We performed an integrated analysis of genome-wide DNA methylation and expression datasets in normal cells and healthy animals exposed to polyphenols with estrogenic activity (i.e. phytoestrogens). We identified that phytoestrogens target genes linked to disrupted cellular homeostasis, e.g. genes limiting DNA break repair (RNF169) or promoting ribosomal biogenesis (rDNA). Existing evidence suggests that DNA methylation may be governed by sirtuin 1 (SIRT1) deacetylase via interactions with DNA methylating enzymes, specifically DNMT3B. Since SIRT1 was reported to be regulated by phytoestrogens, we test whether phytoestrogens suppress genes related to disrupted homeostasis via SIRT1/DNMT3B-mediated transcriptional silencing. Human MCF10A mammary epithelial cells were treated with phytoestrogens, pterostilbene (PTS) or genistein (GEN), followed by analysis of cell growth, DNA methylation, gene expression, and SIRT1/DNMT3B binding. SIRT1 occupancy at the selected phytoestrogen-target genes, RNF169 and rDNA, was accompanied by consistent promoter hypermethylation and gene downregulation in response to GEN, but not PTS. GEN-mediated hypermethylation and SIRT1 binding were linked to a robust DNMT3B enrichment at RNF169 and rDNA promoters. This was not observed in cells exposed to PTS, suggesting a distinct mechanism of action. Although both SIRT1 and DNMT3B bind to RNF169 and rDNA promoters upon GEN, the two proteins do not co-occupy the regions. Depletion of SIRT1 abolishes GEN-mediated decrease in rDNA expression, suggesting SIRT1-dependent epigenetic suppression of rDNA by GEN. These findings enhance our understanding of the role of SIRT1-DNMT3B interplay in epigenetic mechanisms mediating the impact of phytoestrogens on cell biology and cellular homeostasis.

This study investigated the pathogenic mechanisms of Aeromonas veronii in macrophages. THP-1 derived macrophages were used as a human macrophage model and were treated with A. veronii strain AS1 isolated from intestinal biopsies of an IBD patient, or Escherichia coli strain K-12. RNA was extracted and subjected to RNA sequencing and comparative transcriptomic analyses. Protein levels of IL-8, IL-1β, IL-18, and TNFα were measured using ELISA, and apoptosis was assessed using caspase 3/7 assays. Both A. veronii AS1 and E. coli K-12 significantly upregulated the expression of many genes involving inflammation. At the protein level, A. veronii AS1 induced significantly higher levels of IL-8, TNFα, mature IL-18 and IL-1β than E. coli K-12, and led to greater elevation of caspase 3/7 activities. Both A. veronii AS1 and E. coli K-12 upregulated the expression of CASP5, but not other caspase genes. A. veronii AS1 significantly downregulated the expression of 20 genes encoding histone proteins that E. coli K-12 did not. The more profound pathogenic effects of A. veronii in inducing inflammation and apoptosis in macrophages than E. coli K-12 are consistent with its role as a human enteric pathogen. The upregulated expression of CASP5 and increased release of IL-1β and IL-18 support the role of CASP5 in activation of non-canonical inflammasome. The downregulation of histone genes by A. veronii suggests a unique impact on host cell gene expression, which may represent a novel virulence strategy. These findings advance the understanding of pathogenic mechanisms of the emerging human enteric pathogen A. veronii.

Inflammasomes regulate the host response to intracellular pathogens including mycobacteria. We have previously shown that the course of Mycobacterium marinum infection in adult zebrafish (Danio rerio) mimics the course of tuberculosis in human. To investigate the role of the inflammasome adaptor pycard in zebrafish M. marinum infection, we produced two zebrafish knockout mutant lines for the pycard gene with CRISPR/Cas9 mutagenesis. Although the zebrafish larvae lacking pycard developed normally and had unaltered resistance against M. marinum, the loss of pycard led to impaired survival and increased bacterial burden in the adult zebrafish. Based on histology, immune cell aggregates, granulomas, were larger in pycard-deficient fish than in wild-type controls. Transcriptome analysis with RNA sequencing of a zebrafish haematopoietic tissue, kidney, suggested a role for pycard in neutrophil-mediated defence, haematopoiesis and myelopoiesis during infection. Transcriptome analysis of fluorescently labelled, pycard-deficient kidney neutrophils identified genes that are associated with compromised resistance, supporting the importance of pycard for neutrophil-mediated immunity against M. marinum. Our results indicate that pycard is essential for resistance against mycobacteria in adult zebrafish.

