Exploring the immune-inflammatory mechanism of Maxing Shigan Decoction in treating influenza virus A-induced pneumonia based on an integrated strategy of single-cell transcriptomics and systems biology.

BACKGROUND: Influenza is an acute respiratory infection caused by influenza virus. Maxing Shigan Decoction (MXSGD) is a commonly used traditional Chinese medicine prescription for the prevention and treatment of influenza. However, its mechanism remains unclear. METHOD: The mice model of influenza A virus pneumonia was established by nasal inoculation. After 3 days of intervention, the lung index was calculated, and the pathological changes of lung tissue were detected by HE staining. Firstly, transcriptomics technology was used to analyze the differential genes and important pathways in mouse lung tissue regulated by MXSGD. Then, real-time fluorescent quantitative PCR (RT-PCR) was used to verify the changes in mRNA expression in lung tissues. Finally, intestinal microbiome and intestinal metabolomics were performed to explore the effect of MXSGD on gut microbiota. RESULTS: The lung inflammatory cell infiltration in the MXSGD group was significantly reduced (p < 0.05). The results of bioinformatics analysis for transcriptomics results show that these genes are mainly involved in inflammatory factors and inflammation-related signal pathways mediated inflammation biological modules, etc. Intestinal microbiome showed that the intestinal flora Actinobacteriota level and Desulfobacterota level increased in MXSGD group, while Planctomycetota in MXSGD group decreased. Metabolites were mainly involved in primary bile acid biosynthesis, thiamine metabolism, etc. This suggests that MXSGD has a microbial-gut-lung axis regulation effect on mice with influenza A virus pneumonia. CONCLUSION: MXSGD may play an anti-inflammatory and immunoregulatory role by regulating intestinal microbiome and intestinal metabolic small molecules, and ultimately play a role in the treatment of influenza A virus pneumonia.

DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.

Systems biology of B cells in COVID-19.

The integration of multi-'omic datasets into complex systems-wide assessments has become a mainstay in immunologic investigation. This focus on high-dimensional data collection and analysis was on full display in the investigation of COVID-19, the respiratory illness resulting from infection by the novel coronavirus SARS-CoV-2. Particularly in the area of B cell biology, tremendous efforts in both cellular and serologic investigation have resulted in an increasingly detailed mapping of the coordinated effector, memory, and antibody secreting cell responses that underpin the development of humoral immunity in response to primary viral infection. Further, the rapid development and deployment of effective vaccines has allowed for the assessment of developing memory responses across a wide variety of immune contexts, including in patients with compromised immune function. The result has been a period of rapid gains in the understanding of B cell biology unrestricted to the study of COVID-19. Here, we outline the systems-level technologies that have been routinely implemented in these investigations throughout the pandemic, and discuss how their use has led to clear and applicable gains in pursuance of the amelioration of human infectious disease and beyond.

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers.

Germinal centers (GCs) are the key histological structures of the adaptive immune system, responsible for the development and selection of B cells producing high-affinity antibodies against antigens. Due to their level of complexity, unexpected malfunctioning may lead to a range of pathologies, including various malignant formations. One promising way to improve the understanding of malignant transformation is to study the underlying gene regulatory networks (GRNs) associated with cell development and differentiation. Evaluation and inference of the GRN structure from gene expression data is a challenging task in systems biology: recent achievements in single-cell (SC) transcriptomics allow the generation of SC gene expression data, which can be used to sharpen the knowledge on GRN structure. In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell differentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell differentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the different types of dysfunction of the regulatory mechanisms.

AI-Aristotle: A physics-informed framework for systems biology gray-box identification.

Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. We present a new physics-informed framework for parameter estimation and missing physics identification (gray-box) in the field of Systems Biology. The proposed framework-named AI-Aristotle-combines the eXtreme Theory of Functional Connections (X-TFC) domain-decomposition and Physics-Informed Neural Networks (PINNs) with symbolic regression (SR) techniques for parameter discovery and gray-box identification. We test the accuracy, speed, flexibility, and robustness of AI-Aristotle based on two benchmark problems in Systems Biology: a pharmacokinetics drug absorption model and an ultradian endocrine model for glucose-insulin interactions. We compare the two machine learning methods (X-TFC and PINNs), and moreover, we employ two different symbolic regression techniques to cross-verify our results. To test the performance of AI-Aristotle, we use sparse synthetic data perturbed by uniformly distributed noise. More broadly, our work provides insights into the accuracy, cost, scalability, and robustness of integrating neural networks with symbolic regressors, offering a comprehensive guide for researchers tackling gray-box identification challenges in complex dynamical systems in biomedicine and beyond.

Comprehensive bioinformatics analysis and systems biology approaches to identify the interplay between COVID-19 and pericarditis.

