Augusta: From RNA-Seq to gene regulatory networks and Boolean models.

Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.

COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery.

Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogeneous biological datasets, which can be challenging without efficient tools. We developed Constraint-based Optimization of Metabolic Objectives (COMO), a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases and disease data to aid drug discovery. COMO can be installed as a Docker Image or with Conda and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for the integration of bulk and single-cell RNA-seq, microarrays and proteomics outputs to develop context-specific metabolic models. Using public databases, open-source solutions for model construction and a streamlined approach for predicting repurposable drugs, COMO enables researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease where metabolic inhibition is relevant. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies. The source code of the COMO pipeline is available at https://github.com/HelikarLab/COMO. The Docker image can be pulled at https://github.com/HelikarLab/COMO/pkgs/container/como.

A multiscale mechanistic model of human dendritic cells for in-silico investigation of immune responses and novel therapeutics discovery.

Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model's components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Arginine Catabolism and Polyamine Biosynthesis Pathway Disparities Within Francisella tularensis Subpopulations.

Francisella tularensis is a highly infectious zoonotic pathogen with as few as 10 organisms causing tularemia, a disease that is fatal if untreated. Although F. tularensis subspecies tularensis (type A) and subspecies holarctica (type B) share over 99.5% average nucleotide identity, notable differences exist in genomic organization and pathogenicity. The type A clade has been further divided into subtypes A.I and A.II, with A.I strains being recognized as some of the most virulent bacterial pathogens known. In this study, we report on major disparities that exist between the F. tularensis subpopulations in arginine catabolism and subsequent polyamine biosynthesis. The genes involved in these pathways include the speHEA and aguAB operons, along with metK. In the hypervirulent F. tularensis A.I clade, such as the A.I prototype strain SCHU S4, these genes were found to be intact and highly transcribed. In contrast, both subtype A.II and type B strains have a truncated speA gene, while the type B clade also has a disrupted aguA and truncated aguB. Ablation of the chromosomal speE gene that encodes a spermidine synthase reduced subtype A.I SCHU S4 growth rate, whereas the growth rate of type B LVS was enhanced. These results demonstrate that spermine synthase SpeE promotes faster replication in the F. tularensis A.I clade, whereas type B strains do not rely on this enzyme for in vitro fitness. Our ongoing studies on amino acid and polyamine flux within hypervirulent A.I strains should provide a better understanding of the factors that contribute to F. tularensis pathogenicity.

A computational approach to investigate constitutive activation of NF-κB.

Nuclear factor kappa B (NF-κB) signaling is the master regulator of inflammatory pathways; therefore, its regulation has been the subject of investigation since last two decades. Multiple models have been published that describes the dynamics of NF-κB activity by stimulated activation and feedback loops. However, there is also paramount evidence of the critical role of posttranslational modifications (PTMs) in the regulation of NF-κB pathway. With the premise that PTMs present alternate routes for activation or repression of the NF-κB pathway, we have developed a model including all PTMs known so far describing the system behavior. We present a pathway network model consisting of 171 proteins forming 315 molecular species and consisting of 482 reactions that describe the NF-κB activity regulation in totality. The overexpression or knockdown of interacting molecular partners that regulate NF-κB transcriptional activity by PTMs is used to infer the dynamics of NF-κB activity and offers qualitative agreement between model predictions and the experimental results heuristically. Finally, we have demonstrated an instance of NF-κB constitutive activation through positive upregulation of cytokines (the stimuli) and IKK complex (NF-κB activator), the characteristic features in several cancer types and metabolic disorders, and its reversal by employing combinatorial activation of PPARG, PIAS3, and P50-homodimer. For the first time, we have presented a NF-κB model that includes transcriptional regulation by PTMs and presented a theoretical strategy for the reversal of NF-κB constitutive activation. The presented model would be important in understanding the NF-κB system, and the described method can be used for other pathways as well.

Essential role of systemic iron mobilization and redistribution for adaptive thermogenesis through HIF2-α/hepcidin axis.

