Systems Biology Research Articles

Regulatory disturbances in the dynamical signaling systems of and NO in fibroblasts cause fibrotic disorders.

Studying the calcium dynamics within a fibroblast cell individually has provided only a restricted understanding of its functions. However, research efforts focusing on systems biology approaches for such investigations have been largely neglected by researchers until now. Fibroblast cells rely on signaling from calcium and nitric oxide (NO) to maintain their physiological functions and structural stability. Various studies have demonstrated the correlation between NO and the control of dynamics in cells. However, there is currently no existing model to assess the disruptions caused by various factors in regulatory dynamics, potentially resulting in diverse fibrotic disorders. A mathematical model has been developed to investigate the effects of changes in parameters such as buffer, receptor, sarcoplasmic endoplasmic reticulum -ATPase (SERCA) pump, and source influx on the regulation and dysregulation of spatiotemporal calcium and NO dynamics in fibroblast cells. This model is based on a system of reaction-diffusion equations, and numerical simulations are conducted using the finite element method. Disturbances in key processes related to calcium and nitric oxide, including source influx, buffer mechanism, SERCA pump, and inositol trisphosphate receptor, may contribute to deregulation in the calcium and NO dynamics within fibroblasts. The findings also provide new insights into the extent and severity of disorders resulting from alterations in various parameters, potentially leading to deregulation and the development of fibrotic disease.

Comparative analysis of dynamic transcriptomes reveals specific COVID-19 features and pathogenesis of immunocompromised populations.

A majority of previous studies focused on the characterization of coronavirus infectious disease 2019 (COVID-19) disease severity in people with normal immunity, while the characterization of COVID-19 in immunocompromised populations is still limited. Our study profiles changes in the transcriptome landscape post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hematological tumor patients and non-tumor individuals. Furthermore, our integrative and comparative systems biology analysis of the interactome, complexome, and transcriptome provides new insights into the tumor-specific pathogenesis of COVID-19. Our findings confirm that SARS-CoV-2 potentially tends to target more non-functional host proteins to indirectly affect host immune responses in hematological tumor patients. The identified unique genes, complexes, functions/pathways, and expression patterns post-SARS-CoV-2 infection in patients with hematological tumors increase our understanding of how SARS-CoV-2 manipulates the host molecular mechanism. Our observed differential genes/complexes and clinical indicators of normal/long infection and deceased COVID-19 patients provide clues for understanding the mechanism of COVID-19 progression in hematological tumors. Finally, our study provides an important data resource that supports the increasing value of the application of publicly accessible data sets to public health.

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560–680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

In silico simulation of glycosylation and related pathways.

Glycans participate in a vast number of recognition systems in diverse organisms in health and in disease. However, glycans cannot be sequenced because there is no sequencer technology that can fully characterize them. There is no "template" for replicating glycans as there are for amino acids and nucleic acids. Instead, glycans are synthesized by a complicated orchestration of multitudes of glycosyltransferases and glycosidases. Thus glycans can vary greatly in structure, but they are not genetically reproducible and are usually isolated in minute amounts. To characterize (sequence) the glycome (defined as the glycans in a particular organism, tissue, cell, or protein), glycosylation pathway prediction using in silico methods based on glycogene expression data, and glycosylation simulations have been attempted. Since many of the mammalian glycogenes have been identified and cloned, it has become possible to predict the glycan biosynthesis pathway in these systems. By then incorporating systems biology and bioprocessing technologies to these pathway models, given the right enzymatic parameters including enzyme and substrate concentrations and kinetic reaction parameters, it is possible to predict the potentially synthesized glycans in the pathway. This review presents information on the data resources that are currently available to enable in silico simulations of glycosylation and related pathways. Then some of the software tools that have been developed in the past to simulate and analyze glycosylation pathways will be described, followed by a summary and vision for the future developments and research directions in this area.

GMCSpy: Efficient and accurate computation of genetic minimal cut sets in python.

