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bioRxiv2017

Determining controllability of sepsis using genetic algorithms on a proxy agent-based model of systemic inflammation

Sepsis, a manifestation of the bodys inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process in the clinical setting. We propose that this is in great part due to the considerable heterogeneity of the clinical trajectories that constitute clinical \"sepsis,\" and that determining how this system can be controlled back into a state of health requires the application of concepts drawn from the field of dynamical systems. In this work, we consider the human immune system to be a random dynamical system, and investigate its potential controllability using an agent-based model of the innate immune response (the Innate Immune Response ABM or IIRABM) as a surrogate, proxy system. Simulation experiments with the IIRABM provide an explanation as to why single/limited cytokine perturbations at a single, or small number of, time points is unlikely to significantly improve the mortality rate of sepsis. We then use genetic algorithms (GA) to explore and characterize multi-targeted control strategies for the random dynamical immune system that guide it from a persistent, non-recovering inflammatory state (functionally equivalent to the clinical states of systemic inflammatory response syndrome (SIRS) or sepsis) to a state of health. We train the GA on a single parameter set with multiple stochastic replicates, and show that while the calculated results show good generalizability, more advanced strategies are needed to achieve the goal of adaptive personalized medicine. This work evaluating the extent of interventions needed to control a simplified surrogate model of sepsis provides insight into the scope of the clinical challenge, and can serve as a guide on the path towards true \"precision control\" of sepsis.\n\nAuthor summarySepsis, characterized by the bodys inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process. In this work, we utilize a computational model of the human immune response to infectious injury to offer an explanation as to why previously attempted treatment strategies are inadequate and why the current approach to drug/therapy-development is inadequate. We then use evolutionary computation algorithms to explore drug-intervention space using this same computational model. This allows us to characterize the scale and scope of interventions needed to successfully control sepsis, as well as the types of data needed to derive these interventions. We demonstrate that multi-point and time-dependent varying controls are necessary and able to control the cytokine network dynamics of the immune system.

systems biology↗

bioRxiv2017

Direct in vivo mapping of functional suppressors in glioblastoma genome

Glioblastoma (GBM) is one of the deadliest cancers, with limited effective treatments and single-digit five-year survival 1-7. A causative understanding of genetic factors that regulate GBM formation is of central importance 8-19. However, a global, quantitative and functional understanding of gliomagenesis in the native brain environment has been lacking due to multiple challenges. Here, we developed an adeno-associated virus (AAV) mediated autochthonous CRISPR screen and directly mapped functional suppressors in the GBM genome. Stereotaxic delivery of an AAV library targeting significantly mutated genes into fully immunocompetent conditional Cas9 mice robustly led to gliomagenesis, resulting in tumors that recapitulate features of human GBM. Targeted capture sequencing revealed deep mutational profiles with diverse patterns across mice, uncovering in vivo roles of previously uncharacterized factors in GBM such as immune regulator B2m, zinc finger protein Zc3h13, transcription repressor Cic, epigenetic regulators Mll2/3 and Arid1b, alongside canonical tumor suppressors Nf1 and Pten. Comparative cancer genomics showed that the mutation frequencies across all genes tested in mice significantly correlate with those in human from two independent patient cohorts. Co-mutation analysis identified frequently co-occurring driver combinations, which were validated using AAV minipools, such as Mll2, B2m-Nf1, Mll3-Nf1 and Zc3h13-Rb1. Distinct from Nf1-oncotype tumors, Rb1-oncotype tumors exhibit undifferentiated histopathology phenotype and aberrant activation of developmental reprogramming signatures such as Homeobox gene clusters. The secondary addition of Zc3h13 or Pten mutations drastically altered the gene expression profiles of Rb1 mutants and rendered them more resistant to the GBM chemotherapeutic temozolomide. Our study provides a systematic functional landscape of GBM suppressors directly in vivo, opening new paths for high-throughput molecular mapping and cancer phenotyping.

systems biology↗

bioRxiv2017

Dynamic balance between vesicle transport and microtubule growth enables neurite growth

