Sepsis, a life-threatening dysregulated host response to infection, involves complex cytokine signaling. Comprehensive bioinformatics analysis of cytokine activity, associated pathways, and immune alterations in sepsis is warranted. Using the sepsis dataset GSE26378 from GEO, we analyzed differential cytokine pathway activity with ssGSEA and identified differentially expressed genes (DEGs). Cytokine-related genes (CRGs) were extracted and overlapped with DEGs. Protein-Protein Interaction (PPI) network analysis and functional enrichment were performed on differentially expressed CRGs. Cytokine activity scores and pathway activities were quantified using Gene Set Variation Analysis (GSVA). Immune cell infiltration was assessed with MCP-counter. Machine learning algorithms (Random Forest, LASSO, SVM) identified diagnostic biomarkers, validated using an independent dataset (GSE26440) and ROC analysis. Cytokine/cytokine receptor pathways were significantly upregulated in sepsis. We identified 617 DEGs and 46 differentially expressed CRGs. Cytokine activity scores were significantly elevated in sepsis and strongly correlated with heightened activity in inflammatory pathways (e.g. TLR, IL-1R, NF-κB, JAK/STAT, hypoxia) and metabolic pathways (e.g. glycolysis, PI3K/AKT/mTOR). Immune analysis showed decreased T cells, NK cells, B cells, and cytotoxic lymphocytes, alongside increased neutrophils and endothelial cells; neutrophil infiltration positively correlated with cytokine scores. Machine learning identified four core genes (C3AR1, XCL1, CSF2RA, IL2RB), consistently dysregulated in sepsis across datasets and demonstrating robust diagnostic accuracy. This integrated bioinformatics study indicates heightened cytokine activity, profound alterations in inflammatory and metabolic pathways, and a dysregulated immune cell landscape in sepsis. The identified hub genes and the four-gene biomarker panel show potential as diagnostic tools, offering insights into sepsis pathophysiology. Insight box This study integrates multi-omics bioinformatics (ssGSEA, GSVA, PPI, immune deconvolution) and machine learning (RF, LASSO, SVM) to dissect sepsis pathophysiology. Innovatively, we quantify cytokine pathway hyperactivity, linking it to inflammatory/metabolic dysregulation (TLR, NF-κB, glycolysis) and immune imbalance. A novel four-gene panel (C3AR1, XCL1, CSF2RA, IL2RB) was identified and validated as a robust diagnostic biomarker, bridging cytokine signaling with clinical utility. The findings provide mechanistic insights into sepsis-driven immune-metabolic crosstalk and offer translational potential for early diagnosis and targeted therapy.
Plant protease inhibitors (PI's) inhibit the activity of gut proteases and thus provide resistance against insect attack. Previously we have published first report on cloning and characterization of a novel Bowman-Birk protease inhibitor gene (RbTI) from ricebean (Vigna umbellata). In this study, the RbTI gene was further characterized and validated as a potential candidate for transferring insect resistance in economically important crops. We have successfully generated transgenic tobacco plants expressing RbTI gene constitutively under CaMV35S promoter using Agrobacterium transformation. Genomic PCR and GUS analysis confirmed the successful integration of RbTI gene into tobacco plant genome. qRT-PCR analysis revealed highest RbTI gene expression in transformed tobacco leaves nearing maturity. Feeding of transformed tobacco leaf tissue showed prominent effect on larval mortality throughout the larval growth stages mainly during first three days of feeding. For functional analysis of RbTI gene, we estimated the inhibitory activity of protein extracts from normal and transformed tobacco plants against gut proteases of Spodoptera litura and H. armigera larval instars. Maximum inhibition of trypsin (82.42% and 73.25%) and chymotrypsin (69.50% and 60.64%) enzymes was recorded at early larval stages of both insects. The results of this study validated the future use of RbTI gene from ricebean legume as a potential candidate for transferring insect resistance in economically important crops. Insight, innovation, integration: Present study was conducted with the aim to utilize the state of art biotechnological techniques for transferring key pest resistant genes from underutilized promising crop ricebean. The tobacco plant has been utilized as modern plant for proof of concept where a protease inhibitor gene from Ricebean has been transferred to tobacco plant which induced larval mortality within first three days of feeding at all larval developmental stages. The biochemical assays on mid-gut total protein extract showed that the transgenic tobacco leaves have inhibiting effect on trypsin and chymotrypsin enzymes of insect which is otherwise required for digestion of food by them. Hence, we provide a novel gene that could be utilized for pest resistance in other crops different developmental stages.