The identification of minimal genetic interventions that modulate metabolic processes constitutes one of the most relevant applications of genome-scale metabolic models (GEMs). The concept of Minimal Cut Sets (MCSs) and its extension at the gene level, genetic Minimal Cut Sets (gMCSs), have attracted increasing interest in the field of Systems Biology to address this task. Different computational tools have been developed to calculate MCSs and gMCSs using both commercial and open-source software. Here, we present gMCSpy, an efficient Python package to calculate gMCSs in GEMs using both commercial and non-commercial optimization solvers. We show that gMCSpy substantially overperforms our previous computational tool GMCS, which exclusively relied on commercial software. Moreover, we compared gMCSpy with recently published competing algorithms in the literature, finding significant improvements in both accuracy and computation time. All these advances make gMCSpy an attractive tool for researchers in the field of Systems Biology for different applications in health and biotechnology. The Python package gMCSpy can be accessed at: https://github.com/PlanesLab/gMCSpy. Supplementary data are available at Bioinformatics online.

Systems biology approach: identification of hub genes, signaling pathways, and molecular docking of COL1A1 gene in cervical insufficiency

Systems biology approach: identification of hub genes, signaling pathways, and molecular docking of COL1A1 gene in cervical insufficiency

A compositional account of motifs, mechanisms, and dynamics in biochemical regulatory networks

Regulatory networks depict promoting or inhibiting interactions between molecules in a biochemical system. We introduce a category-theoretic formalism for regulatory networks, using signed graphs to model the networks and signed functors to describe occurrences of one network in another, especially occurrences of network motifs. With this foundation, we establish functorial mappings between regulatory networks and other mathematical models in biochemistry. We construct a functor from reaction networks, modeled as Petri nets with signed links, to regulatory networks, enabling us to precisely define when a reaction network could be a physical mechanism underlying a regulatory network. Turning to quantitative models, we associate a regulatory network with a Lotka-Volterra system of differential equations, defining a functor from the category of signed graphs to a category of parameterized dynamical systems. We extend this result from closed to open systems, demonstrating that Lotka-Volterra dynamics respects not only inclusions and collapsings of regulatory networks, but also the process of building up complex regulatory networks by gluing together simpler pieces. Formally, we use the theory of structured cospans to produce a lax double functor from the double category of open signed graphs to that of open parameterized dynamical systems. Throughout the paper, we ground the categorical formalism in examples inspired by systems biology.

Climate Crisis and Agricultural Response: Climate Resilient Crops for Sustainability in Food Production Systems

Climate change encompasses altered levels of temperature and humidity, variability in the rainfall pattern, fluctuations in weather parameters, rise in ambient CO2 levels, emission of greenhouse gases, global warming, etc. that results in extreme events and disasters as cyclones, floods, droughts, salinity, nutrient and heavy metal stress, change in arthropod diversity and emergence of new invasive pests. This results in un-usual effects in agro-ecosystems leading to changes in cropping patterns, crop diversity, and their interaction with biotic and abiotic stress factors, threatening livelihood, food, and nutritional security. Population displacement, declining food productivity, and vulnerable agro-ecosystems are the major consequences of altered meteorological events that occur due to climate change. Therefore, substitution of traditional crops with crops that exhibit resilience to climate crisis is the need of the hour. Smart breeding approaches and precision farming technologies as remote sensing and spectral analysis, artificial intelligence, machine learning, speed breeding, genome analysis, genetic manipulation, gene drive systems, system biology study, omics approaches, etc can make agricultural production climate resilient and sustainable. Response of biofortified crops under changing climate must also be assessed to improve the crop productivity and output.

Identification of Ferroptosis-Inflammation Related Hub Genes and the Disease Subtypes in Idiopathic Pulmonary Fibrosis via System Biology Approaches.

