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A deep mutational scan of an acidic activation domain

Transcriptional activation domains are intrinsically disordered peptides with little primary sequence conservation. These properties have made it difficult to identify the sequence features that define activation domains. For example, although acidic activation domains were discovered 30 years ago, we still do not know what role, if any, acidic residues play in these peptides. To address this question we designed a rational mutagenesis scheme to independently test four sequence features theorized to control the strength of activation domains: acidity (negative charge), hydrophobicity, intrinsic disorder, and short linear motifs. To test enough mutants to deconvolve these four features we developed a method to quantify the activities of thousands of activation domain variants in parallel. Our results with Gcn4, a classic acidic activation domain, suggest that acidic residues in particular regions keep two hydrophobic motifs exposed to solvent. We also found that the specific activity of the Gcn4 activation domain increases during amino acid starvation. Our results suggest that Gcn4 may have evolved to have low activity but high inducibility. Our results also demonstrate that high-throughput rational mutation scans will be powerful tools for unraveling the properties that control how intrinsically disordered proteins function.

systems biology

Increased glutaminolytic flux and activation of mitochondrial metabolism by BCL2 hyperactivity in lymphoma

B-cell lymphoma 2 (BCL2) is an important apoptosis regulator during developmental and pathological states, and its overexpression is a key feature of several malignancies. Genomic data from The Cancer Genome Atlas (TCGA) reveals significant somatic copy number amplification, overexpression, and/or elevated protein activity of BCL2 in 50 % of diffuse large B-cell lymphoma (DLBC) patients. While its canonical role in mitochondria-directed apoptosis is well established, the effect of BCL2 on transcriptional and metabolic networks remains elusive. Using an established lymphocytic pro-B-cell line overexpressing BCL2, we identified dysregulated transcriptional and metabolic networks by transcriptomic profiling arrays. Elevated BCL2 levels affect transcription factor complexes and mitogenic programs of NF-{kappa}B/REL, HIF1A/ARNT, AP1, E2F, and STAT factors. Using stable isotope-assisted metabolic flux measurements we quantify that elevated BCL2 expression increases carbon utilization boosting cellular proliferation. Tumorigenic overexpression of BCL2 significantly increases glycolytic flux, glutaminolysis, and anaplerotic flux into the TCA cycle. At the same time, the mitochondrial acetyl-CoA pool is separated from the glycolytic one by inactivating the pyruvate dehydrogenase complex via transcriptional regulation of pyruvate dehydrogenase kinase (PDK3). As compensatory fuel, mitochondrial TCA cycle metabolism is supported by asparagine synthase (ASNS) and oxidative glutaminolysis creating targets for small molecule inhibition of glutaminase. Lymphoma cells overexpressing BCL2 contained more mitochondrial mass and were more sensitive to L-glutamine deprivation and glutaminase inhibition. Cells overexpressing a mutant BCL2 G145E, which is incapable of binding BH domain members, failed to increase proliferation, glycolysis, or glutaminolysis. Taken together, the oncogene BCL2 has the ability to ramp up a metabolic phenotype supporting proliferation independent of its anti-apoptotic role. The cellular model of BCL2 activation supports NF-KB-positive subtypes of DLBC and identifies metabolic bottlenecks with dependency on anaplerotic flux as an actionable BCL2 effector network in cancer.

systems biology

Unraveling mitotic protein networks by 3D multiplexed epitope drug screening

Three-dimensional protein localization intricately determines the functional coordination of cellular processes. The complex spatial context of protein landscape has been assessed by multiplexed immunofluorescent staining1-3 or mass spectrometry4, applied to 2D cell culture with limited physiological relevance5 or tissue sections. Here, we present 3D SPECS, an automated technology for 3D Spatial characterization of Protein Expression Changes by microscopic Screening. This workflow encompasses iterative antibody staining of proteins, high-content imaging, and machine learning based classification of mitotic states. This is followed by mapping of spatial protein localization into a spherical, cellular coordinate system, the basis used for model-based prediction of spatially resolved affinities of various mitotic proteins. As a proof-of-concept, we mapped twelve epitopes in 3D cultured epithelial breast spheroids and investigated the network effects of mitotic cancer drugs with known limited success in clinical trials6-8. Our approach reveals novel insights into spindle fragility and global chromatin stress, and predicts unknown interactions between proteins in specific mitotic pathways. 3D SPECSs ability to map potential drug targets by multiplexed immunofluorescence in 3D cell cultured models combined with our automized high content assay will inspire future functional protein expression and drug assays.