Non-alcoholic fatty liver disease (NAFLD) represents a highly prevalent metabolic disorder; however, the functional role of mitogen-activated protein kinase 10 (MAPK10) in the initiation and progression of NAFLD remains incompletely understood. This study investigated MAPK10's role in NAFLD and its regulatory mechanisms. Bioinformatics analysis was performed on the GSE89632 dataset to screen for differentially expressed genes (DEGs) associated with NAFLD. An in vivo NAFLD model was established in C57BL/6 J mice by feeding a high-fat diet (HFD), and subsequent lentiviral transduction was used to achieve hepatic overexpression or knockdown of MAPK10 and DNA methyltransferase 1 (DNMT1). In vitro, HepG2 cells were transfected with DNMT1 overexpression plasmids. Molecular analyses, including RT-qPCR and Western blot, were used to measure gene and protein expression levels. RNA immunoprecipitation (RIP) and dual-luciferase reporter assays were employed to assess the m5C modification of MAPK10 mRNA and its transcriptional activity, respectively. An RNA stability assay was used to evaluate mRNA half-life. MAPK10 expression was significantly reduced in the liver tissues of HFD-fed mice. Overexpression of MAPK10 alleviated hepatic steatosis, oxidative stress, and mitochondrial damage in vivo. Mechanistically, DNMT1 enhanced MAPK10 expression and stability in an m5C-dependent manner. RIP assay confirmed increased m5C modification of MAPK10 mRNA upon DNMT1 overexpression. Luciferase reporter assay demonstrated that DNMT1 specifically enhanced the activity of wild-type, but not m5C-site-mutated, MAPK10. RNA stability assay further showed that DNMT1 overexpression stabilized MAPK10 mRNA. Rescue experiments indicated that MAPK10 knockdown reversed the protective effects (improved lipid accumulation, oxidative stress, and mitochondrial function) induced by DNMT1 overexpression in HFD-fed mice. MAPK10 plays a protective role in NAFLD by ameliorating hepatic steatosis and mitochondrial dysfunction. Its expression is positively regulated by DNMT1 via m5C-mediated RNA stabilization. This study highlights the DNMT1-MAPK10 axis as a potential therapeutic target for NAFLD. Insight Box This study uncovers a previously unrecognized RNA epigenetic regulatory axis in NAFLD, demonstrating that DNMT1 post-transcriptionally regulates MAPK10 expression and stability through m5C RNA methylation. We establish MAPK10 as a key metabolic protector against NAFLD, ameliorating hepatic steatosis, oxidative stress, and mitochondrial dysfunction. By integrating bioinformatics, molecular biology, and functional validation across in vivo and in vitro models, we delineate a mechanistic pathway where m5C modification critically modulates MAPK10 activity. These findings not only provide fresh insight into the RNA-centric regulatory layer of NAFLD pathogenesis but also spotlight the therapeutic potential of modulating the DNMT1-MAPK10 axis, underscoring the value of integrative research strategies in elucidating complex metabolic diseases. Main points MAPK10 expression was significantly reduced in the liver tissues of HFD-induced NAFLD mice. MAPK10 overexpression alleviated hepatic steatosis and mitochondrial damage in HFD-fed mice. DNMT1 positively regulates MAPK10 expression and mRNA stability in an m5C RNA methylation-dependent manner.
Neurodegenerative disorders are characterised by progressive damage to neurons that leads to cognitive impairment and motor dysfunction. Current treatment options focus only on symptom management and palliative care, without addressing their root cause. In our previous study, we reported the upregulation of the CXC motif chemokine receptor 4 (CXCR4), in Alzheimer's disease (ad) and Parkinson's disease (PD). We reached this conclusion by analysing gene expression patterns of ad and PD patients, compared to healthy individuals of similar age. We used RNA sequencing data from Gene Expression Omnibus to carry out this analysis. Herein, we aim to identify natural compounds that have potential inhibitory activity against CXCR4 through cheminformatics-guided machine learning, to aid drug discovery for neurodegenerative disorders, especially ad and PD. Natural compounds are gaining prominence in the treatment of neurodegenerative disorders due to their biocompatibility and potential neuroprotective properties, including their ability to modulate CXCR4 expression. Recent advances in artificial intelligence (AI) and machine learning (ML) algorithms have opened new avenues for drug discovery research across various therapeutic areas, including neurodegenerative disorders. We aim to produce an ML model using cheminformatics-guided machine learning algorithms using data of compounds with known CXCR4 activity, retrieved from the Binding Database, to analyse various physicochemical attributes of natural compounds obtained from the COCONUT Database and predict their inhibitory activity against CXCR4. Insight Box This work extends our previous study published in Integrative Biology (DOI: 10.1093/intbio/zyad012). We aim to demonstrate the effectiveness of AI and ML in identifying potential treatment options for Alzheimer's and Parkinson's diseases. By analysing vast amounts of data and identifying patterns that may not be apparent to human researchers, AI-powered systems can provide valuable insight into potential treatment options that may have been overlooked through traditional research methods. Our study underscores the significance of interdisciplinary collaboration between computational and experimental scientists in drug discovery and in developing a robust pipeline to identify potential leads for drug development.
Preeclampsia is a type of pregnancy complication that manifests as hypertension and albuminuria, associated with improper development of blood vessels in the placenta. However, the precise cause of preeclampsia is not well defined. Ferroptosis is a type of cell death involving abnormal accumulation of iron and lipid reactive oxygen species (ROS) in cells. Accumulating evidence indicates that ferroptosis may contribute to preeclampsia development, but the underlying mechanism remains unclear. Several ubiquitin-specific proteases (USPs) have been reported to repress ferroptosis, but whether other USPs regulate ferroptosis and preeclampsia development remains elusive. Here we identified USP46 as a potent regulator of erastin-induced ferroptosis in BeWo trophoblasts, which serve as an in vitro model to study preeclampsia. We found that overexpression of USP46 promoted erastin-induced ferroptosis in BeWo cells, while knockdown of USP46 led to resistance to erastin-induced ferroptosis. This resistance could be reversed by excessive cold-inducible RNA-binding protein (CIRBP). Immunoprecipitation experiments showed that USP46 interacts with CIRBP to inhibit its ubiquitination. These findings suggest that USP46 sensitizes BeWo cells to ferroptosis by stabilizing CIRBP. Insight Box Preeclampsia is a severe pregnancy complication with an unknown pathogenesis. Studies have shown that several ubiquitin-specific proteases (USPs) can inhibit ferroptosis and affect the occurrence of preeclampsia. However, given the numerous genes in the USP family, it remains unclear whether other USPs regulate ferroptosis and the development of preeclampsia. In this study, we identified USP46 as a strong regulator of ferroptosis in BeWo cells. USP46 interacts with CIRBP to reduce its ubiquitination and stabilize its expression, thereby promoting ferroptosis. This study reveals the key role of USP46 in regulating ferroptosis and provides a new target for etiological research and treatment of preeclampsia.
