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Kültz D, Gardell AM, DeTomaso A, Stoney G, Rinkevich B, Rinkevich Y, Qarri A, Dong W, Luu B, Lin M. Deep quantitative proteomics of North American Pacific coast star tunicate (Botryllus schlosseri). Proteomics 2024; 24:e2300628. [PMID: 38400697 DOI: 10.1002/pmic.202300628] [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] [Revised: 01/30/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024]
Abstract
Botryllus schlosseri, is a model marine invertebrate for studying immunity, regeneration, and stress-induced evolution. Conditions for validating its predicted proteome were optimized using nanoElute® 2 deep-coverage LCMS, revealing up to 4930 protein groups and 20,984 unique peptides per sample. Spectral libraries were generated and filtered to remove interferences, low-quality transitions, and only retain proteins with >3 unique peptides. The resulting DIA assay library enabled label-free quantitation of 3426 protein groups represented by 22,593 unique peptides. Quantitative comparisons of single systems from a laboratory-raised with two field-collected populations revealed (1) a more unique proteome in the laboratory-raised population, and (2) proteins with high/low individual variabilities in each population. DNA repair/replication, ion transport, and intracellular signaling processes were distinct in laboratory-cultured colonies. Spliceosome and Wnt signaling proteins were the least variable (highly functionally constrained) in all populations. In conclusion, we present the first colonial tunicate's deep quantitative proteome analysis, identifying functional protein clusters associated with laboratory conditions, different habitats, and strong versus relaxed abundance constraints. These results empower research on B. schlosseri with proteomics resources and enable quantitative molecular phenotyping of changes associated with transfer from in situ to ex situ and from in vivo to in vitro culture conditions.
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Affiliation(s)
- Dietmar Kültz
- Department of Animal Sciences & Genome Center, University of California Davis, Meyer Hall, Davis, California, USA
| | - Alison M Gardell
- School of Interdisciplinary Arts and Sciences, University of Washington Tacoma, Tacoma, Washington, USA
| | - Anthony DeTomaso
- Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Goleta, California, USA
| | - Greg Stoney
- Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Goleta, California, USA
| | - Baruch Rinkevich
- Israel Oceanography & Limnological Research, National Institute of Oceanography, Haifa, Israel
| | - Yuval Rinkevich
- Helmholtz Zentrum München, Regenerative Biology and Medicine Institute, Munich, Germany
| | - Andy Qarri
- Israel Oceanography & Limnological Research, National Institute of Oceanography, Haifa, Israel
- Helmholtz Zentrum München, Regenerative Biology and Medicine Institute, Munich, Germany
| | - Weizhen Dong
- Department of Animal Sciences & Genome Center, University of California Davis, Meyer Hall, Davis, California, USA
| | - Brenda Luu
- Department of Animal Sciences & Genome Center, University of California Davis, Meyer Hall, Davis, California, USA
| | - Mandy Lin
- Department of Animal Sciences & Genome Center, University of California Davis, Meyer Hall, Davis, California, USA
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O'Brien JJ, Raj A, Gaun A, Waite A, Li W, Hendrickson DG, Olsson N, McAllister FE. A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments. Nat Methods 2024; 21:290-300. [PMID: 38110636 DOI: 10.1038/s41592-023-02120-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 10/31/2023] [Indexed: 12/20/2023]
Abstract
We present a framework for the analysis of multiplexed mass spectrometry proteomics data that reduces estimation error when combining multiple isobaric batches. Variations in the number and quality of observations have long complicated the analysis of isobaric proteomics data. Here we show that the power to detect statistical associations is substantially improved by utilizing models that directly account for known sources of variation in the number and quality of observations that occur across batches.In a multibatch benchmarking experiment, our open-source software (msTrawler) increases the power to detect changes, especially in the range of less than twofold changes, while simultaneously increasing quantitative proteome coverage by utilizing more low-signal observations. Further analyses of previously published multiplexed datasets of 4 and 23 batches highlight both increased power and the ability to navigate complex missing data patterns without relying on unverifiable imputations or discarding reliable measurements.
