Systems genetics approaches to probe gene function

Genes are the functional units of heredity. However, the functions of many genes remain unknown, which impedes the understanding of the underlying mechanism of complex traits and diseases. Systems genetics approaches try to understand the complexity underlining phenotypic traits using high-throughput experimental and computational approaches. In this thesis, I describe a list of novel systems genetics approaches to identify gene functions based on publicly available datasets. In Chapter 2, I focused on the multi-omics datasets collected from the BXD mouse cohort, which is one of the most studied mouse genetic reference populations. Over the past 40 years, the BXD community has generated tremendous amounts of data covering different omics layers, making the BXD GRP a perfect data source to conduct systems genetics analyses to discover biological insights. By integrating the data from different omics layers, I developed phenome-wide association study (PheWAS) and expression-based PheWAS (ePheWAS) to reveal the associations between genes and phenotypic traits, as well as mediation and reverse-mediation analysis to identify the regulatory connections between genes. In Chapter 3, I analyzed the transcriptome datasets from six different model organisms, ranging from yeast to human. It is commonly believed that genes with similar functions tend to have similar expression patterns. Therefore by using the co-expression patterns of genes, one can annotate a gene from its correlating genes with known functions. I proposed here a new systems genetics method, termed gene-module association determination (G-MAD), which assigns novel functions of genes and proposes new components of pathway modules. Several new associations, including DDT as a novel mitochondrial protein, were experimentally validated. In addition, G-MAD was further extended to determine the interconnection between pathway modules, for example those between mitochondria and proteasome, as well as ribosome and lipid biosynthesis. Altogether, this thesis described several novel systems genetics approaches to identify the associations between genes, pathway modules, phenotypic traits, and diseases. The approaches and data described in this thesis have been deposited in a publicly accessible web source at www.systems-genetics.org, and will hopefully facilitate the identification of new gene functions.

Identifying gene function and module connections by the integration of multispecies expression compendia

Johan Auwerx, Maroun Bou Sleiman, Wen Gao, Qingyao Huang, Elena Katsyuba, Andrea Komljenovic, Hao Li, Yang Li, Stephan Morgenthaler, Changmyung Oh, Daria Rukina, Kristina Schoonjans

The functions of many eukaryotic genes are still poorly understood. Here, we developed and validated a new method, termed GeneBridge, which is based on two linked approaches to impute gene function and bridge genes with biological processes. First, Gene-Module Association Determination (G-MAD) allows the annotation of gene function. Second, Module-Module Association Determination (M-MAD) allows predicting connectivity among modules. We applied the GeneBridge tools to large-scale multispecies expression compendia-1700 data sets with over 300,000 samples from human, mouse, rat, fly, worm, and yeast-collected in this study. G-MAD identifies novel functions of genes-for example, DDT in mitochondrial respiration and WDFY4 in T cell activation-and also suggests novel components for modules, such as for cholesterol biosynthesis. By applying G-MAD on data sets from respective tissues, tissue-specific functions of genes were identified-for instance, the roles of EHHADH in liver and kidney, as well as SLC6A1 in brain and liver. Using M-MAD, we identified a list of module-module associations, such as those between mitochondria and proteasome, mitochondria and histone demethylation, as well as ribosomes and lipid biosynthesis. The GeneBridge tools together with the expression compendia are available as an open resource, which will facilitate the identification of connections linking genes, modules, phenotypes, and diseases.

Cold Spring Harbor Laboratory2019

Quantitative single-cell analysis of S. cerevisiae using a microfluidic live-cell imaging platform