Chronic obstructive pulmonary disease (COPD), multiple sclerosis (MS), and lung cancer are linked by shared inflammatory pathways and immune dysregulation. miRNAs regulate these processes by influencing gene expression, yet their roles in the molecular mechanisms across neurological and respiratory systems are not fully understood. This study aims to identify miRNAs and their target genes regulating inflammatory pathways, advancing the understanding of molecular genetics underlying COPD, MS, and lung cancer. miRNA expression data (GSE61741) were analyzed using a Random Forest (RF) model optimized via Grid Search and validated with Stratified K-Fold cross-validation. Synthetic Minority Oversampling (SMOTE) addressed data imbalance, while SHapley Additive exPlanations (SHAP) identified key miRNAs. Functional enrichment and pathway analyses explored miRNA-gene interactions. Single-cell level analysis further validated the cell-specific roles of these genes. An independent dataset (GSE31568) was used for validation. Key miRNAs, including hsa-let-7c, hsa-miR-454, hsa-miR-92a, and hsa-miR-223, were identified as regulators of hallmark inflammatory genes such as CCL2, IL6, ITGB3, and MYC. These genes are critical for cytokine signaling, epithelial repair, and immune modulation. Single-cell analysis highlighted the role of inflammatory fibroblasts in localized inflammation and tissue remodeling. The RF model achieved an accuracy of 81.58%, validated at 82.55%. Pathway analysis emphasized cytokine-cytokine receptor interactions and shared pathways between neurological and respiratory diseases. This study identifies miRNAs and their target genes as critical regulators of inflammation in COPD, MS, and lung cancer. Single-cell insights and pathway enrichment provide a comprehensive view of shared molecular mechanisms, contributing to biomarker discovery and therapeutic strategies for precision medicine in inflammatory diseases.
Tuberculosis (TB) remains a major global health challenge, necessitating the development of novel therapeutic interventions. Enoyl-acyl carrier protein (ACP) reductase (InhA), a key enzyme in the fatty acid biosynthesis pathway of Mycobacterium tuberculosis, has emerged as a promising target for anti-TB drug discovery. Exploration of InhA-inhibitors is important for advancing the drug discovery process. This study systematically investigates the molecular and pharmacological interactions of bioactive phytochemicals from Indian traditional medicinal plants with InhA to elucidate their therapeutic potential. Among 42 screened phytocompounds, the top five (Ebastine, 2-Phenylaminoadenosine, Gosogliptin, Lorcainide, and Levomefolic acid) showed promising binding affinities towards InhA (PDB: 2X22), with binding free energy ranging from -9.6 to -10.9 kcal/mol. Comprehensive computational analyses, including pharmacokinetic predictions, drug-likeness evaluation, and biological activity assessment, highlighted their potential as a drug candidate. These top-ranking ligands exhibited potential target-lead interactions, forming stable hydrogen bonds and hydrophobic contacts crucial for inhibitory activity. This multidisciplinary computational approach, including molecular docking and dynamic simulations, identifies these phytochemicals as high-affinity and stable inhibitors of InhA. These identified phytocompounds could serve as promising scaffolds for further optimization and experimental validation in the quest for new plant-based anti-TB agents. Insight Our study targets the essential mycobacterial enzyme, enoyl-ACP reductase (InhA), a validated pathway crucial for tuberculosis drug discovery. Understanding structural characteristics of InhA and its interaction with potential phytochemical ligands enhances our grasp of the mechanisms underlying anti-mycobacterial activity. Computational tools such as molecular docking, molecular dynamics simulations, and predictive toxicology models, provides a rational framework for selecting promising leads for further experimental validation. This integrated approach not only facilitates the translation of traditional medicinal plant knowledge into evidence-based drug discovery but also maximizes the efficiency of the drug development pipeline.
MicroRNAs have been implicated in the pathophysiology of several diseases including Parkinson's disease (PD). Endoplasmic reticulum (ER) stress mediated unfolded protein response (UPR) pathway and autophagy play a vital role in preventing the accumulation of α-synuclein, which is one among the major causes of PD. This study presents data on the interactions among miRNAs and genes involved in PD, ER stress and autophagy pathways analysed using computational tools. When the interactions among selected 89 miRNAs and 44 genes were visualised using Cytoscape, three miRNAs- hsa-miR-34a-5p, hsa-miR-9-5p and hsa-miR-214-3p were selected as hub-miRNAs based on their degree of interaction. Further, functional annotation and functional interaction analyses were carried out for the target genes of these hub-miRNAs. Based on ontology and enrichment analyses data, the targets of miR-34a-5p and miR-9-5p such as BCL2, BECN1, ATG5, HMGB1, and ATG7 were observed to be involved in apoptosis and autophagy. Further, the functional interactions of ATG5-BECN1 and BECN1-HMGB1 emphasised their integrative roles in autophagy. On the other hand, the targets of miR-214-3b such as XBP1, ATF4, BCL2L11, and BAX were found to be associated with ER stress and apoptosis. Also, functional interactions observed between XBP1-ATF4, ATF4-BCL2L11, and BCL2L11-BAX highlighted their integrative roles in neuronal apoptosis and ER stress pathways. Overall findings indicated that dysfunctions of these miRNAs might contribute to neuronal apoptosis through their regulatory roles in autophagy and ER stress pathways.
