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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 433 records · Page 24Linked to original sources

bioRxiv2019

A Multicellular Network Mechanism for Temperature-Robust Food Sensing

Responsiveness to external cues is a hallmark of biological systems. In complex environments, organisms must remain responsive to specific inputs even as other internal or external factors fluctuate. Here we show how Caenorhabditis elegans can discriminate between food levels to modulate lifespan despite temperature perturbations. While robustness of fixed outputs has been described, our findings uncover a more complex robustness process that maintains food-responsiveness. This end-to-end robustness from environment to physiology is mediated by food-sensing neurons that communicate via TGF-{beta} and serotonin signals to form a multicellular gene network. Mechanistically, specific regulations in this network change with temperature to maintain similar food-responsiveness in the lifespan output. Together, our findings provide a basis for gene-environment interactions and unveil computations that integrate environmental cues to govern physiology.

systems biology↗

bioRxiv2026

A Computational Framework for Extracting Mechanistic Hypotheses from Quantitative Data of Morphological Dynamics

Advances in live-cell imaging and image analysis have made it possible to quantitatively measure the spatiotemporal morphological dynamics of biological phenomena at scale. However, a general framework is still lacking for systematically extracting, from the resulting multivariate data, which relationships between phenotypic characters are mechanistically interpretable and which gene perturbations disrupt those relationships. Here, we propose a computational framework for extracting mechanistic hypotheses and candidate genes from quantitative data of morphological dynamics. First, we detect reproducible correlations between phenotypic characters in wild-type data and interpret them as mechanistic hypotheses in light of existing knowledge. Next, we perform outlier analysis on data obtained under gene perturbation and extract, as candidates, genes that selectively disrupt relationships between phenotypic characters maintained in the wild type. We further integrate the extracted relationships into a spatiotemporal network to provide an overview of how phenotypic characters are linked across the developmental process. As a proof of concept, we applied the framework to quantitative data on nuclear division dynamics during early embryogenesis in Caenorhabditis elegans and recovered relationships between phenotypic characters consistent with known mechanisms while prioritizing candidate genes. This framework provides a useful basis for efficiently generating testable mechanistic hypotheses from quantitative data of morphological dynamics. Author SummaryHow does a single cell give rise to a complex organism? Answering this question requires understanding not only which genes are active, but how the physical behavior of cells--their shapes, positions, and movements--is coordinated across time and space. Live-cell imaging now allows researchers to measure these morphological dynamics in quantitative detail, yet extracting biological meaning from the resulting large, high-dimensional datasets remains a challenge. Here we present a computational framework that addresses this challenge by treating correlations between quantitative morphological measurements as windows into the underlying biological machinery. Applied to the nematode Caenorhabditis elegans, a powerful model organism whose early development is exquisitely reproducible, our approach automatically identifies pairs of cellular measurements that reliably co-vary in normal embryos and interprets these relationships as reflecting shared biological mechanisms. When genes are inactivated one at a time and the resulting embryos deviate from the expected co-variation, those genes are flagged as candidates for the disrupted mechanism. In a systematic test using embryos in which 263 genes had been individually inactivated, the framework correctly prioritized genes with known roles in spindle positioning and cell polarity. By converting large-scale morphodynamic datasets into a network of testable mechanistic hypotheses, this framework offers a broadly applicable strategy for moving from quantitative phenotyping to mechanistic understanding across diverse biological systems.

Systems Biology↗

bioRxiv2026

Deep learning of dynamic signatures resolves mechanistic ambiguity in complex signaling networks

Sparse experimental data often yields vast mechanistic hypothesis spaces with numerous equally probable models. Traditional model selection metrics like the Akaike Information Criterion fall short because they reduce models nonlinear dynamics to a scalar score that masks crucial mechanistic details. Here, we introduce an AI-driven framework treating model dynamics as learnable signatures. Using deep learning autoencoders, we embed the dynamic signatures of thousands of competing models into a low-dimensional latent space. Iterative clustering and physiological constraints systematically refine this space to a manageable number of testable hypotheses. Applying this to the Integrated Stress Response, a fundamental cellular homeostasis mechanism, we converged on 12 compelling mechanistic hypotheses from over 12,000 candidates. Notably, this framework confidently rejected structure-derived kinetic assumptions regarding higher-order PKR activation that traditional metrics could not resolve. Ultimately, this method provides a rigorous, full-state alternative to scalar metrics, accelerating the discovery of driving principles in complex biological systems.

