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Results for “Systems Biology”

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bioRxiv2018

Methionine coordinates a hierarchically organized anabolic program through GCN4

Methionine availability during overall amino acid limitation metabolically reprograms cells to support proliferation, the underlying basis for which remains unclear. Here, we construct the organization of this methionine mediated anabolic program, using yeast. Combining comparative transcriptome analysis, biochemical and metabolic flux based approaches, we discover that methionine rewires overall metabolic outputs by increasing the activity of three key regulatory nodes. These are: the pentose phosphate pathway coupled with reductive biosynthesis, and overall transamination capacity, including the synthesis of glutamate/glutamine. These provides the cofactors or substrates that enhance subsequent rate-limiting reactions in the synthesis of costly amino acids, and nucleotides, which are also induced in a methionine dependent manner. This thereby results in a biochemical cascade establishing an overall anabolic program. For this methionine mediated anabolic program leading to proliferation, cells co-opt a \"starvation stress response\" regulator, Gcn4p. Collectively, our data suggest a hierarchical metabolic framework explaining how methionine mediates an anabolic switch.

systems biology↗

bioRxiv2018

The Innate Immune Response to Ischemic Injury: a Multiscale Modeling Perspective

BackgroundCell death as a result of ischemic injury triggers powerful mechanisms regulated by germline-encoded Pattern Recognition Receptors (PRRs) with shared specificity that recognize invading pathogens and endogenous ligands released from dying cells, and as such are essential to human health. Alternatively, dysregulation of these mechanisms contributes to extreme inflammation, deleterious tissue damage and impaired healing in various diseases. The Toll-like receptors (TLRs) are a prototypical family of PRRs that may be powerful anti-inflammatory targets if agents can be designed that antagonize their harmful effects while preserving host defense functions. This requires an understanding of the complex interactions and consequences of targeting the TLR-mediated pathways as well as technologies to analyze and interpret these, which will then allow the simulation of perturbations targeting specific pathway components, predict potential outcomes and identify safe and effective therapeutic targets.\n\nResultsWe constructed a multiscale mathematical model that spans the tissue and intracellular scales, and captures the consequences of targeting various regulatory components of injury-induced TLR4 signal transduction on potential pro-inflammatory or pro-healing outcomes. We applied known interactions to simulate how inactivation of specific regulatory nodes affects dynamics in the context of injury and to predict phenotypes of potential therapeutic interventions. We propose rules to link model behavior to qualitative estimates of pro-inflammatory signal activation, macrophage infiltration, production of reactive oxygen species and resolution. We tested the validity of the model by assessing its ability to reproduce published data not used in its construction.\n\nConclusionsThese studies will enable us to form a conceptual framework focusing on TLR4-mediated ischemic repair to assess potential molecular targets that can be utilized therapeutically to improve efficacy and safety in treating ischemic/inflammatory injury.

systems biology↗

bioRxiv2018

Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers

Many tumors are characterized by large genomic heterogeneity and it remains unclear to what extent this impacts on protein biomarker discovery. Here, we quantified proteome intra-tissue heterogeneity (ITH) based on a multi-region analysis of 30 biopsy-scale prostate tissues using pressure cycling technology and SWATH mass spectrometry. We quantified 8,248 proteins and analyzed the ITH of 3,700 proteins. The level of ITH varied significantly depending on proteins and tissue types. Benign tissues exhibited generally more complex ITH patterns than malignant tissues. Spatial variability of ten prostate biomarkers was further validated by immunohistochemistry in an independent cohort (n=83) using tissue microarrays. PSA was preferentially variable in benign prostatic hyperplasia, while GDF15 substantially varied in prostate adenocarcinomas. Further, we found that DNA repair pathways exhibited a high degree of variability in tumorous tissues, which may contribute to the genetic heterogeneity of tumors. This study conceptually adds a new perspective to protein biomarker discovery by quantifying spatial proteome variation and it demonstrates the feasibility by exploiting recent technological progress.

