Circuit building blocks of gene regulatory networks (GRN) have been identified through the fibration symmetries of the underlying biological graph. Here, we analyse analytically six of these circuits that occur as functional and synchronous building blocks in these networks. Of these, the lock-on, toggle switch, Smolen oscillator, feed-forward fibre and Fibonacci fibre circuits occur in living organisms, notably Escherichia coli; the sixth, the repressilator, is a synthetic GRN. We consider synchronous steady states determined by a fibration symmetry (or balanced colouring) and determine analytic conditions for local bifurcation from such states, which can in principle be either steady-state or Hopf bifurcations. We identify conditions that characterize the first bifurcation, the only one that can be stable near the bifurcation point. We model the state of each gene in terms of two variables: mRNA and protein concentration. We consider all possible \'admissible\' models-those compatible with the network structure-and then specialize these general results to simple models based on Hill functions and linear degradation. The results systematically classify using graph symmetries the complexity and dynamics of these circuits, which are relevant to understand the functionality of natural and synthetic cells.

BACKGROUND: Preeclampsia (PE) is a serious pregnancy-related complication caused by high blood pressure in pregnant women. The severe form has more devastating effects. According to the growing evidence, the placenta is a crucial component in the pathogenesis of PE, and eliminating it will alleviate symptoms.
METHODS: GEO\'s severe preeclampsia placenta microarray datasets; GSE147776, GSE66273, GSE102897, and GSE10588, were chosen to identify differentially expressed genes (DEGs) in different biological pathways. The analysis of hub genes and related non-coding RNAs was done as well.
RESULTS: A total of 347 DEGs with adj p-value <0.05 and ǀlog2FoldChangeǀ> 0.5 were discovered between severe PEs and healthy pregnancies, including 204 over-expressed genes and 143 under-expressed genes. The MCC method identified ISG15, IFI44L, MX2, OAS2, MX1, FN1, LDHA, ITGB3, TKT, HK2 genes as the top ten hub genes. Interactions between hub genes and noncoding RNAs were also conducted. The most enriched pathways were as follows; HIF-1 signaling pathway; Pathways in cancer; Alanine, aspartate and glutamate metabolism; Arginine biosynthesis; Human papillomavirus infection; Glycolysis/Gluconeogenesis; Central carbon metabolism in cancer; Valine, leucine and isoleucine degradation; Cysteine and methionine metabolism; and Galactose metabolism.
CONCLUSIONS: This is a secondary data analysis conducted on severe preeclampsia placenta to identify differentially expressed genes, biological pathways, hub-genes, and related noncoding RNAs. Functional studies are crucial to understanding the precise role of these genes in the pathogenesis of PE. Also, accepting a gene as a diagnostic or prognostic marker for early diagnosis and management of PE requires multiple lines of evidence.

Bombinin-BO1 (BO1), a bombinin peptide derived from the skin secretion of Bombina orientalis, exhibits broad-spectrum antimicrobial activity. To date, the anticancer effect of BO1 remains unclear. This study confirmed cytotoxicity of BO1 on hepatocellular carcinoma cells by inducing S-phase cycle block and apoptosis. In addition, BO1 was found to be localized in cytoplasm through endocytosis. The combined results of pull down, mass spectrometry, and co-immunoprecipitation suggested that BO1 induced misfolding of CDK1 and degradation by competitively binding HSP90A with Cdc37. It was verified that overexpression of HSP90A in BO1-treated cells significantly inhibited degradation of CDK1. In vivo, BO1 inhibited tumor without being toxic to individuals. This study reveals the anti-tumor mechanism of BO1 in inducing cell-cycle arrest and apoptosis by interfering with HSP90A-Cdc37-CDK1 system. This is the first study to analyze the mechanism of BO1 regulation of tumor cells, providing theoretical basis for BO1 treatment of hepatocellular carcinoma.

Glyphosate, the world\'s most widely used herbicide, has a low toxicity rating despite substantial evidence of adverse health effects. Furthermore, glyphosate-based formulations (GBFs) contain several other chemicals, some of which are known to be harmful. Additionally, chronic, and acute exposure to GBFs among rural workers may lead to health impairments, such as neurodegenerative diseases and cancer. P53 is known as a tumor suppressor protein, acting as a key regulator of the cellular response to stress and DNA damage. Therefore, mutations in the TP53 gene, which encodes p53, are common genetic alterations found in various types of cancer. Therefore, this study aimed to evaluate the cytotoxicity and genotoxicity of GBF in two glioblastoma cell lines: U87MG (TP53-proficient) and U251MG (TP53-mutant). Additionally, the study aimed to identify the main proteins involved in the response to GBF exposure using Systems Biology in a network containing p53 and another network without p53. The MTT assay was used to study the toxicity of GBF in the cell lines, the clonogenic assay was used to investigate cell survival, and the Comet Assay was used for genotoxicity evaluation. For data analysis, bioinformatics tools such as String 12.0 and Stitch 5.0 were applied, serving as a basis for designing binary networks in the Cytoscape 3.10.1 program. From the in vitro test analyses, it was observed a decrease in cell viability at doses starting from 10 ppm. Comet Assay at concentrations of 10 ppm and 30 ppm for the U251MG and U87MG cell lines, respectively observed DNA damage. The network generated with systems biology showed that the presence of p53 is important for the regulation of biological processes involved in genetic stability and neurotoxicity, processes that did not appear in the TP53-mutant network.