Iron is an essential biometal, but is toxic if it exists in excess. Therefore, iron content is tightly regulated at cellular and systemic levels to meet metabolic demands but to avoid toxicity. We have recently reported that adaptive thermogenesis, a critical metabolic pathway to maintain whole-body energy homeostasis, is an iron-demanding process for rapid biogenesis of mitochondria. However, little information is available on iron mobilization from storage sites to thermogenic fat. This study aimed to determine the iron-regulatory network that underlies beige adipogenesis. We hypothesized that thermogenic stimulus initiates the signaling interplay between adipocyte iron demands and systemic iron liberation, resulting in iron redistribution into beige fat. To test this hypothesis, we induced reversible activation of beige adipogenesis in C57BL/6 mice by administering a ß3-adrenoreceptor agonist CL 316,243 (CL). Our results revealed that CL stimulation induced the iron-regulatory protein-mediated iron import into adipocytes, suppressed hepcidin transcription, and mobilized iron from the spleen. Mechanistically, CL stimulation induced an acute activation of hypoxia-inducible factor 2-α (HIF2-α), erythropoietin production, and splenic erythroid maturation, leading to hepcidin suppression. Disruption of systemic iron homeostasis by pharmacological HIF2-α inhibitor PT2385 or exogenous administration of hepcidin-25 significantly impaired beige fat development. Our findings suggest that securing iron availability via coordinated interplay between renal hypoxia and hepcidin down-regulation is a fundamental mechanism to activate adaptive thermogenesis. It also provides an insight into the effects of adaptive thermogenesis on systemic iron mobilization and redistribution.

A multi-approach and multi-scale platform to model CD4+ T cells responding to infections.

Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node's ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology.

An in silico, structural, and biological analysis of lactoferrin of different mammals.

Lactoferrin (LF) belongs to the family of transferrins having multifunctional roles associated with the immune system of animals. To follow the aims for this study was selected 20 sequences of LF from mammalian species to evaluate the chemical, biological, and structural properties. Bioinformatics approaches used programs such as MAFFT for sequence alignment; PartitionFinder and MrBayes for phylogenetic approaches; I-TASSER, PROCHECK, Molecular Operating Environment (MOE), SWISS Model server, Peptide DB and Expasy ProtParam to estimate the physicochemical properties, to model the protein and predicted secondary structures. A phylogenic analysis shows species with genetic similarities clustered by complexity and unique grouping between Capra hircus, Macaca mulatta, and Myotis lucifugus, since they presented more amino acids but not overall changes in the iron-binding sites or biological aspects. Structural deviations in these clusters obtained in LF from those species were found in residues 46 (position 406-450), that is part of alpha-helix, and 37 (position 295-331), that is part of the beta-sheets. Our predicted model can be used to investigate more about structural aspects of LF and be applied for medicinal research.

Integrative network analyses of transcriptomics data reveal potential drug targets for acute radiation syndrome.

Recent political unrest has highlighted the importance of understanding the short- and long-term effects of gamma-radiation exposure on human health and survivability. In this regard, effective treatment for acute radiation syndrome (ARS) is a necessity in cases of nuclear disasters. Here, we propose 20 therapeutic targets for ARS identified using a systematic approach that integrates gene coexpression networks obtained under radiation treatment in humans and mice, drug databases, disease-gene association, radiation-induced differential gene expression, and literature mining. By selecting gene targets with existing drugs, we identified potential candidates for drug repurposing. Eight of these genes (BRD4, NFKBIA, CDKN1A, TFPI, MMP9, CBR1, ZAP70, IDH3B) were confirmed through literature to have shown radioprotective effect upon perturbation. This study provided a new perspective for the treatment of ARS using systems-level gene associations integrated with multiple biological information. The identified genes might provide high confidence drug target candidates for potential drug repurposing for ARS.

Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders.

CD4+ T cells provide adaptive immunity against pathogens and abnormal cells, and they are also associated with various immune-related diseases. CD4+ T cells' metabolism is dysregulated in these pathologies and represents an opportunity for drug discovery and development. Genome-scale metabolic modeling offers an opportunity to accelerate drug discovery by providing high-quality information about possible target space in the context of a modeled disease. Here, we develop genome-scale models of naïve, Th1, Th2, and Th17 CD4+ T-cell subtypes to map metabolic perturbations in rheumatoid arthritis, multiple sclerosis, and primary biliary cholangitis. We subjected these models to in silico simulations for drug response analysis of existing FDA-approved drugs and compounds. Integration of disease-specific differentially expressed genes with altered reactions in response to metabolic perturbations identified 68 drug targets for the three autoimmune diseases. In vitro experimental validation, together with literature-based evidence, showed that modulation of fifty percent of identified drug targets suppressed CD4+ T cells, further increasing their potential impact as therapeutic interventions. Our approach can be generalized in the context of other diseases, and the metabolic models can be further used to dissect CD4+ T-cell metabolism.

Differences in Blood-derived Francisella tularensis Type B Strains from Clinical Cases of Tularemia.