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Affiliation(s)
| | - Anil Raj
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | | | - Adam Waite
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | - Wenzhou Li
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | | | - Niclas Olsson
- Calico Life Sciences LLC, South San Francisco, CA, USA
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Okamoto F, Chitre AS, Missfeldt Sanches T, Chen D, Munro D, Polesskaya O, Palmer AA. Y and Mitochondrial Chromosomes in the Heterogeneous Stock Rat Population. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.566473. [PMID: 38076923 PMCID: PMC10705385 DOI: 10.1101/2023.11.29.566473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Genome-wide association studies typically evaluate the autosomes and sometimes the X Chromosome, but seldom consider the Y or mitochondrial Chromosomes. We genotyped the Y and mitochondrial chromosomes in heterogeneous stock rats (Rattus norvegicus), which were created in 1984 by intercrossing eight inbred strains and have subsequently been maintained as an outbred population for 100 generations. As the Y and mitochondrial Chromosomes do not recombine, we determined which founder had contributed these chromosomes for each rat, and then performed association analysis for all complex traits (n=12,055; intersection of 12,116 phenotyped and 15,042 haplotyped rats). We found the eight founders had 8 distinct Y and 4 distinct mitochondrial Chromosomes, however only two of each were observed in our modern heterogeneous stock rat population (Generations 81-97). Despite the unusually large sample size, the p-value distribution did not deviate from expectations; there were no significant associations for behavioral, physiological, metabolome, or microbiome traits after correcting for multiple comparisons. However, both Y and mitochondrial Chromosomes were strongly associated with expression of a few genes located on those chromosomes, which provided a positive control. Our results suggest that within modern heterogeneous stock rats there are no Y and mitochondrial Chromosomes differences that strongly influence behavioral or physiological traits. These results do not address other ancestral Y and mitochondrial Chromosomes that do not appear in modern heterogeneous stock rats, nor do they address effects that may exist in other rat populations, or in other species.
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Affiliation(s)
- Faith Okamoto
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Apurva S Chitre
- Bioinformatics and System Biology Program, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Thiago Missfeldt Sanches
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Denghui Chen
- Bioinformatics and System Biology Program, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Daniel Munro
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | | | - Oksana Polesskaya
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
- Institute for Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
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4
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Risemberg EL, Smeekens JM, Cisneros MCC, Hampton BK, Hock P, Linnertz CL, Miller DR, Orgel K, Shaw GD, de Villena FPM, Burks AW, Valdar W, Kulis MD, Ferris MT. A mutation in Themis contributes to peanut-induced oral anaphylaxis in CC027 mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.13.557467. [PMID: 37745496 PMCID: PMC10515941 DOI: 10.1101/2023.09.13.557467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Background The development of peanut allergy is due to a combination of genetic and environmental factors, although specific genes have proven difficult to identify. Previously, we reported that peanut-sensitized CC027/GeniUnc (CC027) mice develop anaphylaxis upon oral challenge to peanut, unlike C3H/HeJ (C3H) mice. Objective To determine the genetic basis of orally-induced anaphylaxis to peanut in CC027 mice. Methods A genetic mapping population between CC027 and C3H mice was designed to identify the genetic factors that drive oral anaphylaxis. A total of 356 CC027xC3H backcrossed mice were generated, sensitized to peanut, then challenged to peanut by oral gavage. Anaphylaxis and peanut-specific IgE were quantified for all mice. T-cell phenotyping was conducted on CC027 and five additional CC strains. Results Anaphylaxis to peanut was absent in 77% of backcrossed mice, with 19% showing moderate anaphylaxis, and 4% having severe anaphylaxis. A total of eight genetic loci were associated with variation in response to peanut challenge, six associated with anaphylaxis (temperature decrease) and two associated with peanut-specific IgE levels. There were two major loci that impacted multiple aspects of the severity of acute anaphylaxis, at which the CC027 allele was associated with worse outcome. At one of these loci, CC027 has a private genetic variant in the Themis (thymocyte-expressed molecule involved in selection) gene. Consistent with Themis' described functions, we found that CC027 have more immature T cells with fewer CD8+, CD4+, and CD4+CD25+CD127- regulatory T cells. Conclusion Our results demonstrate a key role for Themis in the orally-reactive CC027 mouse model of peanut allergy.