systems biology↗

bioRxiv2026

Agentic-AI-ready genome-wide poxvirus-host interaction screen refined by a protein language model

Recent mpox outbreaks highlight the necessity to understand the interactions between poxviruses and the human host. These can be discovered systematically through screening for host genes involved in infection at the single-cell level using RNA interference. However, off-target effects and assay noise obscure true gene-phenotype relationships, hampering the discovery of therapeutically relevant targets. Here, we show that integrating protein-protein interaction information derived from a protein language model boosts the discovery of vaccinia virus-host interactions. We propose ICARus - a positive-unlabelled read-out refinement framework to achieve this. Our approach enhances the identification of human genes with potential antiviral function. We provide the raw and refined read-outs of a genome-wide screen for vaccinia virus host factors as an agentic-AI-enabled community resource. Our findings provide a generalisable strategy for robust hit prioritisation in functional screens, accelerating discovery across complex biological systems.

systems biology↗

bioRxiv2026

Scalable and semi-automated Boolean network inference from multi-condition single-cell transcriptomes with scBOLT

Predictive logical models have made substantial contributions to biomedical research by facilitating the identification of regulatory mechanisms driving biological processes. Yet, their construction remains a major bottleneck, as it typically relies on extensive prior knowledge at the genome scale. The emergence of high-throughput omics technologies has opened new opportunities for data-driven logical model construction by providing high-resolution observations of biological states. In parallel, formal constraint-based approaches have emerged, reformulating executable model inference as a constraint-satisfaction problem. However, transforming omics observations into biologically meaningful Boolean state abstractions and dynamical constraints suitable for logical synthesis engines remains challenging, particularly for poorly understood biological systems and multi-condition experimental settings. To address these challenges, we introduce scBOLT, a semi-automated framework for Boolean constraint engineering and logical model inference from multi-condition single-cell transcriptomes. Beyond these methodological contributions, scBOLT substantially improves computational tractability, enabling the inference of Boolean networks comprising several hundred components within practical runtimes.

systems biology↗

bioRxiv2026

FluxSprout: Single-cell metabolic modeling of vascular sprouting in zebrafish

Genome-scale metabolic modeling offers a powerful framework for identifying cell-state-specific metabolic vulnerabilities, yet its application to heterogeneous biological systems remains limited by uncertainty in network annotations, thermodynamic feasibility, transcriptome-to-flux inference, and target specificity. Here, we introduce FluxSprout, a single-cell transcriptomics-informed genome-scale metabolic modeling framework to resolve metabolic heterogeneity during zebrafish sprouting angiogenesis. By integrating single-cell RNA sequencing of 10,090 quality-controlled cells with Zebrafish-GEM, we reconstructed network ensembles for 14 endothelial and hemogenic states. We established four safeguards: stoichiometric auditing, loop auditing, cell-cycle-controlled testing, and ensemble specificity screening. First, we identified four inorganic pyrophosphatase reactions misannotated as oxidative phosphorylation, showing that apparent pathway essentiality arose only with unbounded exchange fluxes. Second, 19.5% of reactions could carry thermodynamically infeasible loop flux, including every candidate of a conventional screen. Third, tip-like and stalk-like cells differed mainly in cell-cycle phase, and elevated glycolysis separated sprouting from established vessels only in gene expression. In contrast, sprouting vessels had higher predicted growth capacity at both constraint strengths, with serine and nucleotide synthesis following in fixed proportion. Fourth, screening 825 genes across four network-extraction settings and 13 non-endothelial lineages left no candidate called by more than one setting. Notably, SLC25A20, that screen's lead candidate, had no predicted effect in any FASTCORE network. Although the data derive from a single pooled clutch and require experimental validation, FluxSprout provides a transferable, uncertainty-aware framework for distinguishing genuine metabolic dependencies from artifacts of network reconstruction, model assumptions, and cellular heterogeneity, and identifies biosynthetic capacity as a testable feature of sprouting vessels.

systems biology↗

bioRxiv2026

Uncovering disease progression trajectories across multiple timescales with hybrid ODE-mixture models