Despite the considerable decline of cervical cancer incidence in developed countries, the disease remains a public health problem in low-income and middle-income countries due to the low popularity of human papillomavirus vaccination and cervical cancer screening. Mainly treated with radiotherapy, the number of recurrences linked to radioresistance increases in women suffering from this disease and constitutes major obstacle. Here, we perform a combined proteomic and phosphoproteomic profiling of HeLa cervical cancer cells after in vitro treatment with X-rays and carbon ions. We observed differential and extensive alterations at the proteins and phosphoproteins levels. In total, we observed 96 and 102 differentially expressed proteins (DEPs) after X-rays and C-ions irradiation, respectively. For phosphoproteins, our results revealed 21 and 41 DEPs in response to C-ions and X-rays ionizing radiation respectively. Furthermore, our study revealed several mechanisms significantly activated by cells in response to ionizing radiation, potentially related to cancer radioresistance, including sister chromatid segregation, rRNA processing, ribosomal large subunit biogenesis, positive regulation of phagocytosis, engulfment, peptidase regulatory activity and negative regulation of ERK1/2 cascade. We also identified three proteins IPM3, DUSP3 and COQ7, oppositely expressed across the C-ions and X-rays groups while MX2 phosphorylation was downregulated in both radiation qualities. Finally, our study revealed a specific kinase signature, associated with Hela cells radioresistance: CDK5, MTOR and CDK2 kinases were predicted in the group of X-rays irradiation while CDK1, PLK1 SRC and MAPK1 kinases were predicted in the group of C-ions irradiation. Taken together, these findings could help to define new potential pathways and biomarkers to be targeted in the treatment of cervical cancer. Insight Box Statement of Integration, Innovation and Insight In this study, a robust proteomic and phospho-proteomic strategy was developed in order to display HELA cells responses to radiations. Two time points were selected to highlight the early responses of cells, following irradiation with low and high LET. CDK1, SRC, MAPK1 kinases were predicted to be activated in response to carbon ions irradiation, while CDK5, MTOR, ATM kinases were predicted in response to X-rays. Several accessions, playing pivotal role in cell proliferation and resistance, were upregulated in X-rays irradiated cells and down regulated in carbon ions irradiated cells. This study gives an accurate picture of molecular events linked with HELA cells radioresistance and offer potential drug targets for optimization of cervical cancer radiotherapy.
Ferroptosis plays a crucial role in inhibiting tumor progression. La Ribonucleoprotein 4B (LARP4B) is known to function as a pro-oncogenic factor in digestive tumors, but its specific role and potential mechanisms remain unclear in pancreatic cancer (PC). In this study, we found that LARP4B was upregulated in PC tissues and cells. Overexpression of LARP4B promoted PC cell proliferation and invasion, while knockdown of LARP4B inhibited PC cell proliferation and invasion. Furthermore, knockdown of LARP4B was associated with intracellular iron overload, increased levels of reactive oxygen species (ROS) and malondialdehyde (MDA), and decreased glutathione (GSH) content and superoxide dismutase (SOD) activity in PC cells. Mechanistically, LARP4B binds to mRNA of with-no-lysine kinase 1 (WNK1) and promotes its stability, and WNK1 competitively binds to the partial Kelch domain of Kelch-like ECH-associated protein 1 (Keap1) to promote the nuclear translocation of nuclear factor erythroid-2-related factor 2 (NRF2), thereby activating the NRF2/GCH1/BH4 pathway and inhibiting ferroptosis in PC cells. ML385, a NRF2 nuclear translocation inhibitor, partially rescued the inhibitory effect of WNK1 on ferroptosis in PC cells. Finally, in vivo experiments showed that knockdown of LARP4B suppressed tumor growth in PC xenograft mice. In conclusion, our study demonstrated that LARP4B inhibited ferroptosis by activating the WNK1-mediated NRF2/GCH1/BH4 pathway, thereby promoting PC progression. Insight Box This work provides evidence for LARP4B as a pro-oncogenic factor in pancreatic cancer, while also offers new insights into the further understanding of the biological functions of LARP4B and the oncological mechanisms of pancreatic cancer. We found that LARP4B is upregulated in PC tissues and cells, and its overexpression promotes the proliferation and invasion of PC cells. Additionally, we discovered that LARP4B binds to WNK1 mRNA and enhances its stability. WNK1 competitively binds to Keap1 to facilitate NRF2 nuclear translocation, thereby activating the NRF2/GCH1/BH4 pathway and inhibiting ferroptosis in PC cells. These findings provide significant insights for further research on PC and the development of therapeutic strategies.