SRSF2 (serine/arginine-rich splicing factor 2) is a critical regulator of pre-messenger RNA splicing, which also plays noncanonical functions in transcription initiation and elongation. Although elevated levels of SRSF2 are associated with advanced stages of lung adenocarcinoma (LUAD), the mechanisms connecting SRSF2 to lung tumor progression remain unknown. We show that SRSF2 overexpression increases global transcription and replicative stress in LUAD cells, which correlates with the production of DNA damage, notably double-strand breaks (DSBs), likely resulting from conflicts between transcription and replication. Moreover, SRSF2 regulates DNA repair pathways by promoting homologous recombination and inhibiting nonhomologous end joining. Mechanistically, SRSF2 interacts with and enhances MRE11 (meiotic recombination 11) recruitment to chromatin, while downregulating 53BP1 messenger RNA and protein levels. Both events are likely contributing to SRSF2-mediated DNA repair process rerouting. Lastly, we show that SRSF2 and MRE11 expression is commonly elevated in LUAD and predicts poor outcome of patients. Altogether, our results identify a mechanism by which SRSF2 overexpression promotes lung cancer progression through a fine control of both DSB production and repair. Finally, we show that SRSF2 knockdown impairs late repair of ionizing radiation-induced DSBs, suggesting a more global function of SRSF2 in DSB repair by homologous recombination.

Osteoporosis (OP) is a common disease of aging, which is closely related to the osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). DNA damage, as a senescence-associated secretory phenotype (SASP), plays an important role in aging diseases including OP. GIT2 has been identified as a DNA repair gene and alleviates aging-related phenotypes. However, the relationship between GIT2 and osteogenic differentiation of BMSCs remains unclear.

Here, we used bioinformatics analysis to identify the gene GIT2, which is closely related to aging, OP and DNA damage, and its downstream targets. Then, H2O2 -induced BMSCs senescence model and ovariectomy-induced mice OP model was established in vitro and in vivo, respectively. Micro-CT, H&E staining, toluidine blue staining, and calcein double labeling were used to analyze bone mass, osteogenic differentiation phenotype, and bone formation rate. Comet assay, Elisa and immunofluorescence were used to analyze senescence-related phenotypes. Western blotting was used to detect the protein levels of GIT2/TRAF3/NF-κB axis and osteogenesis-related markers.

Our results showed that GTI2 and TRAF3 were positively correlated with OP-related markers. On the one hand, GIT2 could inhibit the activation of both canonical and non-canonical NF-κB signaling pathways by positively regulating TRAF3. On the other hand, GIT2 could directly bind to P65, a component of the classical NF-κB signaling pathway, and P52, a component of the non-classical NF-κB signaling pathway, to inhibit their activation, improve DNA damage repair, alleviate cell senescence, and further promote osteogenic differentiation of BMSCs.

In summary, the present study demonstrates that GIT2 plays a crucial regulatory role in promoting osteogenic differentiation of BMSCs, which provides new ideas for the prevention and treatment of OP and other aging-related diseases.

Oral submucous fibrosis (OSMF) is categorized as an oral potentially malignant disorder (OPMD) with an increased risk of occurrence of oral squamous cell carcinoma (OSCC). In this study, we aimed to identify the hub genes associated with OSMF and OSCC.

Using RStudio, a set of differentially expressed genes (DEGs) were identified in (A) OSMF, (B) OSCC, and (C) comparative OSMF-OSCC category, obtained from Gene Expression Omnibus (GEO). The Protein to Protein Interaction (PPI) Network, hub genes, and functional annotation were determined using Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, and SR-Plot, Database for Annotation, Visualization and Integrated Discovery (DAVID).

A total of 2081, 2320, and 3295 DEGs were obtained from the OSMF, OSCC, and comparative categories, respectively. Hub gene and gene enrichment analysis revealed that the genes in (A) MYH6, TTN, TNNT3, MYL1, TPM2, ACTN3, NEB, MYL2, TNNT1, and TPM1; (B) CD4, SELL, CD28, CD27, PRF1, CD80, GZMB, CD40LG, ITGAX, and IL4; and (C) CD4, CD8A, CTLA4, CD28, GZMB, IL79, CD69, CD40LG, IFNG, and CD80 categories, were associated with muscle contraction, cell proliferation, and malignant transformation.

Hub genes and functional enrichment analysis revealed the diagnostic genes and the genes responsible for the malignant transformation in OSMF, OSCC, and the comparative category. A panel of identified genes will be of clinical significance in targeted therapy in future studies. (Oral Surg Oral Med Oral Pathol Oral Radiol YEAR;VOL:page range).

Atherosclerosis is the major cause of cardiovascular diseases worldwide, and AIDS linked with chronic inflammation and immune activation, increases atherosclerosis risk. The application of bioinformatics and machine learning to identify hub genes for atherosclerosis and AIDS has yet to be reported. Thus, this study aims to identify the hub genes for atherosclerosis and AIDS. Gene expression profiles were downloaded from the Gene Expression Omnibus database. The Robust Multichip Average was performed for data preprocessing, and the limma package was used for screening differentially expressed genes. Enrichment analysis employed GO and KEGG, protein-protein interaction network was constructed. Hub genes were filtered using topological and machine learning algorithms and validated in external cohorts. Then immune infiltration and correlation analysis of hub genes were constructed. Nomogram, receiver operating curve, and single-sample gene set enrichment analysis were applied to evaluate hub genes. This study identified 48 intersecting genes. Enrichment analyses indicated that these genes are significantly enriched in viral response, inflammatory response, and cytokine signaling pathways. CCR5 and OAS1 were identified as common hub genes in atherosclerosis and AIDS for the first time, highlighting their roles in antiviral immunity, inflammation and immune infiltration. These findings contributed to understanding the shared pathogenesis of Atherosclerosis and AIDS and provided possible potential therapeutic targets for immunomodulatory therapy.