We aim to screen and analyze the ferroptosis inflammation-related hub genes associated with idiopathic pulmonary fibrosis (IPF). The GSE52463 and GSE110147 datasets were obtained from the GEO database and merged. The DEGs were selected by differential analysis and intersected with inflammation-related genes and ferroptosis-related genes to acquire the ferroptosis-related differentially expressed genes (FRDEGs). GO, KEGG, GSEA, and GSVA were performed to investigate the features of FRDEGs. The key module genes were selected by WGCNA and employed to generate the PPI network using Cytoscape. Subsequently, the hub genes were identified using cytoHubba and validated by ROC curves generated by survivalROC. Finally, the correlations of hub genes were analyzed through Spearman and the subtypes of IPF were constructed using ConsensusClusterPlus. A total of 1814 DEGs were screened out and 18 FRDEGs were acquired from the intersection of DEGs, ferroptosis-related genes, and inflammation-related genes. GO and KEGG analysis revealed that FRDEGs were primarily involved in bacterial-origin molecular, response infectious disease, and iron ion transport. GSEA results suggested a predominant association with autoimmune diseases and GSVA identified ten different pathways between PF and control. Through WGCNA, three highly correlated modules were identified and ten key module genes were obtained by intersecting genes in the three modules with FRDEGs. Finally, employing three algorithms within the cytoHubba led to the identification of eight hub genes: CCND1, TP53, STAT3, CTNNB1 CDH1, ESR1, HSP90AA1, and EP300. Eventually, two distinct subtypes of IPF were identified. The present research successfully identified the hub genes associated with ferroptosis and inflammation and their biological effects on IPF. Furthermore, two disease subtypes of IPF were constructed.

Molecular characteristics of the protective effect of Bacillus Calmette-Guerin against neonatal sepsis

Purpose: To investigate the molecular characteristics and potential mechanisms underlying the immune-protective effect of Bacillus Calmette-Guerin (BCG) against neonatal sepsis (NS). Methods: Data on transcriptome sequencing of BCG infection, NS, post-BCG infection sepsis, and the respective controls were retrieved from public databases. Systems biology screening was used to identify differentially expressed genes (DEGs) amongst the groups, and common DEGs with high connectivity were selected using protein-protein interaction network analysis. Results: A total of 15 common DEGs were related to the expressions of macrophages and neutrophils after BCG infection and NS infection. Some of the genes were involved in IL-4/IL-13 signal pathway and neutrophil degranulation pathway, and they were highly correlated with the expressions of TNF-alpha, IL-6 and type I interferon-response signal pathway. This may reduce the poor prognosis of NS after BCG infection. Conclusion: These findings provide insights into how BCG-induced trained immunity may provide protection against NS.

From Androgen Dependence to Independence in Prostate Cancer: Unraveling Therapeutic Potential and Proteomic Landscape of Hydroxychloroquine as an Autophagy Inhibitor.

Prostate cancer is a major planetary health challenge wherein new ways of thinking drug discovery and therapeutics innovation are much needed. Numerous studies have shown that autophagy inhibition holds a significant role as an adjunctive intervention in prostate cancer. Hydroxychloroquine (HCQ) has gained considerable attention due to its established role as an autophagy inhibitor across diverse cancer types, but its proteomics landscape and systems biology in prostate cancer are currently lacking in the literature. This study reports the proteomic responses to HCQ in prostate cancer cells, namely, androgen-dependent LNCaP and androgen-independent PC3 cells. Differentially expressed proteins and proteome in HCQ-treated cells were determined by label-free quantification with nano-high-performance liquid chromatography and tandem mass spectrometry (nHPLC-MS/MS), and harnessing bioinformatics tools. In PC3 cells, there was a marked shift toward metabolic reprogramming, highlighted by an upregulation of mitochondrial proteins in oxidative phosphorylation and tricarboxylic acid cycle, suggesting an adaptive mechanism to maintain energy production under therapeutic stress. In contrast, LNCaP cells prioritized proteostasis and cell cycle regulation, indicating a more conservative adaptation strategy. To the best of our knowledge, this study is the first to demonstrate the differential responses of prostate cancer cells to autophagy inhibition by HCQ, suggesting that a combination therapy approach, targeting distinct pathways in androgen-independent and androgen-dependent cells, could represent a promising treatment strategy. Moreover, the varied proteomic responses observed between these cell lines underscore the importance of personalized medicine in cancer therapy. Future translational and clinical research on HCQ and prostate cancer are called for.

Targeting Metabolic Syndrome Pathways: Carrot microRNAs As Potential Modulators.

