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bioRxiv2018

Estimating Optimal Lysogenic Propensity for Viruses in Stressed Environments

Abstract.Having infected a bacterial cell, a temperate phage has to make a choice between (a) integrating itself into the bacterial genome, i.e. lysogeny, and (b) using the bacterial machinery to create multiple copies of itself and lysing the cell in the process, i.e. lysis. In order to maximize its long-term growth rate, phages need to ensure that they do not wipe off their bacterial hosts. Temperate phages have been observed to exhibit lysogenic propensities dependent on the MoI (Multiplicity of Infection), among other factors. We propose a model to estimate the propensity of lysogeny opted for by the phages in order to maximize coexistence. One possible approach to do so is to adopt a strategy that would help to attain and maintain an approximately equal proportion of phages with respect to their host. We find that the optimal fraction of phages opting for lysogeny follows a sigmoidal relationship with the MoI and is comparable to results obtained experimentally. We further assess the impact of phage and bacterial environmental stresses on the lysogenic propensity. Our results indicate that the optimal value of lysogenic propensity is greatly dependent on the intensity of these stresses.

systems biology↗

bioRxiv2018

Estimating parasite load dynamics to reveal novel resistance mechanisms to human malaria

Improved methods are needed to identify host mechanisms which directly protect against human infectious diseases in order to develop better vaccines and therapeutics1,2. Pathogen load determines the outcome of many infections3, and is a consequence of pathogen multiplication rate, duration of the infection, and inhibition or killing of pathogen by the host (resistance). If these determinants of pathogen load could be quantified then their mechanistic correlates might be determined. In humans the timing of infection is rarely known and treatment cannot usually be withheld to monitor serial changes in pathogen load and host response. Here we present an approach to overcome this and identify potential mechanisms of resistance which control parasite load in Plasmodium falciparum malaria. Using a mathematical model of longitudinal infection dynamics for orientation, we made individualized estimates of parasite multiplication and growth inhibition in Gambian children at presentation with acute malaria and used whole blood RNA-sequencing to identify their correlates. We identified novel roles for secreted proteases cathepsin G and matrix metallopeptidase 9 (MMP9) as direct effector molecules which inhibit P. falciparum growth. Cathepsin G acts on the erythrocyte membrane, cleaving surface receptors required for parasite invasion, whilst MMP9 acts on the parasite. In contrast, the type 1 interferon response and expression of CXCL10 (IFN-{gamma}-inducible protein of 10 kDa, IP-10) were detrimental to control of parasite growth. Natural variation in iron status and plasma levels of complement factor H were determinants of parasite multiplication rate. Our findings demonstrate the importance of accounting for the dynamic interaction between host and pathogen when seeking to identify correlates of protection, and reveal novel mechanisms controlling parasite growth in humans. This approach could be extended to identify additional mechanistic correlates of natural- and vaccine-induced immunity to malaria and other infections.

systems biology↗

bioRxiv2018

Machine characterization of rat toxin responses identifies disease states, tolerance mechanisms and organ to whole-body communication

BackgroundLiving organisms are constantly exposed to toxic xenobiotics and have therefore evolved protective responses. In mammals, the liver and kidney play central roles in protecting the organism from xenobiotics, and are at high risk of xenobiotic-induced injury. Liver and kidney damage by drugs and industrial toxins have been extensively studied from both classical histopathologic and biochemical perspectives.\n\nMethods and FindingsWe introduce a machine learning approach for the analysis of toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin-induced disease states. Transcriptome analysis showed that some of the machine-identified disease states correspond to known pathology, and to known effects of certain toxin classes, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly in the direction of decreased pathology, which implies induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and novel decreased ferroptosis sensitivity biomarkers. These data reinforce emerging evidence that ferroptosis drives organ pathology, and suggest that its downreagulation may promote tolerance and recovery. Lastly, mechanism of body weight decrease, a known primary marker for toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ disease states promote cachexia by whole-body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.\n\nConclusionsApplication of machine learning to systematic data collection of physiology, histopathology, transcriptome reveals multiple disease states, tolerance mechanisms and organ to whole-body communication.

systems biology↗

bioRxiv2018

Disproportionate feedback interactions govern cell-type specific proliferation in mammalian cells

