Formal Concept Analysis and Knowledge Integration for Highlighting Statistically Enriched Functions from Microarrays Data

International audienceIn this paper we introduce a new method for extracting enriched biological functions from transcriptomic databases using an integrative bi-classication approach. The initial gene datasets are firstly represented as a formal context (objects attributes), where objects are genes, and attributes are their expression profiles and complementary information of different knowledge bases. After that, Formal Concept Analysis (FCA) is applied for extracting formal concepts regrouping genes having similar transcriptomic profiles and functional behaviors. An enrichment analysis is then performed in order to identify the pertinent formal concepts from the generated Galois lattice, and to extract biological functions that could participate in the proliferation of cancers

Unique networks: a method to identity disease-specific regulatory networks from microarray data

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The survival of any organismis determined by the mechanisms triggered in response to the inputs received. Underlying mechanisms are described by graphical networks that can be inferred from different types of data such as microarrays. Deriving robust and reliable networks can be complicated due to the microarray structure of the data characterized by a discrepancy between the number of genes and samples of several orders of magnitude, bias and noise. Researchers overcome this problem by integrating independent data together and deriving the common mechanisms through consensus network analysis. Different conditions generate different inputs to the organism which reacts triggering different mechanisms with similarities and differences. A lot of effort has been spent into identifying the commonalities under different conditions. Highlighting similarities may overshadow the differences which often identify the main characteristics of the triggered mechanisms. In this thesis we introduce the concept of study-specific mechanism. We develop a pipeline to semiautomatically identify study-specific networks called unique-networks through a combination of consensus approach, graphical similarities and network analysis. The main pipeline called UNIP (Unique Networks Identification Pipeline) takes a set of independent studies, builds gene regulatory networks for each of them, calculates an adaptation of the sensitivity measure based on the networks graphical similarities, applies clustering to group the studies who generate the most similar networks into study-clusters and derives the consensus networks. Once each study-cluster is associated with a consensus-network, we identify the links that appear only in the consensus network under consideration but not in the others (unique-connections). Considering the genes involved in the unique-connections we build Bayesian networks to derive the unique-networks. Finally, we exploit the inference tool to calculate each gene prediction-accuracy across all studies to further refine the unique-networks. Biological validation through different software and the literature are explored to validate our method. UNIP is first applied to a set of synthetic data perturbed with different levels of noise to study the performance and verify its reliability. Then, wheat under stress conditions and different types of cancer are explored. Finally, we develop a user-friendly interface to combine the set of studies by using AND and NOT logic operators. Based on the findings, UNIP is a robust and reliable method to analyse large sets of transcriptomic data. It easily detects the main complex relationships between transcriptional expression of genes specific for different conditions and also highlights structures and nodes that could be potential targets for further research

Bioinformatics protocols for analysis of functional genomics data applied to neuropathy microarray datasets

Microarray technology allows the simultaneous measurement of the abundance of thousands of transcripts in living cells. The high-throughput nature of microarray technology means that automatic analytical procedures are required to handle the sheer amount of data, typically generated in a single microarray experiment. Along these lines, this work presents a contribution to the automatic analysis of microarray data by attempting to construct protocols for the validation of publicly available methods for microarray. At the experimental level, an evaluation of amplification of RNA targets prior to hybridisation with the physical array was undertaken. This had the important consequence of revealing the extent to which the significance of intensity ratios between varying biological conditions may be compromised following amplification as well as identifying the underlying cause of this effect. On the basis of these findings, recommendations regarding the usability of RNA amplification protocols with microarray screening were drawn in the context of varying microarray experimental conditions. On the data analysis side, this work has had the important outcome of developing an automatic framework for the validation of functional analysis methods for microarray. This is based on using a GO semantic similarity scoring metric to assess the similarity between functional terms found enriched by functional analysis of a model dataset and those anticipated from prior knowledge of the biological phenomenon under study. Using such validation system, this work has shown, for the first time, that ‘Catmap’, an early functional analysis method performs better than the more recent and most popular methods of its kind. Crucially, the effectiveness of this validation system implies that such system may be reliably adopted for validation of newly developed functional analysis methods for microarray

Formal Concept Analysis Applications in Bioinformatics

Bioinformatics is an important field that seeks to solve biological problems with the help of computation. One specific field in bioinformatics is that of genomics, the study of genes and their functions. Genomics can provide valuable analysis as to the interaction between how genes interact with their environment. One such way to measure the interaction is through gene expression data, which determines whether (and how much) a certain gene activates in a situation. Analyzing this data can be critical for predicting diseases or other biological reactions. One method used for analysis is Formal Concept Analysis (FCA), a computing technique based in partial orders that allows the user to examine the structural properties of binary data based on which subsets of the data set depend on each other. This thesis surveys, in breadth and depth, the current literature related to the use of FCA for bioinformatics, with particular focus on gene expression data. This includes descriptions of current data management techniques specific to FCA, such as lattice reduction, discretization, and variations of FCA to account for different data types. Advantages and shortcomings of using FCA for genomic investigations, as well as the feasibility of using FCA for this application are addressed. Finally, several areas for future doctoral research are proposed. Adviser: Jitender S. Deogu

