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Background: Mycobacterium tuberculosis (Mtb) carbonic anhydrases (CAs) are critical enzymes that regulate pH by converting CO2 to HCO3 -, essential for Mtb's survival in acidic environments. Inhibiting γ-CAs presents a potential target for novel antituberculosis drugs with unique mechanisms of action. Objective: This study aimed to explore the biological connections underlying Mtb pathogenesis and investigate the mechanistic actions of antituberculosis compounds targeting the Cas9 protein. Methods: We employed homology modeling and virtual screening to identify compounds with high binding affinities for Cas9 protein. This study used the homology modeling approach employing high-quality AlphaFold DB models for γ-CA. Furthermore, the systems biology approach was used for analyzing the integrated modelling of compounds, integrating data on genes, pathways, phenotypes, and molecular descriptors. Single-cell RNA sequencing was also conducted to profile gene expression. Results: Three compounds, F10921405, F08060425, and F14437079, potentially binding to Cas9 protein, have been identified. F10921405 and F08060425 showed significant overlap in their effects on pathways related to the immune response, while F14437079 displayed distinct mechanistic pathways. Expression profiling revealed high levels of genes such as PDE4D, ROCK2, ITK, MAPK10, and SYK in response to F1092-1405 and F0806-0425, and MMP2 and CALCRL in response to F1443-7079. These genes, which play a role in immune modulation and lung tissue integrity, are essential to fight against Mtb. Conclusion: The molecular relationship and pathways linked to the mentioned compounds give the study a holistic perspective of targeting Mtb, which is essential in designing specific therapeutic approaches. Subsequent research will involve experimental validation to demonstrate the efficacy of the promising candidates in Mtb infections.

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LipidOne 2.0 (https://lipidone.eu) is a new web bioinformatic tool for the analysis of lipidomic data. It facilitates the exploration of the three structural levels of lipids: classes, molecular species, and lipid building blocks (acyl, alkyl, or alkenes chains). The tool's flexibility empowers users to seamlessly include or exclude experimental groups and lipid classes at any stage of the analysis. LipidOne 2.0 offers a range of mono- and multivariate statistical analyses, specifically tailored to each structural level. This includes a novel lipid biomarker identification function, integrating four diverse statistical parameters. LipidOne 2.0 incorporates Lipid Pathway analysis across all three structural levels of lipids. Users can identify lipid-involved reactions through case-control comparisons, generating lists of genes/enzymes and their activation states based on Z scores. Accessible without the need for registration, LipidOne 2.0 provides a user-friendly and efficient platform for exploring and analyzing lipidomic data. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Dataset preparation for LipidOne 2.0 Support Protocol: Lipid nomenclature from spectrometric experiments Basic Protocol 2: Uploading a dataset into LipidOne 2.0 Basic Protocol 3: Data mining of lipidomic dataset by LipidOne 2.0.

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Colorectal adenomas (CRAs) are potential precursor lesions to adenocarcinomas, currently classified by morphological features. We aimed to establish a molecular feature-based risk allocation framework toward improved patient stratification. Deep visual proteomics (DVP) is an approach that combines image-based artificial intelligence with automated microdissection and ultra-high sensitive mass spectrometry. Here, we used DVP on formalin-fixed, paraffin-embedded (FFPE) CRA tissues from nine male patients, immunohistologically stained for caudal-type homeobox 2 (CDX2), a protein implicated in colorectal cancer, enabling the characterization of cellular heterogeneity within distinct tissue regions and across patients. DVP identified DMBT1, MARCKS, and CD99 as protein markers linked to recurrence, suggesting their potential for risk assessment. It also detected a metabolic shift to anaerobic glycolysis in cells with high CDX2 expression. Our findings underscore the potential of spatial proteomics to refine early stage detection and contribute to personalized patient management strategies and provided novel insights into metabolic reprogramming.

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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.

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INTRODUCTION: 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. DISCUSSION: 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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OBJECTIVES: 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. DESIGN: 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. LIMITATIONS: 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.

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Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.

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Depression is a common psychiatric comorbidity among patients with epilepsy (PWE), affecting more than a third of PWE. Management of depression may improve quality of life of epileptic patients. Unfortunately, available antidepressants worsen epilepsy by reducing the seizure threshold. This situation demands search of new safer target for combined directorate of epilepsy and comorbid depression. A system biology approach may be useful to find novel pathways/markers for the cure of both epilepsy and associated depression via analyzing available genomic and proteomic information. Hence, the system biology approach using curated 64 seed genes involved in temporal lobe epilepsy and mental depression was applied. The interplay of 600 potential proteins was revealed by the Disease Module Detection (DIAMOnD) Algorithm for the treatment of both epilepsy and comorbid depression using these seed genes. The gene enrichment analysis of seed and diamond genes through DAVID suggested 95 pathways. Selected pathways were refined based on their syn or anti role in epilepsy and depression. In conclusion, total 8 pathways and 27 DIAMOnD genes/proteins were finally deduced as potential new targets for modulation of selected pathways to manage epilepsy and comorbid depression. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00208-1.

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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.

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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.

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Calcitriol circulates at low levels in normal human and rodent fetuses, in part due to increased 24-hydroxylation of calcitriol and 25-hydroxyvitamin D by 24-hydroxylase (CYP24A1). Inactivating mutations of CYP24A1 cause high postnatal levels of calcitriol and the human condition of infantile hypercalcemia type 1, but whether the fetus is disturbed by the loss of CYP24A1 is unknown. We hypothesized that loss of Cyp24a1 in fetal mice will cause high calcitriol, hypercalcemia, and increased placental calcium transport. The Cyp24a1+/- mice were mated to create pregnancies with wildtype, Cyp24a1+/-, and Cyp24a1 null fetuses. The null fetuses were hypercalcemic, modestly hypophosphatemic (compared to Cyp24a1+/- fetuses only), with 3.5-fold increased calcitriol, 4-fold increased fibroblast growth factor 23 (FGF23), and unchanged parathyroid hormone. The quantitative RT-PCR confirmed the absence of Cyp24a1 and 2-fold increases in S100g, sodium-calcium exchanger type 1, and calcium-sensing receptor in null placentas but not in fetal kidneys; these changes predicted an increase in placental calcium transport. However, placental 45Ca and 32P transport were unchanged in null fetuses. Fetal ash weight and mineral content, placental weight, crown-rump length, and skeletal morphology did not differ among the genotypes. Serum procollagen 1 intact N-terminal propeptide and bone expression of sclerostin and Blgap were reduced while calcitonin receptor was increased in nulls. In conclusion, loss of Cyp24a1 in fetal mice causes hypercalcemia, modest hypophosphatemia, and increased FGF23, but no alteration in skeletal development. Reduced incorporation of calcium into bone may contribute to the hypercalcemia without causing a detectable decrease in the skeletal mineral content. The results predict that human fetuses bearing homozygous or compound heterozygous inactivating mutations of CYP24A1 will also be hypercalcemic in utero but with normal skeletal development.