In mammalian cells, the critical decision to maintain quiescence over proliferation commitment in and around the G1-S transition depends on more than one intertwined feedback interaction, and is highly cell-type dependent. However, the precise role played by these individual feedback regulations, in order to generate such diverse nature of proliferation commitment, is still poorly understood. Herein, we propose a generic mathematical model of G1-S transition in mammalian cells that not only reconciles distinct single cell experimental observations in a cell-type specific manner, but also makes experimentally testable non-intuitive predictions. Intriguingly, The model analysis reveals that the feedback motifs responsible for the G1-S transition act in a disparate fashion to organize the cell-type specific proliferation response in different mammalian cells. The proposed model, in principle, can be effectively tuned to explore proliferation dynamics in a cell-type specific way to gain crucial insights about novel therapeutic intervention to prevent unwanted cellular proliferation.

systems biology↗

bioRxiv2018

Effect of fosfomycin, Cynara scolymus extract, deoxynivalenol and their combinations on intestinal health of weaned piglets

Intestinal health of weaning piglets was studied after oral treatments with fosfomycin (FOS), Cynara scolymus extract (CSE), deoxynivalenol (DON) and their combinations. Piglets were divided in groups and received different treatments during 15 days, namely DON (1mg/kg of feed), FOS administered into the drinking water (30 mg/kg b.w.), CSE (300 g/ton of feed) and all possible combinations including a control group that received clean balanced diet. At day 15, three piglets from each group were euthanized and gastrointestinal tract samples were immediately taken to evaluate pH, bacteriology (enterobacteria and lactic acid bacteria), volatile fatty acids concentration (VFAs), disaccharidases activity (lactase, sucrase and maltase), histology (intestinal absorptive area [IAA] and goblet cells count) and adherence of bacteria to intestinal mucus. Animals receiving FOS and CSE treatments exhibited evident beneficial intestinal effects compared to animals receiving diets free from these compounds. This was revealed by a lower enterobacteria population together with a lower E/L, an enhanced production of butyric acid, an increased enzymatic activity (particularly maltase), and a greater IAA and goblet cells count along with an increase in pathogenic bacteria adherence to intestinal mucus. Interactions between both treatments resulted in similar beneficial effects as their individual administration. On the contrary, DON produced detrimental effects on intestinal health as a decrease was observed on volatile fatty acids production, enzymatic activity and goblet cells count in animals receiving diets containing sub- toxic concentrations of this mycotoxin. The knowledge of the intestinal effects of these compounds contributes to understand the physiological and pathological gut changes and their potential productive consequences.

systems biology↗

bioRxiv2018

A computational model to understand mouse iron physiology and diseases

It is well known that iron is an essential element for life but is toxic when in excess or in certain forms. Accordingly there are many diseases that result directly from either lack or excess of iron. Iron has also been associated with a wide range of other diseases and may have an important role in aging. Yet many molecular and physiological aspects of iron regulation have only been discovered recently and others are still awaiting elucidation. In the last 18 years, after the discovery of the hormone hepcidin, many details of iron regulation have become better understood and a clearer picture is starting to emerge, at least in qualitative terms. However there is still no good quantitative and dynamic description of iron absorption, distribution, storage and mobilization that agrees with the wide array of phenotypes presented in several iron-related diseases. The present work addresses this issue by developing a mathematical model of iron distribution in mice that was calibrated with existing ferrokinetic data and subsequently validated against data from a series of iron disorders, such as hemochromatosis, {beta}-thalassemia, atransferrinemia and anemia of inflammation. To adequately fit the ferrokinetic data required including the following mechanisms: a) the role of transferrin in deliving iron to tissues, b) the induction of hepcidin by high levels of transferrin-bound iron, c) the ferroportin-dependent hepcidin-regulated iron export from tissues, d) the erythropoietin regulation of erythropoiesis, and e) direct NTBI uptake by the liver. The utility of such a model to simulate disease interventions was demonstrated by using it to investigate the outcome of different schedules of transferrin treatment in {beta}-thalassemia. The present model is a successful step towards a comprehensive mathematical model of iron physiology incorporating cellular and organ level details.

systems biology↗

bioRxiv2018

Development of Convolutional Neural Network Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Mouse Development

