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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

The Translation Machinery Is Immune from miRNA Perturbations: A Cell-Based Probabilistic Approach

Mature microRNAs (miRNAs) regulate most human genes through direct base-pairing with mRNAs. We investigate some underlying principles of such regulation. To this end, we overexpressed miRNAs in different cell types and measured the mRNA decay rate under transcriptional arrest. Parameters extracted from these experiments were incorporated into a computational stochastic framework which was developed to simulate the cooperative action of miRNAs in living cells. We identified gene sets that exhibit coordinated behavior with respect to all major miRNAs, over a broad range of overexpression levels. While a small set of genes is highly sensitive to miRNA manipulations, about 180 genes are insensitive to miRNA manipulations as measured by their degree of mRNA retention. The insensitive genes are associated with the translation machinery. We conclude that the stochastic nature of miRNAs reveals an unexpected robustness of gene expression in living cells. Moreover, the use of a systematic probabilistic approach exposes design principles of cells states and in particular, the translational machinery.\n\nHighlightsO_LIA probabilistic-based simulator assesses the cellular response to thousands of miRNA overexpression manipulations\nC_LIO_LIThe translational machinery displays an exceptional resistance to manipulations of miRNAs.\nC_LIO_LIThe insensitivity of the translation machinery to miRNA manipulations is shared by different cell types\nC_LIO_LIThe composition of the most abundant miRNAs dominates cell identity\nC_LI

systems biology↗

bioRxiv2018

Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems

Metabolic exchange can mediate beneficial interactions among microbes, helping explain diversity in microbial communities. These interactions are often assumed to involve a fitness cost, prompting questions on how cooperative phenotypes can be stable and withstand the emergence of cheaters. Here we use genome-scale models of metabolism to investigate whether a radically different scenario, the pervasive release of \"costless\" metabolites (i.e. those that cause no fitness cost to the producing organism), can serve as a prominent mechanism for inter-microbial interactions. By carrying out over 1 million pairwise growth simulations for 14 microbial species in a combinatorial assortment of environmental conditions, we find that there is indeed a large space of metabolites that can be secreted at no cost, which can generate ample cross-feeding opportunities. In addition to providing an atlas of putative costless interdependencies, our modeling also demonstrates that oxygen availability significantly enhances mutualistic interactions by providing more opportunities for metabolic exchange through costless metabolites, resulting in an over-representation of specific ecological network motifs. In addition to helping explain natural diversity, we show how the exchange of costless metabolites can facilitate the engineering of stable synthetic microbial consortia.

systems biology↗

bioRxiv2018

Dilution and titration of cell-cycle regulators may control cell size in budding yeast

The size of a cell sets the scale for all biochemical processes within it, thereby affecting cellular fitness and survival. Hence, cell size needs to be kept within certain limits and relatively constant over multiple generations. However, how cells measure their size and use this information to regulate growth and division remains controversial. Here, we present two mechanistic mathematical models of the budding yeast (S. cerevisiae) cell cycle to investigate competing hypotheses on size control: inhibitor dilution and titration of nuclear sites. Our results suggest that an inhibitor-dilution mechanism, in which cell growth dilutes the transcriptional inhibitor Whi5 against the constant activator Cln3, can facilitate size homeostasis. This is achieved by utilising a positive feedback loop to establish a fixed size threshold for the SO_SCPCAPTARTC_SCPCAP transition, which efficiently couples cell growth to cell cycle progression. Yet, we show that inhibitor dilution cannot reproduce the size of mutants that alter the cells overall ploidy and WHI5 gene copy number. By contrast, size control through titration of Cln3 against a constant number of genomic binding sites for the transcription factor SBF recapitulates both size homeostasis and the size of these mutant strains. Moreover, this model produces an imperfect sizer behaviour in G1 and a timer in S/G2/M, which combine to yield an adder over the whole cell cycle; an observation recently made in experiments. Hence, our model connects these phenomenological data with the molecular details of the cell cycle, providing a systems-level perspective of budding yeast size control.

