Inferring modulators of genetic interactions with epistatic nested effects models

Maps of genetic interactions can dissect functional redundancies in cellular networks. Gene expression profiles as high-dimensional molecular readouts of combinatorial perturbations provide a detailed view of genetic interactions, but can be hard to interpret if different gene sets respond in different ways (called mixed epistasis). Here we test the hypothesis that mixed epistasis between a gene pair can be explained by the action of a third gene that modulates the interaction. We have extended the framework of Nested Effects Models (NEMs), a type of graphical model specifically tailored to analyze high-dimensional gene perturbation data, to incorporate logical functions that describe interactions between regulators on downstream genes and proteins. We benchmark our approach in the controlled setting of a simulation study and show high accuracy in inferring the correct model. In an application to data from deletion mutants of kinases and phosphatases in S. cerevisiae we show that epistatic NEMs can point to modulators of genetic interactions. Our approach is implemented in the R-package 'epiNEM' available from https://github.com/cbg-ethz/epiNEM and https://bioconductor.org/packages/epiNEM/

Learning Gene Interactions and Networks from Perturbation Screens and Expression Data

We investigate a variety of methods to first discover and then understand genetic interactions. Beginning with pairwise interactions, we propose a method for inferring pairwise gene interactions en masse from short- interfering RNA screens. We use the siRNA off-target effects to form a matrix of knocked-down genes, and consider the observed fitness to be a linear combination of individual and pairwise effects in this matrix. These effects can then be inferred using a variety of statistical learning methods. We evaluate two such methods for this task, xyz and glinternet. Using either method, we are able to find interactions in small simulated data sets. Neither method scales to genome-scale data sets, however. In our larger simulations both methods suffer from scalability problems, either with their accuracy or running time. We overcome these limitations by developing our own lasso-based regression method, which takes into account the binary nature of our perturbation screens. Using a compressed sparse representation of the pairwise interaction matrix, and parallelising updates, we are able to run this method on exome-scale data. Generalising from pairwise interactions we then consider network models, in which pairwise gene interactions form edges of a graph. Such networks are often understood in terms of functional modules, groups of genes that act together to perform a task. We develop a method that combines pairwise interaction and gene expression data to effectively find functional modules in simulated data

IST Austria Thesis

Antibiotic resistance can emerge spontaneously through genomic mutation and render treatment ineffective. To counteract this process, in addition to the discovery and description of resistance mechanisms,a deeper understanding of resistanceevolvabilityand its determinantsis needed. To address this challenge, this thesisuncoversnew genetic determinants of resistance evolvability using a customized robotic setup, exploressystematic ways in which resistance evolution is perturbed due to dose-responsecharacteristics of drugs and mutation rate differences,and mathematically investigates the evolutionary fate of one specific type of evolvability modifier -a stress-induced mutagenesis allele.We find severalgenes which strongly inhibit or potentiate resistance evolution. In order to identify them, we first developedan automated high-throughput feedback-controlled protocol whichkeeps the population size and selection pressure approximately constant for hundreds of cultures by dynamically re-diluting the cultures and adjusting the antibiotic concentration. We implementedthis protocol on a customized liquid handling robot and propagated 100 different gene deletion strains of Escherichia coliin triplicate for over 100 generations in tetracycline and in chloramphenicol, and comparedtheir adaptation rates.We find a diminishing returns pattern, where initially sensitive strains adapted more compared to less sensitive ones. Our data uncover that deletions of certain genes which do not affect mutation rate,including efflux pump components, a chaperone and severalstructural and regulatory genes can strongly and reproducibly alterresistance evolution. Sequencing analysis of evolved populations indicates that epistasis with resistance mutations is the most likelyexplanation. This work could inspire treatment strategies in which targeted inhibitors of evolvability mechanisms will be given alongside antibiotics to slow down resistance evolution and extend theefficacy of antibiotics.We implemented astochasticpopulation genetics model, toverifyways in which general properties, namely, dose-response characteristics of drugs and mutation rates, influence evolutionary dynamics. In particular, under the exposure to antibiotics with shallow dose-response curves,bacteria have narrower distributions of fitness effects of new mutations. We show that in silicothis also leads to slower resistance evolution. We see and confirm with experiments that increased mutation rates, apart from speeding up evolution, also leadto high reproducibility of phenotypic adaptation in a context of continually strong selection pressure.Knowledge of these patterns can aid in predicting the dynamics of antibiotic resistance evolutionand adapting treatment schemes accordingly.Focusing on a previously described type of evolvability modifier –a stress-induced mutagenesis allele –we find conditions under which it can persist in a population under periodic selectionakin to clinical treatment. We set up a deterministic infinite populationcontinuous time model tracking the frequencies of a mutator and resistance allele and evaluate various treatment schemes in how well they maintain a stress-induced mutator allele. In particular,a high diversity of stresses is crucial for the persistence of the mutator allele. This leads to a general trade-off where exactly those diversifying treatment schemes which are likely to decrease levels of resistance could lead to stronger selection of highly evolvable genotypes.In the long run, this work will lead to a deeper understanding of the genetic and cellular mechanisms involved in antibiotic resistance evolution and could inspire new strategies for slowing down its rate