In embryology, analytical approaches such as segmentation are applied to acquiring quantitative criteria from time-series three-dimensional microscopic images. When used to segment cells or intracellular organelles, several current machine learning techniques outperform traditional image processing algorithms. However segmentation algorithms still have unsolved problems, especially in bioimage processing. The most critical issue is that the existing algorithms can perform only semantic segmentation, which distin-guishes whether a pixel is within an object (for example, nucleus) or not. In this study, we implemented a novel segmentation algorithm, based on deep learning, which segments each nucleus and adds different labels to the detected objects. This segmentation algorithm is called instance segmentation. Our instance segmentation algorithm, implemented as a neural network, which we named QCA Net, substantially out-performed 3D U-Net, which is the best semantic segmentation algorithm that uses deep learning. Using QCA Net, we quantified the nuclear number, volume, surface area, and center of gravity coordinates during the development of mouse embryos. In particular, QCA Net distinguished nuclei of embryonic cells from those of polar bodies formed in meiosis. We consider that QCA Net can greatly contribute to bioimage segmentation in embryology by generating quantitative criteria from segmented images.

systems biology↗

bioRxiv2018

SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework

A recurrent problem in genome-scale metabolic models (GEMs) is to correctly represent lipids as biomass requirements, due to the numerous of possible combinations of individual lipid species and the corresponding lack of fully detailed data. In this study we present SLIMEr, a formalism for correctly representing lipid requirements in GEMs using commonly available experimental data. SLIMEr enhances a GEM with mathematical constructs where we Split Lipids Into Measurable Entities (SLIME reactions), in addition to constraints on both the lipid classes and the acyl chain distribution. By implementing SLIMEr on the consensus GEM of Saccharomyces cerevisiae, we can predict accurate amounts of lipid species, analyze the flexibility of the resulting distribution, and compute the energy costs of moving from one metabolic state to another. The approach shows potential for better understanding lipid metabolism in yeast under different conditions. SLIMEr is freely available at https://github.com/SysBioChalmers/SLIMEr.

systems biology↗

bioRxiv2018

Splicing and epigenetic factors jointly regulate epidermal differentiation

Epidermal homeostasis requires a continuous balance between progenitor cell proliferation and loss of differentiated cells from its surface. During this process cells undergo major changes in their transcriptional programs to accommodate new cellular functions. We found that transcriptional and post-transcriptional mechanisms underlying these changes are functionally connected and jointly control genes involved in cell adhesion, a key process in epidermal maintenance. Using siRNA-based perturbation screens, we identified novel DNA/RNA binding regulators of epidermal differentiation. Computational modeling and experimental validation identified functional interactions between the matrin-type 2 zinc-finger protein ZMAT2 and the epigenetic modifiers ING5, SMARCA5, BRD1, UHRF1, BPTF, SMARCC2. ZMAT2 is required to keep cells in an undifferentiated, proliferative state and quantitative proteomics identified ZMAT2 as an interactor of the pre-spliceosome. RNA-Immunoprecipitation and transcriptome-wide RNA splicing analysis showed that ZMAT2 associates with and regulates transcripts involved in cell adhesion in conjuction with ING5. Thus, joint control by post-transcriptional and epigenetic mechanisms is important to maintain epidermal cells in an undifferentiated state.\n\nHighlights- Gene-perturbation screens identify a role for ZMAT2 in the control of human epidermal differentiation.\n- ZMAT2 functionally interacts with known epigenetic regulators of epidermal differentiation.\n- ZMAT2 interacts with the pre-spliceosome and transcripts involved in cell adhesion.\n- ZMAT2 mediated splicing and epigenetic control jointly target an adhesion related transcriptional program in human epidermal stem cells.

systems biology↗

bioRxiv2018

Synthetic STARR-seq reveals how DNA shape and sequence modulate transcriptional output and noise.