systems biology↗

bioRxiv2018

In silico mouse study identifies tumor growth kinetics as biomarkers for the outcome of anti-angiogenic treatment

Angiogenesis is a crucial step in tumor progression, as this process allows tumors to recruit new blood vessels and obtain oxygen and nutrients to sustain growth. Therefore, inhibiting angiogenesis remains a viable strategy for cancer therapy. However, anti-angiogenic therapy has not proved to be effective in reducing tumor growth across a wide range of tumors, and no reliable predictive biomarkers have been found to determine the efficacy of anti-angiogenic treatment. Using our previously established computational model of tumor-bearing mice, we sought to determine whether tumor growth kinetic parameters could be used to predict the outcome of anti-angiogenic treatment. A model trained with datasets from six in vivo mice studies was used to generate a randomized in silico tumor-bearing mouse population. We analyzed tumor growth in untreated mice (control) and mice treated with an anti-angiogenic agent and determined the Kaplan-Meier survival estimates based on simulated tumor volume data. We found that the ratio between two kinetic parameters, k0 and k1, which characterize the tumors exponential and linear growth rates, as well as k1 alone, can be used as prognostic biomarkers of population survival outcome. Our work demonstrates a robust, quantitative approach for identifying tumor growth kinetic parameters as prognostic biomarkers and serves as a template that can be used to identify other biomarkers for anti-angiogenic treatment.

systems biology↗

bioRxiv2018

Identification of protein abundance changes in biopsy-level hepatocellular carcinoma tissues using PCT-SWATH

In this study, we optimized the pressure-cycling technology (PCT) and SWATH mass spectrometry workflow to analyze biopsy-level tissue samples (2 mg wet weight) from 19 hepatocellular carcinoma (HCC) patients. Using OpenSWATH and pan-human spectral library, we quantified 11,787 proteotypic peptides from 2,579 SwissProt proteins in 76 HCC tissue samples within about 9 working days (from receiving tissue to SWATH data). The coefficient of variation (CV) of peptide yield using PCT was 32.9%, and the R2 of peptide quantification was 0.9729. We identified protein changes in malignant tissues compared to matched control samples in HCC patients, and further stratified patient samples into groups with high -fetoprotein (AFP) expression or HBV infection. In aggregate, the data identified 23 upregulated pathways and 13 ones. We observed enhanced biomolecule synthesis and suppressed small molecular metabolism in liver tumor tissues. 16 proteins of high documented relevance to HCC are highlighted in our data. We also identified changes of virus-infection-related proteins including PKM, CTPS1 and ALDOB in the HBV+ HCC subcohort. In conclusion, we demonstrate the practicality of performing proteomic analysis of biopsy-level tissue samples with PCT-SWATH methodology with moderate effort and within a relatively short timeframe.

systems biology↗

bioRxiv2018

Nicotinic modulation of hierarchal inhibitory control over prefrontal cortex resting state dynamics: modeling of genetic modification and schizophrenia-related pathology

Nicotinic acetylcholine receptors (nAChRs) strongly modulate the cholinergic drive to a hierarchy of inhibitory neurons in the superficial layers of the PFC, critical to cognitive processes. Genetic deletion of various types of nAChRs, located on specific interneurons, impacts the properties of ultra-slow transitions between high and low activity states (H-states and L-states, respectively), recorded in mice during quiet wakefulness. In addition, recent data indicate that a genetic mutation of the 5 nAChR subunit located on vasoactive intestinal polypeptide (VIP) inhibitory neurons, the rs16969968 single nucleotide polymorphism (5 SNP), appears to be responsible for \"hypofrontality\" observed in schizophrenia. Chronic nicotine application to 5 SNP mice restores neural activity to control levels. Using firing rate models of hierarchically organized neural populations, we showed that the change of activity patterns recorded in the genetically modified mice can be explained by a change of activity state stability, differentially modulated by cholinergic inputs to parvalbumin (PV), somatostatin (SOM) or VIP inhibitory populations. A change in amplitude, but not duration of H-states fully account for the lowered pyramidal (PYR) firing rates recorded in 5 SNP mice. We demonstrate that desensitization and upregulation of {beta}2 nAChRs located on SOM interneurons, but not activation of 5 nAChRs located on VIP interneurons, by chronic nicotine application could account for activity normalization recorded in 5 SNP mice. The model implies that subsequent nicotine withdrawal should lead to PYR activity depression more severe than the original hypofrontality caused by SNP mutation.