Learning by Fusing Heterogeneous Data

It has become increasingly common in science and technology to gather data about systems at different levels of granularity or from different perspectives. This often gives rise to data that are represented in totally different input spaces. A basic premise behind the study of learning from heterogeneous data is that in many such cases, there exists some correspondence among certain input dimensions of different input spaces. In our work we found that a key bottleneck that prevents us from better understanding and truly fusing heterogeneous data at large scales is identifying the kind of knowledge that can be transferred between related data views, entities and tasks. We develop interesting and accurate data fusion methods for predictive modeling, which reduce or entirely eliminate some of the basic feature engineering steps that were needed in the past when inferring prediction models from disparate data. In addition, our work has a wide range of applications of which we focus on those from molecular and systems biology: it can help us predict gene functions, forecast pharmacological actions of small chemicals, prioritize genes for further studies, mine disease associations, detect drug toxicity and regress cancer patient survival data. Another important aspect of our research is the study of latent factor models. We aim to design latent models with factorized parameters that simultaneously tackle multiple types of data heterogeneity, where data diversity spans across heterogeneous input spaces, multiple types of features, and a variety of related prediction tasks. Our algorithms are capable of retaining the relational structure of a data system during model inference, which turns out to be vital for good performance of data fusion in certain applications. Our recent work included the study of network inference from many potentially nonidentical data distributions and its application to cancer genomic data. We also model the epistasis, an important concept from genetics, and propose algorithms to efficiently find the ordering of genes in cellular pathways. A central topic of our Thesis is also the analysis of large data compendia as predictions about certain phenomena, such as associations between diseases and involvement of genes in a certain phenotype, are only possible when dealing with lots of data. Among others, we analyze 30 heterogeneous data sets to assess drug toxicity and over 40 human gene association data collections, the largest number of data sets considered by a collective latent factor model up to date. We also make interesting observations about deciding which data should be considered for fusion and develop a generic approach that can estimate the sensitivities between different data sets

The Transition from Seed to Seedling

The transition from a growth-arrested seed to a germinating seed and then to a seedling is a crucial developmental step, dramatically affecting plant growth and viability. Before plants enter the vegetative phase of their ontogenesis, massive rearrangements of signaling pathways and switching of gene expression programs are required. It results in the suppression of the genes controlling seed maturation and the activation of those involved in regulating vegetative growth. These events are governed by the hormonal balance, primarily the abscisic acid and gibberellins, although other hormones are also involved. This book compiled the review and research papers published during the 2021–2022 biennium in the "Plant Development and Morphogenesis" section. The selection of papers covers a topic related to the most recent advances in the molecular mechanisms of metabolic switches during the seed-to-seedling transition and the relationship between seed longevity and seedling viability. It can help understand the complex regulatory mechanisms underlying the seedling establishment and facilitate the breeding of plants more tolerant to abiotic stresses

Dissecting the genetic basis of wheat yellow rust resistance in the NIAB Elite MAGIC population