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Affiliation(s)
- Ellen L. Risemberg
- Curriculum in Bioinformatics and Computational Biology, UNC Chapel Hill
- Department of Genetics, UNC Chapel Hill
| | - Johanna M. Smeekens
- Department of Pediatrics, Division of Allergy and Immunology, UNC Chapel Hill
| | - Marta C. Cruz Cisneros
- Department of Genetics, UNC Chapel Hill
- Curriculum in Genetics and Molecular Biology, UNC Chapel Hill
| | - Brea K. Hampton
- Department of Genetics, UNC Chapel Hill
- Curriculum in Genetics and Molecular Biology, UNC Chapel Hill
| | | | | | | | - Kelly Orgel
- Department of Pediatrics, Division of Allergy and Immunology, UNC Chapel Hill
| | - Ginger D. Shaw
- Department of Genetics, UNC Chapel Hill
- Lineberger Comprehensive Cancer Center, UNC Chapel Hill
| | | | - A. Wesley Burks
- Department of Pediatrics, Division of Allergy and Immunology, UNC Chapel Hill
| | - William Valdar
- Department of Genetics, UNC Chapel Hill
- Lineberger Comprehensive Cancer Center, UNC Chapel Hill
| | - Michael D. Kulis
- Department of Pediatrics, Division of Allergy and Immunology, UNC Chapel Hill
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Philip VM, He H, Saul MC, Dickson PE, Bubier JA, Chesler EJ. Gene expression genetics of the striatum of Diversity Outbred mice. Sci Data 2023; 10:522. [PMID: 37543624 PMCID: PMC10404230 DOI: 10.1038/s41597-023-02426-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 07/28/2023] [Indexed: 08/07/2023] Open
Abstract
Brain transcriptional variation is a heritable trait that mediates complex behaviors, including addiction. Expression quantitative trait locus (eQTL) mapping reveals genomic regions harboring genetic variants that influence transcript abundance. In this study, we profiled transcript abundance in the striatum of 386 Diversity Outbred (J:DO) mice of both sexes using RNA-Seq. All mice were characterized using a behavioral battery of widely-used exploratory and risk-taking assays prior to transcriptional profiling. We performed eQTL mapping, incorporated the results into a browser-based eQTL viewer, and deposited co-expression network members in GeneWeaver. The eQTL viewer allows researchers to query specific genes to obtain allelic effect plots, analyze SNP associations, assess gene expression correlations, and apply mediation analysis to evaluate whether the regulatory variant is acting through the expression of another gene. GeneWeaver allows multi-species comparison of gene sets using statistical and combinatorial tools. This data resource allows users to find genetic variants that regulate differentially expressed transcripts and place them in the context of other studies of striatal gene expression and function in addiction-related behavior.
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Affiliation(s)
- Vivek M Philip
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, 04605, USA
| | - Hao He
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Michael C Saul
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, 04605, USA
| | - Price E Dickson
- Department of Biomedical Sciences, Joan C. Edwards School of Medicine Marshall University, Huntington, WV, 25703, USA
| | - Jason A Bubier
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, 04605, USA
| | - Elissa J Chesler
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, 04605, USA.
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6
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Philip VM, He H, Saul MC, Dickson PE, Bubier JA, Chesler EJ. Gene expression genetics of the striatum of Diversity Outbred mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.11.540390. [PMID: 37214980 PMCID: PMC10197688 DOI: 10.1101/2023.05.11.540390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Brain transcriptional variation is a heritable trait that mediates complex behaviors, including addiction. Expression quantitative trait locus (eQTL) mapping reveals genomic regions harboring genetic variants that influence transcript abundance. In this study, we profiled transcript abundance in the striatum of 386 Diversity Outbred (J:DO) mice of both sexes using RNA-Seq. All mice were characterized using a behavioral battery of widely-used exploratory and risk-taking assays prior to transcriptional profiling. We performed eQTL mapping, incorporated the results into a browser-based eQTL viewer, and deposited co-expression network members in GeneWeaver. The eQTL viewer allows researchers to query specific genes to obtain allelic effect plots, analyze SNP associations, assess gene expression correlations, and apply mediation analysis to evaluate whether the regulatory variant is acting through the expression of another gene. GeneWeaver allows multi-species comparison of gene sets using statistical and combinatorial tools. This data resource allows users to find genetic variants that regulate differentially expressed transcripts and place them in the context of other studies of striatal gene expression and function in addiction-related behavior.
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Affiliation(s)
- Vivek M. Philip
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME 04605
| | - Hao He
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032
| | - Michael C. Saul
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME 04605
| | - Price E. Dickson
- Department of Biomedical Sciences, Joan C. Edwards School of Medicine Marshall University, 1700 3rd Ave. Huntington, WV 25703
| | - Jason A. Bubier
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME 04605
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Gastonguay MS, Keele GR, Churchill GA. The trouble with triples: Examining the impact of measurement error in mediation analysis. Genetics 2023; 224:iyad045. [PMID: 36932658 PMCID: PMC10158839 DOI: 10.1093/genetics/iyad045] [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: 01/03/2023] [Revised: 01/03/2023] [Accepted: 02/11/2023] [Indexed: 03/19/2023] Open
Abstract
Mediation analysis is used in genetic mapping studies to identify candidate gene mediators of quantitative trait loci (QTL). We consider genetic mediation analysis of triplets-sets of three variables consisting of a target trait, the genotype at a QTL for the target trait, and a candidate mediator that is the abundance of a transcript or protein whose coding gene co-locates with the QTL. We show that, in the presence of measurement error, mediation analysis can infer partial mediation even in the absence of a causal relationship between the candidate mediator and the target. We describe a measurement error model and a corresponding latent variable model with estimable parameters that are combinations of the causal effects and measurement errors across all three variables. The relative magnitudes of the latent variable correlations determine whether or not mediation analysis will tend to infer the correct causal relationship in large samples. We examine case studies that illustrate the common failure modes of genetic mediation analysis and demonstrate how to evaluate the effects of measurement error. While genetic mediation analysis is a powerful tool for identifying candidate genes, we recommend caution when interpreting mediation analysis findings.
