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bioRxiv2018

Mathematical Modelling of Actin treadmill in Apicomplexans

Plasmodium parasite, a representative member of phylum Apicomplexa is a causative agent of malaria in human as well as other animals. To infect host cells, Plasmodium first finds receptors on the host cell surface, then binds specifically, and finally penetrates host cell membrane to acquire the host cellular resources. The motility for moving on the cell surface is equipped by the precise and tight control of actin treadmill. Several regulators are required to achieve precision and robustness in the control of actin treadmill. However, the mechanistic detail of the treadmill regulatory network and the cross-talk among regulators are not well understood. We developed a stochastic model of treadmill regulation and explored the dynamics of filament growth, nucleation time, and elongation time. Our study mainly highlighted on how and what helps cells to maintain an average size of the actin filaments within a species. This is particularly important, since, excessive growth of filament can lead to cell lysis. Moreover, we also explore how the regulators interact to fine-tune the control elements in the actin treadmill.

systems biology↗

bioRxiv2018

Two parallel pathways implement robust propionate catabolism and detoxification in mycobacteria

Tuberculosis remains a major global health threat with over 1.5 million deaths each year. Mycobacterium tuberculosis success story is related to a flexible metabolism, allowing growth despite restrictive conditions within the human host.\n\nHost lipids stores are a major carbon source in vivo. Their catabolism yields propionyl-CoA, which is processed by two parallel pathways, the methylmalonyl CoA pathway and the methylcitrate pathway. Both pathways are considered potential drug targets. The methylcitrate pathway is upregulated in the pathological context. However, intermediates of this pathway can be cytotoxic and Mtbs preference for its usage remains unclear.\n\nWe combine thermodynamic kinetic modeling, quantitative proteomics and time-resolved metabolomics to characterize the interplay between the two pathways and to show their functionalities in an efficient and fast propionate catabolism.\n\nWe find that the methylcitrate pathway acts as a transcriptionally regulated, high capacity catabolic pathway due to its favorable thermodynamics and metabolic control distribution. In contrast, the methylmalonyl pathway is constitutively fulfilling biosynthetic tasks and can quickly detoxify propionate pulses, but is thermodynamically restricted to lower capacity.

systems biology↗

bioRxiv2018

Determination of growth-coupling strategies and their underlying principles

Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. We have developed the computational tool gcOpt, which identifies knockout strategies leading to the best possible GC by maximizing the minimally guaranteed product yield. gcOpt implicitly favors solutions resulting in strict coupling of product synthesis to growth and metabolic activity while avoiding solutions inferring weak, conditional coupling.\n\nGC intervention strategies identified by gcOpt were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies. Thus, generating a dependency between supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC.\n\nAbbreviations

systems biology↗

bioRxiv2018

Reprogramming protein abundance fluctuations in single cells by degradation

Isogenic cells living in the same environment show a natural heterogeneity associated with fluctuations in gene expression. When these fluctuations propagate through cellular regulatory networks, they can give rise to noise regulons, whereby multiple genes fluctuate in a coordinated fashion in single cells. The propagation of these fluctuations has been extensively characterized at the transcriptional level. For example, variations in transcription factor concentration induce correlated fluctuations in the abundance of target gene products. Here, we find that such noise regulons can also stem from protein degradation. We expressed pairs of yellow and red fluorescent proteins, subjected them to differential translation or degradation, and analyzed their fluctuations in single cells. While differential translation had little impact on fluctuations, protein degradation was found to be a dominant contributor. A mathematical model to decompose fluctuations arising from multiple sources of regulation revealed that cells with higher protein production capacity also exhibited higher protein degradation capacity. This association uncouples fluctuations in protein abundance from fluctuations in production rate, and can generate orthogonal noise regulons even for proteins relying on the same transcriptional program.

systems biology↗

bioRxiv2018

A Kirchhoff-Nernst-Planck framework for modeling large scale extracellular electrodiffusion surrounding morphologically detailed neurons

