish isoform changes that could impact RNA regulation or protein function. Coupling LRS with technologies such as single cell sequencing, targeted sequencing and spatial transcriptomics is producing a rapidly expanding suite of technological approaches to profile alternative splicing at the isoform level with unprecedented detail. In addition, integrating LRS with genotype now allows the impact of genetic variation on isoform expression to be determined. Recent results demonstrate the potential of these techniques to elucidate the landscape of splicing, including in tissues such as the brain where AS is particularly prevalent. Finally, we also discuss how AS can impact protein function, potentially leading to novel therapeutic targets for a range of diseases.
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Affiliation(s)
| | | | - Michael B. Clark
- Centre for Stem Cell Systems, Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, Australia
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