Most of the chemical energy that organisms rely on to support cellular function is generated through oxidative phosphorylation, a metabolic pathway in which electron donors NADH and FADH are oxidized through a series of successive steps to generate adenosine triphosphate. These redox reactions are orchestrated by a series of five protein complexes that sit within the mitochondrial membrane. Deficiency of cytochrome c oxidase, the fourth of these complexes, is a recognized cause of mitochondrial disease. COXFA4 encodes one of the protein subunits of cytochrome c oxidase, and variants in COXFA4 have recently been reported in individuals with a range of symptoms. These symptoms can include feeding difficulties, poor growth, cardiomyopathy, Leigh or Leigh-like disease, and neurodevelopmental delay, although these symptoms vary widely between individuals. However, a mechanistic understanding of the connection between COXFA4 loss and these varied disease manifestations is lacking. Using animal modeling in Xenopus, we explored the ramifications of coxfa4 loss of function on the early developing heart. We then conducted a hypothesis naive analysis of cellular gene expression in the context of COXFA4 deletion and discovered a downstream deficiency in the ornithine decarboxylase pathway. Small-molecule-based modulation of the ornithine decarboxylase pathway in our model modified the extent of disease, including improvement of cardiac function. Our findings point to a mechanism by which COXFA4 dysfunction leads to tissue-specific disease.

It is well known that Shaoyao Decoction (SYD), as a commonly used formula of traditional Chinese medicine (TCM), has a beneficial effect on the treatment of ulcerative colitis (UC). It is found that SYD can also prevent colitis-associated colorectal cancer (CAC). However, its potential anti-cancer mechanism is still waiting to be revealed.

The aim of this study is to investigate the underlying mechanisms of SYD in inhibiting CAC through silico analysis as well as animal experiment validation.

The primary active compounds, potential therapeutic targets and intervening signaling pathways, which SYD might inhibit the CAC process were predicted by network pharmacology analysis combined with our previous research result of high performance liquid chromatography (HPLC). We attempted to validate the acquired hub targets from molecular docking combined with the Gene Expression Profiling Interactive Analysis (GEPIA), the Human Protein Atlas (HPA), and the cBioPortal database comprehensively. Subsequently, an animal model of CAC mice induced by azoxymethane (AOM) and dextran sulfate sodium (DSS) was constructed and treated with SYD for 14 weeks, and tumor-related physical indicators were evaluated after sacrificed. In addition, samples of colon tissues were obtained for histologic and protein level studies to verify the predicted mechanism.

We obtained 166 active ingredients of SYD and predicted 148 potential targets through network pharmacology analysis, among which quercetin, berberine, kaempferol, wogonin and naringenin were selected as core drug ingredients, and TP53, AKT1, CASP3, PTGS2 and CCND1 were identified and included into the range of core targets. GO and KEGG analyses suggested that the PI3K-Akt signaling pathway might hold a crucial role in CAC prevention and treatment by promoting apoptosis and inhibiting tumor proliferation. In the animal experiment, both SYD and SASP treatments improved the inflammatory condition and pathological damage of the colon tissues in mice. After treatments with SYD and SASP, it was found that decreases of Cyclin D1 and Survivin expression levels and increases of p53 and Cleaved caspase-3 expression levels could be mediated by decreasing the phosphorylation levels of PI3K and Akt proteins in the colon tissues of mice.

The results of our study provide supports that SYD effectively inhibits CAC based on modulating PI3K-Akt signaling pathway to suppress tumor proliferation process as well as to promote tumor apoptosis process.

Traditional paradigms of pharmaceutical innovation have long relied on the "one drug, one disease" premise. However, a network mindset in unpacking disease mechanisms can be fruitful to move toward a "one drug, polydisease" paradigm of drug discovery and development. A case in point is obstructive sleep apnea (OSA) and lung cancer, which are two prevalent respiratory disorders that share common risk factors and may potentially exhibit overlapping molecular mechanisms. The putative mechanistic linkages between OSA and lung cancer remain underexplored; however, this study offers new evidence on overlapping genetic signatures between OSA and lung cancer with an in-silico approach. Bioinformatics analysis of the publicly available datasets (GSE135917 and GSE268175) identified 123 upregulated and 13 downregulated genes in OSA and 3175 upregulated and 2272 downregulated genes in lung cancer. A total of four genes (C1GALT1, TMEM106B, ZNF117, and ZNF486) were significantly upregulated with both disorders, highlighting potentially shared genetic and molecular mechanisms. Pathway and cell enrichment analysis indicated that mucin type O-glycan biosynthesis pathway and endothelial cells are strongly associated with these shared genes, lending support for their potential roles in both diseases. Moreover, hsa-miR-34a-5p, hsa-let-7g-5p, and hsa-miR-19a-3p were found to be associated with these common genes. Validation using the GEPIA2 tool confirmed the consistent expression patterns of these four genes in lung cancer. Machine learning analysis highlighted TMEM106B as the most significant biomarker candidate for distinguishing OSA and lung cancer from controls. In summary, this study supports the overarching concept that human diseases can have shared mechanistic pathways in the specific example of OSA and lung cancer. While these findings call for further research and validation, they invite rethinking the current pharmaceutical innovation paradigms to move beyond the "one drug, one disease" concept.