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Aydin S, Pham DT, Zhang T, Keele GR, Skelly DA, Paulo JA, Pankratz M, Choi T, Gygi SP, Reinholdt LG, Baker CL, Churchill GA, Munger SC. Genetic dissection of the pluripotent proteome through multi-omics data integration. CELL GENOMICS 2023; 3:100283. [PMID: 37082146 PMCID: PMC10112288 DOI: 10.1016/j.xgen.2023.100283] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 09/12/2022] [Accepted: 02/27/2023] [Indexed: 04/22/2023]
Abstract
Genetic background drives phenotypic variability in pluripotent stem cells (PSCs). Most studies to date have used transcript abundance as the primary molecular readout of cell state in PSCs. We performed a comprehensive proteogenomics analysis of 190 genetically diverse mouse embryonic stem cell (mESC) lines. The quantitative proteome is highly variable across lines, and we identified pluripotency-associated pathways that were differentially activated in the proteomics data that were not evident in transcriptome data from the same lines. Integration of protein abundance to transcript levels and chromatin accessibility revealed broad co-variation across molecular layers as well as shared and unique drivers of quantitative variation in pluripotency-associated pathways. Quantitative trait locus (QTL) mapping localized the drivers of these multi-omic signatures to genomic hotspots. This study reveals post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome and provides a regulatory map for mESCs that can provide a basis for future mechanistic studies.
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Affiliation(s)
- Selcan Aydin
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Duy T. Pham
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Tian Zhang
- Harvard Medical School, Boston, MA 02115, USA
| | | | | | | | | | - Ted Choi
- Predictive Biology, Inc., Carlsbad, CA 92010, USA
| | | | - Laura G. Reinholdt
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Christopher L. Baker
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Gary A. Churchill
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Steven C. Munger
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
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Keele GR. Which mouse multiparental population is right for your study? The Collaborative Cross inbred strains, their F1 hybrids, or the Diversity Outbred population. G3 (BETHESDA, MD.) 2023; 13:jkad027. [PMID: 36735601 PMCID: PMC10085760 DOI: 10.1093/g3journal/jkad027] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 12/30/2022] [Accepted: 01/23/2023] [Indexed: 02/04/2023]
Abstract
Multiparental populations (MPPs) encompass greater genetic diversity than traditional experimental crosses of two inbred strains, enabling broader surveys of genetic variation underlying complex traits. Two such mouse MPPs are the Collaborative Cross (CC) inbred panel and the Diversity Outbred (DO) population, which are descended from the same eight inbred strains. Additionally, the F1 intercrosses of CC strains (CC-RIX) have been used and enable study designs with replicate outbred mice. Genetic analyses commonly used by researchers to investigate complex traits in these populations include characterizing how heritable a trait is, i.e. its heritability, and mapping its underlying genetic loci, i.e. its quantitative trait loci (QTLs). Here we evaluate the relative merits of these populations for these tasks through simulation, as well as provide recommendations for performing the quantitative genetic analyses. We find that sample populations that include replicate animals, as possible with the CC and CC-RIX, provide more efficient and precise estimates of heritability. We report QTL mapping power curves for the CC, CC-RIX, and DO across a range of QTL effect sizes and polygenic backgrounds for samples of 174 and 500 mice. The utility of replicate animals in the CC and CC-RIX for mapping QTLs rapidly decreased as traits became more polygenic. Only large sample populations of 500 DO mice were well-powered to detect smaller effect loci (7.5-10%) for highly complex traits (80% polygenic background). All results were generated with our R package musppr, which we developed to simulate data from these MPPs and evaluate genetic analyses from user-provided genotypes.
