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bioRxiv2017

Deciphering Molecular Cascades In A Novel Acclimatization Strategy For Rapid Ascent To High Altitude

The repercussions of hypobaric hypoxia are dependent upon two factors-time and intensity of exposure. The effects of intensity i.e. variation of altitude are yet unknown although it is a significant factor in terms of acclimatization protocols. In this study we present the effects of acute (24 h) exposure to high (10,000 ft), very high (15,000 ft) and extreme altitude (25,000 ft) zones on lung and plasma using semi-quantitative redox specific transcripts and quantitative proteo-bioinformatics workflow in conjunction with redox stress assays. Our findings indicate that very high altitude exposure elicits systemic redox homeostatic processes due to failure of lung redox homeostasis without causing mortality. We also document a rapid acclimatization protocol causing a shift from 0 to 100% survival at 25,000 ft in male SD rats upon rapid induction. Finally we posit the various processes involved and the plasma proteins that can be used to ascertain the acclimatization status of an individual.

systems biology↗

bioRxiv2017

A Novel Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.

systems biology↗

bioRxiv2017

Dynamical predictors of an imminent phenotypic switch in bacteria

Single cells can stochastically switch across thresholds imposed by regulatory networks. Such thresholds can act as a tipping point, drastically changing global phenotypic states. In ecology and economics, imminent transitions across such tipping points can be predicted using dynamical early warning indicators. A typical example is \"flickering\" of a fast variable, predicting a longer-lasting switch from a low to a high state or vice versa. Considering the different timescales between metabolite and protein fluctuations in bacteria, we hypothesized that metabolic early warning indicators predict imminent transitions across a network threshold caused by enzyme saturation. We used stochastic simulations to determine if flickering predicts phenotypic transitions, accounting for a variety of molecular physiological parameters, including enzyme affinity, burstiness of enzyme gene expression, homeostatic feedback, and rates of metabolic precursor influx. In most cases, we found that metabolic flickering rates are robustly peaked near the enzyme saturation threshold. The degree of fluctuation was amplified by product inhibition of the enzyme. We conclude that sensitivity to flickering in fast variables may be a possible natural or synthetic strategy to prepare physiological states for an imminent transition.

systems biology↗

bioRxiv2017

Proteolytically Coordinated Activation of Toxin-Antitoxin Modules

Chronic bacterial infections present a serious threat to the health of humans by decreasing life expectancy and quality. Resilience of these populations is closely linked to a small fraction of persister cells that are capable of surviving a wide range of environmental stressors that include starvation, DNA damage, heat shock, and antibiotics. In contrast to inherited resistance, persistence arises from a rare and reversible phenotypic change that protects the cell for one or a few generations. The frequency and character of persistence is controlled in part by the dynamics of numerous toxin-antitoxin (TA) modules, operons with an evolutionarily conserved motif including a toxin that slows cell growth and an antitoxin that can neutralize the toxin. While many such modules have been identified and studied in a wide range of organisms, relatively little consideration of the interactions between multiple TA modules within a single host has been made. Particularly, a multitude of different protein-based antitoxin species are known to be actively degraded by a limited number of shared proteolytic pathways, strongly suggesting interaction via competition between antitoxins for degradation machinery. Here we present a theoretical understanding of the dynamics of multiple TA modules whose activity is coupled through either proteolytic activity, a toxic effect on cell growth rate, or both. We also present a generalizable theoretical mechanism by which a toxic state is tunable by regulation of proteolysis. Such regulation or indirect coordination between multiple TA modules may be at the heart of the flexibility and robustness observed for bacterial persistence.

systems biology↗

bioRxiv2017

Design Principle of Lysis/Lysogeny Decision vis-a-vis Multiplicity of Infection

Bacteriophage lambda makes a decision between lysis and lysogeny based on the number of coinfecting phages, namely the multiplicity of infection (MoI): lysis at low MoIs; lysogeny at high MoIs. Here, by evaluating various rationally designed models on their ability a) to make the lytic decision at MoI of 1 and the lysogeny decision at MoI of 2, b) to exhibit bistability at both MoIs, and c) to perform accurately in the presence of noise, it is demonstrated that lambdas lysis/lysogeny decision is based on three features, namely a) mutual repression, b) cooperative positive autoregulation of CI, and c) cooperative binding of the activator protein, not basal expression, triggering positive autoregulatory loop of CI. Cro and CI are sufficient to acquire the first two features. CII is required to acquire the third feature. The quasi-minimal two-protein model for the switch is justified by showing its qualitative equivalence, except for Cro repression of pRM, to the lambdas gene regulatory network responsible for the decision. A three-protein simplified version of the lambdas switch is shown to possess all the three design features. Bistability at MoI of 1 is responsible for lysogen stability, whereas bistability at MoI of 2 imparts stability to lytic development post-infection and especially during prophage induction.

