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bioRxiv2018

Self-organization of conducting pathways explains electrical wave propagation in cardiac tissues with high fibrosis

Cardiac fibrosis occurs in many forms of heart disease and is considered to be one of the main arrhythmogenic factors. Regions with a high density of fibrosis are likely to cause blocks of wave propagation that give rise to dangerous cardiac arrhythmias. Therefore, studies of the wave propagation through these regions are very important, yet the precise mechanisms leading to arrhythmia formation in fibrotic cardiac tissue remain poorly understood. Particularly, it is not clear how wave propagation is organized at the cellular level, as experiments show that the regions with a high percentage of fibrosis (65-75%) are still conducting electrical signals, whereas geometric analysis of randomly distributed cells predicts connectivity loss at 40% at the most (percolation threshold). To address this question, we used a joint in vitro-in silico approach, which combined experiments in neonatal rat cardiac monolayers with morphological and electrophysiological computer simulations. We have shown that the main reason for sustainable wave propagation in highly fibrotic samples is the formation of a branching network of cardiomyocytes. We have successfully reproduced the morphology of conductive pathways in computer modelling, assuming that cardiomyocytes align their cytoskeletons to fuse into cardiac syncytium. The electrophysiological properties of the monolayers, such as conduction velocity, conduction blocks and wave fractionation, were reproduced as well. In a virtual cardiac tissue, we have also examined the wave propagation at the subcellular level, detected wavebreaks formation and its relation to the structure of fibrosis and, thus, analysed the processes leading to the onset of arrhythmias.\n\nAuthor summaryCardiac arrhythmias are one of the major causes of death in the industrialized world. The most dangerous ones are often caused by the blocks of propagation of electrical signals. One of the common factors that contribute to the likelihood of these blocks, is a condition called cardiac fibrosis. In fibrosis, excitable cardiac tissue is partially replaced with the inexcitable connective tissue. The precise mechanisms leading to arrhythmia formation in fibrotic cardiac tissue remain poorly understood. Therefore, it is important to study wave propagation in fibrosis from cellular to tissue level. In this paper, we study fibrosis of high density in experiments and computer simulations. We have observed a paradoxical ability of the tissue with extremely high fibrosis (up to 75% of fibroblasts) to conduct electrical signals and contract synchronously, whereas geometric analysis of randomly distributed cells predicted connectivity loss at 40% at the most. To explain this phenomenon, we have studied the patterns that cardiac cells form in the tissue and reproduced their self-organisation in a computer model. Our virtual model also took into account the polygonal shapes of the spreading cells and explained high arrhythmogenicity of fibrotic tissue.

systems biology↗

bioRxiv2018

Analysing protein post-translational modform regions by linear programming

Post-translational modifications (PTMs) at multiple sites can collectively influence protein function but the scope of such PTM coding has been challenging to determine. The number of potential combinatorial patterns of PTMs on a single molecule increases exponentially with the number of modification sites and a population of molecules exhibits a distribution of such \"modforms\". Estimating these \"modform distributions\" is central to understanding how PTMs influence protein function. Although mass-spectrometry (MS) has made modforms more accessible, we have previously shown that current MS technology cannot recover the modform distribution of heavily modified proteins. However, MS data yield linear equations for modform amounts, which constrain the distribution within a high-dimensional, polyhedral \"modform region\". Here, we show that linear programming (LP) can efficiently determine a range within which each modform value must lie, thereby approximating the modform region. We use this method on simulated data for mitogen-activated protein kinase 1 with the 7 phosphorylations reported on UniProt, giving a modform region in a 128 dimensional space. The exact dimension of the region is determined by the number of linearly independent equations but its size and shape depend on the data. The average modform range, which is a measure of size, reduces when data from bottom-up (BU) MS, in which proteins are first digested into peptides, is combined with data from top-down (TD) MS, in which whole proteins are analysed. Furthermore, when the modform distribution is structured, as might be expected of real distributions, the modform region for BU and TD combined has a more intricate polyhedral shape and is substantially more constrained than that of a random distribution. These results give the first insights into high-dimensional modform regions and confirm that fast LP methods can be used to analyse them. We discuss the problems of using modform regions with real data, when the actual modform distribution will not be known.