The binding of transcription factors to short recognition sequences plays a pivotal role in controlling the expression of genes. The sequence and shape characteristics of binding sites influence DNA binding specificity and have also been implicated in modulating the activity of transcription factors downstream of binding. To quantitatively assess the transcriptional activity of dozens of thousands of designed synthetic sites in parallel, we developed a synthetic version of STARR-seq (synSTARR-seq). We used the approach to systematically analyze how variations in the recognition sequence of the glucocorticoid receptor (GR) affect transcriptional regulation. Our approach resulted in the identification of a novel highly active functional GR binding sequence and revealed that sequence variation both within and flanking GRs core binding site can modulate GR activity without apparent changes in DNA binding affinity. Notably, we found that the sequence composition of variants with similar activity profiles was highly diverse. In contrast, groups of variants with similar activity profiles showed distinct DNA shape characteristics indicating that DNA shape may be a better predictor of activity than DNA sequence. Finally, using single cell experiments with individual enhancer variants, we obtained clues indicating that the architecture of the response element can independently tune expression mean and cell-to cell variability in gene expression (noise). Together, our studies establish synSTARR as a powerful method to systematically study how DNA sequence and shape modulate transcriptional output and noise.

systems biology↗

bioRxiv2018

A novel approach of human geroprotector discovery by targeting the converging subnetworks of aging and age-related diseases

A key goal of geroscience research is to discover effective interventions to extend human healthspan, the years of healthy life. Currently, majority of the geroprotectors are found by testing compounds in model organisms; whether these compounds will be effective in humans is largely unknown. Here we present a novel strategy called ANDRU (aging network based drug discovery) to help the discovery of human geroprotectors. Instead of relying on model organisms, this approach is driven by human genomic and pharmacogenomic data. It first identifies human aging subnetworks that putatively function at the interface between aging and age-related diseases; it then screens for pharmacological or genetic interventions that may \"reverse\" the age-associated transcriptional changes seen in these subnetworks. We applied ANDRU to human adipose and artery tissues. In adipose tissue, PTPN1, a target for diabetes treatment and APOE, a known genetic factor for human longevity and diseases like Alzheimers disease, were ranked at the top. For small molecules, conjugated linoleic acid and metformin, a drug commonly used to treat type 2 diabetes, were ranked among the top compounds. In artery tissue, N-methyl-D-aspartate antagonists and curcumin were ranked at the top. In summary, ANDRU represents a promising human data-driven strategy that may speed up the discovery of interventions to extend human healthspan.

systems biology↗

bioRxiv2018

Insights on the impact of mitochondrial organisation on bioenergetics in high-resolution computational models of cardiac cell architecture

Recent electron microscopy data have revealed that cardiac mitochondria are not arranged in crystalline columns, but are organised with several mitochondria aggregated into columns of varying sizes often spanning the cell cross-section. This raises the question - how does the mitochondrial arrangement affect the metabolite distributions within cardiomyocytes and their impact on force dynamics? Here we employed finite element modelling of cardiac bioenergetics, using computational meshes derived from electron microscope images, to address this question. Our results indicate that heterogeneous mitochondrial distributions can lead to significant spatial variation across the cell in concentrations of inorganic phosphate, creatine (Cr) and creatine phosphate (PCr). However, our model predicts that sufficient activity of the creatine kinase (CK) system, coupled with rapid diffusion of Cr and PCr, maintains near uniform ATP and ADP ratios across the cell cross sections. This homogenous distribution of ATP and ADP should also evenly distribute force production and twitch duration with contraction. These results suggest that the PCr shuttle, and associated enzymatic reactions, act to maintain uniform force dynamics in the cell despite the heterogeneous mitochondrial organization. However, our model also predicts that under hypoxia - activity of mitochondrial CK enzyme and diffusion of high-energy phosphate compounds may be insufficient to sustain uniform ATP/ADP distribution and hence force generation.

systems biology↗

bioRxiv2018

Reconstruction of consensus tissue-specific metabolic models

Genome-Scale Metabolic Models have shown promising results in biomedical applications, such as understanding cancer metabolism and drug discovery. However, to take full advantage of these models there is the need to address the representation and simulation of the metabolic phenotypes of distinct cell types. With this aim, several algorithms have been recently proposed to reconstruct tissue-specific metabolic models based on available data. Here, the most promising were implemented and used to reconstruct models for two case studies, using omics data from distinct sources. The set of obtained models were compared and analyzed, being shown they are highly variable and that no combination of algorithm and data source can achieve models with acceptable phenotype predictions. We propose an algorithm to achieve a consensus model from the set of models available for a given tissue/cell line, and to improve it given functional data (e.g. known metabolic tasks). The results show that the resulting models are more accurate, both considering the prediction of known metabolic phenotypes and of experimental data not used in the model construction. Two case studies used for model validation consider healthy hepatocytes and a glioblastoma cell line. The open-source implementation of the algorithms is provided, together with the models built, in a software container, allowing full reproducibility, and representing by itself a contribution for the community.