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation of the synovial membrane that leads to the destruction of cartilage and bone. Currently, pharmacological targeting of ion channels is being increasingly recognized as an attractive and feasible strategy for the treatment of RA. The present work employs a network analysis approach to predict the most promising ion channel target for potential RA-treating drugs. A protein-protein interaction map was generated for 343 genes associated with inflammation in RA and ion channel genes using Search Tool for the Retrieval of Interacting Genes and visualized using Cytoscape. Based on the betweenness centrality and traffic values as key topological parameters, 17 hub nodes were identified, including FOS (9800.85), tumor necrosis factor (3654.60), TGFB1 (3305.75), and VEGFA (3052.88). The backbone network constructed with these 17 hub genes was intensely analyzed to identify the most promising ion channel target using network analyzer. Calcium permeating ion channels, especially store-operated calcium entry channels, and their associated regulatory proteins were found to highly interact with RA inflammatory hub genes. This significant ion channel target for RA identified by theoretical and statistical studies was further validated by a pilot case-control gene expression study. Experimental verification of the above findings in 75 RA cases and 25 controls showed increased ORAI1 expression. Thus, with a combination of network analysis approach and gene expression studies, we have explored potential targets for RA treatment.

Systems biology is an interdisciplinary field that aims to understand complex biological processes at the system level. The data, driven by high-throughput omics technologies, can be used to study the underpinning mechanisms of metabolite production under different conditions to harness this knowledge for the construction of regulatory networks, protein networks, metabolic models, and engineering of strains with enhanced target metabolite production in microalgae. In the current study, we comprehensively reviewed the recent progress in the application of these technologies for the characterization of carotenoid biosynthesis pathways in microalgae. Moreover, harnessing integrated approaches such as network analysis, meta-analysis, and machine learning models for deciphering the complexity of carotenoid biosynthesis pathways were comprehensively discussed.

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development.

Sarcopenia is a major public health concern among older adults, leading to disabilities, falls, fractures, and mortality. This study aimed to elucidate the pathophysiological mechanisms of sarcopenia and identify potential therapeutic targets using systems biology approaches. RNA-seq data from muscle biopsies of 24 sarcopenic and 29 healthy individuals from a previous cohort were analysed. Differential expression, gene set enrichment, gene co-expression network, and topology analyses were conducted to identify target genes implicated in sarcopenia pathogenesis, resulting in the selection of 6 hub genes (PDHX, AGL, SEMA6C, CASQ1, MYORG, and CCDC69). A drug repurposing approach was then employed to identify new pharmacological treatment options for sarcopenia (clofibric-acid, troglitazone, withaferin-a, palbociclib, MG-132, bortezomib). Finally, validation experiments in muscle cell line (C2C12) revealed MG-132 and troglitazone as promising candidates for sarcopenia treatment. Our approach, based on systems biology and drug repositioning, provides insight into the molecular mechanisms of sarcopenia and offers potential new treatment options using existing drugs.

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.

OBJECTIVE: The purpose of this study was to define the underlying biological mechanisms of polycystic ovarian syndrome (PCOS) utilizing the protein-protein interaction networks (PPINs) that were constructed based on the putative disease-causing genes for PCOS.
METHODS: No animals were used in this research because this is an in silico study that mainly uses software and online analysis tools. Participants/Materials, Settings: Gene datasets related to PCOS were obtained from Genecards.
METHODS: The PPINs of PCOS were created using the String Database after genes related to PCOS were obtained from Genecards. After that, we performed an analysis of the hub-gene clusters extracted from the PPIN using the ShinyGO algorithm. In the final step of this research project, functional enrichment analysis was used to investigate the primary biological activities and signaling pathways that were associated with the hub clusters.
RESULTS: The Genecards database provided the source for the identification of a total of 1,072 potential genes related to PCOS. The PPIN that was generated by using the genes that we collected above contained a total of 82 genes and three different types of cluster interaction interactions. In addition, after conducting research on the PPIN with the shinyGO plug-in, 19 of the most important gene clusters were discovered. The primary biological functions that were enriched in the key clusters that were developed were ovarian steroidogenesis, the breast cancer pathway, regulation of lipid and glucose metabolism by the AMPK pathway, and ovarian steroidogenesis. The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated with the pathogenesis of PCOS.
CONCLUSIONS: Several of the significant genes that were identified in this study, such as ACVR1, SMAD5, BMP6, SMAD3, SMAD4, and anti-mullerian hormone. It is necessary to do additional research using large samples, several centers, and multiple ethnicities in order to verify these findings.
CONCLUSIONS: The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated with the pathogenesis of PCOS. This information may possibly bring unique insights for the treatment of PCOS as well as the investigation of its underlying pathogenic mechanism.