Whole cell responses involve multiple subcellular processes (SCPs). To understand how balance between SCPs controls the dynamics of whole cell responses we studied neurite outgrowth in rat primary cortical neurons in culture. We used a combination of dynamical models and experiments to understand the conditions that permitted growth at a specified velocity and when aberrant growth could lead to the formation of dystrophic bulbs. We hypothesized that dystrophic bulb formation is due to quantitative imbalances between SCPs. Simulations predict redundancies between lower level sibling SCPs within each type of high level SCP. In contrast, higher level SCPs, such as vesicle transport and exocytosis or microtubule growth characteristic of each type need to be strictly coordinated with each other and imbalances result in stalling of neurite outgrowth. From these simulations, we predicted the effect of changing the activities of SCPs involved in vesicle exocytosis or microtubule growth could lead to formation of dystrophic bulbs. siRNA ablation experiments verified these predictions. We conclude that whole cell dynamics requires balance between the higher-level SCPs involved and imbalances can terminate whole cell responses such as neurite outgrowth.

systems biology↗

bioRxiv2017

Pooled AAV-CRISPR Screen with Targeted Amplicon Sequencing

High-resolution, high-throughput direct in vivo screening of functional genetic factors in native tissues has long been challenging. Adeno-associated viruses (AAV) are powerful carriers of transgenes and have been shown to mediate efficient genome editing in various organs in mice. Here, we developed a new technological approach, Pooled AAV-CRISPR Screen with Targeted Amplicon Sequencing (PASTAS), and demonstrated its application for directly mapping functional cancer driver variants in the mouse liver in an autochthonous manner. Intravenous delivery of an AAV-CRISPR library targeting a set of the most frequently mutated tumor suppressor genes into fully immunocompetent conditional Cas9 knock-in mice consistently generated highly complex autochthonous liver tumors. The molecular landscapes of these genetically diverse tumors were mapped out by deep direct readout of Cas9-generated variants at predicted sgRNA cut sites using molecular inversion probe sequencing. Co-occurrence and correlation analyses as well as validation with lower complexity minipools further confirmed the potency of various co-mutated drivers. The PASTAS method can be applied to virtually any gene sets, any cancer types, or any type of in vivo genetic studies other than cancer.

systems biology↗

bioRxiv2017

Drug treatment efficiency depends on the initial state of activation in nonlinear pathways

An accurate prediction of the outcome of a given drug treatment requires quantitative values for all parameters and concentrations involved as well as a detailed characterization of the network of interactions where the target molecule is embedded. Here, we present a high-throughput in silico screening of all potential networks of three interacting nodes to study the effect of the initial conditions of the network in the efficiency of drug inhibition. Our study shows that most network topologies can induce multiple dose-response curves, where the treatment has an enhanced, reduced or even no effect depending on the initial conditions. The type of dual response observed depends on how the potential bistable regimes interplay with the inhibition of one of the nodes inside a nonlinear pathway architecture. We propose that this dependence of the strength of the drug on the initial state of activation of the pathway may be affecting the outcome and the reproducibility of drug studies and clinical trials.

systems biology↗

bioRxiv2017

Learning Edge Rewiring in EMT from Single Cell Data

Cellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in gene expression and protein confirmations. However, typical computational approaches treat them as static interaction networks derived from a single experimental time point. Here, we provide a method for learning the dynamic modulation, or rewiring of pairwise relationships (edges) from a static single-cell data. We use the epithelial-to-mesenchymal transition (EMT) in murine breast cancer cells as a model system, and measure mass cytometry data three days after induction of the transition by TGF{beta}. We take advantage of transitional rate variability between cells in the data by deriving a pseudo-time EMT trajectory. Then we propose methods for visualizing and quantifying time-varying edge behavior over the trajectory and use these methods: TIDES (Trajectory Imputed DREMI scores), and measure of edge dynamism (3DDREMI) to predict and validate the effect of drug perturbations on EMT.

systems biology↗

bioRxiv2017

Linearly changing stress environment causes cellular growth phenotype

Cells are exposed to changes in extracellular stimulus concentration that vary as a function of rate. However, the effect of stimulation rate on cell behavior and signaling remains poorly understood. Here, we examined how varying the rate of stress application alters budding yeast cell viability and mitogen-activated protein kinase (MAPK) signaling at the single-cell level. We show that cell survival and signaling depend on a rate threshold that operates in conjunction with a concentration threshold to determine the timing of MAPK signaling during rate-varying stimulus treatments. We also discovered that the stimulation rate threshold is sensitive to changes in the expression levels of the Ptp2 phosphatase, but not of another phosphatase that similarly regulates osmostress signaling during switch-like treatments. Our results demonstrate that stimulation rate is a regulated determinant of signaling output and provide a paradigm to guide the dissection of major stimulation rate-dependent mechanisms in other systems.