Background: Increasing evidence indicating that coronavirus disease 2019 (COVID-19) increased the incidence and related risks of pericarditis and whether COVID-19 vaccine is related to pericarditis has triggered research and discussion. However, mechanisms behind the link between COVID-19 and pericarditis are still unknown. The objective of this study was to further elucidate the molecular mechanisms of COVID-19 with pericarditis at the gene level using bioinformatics analysis. Methods: Genes associated with COVID-19 and pericarditis were collected from databases using limited screening criteria and intersected to identify the common genes of COVID-19 and pericarditis. Subsequently, gene ontology, pathway enrichment, protein-protein interaction, and immune infiltration analyses were conducted. Finally, TF-gene, gene-miRNA, gene-disease, protein-chemical, and protein-drug interaction networks were constructed based on hub gene identification. Results: A total of 313 common genes were selected, and enrichment analyses were performed to determine their biological functions and signaling pathways. Eight hub genes (IL-1ß, CD8A, IL-10, CD4, IL-6, TLR4, CCL2, and PTPRC) were identified using the protein-protein interaction network, and immune infiltration analysis was then carried out to examine the functional relationship between the eight hub genes and immune cells as well as changes in immune cells in disease. Transcription factors, miRNAs, diseases, chemicals, and drugs with high correlation with hub genes were predicted using bioinformatics analysis. Conclusions: This study revealed a common gene interaction network between COVID-19 and pericarditis. The screened functional pathways, hub genes, potential compounds, and drugs provided new insights for further research on COVID-19 associated with pericarditis.

A Practical Guide to the Representation of Protein Regulation in the Web Application ChloroKB.

ChloroKB ( http://chlorokb.fr ) is a knowledge base providing synoptic representations of the metabolism of the model plant Arabidopsis thaliana and its regulation. Initially focused on plastid metabolism, ChloroKB now accounts for the metabolism throughout the cell. ChloroKB is based on the CellDesigner formalism. CellDesigner supports graphical notation and listing of the corresponding symbols based on the Systems Biology Graphical Notation. Thus, this formalism allows biologists to represent detailed biochemical processes in a way that can be easily understood and shared, facilitating communication between researchers. In this chapter, we will focus on a specificity of ChloroKB, the representation of multilayered regulation of protein activity. Information on regulation of protein activity is indeed central to understanding the plant response to fluctuating environmental conditions. However, the intrinsic diversity of the regulatory modes and the abundance of detail may hamper comprehension of the regulatory processes described in ChloroKB. With this chapter, ChloroKB users will be guided through the representation of these sophisticated biological processes of prime importance to understanding metabolism or for applied purposes. The descriptions provided, which summarize years of work and a broad bibliography in a few pages, can help speed up the integration of regulatory processes in kinetic models of plant metabolism.

BioKC: a collaborative platform for curation and annotation of molecular interactions.

Curation of biomedical knowledge into systems biology diagrammatic or computational models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever-increasing growth of domain literature. New findings demonstrating elaborate relationships between multiple molecules, pathways and cells have to be represented in a format suitable for systems biology applications. Importantly, curation should capture the complexity of molecular interactions in such a format together with annotations of the involved elements and support stable identifiers and versioning. This challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, community-based curation, an important source of curated knowledge, requires support in role management, reviewing features and versioning. Here, we present Biological Knowledge Curation (BioKC), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML). BioKC offers a graphical user interface for curation of complex molecular interactions and their annotation with stable identifiers and supporting sentences. With the support of collaborative curation and review, it allows to construct building blocks for systems biology diagrams and computational models. These building blocks can be published under stable identifiers and versioned and used as annotations, supporting knowledge building for modelling activities.

Prediction of Essential Proteins of Klebsiella pneumoniae using Integrative Bioinformatics and Systems Biology Approach: Unveiling New Avenues for Drug Discovery.

Klebsiella pneumoniae is an opportunistic multidrug-resistant bacterial pathogen responsible for various health care-associated infections. The prediction of proteins that are essential for the survival of bacterial pathogens can greatly facilitate the drug development and discovery pipeline toward target identification. To this end, the present study reports a comprehensive computational approach integrating bioinformatics and systems biology-based methods to identify essential proteins of K. pneumoniae involved in vital processes. From the proteome of this pathogen, we predicted a total of 854 essential proteins based on sequence, protein-protein interaction (PPI) and genome-scale metabolic model methods. These predicted essential proteins are involved in vital processes for cellular regulation such as translation, metabolism, and biosynthesis of essential factors, among others. Cluster analysis of the PPI network revealed the highly connected modules involved in the basic functionality of the organism. Further, the predicted consensus set of essential proteins of K. pneumoniae was evaluated by comparing them with existing resources (NetGenes and PATHOgenex) and literature. The findings of this study offer guidance toward understanding cell functionality, thereby facilitating the understanding of pathogen systems and providing a way forward to shortlist potential therapeutic candidates for developing novel antimicrobial agents against K. pneumoniae. In addition, the research strategy presented herein is a fusion of sequence and systems biology-based approaches that offers prospects as a model to predict essential proteins for other pathogens.