systems biology↗

bioRxiv2018

Single Cell Molecular Alterations Reveal Pathogenesis and Targets of Concussive Brain Injury

The complex neuropathology of traumatic brain injury (TBI) is difficult to dissect in the hippocampus considering the convoluted hippocampal cytoarchitecture. As a major casualty of TBI, hippocampal dysfunction results in cognitive decline that may escalate to other neurological disorders, and the molecular basis is hidden in the genomic programs of individual hippocampal cells. Using the unbiased single cell sequencing method Drop-seq, we uncovered the hippocampal cell types most sensitive to concussive mild TBI (mTBI) as well as the vulnerable genes, pathways and cell-cell interactions predictive of disease pathogenesis in a cell-type specific manner, revealing hidden pathogenic mechanisms and potential targets. Targeting Ttr, encoding the thyroid hormone T4 transporter transthyretin, mitigated the genomic and behavioral abnormalities associated with mTBI. Single cell genomics provides unique evidence about altered circuits and pathogenic pathways, and pinpoints new targets amenable to therapeutics in mTBI and related disorders.

systems biology↗

bioRxiv2018

Charting the cross-functional map between transcription factors and cancer metabolism

Transcriptional reprogramming of cellular metabolism is a hallmark feature of cancer. However, a systematic approach to study the role of transcription factors (TFs) in mediating cancer metabolic rewiring is missing. Here, we chart a genome-scale map of TF-metabolite associations in human using a new combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines, and the integration of newly generated intracellular metabolic profiles of 54 cancer cell lines with transcriptomic and proteomic data. We unravel a large space of dependencies between TFs and central metabolic pathways, suggesting that the regulation of carbon metabolism in tumors may be more diverse and flexible than previously appreciated. This map provides an unprecedented resource to predict TFs responsible for metabolic transformation in patient-derived tumor samples, opening new opportunities in designing modulators of oncogenic TFs and in understanding disease etiology.

systems biology↗

bioRxiv2018

Molecular crypsis by pathogenic fungi using human factor H. A numerical model

Molecular mimicry is the formation of specific molecules by microbial pathogens to avoid recognition and attack by the immune system of the host. Several pathogenic Ascomycota and Zygomycota show such a behaviour by utilizing human complement factor H to hide in the blood stream. We call this type of mimicry molecular crypsis. Such a crypsis can reach a point where the immune system can no longer clearly distinguish between self and non-self cells. Thus, a trade-off between attacking disguised pathogens and erroneously attacking host cells has to be made, which can lead to autoreactivity. Based on signalling theory and protein-interaction modelling, we here present a mathematical model of molecular crypsis of pathogenic fungi using the example of Candida albicans. We tackle the question whether perfect crypsis is feasible, which would imply that protection of human cells by complement factors would be useless. The model identifies pathogen abundance relative to host cell abundance as the predominant factor influencing successful or unsuccessful molecular crypsis. If pathogen cells gain a (locally) quantitative advantage over host cells, even autoreactivity may occur. Our new model enables insights into the mechanisms of candidiasis-induced sepsis and complement associated autoimmune diseases.

systems biology↗

bioRxiv2018

A framework for large-scale dynamic metabolome drug profiling in mammalian cells: a case study analysis of the anti-cancer drug dichloroacetate

Metabolic profiling of cell line collections have become an invaluable tool to study disease etiology, drug modes of action and personalized medicine. However, large-scale in vitro dynamic metabolic profiling is limited by time-consuming sampling and complex measurement procedures. By adapting an MS-based metabolomics workflow for high-throughput profiling of diverse adherent mammalian cells, we establish a technique for the rapid measurement and analysis of drug-induced dynamic changes in intracellular metabolites. This methodology is scalable to large compound libraries and is here applied to study the mechanism underlying the toxic effect of dichloroacetate in ovarian cancer cell lines. System-level analysis of the metabolic responses revealed a key and unexpected role of CoA imbalance in dichloroacetate toxicity. The herein proposed strategy for large-scale drug metabolic profiling is complementary to other molecular profiling techniques, opening new scientific and drug-discovery opportunities.