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Affiliation(s)
- Gregory R Keele
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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10
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Zhang T, Keele GR, Gyuricza IG, Vincent M, Brunton C, Bell TA, Hock P, Shaw GD, Munger SC, de Villena FPM, Ferris MT, Paulo JA, Gygi SP, Churchill GA. Multi-omics analysis identifies drivers of protein phosphorylation. Genome Biol 2023; 24:52. [PMID: 36944993 PMCID: PMC10031968 DOI: 10.1186/s13059-023-02892-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 03/09/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Phosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a phosphorylated peptide (phosphopeptide) is determined by the abundance of its parent protein and the proportion of target sites that are phosphorylated. RESULTS We quantified phosphopeptides, proteins, and transcripts in heart, liver, and kidney tissue samples of mice from 58 strains of the Collaborative Cross strain panel. We mapped ~700 phosphorylation quantitative trait loci (phQTL) across the three tissues and applied genetic mediation analysis to identify causal drivers of phosphorylation. We identified kinases, phosphatases, cytokines, and other factors, including both known and potentially novel interactions between target proteins and genes that regulate site-specific phosphorylation. Our analysis highlights multiple targets of pyruvate dehydrogenase kinase 1 (PDK1), a regulator of mitochondrial function that shows reduced activity in the NZO/HILtJ mouse, a polygenic model of obesity and type 2 diabetes. CONCLUSIONS Together, this integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. The data generated in this study provides a resource for further exploration.
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Affiliation(s)
- Tian Zhang
- Harvard Medical School, Boston, MA, 02115, USA
| | | | | | | | | | - Timothy A Bell
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Pablo Hock
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Ginger D Shaw
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | | | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Martin T Ferris
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
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11
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Toikumo S, Xu H, Gelernter J, Kember RL, Kranzler HR. Integrating human brain proteomic data with genome-wide association study findings identifies novel brain proteins in substance use traits. Neuropsychopharmacology 2022; 47:2292-2299. [PMID: 35941285 PMCID: PMC9630289 DOI: 10.1038/s41386-022-01406-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/13/2022] [Accepted: 07/16/2022] [Indexed: 11/09/2022]
Abstract
Despite the identification of a growing number of genetic risk loci for substance use traits (SUTs), the impact of these loci on protein abundance and the potential utility of relevant proteins as therapeutic targets are unknown. We conducted a proteome-wide association study (PWAS) in which we integrated human brain proteomes from discovery (Banner; N = 152) and validation (ROSMAP; N = 376) datasets with genome-wide association study (GWAS) summary statistics for 4 SUTs. The 4 samples comprised GWAS of European-ancestry individuals for smoking initiation [Smk] (N = 1,232,091), alcohol use disorder [AUD] (N = 313,959), cannabis use disorder [CUD] (N = 384,032), and opioid use disorder [OUD] (N = 302,585). We conducted transcriptome-wide association studies (TWAS) with human brain transcriptomic data to examine the overlap of genetic effects at the proteomic and transcriptomic levels and characterize significant genes through conditional, colocalization, and fine-mapping analyses. We identified 27 genes (Smk = 21, AUD = 3, CUD = 2, OUD = 1) that were significantly associated with cis-regulated brain protein abundance. Of these, 7 showed evidence for causality (Smk: NT5C2, GMPPB, NQO1, RHOT2, SRR and ACTR1B; and AUD: CTNND1). Cis-regulated transcript levels for 8 genes (Smk = 6, CUD = 1, OUD = 1) were associated with SUTs, indicating that genetic loci could confer risk for these SUTs by modulating both gene expression and proteomic abundance. Functional studies of the high-confidence risk proteins identified here are needed to determine whether they are modifiable targets and useful in developing medications and biomarkers for these SUTs.
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Affiliation(s)
- Sylvanus Toikumo
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Rachel L Kember
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA.
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA.
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12
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Xiao H, Bozi LHM, Sun Y, Riley CL, Philip VM, Chen M, Li J, Zhang T, Mills EL, Emont MP, Sun W, Reddy A, Garrity R, Long J, Becher T, Vitas LP, Laznik-Bogoslavski D, Ordonez M, Liu X, Chen X, Wang Y, Liu W, Tran N, Liu Y, Zhang Y, Cypess AM, White AP, He Y, Deng R, Schöder H, Paulo JA, Jedrychowski MP, Banks AS, Tseng YH, Cohen P, Tsai LT, Rosen ED, Klein S, Chondronikola M, McAllister FE, Van Bruggen N, Huttlin EL, Spiegelman BM, Churchill GA, Gygi SP, Chouchani ET. Architecture of the outbred brown fat proteome defines regulators of metabolic physiology. Cell 2022; 185:4654-4673.e28. [PMID: 36334589 PMCID: PMC10040263 DOI: 10.1016/j.cell.2022.10.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 07/18/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
Abstract
Brown adipose tissue (BAT) regulates metabolic physiology. However, nearly all mechanistic studies of BAT protein function occur in a single inbred mouse strain, which has limited the understanding of generalizable mechanisms of BAT regulation over physiology. Here, we perform deep quantitative proteomics of BAT across a cohort of 163 genetically defined diversity outbred mice, a model that parallels the genetic and phenotypic variation found in humans. We leverage this diversity to define the functional architecture of the outbred BAT proteome, comprising 10,479 proteins. We assign co-operative functions to 2,578 proteins, enabling systematic discovery of regulators of BAT. We also identify 638 proteins that correlate with protection from, or sensitivity to, at least one parameter of metabolic disease. We use these findings to uncover SFXN5, LETMD1, and ATP1A2 as modulators of BAT thermogenesis or adiposity, and provide OPABAT as a resource for understanding the conserved mechanisms of BAT regulation over metabolic physiology.