systems biology↗

bioRxiv2017

Unfolding the hippocampus: An intrinsic coordinate system for subfield segmentations and quantitative mapping

The hippocampus, like the neocortex, has a morphological structure that is complex and variable in its folding pattern, especially in the hippocampal head. The current study presents a computational method to unfold hippocampal grey matter, with a particular focus on the hippocampal head where complexity is highest due to medial curving of the structure and the variable presence of digitations. This unfolding was performed on segmentations from high-resolution, T2-weighted 7T MRI data from 12 healthy participants and one surgical patient with epilepsy whose resected hippocampal tissue was used for histological validation. We traced a critical hippocampal component, the hippocampal sulcus and stratum radiatum, lacunosum moleculaire, (SRLM) in these images, then employed user-guided semi-automated techniques to detect and subsequently unfold the surrounding hippocampal grey matter. This unfolding was performed by solving Laplaces equation in three dimensions of interest (long-axis, proximal-distal, and laminar). The resulting unfolded coordinate space provides an intuitive way of mapping the hippocampal subfields in 2D space (long-axis and proximal-distal), such that similar borders can be applied in the head, body, and tail of the hippocampus independently of variability in folding. This unfolded coordinate space was employed to map intracortical myelin and thickness in relation to subfield borders, which revealed intracortical myelin differences that closely follow the subfield borders used here. Examination of a histological sample from a patient with epilepsy reveals that our unfolded coordinate system shows biological validity, and that subfield segmentations applied in this space are able to capture features not seen in manual tracing protocols.\n\nResearch highlightsO_LISRLM in hippocampal head consistently detected with 7T, T2 isotropic MRI\nC_LIO_LIHippocampal grey matter unfolded using Laplaces equation in 3D\nC_LIO_LIIntracortical myelin and thickness mapped in unfolded coordinate space\nC_LIO_LIUnfolded subfields capture critical structural regularities and agree with histology\nC_LI

neuroscience↗

bioRxiv2017

Theoretical quantification of interference in the TASEP: Application to mRNA translation shows near-optimality of termination rates

The Totally Asymmetric Exclusion Process (TASEP) is a classical stochastic model for describing the transport of interacting particles, such as ribosomes moving along the mRNA during translation. Although this model has been widely studied in the past, the extent of collision between particles and the average distance between a particle to its nearest neighbor have not been quantified explicitly. We provide here a theoretical analysis of such quantities via the distribution of isolated particles. In the classical form of the model in which each particle occupies only a single site, we obtain an exact analytic solution using the Matrix Ansatz. We then employ a refined mean field approach to extend the analysis to a generalized TASEP with particles of an arbitrary size. Our theoretical study has direct applications in mRNA translation and the interpretation of experimental ribosome profiling data. In particular, our analysis of data from S. cerevisiae suggests a potential bias against the detection of nearby ribosomes with gap distance less than ~ 3 codons, which leads to some ambiguity in estimating the initiation rate and protein production flux for a substantial fraction of genes. Despite such ambiguity, however, we demonstrate theoretically that the interference rate associated with collisions can be robustly estimated, and show that approximately 1% of the translating ribosomes get obstructed.

systems biology↗

bioRxiv2017

A Negative Feedback Regulates The Flow Of Signal Through Akt/mTORC1/S6K1 Pathway

AbstractSeveral growth factors, cytokines, hormones activate PI3K/Akt pathway. Akt is a key node in this pathway and activates different downstream paths. One such path is Akt/mTORC1/S6K1 that controls protein synthesis, cell survival, and proliferation. Here we show that a negative feedback controls activation of S6K1 through this pathway. Due to this negative feedback, a sustained phospho-Akt signal generates a transient pulse of phospho-S6K1. We have created a mathematical model for this circuit. Analysis of this model shows that the negative feedback acts as a filter and preferentially allows a signal, with sharp and faster rise of phospho-Akt, to induce pronounced activation of S6K1. It blocks an input with a fast oscillation of phospho-Akt to flow through this path. We show that this negative feedback leads to differential activation of S6K1 by Insulin and Insulin-like Growth Factor 1. Such differential effect may explain the difference in the mitogenic effect of these two molecules.

systems biology↗

bioRxiv2017

The adaptive acquisition of single DNA segments drives metabolic evolution across E. coli lineages