systems biology↗

bioRxiv2018

Delays, behaviour and transmission: modelling the impact of effective private provider engagement on tuberculosis control in urban India

In India, the country with the worlds largest burden of tuberculosis (TB), most patients first seek care in the private healthcare sector, which is fragmented and unregulated. Ongoing initiatives are demonstrating effective approaches for engaging with this sector, and form a central part of Indias recent National Strategic Plan: here we aimed to address their potential impact on TB transmission in urban settings, when taken to scale. We developed a mathematical model of TB transmission dynamics, calibrated to urban populations in Mumbai and Patna, two major cities in India where pilot interventions are currently ongoing.\n\nWe found that, when taken to sufficient scale to capture 75% of patient-provider interactions, the intervention could reduce incidence by upto 21.3% (95% Bayesian credible interval (CrI) 13.0 - 32.5%) and 15.8% (95% CrI 7.8 - 28.2%) in Mumbai and Patna respectively, between 2018 and 2025. There is a stronger impact on TB mortality, with a reduction of up to 38.1% (95% CrI 20.0 - 55.1%) in the example of Mumbai. The incidence impact of this intervention alone may be limited by the amount of transmission that has already occurred by the time a patient first presents for care: model estimates suggest an initial patient delay of 4-5 months before first seeking care, followed by a diagnostic delay of 1-2 months before ultimately initiating TB treatment. Our results suggest that the transmission impact of such interventions could be maximised by additional measures to encourage early uptake of TB services.

systems biology↗

bioRxiv2018

Timing Polymerase Pausing with TV-PRO-seq

Transcription of many genes in metazoans is subject to polymerase pausing, which is the transient stop of transcriptionally-engaged polymerases. This is known to mainly occur in promoter proximal regions but it is not well understood. In particular, a genome-wide measurement of pausing times at high resolution has been lacking. We present here the time-variant precision nuclear run-on and sequencing (TV-PRO-seq) assay, an extension of the standard PRO-seq that allows us to estimate genome-wide pausing times at single-base resolution. Its application to human cells demonstrates that, proximal to promoters, polymerases pause more frequently but for shorter times than in other genomic regions. Pausing release by the detergent sarkosyl, previously believed to be linked to the factor NELF at the promoter proximal region only, is independent of the latter. Comparison with single-cell gene expression data reveals that the polymerase pausing times are longer in highly expressed genes, while transcriptionally noisier genes have higher pausing frequencies and slightly longer pausing times. Analyses of histone modifications suggest that the marker H3K36me3 is related to the polymerase pausing.

systems biology↗

bioRxiv2018

Cellular response to small molecules that selectively stall protein synthesis by the ribosome

Identifying small molecules that inhibit protein synthesis by selectively stalling the ribosome constitutes a new strategy for therapeutic development. Compounds that inhibit the translation of PCSK9, a major regulator of low-density lipoprotein cholesterol, have been identified that reduce LDL cholesterol in preclinical models and that affect the translation of only a few off-target proteins. Although some of these compounds hold potential for future therapeutic development, it is not known how they impact the physiology of cells or ribosome quality control pathways. Here we used a genome-wide CRISPRi screen to identify proteins and pathways that modulate cell growth in the presence of high doses of a selective PCSK9 translational inhibitor, PF-06378503 (PF8503). The two most potent genetic modifiers of cell fitness in the presence of PF8503, the ubiquitin binding protein ASCC2 and helicase ASCC3, bind to the ribosome and protect cells from toxic effects of high concentrations of the compound. Surprisingly, translation quality control proteins Pelota (PELO) and HBS1L sensitize cells to PF8503 treatment. In genetic interaction experiments, ASCC3 acts together with ASCC2, and functions downstream of HBS1L. Taken together, these results identify new connections between ribosome quality control pathways, and provide new insights into the selectivity of compounds that stall human translation that will aid the development of next-generation selective translation stalling compounds to treat disease.