Biological systems are capable of evolving across multiple timescales, yet most modeling approaches capture either short-term physiological responses or long-term disease progression. Additionally, methods that combine these time scales either struggle with inter-individual variation, or require large volumes of data to work. We present Latent Class Parameter Trajectory Analysis (LCPTA), a hybrid framework that combines mechanistic ordinary differential equation (ODE) modeling of short-term dynamics with latent class trajectory modeling of long-term parameter evolution. To support this approach, we developed TemporalMixtureModels.jl, a Julia package for univariate and multivariate temporal mixture modeling. We evaluated LCPTA in two synthetic longitudinal studies, covering a simulation of a heterogeneous population developing hypertension, as well as a synthetic longitudinal study of 75 virtual individuals followed over 30 years, representing muscle insulin resistance, liver insulin resistance, and healthy trajectories. We compared LCPTA with a conditional neural ODE and a conditional neural network for reconstruction of long-term parameter trajectories and subgroup separation. In both studies LCPTA recovered relevant subgroup structure and trajectory patterns. In the cardiovascular study, the neural approaches were able to distinguish inter-individual differences, but in the insulin sensitivity study, neural approaches failed to represent parameter trajectories faithfully, primarily separating individuals along a single dominant parameter and showing weaker multivariate trajectory reconstruction. In additional experiments with sparse and irregularly sampled unseen individuals, LCPTA maintained strong classification performance. We show that LCPTA is an efficient and interpretable strategy for multiscale modeling of heterogeneous metabolic disease progression, with potential utility for data-efficient digital twin applications.

systems biology↗

bioRxiv2018

Investigation of Electrical Stimulation Effect on Intracellular Calcium Dynamics Using Locally Uniform Mesh Refinement

Modeling-based methods are conventionally exploited to simulate and predict the dynamics of a system in various fields of engineering. However, due to the intricacy of biological systems, these methods were rarely utilized in bioengineering until recently. By developing advanced computers with the ability to cope with enormous calculations and extending our knowledge about biological systems, modeling-based approaches have been adopted to manipulate biological systems. While utilizing the models to investigate the behavior of a system has some advantages including lower cost and time consumption, such methods were used for tissue engineering scarcely. Exploiting modeling-based methods to tissue engineering field requires developing and utilizing efficient computational methods to model gene regulation and signaling networks, which determine cell response to environmental changes and cellular fate.\n\nIn the present work, a novel spatio-temporal method was proposed, predicated on locally uniform mesh refinement. A benchmark comparison was performed with the previously used method and the results indicated the better performance of the incipiently adopted method. Besides, the model was utilized to investigate the effect of a popular differentiating stimulator, namely electrical stimulation, on mesenchymal stem cells as a type of stem cells widely used in tissue engineering. The results of the simulations demonstrated the puissance of such computational models in analyzing and predicting the effect of biochemical and biophysical perturbations on the cells and hence, their potential utility in tissue engineering.

systems biology↗

bioRxiv2026

A Master-Key DNA System Enabling Programmable Cross-Talks in Biomimetic Networks via An Artificial Chaperone

Biological systems operate through complex molecular networks programmed by genetic information; however, constructing artificial systems with multilayered control remains a significant challenge. Here, we report a simple and integrated master-key system governed by lock DNA and master key DNA, reversibly switching diverse downstream processes ON/OFF and achieving dynamic cross-talks among distinct molecular components. The system utilizes cationic copolymer chaperones as control nodes, based on poly(L-lysine) or poly(allylamine) grafted with hydrophilic side chains, with a peptide nucleic acid (PNA) plug-in that grants sequence-specificity. We demonstrated two proof-of-concept systems: a nucleic acid-based catalytic network responsive to microRNA let-7b and a peptide-mediated transformation of lipid bilayers from two-dimensional sheets to three-dimensional vesicles. Both systems exhibited precise, modular, and programmable control with high robustness, mimicking the governing role of nucleic acids in biological systems. This strategy provides a versatile design framework for constructing biomimetic molecular networks and studying biological systems.

bioengineering↗

bioRxiv2023

Biphasic responses in cell signalling: A unified approach

Biphasic responses are encountered at all levels in biological systems. At the cellular level, biphasic dose-responses are widely encountered in cell signalling and post-translational modification systems and represent safeguards against over-activation or overexpression of species. In this paper we provide a unified theoretical synthesis of biphasic responses in cell signalling systems, by assessing signalling systems ranging from basic biochemical building blocks to canonical network structures to well-characterized exemplars on one hand, and examining different types of doses on the other. By using analytical and computational approaches applied to a range of systems across levels (described by broadly employed models) we reveal (i) design principles enabling the presence of biphasic responses, including in almost all instances, an explicit characterization of the parameter space (ii) structural factors which preclude the possibility of biphasic responses (iii) different combinations of the presence or absence of enzyme-biphasic and substrate-biphasic responses, representing safeguards against overactivation and overexpression respectively (iv) the possibility of broadly robust biphasic responses (v) the complete alteration of signalling behaviour in a network due to biphasic interactions between species (biphasic regulation) (vi) the propensity of different co-existing biphasic responses in the Erk signalling network. These results both individually and in totality have a number of important consequences for systems and synthetic biology.