1Many pathological conditions, such as seizures, stroke, and spreading depression, are associated with substantial changes in ion concentrations in the extracellular space (ECS) of the brain. An understanding of the mechanisms that govern ECS concentration dynamics may be a prerequisite for understanding such pathologies. To estimate the transport of ions due to electrodiffusive effects, one must keep track of both the ion concentrations and the electric potential simultaneously in the relevant regions of the brain. Although this is currently unfeasible experimentally, it is in principle achievable with computational models based on biophysical principles and constraints. Previous computational models of extracellular ion-concentration dynamics have required extensive computing power, and therefore have been limited to either phenomena on very small spatiotemporal scales (micrometers and milliseconds), or simplified and idealized 1-dimensional (1-D) transport processes on a larger scale. Here, we present the 3-D Kirchhoff-Nernst-Planck (KNP) framework, tailored to explore electrodiffusive effects on large spatiotemporal scales. By assuming electroneutrality, the KNP-framework circumvents charge-relaxation processes on the spatiotemporal scales of nanometers and nanoseconds, and makes it feasible to run simulations on the spatiotemporal scales of millimeters and seconds on a standard desktop computer. In the present work, we use the 3-D KNP framework to simulate the dynamics of ion concentrations and the electrical potential surrounding a morphologically detailed pyramidal cell. In addition to elucidating the single neuron contribution to electrodiffusive effects in the ECS, the simulation demonstrates the efficiency of the 3-D KNP framework. We envision that future applications of the framework to more complex and biologically realistic systems will be useful in exploring pathological conditions associated with large concentration variations in the ECS.\n\n2 Author summaryMany pathological conditions, such as epilepsy and cortical spreading depression, are linked to abnormal extracellular ion concentrations in the brain. Understanding the underlying principles of such conditions may prove important in developing treatments for these illnesses, which incur societal costs of tens of billions annually. In order to investigate the role of ion-concentration dynamics in the pathological conditions, one must measure the spatial distribution of all ion concentrations over time. This remains challenging experimentally, which makes computational modeling an attractive tool. We have previously introduced the Kirchhoff-Nernst-Planck framework, an efficient framework for modeling electrodiffusion. In this study, we introduce a 3-dimensional version of this framework and use it to model the electrodiffusion of ions surrounding a morphologically detailed neuron. The simulation covered a 1 mm3 cylinder of tissue for over a minute and was performed in less than a day on a standard desktop computer, demonstrating the frameworks efficiency. We believe this to be an important step on the way to understanding phenomena involving ion concentration shifts at the tissue level.

neuroscience↗

bioRxiv2018

Computational elucidation of regulatory network responding to acid stress in Lactococcus lactis MG1363

Acid stress caused by lactate increment can lead to the growth inhibition of bacteria and yes has not been fully defined. Regulons, serve as co-regulated gene groups contribute to the transcriptional regulation of microbe genome, have the potential in understanding the underlying regulatory mechanism. Lactococcus lactis is one of the most important Gram-positive lactic acid-producing bacteria, widely used in food industry and has been proved to have advantages in oral delivery of drug and vaccine. In this study, we designed a novel computational pipeline, RECTA, for regulon prediction. The pipeline carried out differentially expressed gene prediction, gene co-expression analysis, cis-regulatory motif finding, and comparative genomic study to predict and validate regulons related to acid stress response in Lactococcus lactis MG1363. A total of 51 regulons were identified, and 14 of them have computational verified significance. Among these 14 regulons, five of them were computationally predicted to be connected with acid stress response with (i) known transcriptional factors in MEME suite database successfully mapped in Lactococcus lactis MG1363; and (ii) differentially expressed genes between pH values of 6.5 (control) and 5.1 (treatment). Validated by 36 literature confirmed acid stress response related proteins and genes, 33 genes in Lactococcus lactis MG1363 were found having orthologous genes using BLAST, associated to six regulons. An acid response related regulatory network was constructed, involving two trans-membrane proteins, eight regulons (llrA, llrC, hllA, ccpA, NHP6A, rcfB, regulons #8 and #39), nine functional modules, and 33 genes with orthologous genes known to be associated to acid stress. Our RECTA pipeline provides an effective way to construct a reliable gene regulatory network based on regulon elucidation. The predicted resistance pathways could serve as promising candidates for better acid tolerance engineering in Lactococcus lactis. It has a strong application power and can be effectively applied to other bacterial genomes, where the elucidation of the transcriptional regulation network is needed.