Even closely related prokaryotes show an astounding diversity in their ability to grow in different nutritional environments1,2. Mechanistically, this diversity arises predominantly through horizontal gene transfer, the exchange of DNA between individuals from different strains3. It has been hypothesized that complex metabolic adaptations - those requiring the acquisition of multiple distinct DNA segments - can evolve via selectively neutral intermediate steps4,5; an alternative explanation rests on the existence of intermediate environments that make each individual DNA acquisition adaptive6 However, it is unclear how important changing environments6 are compared to neutral explorations of phenotype space5; more fundamentally, it is unknown what fraction of metabolic adaptations are indeed complex. Here, we use metabolic network simulations to show that all 3,363 phenotypic innovations observed in the evolutionary history of 53 E. coli strains arose through the acquisition of a single DNA segment; while we found no evidence for the contribution of selectively neutral processes, 10.6% of adaptations to previously unviable environments relied on the support of DNA acquisitions on earlier phylogenetic branches. 97.0% of all metabolic phenotypes accessible for the E. coli pan-genome could be bestowed on any ancestral strain by transferring a single DNA segment from one of the extant strains. These results demonstrate an amazing ability of the E. coli lineage to quickly adapt to previously inaccessible environments through a single DNA acquisition, an ability likely to be mirrored in other clades of generalist bacteria.

systems biology↗

bioRxiv2017

A Rational Drug Combination Design Proves to Inhibit Epithelial-Mesenchymal Transition in a Three-Dimensional Microenvironment

Epithelial-Mesenchymal Transition (EMT) is a major player of tumor invasiveness whose inhibition is challenged by redundancy of multiple inducing factors. We applied a systems-pharmacology approach by integrating network-based analyses with multiple bioinformatic resources to design a drug combination regimen reversing EMT phenotype in aggressive cancers. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To validate the anti-EMT efficacy of the target combinations, we inhibited the pinpointed proteins with already prescribed drugs and observed that whereas low dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward co-cultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.

systems biology↗

bioRxiv2017

Variable sensitivity to DNA damaging chemotherapeutic modulated by cell type-dependent bimodal p53 dynamics

Mechanisms that determine drug sensitivity of distinct cancer types is poorly understood for most cytotoxic chemotherapy. In this study, we elucidated a new resistance mechanism to DNA damaging chemotherapeutic through modulation of p53 dynamics. While both sensitive and resistant cancer cell lines activated similar p53 oscillation followed by cell-cycle arrest in response to low dose of DNA damaging drug, they switched in a bimodal manner to monotonic or single pulse dynamics at high drug dose. Cell lines with monotonically increasing p53 underwent rapid and extensive drug-induced apoptosis, while those exhibiting a single p53 pulse mostly survived. By combining single cell imaging with computational modeling, we characterized a regulatory module involving ATM, p53, Mdm2 and Wip1, which generates bimodal p53 dynamics through coupled feed-forward and feedback, and we found that basal expression of ATM determined the differential modular output between drug sensitive and resistant lines. Moreover, we showed combinatorial inhibition of Mdm2 and Wip1 was an effective strategy to alter p53 dynamics in resistant cancer cells and sensitize their apoptotic response. Our results point to p53 pulsing as a potentially druggable mechanism that mediates resistance to cytotoxic chemotherapy.

systems biology↗

bioRxiv2017

Revisiting the General Concept of Network Centralities: A Propose for Centrality Analysis in Network Science

BackgroundNumerous centrality measures have been introduced to identify \"central\" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.\n\nResultsWe used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the networks topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.\n\nConclusionsThe choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.

systems biology↗

bioRxiv2017

Approximate inference of gene regulatory network models from RNA-Seq time series data

Inference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regression with a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variational inference scheme to learn approximate posterior distributions for the model parameters. The methodology is benchmarked on synthetic data designed to replicate the distribution of real world RNA-Seq data. We compare our method to other sparse regression approaches and information theoretic methods. We demonstrate an application of our method to a publicly available human neuronal stem cell differentiation RNA-Seq time series.

systems biology↗

bioRxiv2017

Post-translational regulation of metabolism in fumarate hydratase deficient cancer cells

Deregulated signal transduction pathways and energy metabolism are hallmarks of cancer and both play a fundamental role in the process of tumorigenesis. While it is increasingly recognised that signalling and metabolism are highly interconnected, the underpinning mechanisms of their co-regulation are still largely unknown. Here we designed and acquired proteomics, phosphoproteomics, and metabolomics experiments in fumarate hydratase (FH) deficient cells and developed a computational modelling approach to identify putative regulatory phosphorylation-sites of metabolic enzymes. We identified previously reported functionally relevant phosphosites and potentially novel regulatory residues in enzymes of the central carbon metabolism. In particular, we show that pyruvate dehydrogenase (PDHA1) enzymatic activity is inhibited by increased phosphorylation in FH-deficient cells. Our work provides a novel approach to investigate how post-translational modifications of enzymes regulate metabolism and could have important implications for understanding the metabolic transformation of FH-deficient cancers.