Yellow rust, caused by the biotrophic fungus Puccinia striiformis f. sp. tritici (Pst), poses a major challenge for wheat breeders and growers globally. The past two decades have seen the rise of Pst populations that are more genetically diverse, more aggressive and that have adapted to warmer temperatures. These features, likely further aided by increased international travel, have led to important epidemic outbreaks and have jeopardised wheat yellow rust resistance levels in the main wheat-producing regions globally. Widespread epidemics have been further facilitated by the deployment of genetically uniform material underpinned by major resistance over large areas. With such a rapidly changing Pst population landscape, disease resistance breeding strategies must adapt accordingly, and this starts with the continued characterisation of adequate yellow rust resistance loci and accompanying molecular and genomic tools. It is crucial that these loci are of direct relevance to breeding programmes for rapid varietal deployment. To this end, I used the NIAB Elite Multi-parent Advanced Generation Inter-Cross (MAGIC) population, a multi-founder population that captures 80 % of the genetic variation present in key representative varieties used in UK wheat breeding (1970-2010s), to identify and characterise genetic loci controlling yellow rust resistance in replicated multi-environmental field trials, in both leaves and ears. This approach has further opened up the avenue for dissecting disease resistance beyond the limited scope of single varieties. I found that nine Quantitative Trait Loci (QTLs) conferred resistance to yellow rust, with four consistently detected across environments and explaining nearly 50 % of the phenotypic variation, and the other five explaining 15-20 % with inconsistent detection across environments. There was a strong indication of additivity effects between the four strong-effect QTL. Furthermore, all founders but the most susceptible one contributed towards resistance, indirectly demonstrating that UK breeding germplasm has high resistance potential against yellow rust. In the second part of my thesis, I focus on the physical interval of the eight most significant QTL previously identified, by examining gene annotations from the recently published IWGSC RefSeq v1.0 genome assembly. Five QTLs were characterised by NBS-LRR clusters. The presence of NBS-LRR-encoding genes with integrated domains revealed the potential for effector triggered immunity based on indirect recognition for a subset of those yellow rust resistance loci. The other three QTL were characterised by the absence of NBS-LRR-encoding genes in their physical interval, potentially indicating the role of non-race specific yellow rust resistance in the MAGIC population. Finally, I focus on glume infection, a phenotypic trait largely overlooked in QTL mapping studies, despite repeated reports of outbreaks. Despite high heritability (72 %), the five QTLs detected explained between 3 and 6 % of the phenotypic variation. Three QTLs co-located with QTLs for foliar resistance. The other two were associated with flowering time, suggesting that earlier ear emergence potentially leads to increased susceptibility to yellow rust in the glumes

Historical Contingency and Compensatory Evolution Constrain the Path of Evolution in a Genome Shuffling Experiment with Saccharomyces cerevisiae

The research reported in this thesis builds on an evolutionary engineering experiment (Pinel 2011) that yielded strains of Saccharomyces cerevisiae tolerant to a lignocellulosic hydrolysate. A highly tolerant strain was later characterized by whole genome and transcriptome sequencing (Pinel 2015). The evolutionary trajectories of mutations identified by sequencing were probed by whole population amplicon sequencing, while their significance to the phenotype was assessed by genotyping of additional mutants. Results of this work suggested that our survey of mutations selected during evolutionary engineering was partial. I therefore hypothesized that a complete survey of mutational diversity by whole population genome sequencing would further refine our understanding of lignocellulosic hydrolysate tolerance in S. cerevisiae. I further conjectured that extending this survey to several time points would reveal some of the fundamental evolutionary mechanisms that shape the outcomes of genome shuffling experiments. In parallel, I hypothesized that phenotypic testing of reverse engineered point mutants would identify mutations responsible for lignocellulosic hydrolysate tolerance in our strains of S. cerevisiae. My data revealed that a stong founder effect and prevalent genetic hitchhiking during genome shuffling lead to the domination of compensatory patterns during evolution. Bias introduced by historical contingency lead to the selection of few genuinely beneficial mutations. In the specific context of lignocellulosic hydrolysate tolerance, mutations in genes NRG1 and GSH1, conferring tolerance to acetic acid, oxidative, and potentially other stresses most prominently enhanced the phenotype