Deciphering Breast Cancer Metastasis Cascade: A Systems Biology Approach Integrating Transcriptome and Interactome Insights for Target Discovery.

Breast cancer is the lead cause of cancer-related deaths among women globally. Breast cancer metastasis is a complex and still inadequately understood process and a key dimension of mortality attendant to breast cancer. This study reports dysregulated genes across metastatic stages and tissues, shedding light on their molecular interplay in disease pathogenesis and new possibilities for drug discovery. Comprehensive analyses of gene expression data from primary breast tumor, circulating tumor cells, and distant metastatic sites in the brain, lung, liver, and bone were conducted. Genes dysregulated across multiple stages and tissues were identified as metastatic cascade genes, and are further classified based on functional associations with metastasis-related mechanisms. Their interactions with HUB genes in interactome networks were scrutinized, followed by pathway enrichment analysis. Validation for their potential as targets included assessments for survival, druggability, prognostic marker status, secretome annotation, protein expression, and cell type marker association. Results displayed critical genes in the metastatic cascade and those specific to metastatic sites, revealing the involvement of the collagen degradation and assembly of collagen fibrils and other multimeric structure pathways in driving metastasis. Notably, pivotal cascade genes FABP4, CXCL12, APOD, and IGF1 emerged with high metastatic potential, linked to significant druggability and survival scores, establishing them as potential molecular targets. The significance of this research lies in its potential to uncover novel biomarkers for early detection, therapeutic targets, and a deeper understanding of the molecular mechanisms underpinning the metastatic cascade in breast cancer, and with an eye to precision/personalized medicine.

Bioinformatics and Systems Biology Approaches to Identify the Synergistic Effects of Alcohol Use Disorder on the Progression of Neurological Diseases.

Clinical investigations showed that individuals with Alcohol Use Disorder (AUD) have worse Neurological Disease (ND) development, pointing to possible pathogenic relationships between AUD and NDs. It remains difficult to identify risk factors that are predisposing between AUD and NDs. In order to fix these issues, we created the bioinformatics pipeline and network-based approaches for employing unbiased methods to discover genes abnormally stated in both AUD and NDs and to pinpoint some of the common molecular pathways that might underlie AUD and ND interaction. We found 100 differentially expressed genes (DEGs) in both the AUD and ND patient's tissue samples. The most important Gene Ontology (GO) terms and metabolic pathways, including positive control of cytotoxicity caused by T cells, proinflammatory responses, antigen processing and presentation, and platelet-triggered interactions with vascular and circulating cell pathways were then extracted using the overlapped DEGs. Protein-protein interaction analysis was used to identify hub proteins, including CCL2, IL1B, TH, MYCN, HLA-DRB1, SLC17A7, and HNF4A, in the pathways that have been reported as playing a function in these disorders. We determined several TFs (HNF4A, C4A, HLA-B, SNCA, HLA-DMB, SLC17A7, HLA-DRB1, HLA-C, HLA-A, and HLA-DPB1) and potential miRNAs (hsa-mir-34a-5p, hsa-mir-34c-5p, hsa-mir-449a, hsa-mir-155-5p, and hsa-mir-1-3p) were crucial for regulating the expression of AUD and ND which could serve as prospective targets for treatment. Our methodologies discovered unique putative biomarkers that point to the interaction between AUD and various neurological disorders, as well as pathways that could one day be the focus of therapeutic intervention.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine.

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Mechanisms Involved in Therapeutic Effects of Scutellaria baicalensis Georgi in Oral Squamous Cell Carcinoma Based on Systems Biology and Structural Bioinformatics Approaches.