Rhizomes of Drynaria quercifolia have long been traditionally used to manage rheumatic pain. However, there is limited research supporting this traditional practice and insufficient evidence demonstrating the molecular mechanisms of action of plant-derived bioactives in rheumatoid arthritis (RA). The current study aims to identify the effective components in Drynaria quercifolia methanol rhizome extract (DME) and their probable pharmacological mechanisms in alleviating Rheumatoid Arthritis (RA) using network-pharmacology, molecular docking, molecular-dynamics simulations, and gene expression-based validation. Gas chromatography-mass spectrometry (GC-MS) based screening identified 41 volatile phytocomponents from DME having drug-like potentiality. Network pharmacology-based screening revealed 117 therapeutic targets for RA of which 11 have been identified as core targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that key target genes were mostly enriched in the inflammatory response associated with multiple signalling pathways. Molecular docking and molecular dynamics studies revealed that key target proteins like serine/threonine-protein kinase (AKT1), peroxisome proliferator-activated receptor alpha (PPARA), and peroxisome proliferator-activated receptor gamma (PPARG), exhibited strong binding affinity and stable interactions with multiple phytocomponents present in DME. For experimental verification FCA (Freund's complete adjuvant)-induced chronic arthritis model employed for further molecular investigation. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) results validated that DME significantly (p ≤ 0.05) regulate the expression of key identified target genes AKT and PPARG in experimental RA model. Moreover, this study further confirmed that DME significantly (p ≤ 0.05) downregulated pro-inflammatory mediators like COX-2, IL-6 and TNF-α at gene and protein levels and also normalized (p ≤ 0.05) different oxidative stress parameters in both the low and high dose groups of DME-treated arthritic animals. In conclusion, the network-based in silico approach indicated that the phytocomponents present in DME probably act in a synergistic way to modulate key identified targets associated with RA, which was further validated by experimental studies. Therefore, DME could be a potential alternative in immunomodulatory therapies to combat RA and related chronic inflammatory conditions.

Glioblastoma multiforme (GBM) presents significant therapeutic challenges due to its heterogeneous tumorigenicity, drug resistance, and immunosuppression. Although several molecular markers have been developed, there still lack of sensitive molecular for accurately detection. Studying the mechanisms underlying the development of GBM and finding relevant prognostic biomarkers remains crucial.

Single-cell RNA sequencing, bulk RNA-seq, and cancer immune cycle activities of GBM were used to assess the expression of different molecular related to GBM. Bioinformatics analyses were carried to evaluate the functional of the high mobility group protein B3 (HMGB3) in GBM.

HMGB3 was highly expressed in GBM tissues and influenced the interpatient and intratumoral transcriptomic heterogeneity as well as immunosuppression in GBM. HMGB3 also contributes to a no inflamed tumor microenvironment (TME) and has an inhibitory effect on tumor-associated immune cell infiltration. Besides, HMGB3 participated GBM chemotherapeutic sensitivity and negative correlation with 140 medicines.

HMGB3 as a heterogeneous and immunosuppressive molecule in the GBM TME, making it a potential target for precision therapy for GBM.

Parkinson's disease (PD), a globally prevalent neurodegenerative disorder, has been implicated with oxidative stress (OS) as a central pathomechanism. Excessive reactive oxygen species (ROS) trigger neuronal damage and may induce disulfidptosis-a novel cell death modality not yet characterized in PD pathogenesis.

Integrated bioinformatics analyses were conducted using GEO datasets to identify PD-associated differentially expressed genes (DEGs). These datasets were subjected to: immune infiltration analysis, gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), intersection analysis of oxidative stress-related genes (ORGs) and disulfidptosis-related genes (DRGs) for functional enrichment annotation. Following hub gene identification, diagnostic performance was validated using independent cohorts. LASSO regression was applied for feature selection, with subsequent experimental validation in MPTP-induced PD mouse models. Single-cell transcriptomic profiling and molecular docking studies were performed to map target gene expression and assess drug-target interactions.