Epigenetic alterations, particularly DNA methylation, play a crucial role in the progression of oral squamous cell carcinoma (OSCC) from oral leukoplakia (OL). However, the molecular mechanisms driving this transition remain poorly understood. Using interpretable machine learning (IML) on genome-wide methylation data from 118 samples (22 OL, 74 OSCC, and 22 controls), we identified 20 key CpG sites among 820 193 loci through SHAP (SHapley Additive exPlanations) analysis. Notably, cg19853638, cg25393842, cg01743793, and cg10784570 mapped to pivotal genes such as TNFRSF19, ALOX5, and SH3PXD2A, which regulate cell morphology, inflammatory pathways, and immune responses- critical processes influencing OSCC malignancy and progression. To assess generalizability and confirm the robustness of classifier, the predictive model was validated on an independent Taiwanese cohort (GSE38532) profiled on a different array platform, achieving 98.8% accuracy and ROC-AUC of 0.999 demonstrating robust cross-population performance. Furthermore, cross-omics integration with an independent transcriptomic dataset (GSE31056) identified eight genes, including ALOX5, FOXP1, and VTI1A, showing consistent methylation and expression patterns, underscoring their biological relevance. Our findings highlight the functional relevance of SH3PXD2A, TNFRSF19, and ALOX5 in OSCC pathophysiology: SH3PXD2A mediates cell migration and invasion, TNFRSF19 is involved in survival signaling, and ALOX5 regulates inflammatory responses. These multi-layered analyses provide novel insights into epigenetic mechanisms underlying OL to OSCC progression and highlight candidate biomarkers with strong translational potential. By combining IML based methylation modeling with external and cross-omics validation, this study advances the development of reliable, interpretable biomarkers for precision oral cancer diagnostics and management.
A recent study delves into intricate relationship between microRNAs (miRNAs), specifically focusing on their role in cancer development. miRNAs are highlighted for their capacity to modify genetic profile and modulate epigenetic architecture, establishing regulatory circuit between epigenetic modulation and miRNAs. Notably, antipsychotic drugs, particularly pimozide, is reported to influence miRNA expression, impacting essential processes in cancer development such as cell proliferation and apoptosis. Anti-cancer properties of pimozide have sparked interest in its potential role in cancer treatment, although precise mechanism of its antitumor function remains elusive. The study focuses on a newly discovered miRNA, miR-2909, encoded by the apoptosis antagonizing transcription factor (AATF) gene, which has been implicated in oncogenesis. This research aims to unravel the interplay between pimozide and miR-2909, investigating their influence on epigenetic modulations and their functional relevance to cellular proliferation and apoptosis in cancer cells. Utilizing bioinformatic tools for structural prediction, pharmacokinetic property assessment, and molecular docking interactions, the study reveals a strong binding affinity between pimozide and miR-2909.Validation through various experimental methods, including qRT-PCR, western blotting, immunofluorescence, transient transfection, CHIP assay, and flow cytometry confirms the interplay between pimozide and miR-2909. The results demonstrate a pivotal role in the regulation of genes responsible for cellular proliferation and apoptosis. Additionally, the study uncovers that pimozide's pharmaco-epigenomic response is mediated through miR-2909, promoting DNA methylation and leading to decreased cellular proliferation, increased apoptosis, ROS generation, and altered cell-cycle dynamics. In conclusion, the findings identify pimozide as a potential chemotherapeutic agent acting through the regulation of the oncomiR-2909. Insight Box This study uncovers a novel pharmaco-epigenomic mechanism by which the antipsychotic drug pimozide exerts anti-cancer effects in breast and lung cancer cells. We demonstrate that pimozide downregulates oncogenic miR-2909, leading to the upregulation of DNMT3B, which mediates epigenetic silencing of key proliferation genes and promotes apoptosis. This integrative approach-combining molecular biology, gene expression profiling, and epigenetic analysis-provides new biological insight into the repurposing of psychiatric drugs for cancer therapy. Our findings highlight the therapeutic potential of targeting microRNA-epigenetic interactions and suggest a broader applicability of integrating pharmacology with systems biology to uncover unconventional roles of known drugs in cancer treatment.
We screened a random peptide phage display library using Russell's viper venom phospholipase A2 (RV-PLA2) as bait. Sequence information from the resulting set of bio-panned heptapeptides was analyzed and mined to determine likely sites of interaction between two subunits of RV-PLA2 homo dimers and between RV-PLA2 and the γPLA2 inhibitor PIP from Malayopython reticulatus. This was accomplished in part by sequence alignment of the affinity-selected peptides with the sequences of RV-PLA2 and PIP. Because similarity scores calculated from sequence alignments proved inadequate to determine interaction interfaces accurately for RV-PLA2 dimers, we explored the use of amino acid frequency-based interactions scores (SFI/SFIN) for a more accurate prediction of protein-protein interaction sites. Heptamers with elevated SFI(N) scores were compared to interfaces of interaction observed in crystal structures of RV-PLA2 homodimers and to sites of interaction predicted by protein-protein docking between structures of RV-PLA2 and model of PIP. Segments with a high density of protein-protein contacts coincided with heptamer sequences exhibiting SFI and/or SFIN scores significantly above average, in both RV-PLA2 homodimers and in RV-PLA2 γPLI heteromeric structures. Elevated SFI and SFIN scores were associated with peptide function since the heptamers with some of the highest SFI and SFI(N) scores, LPGLPLS, GLPLSLQ and SLQNGLY constitute the known PLA2 inhibitor P-PB.I (LPGLPLSLQNGLY) while KLGRVDI, and WDGVYIR, constitute PIP-17 (LGRVDIHVWDGVYIRGR), IC50 for hsPLA2: 5.3 μM. A graph showing the alignment of maxima between SFI scores and average solvent accessibility (per heptamer) suggests that solvent accessibility is a major driver of both protein-protein interaction and phage selection. Insights We show by computational methods that in sets of small phage-displayed peptides of the same length selected for binding to the same target protein, amino acids contributing to binding at a particular position occur at higher frequencies than in random peptides. This position-specific selection of particular amino acids can be detected in the position-specific amino acid frequency distribution of that set of selected peptides. Therefore, when this position-specific amino acid frequency is mapped back onto a particular amino acid sequence of the same length, the sum of these frequencies can function as a measure of enrichment of selected amino acids.