systems biology↗

bioRxiv2018

Identification and characterization of moonlighting long non-coding RNAs based on RNA and protein interactome

Moonlighting proteins are a class of proteins having multiple distinct functions, which play essential roles in a variety of cellular and enzymatic functioning systems. Although there have long been calls for computational algorithms for the identification of moonlighting proteins, research on approaches to identify moonlighting long non-coding RNAs (lncRNAs) has never been undertaken. Here, we introduce a methodology, MoonFinder, for the identification of moonlighting lncRNAs. MoonFinder is a statistical algorithm identifying moonlighting lncRNAs without a priori knowledge through the integration of protein interactome, RNA-protein interactions, and functional annotation of proteins. We identify 155 moonlighting lncRNA candidates and uncover that they are a distinct class of lncRNAs characterized by specific sequence and cellular localization features. The non-coding genes that transcript moonlighting lncRNAs tend to have shorter but more exons and the moonlighting lncRNAs have a localization tendency of residing in the cytoplasmic compartment in comparison with the nuclear compartment. Moreover, moonlighting lncRNAs and moonlighting proteins are rather mutually exclusive in terms of both their direct interactions and interacting partners. Our results also shed light on how the moonlighting candidates and their interacting proteins implicated in the formation and development of cancers and other diseases.

systems biology↗

bioRxiv2018

Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis

The intestinal epithelium is a highly structured tissue composed of repeating crypt-villus units1,2. Enterocytes, which constitute the most abundant cell type, perform the diverse tasks of absorbing a wide range of nutrients while protecting the body from the harsh bacterial-rich environment. It is unknown if these tasks are equally performed by all enterocytes or whether they are spatially zonated along the villus axis3. Here, we performed whole-transcriptome measurements of laser-capture-microdissected villus segments to extract a large panel of landmark genes, expressed in a zonated manner. We used these genes to localize single sequenced enterocytes along the villus axis, thus reconstructing a global spatial expression map. We found that most enterocyte genes were zonated. Enterocytes at villi bottoms expressed an anti-bacterial Reg gene program in a microbiome-dependent manner, potentially reducing the crypt pathogen exposure. Translation, splicing and respiration genes steadily decreased in expression towards the villi tops, whereas distinct mid-top villus zones sub-specialized in the absorption of carbohydrates, peptides and fat. Enterocytes at the villi tips exhibited a unique gene-expression signature consisting of Klf4, Egfr, Neat1, Malat1, cell adhesion and purine metabolism genes. Our study exposes broad spatial heterogeneity of enterocytes, which could be important for achieving their diverse tasks.

systems biology↗

bioRxiv2018

Emergence of the erythroid lineage from multipotent hematopoiesis

Red cell formation begins with the hematopoietic stem cell, but the manner by which it gives rise to erythroid progenitors, and their subsequent developmental path, remain unclear. Here we combined single-cell transcriptomics of murine hematopoietic tissues with fate potential assays to infer a continuous yet hierarchical structure for the hematopoietic network. We define the erythroid differentiation trajectory as it emerges from multipotency and diverges from 6 other blood lineages. With the aid of a new flow-cytometric sorting strategy, we validated predicted cell fate potentials at the single cell level, revealing a coupling between erythroid and basophil/mast cell fates. We uncovered novel growth factor receptor regulators of the erythroid trajectory, including the proinflammatory IL-17RA, found to be a strong erythroid stimulator; and identified a global hematopoietic response to stress erythropoiesis. We further identified transcriptional and high-purity FACS gates for the complete isolation of all classically-defined erythroid burst-forming (BFU-e) and colony-forming progenitors (CFU-e), finding that they express a dedicated transcriptional program, distinct from that of terminally-differentiating erythroblasts. Intriguingly, profound remodeling of the cell cycle is intimately entwined with CFU-e developmental progression and with a sharp transcriptional switch that extinguishes the CFU-e stage and activates terminal differentiation. Underlying these results, our work showcases the utility of theoretic approaches linking transcriptomic data to predictive fate models, providing key insights into lineage development in vivo.