systems biology↗

bioRxiv2021

AI-assisted Biology: Predict the Conditional Probability Distributions from Noisy Measurements

Biological systems are inherently noisy so that two genetically identical cells in the exact same environment will sometimes behave in dramatically different ways. This imposes a big challenge in building traditional supervised machine learning models that can only predict determined phenotypic variables or categories per specific input condition. Furthermore, biological noise has been proven to play a crucial role in gene regulation mechanisms. The prediction of the average value of a given phenotype is not always sufficient to fully characterize a given biological system. In this study, we develop a deep learning algorithm that can predict the conditional probability distribution of a phenotype of interest with a small number of observations per input condition. The key innovation of this study is that the deep neural network automatically generates the probability distributions based on only few (10 or less) noisy measurements for each input condition, with no prior knowledge or assumption of the probability distributions. This is extremely useful for exploring unknown biological systems with limited measurements for each input condition, which is linked not only to a better quantitative understanding of biological systems, but also to the design of new ones, as it is in the case of synthetic biology and cellular engineering.

systems biology↗

bioRxiv2024

Bayesian Occam's Razor to Optimize Models for Complex Systems

Modeling complex biological systems necessitates the integration of vast and multifaceted information spanning various aspects of these systems, and is expected to yield more insights into the system than any of the inputs. Metamodeling, a specialized form of integrative modeling, addresses this by integrating existing models. Developing and optimizing metamodels pose challenges due to the complexities introduced by diverse input models and their inherent uncertainties. In this study, we employ Bayesian formalism to rigorously analyze the propagation of probability throughout the metamodeling process and propose quantitative assessments for it. Building on this, we introduce a method for optimizing metamodeling that adheres to the Bayesian Occams razor rationale, by (i) minimizing model uncertainty; (ii) maximizing model consistency; and (iii) reducing model complexity. To illustrate the benefits of this method, we apply it to the dynamic system of glucose-stimulated insulin secretion in pancreatic {beta}-cells. The optimized metamodel delivers more accurate estimates of impaired {beta}-cell dynamics and function in T2D subjects compared to the non-optimized one, underscoring the critical role of optimization in enhancing both model reliability and applicability. This method is implemented through the Integrative Modeling Platform (IMP), facilitating the development of accurate, precise, and sufficiently simple models for a variety of complex systems.

bioinformatics↗

bioRxiv2023

Cladosporium species detoxify multiple water micropollutants of emerging concern using diverse strategies

The accumulation of micropollutants of emerging concern in aqueous systems raises safety concerns regarding biological systems and human health. Mycoremediation is a promising and green strategy to mitigate the micropollutant challenge. Hitherto, focus has mainly been on white-rot Basidiomycota and micropollutant transformation by ascomycetes remains underexplored. Here, we assayed 53 Ascomycota isolates from 10 genera for the removal of 22 micropollutants. Notably, 9 out of 22 micropollutants were removed from fungal culture supernatant at efficacies >45%. Temporal analysis of the nine top- performing strains, highlighted remarkable potency of Cladosporium isolates in removal of multiple micropollutants. Importantly, Cladosporium considerably reduced the toxicity of a micropollutant cocktail based on growth assays. Metabolomics analyses identified oxidation for 5-methyl-1H-benzotriazole and citalopram, whereas methylation and carboxylation were observed for 5-chlorobenzotriazole. No transformation products were detected for ciprofloxacin, sulfamethoxazole, and sertraline, hinting their extensive degradation. These findings suggest micropollutant transformation via diverse catalytic routes by Cladosporium. Genome sequencing and proteomic analyses of the top-performing isolates were consistent with the observed transformations and tentatively identified the molecular apparatus, conferring micropollutant transformation. This unprecedented study brings novel insight into the micropollutant transformation and detoxification capabilities of the prevalent Cladosporium species, thereby revealing a considerable and hitherto underappreciated potential of this genus and potentially other ascomycetes in micropollutant transformation. ImportanceAt present, conventional wastewater treatment plants (WWTPs) are not designed for removing micropollutants, which are released into aqueous systems. This raises concerns due to the poor insight into micropollutant long-term interplay with biological systems. Innovating biotechnological solutions to tackle micropollutant require addressing the paucity of knowledge on microbial groups and molecular pathways, which mediate micropollutant transformation. Our study highlights the considerable potential of the Cladosporium genus that remains underexplored in the arena of micropollutant transformation. We report the first genomes sequences for three Cladosporium species: C. allicinum, C. inversicolor, and C. fusiforme, which sets the stage for further analyses of micropollutant transformation, but also offers an important resource on this ecologically significant, albeit under-studied genus and related Ascomycota.