To develop efficient diagnostic and treatment approaches, gaining an in-depth knowledge of the molecular mechanisms and potential targets causing childhood asthma is of utmost significance. Childhood asthma datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between asthmatic child and healthy people were screened by the Limma package. DEGs were subjected to further analyses utilizing GO, KEGG and GSEA analysis. The hub genes associated with childhood asthma were discovered by PPI analysis. The drugs target hub genes were accessed from the DrugBank database. Autodock vina was used to explore the binding ability of targeted drugs to hub genes. Total 80 DEGs were selected from GSE152004 and GSE65204 datasets. The cytokine-cytokine receptor interaction was the key pathway identified by functional enrichment analysis of shared DEGs. A total of 4 hub genes (CCL26, CXCR6, IL18RAP and CCL20) were identified by the constructed PPI network, among which CXCR6, IL18RAP and CCL20 were significantly decreased in childhood asthma datasets. Whereas, the CCL26 was significantly increased in childhood asthma datasets. Additionally, the extra dataset GSE19187 and GSE240567 were employed for validation. Ultimately, drugs (Cimetidine, Cefaclor and Propofol) that target hub genes have favorable combination ability. We have determined that CCL26, CXCR6, IL18RAP and CCL20 might have crucial involvement in the advancement of childhood asthma, thus having the potential to be targeted therapeutically in order to enhance treatment choices for childhood asthma. Statement of Integration, Innovation and Insight: The cytokine-cytokine receptor interaction is a key pathway in the occurrence of childhood asthma. The hub genes (CCL26, CXCR6, IL18RAP and CCL20) affect the development of childhood asthma. The drugs (Cimetidine, Cefaclor and Propofol) that target hub genes have favorable combination ability.
Our study aims to explore the early genomic diagnostic markers of OSA and the corresponding drug prediction targets using network pharmacology analysis, and to elucidate the etiology and pathogenesis of OSA from the genetic level. We selected OSA-related gene datasets (GSE135917 and GSE38792) from the Gene Expression Omnibus (GEO). Principal component analysis (PCA) was performed to remove outlier samples, and batch correction was applied to the two datasets. The raw expression matrix was log2-transformed, and samples were divided into normal and OSA groups. Differentially expressed genes (DEGs) were identified, and WGCNA analysis was performed on these DEGs to identify mitochondria-associated hub genes, followed by functional enrichment analysis, PPI network construction, core lncRNA-related ceRNA network construction. We then screened core genes with a high risk of OSA. Based on the core genes, we established an easy-to-use nomogram and verified its accuracy in identifying OSA patients then performed a differential expression analysis of the core genes, GSEA and GSVA analyses, and immune infiltration analysis. Finally, we constructed the disease prediction model and predicted the drug targets, thereby obtain a genomic prediction model for OSA. After PCA and batch correction, an expression matrix comprising 13 normal samples and 19 OSA patient samples was finally included. We identified 1500 differentially expressed genes (DEGs) through differential expression analysis, then screened 61 hub genes by WGCNA analysis, and established an OSA-associated ceRNA network containing 75 predictive miRNAs, 129 lncRNAs and 5 mRNAs. Six robust key genes were identified through PPI network construction: TUFM, CYCS, UQCRC1, COX4I1, TIMM50, and NDUFV1. Finally, after LASSO regression and nomogram validation, a predictive model containing 2 core genes (UQCRC1 and COX4I1) was obtained, and its area under the ROC curve (AUC) was 0.919. Drug target prediction of the core genes showed that 1-Methyl-4-phenyl-2,3-dihydropyridinium CTD 00002003, Cube root extract CTD 00006707, Disodium selenite CTD 00007229, and mitotane CTD 00006344 had good effects. Our current findings provide a rationale for identifying therapeutic targets in the diagnosis and treatment of OSA. In addition, these findings have the potential to facilitate the translation of our study to clinical applications in the future. Insight Box Different from other single-gene predictors of OSA, network pharmacological analysis identified differential genes and explored biomolecular markers of OSA from multi-gene and multi-target perspectives through enrichment analysis, construction of ceRNA gene network, and correlation analysis and explore early genomic diagnostic indicators of OSA and corresponding drug prediction targets using network pharmacological analysis and to elucidate the etiology and pathogenesis of OSA from the genetic level. To provide a more accurate method for the diagnosis, prevention, and treatment of clinical OSA, it is expected to provide a research basis for the accurate diagnosis of clinical OSA and the pathogenesis of OSA.