systems biology↗

bioRxiv2018

Computational model of chimeric antigen receptors explains site-specific phosphorylation kinetics

Chimeric antigen receptors (CARs) have recently been approved for the treatment of hematological malignancies, but our lack of understanding of the basic mechanisms that activate these proteins has made it difficult to optimize and control CAR-based therapies. In this study, we use phospho-proteomic mass spectrometry and mechanistic computational modeling to quantify the in vitro kinetics of individual tyrosine phosphorylation on a variety of CARs. We show that each of the ten tyrosine sites on the CD28-CD3{zeta} CAR is phosphorylated by LCK with distinct kinetics. The addition of CD28 at the N-terminal of CD3{zeta} increases the overall rate of CD3{zeta} phosphorylation. Our computational model identifies that LCK phosphorylates CD3{zeta} through a mechanism of competitive inhibition. This model agrees with previously published data in the literature and predicts that phosphatases in this system interact with CD3{zeta} through a similar mechanism of competitive inhibition. This quantitative modeling framework can be used to better understand CAR signaling and T cell activation.

systems biology↗

bioRxiv2018

Clonal Level Lineage Commitment Pathways of Hematopoietic Stem Cells In Vivo

While hematopoietic stem cells (HSCs) have been extensively studied at the population level, little is known about the lineage commitment of individual clones. Here, we provide comprehensive maps of in vivo HSC clonal development in mice under homeostasis and after depletion of the endogenous hematopoietic system. Under homeostasis, all donor-derived HSC clones regenerate blood homogeneously throughout all measured stages and lineages of hematopoiesis. In contrast, after the hematopoietic system has been depleted by irradiation or by an anti-ckit antibody, only a small fraction of donor-derived HSC clones differentiates while dominantly expanding and exhibiting lineage bias. We identified the cellular origins of clonal dominance and lineage bias, and uncovered the lineage commitment pathways that lead HSC clones to differential blood production. This study reveals surprising alterations in HSC regulation by irradiation, and identifies the key hematopoiesis stages that may be manipulated to control blood production and balance.\n\nSIGNIFICANCE STATEMENTHematopoietic stem cells (HSCs) sustain daily blood production through a complex step-wise lineage commitment process. In this work, we present the first comprehensive study of HSC lineage commitment at the clonal level and identify new HSC regulatory mechanisms that are undetectable by conventional population level studies. First, we uncover distinct HSC clonal pathways that lead to differential blood production and imbalances. Second, we reveal that HSC regulation under physiological conditions is strikingly different from that after injury. Third, we present a comprehensive map of HSC activities in vivo at the clonal level.

systems biology↗

bioRxiv2018

Multi-Omic Profiling Reveals the Opposing Forces of Excess Dietary Sugar and Fat on Liver Mitochondria Protein Acetylation and Succinylation

