Analytical and computational methods towards a metabolic model of ageing in Caenorhabditis elegans

Human life expectancy is increasing globally. This has major socioeconomic implications, but also raises scientific questions about the biological bases of ageing and longevity. Research on appropriate model organisms, such as the nematode worm Caenorhabditis elegans, is a key component of answering these questions. Ageing is a complex phenomenon, with both environmental and genetic influences. Metabolomics, the analysis of all small molecules within a biological system, offers the ability to integrate these complex factors to help understand the role of metabolism in ageing. This thesis addresses the current lack of methods for C. elegans metabolite analysis, with a particular focus on combining analytical and computational approaches. As a first essential step, C. elegans metabolite extraction protocols for NMR, GC-MS and LC-MS based analysis were optimized. Several methods to improve the coverage, automatic annotation and data analysis steps of NMR and GC-MS are proposed. Next, stable isotope labelling was explored as a tool for C. elegans metabolomics. An automated stable isotope based workflow was developed, which identifies all biological, non-redundant features within a LC-MS acquisition and annotates them with molecular compositions. This demonstrated that the vast majority (> 99.5%) of detected features inside LC-MS metabolomics experiments are not of biological origin or redundant. This stable isotope workflow was then used to compare the metabolism of 24 different C. elegans mutant strains from different pathways (e.g. insulin signalling, TOR pathway, neuronal signalling), with differing levels of lifespan extension compared to wild-type worms. The biologically relevant features (metabolites) were detected and annotated, and compared across the mutants. Some metabolites were correlated with longevity across the mutant set, in particular, glycerophospholipids. This led to the formulation of a hypothesis, that lifespan extension in C. elegans requires increased activity of common downstream longevity effector mechanisms (autophagy, and mitochondrial biogenesis), that also involve subcellular compartmentation and hence membrane formation. This results in the alterations in lipid metabolism detected here.Open Acces

Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge

BACKGROUND: Cellular processes are controlled by gene-regulatory networks. Several computational methods are currently used to learn the structure of gene-regulatory networks from data. This study focusses on time series gene expression and gene knock-out data in order to identify the underlying network structure. We compare the performance of different network reconstruction methods using synthetic data generated from an ensemble of reference networks. Data requirements as well as optimal experiments for the reconstruction of gene-regulatory networks are investigated. Additionally, the impact of prior knowledge on network reconstruction as well as the effect of unobserved cellular processes is studied. RESULTS: We identify linear Gaussian dynamic Bayesian networks and variable selection based on F-statistics as suitable methods for the reconstruction of gene-regulatory networks from time series data. Commonly used discrete dynamic Bayesian networks perform inferior and this result can be attributed to the inevitable information loss by discretization of expression data. It is shown that short time series generated under transcription factor knock-out are optimal experiments in order to reveal the structure of gene regulatory networks. Relative to the level of observational noise, we give estimates for the required amount of gene expression data in order to accurately reconstruct gene-regulatory networks. The benefit of using of prior knowledge within a Bayesian learning framework is found to be limited to conditions of small gene expression data size. Unobserved processes, like protein-protein interactions, induce dependencies between gene expression levels similar to direct transcriptional regulation. We show that these dependencies cannot be distinguished from transcription factor mediated gene regulation on the basis of gene expression data alone. CONCLUSION: Currently available data size and data quality make the reconstruction of gene networks from gene expression data a challenge. In this study, we identify an optimal type of experiment, requirements on the gene expression data quality and size as well as appropriate reconstruction methods in order to reverse engineer gene regulatory networks from time series data

Two-Dimensional Patterning by a Trapping/Depletion Mechanism: The Role of TTG1 and GL3 in Arabidopsis Trichome Formation