Metabolic syndrome is a condition characterized by metabolic alterations that culminate in chronic noncommunicable diseases of high morbidity and mortality, such as cardiovascular diseases, type 2 diabetes, nonalcoholic fatty liver disease, and colon cancer. Developing new therapeutic strategies with a multifactorial approach is important since current therapies focus on only one or two components of the metabolic syndrome. In this sense, plant-based gene regulation represents an innovative strategy to prevent or modulate human metabolic pathologies, including metabolic syndrome. Here, using a computational and systems biology approach, it was found that carrot microRNAs can modulate key BMPs/SMAD signaling members, C/EBPs, and KLFs involved in several aspects associated with metabolic syndrome, including the hsa04350:TGF-beta signaling pathway, hsa04931:insulin resistance, hsa04152:AMPK signaling pathway, hsa04933:AGE-RAGE signaling pathway in diabetic complications, hsa04010:MAPK signaling pathway, hsa04350:TGF-beta signaling pathway, hsa01522:endocrine resistance, and hsa04910:insulin signaling pathway. These data demonstrated the potential applications of carrot microRNAs as effective food-based therapeutics for obesity and associated metabolic diseases.

Metabolomics in Children Cow's Milk Protein Allergy: Possible Contribution from a System Biology Approach?

One of the most frequent triggers of food anaphylaxis in pediatric age but also among the most common, early, and complex causes of childhood food allergy is cow's milk protein allergy (CMPA). The diagnostic course and management of this allergy is defined in a complex clinical picture due to several factors. First of all, the epidemiological data are not uniform, mainly as a consequence of the diagnostic methodology used in the various studies and the different age ranges covered. In addition, there is the complexity of terminology, since although CMPA traditionally refers to immune-mediated reactions to cow's milk, it is a term encompassing numerous clinical features with different symptoms and the requirement for specific treatments. Moreover, the differential diagnosis with other very frequent diseases, especially in the first year of life, such as gastro-esophageal reflux disease or colic, is still complex. This can result in misdiagnosis and incorrect treatment, with harmful health consequences and significant economic repercussions. In this context, the combination of several omics sciences together, which have already proved useful in clarifying the allergenicity of cow's milk proteins with greater precision, could improve the diagnostic tests currently in use through the identification of new, more specific, and precise biomarkers that make it possible to improve diagnostic accuracy and predict the patient's response to the various available treatments for the recovery of tolerance.

Database construction and comparative genomics analysis of genes involved in nutritional metabolic diseases in fish

Nutritional metabolic diseases in fish frequently arise in the setting of intensive aquaculture. The etiology and pathogenesis of these conditions involve energy metabolic disorders influenced by both internal genetic factors and external environmental conditions. The exploration of genes associated with nutritional and metabolic disorder has sparked considerable interest within both the aquaculture scientific community and the industry. High-throughput sequencing technology offers researchers extensive genetic information. Effectively mining, analyzing, and securely storing this data is crucial, especially for advancing disease prevention and treatment strategies. Presently, the exploration and application of gene databases concerning nutritional and metabolic disorders in fish are at a nascent stag. Therefore, this study focused on the model organism zebrafish and five primary economic fish species as the subjects of investigation. Using information from KEGG, OMIM, and existing literature, a novel gene database associated with nutritional metabolic diseases in fish was meticulously constructed. This database encompassed 4583 genes for Danio rerio, 6287 for Cyprinus carpio, 3289 for Takifugu rubripes, 3548 for Larimichthys crocea, 3816 for Oreochromis niloticus, and 5708 for Oncorhynchus mykiss. Through a comparative systems biology approach, we discerned a relatively high conservation of genes linked to nutritional metabolic diseases across these fish species, with over 54.9 % of genes being conserved throughout all six species. Additionally, the analysis pinpointed the existence of 13 species-specific genes within the genomes of large yellow croaker, tilapia, and rainbow trout. These genes exhibit the potential to serve as novel candidate targets for addressing nutritional metabolic diseases.

Systems biology of plant metabolic interactions

Systems biology of plant metabolic interactions

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis.

In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.

Glycoproteomic and proteomic analysis of Burkholderia cenocepacia reveals glycosylation events within FliF and MotB are dispensable for motility.

Burkholderia cenocepacia is an opportunistic pathogen of concern within the Cystic Fibrosis community. Despite a greater appreciation of the unique physiology of B. cenocepacia gained over the last 20 years a complete understanding of the proteome and especially the O-glycoproteome, is lacking. In this study, we utilize systems biology approaches to expand the known B. cenocepacia glycoproteome as well as track the dynamics of glycoproteins across growth phases, culturing media and in response to the loss of glycosylation. We show that the glycoproteome of B. cenocepacia is largely stable across conditions and that the loss of glycosylation only impacts five glycoproteins including the motility associated proteins FliF and MotB. Examination of MotB and FliF shows, while these proteins are essential for motility, glycosylation is dispensable. Combined this work supports that B. cenocepacia glycosylation can be dispensable for protein function and may influence protein properties beyond stability.