Johannes Becker

Genome-wide manipulations and measurements have made huge progress over the last decades. In Saccharomyces cerevisiae, a well-studied eukaryotic model organism, homologous recombination allows for systematic deletion or alteration of a majority of its genes. Important products of these manipulation techniques are two libraries of modified strains: A deletion library consisting of all viable knockout mutants, and a GFP library in which 4159 proteins are successfully tagged with GFP. In addition, the development of a method that allows for the systematic construction of double mutants led to a virtually infinite number of potential strains of interest. These advancements in combinatorial biology need to be matched by methods of data measurement and analysis. In order to simultaneously observe the spatio-temporal dynamics of thousands of strains from the GFP library, Dénervaud et al. developed a microfluidic platform that allows for parallel imaging of 1152 strains in a single experiment. On this platform, strains can be grown and monitored in a controllable environment for several days, which results in the imaging of several millions of cells during one experiment. To objectively and quantitatively analyze this immense amount of information, we implemented an image analysis pipeline, which can extract experiment-wide information on single-cell protein abundance and subcellular localization. The construction of a supervised classifier to quantify localization information on a single cell level is a new approach and was invaluable to detect dynamic localization changes within the proteome. Using five different stress conditions, we gained insight into temporal changes of abundance and localization of multiple proteins. For example, we found that while localization changes can often be fast and transient, long-term response of a cell is usually enabled by changes in abundance. This shows a well-orchestrated response of a cell to external stimuli. To extend knowledge about cellular mechanisms, we used our microfluidic platform for two separate screens, combining GFP-reporter with additional deletion mutants. The advantage of our platform in comparison to more common approaches lies in its simultaneous measurement of fluorescence and phenotypic information on cell size and growth. For each deletion, we can quantify not only its influence onto the respective GFP-reporter under changing conditions, but also its effect on cell growth and size. We showed that it is advantageous to combine this information, as it allows pointing out possible underlying mechanisms of gene network regulations. In a first screen we investigated the behavior of several gene networks upon UV irradiation damage. We were able to show that four gene deletions influenced the localization of ribonucleotide-diphosphate reductase (Rnr4p). A second screen was designed to find genes that influence the induction of the galactose network. This screen uses more than 500 deletions of genes mostly related to chromatin in combination with two different reporter strains. A main focus of this study was the inheritance of memory during galactose reinduction. We found several previously unknown genes that potentially influence either induction or reinduction and were picked as candidates for further inheritance studies. Our microfluidic platform allows for unprecedented studies of proteomes in flux. [...]

EPFL2015

The phenotypic impact of naturally occurring genetic and molecular mitochondrial variation in a Drosophila Genetic Reference Population

Roel Paulus Josephus Bevers

Genome-wide association (GWA) studies aim to dissect the relationship between genotype and phenotype. So far, the focus has been on nuclear genetic determinants as variation in the mitochondrial genome is often low in the study cohorts. Despite this observation, mitochondrial genomic variants are increasingly linked to metabolic and neurological disorders. Thus, there is much to gain from studying how mitochondrial variation interacts with nuclear variation and/or environment, and how this subsequently affects phenotypes. In my thesis I address these topics using Drosophila melanogaster as a model system, and specifically the Drosophila Genetic Reference Panel (DGRP). The DGRP consists of 200 sequenced and genetically diverse Drosophila populations and has been extensively used to study a wide-range of quantitative traits. However, in all of these studies, researchers have focussed on the influence of nuclear genetic variants in the absence of a high-resolution map of mitochondrial variants. To address this problem, we have developed a high-throughput method to specifically sequence mitochondrial DNA that enabled us to characterize mitochondrial genomic variation to an unprecedented depth. On average, each DGRP line had 25 mitochondrial variants (1 in 600 bp) and that most of the variants detected were unique for each line. However, we also found that some variants are more prevalent and show they can be used to construct mitochondrial haplotypes. We then used these haplotypes to test whether mitochondrial variation affected previously published phenotypes. We detected significant associations with 12 out of 259 publicly accessible phenotypes and found that these were mostly related to metabolism and responses to environmental cues. We selected one of the significant phenotypes, food intake in males, to verify our findings. We demonstrated the effect of mitochondrial haplotypes on the phenotype by swapping mitochondria from ¿high food intake¿ lines with mitochondria from ¿low food intake¿ lines and vice versa. We observed that, largely independent of the nuclear background, DGRP lines with a ¿high food intake mitochondria¿ consumed more food and concomitantly, DGRP lines with the ¿low food intake mitochondria¿ consumed less food. Furthermore, we investigated the influence of natural genetic variation was on the response to a high-fat diet. We designed a control and a high-fat diet and fed this to >100 DGRP lines and measured the lifespan on each diet. On average, lifespan was decreased by 48% on a high-fat diet, however this ranged from an increase of 2% to a decrease of 60% accentuating the complexity of the underlying genetic architecture. Our dataset was relatively small and limited our genome-wide and mitochondrial haplotype association analyses. Nonetheless, our GWA analysis detected variants related to genes involved in lipid metabolism and mitochondria but we did not detect significant associations with mitochondrial haplotypes. This suggests that while mitochondrial function may be important for the response to a high-fat diet, it is not dependent on the mitochondrial haplotype. Taken together, the work described in my thesis demonstrates the significance of accounting for mitochondrial haplotypes in phenotype association analyses. By doing so, it can open up new insights into genotype-to-phenotype relationships and gene regulation.