Modelling genetic networks involved in the activity-dependent modulation of adult neurogenesis

Die Bildung neuen Nervenzellen im erwachsenen Gehirn—adulte Neurogenese—ist bei Säugetieren auf spezifische Regionen beschränkt. Eine der beiden bekannten ist der Hippokampus, eine Gehirnstruktur, die eine wichtige Rolle beim Lernen sowie der Gedächtnisbildung spielt. Ein Reservoir von neuralen Stammzellen befindet sich in der subgranulären Zone des hippokampalen Gyrus dentatus. Diese Zellen teilen sich fortwährend und bilden neue Nervenzellen. Die Regulation adulter hippokampaler Neurogenese wird sowohl von der Umgebung beeinflusst als auch von mehreren Genen gesteuert. In der vorliegenden Arbeit wurden mittels Hochdurchsatz- Genexpressionsverfahren die an der Neurogenese beteiligten Gene identifiziert und ihr Zusammenspiel untersucht. Anhand von genetischen, umgebungsbedingten und zeitlichen Angaben und Variationen wurde ein vielseitiger Datensatz erstellt, der einen multidimensionalen Blick auf den proliferativen Phänotyp verschafft. Netzwerke aus Gen-Gen und Gen-Phänotyp Interaktionen wurden beschrieben und in einer mehrschichtigen Ressource zusammengefasst. Ein Kern-Netzwerk bestehend aus immerwiederkehrenden Modulen aus verschiedenen Ebenen wurde anhand von Proliferation als Keim-Phänotyp identifiziert. Aus diesem Kern-Netzwerk sind neue Gene und ihre Interaktionen hervorgegangen, die potentiell bei der Regulierung adulter Neurogenesis beteiligt sind.:Zusammenfassung i Abstract iii Acknowledgements vii Contents ix Preface xiii General Introduction 1 Adult Neurogenesis 1 Historical setting 1 Neurogenesis exists in two regions of the adult mammalian brain 1 Implications of neurogenesis in the hippocampus 1 The Hippocampal Formation 2 Function of the hippocampus in learning and memory 2 The functional role of adult neurogenesis 2 Anatomy of the hippocampal formation 2 Neural Precursor Biology 3 The subgranular zone as a neurogenic niche 3 Neuronal maturation is a multi-step pathway 3 Regulation of Adult Neurogenesis 3 Neurogenesis is modulated by age 3 Neurogenesis is modulated by environmental factors 4 Neurogenesis is modulated by genetic background 4 Genetics of the BXD RI Cross 5 C57BL/6 and DBA/2 5 Recombinant Inbred Lines 5 The BXD panel 6 Quantitative genetics 6 Microarray Analysis 7 The concept of ‘whole genome’ expression analysis 7 Technical considerations 8 Theoretical considerations 9 Current Analytical Methods 9 Network Analysis 10 Network Description and Terminology 10 Graph Theory 10 Multiple-Network Comparison 11 Biological networks 11 Types of Biological Network 11 Sources of Network Data 12 Biological Significance of Networks 12 Aim of the current work 13 Methods and Materials 15 Animals 15 BXD panel 15 Progenitor strains 15 Animal behaviour 15 Running wheel activity 15 Enriched environment 16 Morris water maze 16 Open field test 16 Corticosterone assay 16 Histology 17 Tissue collection 17 BrdU staining 17 Statistics 17 Cell culture 18 Maintenance and differentiation 18 Immunostaining 18 RNA isolation 18 Microarray processing 18 Affymetrix arrays 18 M430v2 probe reannotation 19 Illumina arrays 19 Illumina probe reannotation 19 Bioinformatics 19 Translating the STRING network 19 QTL mapping 20 Network graph layout 20 Triplot 20 Enrichment analysis 20 Mammalian Adult Neurogenesis Gene Ontology 21 Introduction 21 Results 25 The cell stage ontology 25 The process ontology 25 Genes known to regulate hippocampal adult neurogenesis 26 Enrichment analysis 27 The MANGO gene network 27 Discussion 28 Hippocampal Coexpression Networks from the BXD Panel 31 Introduction 31 Results 32 Variation and covariation of gene expression across a panel of inbred lines 32 A hippocampal expression correlation network 32 Diverse neurogenesis phenotypes associate with discrete transcript networks 34 Discussion 34 Interactions Between Gene Expression Phenotypes and Genotype 37 Introduction 37 Results 39 QTL analysis and interval definitions 39 Pleiotropic loci and ‘trans-bands’ 39 Transcript expression proxy-QTLs can help in dissection of complex phenotypes 41 Interaction network 43 Discussion 43 Strain-Dependent Effects of Environment 47 Introduction 47 Results 48 Effects of strain and environment on precursor cell proliferation 48 Effects of strain and environment on learning behaviour 52 Transcript expression associated with different housing environments 53 Strain differences in transcript regulation 55 Distance-weighted coexpression networks 57 Discussion 58 Expression Time Course from Differentiating Cell Culture 61 Introduction 61 Results 63 Differentiation of proliferating precursors into neurons in vitro 63 Transcripts associated with stages of differentiation 63 Early events in NPC differentiation 64 A network of transcript coexpression during in vitro differentiation 66 Discussion 67 Integrated Gene Interaction Networks 71 Introduction 71 Results 72 Description of network layers 72 Merging of network layers to a multigraph 74 A network of genes controls neural precursor proliferation in the adult hippocampus 75 Novel candidate regulators of adult hippocampal neurogenesis 77 Novel pathways regulating adult hippocampal