Network pharmacology speaking to ethnopharmacology: new data on an ancient remedy

Network pharmacology as a “green approach”, predicting metabolite behaviours chemically and biologically and guid¬ing biological experimental design, is a new strategy aiming to uncover the mechanism of action of natural products as drug candidates. It provides a powerful way to identify novel mechanisms of natural products with potential thera¬peutic effects. This approach has emerged as a powerful tool to overcome the limitations of traditional methods, such as the ability to predict the adverse effects of a drug and the likelihood of failure during clinical trials, by applying systems biology principles to the field of pharmacology. This method combines the multi-omics dataset, computer modeling, and chemical biology so as to reveal pharmaceutical actions and guide drug discovery. Therefore, computer-aided drug design combined with network pharmacology can be viewed as a novel in silico screening ap¬proach to drug discovery, by utilising chemoinformatics, bioinformatics, structure biology, and chemical biology. This strategy includes target-based virtual screening - molecular docking, ligand similarity-based virtual screening, and inverse screening (Inver-dock), providing a powerful tool for target identification of drug candidates, multitarget dis¬covery, and natural bioactive product profiling. It can also be used for selectivity profiling of drugs, drug repositioning, safety profiling, and metabolism profiling prediction (ADMET).

Biomarkers of Alzheimer's Disease Associated with Programmed Cell Death Reveal Four Repurposed Drugs.

Alzheimer's disease (AD) is a neurodegenerative disorder and the most common cause of dementia. Programmed cell death (PCD) is mainly characterized by unique morphological features and energy-dependent biochemical processes. The predominant pathway leading to cell death in AD has not been thoroughly analyzed, although there is evidence of neuron loss in AD and numerous pathways of PCD have been associated with this process. A better understanding of the systems biology underlying the relationship between AD and PCD could lead to the development of new therapeutic approaches. To this end, publicly available transcriptome data were examined using bioinformatic methods such as differential gene expression and weighted gene coexpression network analysis (WGCNA) to find PCD-related AD biomarkers. The diagnostic significance of these biomarkers was evaluated using a logistic regression-based predictive model. Using these biomarkers, a multifactorial regulatory network was developed. Last, a drug repositioning study was conducted to propose new drugs for the treatment of AD targeting PCD. The development of 3PM (predictive, preventive, and personalized) drugs for the treatment of AD would be enabled by additional research on the effects of these drugs on this disease.

Natural products act as game-changer potentially in treatment and management of sepsis-mediated inflammation: A clinical perspective

BackgroundSepsis, a life-threatening condition resulting from uncontrolled host responses to infection, poses a global health challenge with limited therapeutic options. Due to high heterogeneity, sepsis lacks specific therapeutic drugs. Additionally, there remains a significant gap in the clinical management of sepsis regarding personalized and precise medicine. PurposeThis review critically examines the scientific landscape surrounding natural products in sepsis and sepsis-mediated inflammation, highlighting their clinical potential. MethodsFollowing the PRISMA guidelines, we retrieved articles from PubMed to explore potential natural products with therapeutic effects in sepsis-mediated inflammation. Results434 relevant in vitro and in vivo studies were identified and screened. Ultimately, 55 studies were obtained as the supporting resources for the present review. We divided the 55 natural products into three categories: those influencing the synthesis of inflammatory factors, those affecting surface receptors and modulatory factors, and those influencing signaling pathways and the inflammatory cascade. ConclusionNatural products' potential as game-changers in sepsis-mediated inflammation management lies in their ability to modulate hallmarks in sepsis, including inflammation, immunity, and coagulopathy, which provides new therapeutic avenues that are readily accessible and capable of undergoing rapid clinical validation and deployment, offering a gift from nature to humanity. Innovative techniques like bioinformatics, metabolomics, and systems biology offer promising solutions to overcome these obstacles and facilitate the development of natural product-based therapeutics, holding promise for personalized and precise sepsis management and improving patient outcomes. However, standardization, bioavailability, and safety challenges arise during experimental validation and clinical trials of natural products.