systems biology↗

bioRxiv2018

Noise induced bimodality in genetic circuits with monostable positive feedback

The expression of individual genes can be maintained through positive feedback loop mechanisms. If genes are expressed in bursts, then feedback either affects the frequency with which bursts occur or their size. Here we use a tractable hybrid modelling framework to evaluate how noncooperative positive feedback in burst frequency or burst size impacts the protein-level distribution. We confirm the results of previous studies that noncooperative positive feedback in burst frequency can support bimodal distributions. Intriguingly, bimodal distributions are unavailable in the case of feedback in burst size in the hybrid framework. However, kinetic Monte Carlo simulations of a full discrete model show that bimodality can reappear due to low-copy number effects. The two types of feedbacks lead to dramatically different values of protein mean and noise. We show that small values of leakage imply a small protein mean for feedback in burst frequency but not necessarily for feedback in burst size. We also show that protein noise decreases in response to gene activation if feedback is in burst frequency but there is a transient noise amplification if feedback acts on burst size. Our results suggest that feedback in burst size and feedback in burst frequency may play fundamentally different roles in maintaining and controlling stochastic gene expression.

systems biology↗

bioRxiv2018

Rational Discovery of Dual-Action Multi-Target Kinase Inhibitor for Precision Anti-Cancer Therapy Using Structural Systems Pharmacology

Although remarkable progresses have been made in the cancer treatment, existing anti-cancer drugs are associated with increasing risk of heart failure, variable drug response, and acquired drug resistance. To address these challenges, for the first time, we develop a novel genome-scale multi-target screening platform 3D-REMAP that integrates data from structural genomics and chemical genomics as well as synthesize methods from structural bioinformatics, biophysics, and machine learning. 3D-REMAP enables us to discover marked drugs for dual-action agents that can both reduce the risk of heart failure and present anti-cancer activity. 3D-REMAP predicts that levosimendan, a drug for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to identify cancer cell-lines and patients that may respond to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe and effective multi-targeted cancer therapy, and demonstrate the potential of genome-wide multi-target screening in designing polypharmacology and drug repurposing for precision medicine.\n\nAuthor SummaryMulti-target drug design (a.k.a targeted polypharmacology) has emerged as a new strategy for discovering novel therapeutics that can enhance therapeutic efficacy and overcome drug resistance in tackling multi-genic diseases such as cancer. However, it is extremely challenging for conventional computational tools that are either receptor-based or ligand-based to screen compounds for selectively targeting multiple receptors. Existing multi-target drug design mainly focuses on compound screening against receptors within the same gene family but not across different gene families. Here, we develop a new computational tool 3D-REMAP that enables us to identify chemical-protein interactions across fold space on a genome scale. The genome-scale chemical-protein interaction network allows us to discover dual-action drugs that can bind to two types of targets simultaneously, one for mitigating side effect and another for enhancing the therapeutic effect. Using 3D-REMAP, we predict and subsequently experiments validate that levosimendan, a drug for heart failure, is active against multiple cancers, notably, lymphoma. This study demonstrates the potential of genome-wide multi-target screening in designing polypharmacology and drug repurposing for precision medicine.

systems biology↗

bioRxiv2018

Metastable regimes and tipping points of biochemical networks with potential applications in precision medicine

The concept of attractor of dynamic biochemical networks has been used to explain cell types and cell alterations in health and disease. We have recently proposed an extension of the notion of attractor to take into account metastable regimes, de[fi]ned as long lived dynamical states of the network. These regimes correspond to slow dynamics on low dimensional invariant manifolds of the biochemical networks. Methods based on tropical geometry allow to compute the metastable regimes and represent them as polyhedra in the space of logarithms of the species concentrations. We are looking for sensitive parameters and tipping points of the networks by analyzing how these polyhedra depend on the model parameters. Using the coupled MAPK and PI3K/Akt signaling networks as an example, we test the idea that large changes of the metastable states can be associated to cancer disease speci[fi]c alterations of the network. In particular, we show that for model parameters representing protein concentrations, the protein differential level between tumors of different types is reasonably re[fl]ected in the sensitivity scores, with sensitive parameters corresponding to differential proteins.