Dietary macronutrient composition alters metabolism through several mechanisms, including post-translational modification (PTM) of proteins. To connect diet and molecular changes, here we performed short- and long-term feeding of mice with standard chow diet (SCD) and high-fat diet (HFD), with or without glucose or fructose supplementation, and quantified liver metabolites, 861 proteins, and 1,815 protein level-corrected mitochondrial acetylation and succinylation sites. Nearly half the acylation sites were altered by at least one diet; nutrient-specific changes in protein acylation sometimes encompass entire pathways. Although acetyl-CoA is an intermediate in both sugar and fat metabolism, acetyl-CoA had a dichotomous fate depending on its source; chronic feeding of dietary sugars induced protein hyperacetylation, whereas the same duration of HFD did not. Instead, HFD resulted in citrate accumulation, anaplerotic metabolism of amino acids, and protein hypo-succinylation. Together, our results demonstrate novel connections between dietary macronutrients, protein post-translational modifications, and regulation of fuel selection in liver.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/263426_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (53K):\norg.highwire.dtl.DTLVardef@1419c64org.highwire.dtl.DTLVardef@82a874org.highwire.dtl.DTLVardef@16023org.highwire.dtl.DTLVardef@4ed44e_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract\n\nC_FIG

systems biology↗

bioRxiv2018

Transition State Characteristics During Cell Differentiation

Models describing the process of stem-cell differentiation are plentiful, and may offer insights into the underlying mechanisms and experimentally observed behaviour. Waddingtons epigenetic landscape has been providing a conceptual framework for differentiation processes since its inception. It also allows, however, for detailed mathematical and quantitative analyses, as the landscape can, at least in principle, be related to mathematical models of dynamical systems. Here we focus on a set of dynamical systems features that are intimately linked to cell differentiation, by considering exemplar dynamical models that capture important aspects of stem cell differentiation dynamics. These models allow us to map the paths that cells take through gene expression space as they move from one fate to another, e.g. from a stem-cell to a more specialized cell type. Our analysis highlights the role of the transition state (TS) that separates distinct cell fates, and how the nature of the TS changes as the underlying landscape changes-- change that can be induced by e.g. cellular signalling. We demonstrate that models for stem cell differentiation may be interpreted in terms of either a static or transitory landscape. For the static case the TS represents a particular transcriptional profile that all cells approach during differentiation. Alternatively, the TS may refer to the commonly observed period of heterogeneity as cells undergo stochastic transitions.

systems biology↗

bioRxiv2018

Evidences of conditioned behavior in Amoeba proteus

Associative memory is the main type of learning wherein complex organisms endowed with evolved nervous systems respond efficiently to determined environmental stimuli. This fundamental cognitive property has been evidenced in different multicellular species, from cephalopods to Humans, but never in individual cells. Here, following Pavlovs experiments with dogs that founded the principles of classical conditioning, we have observed the development of an associative memory in Amoeba proteus, which corresponds to the emergence of a new systemic motility pattern. In our cellular version of this conditioning behavior, we have used a controlled direct current electric field as the conditioned stimulus and a specific chemotactic peptide as the non-conditioned stimulus. Our study allowed us to demonstrate that Amoeba proteus are capable of linking two independent past events, and the induced associative memory can be recorded for up to at least four hours. For the first time, it has been observed that a systemic response to a specific stimulus can be modified by learning in unicellular organisms. This finding opens up a new framework in the understanding of the mechanisms underlying the complex systemic behavior involved in the cellular migration and the adaptive capacity of cells to the external medium.

systems biology↗

bioRxiv2018

An integrated approach to comprehensively map the molecular context of proteins

Protein-protein interactions underlie almost all cellular functions. The comprehensive mapping of these complex cellular networks of stable and transient associations has been made available by affinity purification mass spectrometry (AP-MS) and more recently by proximity based labelling methods such as BioID. Due the advancements in both methods and MS instrumentation, an in-depth analysis of the whole human proteome is at grasps. In order to facilitate this, we designed and optimized an integrated approach utilizing MAC-tag combining both AP-MS and BioID in a single construct. We systematically applied this approach to 18 subcellular localization markers and generated a molecular context database, which can be used to define molecular locations for any protein of interest. In addition, we show that by combining the AP-MS and BioID results we can also obtain interaction distances within a complex. Taken together, our combined strategy offers comprehensive approach for mapping physical and functional protein interactions.