A total of 1615 PD DEGs and 200 WGCNA DEGs were obtained, and the intersection with ORGs and DRGs resulted in 202 DEORGs, 11 DEDRGs, and 5 DED-ORGs (NDUFS2, LRPPRC, NDUFS1, GLUD1, and MYH6). These genes are mainly associated with oxidative stress, the respiratory electron transport chain, the ATP metabolic process, oxidative phosphorylation, mitochondrial respiration, and the TCA cycle. 10 hub genes have good diagnostic value, including in the validation dataset (AUC ≥ 0.507). LASSO analysis of hub genes yielded a total of 6 target genes, ACO2, CYCS, HSPA9, SNCA, SDHA, and VDAC1. In the MPTP-induced PD mice model, the expression of ACO2, HSPA9, and SDHA was decreased while the expression of CYCS, SNCA, and VDAC1 was increased, and the expression of the 5 DED-ORGs was decreased. Additionally, it was discovered that N-Acetylcysteine (NAC) could inhibit the occurrence of disulfidptosis in the MPTP-induced PD model. Subsequently, the distribution of target genes with AUC > 0.7 in different cell types of the brain was analyzed. Finally, molecular docking was performed between the anti-PD drugs entering clinical phase IV and the target genes. LRPPRC has low binding energy and strong affinity with duloxetine and donepezil, with binding energies of -7.6 kcal/mol and - 8.7 kcal/mol, respectively.

This study elucidates the pathogenic role of OS-induced disulfidptosis in PD progression. By identifying novel diagnostic biomarkers (e.g., DED-ORGs) and therapeutic targets (e.g., LRPPRC), our findings provide a mechanistic framework for PD management and lay the groundwork for future therapeutic development.

Dystrophin is a protein crucial for maintaining the structural integrity of skeletal muscle. So far, attention has been focused on the role of dystrophin in muscle, in view of the devastating progression of weakness and early death that characterizes Duchenne muscular dystrophy. However, in the last few years, the role of shorter dystrophin isoforms, including development and adult expression-specific mechanisms, has been a greater focus. Within the cerebral landscape, various cell types, such as glia, oligodendrocytes and Purkinje, cerebellar granule and vascular-associated cells express a spectrum of dystrophin isoforms, including Dp427, Dp140, Dp71 and Dp40. The interaction of these isoforms with a multitude of proteins suggests their involvement in neurotransmission, influencing several circuit functions. This review presents the intricate interactions among dystrophin isoforms and diverse protein complexes across different cell types and brain regions, as well as the associated clinical complications. We focus on studies investigating protein interactions with dystrophin in the past 30 years at a biochemical level. In essence, the brain's dystrophin landscape is a thrilling exploration of diversity, challenging preconceptions and opening new avenues for understanding CNS physiology. It also holds potential therapeutic implications for neurological complications involving brain dystrophin deficiency. By revealing the molecular complexities related to dystrophin, this review paves the way for future investigations and therapeutic interventions for this CNS aspect of Duchenne muscular dystrophy.

Inflammatory Bowel Disease (IBD), which includes Crohn's disease and ulcerative colitis, is associated with an increased risk of Acute Myocardial Infarction (AMI). The genetic mechanisms underlying this link are not well understood.

We downloaded IBD and AMI-related microarray datasets from the NCBI Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and analyzed using enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Machine learning techniques, including LASSO, random forest, and Boruta, were employed to screen for hub genes. These genes were validated through qRT-PCR and Western blotting. Single-cell sequencing was used to confirm findings. Additionally, potential therapeutic targets were identified using the Connectivity Map (CMap) database.

Five key hub genes-THBD, FOSB, ADGPR3, IL1R2, and PLAUR-were identified as significantly involved in both IBD and AMI pathogenesis. A diagnostic model for AMI constructed using these hub genes demonstrated high predictive accuracy. Single-cell sequencing analysis and several potential drugs targeting these hub genes were identified, offering new therapeutic avenues.

This study highlights the crucial role of FOSB and other hub genes in the comorbidity of IBD and AMI. The findings provide novel insights for early diagnosis and potential therapeutic strategies, emphasizing the importance of further investigation into these genetic links.

The rat sub-total nephrectomy (SNx) is a functional model of general chronic kidney disease (CKD) where the main pathological driver is glomerular hypertension representative of several subtypes of CKD. Comprehensive transcriptomics and proteomics analyses on the SNx rats were performed to identify biomarkers in plasma or urine that correlate with kidney disease and functional kidney loss. Kidneys were subjected to collagen I and III staining for fibrosis scoring, SWATH-MS proteomics and bulk RNA-sequencing transcriptomics, with SWATH-MS also performed on plasma and urine. Differential expression analysis demonstrated significant dysregulation of genes and proteins involved in fibrosis, metabolism, and immune response in the SNx rats compared to controls. Gene ontology analysis of the intersecting genes and proteins from both studies demonstrated common biology between animal cohorts that reached the predefined kidney disease thresholds (serum creatinine > two-fold or proteinuria > three-fold increase over sham-operated). Thirteen significantly differential molecules were detected with consistent directional changes in both omics datasets. These molecules were detected independently in kidney (both RNA and protein) and urine (protein only), but not in plasma. Bioinformatics analysis enabled the identification of mechanistic CKD biomarkers including lumican and collagen alpha-1(III) chain, whose co-expression has previously been both implicated in fibrosis and detected in urine in CKD patients.