systems biology

Epithelial stratification shapes infection dynamics

Infections of stratified epithelia collectively represent a large burden on global health. Experimental models provide a means to understand how the cell dynamics themselves influence the outcomes of these infections. Mathematical approaches are needed to improve quantification and theoretical advancement of these complex systems. Here, we develop a general ecology-inspired model for stratified epithelial dynamics, which allows us to simulate infections and to estimate parameters that are difficult to measure with organotypic cell cultures. To explore how epithelial cell dynamics affect infection dynamics, we focus on two contrasting pathogens of the cervicovaginal epithelium: Chlamydia trachomatis and Human papillomaviruses. We find that key infection symptoms stem from differential interactions with the layers, while clearance and pathogen burden are bottom-up processes. Cell protective responses to infections (e.g. increased cell proliferation) generally lowered pathogen load but there were specific effects based on infection strategies. These generic responses by the epithelium, then, will have varying results depending on the pathogens infection strategy. Our modeling approach opens new perspectives for 3D tissue culture experimental systems of infections and, more generally, for developing and testing hypotheses related to infections of stratified epithelia.

systems biology

Modeling systemic colorimetric parameters as a tool for processing images of clumps of toxic cyanobacteria targeted at their boundaries detection

Global climate change, along with other large-scale consequences of human impact upon the nature, increases the risk of biosafety threats associated with the disturbance of stability of communities of living organisms. In this regard, the topicality of the challenge of developing methods for monitoring and correcting homeostasis mechanisms that can support this stability is a problem of premium importance.\n\nThe work aims at investigation of techniques of remote detection of toxic cyanobacteria clumps in water area, with the use of dynamical modeling.

systems biology

A quantitative network modeling approach to evaluate the role of cytokine combinations on CD4+ T cell differentiation, partial polarization, and plasticity

Diverse cellular polarization states with different phenotypes and functions are derived from the differentiation of activated CD4+ T naive lymphocytes in the presence of particular cytokines. In addition, conversion of polarized cells to phenotypes different from that originally induced has been documented, highlighting the capacity of the immune response for adaptation to changing circumstances. In a recent study, we proposed a minimal Boolean regulatory network of CD4+ T differentiation that incorporates transcription factors, signaling pathways, and autocrine and exogenous cytokines. The qualitative model effectively reproduced the main polarized phenotypes of CD4+ T cells and several of the plasticity events reported in the literature. Yet, the amount and the expression of cytokines relative to expression of other factors influence CD4+ T cell transitions. In this paper, we have extended the Boolean network to a continuous model that allows us to assess the effect of quantitative differences in the concentrations and combinations of exogenous and endogenous cytokines, as well as diverse levels of transcription factors expression, in order to assess the role of intracellular and extracellular components in CD4+ T differentiation and plasticity. Interestingly, the model predicts either abrupt or gradual differentiation patterns between observed phenotypes depending on critical concentrations of single or multiple environmental cytokines. Plastic changes induced by environmental cytokines were observed in conditions of partial phenotype polarization in the Th1/Th2 transition. On the other hand, the Th17/iTreg transition was highly dependent on cytokine concentrations in the environment. Thus, modeling shows how the concentration of exogenous factors, the degree of initial polarization, and cell heterogeneity, may determine the differentiation and plasticity capacity of CD4+ T cells. The model and results presented here are useful to further understand system-level mechanisms underlying observed patterns of CD4+ T differentiation and plasticity.

systems biology

Metagenomic signature of natural strongyle infection in susceptible and resistant horses