systems biology↗

bioRxiv2017

Novel ‘housekeeping’ genes and an unusually heterogeneous distribution of transporter expression profiles in human tissues and cell lines, assessed using the Gini coefficient

We analyse two comprehensive transcriptome datasets from human tissues and human-derived cell lines in terms of the expression profiles of the SLC and ABC families of membrane transporters. The Gini index (coefficient) characterises inequalities of distributions, and is used in a novel way to describe the distribution of the expression of each transporter among the different tissues and cell lines. In many cases, transporters exhibit extremely high Gini coefficients, even when their supposed substrates might be expected to be available to all tissues, indicating a much higher degree of specialisation than is usually assumed. This is consistent with divergent evolution from a more restricted set of ancestors. Similar trends hold true for the expression profiles of transporters in different cell lines, suggesting that cell lines exhibit largely similar transport behaviour to that of tissues. By contrast, the Gini coefficients for ABC transporters tend to be larger in cell lines than in tissues, implying that some kind of a selection process has taken place. In particular, with some exceptions such as olfactory receptors and genes involved in keratin production, transporter genes are significantly more heterogeneously expressed than are most non-transporter genes. The Gini index also allows us to determine those transcripts with the most stable expression; these often differ significantly from the housekeeping genes commonly used for normalisation in transcriptomics and qPCR studies. The lowest four in tissues are FAM32A, ABCB7, MRPL21 and PCBP1, while the lowest three in cell lines are SF3B2, NXF1 and RBM45. PCBP1 is both reasonably highly expressed and has a low Gini coefficient in both tissues and cell lines, and is an excellent novel housekeeping gene. Overall, our analyses provide novel opportunities for the normalisation of genome-wide expression profiling data.

systems biology↗

bioRxiv2017

Molecular analysis of the midbrain dopaminergic niche during neurogenesis

Midbrain dopaminergic (mDA) neurons degenerate in Parkinsons disease and are one of the main targets for cell replacement therapies. However, a comprehensive view of the signals and cell types contributing to mDA neurogenesis is not yet available. By analyzing the transcriptome of the mouse ventral midbrain at a tissue and single-cell level during mDA neurogenesis we found that three recently identified radial glia types 1-3 (Rgl1-3) contribute to different key aspects of mDA neurogenesis. While Rgl3 expressed most extracellular matrix components and multiple ligands for various pathways controlling mDA neuron development, such as Wnt and Shh, Rgl1-2 expressed most receptors. Moreover, we found that specific transcription factor networks explain the transcriptome and suggest a function for each individual radial glia. A network controlling neurogenesis was found in Rgl1, progenitor maintenance in Rgl2 and the secretion of factors forming the mDA niche by Rgl3. Our results thus uncover a broad repertoire of developmental signals expressed by each midbrain cell type during mDA neurogenesis. Cells identified for their emerging importance are Rgl3, a niche cell type, and Rgl1, a neurogenic progenitor that expresses ARNTL, a transcription factor that we find is required for mDA neurogenesis.

systems biology↗

bioRxiv2017

Molecular structures enumeration and virtual screening in the chemical space with RetroPath2.0

BackgroundNetwork generation tools coupled with chemical reaction rules have been mainly developed for synthesis planning and more recently for metabolic engineering. Using the same core algorithm, these tools apply a set of rules to a source set of compounds, stopping when a sink set of compounds has been produced. When using the appropriate sink, source and rules, this core algorithm can be used for a variety of applications beyond those it has been developed for.\n\nResultsHere, we showcase the use of the open source workflow RetroPath2.0. First, we mathematically prove that we can generate all structural isomers of a molecule using a reduced set of reaction rules. We then use this enumeration strategy to screen the chemical space around a set of monomers and predict their glass transition temperatures, as well as around aminoglycosides to search structures maximizing antibacterial activity. We also perform a screening around aminoglycosides with enzymatic reaction rules to ensure biosynthetic accessibility. We finally use our workflow on an E. coli model to complete E. coli metabolome, with novel molecules generated using promiscuous enzymatic reaction rules. These novel molecules are searched on the MS spectra of an E. coli cell lysate interfacing our workflow with OpenMS through the KNIME analytics platform.\n\nConclusionWe provide an easy to use and modify, modular, and open-source workflow. We demonstrate its versatility through a variety of use cases including, molecular structure enumeration, virtual screening in the chemical space, and metabolome completion. Because it is open source and freely available on MyExperiment.org, workflow community contributions should likely expand further the features of the tool, even beyond the use cases presented in the paper.