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

Statistical modelling of masked gene regulatory pathway changes across microarray studies of interferon gamma activated macrophages

Interferon gamma (IFN-γ) regulation of macrophages plays an essential role in innate immunity and pathogenicity of viral infections by directing large and small genome-wide changes in the transcriptional program of macrophages. Smaller changes at the transcriptional level are difficult to detect but can have profound biological effects, motivating the hypothesis of this thesis that responses of macrophages to immune activation by IFN-γ include small quantitative changes that are masked by noise but represent meaningful transcriptional systems in pathways against infection. To test this hypothesis, statistical meta-analysis of microarray studies is investigated as a tool to obtain the necessary increase in analysis sensitivity. Three meta-analysis models (Effect size model, Rank Product model, Fisher’s sum of logs) and three further modified versions were applied to a heterogeneous set of four microarray studies on the effect of IFN-γ on murine macrophages. Performance assessments include recovery of known biology and are followed by development of novel biological hypotheses through secondary analysis of meta-analysis outcomes in context of independent biological data sources. A separate network analysis of a microarray time course study investigate s if gene sets with coordinated time-dependent relationships overlap can also identify subtle IFN-γ related transcriptional changes in macrophages that match those identified through meta-analysis. It was found that all meta-analysis models can identify biologically meaningful transcription at enhanced sensitivity levels, with slightly improved performance advantages for a non-parametric model (Rank Product meta-analysis). Meta-analysis yielded consistently regulated genes, hidden in individual microarray studies, related to sterol biosynthesis (Stard3, Pgrmc1, Galnt6, Rab11a, Golga4, Lrp10), implicated in cross-talk between type II and type I interferon or IL-10 signalling (Tbk1, Ikbke, Clic4, Ptpre, Batf), and circadian rhythm (Csnk1e). Further network analysis confirms that meta-analysis findings are highly concentrated in a distinct immune response cluster of co-expressed genes, and also identifies global expression modularisation in IFN-γ treated macrophages, pointing to Trafd1 as a central anti-correlated node topologically linked to interactions with down-regulated sterol biosynthesis pathway members. Outcomes from this thesis suggest that small transcriptional changes in IFN-γ activated macrophages can be detected by enhancing sensitivity through combination of multiple microarray studies. Together with use of bioinformatical resources, independent data sets and network analysis, further validation assigns a potential role for low or variable transcription genes in linking type II interferon signalling to type I and TLR signalling, as well as the sterol metabolic network

Computationally Linking Chemical Exposure to Molecular Effects with Complex Data: Comparing Methods to Disentangle Chemical Drivers in Environmental Mixtures and Knowledge-based Deep Learning for Predictions in Environmental Toxicology