systems biology↗

bioRxiv2017

Guanine-nucleotide Exchange Modulator, GIV/Girdin, Serves as a Tunable Valve for Growth Factor-Stimulated Cyclic AMP Signals

Cellular levels of the versatile second messenger, cyclic-(c)AMP are regulated by the antagonistic actions of the canonical G protein[->]adenylyl cyclase pathway that is initiated by G-protein-coupled receptors (GPCRs) and by phosphodiesterases (PDEs); dysregulated cAMP signaling drives many diseases, including cancers. Recently, an alternative paradigm for cAMP signaling has emerged, in which growth factor-receptor tyrosine kinases (RTKs; e.g., EGFR) access and modulate G proteins via cytosolic guanine-nucleotide exchange modulator (GEM), GIV/Girdin; dysregulation of this pathway is frequently encountered in cancers. Here we present a comprehensive network-based compartmental model for the paradigm of GEM-dependent signaling that reveals unforeseen crosstalk and network dynamics between upstream events and the various feedback-loops that fine-tune the GEM action of GIV, and captures the experimentally determined dynamics of cAMP. The model also reveals that GIV acts a tunable control-valve within the RTK[->]cAMP pathway; hence, it modulates cAMP via mechanisms distinct from the two most-often targeted classes of cAMP modulators, GPCRs and PDEs.

systems biology↗

bioRxiv2017

ProbAnnoWeb and ProbAnnoPy: probabilistic annotation and gap-filling of metabolic reconstructions

SummaryGap-filling is a necessary step to produce quality genome-scale metabolic reconstructions capable of flux-balance simulation. Most available gap-filling tools use an organism-agnostic approach, where reactions are selected from a database to fill gaps without consideration of the target organism. Conversely, our likelihood based gap-filling with probabilistic annotations selects candidate reactions based on a likelihood score derived specifically from the target organisms genome. Here, we present two new implementations of probabilistic annotation and likelihood based gap-filling: a web service called ProbAnnoWeb, and a standalone python package called ProbAnnoPy.\n\nAvailability and ImplementationOur tools are available as a web service with no installation needed (ProbAnnoWeb), available at http://probannoweb.systemsbiology.net, and as a local python package implementation (ProbAnnoPy), available for download at http://github.com/PriceLab/probannopy.\n\nContacthttp://Evangelos.Simeonidis@systemsbiology.org; http://Nathan.Price@systemsbiology.org

systems biology↗

bioRxiv2017

Transcription Factor Target Gene Network governs the Logical Abstraction Analysis of the Synthetic Circuit in Leishmaniasis

Stochastic variations in the transcription factor target gene network influences the dynamics of protein levels. The mathematical model built, here, is useful to study the cytokine response and the resulting dynamical patterns in leishmaniasis. The fluctuations produced affect the expression rate of its target in the regulatory synthetic circuit designed by means of a negative feedback loop insertion. Thus, the faster the response time, smaller is the fluctuation of the system observed and it can be justified that the TFTG network embedded can be understood with the recurring dynamics of the designed synthetic circuit.

systems biology↗

bioRxiv2017

Developmental switching in Physarum polycephalum: Petri net analysis of single cell trajectories of gene expression indicates responsiveness and genetic plasticity of the Waddington quasipotential landscape

The developmental switch to sporulation in Physarum polycephalum is a phytochrome-mediated far-red light-induced cell fate decision that synchronously encompasses the entire multinucleate plasmodial cell and is associated with extensive reprogramming of the transcriptome. By repeatedly taking samples of single cells after delivery of a light stimulus pulse, we analysed differential gene expression in two mutant strains and in a heterokaryon of the two strains all of which display a different propensity for making the cell fate decision. Multidimensional scaling of the gene expression data revealed individually different single cell trajectories eventually leading to sporulation. Characterization of the trajectories as walks through states of gene expression discretized by hierarchical clustering allowed the reconstruction of Petri nets that model and predict the observed behavior. Structural analyses of the Petri nets indicated stimulus- and genotype-dependence of both, single cell trajectories and of the quasipotential landscape through which these trajectories are taken. The Petri net-based approach to the analysis and decomposition of complex cellular responses and of complex mutant phenotypes may provide a scaffold for the data-driven reconstruction of causal molecular mechanisms that shape the topology of the quasipotential landscape.

systems biology↗