systems biology↗

bioRxiv2018

Model-driven design and evolution of non-trivial synthetic syntrophic pairs

Synthetic microbial communities are attractive for applied biotechnology and healthcare applications through their ability to efficiently partition complex metabolic functions. By pairing auxotrophic mutants in co-culture, nascent E. coli communities can be established where strain pairs are metabolically coupled. Intuitive synthetic communities have been demonstrated, but the full space of cross-feeding metabolites has yet to be explored. A novel algorithm, OptAux, was constructed to design 66 multi-knockout E. coli auxotrophic strains that require significant metabolite cross-feeding when paired in co-culture. Three OptAux predicted auxotrophic strains were co-cultured with an L-histidine auxotroph and validated via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community fitness and provided insights on mechanisms for sharing and adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents a novel computational method to elucidate metabolic changes that empower community formation and thus guide the optimization of co-cultures for a desired application.

systems biology↗

bioRxiv2018

Exploring the impact of inoculum dose on host immunity and morbidity to inform model-based vaccine design

BackgroundVaccination is an effective method to protect against infectious diseases. An important consideration in any vaccine formulation is the inoculum dose, i.e., amount of antigen or live attenuated pathogen that is used. Higher levels generally lead to better stimulation of the immune response but might cause more severe side effects and allow for less population coverage in the presence of vaccine shortages. Determining the optimal amount of inoculum dose is an important component of rational vaccine design. A combination of mathematical models with experimental data can help determine the impact of the inoculum dose.\n\nMethodsWe designed mathematical models and fit them to data from influenza A virus (IAV) infection of mice and human parainfluenza virus (HPIV) of cotton rats at different inoculum doses. We used the model to predict the level of immune protection and morbidity for different inoculum doses and to explore what an optimal inoculum dose might be.\n\nResultsWe show how a framework that combines mathematical models with experimental data can be used to study the impact of inoculum dose on important outcomes such as immune protection and morbidity. We find that the impact of inoculum dose on immune protection and morbidity depends on the pathogen and both protection and morbidity do not always increase with increasing inoculum dose. An intermediate inoculum dose can provide the best balance between immune protection and morbidity, though this depends on the specific weighting of protection and morbidity.\n\nConclusionsOnce vaccine design goals are specified with required levels of protection and acceptable levels of morbidity, our proposed framework which combines data and models can help in the rational design of vaccines and determination of the optimal amount of inoculum.

systems biology↗

bioRxiv2018

The Y-ome defines the thirty-four percent of Escherichia coli genes that lack experimental evidence of function

Experimental studies of Escherichia coli K-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What information is available for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models of E. coli inevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt, RefSeq, and RegulonDB. We identified the genes that lack direct experimental evidence of function (the \"y-ome\") which include 1563 of 4653 unique genes (34%), of which 131 have absolutely no evidence of function. An additional 304 genes (6.6%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of the E. coli chromosome. Where evidence is available for y-ome genes, it most often points to them being membrane proteins and transporters. We resolve the misconception that a gene in E. coli whose primary name starts with \"y\" is unannotated, and we discuss the value of the y-ome for systematic improvement of E. coli knowledge bases and its extension to other organisms.

systems biology↗

bioRxiv2018

Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico

Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/

systems biology↗

bioRxiv2018

A genome-scale metabolic model for Methylococcus capsulatus predicts reduced efficiency uphill electron transfer to pMMO.

BackgroundGenome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modifications in silico, without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts like E. coli and S. cerevisiae, few such models exist for one-carbon (C1) metabolizers.\n\nResultsHere we present a genome-scale metabolic model for Methylococcus capsulatus, a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 877 metabolites connected via 898 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base for M. capsulatus. With it, we determined that oxidation of methane by the particulate methane monooxygenase is most likely driven through uphill electron transfer operating at reduced efficiency as this scenario matches best with experimental data from literature.\n\nConclusionsThe metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies towards improved SCP production processes in M. capsulatus.

systems biology↗