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Affiliation(s)
- Haopeng Xiao
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
| | - Luiz H M Bozi
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Yizhi Sun
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Christopher L Riley
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Mandy Chen
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Jiaming Li
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Tian Zhang
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Evanna L Mills
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Margo P Emont
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Wenfei Sun
- Department of Bioengineering, Stanford University, CA 94305, USA; Department of Molecular and Cellular Physiology, Stanford University, CA 94305, USA
| | - Anita Reddy
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Ryan Garrity
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jiani Long
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Tobias Becher
- Laboratory of Molecular Metabolism, The Rockefeller University, New York, NY 10065, USA
| | - Laura Potano Vitas
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Martha Ordonez
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Xinyue Liu
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Xiong Chen
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Yun Wang
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, Guangdong 510275, China
| | - Weihai Liu
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Nhien Tran
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Yitong Liu
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Yang Zhang
- Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Aaron M Cypess
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Andrew P White
- Department of Orthopedic Surgery, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Yuchen He
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Rebecca Deng
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Mark P Jedrychowski
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Alexander S Banks
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Yu-Hua Tseng
- Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Paul Cohen
- Laboratory of Molecular Metabolism, The Rockefeller University, New York, NY 10065, USA
| | - Linus T Tsai
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Evan D Rosen
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215, USA
| | - Samuel Klein
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO 63110, USA
| | | | | | | | - Edward L Huttlin
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Bruce M Spiegelman
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Edward T Chouchani
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
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13
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Li H, Perino A, Huang Q, Von Alvensleben GVG, Banaei-Esfahani A, Velazquez-Villegas LA, Gariani K, Korbelius M, Bou Sleiman M, Imbach J, Sun Y, Li X, Bachmann A, Goeminne LJE, Gallart-Ayala H, Williams EG, Ivanisevic J, Auwerx J, Schoonjans K. Integrative systems analysis identifies genetic and dietary modulators of bile acid homeostasis. Cell Metab 2022; 34:1594-1610.e4. [PMID: 36099916 PMCID: PMC9534359 DOI: 10.1016/j.cmet.2022.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 06/22/2022] [Accepted: 08/16/2022] [Indexed: 11/16/2022]
Abstract
Bile acids (BAs) are complex and incompletely understood enterohepatic-derived hormones that control whole-body metabolism. Here, we profiled postprandial BAs in the liver, feces, and plasma of 360 chow- or high-fat-diet-fed BXD male mice and demonstrated that both genetics and diet strongly influence BA abundance, composition, and correlation with metabolic traits. Through an integrated systems approach, we mapped hundreds of quantitative trait loci that modulate BAs and identified both known and unknown regulators of BA homeostasis. In particular, we discovered carboxylesterase 1c (Ces1c) as a genetic determinant of plasma tauroursodeoxycholic acid (TUDCA), a BA species with established disease-preventing actions. The association between Ces1c and plasma TUDCA was validated using data from independent mouse cohorts and a Ces1c knockout mouse model. Collectively, our data are a unique resource to dissect the physiological importance of BAs as determinants of metabolic traits, as underscored by the identification of CES1C as a master regulator of plasma TUDCA levels.