systems biology↗

bioRxiv2018

Bayesian estimation for stochastic gene expression using multifidelity models

The finite state projection (FSP) approach to solving the chemical master equation has enabled successful inference of discrete stochastic models to predict single-cell gene regulation dynamics. Unfortunately, the FSP approach is highly computationally intensive for all but the simplest models, an issue that is highly problematic when parameter inference and uncertainty quantification takes enormous numbers of parameter evaluations. To address this issue, we propose two new computational methods for the Bayesian inference of stochastic gene expression parameters given single-cell experiments. We formulate and verify an Adaptive Delayed Acceptance Metropolis-Hastings (ADAMH) algorithm to utilize with reduced Krylov-basis projections of the FSP. We then introduce an extension of the ADAMH into a Hybrid scheme that consists of an initial phase to construct a reduced model and a faster second phase to sample from the approximate posterior distribution determined by the constructed model. We test and compare both algorithms to an adaptive Metropolis algorithm with full FSP-based likelihood evaluations on three example models and simulated data to show that the new ADAMH variants achieve substantial speedup in comparison to the full FSP approach. By reducing the computational costs of parameter estimation, we expect the ADAMH approach to enable efficient data-driven estimation for more complex gene regulation models.

systems biology↗

bioRxiv2018

Organoid single-cell profiling identifies a transcriptional signature of glomerular disease

Podocyte injury is central to many forms of kidney disease, but transcriptional signatures reflecting podocyte injury and compensation mechanisms are challenging to analyze in vivo. Human kidney organoids derived from pluripotent stem cells (PSCs), a new model for disease and regeneration, present an opportunity to explore the transcriptional plasticity of podocytes. Here, transcriptional profiling of over 12,000 single cells from human PSC-derived kidney organoid cultures was used to identify robust and reproducible cell-lineage gene expression signatures shared with developing human kidneys based on trajectory analysis. Surprisingly, the gene expression signature characteristic of developing glomerular epithelial cells was also observed in glomerular tissue from a kidney disease cohort. This signature correlated with proteinuria and inverse eGFR, and was confirmed in an independent podocytopathy cohort. Three genes in particular were further identified as critical components of the glomerular disease signature. We conclude that cells in human PSC-derived kidney organoids reliably recapitulate the developmental transcriptional program of podocytes and other cell lineages in the human kidney, and that the early transcriptional profile seen in developing podocytes is reactivated in glomerular disease. Our findings demonstrate an innovative approach to identifying novel molecular programs involved in the pathogenesis of glomerulopathies.

systems biology↗

bioRxiv2018

The strength of protein-protein interactions controls the information capacity and dynamical response of signaling networks

Eukaryotic cells transmit information by signaling through complex networks of interacting proteins. Here we develop a theoretical and computational framework that relates the biophysics of protein-protein interactions (PPIs) within a signaling network to its information processing properties. To do so, we generalize statistical physics-inspired models for protein binding to account for interactions that depend on post-translational state (e.g. phosphorylation). By combining these models with information-theoretic methods, we find that PPIs are a key determinant of information transmission within a signaling network, with weak interactions giving rise to \"noise\" that diminishes information transmission. While noise can be mitigated by increasing interaction strength, the accompanying increase in transmission comes at the expense of a slower dynamical response. This suggests that the biophysics of signaling protein interactions give rise to a fundamental \"speed-information\" trade-off. Surprisingly, we find that cross-talk between pathways in complex signaling networks do not significantly alter information capacity-an observation that may partially explain the promiscuity and ubiquity of weak PPIs in heavily interconnected networks. We conclude by showing how our framework can be used to design synthetic biochemical networks that maximize information transmission, a procedure we dub \"InfoMax\" design.

systems biology↗

bioRxiv2018

High Resolution Comparison of Cancer-Related Developmental Processes Using Trajectory Alignment