systems biology↗

bioRxiv2017

Is obesity a genetic disease? Human obese transcriptome analysis in monozygotic twins

Obesity is a pandemic disease with a critical increase in childhood. An important unanswered question is to understand if this disease is due to genetic causes or to the life-style of the subjects. To address this question, we have analyzed if monozygotic twins show the same robust transcriptomic signature (5{sigma}, as for the Higgs Boson) that we have recently revealed in obese subjects. Our results show that our signature correlates with BMI in paired transcriptomes of monozygotic twins, suggesting that the signature does not reflect underlying genetic causes.

systems biology↗

bioRxiv2017

Viral Complexity: Amino acid co-evolution in viral genomes as a possible metric

Viruses are simultaneously simple and complex. Simple because they have barely around ten types of proteins compared to tens of thousands of proteins in bacteria. Complex because amino acid mutation rates are very high, challenging host immune system and drugs. In this work we use the co-evolution of amino acids and the network characteristics that arise out of it to describe the complexity hidden in the multitude of variations in a viral genome. Using large-scale genomic data, the complexity in several viruses was compared. Interestingly, the co-evolutionary relations were primarily intra-protein in avian influenza and inter-protein in HIV-1. The network degree distributions showed two universality classes: a power-law with exponent -1 in HIV-1 and avian-influenza, random co-evolutionary behavior in human flu and dengue, suggesting the co-evolution as one way to statistically classify the complexity in viruses. The observed correlation between the network densities and the strengths on virus Richter scale raises interesting questions on whether it is possible to define the complexity of viruses using their evolutionary networks.

systems biology↗

bioRxiv2017

Efficient strategies for screening large-scale genetic interaction networks

Large-scale genetic interaction screening is a powerful approach for unbiased characterization of gene function and understanding systems-level cellular organization. While genome-wide screens are desirable as they provide the most comprehensive interaction profiles, they are resource and time-intensive and sometimes infeasible, depending on the species and experimental platform. For these scenarios, optimal methods for more efficient screening while still producing the maximal amount of information from the resulting profiles are of interest.\n\nTo address this problem, we developed an optimal algorithm, called COMPRESS-GI, which selects a small but informative set of genes that captures most of the functional information contained within genome-wide genetic interaction profiles. The utility of this algorithm is demonstrated through an application of the approach to define a diagnostic mutant set for large-scale chemical genetic screens, where more than 13,000 compound screens were achieved through the increased throughput enabled by the approach. COMPRESS-GI can be broadly applied for directing genetic interaction screens in other contexts, including in species with little or no prior genetic-interaction data.

systems biology↗

bioRxiv2017

Quantifying 35 transcripts in a single tube: Model-based calibration of the GeXP RT-PCR assay

Quantitative analysis of differential gene expression is of central importance in molecular life sciences. The Gene eXpression Profiling technology (GeXP) relies on multiplex RT-PCR and subsequent capillary electrophoretic separation of the amplification products and allows to quantify the transcripts of up to approximately 35 genes with a single reaction and one dye. Here, we provide a kinetic model of primer binding and PCR product formation as the rational basis for taking and evaluating calibration curves. With the help of a purposeful designed data processing workflow supported by easy-to-use Perl scripts for calibration, data evaluation, and quality control, the calibration procedure and the model predictions were confirmed and the robustness and linearity of transcript quantification demonstrated for differentiating Physarum polycephalum plasmodial cells. We conclude that GeXP analysis is a robust, sensitive, and useful method when the transcripts of tens to few hundred genes are to be precisely quantified in a high number of samples.

systems biology↗

bioRxiv2017

Improving Designer Glycan Production in Escherichia coli through Model-Guided Metabolic Engineering

