A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma

Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses.

Elucidating regulatory mechanisms shaping the transcriptome and proteome of the gut immune response in Drosophila melanogaster

Michael Vincent Frochaux

Work on Drosophila melanogaster paved the way to our current understanding of modern genetics. Since then, this model organism has contributed greatly to various fields such as neurobiology, development, and immunology. The discovery and analysis of the various pathways of the Drosophila immune response led the way to the in-depth characterization of the gut immune response. Furthermore, a genome-wide association study on a population of Drosophila identified resistance to infection by the entomopathogenic bacteria Pseudomonas entomophila (P.e.) as a complex phenotype, with highly resistant and susceptible lines, and highlighted genes and pathways mediating inter-individual differences. However, the molecular mechanisms mediating the resistance to enteric infection remain largely uncharacterized. This study uses the same Drosophila population to analyze what are the molecular mechanisms mediating the resistance to enteric infection. We analyzed the genetic determinants of gene expression variation among the most resistant and susceptible lines in response to infection using expression quantitative trait loci (eQTL) analysis. We also characterized the mode of action of these eQTLs into cis- and trans-acting. Furthermore, we identified the gene nutcracker (ntc) as the only differentially-expressed gene between resistance classes. We then demonstrate that loss of function ntc mutants are significantly more susceptible to infection and then identify a single nucleotide polymorphism (SNP) located in a transcription factor binding site (TFBS) which affects the binding affinity of the transcription factor Broad leading to allele-specific expression variation of the gene ntc. If the transcriptomic response to infection has been thoroughly characterized, it is not the case for the proteomic response. We thus sought to characterize for the first time the proteomic response of the Drosophila to enteric infection. We performed mass spectrometry and RNA sequencing on dissected guts from flies infected by two Gram-negative bacteria, Erwinia carotovora carotovora 15 (Ecc15) or Pseudomonas entomophila, 4h and 16h after infection. We found that a large portion of the measurable proteome (12%) varies after infection and that protein changes are strongly time- and infection-dependent. We showed the relatively poor correlation between gene expression and protein abundance in the Drosophila enteric immune response. Finally, we performed a screen of several proteins that were identified in our proteomics work but had previously not been found when assessing the gene expression response to infection using loss-of-function mutants and identifying 7 genes that modulate overall susceptibility to P.e. infection. In summary, this work analyze the genetic determinants of gene expression variation in the DGRP population upon infection and characterize them according to their mode of action. Furthermore, we describe how a non-coding variant lowers resistance to infection by modulating ntc gene expression through altered Broad repressor binding. Finally, it provides the first comprehensive characterization of the Drosophila gut proteome upon infection by Gram-negative bacteria which can be the basis of future proteomic analysis of the enteric immune response

EPFL2019

Large-scale variational inference for Bayesian joint regression modelling of high-dimensional genetic data

Hélène Ruffieux

Genetic association studies have become increasingly important in understanding the molecular bases of complex human traits. The specific analysis of intermediate molecular traits, via quantitative trait locus (QTL) studies, has recently received much attention, prompted by the advance of high-throughput technologies for quantifying gene, protein and metabolite levels. Of great interest is the detection of weak trans-regulatory effects between a genetic variant and a distal gene product. In particular, hotspot genetic variants, which remotely control the levels of many molecular outcomes, may initiate decisive functional mechanisms underlying disease endpoints. This thesis proposes a Bayesian hierarchical approach for joint analysis of QTL data on a genome-wide scale. We consider a series of parallel sparse regressions combined in a hierarchical manner to flexibly accommodate high-dimensional responses (molecular levels) and predictors (genetic variants), and we present new methods for large-scale inference. Existing approaches have limitations. Conventional marginal screening does not account for local dependencies and association patterns common to multiple outcomes and genetic variants, whereas joint modelling approaches are restricted to relatively small datasets by computational constraints. Our novel framework allows information-sharing across outcomes and variants, thereby enhancing the detection of weak trans and hotspot effects, and implements tailored variational inference procedures that allow simultaneous analysis of data for an entire QTL study, comprising hundreds of thousands of predictors, and thousands of responses and samples. The present work also describes extensions to leverage spatial and functional information on the genetic variants, for example, using predictor-level covariates such as epigenomic marks. Moreover, we augment variational inference with simulated annealing and parallel expectation-maximisation schemes in order to enhance exploration of highly multimodal spaces and allow efficient empirical Bayes estimation. Our methods, publicly available as packages implemented in R and C++, are extensively assessed in realistic simulations. Their advantages are illustrated in several QTL applications, including a large-scale proteomic QTL study on two clinical cohorts that highlights novel candidate biomarkers for metabolic disorders.

EPFL2019

Analysis of expression of PDCP and MAL13P1.308 of Plasmodium falciparum employing a quantitative proteomics approach based on SILAC

Céline Freymond

Plasmodium falciparum is a deadly parasite that causes malaria in humans. This disease causes the death of one million people every year. To find new means to fight this parasite, it is important to learn more about its biology. As the pathways of protein expression are better understood, it becomes easier to find out how to block these mechanisms. The completion of the genome sequencing has opened new perspective of genome wide analysis. One of these studies was done by LaCount et al. who used the yeast1two1hybrid system to map the complete interaction network between proteins of P. falciparum. ¨ From this network of interaction an interesting protein was studied further by Daubenberger et al. which is PDCP. This protein is closely related to MSP11 in this protein network, a protein involved in erythrocyte invasion. PDCP is a CCCH1type zinc finger protein, a family of proteins that are involved in protein1protein interaction, nucleic acid binding and binding in small ligands. It was shown that its expression was dependant on the density of the parasites. In this study we used the SILAC technology to confirm these previous results. A culture is grown with isotopically heavy isoleucine in the medium as a control sample. After a few cell cycles, almost all the natural isoleucines are replaced with the labeled ones. Three cultures with parasitemia of 2, 5 and 10% are grown and mixed with an equal amount of the control culture. When the proteins are analyzed by mass spectrometry, the peptides that contained isoleucine will be detected as two separate peaks. After normalization of each peptide area with this internal control, the quantity of PDCP can be compared between cultures of different parasitemias. 11 peptides of PDCP have been detected by mass spectrometry, proving its existence for the first time. These peptides were labeled to more than 95%, allowing the comparison of PDCP expression between the cultures. But because of difficulty of reproducibility in in vitro cultures, the regulation of PDCP by the parasitemia has not been observed by SILAC. We wanted to study further the protein interaction network in which PDCP is involved. MAL13P1.308, a protein directly interacting with PDCP and MSP11 in the protein network map, was analyzed by IFA. It was found to be expressed during all stages of the asexual blood stage cycle and it was not imported in the host cell. The next step will be to see if it interacts with PDCP by co1localization studies

2008

Elucidating regulatory mechanisms shaping the transcriptome and proteome of the gut immune response in Drosophila melanogaster

Michael Vincent Frochaux

EPFL2019

Large-scale variational inference for Bayesian joint regression modelling of high-dimensional genetic data

Hélène Ruffieux

EPFL2019

Analysis of expression of PDCP and MAL13P1.308 of Plasmodium falciparum employing a quantitative proteomics approach based on SILAC

Céline Freymond

2008