Abnormal differentiation is a key feature of cancer, yet currently there is no framework that enables a comparative analysis of differentiation processes across patients while preserving their individual-level resolution. Here, we present devMap, an algorithm that uses high-dimensional trajectory alignment to anchor cancer-related developmental processes to a common backbone process, thus allowing for their systematic comparison. We applied devMap to bone marrow samples from healthy individuals and AML patients profiled by single-cell mass-cytometry at cancer diagnosis and following treatment. devMap standardization enabled us to infer the developmental status of the AML samples and characterize its evolution following treatment and in relapse. Application of devMap on an external dataset of AML bone marrow samples revealed conserved patterns of developmental signaling responses in AML that were obscured by traditional methodologies for developmental inference.

systems biology↗

bioRxiv2018

Revisiting tradeoffs in Rubisco kinetic parameters

Rubisco is the most abundant enzyme in the biosphere and one of the best-characterized enzymes. Based on correlations between Rubisco kinetic parameters, it is widely posited that tradeoffs embedded in the catalytic mechanism constrain its specificity and maximum catalytic rate. However, the reasoning that established this view was based on data from {approx}20 organisms. We re-examine these tradeoff models using a dataset from {approx}300 organisms. Most correlations are substantially attenuated, with the inverse relationship between carboxylation kcat and specificity SC/O being a key example. Only one tradeoff model survives in our dataset. In this model, increasing catalytic efficiency (kcat/KM) for carboxylation requires increased catalytic efficiency for the competing oxygenation reaction, evidenced by strong power-law correlation between catalytic efficiencies. Our results imply that Rubisco evolution is constrained primarily by the physicochemical limits of O2/CO2 discrimination, which should reframe efforts to engineer this very central enzyme.

systems biology↗

bioRxiv2018

Classifying Asian Rice Cultivars (Oryza sativa L.) into Indica and Japonica Using Logistic Regression Model with Publicly Available Phenotypic Data

This article introduces how to implement the logistic regression model (LRM) with phenotypic variables for classifying Asian rice (Oryza sativa L.) cultivars into two pivotal subpopulations, indica and japonica. This study took advantage of publicly available data attached to a previous paper. The classification accuracy was assessed using an area under curve (AUC) of a receiver operating characteristic (ROC) curve. Given 24 phenotypic variables for 280 indica/japonica accessions, the LRMs were fitted with up to six phenotypic variables of all possible combinations; the highest AUC accounts for 0.9977, obtained with six variables including panicle number per plant, seed number per panicle, florets per panicle, panicle fertility, straighthead susceptibility and blast resistance. Overall, the more variables there are, the higher the resulting AUCs are. The ultimate purpose of this study is to demonstrate the indica/japonica prediction ability of the LRM when applied to unclassified Asian rice cultivars. To estimate the indica/japonica prediction accuracy, ten-fold cross-validations were conducted 100 times with the 280 indica/japonica accessions using the LRM with parameters that yielded the highest AUC. The resulting prediction accuracy accounted for 0.9779. This suggests that the LRM promises to be a highly effective indica/japonica prediction tool using phenotypic variables in Asian cultivated rice.

systems biology↗

bioRxiv2018

Senescent cells and the dynamics of aging

Genetically-identical organisms age at different rates, but the reason for this is unclear. The rate of aging is due to the accumulation of damage, such as senescent cells (SnC) which are a major driver of aging in mammals, but the accumulation dynamics of SnC are unknown. Here, we combine theory and experiments in mice and find that SnC are rapidly produced and removed with a half-life of days in young mice. In old mice SnC half-life grows to weeks, because SnC saturate their own removal process. This saturation leads to persistent fluctuations known as critical-slowing-down: fluctuations are amplified to long-lasting differences in SnC between individuals at old ages. Rapid turnover with saturating removal is a general framework that may apply to any aging-related form of damage, and can quantitatively explain the demographic laws of mortality in worms, flies, mice and humans.

systems biology↗

bioRxiv2018

Quantitative systems pharmacological analysis of drugs of abuse reveals the pleiotropy of their targets and the effector role of mTORC1