Asparagine-linked (N-linked) glycosylation is the most common protein modification in eukaryotes, affecting over two-thirds of the proteome. Glycosylation is also critical to the pharmacokinetic activity and immunogenicity of many therapeutic proteins currently produced in complex eukaryotic hosts. The discovery of a protein glycosylation pathway in the pathogen Campylobacter jejuni and its subsequent transfer into laboratory strains of Escherichia coli has spurred great interest in glycoprotein production in prokaryotes. However, prokaryotic glycoprotein production has several drawbacks, including insufficient availability of non-native glycan precursors. To address this limitation, we used a constraint-based model of E. coli metabolism in combination with heuristic optimization to design gene knockout strains that overproduced glycan precursors. First, we incorporated reactions associated with C. jejuni glycan assembly into a genome-scale model of E. coli metabolism. We then identified gene knockout strains that coupled optimal growth to glycan synthesis. Simulations suggested that these growth-coupled glycan overproducing strains had metabolic imbalances that rerouted flux toward glycan precursor synthesis. We then validated the model-identified knockout strains experimentally by measuring glycan expression using a flow cytometric-based assay involving fluorescent labeling of cell surface-displayed glycans. Overall, this study demonstrates the promising role that metabolic modeling can play in optimizing the performance of a next-generation microbial glycosylation platform.

systems biology↗

bioRxiv2017

Ecological suicide in microbes

The growth and survival of organisms often depend on interactions between them. In many cases, these interactions are positive and caused by a cooperative modification of the environment, such as the cooperative breakdown of complex nutrients in microbes [1]-[3] or the construction of elaborate architectures in social insects [4]. However, organisms can similarly display negative interactions by changing the environment in ways that are detrimental for them, eg by resource depletion or the production of toxic byproducts [5]. Here we find an extreme type of negative interactions, in which bacteria modify the environmental pH to such a degree that it leads to a rapid extinction of the whole population, a phenomenon we call ecological suicide. Modification of the pH is more pronounced at higher population densities, and thus ecological suicide is more likely with increasing bacterial density. Correspondingly, promoting bacterial growth can drive populations extinct whereas inhibiting bacterial growth by the addition of harmful substances - like antibiotics - can rescue them. Moreover, ecological suicide can cause oscillatory dynamics, even in single-species populations. We find ecological suicide in a wide variety of microbes, suggesting that it could play a significant role in microbial ecology and evolution.

systems biology↗

bioRxiv2017

Discrete dynamical model of mechanisms determining the relations of biodiversity and stability at different levels of organization of living matter

The paper aims at building the model of relations of biodiversity and stability at different levels of organization of living matter with the use of discrete dynamical models. The relations revealed in the study are illustrated by case studies of zooplankton community of the eutrophicated lake and the colorimetric parameters of the microalgae community of phytobenthos and phytoperiphyton. The results offer: (1) new approaches to estimating the risk of mass development of toxic cyanobacteria in eutrophicated reservoirs, and (2) new indicators for unmasking animals with protective coloring on the background of plant communities by image processing of digital photos.

systems biology↗

bioRxiv2017

Integrative meta-analysis reveals that most yeast proteins are very stable

Measurements for protein half-lives in yeast Saccharomyces cerevisiae reported large discrepancies, with median values between minutes to several hours. We present a unifying analysis that provides a consistent half-life estimate, based on our re-analysis of three published and one new dataset of cells grown under similar conditions. We found that degradation of many proteins can be approximated by exponential decay. Protein disappearance was primarily driven by dilution due to cell division, with cell doubling times ranging from [~]2 to 3.5 hours across the four experiments. After adjusting for doubling time, protein half-lives increased to median values between [~]7.5 to [~]40 hours. Half-lives correlated with cell doubling time even after adjustment, implying that slow growth also slows protein degradation. All estimates were validated by multiple means and were robust to different analysis methods. Overall, protein stability correlated with abundance and showed weak enrichment for degradation signals such as degrons and disordered regions. Long-lived proteins often functioned in oxidation-reduction and amino acid synthesis. Short-lived proteins often functioned in ribosome biogenesis. Despite some overall differences in behavior, all methods were able to resolve subtle difference in half-lives of ribosomal proteins, e.g. the short lifespan of RPL10. Finally, our results help the design of future experiments: time series measurements need to cover at least two to three cell doubling times for accurate estimates, exponential decay provides a reasonable proxy for protein stability, and it can be sufficiently estimated with four measurement points.

systems biology↗