Metastasis is one of the leading factors of cancer-related deaths worldwide. New potential targets and treatment strategies are needed to extend survival and enhance the quality of life for these patients. We performed an in-depth bioinformatics analysis to identify potential genes and associated potential therapeutic compounds for metastasis of prostate adenocarcinoma. The differentially expressed genes (DEGs) were first identified using four datasets (GSE8511), (GSE3325), (GSE27616) and (GSE6919) present in the Gene Expression Omnibus (GEO) database and analyzed using the GEO2R. WGCNA was performed to find a significant gene cluster. Network analysis was performed using MCODE and Cytohubba plugins of Cytoscape to select hub genes. Moreover, expression validation of key genes was carried out using the TCGA dataset. Functional annotation and pathway enrichment analyses were conducted for validation, while survival analysis was applied to assess potential therapeutic effects. DEGs retrieved from the GEO were submitted to the Connectivity Map database to identify potentially related compounds. Molecular docking, ADMET analysis and drug-likeness properties, MD simulations and MM-GBSA analysis were performed to screen for the best potential drugs. We identified three compounds-Prunetin, Ofloxacin, and ALW-II-49-7 that may help extend disease-free survival in patients with tumor metastasis. Additionally, ACTA2, MYLK, and CNN1 were recognized as potential therapeutic targets for these compounds. These drugs' potential effectiveness and binding efficiency were screened using induced fit molecular docking followed by 100 ns MD-based Simulations and MM-GBSA analysis. However, further in vitro and in vivo studies are needed to confirm these findings. Insight box This study integrates microarray gene expression profiling with bioinformatics tools to identify differentially expressed genes (DEGs) and co-expression networks using WGCNA. Network analysis in Cytoscape was used to screen hub genes, and the Connectivity Map (cMAP) database was searched for potential candidate drugs. Binding efficiency of repurposed drugs was evaluated using molecular docking, molecular dynamics (MD) simulations, and MM-GBSA analysis. Our findings provide the potential therapeutic drugs and targets of prostate adenocarcinoma metastasis with possibilities for follow-up in vitro and in vivo validation.
Oxygen levels vary in the environment. Oxygen availability has a major effect on almost all organisms, and oxygen is far more than a substrate for energy production. However, less is known about related biological processes under hypoxic conditions and about the adaptations to changing oxygen concentrations. The yeast Saccharomyces cerevisiae can adapt its metabolism for growth under different oxygen concentrations and can grow even under anaerobic conditions. Therefore, we developed a microfluidic device that can generate serial, accurately controlled oxygen concentrations for single-cell studies of multiple yeast strains. This device can construct a broad range of oxygen concentrations, [O2] through on-chip gas-mixing channels from two gases fed to the inlets. Gas diffusion through thin polydimethylsiloxane (PDMS) can lead to the equilibration of [O2] in the medium in the cell culture layer under gas cover regions within 2 min. Here, we established six different and stable [O2] varying between ~0.1 and 20.9% in the corresponding layers of the device designed for multiple parallel single-cell culture of four different yeast strains. Using this device, the dynamic responses of different yeast transcription factors and metabolism-related proteins were studied when the [O2] decreased from 20.9% to serial hypoxic concentrations. We showed that different hypoxic conditions induced varying degrees of transcription factor responses and changes in respiratory metabolism levels. This device can also be used in studies of the aging and physiology of yeast under different oxygen conditions and can provide new insights into the relationship between oxygen and organisms. Integration, innovation and insight: Most living cells are sensitive to the oxygen concentration because they depend on oxygen for survival and proper cellular functions. Here, a composite microfluidic device was designed for yeast single-cell studies at a series of accurately controlled oxygen concentrations. Using this device, we studied the dynamic responses of various transcription factors and proteins to changes in the oxygen concentration. This study is the first to examine protein dynamics and temporal behaviors under different hypoxic conditions at the single yeast cell level, which may provide insights into the processes involved in yeast and even mammalian cells. This device also provides a base model that can be extended to oxygen-related biology and can acquire more information about the complex networks of organisms.
Cosmic radiation, composed of high charge and energy (HZE) particles, causes cellular DNA damage that can result in cell death or mutation that can evolve into cancer. In this work, a cell death model is applied to several cell lines exposed to HZE ions spanning a broad range of linear energy transfer (LET) values. We hypothesize that chromatin movement leads to the clustering of multiple double strand breaks (DSB) within one radiation-induced foci (RIF). The survival probability of a cell population is determined by averaging the survival probabilities of individual cells, which is function of the number of pairwise DSB interactions within RIF. The simulation code RITCARD was used to compute DSB. Two clustering approaches were applied to determine the number of RIF per cell. RITCARD outputs were combined with experimental data from four normal human cell lines to derive the model parameters and expand its predictions in response to ions with LET ranging from ~0.2 keV/μm to ~3000 keV/μm. Spherical and ellipsoidal nuclear shapes and two ion beam orientations were modeled to assess the impact of geometrical properties on cell death. The calculated average number of RIF per cell reproduces the saturation trend for high doses and high-LET values that is usually experimentally observed. The cell survival model generates the recognizable bell shape of LET dependence for the relative biological effectiveness (RBE). At low LET, smaller nuclei have lower survival due to increased DNA density and DSB clustering. At high LET, nuclei with a smaller irradiation area-either because of a smaller size or a change in beam orientation-have a higher survival rate due to a change in the distribution of DSB/RIF per cell. If confirmed experimentally, the geometric characteristics of cells would become a significant factor in predicting radiation-induced biological effects. Insight Box: High-charge and energy (HZE) ions are characterized by dense linear energy transfer (LET) that induce unique spatial distributions of DNA damage in cell nuclei that result in a greater biological effect than sparsely ionizing radiation like X-rays. HZE ions are a prominent component of galactic cosmic ray exposure during human spaceflight and specific ions are being used for radiotherapy. Here, we model DNA damage clustering at sub-micrometer scale to predict cell survival. The model is in good agreement with experimental data for a broad range of LET. Notably, the model indicates that nuclear geometry and ion beam orientation affect DNA damage clustering, which reveals their possible role in mediating cell radiosensitivity.