microbiology↗

bioRxiv2026

Antimony 3: Extending human-readable model definitions for SBML Level 3 Core and Packages

Antimony is a human-readable language for defining and sharing models developed by the systems biology community. It enables scientists to describe biochemical networks with a simple syntax, while supporting seamless conversion to and from the Systems Biology Markup Language (SBML) community standard. Since Antimonys original release, both SBML and modeling practices have evolved significantly, creating a need to update Antimony to maintain its standards compliance and practical relevance. In this paper, we introduce Antimony 3, a comprehensive update that formalizes its cumulative improvements and extends its support for SBML Level 3 Core and Flux Balance Constraints (FBC), Distributions, Layout, and Render packages. Antimony 3 enables model specifications that combine kinetic reactions with flux balance analysis, represent uncertainty using probability distributions, add biological context through annotations, and define publication-ready visualizations, all within a unified plain-text format. Antimony 3 is delivered as a lightweight C/C++ library with a stable C API. It is available through official bindings for Python, Julia, and JavaScript/WebAssembly, as well as a cross-platform desktop GUI, which enables straightforward use across scripting environments, desktop applications, and browser-based tools. Antimony 3 is released as open-source software under the BSD 3-Clause License and is available at https://github.com/sys-bio/antimony. Author SummaryBiological models are typically stored in standardized formats that ensure compatibility across different software tools, but these formats rely on verbose, machine-readable syntax that is difficult for humans to write or inspect directly. Antimony addresses this challenge by providing an intuitive, text-based language for defining biological models that can be automatically converted to and from the Systems Biology Markup Language (SBML). Since Antimonys original release in 2009, the SBML standard and common modeling workflows have expanded significantly. We developed Antimony 3 to support these advances, enabling researchers to write a single human-readable text file that defines reaction networks, constraint-based objectives, uncertainty in parameters and initial conditions, semantic annotations linking to biological databases, and model diagrams. Antimony 3 is provided as open-source software with broad support across computational environments, making it accessible to researchers in a wide range of workflows.

systems biology↗

bioRxiv2021

Climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics

Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on earth at high geospatial resolution. The results are captured as networks, in which edges between geolocations are defined if their historical climates exceed a similarity threshold. We then apply Markov clustering and our novel Correlation of Correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37 x 1018 operations/sec) and one of the largest (168.7 x 1021 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Correlation and network analysis methods of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.

systems biology↗

bioRxiv2020

Constructing and analysing dynamic models withn modelbasev1.0 - a software update

BackgroundComputational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies and disease evolution or transmission. Unfortunately, despite community efforts leading to the development of SBML or the BioModels database, many published models have not been fully exploited, largely due to lack of proper documentation or the dependence on proprietary software. To facilitate synergies within the emerging research fields of systems biology and medicine by reusing and further developing existing models, an open-source toolbox that makes the overall process of model construction more consistent, understandable, transparent and reproducible is desired. Results and DiscussionWe provide here the update on the development of modelbase, a free expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. It provides intuitive and unified methods to construct and solve these systems. Significantly expanded visualisation methods allow convenient analyses of structural and dynamic properties of the models. Specifying reaction stoichiometries and rate equations, the system of differential equations is assembled automatically. A newly provided library of common kinetic rate laws highly reduces the repetitiveness of the computer programming code, and provides full SBML compatibility. Previous versions provided functions for automatic construction of networks for isotope labelling studies. Using user-provided label maps, modelbase v1.0 streamlines the expansion of classic models to their isotope-specific versions. Finally, the library of previously published models implemented in modelbase is continuously growing. Ranging from photosynthesis over tumour cell growth to viral infection evolution, all models are available now in a transparent, reusable and unified format using modelbase. ConclusionWith the small price of learning a new software package, which is written in Python, currently one of the most popular programming languages, the user can develop new models and actively profit from the work of others, repeating and reproducing models in a consistent, tractable and expandable manner. Moreover, the expansion of models to their label specific versions enables simulating label propagation, thus providing quantitative information regarding network topology and metabolic fluxes.