Gastrointestinal strongyles are a major threat to horses' health and welfare. Given that strongyles inhabit the same niche as the gut microbiota, they may interact with each other. These beneficial or detrimental interactions are unknown in horses and could partly explain contrasted susceptibility to infection between individuals. To address these questions, an experimental pasture trial with 20 worm-free female Welsh ponies (10 susceptible (S) and 10 resistant (R) to parasite infection) was implemented for five months. Fecal egg counts (FEC), hematological and biochemical data, body weight and gut microbiota composition were studied in each individual after 0, 24, 43, 92 and 132 grazing days.\n\nThe predicted R ponies exhibited lower FEC after 92 and 132 grazing days, and showed higher levels of circulating monocytes and eosinophils, while S ponies developed lymphocytosis by the end of the trial. Although the overall microbiota diversity remained similar between the two groups, R and S ponies exhibited sustained differential abundances in Clostridium XIVa, Ruminococcus, Acetivibrio and unclassified Lachnospiracea at day 0. These bacteria may hence contribute to the intrinsic pony resistance towards strongyle infection. Moreover, Paludibacter, Campylobacter, Bacillus, Pseudomonas, Clostridium III, Acetivibrio, members of the unclassified Eubacteriaceae and Ruminococcaceae and fungi loads were increased in infected S ponies, suggesting that strongyle and fungi may contribute to each others success in the ecological niche of the equine intestines. In contrast, butyrate-producing bacteria such as Ruminococcus, Clostridium XIVa and members of the Lachnospiraceae family decreased in S relative to R ponies. Additionally, these gut microbiota alterations induced changes in several immunological pathways in S ponies, including pathogen sensing, lipid metabolism, and activation of signal transduction that are critical for the regulation of immune system and energy homeostasis. These observations shed light on a putative implication of the gut microbiota in the intrinsic resistance to strongyle infection.\n\nOverall, this longitudinal study provides a foundation to better understand the mechanisms that underpin the relationship between host susceptibility to strongyle infection, immune response and gut microbiota under natural conditions in horses and should contribute to the development of novel biomarkers of strongyle susceptibility and provide additional control options.

systems biology

Prevalence and patterns of higher-order interactions

Interactions and emergent processes are essential for research on complex systems involving many components. Most studies focus solely on pairwise interactions and ignore higher-order interactions among three or more components. To gain deeper insights into higher-order interactions and complex environments, we study antibiotic combinations applied to pathogenic Escherichia coli and obtain unprecedented amounts of detailed data (251 two-drug combinations, 1512 three-drug combinations, 5670 four-drug combinations, and 13608 five-drug combinations). Directly opposite to previous assumptions and reports, we find higher-order interactions increase in frequency with the number of drugs in the bacterias environment. Furthermore, we observe a shift towards net synergy (effect greater than expected based on independent individual effects) and towards emergent antagonism (effect less than expected based on lower-order interaction effects). These findings have implications for the potential efficacy of drug combinations and are crucial for better navigating problems associated with the combinatorial complexity of multi-component systems.

systems biology

The Cell Cycle Browser: an interactive tool for visualizing, simulating, and perturbing cell cycle progression

SUMMARYThe cell cycle is driven by precise temporal coordination among many molecular activities. To understand and explore this process, we developed the Cell Cycle Browser (CCB), an interactive web interface based on real-time reporter data collected in proliferating human cells. This tool facilitates visualizing, simulating, and predicting the outcomes of perturbing cell cycle parameters. Time-series traces from individual cells can be combined to build a multi-layered timeline of molecular activities. Users can simulate the cell cycle using computational models that capture the dynamics of molecular activities and phase transitions. By adjusting individual expression levels and strengths of molecular relationships, users can predict effects on the cell cycle. Virtual assays, such as growth curves and flow cytometry, provide familiar outputs to compare cell cycle behaviors for data and simulations. The CCB serves to unify our understanding of cell cycle dynamics and provides a platform for generating hypotheses through virtual experiments.\n\nHIGHLIGHTSO_LIUsers can stack and align single-cell traces for different molecular reporters\nC_LIO_LIComputational models with adjustable parameters simulate cell cycle progression\nC_LIO_LIVirtual growth curves and flow cytometry assays predict cell cycle behaviors\nC_LI

systems biology

Statistical mechanics of cell decision-making: the cell migration force distribution

Cell decision-making is the cellular process of responding to microenvironmental cues. This can be regarded as the regulation of cells intrinsic variables to extrinsic stimuli. Currently, little is known about the principles dictating cell decision-making. Regarding cells as Bayesian decision-makers under energetic constraints, I postulate the principle of least microenvironmental uncertainty (LEUP). This is translated into a free-energy principle and I develop a statistical mechanics theory for cell decision-making. I exhibit the potential of LEUP in the case of cell migration. In particular, I calculate the dependence of cell locomotion force on the steady state distribution of adhesion receptors. Finally, the associated migration velocity allows for the reproduction of the cell anomalous diffusion, as observed in cell culture experiments.