neurogenesis 77 Discussion 79 General Discussion 81 References 89 Selbständigkeitserklärung 107Neurogenesis, the production of new neurons, is restricted in the adult brain of mammals to only a few regions. One of these sites of adult neurogenesis is the hippocampus, a structure essential for many types of learning. A pool of stem cells is maintained in the subgranular zone of the hippocampal dentate gyrus which proliferate and can differentiate into new neurons, astrocytes and oligodendroctytes. Regulation of adult hippocampal neurogenesis occurs in response to en- vironmental stimuli and is under the control of many genes. This work employs high-throughput gene expression technologies to identify these genes and their interactions with each other and the neurogenesis phenotype. Harnessing variation from genetic, environmental and temporal sources, a multi-faceted dataset has been generated which offers a multidimensional view of the neural precursor proliferation phenotype. Networks of gene-gene and gene-phenotype interac- tions have been described and merged into a multilayer resource. A core subnetwork derived from modules recurring in the different layers has been identified using the proliferation phenotype as a seed. This subnetwork has suggested novel genes and interactions potentially involved in the regulation of adult hippocampal neurogenesis.:Zusammenfassung i Abstract iii Acknowledgements vii Contents ix Preface xiii General Introduction 1 Adult Neurogenesis 1 Historical setting 1 Neurogenesis exists in two regions of the adult mammalian brain 1 Implications of neurogenesis in the hippocampus 1 The Hippocampal Formation 2 Function of the hippocampus in learning and memory 2 The functional role of adult neurogenesis 2 Anatomy of the hippocampal formation 2 Neural Precursor Biology 3 The subgranular zone as a neurogenic niche 3 Neuronal maturation is a multi-step pathway 3 Regulation of Adult Neurogenesis 3 Neurogenesis is modulated by age 3 Neurogenesis is modulated by environmental factors 4 Neurogenesis is modulated by genetic background 4 Genetics of the BXD RI Cross 5 C57BL/6 and DBA/2 5 Recombinant Inbred Lines 5 The BXD panel 6 Quantitative genetics 6 Microarray Analysis 7 The concept of ‘whole genome’ expression analysis 7 Technical considerations 8 Theoretical considerations 9 Current Analytical Methods 9 Network Analysis 10 Network Description and Terminology 10 Graph Theory 10 Multiple-Network Comparison 11 Biological networks 11 Types of Biological Network 11 Sources of Network Data 12 Biological Significance of Networks 12 Aim of the current work 13 Methods and Materials 15 Animals 15 BXD panel 15 Progenitor strains 15 Animal behaviour 15 Running wheel activity 15 Enriched environment 16 Morris water maze 16 Open field test 16 Corticosterone assay 16 Histology 17 Tissue collection 17 BrdU staining 17 Statistics 17 Cell culture 18 Maintenance and differentiation 18 Immunostaining 18 RNA isolation 18 Microarray processing 18 Affymetrix arrays 18 M430v2 probe reannotation 19 Illumina arrays 19 Illumina probe reannotation 19 Bioinformatics 19 Translating the STRING network 19 QTL mapping 20 Network graph layout 20 Triplot 20 Enrichment analysis 20 Mammalian Adult Neurogenesis Gene Ontology 21 Introduction 21 Results 25 The cell stage ontology 25 The process ontology 25 Genes known to regulate hippocampal adult neurogenesis 26 Enrichment analysis 27 The MANGO gene network 27 Discussion 28 Hippocampal Coexpression Networks from the BXD Panel 31 Introduction 31 Results 32 Variation and covariation of gene expression across a panel of inbred lines 32 A hippocampal expression correlation network 32 Diverse neurogenesis phenotypes associate with discrete transcript networks 34 Discussion 34 Interactions Between Gene Expression Phenotypes and Genotype 37 Introduction 37 Results 39 QTL analysis and interval definitions 39 Pleiotropic loci and ‘trans-bands’ 39 Transcript expression proxy-QTLs can help in dissection of complex phenotypes 41 Interaction network 43 Discussion 43 Strain-Dependent Effects of Environment 47 Introduction 47 Results 48 Effects of strain and environment on precursor cell proliferation 48 Effects of strain and environment on learning behaviour 52 Transcript expression associated with different housing environments 53 Strain differences in transcript regulation 55 Distance-weighted coexpression networks 57 Discussion 58 Expression Time Course from Differentiating Cell Culture 61 Introduction 61 Results 63 Differentiation of proliferating precursors into neurons in vitro 63 Transcripts associated with stages of differentiation 63 Early events in NPC differentiation 64 A network of transcript coexpression during in vitro differentiation 66 Discussion 67 Integrated Gene Interaction Networks 71 Introduction 71 Results 72 Description of network layers 72 Merging of network layers to a multigraph 74 A network of genes controls neural precursor proliferation in the adult hippocampus 75 Novel candidate regulators of adult hippocampal neurogenesis 77 Novel pathways regulating adult hippocampal neurogenesis 77 Discussion 79 General Discussion 81 References 89 Selbständigkeitserklärung 10