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Affiliation(s)
- Hao Li
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland; Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Alessia Perino
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Qingyao Huang
- Laboratory of Metabolic Signaling, 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
| | - Amir Banaei-Esfahani
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Laura A Velazquez-Villegas
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Karim Gariani
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Melanie Korbelius
- Laboratory of Metabolic Signaling, 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
| | - Jéromine Imbach
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Yu Sun
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Xiaoxu Li
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Alexis Bachmann
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Ludger J E Goeminne
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Hector Gallart-Ayala
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, 1005 Lausanne, Switzerland
| | - Evan G Williams
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, 1005 Lausanne, Switzerland
| | - Johan Auwerx
- Laboratory of Integrative Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
| | - Kristina Schoonjans
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
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14
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Paulo JA. Isobaric labeling: Expanding the breadth, accuracy, depth, and diversity of sample multiplexing. Proteomics 2022; 22:e2200328. [PMID: 36089831 PMCID: PMC10777124 DOI: 10.1002/pmic.202200328] [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: 08/25/2022] [Revised: 08/25/2022] [Accepted: 08/26/2022] [Indexed: 11/10/2022]
Abstract
Isobaric labeling has rapidly become a predominant strategy for proteome-wide abundance measurements. Coupled to mass spectrometry, sample multiplexing techniques using isobaric labeling are unparalleled for profiling proteins and posttranslational modifications across multiple samples in a single experiment. Here, I highlight aspects of isobaric labeling in the context of expanding the breadth of multiplexing, improving quantitative accuracy and proteome depth, and developing a wide range of diverse applications. I underscore two facets that enhance quantitative accuracy and reproducibility, specifically the availability of quality control standards for isobaric labeling experiments and the evolution of data acquisition methods. I also emphasize the necessity for standardized methodologies, particularly for emerging high-throughput workflows. Future developments in sample multiplexing will further strengthen the importance of isobaric labeling for comprehensive proteome profiling.
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Affiliation(s)
- Joao A Paulo
- Department of Cell Biology, Blavatnik Institute at Harvard Medical School, Boston, Massachusetts, USA
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15
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Vincent M, Gerdes Gyuricza I, Keele GR, Gatti DM, Keller MP, Broman KW, Churchill GA. QTLViewer: an interactive webtool for genetic analysis in the Collaborative Cross and Diversity Outbred mouse populations. G3 (BETHESDA, MD.) 2022; 12:jkac146. [PMID: 35703938 PMCID: PMC9339332 DOI: 10.1093/g3journal/jkac146] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/28/2022] [Indexed: 01/10/2023]
Abstract
The Collaborative Cross and the Diversity Outbred mouse populations are related multiparental populations, derived from the same 8 isogenic founder strains. They carry >50 M known genetic variants, which makes them ideal tools for mapping genetic loci that regulate phenotypes, including physiological and molecular traits. Mapping quantitative trait loci requires statistical and computational training, which can present a barrier to access for some researchers. The QTLViewer software allows users to graphically explore Collaborative Cross and Diversity Outbred quantitative trait locus mapping and related analyses performed through the R/qtl2 package. Additionally, the QTLViewer website serves as a repository for published Collaborative Cross and Diversity Outbred studies, increasing the accessibility of these genetic resources to the broader scientific community.
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Affiliation(s)
| | | | | | | | - Mark P Keller
- Department of Biochemistry, University of Wisconsin–Madison, Madison, WI 53706-1544, USA
| | - Karl W Broman
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI 53706-1544, USA
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16
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Grossbach J, Gillet L, Clément‐Ziza M, Schmalohr CL, Schubert OT, Schütter M, Mawer JSP, Barnes CA, Bludau I, Weith M, Tessarz P, Graef M, Aebersold R, Beyer A. The impact of genomic variation on protein phosphorylation states and regulatory networks. Mol Syst Biol 2022; 18:e10712. [PMID: 35574625 PMCID: PMC9109056 DOI: 10.15252/msb.202110712] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 12/11/2022] Open
Abstract
Genomic variation impacts on cellular networks by affecting the abundance (e.g., protein levels) and the functional states (e.g., protein phosphorylation) of their components. Previous work has focused on the former, while in this context, the functional states of proteins have largely remained neglected. Here, we generated high-quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genomically well-defined yeast strains. Genetic effects on transcripts were generally transmitted to the protein layer, but specific gene groups, such as ribosomal proteins, showed diverging effects on protein levels compared with RNA levels. Phosphorylation states proved crucial to unravel genetic effects on signaling networks. Correspondingly, genetic variants that cause phosphorylation changes were mostly different from those causing abundance changes in the respective proteins. Underscoring their relevance for cell physiology, phosphorylation traits were more strongly correlated with cell physiological traits such as chemical compound resistance or cell morphology, compared with transcript or protein abundance. This study demonstrates how molecular networks mediate the effects of genomic variants to cellular traits and highlights the particular importance of protein phosphorylation.