systems biology

Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer

The human papillomavirus (HPV) is present in a significant fraction of head-and-neck squamous cell cancer (HNSCC). However, a comprehensive understanding of disease progression profiles comparing HPV+ and HPV- HNSCC cases is still lacking. The main goal of this study was to identify distinct co-expression patterns between HPV+ and HPV- HNSCC and to provide insights into potential regulatory mechanisms/effects (such as methylation and mutation) within the analyzed networks. For conducting this, we selected 276 samples from The Cancer Genome Atlas database comprising data of gene expression, methylation profiles and mutational patterns, in addition to clinical information (HPV status and tumor staging). We further added external information such as the identification of transcription factors to the networks. Genes were selected as differentially expressed and differentially methylated based on HPV status, of which 12 genes were doubly selected, including SYCP2, GJB6, FLRT3, PITX2 and CCNA1. Weight correlation network analysis was used to identify co-expression modules and a systematic approach was applied to refine them and identify key regulatory elements integrating results from the other omics. Three main modules were associated with distinct co-expression patterns in HPV+ versus HPV- HNSCC. The molecular signatures found were mainly related to cell fate specification, keratinocyte differentiation, focal adhesion and regulation of protein oligomerization. This study provides comprehensive insights into complex genetic and epigenetic particularities in the development and progression of HNSCC in patients according to HPV status, identifying unseen gene interactions, and may contribute to unveiling specific genes/pathways as novel therapeutic targets for HNSCC.

systems biology

Fibroblast state switching orchestrates dermal maturation and wound healing

Murine dermis contains functionally and spatially distinct fibroblast lineages that cease to proliferate in early postnatal life. Here we propose a model in which a negative feedback loop between extracellular matrix (ECM) deposition and fibroblast proliferation determines dermal architecture. Virtual-tissue simulations of our model faithfully recapitulate dermal maturation, predicting a loss of spatial segregation of fibroblast lineages and dictating that fibroblast migration is only required for wound healing. To test this, we performed in vivo live imaging of dermal fibroblasts, which revealed that homeostatic tissue architecture is achieved without active cell migration. In contrast, both fibroblast proliferation and migration are key determinants of tissue repair following wounding. The results show that tissue-scale coordination is driven by the interdependence of cell proliferation and ECM deposition, paving the way for identifying new therapeutic strategies to enhance skin regeneration.\n\nStandfirst textWe show that fibroblast behaviour switching between two distinct states - proliferating and depositing ECM - is necessary and sufficient to define dermal architecture. Understanding this interdependence is critical for identifying new therapeutic strategies to enhance skin regeneration.\n\nHighlightsO_LITissue-scale coordination in murine dermis is driven by the interdependence of cell proliferation and ECM deposition\nC_LIO_LIThe tissue architecture is set by a negative feedback loop between ECM deposition/remodelling and proliferation\nC_LIO_LIFibroblast lineages lose segregation with age\nC_LIO_LIFibroblast migration is the critical discriminator between dermal development and wound healing\nC_LI

systems biology

Functional Compensation Between Hematopoietic Stem Cells In Vivo

In most organ systems, regeneration is a coordinated effort that involves many stem cells, but little is known about whether and how individual stem cells compensate for the functional deficiencies of other stem cells. Functional compensation between stem cells is critically important during disease progression and treatment. Here, we show how individual hematopoietic stem cells (HSCs) in a mouse heterogeneously compensate for the deficiencies of other HSCs during lymphopoiesis by increasing their clonal expansion at specific differentiation stages. This compensation rescues the overall blood supply and influences blood cell types outside of the deficient lineages in distinct patterns. We have identified the molecular regulators and signaling pathways in HSCs that are involved in this process. Our data demonstrate how stem cells interact with each other to constitute a coordinated network that is robust enough to withstand minor functional disruptions. Exploiting the innate compensation capacity of stem cell networks may improve the prognosis and treatment of many diseases.