Integrins are transmembrane receptors that play a crucial role in cell adhesion and signaling by connecting the extracellular environment to the intracellular cytoskeleton. After binding with specific ligands in the extracellular matrix (ECM), integrins undergo conformational changes that transmit signals across the cell membrane. The integrin-mediated bidirectional signaling triggers various cellular responses, such as changes in cell shape, migration and proliferation. Irregular integrin expression and activity are closely linked to tumor initiation, angiogenesis, cell motility, invasion, and metastasis. Thus, understanding the intricate regulatory mechanism is essential for slowing cancer progression and preventing carcinogenesis. Among the four classes of integrins, the arginine-glycine-aspartic acid (RGD)-binding integrins stand out as the most crucial integrin receptor subfamily in cancer and its metastasis. Dysregulation of almost all RGD-binding integrins promotes ECM degradation in ovarian cancer, resulting in ovarian carcinoma progression and resistance to therapy. Preclinical studies have demonstrated that targeting these integrins with therapeutic antibodies and ligands, such as RGD-containing peptides and their derivatives, can enhance the precision of these therapeutic agents in treating ovarian cancer. Therefore, the development of novel therapeutic agents is essential for treating ovarian cancer. This review mainly discusses genes and their importance across different ovarian cancer subtypes, the involvement of RGD motif-containing ECM proteins in integrin-mediated signaling in ovarian carcinoma, ongoing, completed, partially completed, and unsuccessful clinical trials of therapeutic agents, as well as existing limitations and challenges, advancements made so far, potential strategies, and directions for future research in the field. Insight Box Integrin-mediated signaling regulates cell migration, proliferation and differentiation. Dysregulated integrin expression and activity promote tumor growth and dissemination. Thus, a proper understanding of this complex regulatory mechanism is essential to delay cancer progression and prevent carcinogenesis. Notably, integrins binding to RGD motifs play an important role in tumor initiation, evolution, and metastasis. Preclinical studies have demonstrated that therapeutic agents, such as antibodies and small molecules with RGD motifs, target RGD-binding integrins and disrupt their interactions with the ECM, thereby inhibiting ovarian cancer proliferation and migration. Altogether, this review highlights the potential of RGD-binding integrins in providing new insights into the progression and metastasis of ovarian cancer and how these integrins have been utilized to develop effective treatment plans.
Cells dynamically remodel their internal structures by modulating the arrangement of actin filaments (AFs). In this process, individual AFs exhibit stochastic behavior without knowing the macroscopic higher-order structures they are meant to create or disintegrate, but the mechanism allowing for such stochastic process-driven remodeling of subcellular structures remains incompletely understood. Here we employ percolation theory to explore how AFs interacting only with neighboring ones without recognizing the overall configuration can nonetheless create a substantial structure referred to as stress fibers (SFs) at particular locations. We determined the interaction probabilities of AFs undergoing cellular tensional homeostasis, a fundamental property maintaining intracellular tension. We showed that the duration required for the creation of SFs is shortened by the increased amount of preexisting actin meshwork, while the disintegration occurs independently of the presence of actin meshwork, suggesting that the coexistence of tension-bearing and non-bearing elements allows cells to promptly transition to new states in accordance with transient environmental changes. The origin of this asymmetry between creation and disintegration, consistently observed in actual cells, is elucidated through a minimal model analysis by examining the intrinsic nature of mechano-signal transmission. Specifically, unlike the symmetric case involving biochemical communication, physical communication to sense environmental changes is facilitated via AFs under tension, while other free AFs dissociated from tension-bearing structures exhibit stochastic behavior. Thus, both the numerical and minimal models demonstrate the essence of intracellular percolation, in which macroscopic asymmetry observed at the cellular level emerges not from microscopic asymmetry in the interaction probabilities of individual molecules, but rather only as a consequence of the manner of the mechano-signal transmission. These results provide novel insights into the role of the mutual interplay between distinct subcellular structures with and without tension-bearing capability. Insight: Cells continuously remodel their internal elements or structural proteins in response to environmental changes. Despite the stochastic behavior of individual structural proteins, which lack awareness of the larger subcellular structures they are meant to create or disintegrate, this self-assembly process somehow occurs to enable adaptation to the environment. Here we demonstrated through percolation simulations and minimal model analyses that there is an asymmetry in the response between the creation and disintegration of subcellular structures, which can aid environmental adaptation. This asymmetry inherently arises from the nature of mechano-signal transmission through structural proteins, namely tension-mediated information exchange within cells, despite the stochastic behavior of individual proteins lacking asymmetric characters in themselves.