IDH mutant gliomas often exhibit recurrence and progression, with the mTORC1 pathway and tumor-associated macrophages potentially contributing to these processes. However, the precise mechanisms are not fully understood. This study seeks to investigate these relationships using proteomic, phosphoproteomic, and multi-dimensional transcriptomic approaches.

This study established a matched transcriptomic, proteomic, and phosphoproteomic cohort of IDH-mutant gliomas with recurrence and progression, incorporating multiple glioma-related datasets. We first identified the genomic landscape of recurrent IDH-mutant gliomas through multi-dimensional differential enrichment, GSVA, and deconvolution analyses. Next, we explored tumor-associated macrophage subpopulations using single-cell sequencing in mouse models of IDH-mutant and wild-type gliomas, analyzing transcriptional changes via AddmodelScore and pseudotime analysis. We then identified these subpopulations in matched primary and recurrent IDH-mutant datasets, investigating their interactions with the tumor microenvironment and performing deconvolution to explore their contribution to glioma progression. Finally, spatial transcriptomics was used to map these subpopulations to glioma tissue sections, revealing spatial co-localization with mTORC1 and angiogenesis-related pathways.

Multi-dimensional differential enrichment, GSVA, and deconvolution analyses indicated that the mTORC1 pathway and the proportion of M2 macrophages are upregulated during the recurrence and progression of IDH-mutant gliomas. CGGA database analysis showed that mTORC1 activity is significantly higher in recurrent IDH-mutant gliomas compared to IDH-wildtype, with a correlation to M2 macrophage infiltration. KSEA revealed that AURKA is enriched during progression, and its inhibition reduces mTORC1 pathway activity. Single-cell sequencing in mouse models identified a distinct glioma subpopulation with upregulated mTORC1, exhibiting both M2 macrophage and angiogenesis transcriptional features, which increased after implantation of IDH-mutant tumor cells. Similarly, human glioma single-cell data revealed the same subpopulation, with cell-cell communication analysis showing active VEGF signaling. Finally, spatial transcriptomics deconvolution confirmed the co-localization of this subpopulation with mTORC1 and VEGFA in high-grade IDH-mutant gliomas.

Our findings suggest mTORC1 activation and Angio-TAMs play key roles in the recurrence and progression of IDH-mutant gliomas.

Lung adenocarcinoma (LUAD) is a highly heterogeneous cancer type with a poor prognosis. Accurate subtype identification can help guide its treatment. The traditional subtype identification methods using a single-omics approach make it difficult to comprehensively characterize the molecular features of LUAD. Identification of subtypes through multi-omics association strategies can effectively supplement the shortcomings of single-omics information.

In this study, we used the Generative Adversarial Network (GAN) to mine transcriptomic, proteomic, and epigenomic information and generate an integrated data set. The newly integrated data were then used to identify LUAD immune subtypes. In the improved GAN (MOGAN) method, we not only integrated multiple omics datasets but also included the interactions between proteins and genes and between methylation and genes. Thus, we achieved effective complementarity of multi-omics information.

Two subtypes, MOGANTPM_S1 and MOGANTPM_S2, were identified using immune cell infiltration analysis and the integrated multi-omics data. MOGANTPM_S1 patients displayed higher immune cell infiltration, better prognosis, and sensitivity to immune checkpoint inhibitors (ICIs), while MOGANTPM_S2 had lower immune cell infiltration, poorer prognosis, and were insensitive to ICIs. Therefore, immunotherapy was more suitable for MOGANTPM_S1 patients in clinical practice. In addition, this study developed a LUAD subtype diagnostic model using the transcriptomic and proteomic features of five genes, which can be used to guide clinical subtype diagnosis.

In summary, the MOGAN method was applied to integrate three omics data types and successfully identify two LUAD immune subtypes with significant survival differences. This classification method may be useful for LUAD treatment decisions.

There has been interested in the microRNAs' roles in pancreatic cancer (PC) cell biology, particularly in regulating pathways related to tumorigenesis. The study aimed to explore the hub miRNAs in PC and underlying mechanisms by bioinformatics and fundamental experiments. RNA datasets collected from the Gene Expression Omnibus were analysed to find out differentially expressed RNAs (DERNAs). The miRNA-mRNA and protein-protein interaction (PPI) networks were built. The clinicopathological features and expressions of hub miRNAs and hub mRNAs were explored. Dual-luciferase reporter gene assay was performed to assess the interaction between microRNA and target gene. RT-qPCR and western blot were employed to explore RNA expression. The roles of RNA were detected by CCK-8 test, wound healing, transwell, and flow cytometry experiment. We verified 40 DEmiRNAs and 1613 DEmRNAs, then detected a total of 69 final functional mRNAs (FmRNAs) and 23 DEmiRNAs. In the miRNA-mRNA networks, microRNA-130b (miR-130b) was the hub RNA with highest degrees. Clinical analysis revealed that miR-130b was considerably lower expressed in cancerous tissues than in healthy ones, and patients with higher-expressed miR-130b had a better prognosis. Mechanically, miR-130b directly targeted MET in PC cells. Cell functional experiments verified that miR-130b suppressed cell proliferation, migration, promoted apoptosis, and inhibited the PI3K/Akt pathway by targeting MET in PC cells. Our findings illustrated the specific molecular mechanism of miR-130b regulating PC progress. The miR-130b/MET axis may be an alternative target in the therapeutic intervention of PC and provide an opportunity to deepen our understanding of the pathogenesis of PC.