systems biology↗

bioRxiv2018

Topographer Reveals Stochastic Dynamics of Cell Fate Decisions from Single-Cell RNA-Seq Data

Cell fate decisions play a pivotal role in development but technologies for dissecting them are limited. We developed a multifunction new method, Topographer to construct a quantitative Waddingtons landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point ([~]97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic) and gene expression (microscopic).

systems biology↗

bioRxiv2018

Higher transcriptome stability during aging in long-lived giant mole-rats compared to short-lived rats

Many aging-associated physiological changes are known to come up in short- and long-lived species with a different trajectory and emerging evidence suggests that large parts of life history trait differences between species are based on inter-species variation in gene expression. Little information is yet available, however, about transcriptome changes during aging when comparing mammals with different lifespans. For this reason, we studied the transcriptomes of five tissues and two age cohorts in two similar sized rodent species with very different lifespans: rat (Rattus norvegicus) and giant mole-rat (Fukomys mechowii) with maximum lifespans of 3.8 and >20 years, respectively. Our results show that giant mole-rats exhibit higher transcriptome stability during aging than the rat. While well-known aging signatures (e.g. up-regulation of pro-inflammatory genes) were detected in all rat tissues, they showed up only in one giant mole-rat tissue. Furthermore, many differentially expressed genes that were found in both species, were regulated in opposite directions during aging. This suggests that expression changes that cause aging in short-lived species are counteracted in long-lived species. Taken together, transcriptome stability may be one key causal factor of the long life- and healthspan of giant mole-rats and maybe of African mole-rats in general.

systems biology↗

bioRxiv2018

Gene Expression-Based Drug Repurposing to Target Ageing

Ageing is the largest risk factor for a variety of non-communicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both life- and health-span. Ageing is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target-centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat ageing in human brain. Using multiple gene expression datasets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterise ageing. Then, we compared these changes in gene expression with drug perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as anti-ageing drugs by reversing the detrimental changes that occur during ageing, others by mimicking the cellular defense mechanisms. The drugs that we identified included significant number of already identified pro-longevity drugs, indicating that the method can discover de novo drugs that meliorate ageing. The approach has the advantages that, by using data from human brain ageing data it focuses on processes relevant in human ageing and that it is unbiased, making it possible to discover new targets for ageing studies.

systems biology↗

bioRxiv2018

Influenza Virus Infection Model With Density Dependence Supports Biphasic Viral Decay

Mathematical models that describe infection kinetics help elucidate the time scales, effectiveness, and mechanisms underlying viral growth and infection resolution. For influenza A virus (IAV) infections, the standard viral kinetic model has been used to investigate the effect of different IAV proteins, immune mechanisms, antiviral actions, and bacterial coinfection, among others. We sought to further define the kinetics of IAV infections by infecting mice with influenza A/PR8 and measuring viral loads with high frequency and precision over the course of infection. The data highlighted dynamics that were not previously noted, including viral titers that remain elevated for several days during mid-infection and a sharp 4-5 log10 decline in virus within one day as the infection resolves. The standard viral kinetic model, which has been widely used within the field, could not capture these dynamics. Thus, we developed a new model that could simultaneously quantify the different phases of viral growth and decay with high accuracy. The model suggests that the slow and fast phases of virus decay are due to the infected cell clearance rate changing as the density of infected cells changes. To characterize this model, we fit the model to the viral load data, examined the parameter behavior, and connected the results and parameters to linear regression estimates. The resulting parameters and model dynamics revealed that the rate of viral clearance during resolution occurs 25 times faster than the clearance during mid-infection and that small decreases to this rate can significantly prolong the infection. This likely reflects the high efficiency of the adaptive immune response. The new model provides a well-characterized representation of IAV infection dynamics, is useful for analyzing and interpreting viral load dynamics in the absence of immunological data, and gives further insight into the regulation of viral control.