systems biology↗

bioRxiv2018

Population level rhythms in human skin: implications for circadian medicine

Skin is the largest organ in the body and serves important barrier, regulatory, and sensory functions. Like other tissues, skin is subject to temporal fluctuations in physiological responses under both homeostatic and stressed states. To gain insight into these fluctuations, we investigated the role of the circadian clock in the transcriptional regulation of epidermis using a hybrid experimental design, where a limited set of human subjects (n=20) were sampled throughout the 24 h cycle and a larger population (n=219) were sampled once. By looking at pairwise correlations of core clock genes in 298 skin samples, we found a robust circadian oscillator in skin at the population level. Encouraged by this, we used CYCLOPS to reconstruct the temporal order of all samples and identified hundreds of rhythmically-expressed genes at the population level in human skin. We compared these results with published time-series skin data from mouse and show strong concordance in circadian phase across species for both transcripts and pathways. Further, like blood, skin is readily accessible and a potential source of biomarkers. Using ZeitZeiger, we identified a biomarker set for human skin that is capable of reporting circadian phase to within 3 h from a single sample. In summary, we show rhythms in human skin that persist at the population scale and a path to develop robust single-sample circadian biomarkers.\n\nOne Sentence SummaryHuman epidermis shows strong circadian rhythms at the population scale and provides a better source for developing robust, single-sample circadian phase biomarkers than human blood.

systems biology↗

bioRxiv2018

MaSS-Simulator: A highly configurable MS/MS simulator for generating test datasets for big data algorithms.

Mass Spectrometry (MS) based proteomics has become an essential tool in the study of proteins. The big data from MS machines has led to the development of novel serial and parallel algorithmic tools. However, the absence of data benchmarks and ground truth makes the algorithmic integrity testing and reproducibility a challenging problem. To this end, we present MaSS-Simulator, which is an easy to use simulator and can be configured to generate MS/MS datasets for a wide variety of conditions with known ground truths. MaSS-Simulator offers a large number of configuration options to simulate control datasets with desired properties thus enabling rigorous and large scale algorithmic testing. We assessed 8,031 spectra generated by MaSS-Simulator by comparing them against the experimentally generated spectra of same peptides. Our results showed that MaSS-Simulator generated spectra were very close to the real-experimental spectra and had a relative-error distribution centered around 25%. In contrast the theoretical spectra for same peptides had relative-error distribution centered around 150%. Source code, executables and a user manual can be downloaded from https://github.com/pcdslab/MaSS-Simulator

systems biology↗

bioRxiv2018

Network analysis reveals differential metabolic functionality in antibiotic-resistant Pseudomonas aeruginosa

Metabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To interrogate this relationship, we profiled the growth of lab-evolved antibiotic-resistant lineages of the opportunistic pathogen Pseudomonas aeruginosa across 190 unique carbon sources. We semi-automatically calculated growth dynamics (maximum growth density, growth rate, and time to mid-exponential phase) of over 2,800 growth curves. These data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. Drug-resistant lineages predominantly displayed decreased growth relative to the ancestral lineage; however, resistant lineages occasionally displayed enhanced growth on certain carbon sources, indicating that adaption to drug can provide a growth advantage in certain environments. A genome-scale metabolic network reconstruction (GENRE) of P. aeruginosa strain UCBPP-PA14 was paired with whole-genome sequencing data of one of the drug-evolved lineages to predict genes contributing to observed changes in metabolism. Finally, we experimentally validated in silico predictions to identify genes mutated in resistant P. aeruginosa affecting loss of catabolic function. Our results build upon previous mechanistic knowledge of drug-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens. Robust drug-driven changes in bacterial metabolism have the potential to be exploited to select against antibiotic-resistant populations in chronic infections.