Neuropathic pain (NP) is a debilitating disease and is associated with energy metabolism alterations. This study aimed to identify energy metabolism-related differentially expressed genes (EMRDEGs) in NP, construct a diagnostic model, and analyze immune cell infiltration and single-cell gene expression characteristics of NP. GSE89224, GSE123919, and GSE134003 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) analysis and an intersection with highly energy metabolism-related modules in weighted gene co-expression network analysis (WGCNA) was performed in GSE89224. Least absolute shrinkage and selection operator (LASSO), random forest, and logistic regression were used for model genes selection. NP samples were divided into high- and low-risk groups and different disease subtypes based on risk score of LASSO algorithm and consensus clustering analysis, respectively. Immune cell composition was estimated in different risk groups and NP subtypes. Datasets 134,003 were performed for identification of single-cell DEGs and functional enrichment. Cell-cell communications and pseudo-time analysis to reveal the expression profile of NP. A total of 38 EMRDEGs were obtained and are majorly enriched in metabolism about glioma and inflammation. LASSO, random forest, and logistic regression identified 6 model genes, which were Itpr1, Gng8, Socs3, Fscn1, Cckbr, and Camk1. The nomogram, based on six model genes, had a good predictive ability, concordance, and diagnostic value. The comparisons between different risk groups and NP subtypes identified important pathways and different immune cells component. The immune infiltration results majorly associated with inflammation and energy metabolism. Single-cell analysis revealed cell-cell communications and cells differentiation characteristics of NP. In conclusion, our results not only elucidate the involvement of energy metabolism in NP but also provides a robust diagnostic tool with six model genes. These findings might give insight into the pathogenesis of NP and provide effective therapeutic regimens for the treatment of NP.

Drug resistance poses a major obstacle to the efficient treatment of colorectal cancer (CRC), which is one of the cancers that kill people most often in the United States. Advanced colorectal cancer patients frequently pass away from the illness, even with advancements in chemotherapy and targeted therapies. Developing new biomarkers and therapeutic targets is essential to enhancing prognosis and therapy effectiveness. My goal in this study was to use bioinformatics analysis of microarray data to find possible biomarkers and treatment targets for colorectal cancer. Using an ArrayExpress database, I examined a dataset on colon cancer to find genes that were differentially expressed (DEGs) in tumor versus healthy tissues. Integration of advanced bioinformatics tools provided robust insights into the identification and analysis of EGFR as a key player. STRING and Cytoscape enabled the construction and visualization of protein-protein interaction networks, highlighting EGFR as a hub gene due to its centrality and interaction profile. Functional enrichment analysis through DAVID revealed EGFR's involvement in critical biological pathways, as identified in GO and KEGG analyses. This underscores the power of combining computational tools to uncover significant biomarkers like EGFR. Autodock Vina screening of the NCI diversity dataset identified two potential EGFR inhibitors, ZINC13597410 and ZINC04896472. MD simulation data revealed that ZINC04896472 could be potential anticancer inhibitor. These findings serve as a basis for the creation of novel therapeutic approaches that target EGFR and other discovered pathways in CRC. The suggested strategy may improve the efficacy of CRC therapy and advance personalized medicine.

Epigenetic clocks, DNA methylation-based predictive models of chronological age, are often utilized to study aging associated biology. Despite their widespread use, these methods do not account for other factors that also contribute to the variability of DNA methylation data. For example, many CpG sites show strong sex-specific or cell-type-specific patterns that likely impact the predictions of epigenetic age. To overcome these limitations, we developed a multidimensional extension of the Epigenetic Pacemaker, the Multi-state Epigenetic Pacemaker (MSEPM). We show that the MSEPM is capable of accurately modeling multiple methylation-associated factors simultaneously, while also providing site-specific models that describe the per site relationship between methylation and these factors. We utilized the MSEPM with a large aggregate cohort of blood methylation data to construct models of the effects of age-, sex-, and cell-type heterogeneity on DNA methylation. We found that these models capture a large faction of the variability at thousands of DNA methylation sites. Moreover, this approach allows us to identify sites that are primarily affected by aging and no other factors. An analysis of these sites reveals that those that lose methylation over time are enriched for CTCF transcription factor chip peaks, while those that gain methylation over time are associated with bivalent promoters of genes that are not expressed in blood. These observations suggest mechanisms that underlie age-associated methylation changes and suggest that age-associated increases in methylation may not have strong functional consequences on cell states. In conclusion, the MSEPM is capable of accurately modeling multiple methylation-associated factors, and the models produced can illuminate site-specific combinations of factors that affect methylation dynamics.