systems biology↗

bioRxiv2018

Parametric and Non-parametric Gradient Matching for Network Inference

Reverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression. To avoid the computational cost of large-scale simulations, a two-step Gaussian process interpolation based gradient matching approach has been proposed to solve differential equations approximately. Based on this gradient matching approach, we evaluate the fits of parametric as well as non-parametric candidate models to the data under various settings for different inference objectives. We also use model averaging, based on the Bayesian Information Criterion (BIC), in order to combine the different inferences. We found that parametric methods can provide comparable, and often improved inference compared to non-parametric methods; the latter, however, require no kinetic information and are computationally more efficient.\n\nThe code used in this work is available at https://github.com/ld2113/Final-Project.

systems biology↗

bioRxiv2018

Thermodynamic analysis of mitochondrial DNA breakpoints reveals mechanistic details of deletion mutagenesis

Broad evidence support double-strand breaks (DSBs) as initiators of mitochondrial DNA (mtDNA) deletion mutations. But the mechanism of DSB-induced deletions, including the DSB repair pathway(s) involved, remains to be established. Here, we used DNA hybridization thermodynamics to analyze misalignment lengths surrounding deletion breakpoints. Our analysis of 9,655 previously reported mammalian mtDNA deletions and 1,307 novel Caenorhabditis elegans mtDNA deletions, indicates a significant role of 0-25bp misalignments, supporting the role of erroneous non-homologous and micro-homology dependent DSB repair in deletion formation. Based on these insights we propose that DSB-induced mtDNA deletions occur via the misjoining of DSB ends and/or strand invasion of open mtDNA regions by DSB ends.

systems biology↗

bioRxiv2018

Capturing context-specific regulation in molecular interaction networks

MotivationGene expression changes over time in response to perturbations. These changes are coordinated into functional modules via regulatory interactions. The genes within a functional module are expected to be differentially expressed in a manner coherent with their regulatory network. This perspective presents a promising approach to increase power to detect differential signals as well as for describing regulated modules from a mechanistic point of view.\n\nResultsWe present an effective procedure for identifying differentially activated subnetworks in molecular interaction networks. Differential gene expression coherent with the regulatory nature of the network is identified. Sequentially controlling error on genes and links results in more efficient inference. By focusing on local inference, our method is ignorant of the global topology, and as a result equally effective on exponential and scale-free networks. We apply our procedure both to systematically simulated data, comparing its performance to alternative methods, and to the transcription regulatory network in the context of particle-induced pulmonary inflammation, recapitulating and proposing additional candidates to some previously obtained results.\n\nContactstephen.rush@oru.se, dirk.repsilber@oru.se

systems biology↗

bioRxiv2018

When metabolism meets physiology: Harvey and Harvetta

Precision medicine is an emerging paradigm that requires realistic, mechanistic models capturing the complexity of the human body. We present two comprehensive molecular to physiological-level, gender-specific whole-body metabolism (WBM) reconstructions, named Harvey, in recognition of William Harvey, and Harvetta. These validated, knowledge-based WBM reconstructions capture the metabolism of 20 organs, six sex organs, six blood cells, the gastrointestinal lumen, systemic blood circulation, and the blood-brain barrier. They represent 99% of the human body weight, when excluding the weight of the skeleton. Harvey and Harvetta can be parameterized based on physiological, dietary, and omics data. They correctly predict inter-organ metabolic cycles, basal metabolic rates, and energy use. We demonstrate the integration of microbiome data thereby allowing the assessment of individual-specific, organ-level modulation of host metabolism by the gut microbiota. The WBM reconstructions and the individual organ reconstructions are available under http://vmh.life. Harvey and Harvetta represent a pivotal step towards virtual physiological humans.

systems biology↗

bioRxiv2018

Systematic detection of amino acid substitutions in proteome reveals a mechanistic basis of ribosome errors