systems biology↗

bioRxiv2018

A novel approach to modeling transcriptional heterogeneity identifies the oncogene candidate CBX2 in invasive breast carcinoma

Oncogenes promote the development of and serve as therapeutic targets against subsets of cancers. Here, a new statistical approach that captures transcriptional heterogeneity in tumor and adjacent normal (i.e. tumor-free) mRNA expression profiles was developed to identify oncogene candidates that were overexpressed in a subset of breast tumors. Intronic DNA methylation was strongly associated with the overexpression of chromobox 2 (CBX2), an oncogene candidate that was identified using our method but not through prior analytical approaches. CBX2 overexpression in breast tumors was associated with the upregulation of genes involved in cell cycle progression and is associated with poorer 5-year survival. The predicted function of CBX2 was confirmed in vitro providing the first experimental evidence that CBX2 promotes breast cancer cell growth. Modeling mRNA expression heterogeneity in tumors is a novel powerful approach with the potential to uncover therapeutic targets that benefit subsets of cancer patients.

systems biology↗

bioRxiv2018

Co-existing feedback loops generate tissue-specific circadian rhythms

Gene regulatory feedback loops generate autonomous circadian rhythms in mammalian tissues. The well-studied core clock network contains many negative and positive regulations. Multiple feedback loops have been discussed as primary rhythm generators but the design principles of the core clock and differences between tissues are still under debate.\n\nHere we use global optimization techniques to fit mathematical models to circadian gene expression profiles for different mammalian tissues. It turns out that for every investigated tissue multiple model parameter sets reproduce the experimental data. We extract for all model versions the most essential feedback loops and find auto-inhibitions of Period and Cryptochrome genes, Bmal1/Rev-erb- loops, and repressilator motifs as possible rhythm generators. Interestingly, the essential feedback loops differ between tissues, pointing to specific design principles within the hierarchy of mammalian tissue clocks. Self-inhibitions of Per and Cry genes are characteristic for models of SCN clocks, while in liver models many loops act in synergy and are connected by a repressilator motif. Tissue-specific use of a network of co-existing synergistic feedback loops could account for functional differences between organs.

systems biology↗

bioRxiv2018

Self-organised segregation of bacterial chromosomal origins

In spite of much effort, many aspects of chromosome organisation and segregation in bacteria remain unclear. Even for Escherichia coli, the most widely studied bacterial model organism, we still do not know the underlying mechanisms. Like many other bacteria, the chromosomal origin of replication in E. coli is dynamically positioned throughout the cell cycle. Initially maintained at mid-cell, where replication occurs, origins are subsequently partitioned to opposite quarter positions. The Structural Maintenance of Chromosomes (SMC) complex, MukBEF, which is required for correct chromosome compaction and organisation, has been implicated in this behaviour but the mode of action is unknown. Here, we build on a recent self-organising model for the positioning of E. coli MukBEF, to propose an explanation for the positioning and partitioning of origins. We propose that a specific association of MukBEF with the origin region, results in a non-trivial feedback between the self-organising MukBEF gradient and the origins, leading to accurate positioning and partitioning as an emergent property. We compare the model to quantitative experimental data of origin dynamics and their colocalisation with MukBEF clusters and find excellent agreement. Overall, the model suggests that MukBEF and origins act together as a self-organising system for chromosome segregation and introduces protein self-organisation as an important consideration for future studies of chromosome dynamics.