systems biology

Information Flow in Planar Polarity

In developing tissues, sheets of cells become planar polarised, enabling coordination of cell behaviours. It has been suggested that signalling of polarity information between cells may occur either bidirectionally or monodirectionally between the molecules Frizzled (Fz) and Van Gogh (Vang). Using computational modelling we find that both bidirectional and monodirectional signalling models reproduce known non-autonomous phenotypes derived from patches of mutant tissue of key molecules, but predict different phenotypes from double mutant tissue, which have previously given conflicting experimental results. Consequently, we re-examine experimental phenotypes in the Drosophila wing, concluding that signalling is most likely bidirectional. Our modelling suggests that bidirectional signalling can be mediated either indirectly via bidirectional feedbacks between asymmetric intercellular protein complexes, or directly via different affinities for protein binding in intercellular complexes, suggesting future avenues for investigation. Our findings offer insight into mechanisms of juxtacrine cell signalling and how tissue-scale properties emerge from individual cell behaviours.

systems biology

Non-parametric mixture models identify trajectories of childhood immune development relevant to asthma and allergy

Events in early life contribute to subsequent risk of asthma; however, the causes and trajectories of childhood wheeze are heterogeneous and do not always result in asthma. Similarly, not all atopic individuals develop wheeze, and vice versa. The reasons for these differences are unclear. Using unsupervised model-based cluster analysis, we identified latent clusters within a prospective birth cohort with deep immunological and respiratory phenotyping. We characterised each cluster in terms of immunological profile and disease risk, and replicated our results in external cohorts from the UK and USA. We discovered three distinct trajectories, one of which is a high-risk \"atopic\" cluster with increased propensity for allergic diseases throughout childhood. Atopy contributes varyingly to later wheeze depending on cluster membership. Our findings demonstrate the utility of unsupervised analysis in elucidating heterogeneity in asthma pathogenesis and provide a foundation for improving management and prevention of childhood asthma.

systems biology

Algorithms for Search for Common Ancestors

Generation theory was developed as a tool for studying self-reproducing systems. In this paper we show that this theory can be applied to a search for common ancestors of living organisms.\n\nWe give two algorithms with flowcharts in pseudocode for finding common ancestors of a set of microbes and describe the connections with the genography problem.

systems biology

The null additivity of multi-drug combinations

From natural ecology 1-4 to clinical therapy 5-8, cells are often exposed to mixtures of multiple drugs. Two competing null models are used to predict the combined effect of drugs: response additivity (Bliss) and dosage additivity (Loewe) 9-11. Here, noting that these models diverge with increased number of drugs, we contrast their predictions with measurements of Escherichia coli growth under combinations of up to 10 different antibiotics. As the number of drugs increases, Bliss maintains accuracy while Loewe systematically loses its predictive power. The total dosage required for growth inhibition, which Loewe predicts should be fixed, steadily increases with the number of drugs, following a square root scaling. This scaling is explained by an approximation to Bliss where, inspired by RA Fishers classical geometric model 12, dosages of independent drugs adds up as orthogonal vectors rather than linearly. This dose-orthogonality approximation provides results similar to Bliss, yet uses the dosage language as Loewe and is hence easier to implement and intuit. The rejection of dosage additivity in favor of effect additivity and dosage orthogonality provides a framework for understanding how multiple drugs and stressors add up in nature and the clinic.

systems biology

A minimal "push-pull" bistability model explains oscillations between quiescent and proliferative cell states.

A minimal model for oscillating between quiescent and growth/proliferation states, dependent on the availability of a central metabolic resource, is presented. From the yeast metabolic cycles (YMCs), metabolic oscillations in oxygen consumption are represented as transitions between quiescent and growth states. We consider metabolic resource availability, growth rates, and switching rates (between states) to model a relaxation oscillator explaining transitions between these states. This frustrated bistability model reveals a required communication between the metabolic resource that determines oscillations, and the quiescent and growth state cells. Cells in each state reflect memory, or hysteresis of their current state, and \"push-pull\" cells from the other state. Finally, a parsimonious argument is made for a specific central metabolite as the controller of switching between quiescence and growth states. We discuss how an oscillator built around the availability of such a metabolic resource is sufficient to generally regulate oscillations between growth and quiescence, through committed transitions.

systems biology