Existing treatments against drug addiction are often ineffective due to the complexity of the networks of protein-drug and protein-protein interactions (PPIs) that mediate the development of drug addiction and related neurobiological disorders. There is an urgent need for understanding the molecular mechanisms that underlie drug addiction toward designing novel preventive or therapeutic strategies. The rapidly accumulating data on addictive drugs and their targets as well as advances in machine learning methods and computing technology now present an opportunity to systematically mine existing data and draw inferences on potential new strategies. To this aim, we carried out a comprehensive analysis of cellular pathways implicated in a diverse set of 50 drugs of abuse using quantitative systems pharmacology methods. The analysis of the drug/ligand-target interactions compiled in DrugBank and STITCH databases revealed 142 known and 48 newly predicted targets, which have been further analyzed to identify the KEGG pathways enriched at different stages of drug addiction cycle, as well as those implicated in cell signaling and regulation events associated with drug abuse. Apart from synaptic neurotransmission pathways detected as a common upstream signaling module that senses the early effects of drugs of abuse, pathways involved in neuroplasticity are distinguished as determinants of neuronal morphological changes. Notably, many signaling pathways converge on important targets such as mTORC1. The latter is proposed to act as a universal effector of the persistent restructuring of neurons in response to continued use of drugs of abuse.

systems biology↗

bioRxiv2018

Identification of multi-targeting and synergistic neuromodulators of epilepsy associated protein-targets in Ayurvedic herbs using network pharmacological approach

Epilepsy comprises a wide spectrum of neuronal disorders and accounts for about one percent of global disease burden affecting people of all age groups. In the traditional medicinal system of Indian antiquity, commonly known as Ayurveda, epilepsy is recognised as apasmara and a plenty of information is documented regarding the effectiveness of various herbs against it. Towards exploring the complex regulatory mechanisms of Ayurvedic herbs at molecular levels, in this study, a network pharmacological framework is developed for thoroughly examining the anti-epileptic potential of 349 drug-like phytochemicals (DPCs) found in 63Ayurvedic herbs. Interaction networks of phytochemicals in anti-epileptic herbs, their protein targets and associated human pathways are designed at various scales and DPCs are mapped on these networks to uncover complex interrelationships. Neuromodulatory prospects of anti-epileptic herbs are probed and, as a special case study, DPCs that can regulate metabotrophic glutamate receptors (mGluRs) are inspected. An account of novel regulatory phytochemicals against epilepsy is reported by systematically analysing the screened DPCs against DrugBank compounds. A repertoire of DPCs having poly-pharmacological similarity with anti-epileptic drugs available in DrugBank and those under clinical trials is also reported. Further, a high-confidence PPI network specific to the protein targets of epilepsy was developed and the potential of DPCs to regulate its functional modules was investigated. The study concludes by highlighting a couple of herbs as potential sources of epileptogenic regulators. We believe that the presented schema can open-up the exhaustive explorations of indigenous herbs towards meticulous identification of DPCs against various diseases and disorders.

systems biology↗

bioRxiv2018

Genome-wide co-localization of RNA-DNA interactions and fusion RNA pairs

Fusion transcripts are used as biomarkers in companion diagnoses. Although more than 15,000 fusion RNAs have been identified from diverse cancer types, few common feature has been reported. Here, we compared 16,410 fusion transcripts detected in cancer (from a published cohort of 9,966 tumor samples of 33 cancer types) with genome-wide RNA-DNA interactions mapped in two normal, non-cancerous cell types (using iMARGI, an enhanced version of the MARGI [Mapping RNA-Genome Interactions assay]). Among the top 10 most significant RNA-DNA interactions in normal cells, 5 co-localized with the gene pairs that formed fusion RNAs in cancer. Furthermore, throughout the genome, the frequency of a gene pair to exhibit RNA-DNA interactions is positively correlated with the probability of this gene pair to present documented fusion transcripts in cancer. To test whether RNA-DNA interactions in normal cells are predictive of fusion RNAs, we analyzed in a validation cohort of 96 lung cancer samples using RNA-seq. We found 34 out of 42 fusion transcripts in the validation cohort exhibited RNA-DNA interactions in normal cells. Finally, combining RNA-seq, single-molecule RNA FISH, and DNA FISH, we detected a cancer sample with Eml4-Alk fusion RNA without forming the Eml4-Alk fusion gene. Collectively, these data suggest a novel RNA-poise model, where spatial proximity of RNA and DNA could poise for creation of fusion transcripts.

systems biology↗