Generalized genetical genomics : advanced methods and applications

Generalized genetical genomics (GGG) is a systems genetics approach that combines the analysis of genetic variation with population-wide assessment of variation in molecular traits in multiple environments to identify genotype-by-environment interactions. This thesis starts by introducing the generalized genetical genomics strategy (Chapter 1). Then, we present a newly developed software, designGG for designing optimal GGG experiments (Chapter 2). Next, two important statistical issues relevant to GGG studies were addressed. We discussed the critical concerns on causal inference with genetic data. In addition, we examined the permutation method used for determining the significance of quantitative trait loci (QTL) hotspots in linkage and association studies (Chapter 3−4). Furthermore, we applied the GGG strategy to three pilot studies: In the first of these, we showed that heritable differences in the plastic responses of gene expression are largely regulated in “trans''. In the second pilot study, we demonstrated that heritable differences in transcript abundance are highly sensitive to cellular differentiation stage. In the third study, we found that the alternative splicing machinery exhibits a general genetic robustness in C. elegans and that only a minor fraction of genes shows heritable variation in splicing forms and relative abundance. (Chapter 5−7). Finally, we conclude by discussing various fundamental issues involved in data preprocessing, QTL mapping, result interpretation and network reconstruction and suggesting future directions yet to be explored in order to expand the reach of systems genetics (Chapter 8).