Translation errors limit the accuracy of information transmission from DNA to proteins. Selective pressures shape the way cells produce their proteins: the translation machinery and the mRNA sequences it decodes co-evolved to ensure that translation proceeds fast and accurately in a wide range of environmental conditions. Our understanding of the causes of amino acid misincorporations and of their effect on the evolution of protein sequences is largely hindered by the lack of experimental methods to observe errors at the full proteome level. Here, we systematically detect and quantify errors in entire proteomes from mass spectrometry data. Following HPLC MS-MS data acquisition, we identify E. coli and S. cerevisiae peptides whose mass and fragment ion spectrum are consistent with that of a peptide bearing a single amino acid substitution, and verify that such spectrum cannot result from a post-translational modification. Our analyses confirm that most substitutions occur due to codon-to-anticodon mispairing within the ribosome. Patterns of errors due to mispairing were similar in bacteria and yeast, suggesting that the error spectrum is chemically constrained. Treating E. coli cells with a drug known to affect ribosomal proofreading increased the error rates due to mispairing at the wobble codon position. Starving bacteria for serine resulted in specific patterns of substitutions reflecting the amino acid deficiency. Overall, translation errors tend to occur at positions that are less evolutionarily conserved, and that minimally affect protein energetic stability, indicating that they are selected against. Genome wide ribosome density data suggest that errors occur at sites where ribosome velocity is relatively high, supporting the notion of a trade-off between speed and accuracy as predicted by proofreading theories. Together our results reveal a mechanistic basis for ribosome errors in translation.

systems biology↗

bioRxiv2018

Lineage space and the propensity of bacterial cells to undergo growth transitions

The molecular makeup of the offspring of a dividing cell gradually becomes phenotypically decorrelated from the parent cell by noise and regulatory mechanisms that amplify phenotypic heterogeneity. Such regulatory mechanisms form networks that contain thresholds between phenotypes. Populations of cells can be poised near the threshold so that a subset of the population probabilistically undergoes the phenotypic transition. We sought to characterize the diversity of bacterial populations around a growth-modulating threshold via analysis of the effect of non-genetic inheritance, similar to conditions that create antibiotictolerant persister cells and other examples of bet hedging. Using simulations and experimental lineage data in Escherichia coli, we present evidence that regulation of growth amplifies the dependence of growth arrest on cellular lineage, causing clusters of related cells undergo growth arrest in certain conditions. Our simulations predict that lineage correlations and the sensitivity of growth to changes in toxin levels coincide in a critical regime. Below the critical regime, the sizes of related growth arrested clusters are distributed exponentially, while in the critical regime clusters sizes are more likely to become large. Furthermore, phenotypic diversity can be nearly as high as possible near the critical regime, but for most parameter values it falls far below the theoretical limit. We conclude that lineage information is indispensable for understanding regulation of cellular growth.\n\nAuthor SummaryOne of the most important characteristics of a cell is whether it is growing. Actively growing cells can multiply exponentially. In the case of infections and cancer, growth causes problems for the host organism. On the other hand, cells that have stopped growing can allocate cellular resources toward different activities, such as bacteria surviving antibiotics and tissues in multicellular organisms performing their physiological roles. Observing small bacterial colonies in a microscope over time, we have found that cells closely related to each other often have similar growth state. We were curious if lineage dependence was an intrinsic property of growth regulation or if other factors were needed to explain this effect. We therefore built a computational model of a growing and dividing cellular colony with an encoded growth regulation network. We found that regulation of growth is sufficient for lineage dependence to emerge. We next asked if lineage dependence constrains how diverse the cellular population can become. We found that cellular diversity can reach a peak that is nearly as high as possible near the conditions that have the highest lineage dependence, but that most conditions do not permit such high diversity. We conclude that lineage is an important constraint and discuss how the growth arrest transition is in some ways like a phase transition from physics, and in some ways strikingly different, making it a unique phenomenon.

systems biology↗

bioRxiv2018

Single cell network analysis with a mixture of Nested Effects Models

MotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.\n\nResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.\n\nAvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/.\n\nContactmartin.pirkl@bsse.ethz.ch, niko.beerenwinkel@bsse.ethz.ch\n\nSupplementary informationSupplementary data are available.online.

systems biology↗