Chemical exposures affect the environment and may lead to adverse outcomes in its organisms. Omics-based approaches, like standardised microarray experiments, have expanded the toolbox to monitor the distribution of chemicals and assess the risk to organisms in the environment. The resulting complex data have extended the scope of toxicological knowledge bases and published literature. A plethora of computational approaches have been applied in environmental toxicology considering systems biology and data integration. Still, the complexity of environmental and biological systems given in data challenges investigations of exposure-related effects. This thesis aimed at computationally linking chemical exposure to biological effects on the molecular level considering sources of complex environmental data. The first study employed data of an omics-based exposure study considering mixture effects in a freshwater environment. We compared three data-driven analyses in their suitability to disentangle mixture effects of chemical exposures to biological effects and their reliability in attributing potentially adverse outcomes to chemical drivers with toxicological databases on gene and pathway levels. Differential gene expression analysis and a network inference approach resulted in toxicologically meaningful outcomes and uncovered individual chemical effects — stand-alone and in combination. We developed an integrative computational strategy to harvest exposure-related gene associations from environmental samples considering mixtures of lowly concentrated compounds. The applied approaches allowed assessing the hazard of chemicals more systematically with correlation-based compound groups. This dissertation presents another achievement toward a data-driven hypothesis generation for molecular exposure effects. The approach combined text-mining and deep learning. The study was entirely data-driven and involved state-of-the-art computational methods of artificial intelligence. We employed literature-based relational data and curated toxicological knowledge to predict chemical-biomolecule interactions. A word embedding neural network with a subsequent feed-forward network was implemented. Data augmentation and recurrent neural networks were beneficial for training with curated toxicological knowledge. The trained models reached accuracies of up to 94% for unseen test data of the employed knowledge base. However, we could not reliably confirm known chemical-gene interactions across selected data sources. Still, the predictive models might derive unknown information from toxicological knowledge sources, like literature, databases or omics-based exposure studies. Thus, the deep learning models might allow predicting hypotheses of exposure-related molecular effects. Both achievements of this dissertation might support the prioritisation of chemicals for testing and an intelligent selection of chemicals for monitoring in future exposure studies.:Table of Contents ... I Abstract ... V Acknowledgements ... VII Prelude ... IX 1 Introduction 1.1 An overview of environmental toxicology ... 2 1.1.1 Environmental toxicology ... 2 1.1.2 Chemicals in the environment ... 4 1.1.3 Systems biological perspectives in environmental toxicology ... 7 Computational toxicology ... 11 1.2.1 Omics-based approaches ... 12 1.2.2 Linking chemical exposure to transcriptional effects ... 14 1.2.3 Up-scaling from the gene level to higher biological organisation levels ... 19 1.2.4 Biomedical literature-based discovery ... 24 1.2.5 Deep learning with knowledge representation ... 27 1.3 Research question and approaches ... 29 2 Methods and Data ... 33 2.1 Linking environmental relevant mixture exposures to transcriptional effects ... 34 2.1.1 Exposure and microarray data ... 34 2.1.2 Preprocessing ... 35 2.1.3 Differential gene expression ... 37 2.1.4 Association rule mining ... 38 2.1.5 Weighted gene correlation network analysis ... 39 2.1.6 Method comparison ... 41 Predicting exposure-related effects on a molecular level ... 44 2.2.1 Input ... 44 2.2.2 Input preparation ... 47 2.2.3 Deep learning models ... 49 2.2.4 Toxicogenomic application ... 54 3 Method comparison to link complex stream water exposures to effects on the transcriptional level ... 57 3.1 Background and motivation ... 58 3.1.1 Workflow ... 61 3.2 Results ... 62 3.2.1 Data preprocessing ... 62 3.2.2 Differential gene expression analysis ... 67 3.2.3 Association rule mining ... 71 3.2.4 Network inference ... 78 3.2.5 Method comparison ... 84 3.2.6 Application case of method integration ... 87 3.3 Discussion ... 91 3.4 Conclusion ... 99 4 Deep learning prediction of chemical-biomolecule interactions ... 101 4.1 Motivation ... 102 4.1.1Workflow ...105 4.2 Results ... 107 4.2.1 Input preparation ... 107 4.2.2 Model selection ... 110 4.2.3 Model comparison ... 118 4.2.4 Toxicogenomic application ... 121 4.2.5 Horizontal augmentation without tail-padding ...123 4.2.6 Four-class problem formulation ... 124 4.2.7 Training with CTD data ... 125 4.3 Discussion ... 129 4.3.1 Transferring biomedical knowledge towards toxicology ... 129 4.3.2 Deep learning with biomedical knowledge representation ...133 4.3.3 Data integration ...136 4.4 Conclusion ... 141 5 Conclusion and Future perspectives ... 143 5.1 Conclusion ... 143 5.1.1 Investigating complex mixtures in the environment ... 144 5.1.2 Complex knowledge from literature and curated databases predict chemical- biomolecule interactions ... 145 5.1.3 Linking chemical exposure to biological effects by integrating CTD ... 146 5.2 Future perspectives ... 147 S1 Supplement Chapter 1 ... 153 S1.1 Example of an estrogen bioassay ... 154 S1.2 Types of mode of action ... 154 S1.3 The dogma of molecular biology ... 157 S1.4 Transcriptomics ... 159 S2 Supplement Chapter 3 ... 161 S3 Supplement Chapter 4 ... 175 S3.1 Hyperparameter tuning results ... 176 S3.2 Functional enrichment with predicted chemical-gene interactions and CTD reference pathway genesets ... 179 S3.3 Reduction of learning rate in a model with large word embedding vectors ... 183 S3.4 Horizontal augmentation without tail-padding ... 183 S3.5 Four-relationship classification ... 185 S3.6 Interpreting loss observations for SemMedDB trained models ... 187 List of Abbreviations ... i List of Figures ... vi List of Tables ... x Bibliography ... xii Curriculum scientiae ... xxxix Selbständigkeitserklärung ... xlii

Gli3 utilizes Hand2 to synergistically regulate tissue-specific transcriptional networks.