Objective: Oral squamous cell carcinoma (OSCC) is the most frequent oral cancer, constituting more than 90% of all oral carcinomas. The 5-year survival rate of OSCC patients is not satisfactory, and therefore, there is an urgent need for new practical therapeutic approaches besides the current therapies to overcome OSCC. Scutellaria baicalensis Georgi (SBG) is a plant of the family Lamiaceae with several pharmaceutical properties such as antioxidant, anti-inflammatory, and anticancer effects. Previous studies have demonstrated the curative effects of SBG in OSCC. Methods: A systems biology approach was conducted to identify differentially expressed miRNAs (DEMs) in OSCC patients with a dismal prognosis compared to OSCC patients with a favorable prognosis. A protein interaction map (PIM) was built based on DEMs targets, and the hub genes within the PIM were indicated. Subsequently, the prognostic role of the hubs was studied using Kaplan-Meier curves. Next, the binding affinity of SBG's main components, including baicalein, wogonin, oroxylin-A, salvigenin, and norwogonin, to the prognostic markers in OSCC was evaluated using molecular docking analysis. Results: Survival analysis showed that overexpression of CAV1, SERPINE1, ACTB, SMAD3, HMGA2, MYC, EIF2S1, HSPA4, HSPA5, and IL6 was significantly related to a poor prognosis in OSCC. Besides, molecular docking analysis demonstrated the ΔGbinding and inhibition constant values between SBG's main components and SERPINE1, ACTB, HMGA2, EIF2S1, HSPA4, and HSPA5 were as <-8.00 kcal/mol and nanomolar concentration, respectively. The most salient binding affinity was observed between wogonin and SERPINE1 with a criterion of ΔGbinding < -10.02 kcal/mol. Conclusion: The present results unraveled potential mechanisms involved in therapeutic effects of SBG in OSCC based on systems biology and structural bioinformatics analyses.

From Average Transient Transporter Currents to Microscopic Mechanism─A Bayesian Analysis.

Electrophysiology studies of secondary active transporters have revealed quantitative mechanistic insights over many decades of research. However, the emergence of new experimental and analytical approaches calls for investigation of the capabilities and limitations of the newer methods. We examine the ability of solid-supported membrane electrophysiology (SSME) to characterize discrete-state kinetic models with >10 rate constants. We use a Bayesian framework applied to synthetic data for three tasks: to quantify and check (i) the precision of parameter estimates under different assumptions, (ii) the ability of computation to guide the selection of experimental conditions, and (iii) the ability of our approach to distinguish among mechanisms based on SSME data. When the general mechanism, i.e., event order, is known in advance, we show that a subset of kinetic parameters can be "practically identified" within ∼1 order of magnitude, based on SSME current traces that visually appear to exhibit simple exponential behavior. This remains true even when accounting for systematic measurement bias and realistic uncertainties in experimental inputs (concentrations) are incorporated into the analysis. When experimental conditions are optimized or different experiments are combined, the number of practically identifiable parameters can be increased substantially. Some parameters remain intrinsically difficult to estimate through SSME data alone, suggesting that additional experiments are required to fully characterize parameters. We also demonstrate the ability to perform model selection and determine the order of events when that is not known in advance, comparing Bayesian and maximum-likelihood approaches. Finally, our studies elucidate good practices for the increasingly popular but subtly challenging Bayesian calculations for structural and systems biology.

Index analysis: An approach to understand signal transduction with application to the EGFR signalling pathway.

In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time- and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a large-scale kinetic model of the EGFR signalling cascade, we identified different phases of signal transduction, the peculiar role of Phosphatase3 during signal activation and Ras recycling during signal onset. In addition, we discuss the challenges and pitfalls of interpreting the relevance of molecular species based on knock-out simulation studies, and provide an alternative view on conflicting results on the importance of parallel EGFR downstream pathways. Beyond the applications in model interpretation, index analysis is envisioned to be a valuable tool in model reduction.

Special Issue "Deployment of Proteomics Approaches in Biomedical Research".

Many angles of personalized medicine, such as diagnostic improvements, systems biology [...].

Bioinformatics and systems-biology approach to identify common pathogenic mechanisms for COVID-19 and systemic lupus erythematosus.

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has brought a heavy burden to the world, interestingly, it shares many clinical symptoms with systemic lupus erythematosus (SLE). It is unclear whether there is a similar pathological process between COVID-9 and SLE. In addition, we don't know how to treat SLE patients with COVID-19. In this study, we analyse the potential similar pathogenesis between SLE and COVID-19 and explore their possible drug regimens using bioinformatics and systems biology approaches. METHODS: The common differentially expressed genes (DEGs) were extracted from the COVID-19 datasets and the SLE datasets for functional enrichment, pathway analysis and candidate drug analysis. RESULT: Based on the two transcriptome datasets between COVID-19 and SLE, 325 common DEGs were selected. Hub genes were identified by protein-protein interaction (PPI) analysis. few found a variety of similar functional changes between COVID-19 and SLE, which may be related to the pathogenesis of COVID-19. Besides, we explored the related regulatory networks. Then, through drug target matching, we found many candidate drugs for patients with COVID-19 only or COVID-19 combined with SLE. CONCLUSION: COVID-19 and SLE patients share many common hub genes, related pathways and regulatory networks. Based on these common targets, we found many potential drugs that could be used in treating patient with COVID-19 or COVID-19 combined with SLE.

Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare.

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.