systems biology↗

bioRxiv2018

Genomic, Proteomic and Phenotypic Heterogeneity in HeLa Cells across Laboratories: Implications for Reproducibility of Research Results

The independent reproduction of research results is a cornerstone of experimental research, yet it is beset by numerous challenges, including the quality and veracity of reagents and materials. Much of life science research depends on life materials, including human tissue culture cells. In this study we aimed at determining the degree of variability in the molecular makeup and the ensuing phenotypic consequences in commonly used human tissue culture cells. We collected 14 stock HeLa aliquots from 13 different laboratories across the globe, cultured them in uniform conditions and profiled the genome-wide copy numbers, mRNAs, proteins and protein turnover rates via genomic techniques and SWATH mass spectrometry, respectively. We also phenotyped each cell line with respect to the ability of transfected Let7 mimics to modulate Salmonella infection.\n\nWe discovered significant heterogeneity between HeLa variants, especially between lines of the CCL2 and Kyoto variety. We also observed progressive divergence within a specific cell line over 50 successive passages. From the aggregate multi-omic datasets we quantified the response of the cells to genomic variability across the transcriptome and proteome. We discovered organelle-specific proteome remodeling and buffering of protein abundance by protein complex stoichiometry, mediated by the adaptation of protein turnover rates. By associating quantitative proteotype and phenotype measurements we identified protein patterns that explained the varying response of the different cell lines to Salmonella infection.\n\nAltogether the results indicate a striking degree of genomic variability, the rapid evolution of genomic variability in culture and its complex translation into distinctive expressed molecular and phenotypic patterns. The results have broad implications for the interpretation and reproducibility of research results obtained from HeLa cells and provide important basis for a general discussion of the value and requirements for communicating research results obtained from human tissue culture cells.

systems biology↗

bioRxiv2018

Quantifying multi-layered expression regulation in response to stress of the endoplasmic reticulum

The mammalian response to endoplasmic reticulum (ER) stress dynamically affects all layers of gene expression regulation. We quantified transcript and protein abundance along with footprints of ribosomes and non-ribosomal proteins for thousands of genes in cervical cancer cells responding to treatment with tunicamycin or hydrogen peroxide over an eight hour time course. We identify shared and stress-specific significant regulatory events at the transcriptional and post-transcriptional level and at different phases of the experiment. ER stress regulators increase transcription and translation at different times supporting an adaptive response. ER stress also induces translation of genes from serine biosynthesis and one-carbon metabolism indicating a shift in energy production. Discordant regulation of DNA repair genes suggests transcriptional priming in which delayed translation fine-tunes the early change in the transcriptome. Finally, case studies on stress-dependent alternative splicing and protein-mRNA binding demonstrate the ability of this resource to generate hypotheses for new regulatory mechanisms.

systems biology↗

bioRxiv2018

Mitochondrial ubiquinone-mediated longevity is marked by reduced cytoplasmic protein translation

Mutations in the clk-1 gene impair mitochondrial ubiquinone biosynthesis and extend the lifespan of C. elegans. We demonstrate here that this life extension is linked to the repression of cytoplasmic protein translation. Clk-1 mutations inhibit polyribosome formation similarly to daf-2 mutations that dampen insulin signaling. Comparisons of total versus polysomal RNAs in clk-1 mutants reveal a reduction in the translational efficiencies of mRNAs coding for elements of the translation machinery and an increase in those coding for the oxidative phosphorylation and autophagy pathways. Knocking down the transcription initiation factor TAF-4, a protein that becomes sequestered in the cytoplasm during early embryogenesis to induce transcriptional silencing, ameliorates the clk-1 inhibition of polyribosome formation. These results underscore a prominent role for the repression of cytoplasmic protein translation in eukaryotic lifespan extension, and suggest that mutations impairing mitochondrial function are able to exploit this repression similarly to reductions of insulin signaling. Moreover, this report reveals an unexpected role for TAF-4 as a repressor of polyribosome formation when ubiquinone biosynthesis is compromised.