systems biology↗

bioRxiv2018

Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data

Gene expression is controlled by networks of transcription factors and regulators, but the structure of these networks is as yet poorly understood and is thus inferred from data. Recent work has shown the efficacy of information theoretical approaches for network reconstruction from single cell transcriptomic data. Such methods use information to estimate dependence between every pair of genes in the dataset, then edges are inferred between top-scoring pairs. Dependence, however, does not indicate significance, and the definition of \"top-scoring\" is often arbitrary and a priori related to expected network size. This makes comparing networks across datasets difficult, because networks of a similar size are not necessarily similarly accurate. We present a method for performing formal hypothesis tests on putative network edges derived from information theory, bringing together empirical Bayes and work on theoretical null distributions for information measures. Thresholding based on empirical Bayes allows us to control network accuracy according to how we intend to use the network. Using single cell data from mouse pluripotent stem cells, we recover known interactions and suggest several new interactions for experimental validation (using a stringent threshold) and discover high-level interactions between sub-networks (using a more relaxed threshold). Furthermore, our method allows for the inclusion of prior information. We use in-silico data to show that even relatively poor quality prior information can increase the accuracy of a network, and demonstrate that the accuracy of networks inferred from single cell data can sometimes be improved by priors from population-level ChIP-Seq and qPCR data.

systems biology↗

bioRxiv2018

Escherichia coli can survive stress by noisy growth modulation

Gene expression can be noisy1,2, as can the growth of single cells3,4. Such cell-to-cell variation has been implicated in survival strategies for bacterial populations5-7. However, it remains unclear how single cells couple gene expression with growth to implement these survival strategies. Here we show how noisy expression of a key stress response regulator, rpoS8, allows E. coli to modulate its growth dynamics to survive future adverse environments. First, we demonstrate that rpoS has a long-tailed distribution of expression in an unstressed population of cells. We next reveal how a dynamic positive feedback loop between rpoS and growth rate produces multi-generation rpoS pulses, which are responsible for the rpoS heterogeneity. We do so experimentally with single-cell, time-lapse microscopy9 and microfluidics10 and theoretically with a stochastic model11,22. Finally, we demonstrate the function of the coupling of heterogeneous rpoS activity and growth. It enables E. coli to survive oxidative attack by causing prolonged periods of slow growth. This dynamic phenotype is captured by the rpoS-growth feedback model. Our synthesis of noisy gene expression, growth, and survival paves the way for further exploration of functional phenotypic variability.

systems biology↗

bioRxiv2018

Delineating feedback activity in the MAPK and AKT pathways using feedback-enabled Inference of Signaling Activity

An important aspect of cellular signaling networks is the existence of feedback mechanisms. However, due to the complexity of signaling networks, as well as the presence of multiple interrelated feedback events, it can be difficult to identify which signaling routes are active in any particular context. We have previously shown that Inference of Signaling Activity (ISA) can be a useful method to study steady-state oncogenic signaling across different cell lines and inhibitor treatments. However, ISA did not explicitly include feedback signaling events. Incorporating feedback will increase the complexity and computational cost of the model, and more data is likely to be needed to infer feedback activities. Here, we developed feedback-ISA (f-ISA), an extension of the ISA modeling approach which incorporates feedback signaling events. It also includes integrated batch correction in order to fit the models to multiple, independent datasets simultaneously. We find that the identifiability of feedback activities can be counter-intuitive, which shows the importance of analyzing the full, joint uncertainty in model parameters. By iteratively adapting the model and including multiple datasets, including both steady state and intervention data, we constructed a model that can explain a large part of the phosphorylation levels of several signaling molecules in the MAPK and AKT pathways, across many breast cancer cell lines and across various conditions. The resulting model delineates which routes in the signaling network are likely to be active in each cell line and condition, given all of the data. Additionally, such models can indicate whether datasets agree with each other, and identify which parts of the data cannot be explained, thereby highlighting gaps in the current knowledge. We conclude that this modeling approach can be useful to quantitatively understand how complex cellular signaling networks behave across different cell lines and conditions.

systems biology↗