Francisella tularensis can cause the zoonotic disease tularemia and is partitioned into subspecies due to differences in chromosomal organization and virulence. The subspecies holarctica (type B) is generally considered more clonal than the other subpopulations with moderate virulence compared to the hypervirulent A.I clade. We performed whole genome sequencing (WGS) on six type B strains isolated from the blood of patients with tularemia within a one-year period from the same United States region, to better understand the associated pathogenicity. The WGS data were compared to the prototype strain for this subspecies, specifically FSC200, which was isolated from a patient with tularemia in Europe. These findings revealed 520-528 single nucleotide polymorphisms (SNPs) between the six United States type B strains compared to FSC200, with slightly higher A+T content in the latter strain. In contrast, comparisons between the six type B isolates showed that five of the six type B isolates had only 4-22 SNPs, while one of the strains had 47-53 SNPs. Analysis of SNPs in the core genome for the six United States type B isolates and the FSC200 strain gave similar results, suggesting that some of these mutations may have been nonsynonymous, resulting in altered protein function and pathogenicity.

Combined TLR4 and TLR9 agonists induce distinct phenotypic changes in innate immunity in vitro and in vivo.

Toll-like receptor (TLR)4 and TLR9 agonists, MPL and CpG, are used as adjuvants in vaccines and have been investigated for their combined potential. However, how these two combined agonists regulate transcriptional changes in innate immune cells and cells at the site of vaccination has not been thoroughly investigated. Here, we utilized transcriptomics to investigate how CpG, MPL, and CpG + MPL impact gene expression in dendritic cells (DC) in vitro. Principal component analysis of transcriptional changes after single and combined treatment indicated that CpG, MPL, and CpG + MPL caused distinct gene signatures. CpG + MPL induced antiviral gene expression and activated the interferon regulatory factor pathway. In vitro changes were associated with lower in vivo morbidity upon viral challenge, elevated systemic cytokine protein production, local cytokine mRNA expression, and increased migratory monocyte derived DC populations in the draining lymph node following vaccination with CpG + MPL. This report suggests that CpG + MPL enhances transcription of antiviral and inflammatory genes and increases DC migration.

A Mechanistic Computational Model Reveals That Plasticity of CD4+ T Cell Differentiation Is a Function of Cytokine Composition and Dosage.

CD4+ T cells provide cell-mediated immunity in response to various antigens. During an immune response, naïve CD4+ T cells differentiate into specialized effector T helper (Th1, Th2, and Th17) cells and induced regulatory (iTreg) cells based on a cytokine milieu. In recent studies, complex phenotypes resembling more than one classical T cell lineage have been experimentally observed. Herein, we sought to characterize the capacity of T cell differentiation in response to the complex extracellular environment. We constructed a comprehensive mechanistic (logical) computational model of the signal transduction that regulates T cell differentiation. The model's dynamics were characterized and analyzed under 511 different environmental conditions. Under these conditions, the model predicted the classical as well as the novel complex (mixed) T cell phenotypes that can co-express transcription factors (TFs) related to multiple differentiated T cell lineages. Analyses of the model suggest that the lineage decision is regulated by both compositions and dosage of signals that constitute the extracellular environment. In this regard, we first characterized the specific patterns of extracellular environments that result in novel T cell phenotypes. Next, we predicted the inputs that can regulate the transition between the canonical and complex T cell phenotypes in a dose-dependent manner. Finally, we predicted the optimal levels of inputs that can simultaneously maximize the activity of multiple lineage-specifying TFs and that can drive a phenotype toward one of the co-expressed TFs. In conclusion, our study provides new insights into the plasticity of CD4+ T cell differentiation, and also acts as a tool to design testable hypotheses for the generation of complex T cell phenotypes by various input combinations and dosages.

T2DiACoD: A Gene Atlas of Type 2 Diabetes Mellitus Associated Complex Disorders.

We performed integrative analysis of genes associated with type 2 Diabetes Mellitus (T2DM) associated complications by automated text mining with manual curation and also gene expression analysis from Gene Expression Omnibus. They were analysed for pathogenic or protective role, trends, interaction with risk factors, Gene Ontology enrichment and tissue wise differential expression. The database T2DiACoD houses 650 genes, and 34 microRNAs associated with T2DM complications. Seven genes AGER, TNFRSF11B, CRK, PON1, ADIPOQ, CRP and NOS3 are associated with all 5 complications. Several genes are studied in multiple years in all complications with high proportion in cardiovascular (75.8%) and atherosclerosis (51.3%). T2DM Patients' skeletal muscle tissues showed high fold change in differentially expressed genes. Among the differentially expressed genes, VEGFA is associated with several complications of T2DM. A few genes ACE2, ADCYAP1, HDAC4, NCF1, NFE2L2, OSM, SMAD1, TGFB1, BDNF, SYVN1, TXNIP, CD36, CYP2J2, NLRP3 with details of protective role are catalogued. Obesity is clearly a dominant risk factor interacting with the genes of T2DM complications followed by inflammation, diet and stress to variable extents. This information emerging from the integrative approach used in this work could benefit further therapeutic approaches. The T2DiACoD is available at www.http://t2diacod.igib.res.in/ .