Trichome patterning in Arabidopsis serves as a model system to study how single cells are selected within a field of initially equivalent cells. Current models explain this pattern by an activator–inhibitor feedback loop. Here, we report that also a newly discovered mechanism is involved by which patterning is governed by the removal of the trichome-promoting factor TRANSPARENT TESTA GLABRA1 (TTG1) from non-trichome cells. We demonstrate by clonal analysis and misexpression studies that Arabidopsis TTG1 can act non-cell-autonomously and by microinjection experiments that TTG1 protein moves between cells. While TTG1 is expressed ubiquitously, TTG1–YFP protein accumulates in trichomes and is depleted in the surrounding cells. TTG1–YFP depletion depends on GLABRA3 (GL3), suggesting that the depletion is governed by a trapping mechanism. To study the potential of the observed trapping/depletion mechanism, we formulated a mathematical model enabling us to evaluate the relevance of each parameter and to identify parameters explaining the paradoxical genetic finding that strong ttg1 alleles are glabrous, while weak alleles exhibit trichome clusters

Long-lived T follicular helper cells retain plasticity and help sustain humoral immunity

CD4; +; memory T cells play an important role in protective immunity and are a key target in vaccine development. Many studies have focused on T central memory (T; cm; ) cells, whereas the existence and functional significance of long-lived T follicular helper (T; fh; ) cells are controversial. Here, we show that T; fh; cells are highly susceptible to NAD-induced cell death (NICD) during isolation from tissues, leading to their underrepresentation in prior studies. NICD blockade reveals the persistence of abundant T; fh; cells with high expression of hallmark T; fh; markers to at least 400 days after infection, by which time T; cm; cells are no longer found. Using single-cell RNA-seq, we demonstrate that long-lived T; fh; cells are transcriptionally distinct from T; cm; cells, maintain stemness and self-renewal gene expression, and, in contrast to T; cm; cells, are multipotent after recall. At the protein level, we show that folate receptor 4 (FR4) robustly discriminates long-lived T; fh; cells from T; cm; cells. Unexpectedly, long-lived T; fh; cells concurrently express a distinct glycolytic signature similar to trained immune cells, including elevated expression of mTOR-, HIF-1-, and cAMP-regulated genes. Late disruption of glycolysis/ICOS signaling leads to T; fh; cell depletion concomitant with decreased splenic plasma cells and circulating antibody titers, demonstrating both unique homeostatic regulation of T; fh; and their sustained function during the memory phase of the immune response. These results highlight the metabolic heterogeneity underlying distinct long-lived T cell subsets and establish T; fh; cells as an attractive target for the induction of durable adaptive immunity

Zebrafish Pou5f1-dependent transcriptional networks in temporal control of early development

Time-resolved transcriptome analysis of early pou5f1 mutant zebrafish embryos identified groups of developmental regulators, including SoxB1 genes, that depend on Pou5f1 activity, and a large cluster of differentiation genes which are prematurely expressed.Pou5f1 represses differentiation genes indirectly via activation of germlayer-specific transcriptional repressor genes, including her3, which may mediate in part Pou5f1-dependent repression of neural genes.A dynamic mathematical model is established for Pou5f1 and SoxB1 activity-dependent temporal behaviour of downstream transcriptional regulatory networks. The model predicts that Pou5f1-dependent increase in SoxB1 activity significantly contributes to developmental timing in the early gastrula.Comparison to mouse Pou5f1/Oct4 reveals evolutionary conserved targets. We show that Pou5f1 developmental function is also conserved by demonstrating rescue of Pou5f1 mutant zebrafish embryos by mouse POU5F1/OCT4

A computational analysis of the dynamic roles of talin, Dok1, and PIPKI for integrin activation

Integrin signaling regulates cell migration and plays a pivotal role in developmental processes and cancer metastasis. Integrin signaling has been studied extensively and much data is available on pathway components and interactions. Yet the data is fragmented and an integrated model is missing. We use a rule-based modeling approach to integrate available data and test biological hypotheses regarding the role of talin, Dok1 and PIPKI in integrin activation. The detailed biochemical characterization of integrin signaling provides us with measured values for most of the kinetics parameters. However, measurements are not fully accurate and the cellular concentrations of signaling proteins are largely unknown and expected to vary substantially across different cellular conditions. By sampling model behaviors over the physiologically realistic parameter range we find that the model exhibits only two different qualitative behaviours and these depend mainly on the relative protein concentrations, which offers a powerful point of control to the cell. Our study highlights the necessity to characterize model behavior not for a single parameter optimum, but to identify parameter sets that characterize different signaling modes