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Affiliation(s)
- Jan Grossbach
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | - Ludovic Gillet
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Mathieu Clément‐Ziza
- Center for Molecular Medicine Cologne (CMMC)Medical Faculty, University of CologneCologneGermany
- Lesaffre InternationalMarcq‐en‐BarœulFrance
| | - Corinna L Schmalohr
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | - Olga T Schubert
- Department of Human GeneticsUniversity of California, Los AngelesLos AngelesCAUSA
| | | | | | | | - Isabell Bludau
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Department of Proteomics and Signal TransductionMax Planck Institute of BiochemistryMartinsriedGermany
| | - Matthias Weith
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | - Peter Tessarz
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
- Max Planck Institute for Biology of AgeingCologneGermany
| | - Martin Graef
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
- Max Planck Institute for Biology of AgeingCologneGermany
| | - Ruedi Aebersold
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Faculty of ScienceUniversity of ZurichZurichSwitzerland
| | - Andreas Beyer
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
- Center for Molecular Medicine Cologne (CMMC)Medical Faculty, University of CologneCologneGermany
- Institute for GeneticsFaculty of Mathematics and Natural SciencesUniversity of CologneCologneGermany
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17
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Crouse WL, Keele GR, Gastonguay MS, Churchill GA, Valdar W. A Bayesian model selection approach to mediation analysis. PLoS Genet 2022; 18:e1010184. [PMID: 35533209 PMCID: PMC9129027 DOI: 10.1371/journal.pgen.1010184] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 05/24/2022] [Accepted: 04/04/2022] [Indexed: 01/09/2023] Open
Abstract
Genetic studies often seek to establish a causal chain of events originating from genetic variation through to molecular and clinical phenotypes. When multiple phenotypes share a common genetic association, one phenotype may act as an intermediate for the genetic effects on the other. Alternatively, the phenotypes may be causally unrelated but share genetic loci. Mediation analysis represents a class of causal inference approaches used to determine which of these scenarios is most plausible. We have developed a general approach to mediation analysis based on Bayesian model selection and have implemented it in an R package, bmediatR. Bayesian model selection provides a flexible framework that can be tailored to different analyses. Our approach can incorporate prior information about the likelihood of models and the strength of causal effects. It can also accommodate multiple genetic variants or multi-state haplotypes. Our approach reports posterior probabilities that can be useful in interpreting uncertainty among competing models. We compared bmediatR with other popular methods, including the Sobel test, Mendelian randomization, and Bayesian network analysis using simulated data. We found that bmediatR performed as well or better than these alternatives in most scenarios. We applied bmediatR to proteome data from Diversity Outbred (DO) mice, a multi-parent population, and demonstrate the power of mediation with multi-state haplotypes. We also applied bmediatR to data from human cell lines to identify transcripts that are mediated through or are expressed independently from local chromatin accessibility. We demonstrate that Bayesian model selection provides a powerful and versatile approach to identify causal relationships in genetic studies using model organism or human data.
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Affiliation(s)
- Wesley L. Crouse
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Gregory R. Keele
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | | | | | - William Valdar
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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18
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Williams EG, Pfister N, Roy S, Statzer C, Haverty J, Ingels J, Bohl C, Hasan M, Čuklina J, Bühlmann P, Zamboni N, Lu L, Ewald CY, Williams RW, Aebersold R. Multiomic profiling of the liver across diets and age in a diverse mouse population. Cell Syst 2022; 13:43-57.e6. [PMID: 34666007 PMCID: PMC8776606 DOI: 10.1016/j.cels.2021.09.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/12/2021] [Accepted: 09/14/2021] [Indexed: 01/21/2023]
Abstract
We profiled the liver transcriptome, proteome, and metabolome in 347 individuals from 58 isogenic strains of the BXD mouse population across age (7 to 24 months) and diet (low or high fat) to link molecular variations to metabolic traits. Several hundred genes are affected by diet and/or age at the transcript and protein levels. Orthologs of two aging-associated genes, St7 and Ctsd, were knocked down in C. elegans, reducing longevity in wild-type and mutant long-lived strains. The multiomics data were analyzed as segregating gene networks according to each independent variable, providing causal insight into dietary and aging effects. Candidates were cross-examined in an independent diversity outbred mouse liver dataset segregating for similar diets, with ∼80%-90% of diet-related candidate genes found in common across datasets. Together, we have developed a large multiomics resource for multivariate analysis of complex traits and demonstrate a methodology for moving from observational associations to causal connections.
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Affiliation(s)
- Evan G Williams
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
| | - Niklas Pfister
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Suheeta Roy
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Cyril Statzer
- Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Jack Haverty
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jesse Ingels
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Casey Bohl
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Moaraj Hasan
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
| | - Jelena Čuklina
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
| | - Peter Bühlmann
- Department of Mathematics, Seminar for Statistics, ETH Zürich, Zurich, Switzerland
| | - Nicola Zamboni
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
| | - Lu Lu
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Collin Y Ewald
- Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland; Faculty of Science, University of Zürich, Zurich, Switzerland
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