systems biology↗

bioRxiv2018

Elevated alpha 1 antitrypsin is a major component of GlycA-associated risk for future morbidity and mortality

Integration of electronic health records with systems-level biomolecular data has led to the discovery that GlycA, a complex nuclear magnetic resonance (NMR) spectroscopy biomarker, predicts long-term risk of disease onset and death from myriad causes. To determine the molecular underpinnings of the disease risk of the heterogeneous GlycA signal, we used machine learning to build imputation models for GlycAs constituent glycoproteins, then estimated glycoprotein levels in 11,861 adults across two population-based cohorts with long-term follow-up. While alpha-1-acid glycoprotein had the strongest correlation with GlycA, our analysis revealed that alpha-1 antitrypsin (AAT) was the most predictive of morbidity and mortality for the widest range of diseases, including heart failure (HR=1.60 per s.d., P=1x10-10), influenza and pneumonia (HR=1.37, P=6x10-10), and liver diseases (HR=1.81, P=1x10-6). Despite emerging evidence of AAT's role in suppressing inflammation, transcriptional analyses revealed elevated expression of diverse inflammatory immune pathways with elevated AAT levels, suggesting AAT is elevating to compensate for low-grade chronic inflammation. This study clarifies the molecular underpinnings of the GlycA biomarker and its associated disease risk, and indicates a previously unrecognised association between elevated AAT and severe disease onset and mortality.

systems biology↗

bioRxiv2018

Interdependence of EGFR with PTPs on juxtaposed membranes generates a growth factor sensing and responding network

The proto-oncogenic epidermal growth factor receptor (EGFR) is a tyrosine kinase whose sensitivity to growth factors (GFs) and signal duration determines cellular behavior. We resolve how EGFRs response to epidermal growth factor (EGF) originates from dynamically established recursive interactions with spatially organized protein tyrosine phosphatases (PTPs). Opposed genetic PTP perturbations enabled identification of receptor-like PTPRG/J at the plasma membrane (PM) and endoplasmic reticulum (ER) associated PTPN2 as the major EGFR dephosphorylating activities. Imaging spatial-temporal PTP reactivity then revealed that vesicular trafficking establishes a spatially-distributed negative feedback with PTPN2 that determines signal duration, whereas single cell dose-response analysis uncovered a ROS-mediated toggle switch between autocatalytically activated monomeric EGFR and the tumor suppressor PTPRG that governs EGFRs sensitivity to EGF. Vesicular recycling of monomeric EGFR unifies the interactions with these PTPs on juxtaposed membranes, dynamically generating a network architecture that can sense and respond to time varying growth factor signals.

systems biology↗

bioRxiv2018

Buffering gene expression noise by microRNA based feedforward regulation

Cells use various regulatory motifs, including feedforward loops, to control the intrinsic noise that arises in gene expression at low copy numbers. Here we study one such system, which is broadly inspired by the interaction between an mRNA molecule and an antagonistic microRNA molecule encoded by the same gene. The two reaction species are synchronously produced, individually degraded, and the second species (microRNA) exerts an antagonistic pressure on the first species (mRNA). Using linear-noise approximation, we show that the noise in the first species, which we quantify by the Fano factor, is sub-Poissonian, and exhibits a nonmonotonic response both to the species lifetime ratio and to the strength of the antagonistic interaction. Additionally, we use the Chemical Reaction Network Theory to prove that the first species distribution is Poissonian if the first species is much more stable than the second. Finally, we identify a special parametric regime, supporting a broad range of behaviour, in which the distribution can be analytically described in terms of the confluent hypergeometric limit function. We verify our analysis against large-scale kinetic Monte Carlo simulations. Our results indicate that, subject to specific physiological constraints, optimal parameter values can be found within the mRNA-microRNA motif that can benefit the cell by lowering the gene-expression noise.

systems biology↗