Despite a common understanding that Gli TFs are utilized to convey a Hh morphogen gradient, genetic analyses suggest craniofacial development does not completely fit this paradigm. Using the mouse model (Mus musculus), we demonstrated that rather than being driven by a Hh threshold, robust Gli3 transcriptional activity during skeletal and glossal development required interaction with the basic helix-loop-helix TF Hand2. Not only did genetic and expression data support a co-factorial relationship, but genomic analysis revealed that Gli3 and Hand2 were enriched at regulatory elements for genes essential for mandibular patterning and development. Interestingly, motif analysis at sites co-occupied by Gli3 and Hand2 uncovered mandibular-specific, low-affinity, \u27divergent\u27 Gli-binding motifs (dGBMs). Functional validation revealed these dGBMs conveyed synergistic activation of Gli targets essential for mandibular patterning and development. In summary, this work elucidates a novel, sequence-dependent mechanism for Gli transcriptional activity within the craniofacial complex that is independent of a graded Hh signal

Transcriptomics of prion diseases

Despite substantial research aiming to elucidate prion disease pathogenesis, the underlying mechanisms of cellular toxicity and neurodegeneration remain poorly characterized. The human brain comprises numerous cell populations with a heterogeneous transcriptional landscape, complicating the interpretation of transcriptomic studies. To untangle this complexity, we first established and validated two single-nucleus sequencing methodologies and a bioinformatics pipeline for data analysis. We then designed a time-course case-control study of RML- and control brain homogenate-inoculated FVB mice (N = 95, time points: 20, 40, 80, 120 dpi and disease end-stage), and a human case-control study in post-mortem and biopsied brain samples (N = 26) and applied our transcriptomics pipeline. We generated 210,000 high-quality cell transcriptomes across 5 time points in mice and identified 26 subclusters of cortical neurons, interneurons, mature oligodendrocytes, oligodendrocyte precursor cells, vascular and leptomeningeal cells, and astrocytes. Glial activation was evident from 80 dpi, while our data suggested a selective transcriptomic response of individual cell clusters to disease. We identified a pattern of neuronal transcriptomic change shortly after RML-brain inoculation that quickly resolved, despite rapidly increasing prion titres in the brain, only to return at later stages when the neuropathology of prion disease was evident. Subsequent pathway analyses identified common perturbed biological pathways associated with synaptic dysfunction and ion homeostasis. Our human tissue samples did not pass quality control criteria, highlighting the need for different methodologies to assay archived samples. Here we provide the first single-cell transcriptomics study of prion diseases in mouse which found cell-type and time-specific patterns. Taken together, findings suggest that prion replication itself does not produce a transcriptomic signature in the brain, rather, a transient pattern of toxicity can be seen immediately following inoculation of prion disease brain homogenate, which becomes re-established as prion disease neuropathology develops

The interdependence between environment and metabolism in microbes and their ecosystems

Microbes are ubiquitous in virtually all habitats on Earth and affect human life in multiple ways, from the health-balancing role of the human microbiome, to the involvement of microbial communities in the global nitrogen and carbon cycles. The capacity of microbes to survive and grow in diverse environments relates directly to their ability to utilize available resources, be they from other microbes or from the environment itself. Hence, understanding how the environment shapes the metabolic functionality of individual microbes and complex communities constitutes an important area of research. In the first part of my thesis work, I explored how environmental nutrient composition and intracellular transcriptional regulation data can be integrated to provide insight into the temporal metabolic behavior of a bacterium through the use of genome-scale stoichiometric modeling approaches (Flux Balance Analysis). Thus I developed the method of Temporal Expression-based Analysis of Metabolism (TEAM), and applied it to Shewanella oneidensis, a bacterium studied for its important bioenergy and bioremediation applications. I found that TEAM improves on previous models' predictions of S. oneidensis metabolic fluxes, and recovers the overflow metabolism that has been seen experimentally. This study demonstrated the value of incorporating environmental context and transcriptional data for the prediction of time-dependent metabolic behavior. In the second part of my work, I extended the exploration of microbial metabolism from single species to complex communities in order to understand the robustness of metabolic functions. Specifically, I implemented novel mathematical analyses of metagenomic sequencing data to ask how functional stability of microbial communities could ensue despite large taxonomic variability. Upon representing in matrix form the metabolic capabilities of all genera found in 202 available metabolic ecosystem datasets, I compared the different communities with each other and with various randomized analogues. My results reveal new connections between the abundance of an organism in the community and the functions that it encodes. Furthermore, I found that genus abundances govern the metabolic robustness of a community more than the distribution of genetically encoded functions among the community members, suggesting that communities rely largely on ecological interactions to regulate their overall functionality