Genetic dissection of the pluripotent proteome through multi-omics data integration

Aydin, Selcan; Pham, Duy T.; Zhang, Tian; Keele, Gregory R.; Skelly, Daniel A.; Paulo, João A.; Pankratz, Matthew; Choi, Tae-Young; Gygi, Steven P.; Reinholdt, Laura G.; Baker, Christopher L.; Churchill, Gary A.; Munger, Steven C.

doi:10.1016/j.xgen.2023.100283

Cited by 7 publications

(13 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We and others previously characterized the genetic architecture of ground-state pluripotency and differentiation propensity in genetically diverse mouse embryonic stem cells (mESCs). This work demonstrated that -omics traits like gene expression, chromatin accessibility, and protein levels in genetically diverse cells, especially when combined (multi-omics), provide molecular readouts that can be used to identify the genetic factors regulating cell state 12–15 . The correlation of cell morphology traits to these underlying -omics traits offers the potential to quantitatively analyze and delineate how cells respond to genetic and environmental perturbations 16–18 .…”

Section: Introductionmentioning

confidence: 99%

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population

O’Connor,

Keele,

Martin

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Genetically heterogenous cell lines from laboratory mice are promising tools for population-based screening as they offer power for genetic mapping, and potentially, predictive value forin vivoexperimentation in genetically matched individuals. To explore this further, we derived a panel of fibroblast lines from a genetic reference population of laboratory mice (the Diversity Outbred, DO). We then used high-content imaging to capture hundreds of cell morphology traits in cells exposed to the oxidative stress-inducing arsenic metabolite monomethylarsonous acid (MMAIII). We employed dose-response modeling to capture latent parameters of response and we then used these parameters to identify several hundred cell morphology quantitative trait loci (cmQTL). Response cmQTL encompass genes with established associations with cellular responses to arsenic exposure, includingAbcc4andTxnrd1, as well as novel gene candidates likeXrcc2. Moreover, baseline trait cmQTL highlight the influence of natural variation on fundamental aspects of nuclear morphology. We show that the natural variants influencing response include both coding and non-coding variation, and that cmQTL haplotypes can be used to predict response in orthogonal cell lines. Our study sheds light on the major molecular initiating events of oxidative stress that are under genetic regulation, including the NRF2-mediated antioxidant response, cellular detoxification pathways, DNA damage repair response, and cell death trajectories.

show abstract

Section: Introductionmentioning

confidence: 99%

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population

O’Connor,

Keele,

Martin

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The across-gene correlation analysis focuses on the overall correlation of a large set of genes coming from the same sample under a given condition to find out how well the absolute abundances of mRNAs and proteins are correlated. This correlation has been widely investigated in several species, such as human [15][16][17][18][19][20][21] , rats and mice [22][23][24][25] , flies 26 , plants 27 or yeast [28][29][30] .…”

Section: Introductionmentioning

confidence: 99%

“…Different studies have investigated this within-gene correlation in different contexts and organisms, but they often show divergent results. Several surveys of tumors, normal human tissues, as well as pluripotent stem cells have highlighted this discrepancy in estimates with median within-gene correlation coefficients ranging from 0.14 to 0.59 15,19,21,22,[31][32][33][34][35][36][37][38][39] . Similarly, the overlap of the detected loci influencing mRNA (eQTL) and protein (pQTL) abundance greatly differed across the datasets.…”

Section: Introductionmentioning

confidence: 99%

“…The across-gene correlation analysis focuses on the overall correlation of a large set of genes coming from the same sample under a given condition to find out how well the absolute abundances of mRNAs and proteins are correlated. This correlation has been widely investigated in several species, such as human 15–21 , rats and mice 22–25 , flies 26 , plants 27 or yeast 28–30 . Across-gene correlations are consistently high and range from 0.4 to 0.8, suggesting that the absolute number of transcripts and proteins are globally correlated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Teyssonnière,

Trébulle,

Muenzner

et al. 2023

Preprint

View full text Add to dashboard Cite

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein co-expression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3.6%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

show abstract

“…The across-gene correlation analysis focuses on the overall correlation of a large set of genes coming from the same sample under a given condition to find out how well the absolute abundances of mRNAs and proteins are correlated. This correlation has been widely investigated in several species, such as humans ( 15 – 21 ), rats and mice ( 22 – 25 ), flies ( 26 ), plants ( 27 ), or yeast ( 28 – 30 ). Across-gene correlations are consistently high and range from 0.4 to 0.8, suggesting that the absolute number of transcripts and proteins are globally correlated.…”

mentioning

confidence: 99%

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Teyssonnière,

Trébulle,

Muenzner

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype–phenotype relationship.

show abstract

Genetic dissection of the pluripotent proteome through multi-omics data integration

Cited by 7 publications

References 75 publications

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Contact Info

Product

Resources

About