systems biology↗

bioRxiv2022

Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome

High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion of 75 g oral glucose. We characterized each molecule by individual dependence (constant/variable) and time dependence (sustained/transient). Three of the four features extracted by TD were characterized by our previous hypothesis-driven study, indicating that TD can extract some of the same features obtained by hypothesis-driven analysis in a non-biased manner. In contrast to the years taken for our previous hypothesis-driven analysis, the data-driven analysis in this study took days, indicating that TD can extract biological features in a non-biased manner without the time-consuming process of hypothesis generation. Author SummaryFor biological interpretation of lage-scale omics data, two analyses, hypothesis-driven analysis and data-driven analysis including tensor decomposition, have been used. These two analyses have their own advantages and disadvantages, and are mutually complementary. However, the direct comparison between these two analyses for omic data is poorly examined. In this study, we applied tensor decomposition to a dataset representing temporal changes in the human 562 blood molecules as data-driven analysis and extracted three features. We have previously analyzed the same data by hypothesis-driven analysis (Fujita et al., 2022). The three features extracted by the tensor decomposition are the same features extracted by the hypothesis-driven analysis, indicating that the tensor decomposition can extract the features in an unbiased manner. Although the same features can be extracted by the tensor decomposition and hypothesis-driven analysis, hypothesis-driven analysis in our earlier study took years (Fujita et al., 2022), while feature extraction by tensor decomposition took only days in this study. Thus, tensor decomposition can extract biological features in a non-biased manner without time-consuming process of hypothesis generation. We propose that tensor decomposition can be the first choice for analysis of omic data.

systems biology↗

bioRxiv2020

Metabolic reprogramming and host-immune response against Crimean-Congo Hemorrhagic Fever Viruses

The pathogenesis and host-viral interactions of the Crimean-Congo hemorrhagic fever orthonairovirus (CCHFV) are convoluted and not well evaluated. Application of the multi-omics system biology approaches including biological network analysis in elucidating the complex host-viral response, allow for interrogating the viral pathogenesis. The present study aimed to fingerprint the system-level alterations during acute CCHFV-infection and the cellular immune responses during productive CCHFV-replication in vitro. We used system-wide network-based system biology analysis of peripheral blood mononuclear cells (PBMCs) from a longitudinal cohort of CCHF patients during the acute phase of infection and after one year of recovery (convalescent phase) and untargeted quantitative proteomics analysis of the most permissive CCHFV-infected Huh7 and SW13 cells. In the RNAseq analysis of the PBMCs, comparing the acute and convalescent-phase, we observed system-level hosts metabolic reprogramming towards central carbon and energy metabolism (CCEM) with distinct upregulation of oxidative phosphorylation (OXPHOS) during CCHFV-infection. Upon application of network-based system biology methods, negative coordination of the biological signaling systems like FOXO/Notch axis and Akt/mTOR/HIF-1 signaling with metabolic pathways during CCHFV-infection were observed. The temporal quantitative proteomics in Huh7 showed a dynamic change in the CCEM over time and was in agreement with the cross-sectional proteomics in SW13 cells. By blocking the two key CCEM pathways, glycolysis and glutaminolysis, viral replication was inhibited in vitro. Activation of key interferon stimulating genes during infection suggested the role of type I and II interferon-mediated antiviral mechanisms both at system-level and during progressive replication. Significance StatementA combination of multi-modal systems-wide host-immune response and in vitro temporal analysis identified molecular re-arrangement in CCEM and fingerprinting the interferon-mediated antiviral mechanism during CCHFV-infection. Using the newly gained insights, we then modulated the key pathways of CCEM by drugs and inhibited the productive CCHFV-replication in in vitro infection models. Our study thus provides a comprehensive, system-level picture of the regulation of cellular and metabolic pathways during productive CCHFV-infection for the first time that aids in identifying novel therapeutic targets and treatment strategies.

microbiology↗