Integration of Metabolic Modeling with Gene Co-expression Reveals Transcriptionally Programmed Reactions Explaining Robustness in Mycobacterium tuberculosis.

Robustness of metabolic networks is accomplished by gene regulation, modularity, re-routing of metabolites and plasticity. Here, we probed robustness against perturbations of biochemical reactions of M. tuberculosis in the form of predicting compensatory trends. In order to investigate the transcriptional programming of genes associated with correlated fluxes, we integrated with gene co-expression network. Knock down of the reactions NADH2r and ATPS responsible for producing the hub metabolites, and Central carbon metabolism had the highest proportion of their associated genes under transcriptional co-expression with genes of their flux correlated reactions. Reciprocal gene expression correlations were observed among compensatory routes, fresh activation of alternative routes and in the multi-copy genes of Cysteine synthase and of Phosphate transporter. Knock down of 46 reactions caused the activation of Isocitrate lyase or Malate synthase or both reactions, which are central to the persistent state of M. tuberculosis. A total of 30 new freshly activated routes including Cytochrome c oxidase, Lactate dehydrogenase, and Glycine cleavage system were predicted, which could be responsible for switching into dormant or persistent state. Thus, our integrated approach of exploring transcriptional programming of flux correlated reactions has the potential to unravel features of system architecture conferring robustness.

Systems Perturbation Analysis of a Large-Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics.

Dysregulation in signal transduction pathways can lead to a variety of complex disorders, including cancer. Computational approaches such as network analysis are important tools to understand system dynamics as well as to identify critical components that could be further explored as therapeutic targets. Here, we performed perturbation analysis of a large-scale signal transduction model in extracellular environments that stimulate cell death, growth, motility, and quiescence. Each of the model's components was perturbed under both loss-of-function and gain-of-function mutations. Using 1,300 simulations under both types of perturbations across various extracellular conditions, we identified the most and least influential components based on the magnitude of their influence on the rest of the system. Based on the premise that the most influential components might serve as better drug targets, we characterized them for biological functions, housekeeping genes, essential genes, and druggable proteins. The most influential components under all environmental conditions were enriched with several biological processes. The inositol pathway was found as most influential under inactivating perturbations, whereas the kinase and small lung cancer pathways were identified as the most influential under activating perturbations. The most influential components were enriched with essential genes and druggable proteins. Moreover, known cancer drug targets were also classified in influential components based on the affected components in the network. Additionally, the systemic perturbation analysis of the model revealed a network motif of most influential components which affect each other. Furthermore, our analysis predicted novel combinations of cancer drug targets with various effects on other most influential components. We found that the combinatorial perturbation consisting of PI3K inactivation and overactivation of IP3R1 can lead to increased activity levels of apoptosis-related components and tumor-suppressor genes, suggesting that this combinatorial perturbation may lead to a better target for decreasing cell proliferation and inducing apoptosis. Finally, our approach shows a potential to identify and prioritize therapeutic targets through systemic perturbation analysis of large-scale computational models of signal transduction. Although some components of the presented computational results have been validated against independent gene expression data sets, more laboratory experiments are warranted to more comprehensively validate the presented results.

Studies on the interactions of SAP-1 (an N-terminal truncated form of cystatin S) with its binding partners by CD-spectroscopic and molecular docking methods.

SAP-1 is a 113 amino acid long single-chain protein which belongs to the type 2 cystatin gene family. In our previous study, we have purified SAP-1 from human seminal plasma and observed its cross-class inhibitory property. At this time, we report the interaction of SAP-1 with diverse proteases and its binding partners by CD-spectroscopic and molecular docking methods. The circular dichroism (CD) spectroscopic studies demonstrate that the conformation of SAP-1 is changed after its complexation with proteases, and the alterations in protein secondary structure are quantitatively calculated with increase of α-helices and reduction of ß-strand content. To get insight into the interactions between SAP-1 and proteases, we make an effort to model the three-dimensional structure of SAP-1 by molecular modeling and verify its stability and viability through molecular dynamics simulations and finally complexed with different proteases using ClusPro 2.0 Server. A high degree of shape complementarity is examined within the complexes, stabilized by a number of hydrogen bonds (HBs) and hydrophobic interactions. Using HB analyses in different protein complexes, we have identified a series of key residues that may be involved in the interactions between SAP-1 and proteases. These findings will assist to understand the mechanism of inhibition of SAP-1 for different proteases and provide intimation for further research.