SGLT2 is selectively expressed in the human kidney. SGLT2 inhibitors have markedly changed diabetes, heart failure, and kidney disease treatment, and are under investigation in cancer. However, the role of SGLT2 in papillary renal cell carcinoma (pRCC) is not known.

We investigated the SGLT2 gene expression, associated clinical-molecular features, and overall survival (OS) in pRCC. The Cancer Genome Atlas Program and Gene Expression Omnibus data were utilized. mRNA expression z-scores of the SGLT2 gene relative to normal samples (log-RNASeqV2-RSEM, threshold ± 2) were analyzed (low, unaltered, high expression).

273 patients were involved. As per mRNA expression, 180 patients (66%) had low, and the remaining had unaltered expression. High correlation (r > 0.6) with SGLT2 was observed in IRX5, STRIP2, LINC00899, SATB2-AS1, FOXC1, IRX3, SLC22A8, SH3BP5 genes (P < .001,q < 0.001 for all) and with the HIF-2α (r:0.43, P < .001,q < 0.001). Tumor mutational burden (P = .365) and aneuploidy scores (P = .976) did not differ, however, among the genes with the highest alteration frequency, SETD2 alterations (15.63% vs. 1.07%, P < .00, q = 0.046) were more frequent in the unaltered-expression group. Differential protein expression analysis showed highly separated proteins (ERBB2, AR, MAPK14, VHL, TGM2 in the low and SHC1, SQSTM1, MYH14, and CDH1 in the unaltered group). The median OS has not reached the median in both groups [Hazard Ratio(HR) for the unaltered group:2.658, 95% Confidence Interval(CI):1.401-5.043, P = .003]. SGLT2 expression remained a significant prognostic factor in multivariable analysis [HR:2.446 (95%CI: 1.199-4.990), P = .014].

This study reveals the first data that SGLT2 might have a role in pRCC as a pathogenic factor and biomarker. Confirmatory mechanistic studies are needed.

Mitochondrial calcium uniporter (MCU) is a central subunit of MCU complex that regulate the levels of calcium ions within mitochondria. A comprehensive understanding the implications of MCU in clinical prognostication, biological understandings and therapeutic opportunity of breast cancer (BC) is yet to be determined.

This study aims to investigate the role of MCU in predictive performance, tumor progression, epigenetic regulation, shaping of tumor immune microenvironment, and pharmacogenetics and the development of anti-tumor therapy for BC.

The downloaded TCGA datasets were used to identify predictive ability of MCU expressions via supervised learning principle. Functional enrichment, mutation landscape, immunological profile, drug sensitivity were examined using bioinformatics analysis and confirmed by experiments exploiting human specimens, in vitro and in vivo models.

MCU copy numbers increase with MCU gene expression. MCU expression, but not MCU genetic alterations, had a positive correlation with known BC prognostic markers. Higher MCU levels in BC showed modest efficacy in predicting overall survival. In addition, high MCU expression was associated with known BC prognostic markers and with malignancy. In BC tumor and sgRNA-treated cell lines, enrichment pathways identified the involvement of cell cycle and immunity. miR-29a was recognized as a negative epigenetic regulator of MCU. High MCU levels were associated with increased mutation levels in oncogene TP53 and tumor suppression gene CDH1, as well as with an immunosuppressive microenvironment. Sigle-cell sequencing indicated that MCU mostly mapped on to tumor cell and CD8 T-cells. Inter-databases verification further confirmed the aforementioned observation. miR-29a-mediated knockdown of MCU resulted in tumor suppression and mitochondrial dysfunction, as well as diminished metastasis. Furthermore, MCU present pharmacogenetic significance in cellular docetaxel sensitivity and in prediction of patients' response to chemotherapeutic regimen.

MCU shows significant implication in prognosis, outcome prediction, microenvironmental shaping and precision medicine for BC. miR-29a-mediated MCU inhibition exerts therapeutic effect in tumor growth and metastasis.

The role of inflammation in the development of type 2 diabetes mellitus (T2DM) related skin complications necessitates further investigation. This study aims to explore the correlation between inflammation and cutaneous alterations in T2DM, enhancing comprehension of underlying mechanism involved.

Utilizing bioinformatics, the GSE38396 and GSE92724 datasets were employed to identify differentially expressed genes (DEGs) and potential hub genes in T2DM-related skin inflammation. Subsequently, gene functional enrichment analysis was employed for functional annotation. Finally, we validated the regulatory impact of hub gene on inflammation during high glucose incubation using the in vitro model.

A comprehensive analysis identified 742 DEGs, including 9 hub genes and 4 potential biomarkers. Compared to the CON group, the expression of M2 macrophages was significantly upregulated in the T2DM group, while resting dendritic cells and eosinophils showed notable decreases, indicating a significant correlation with CEBPA. Furthermore, functional enrichment analysis revealed significant enrichment of DEGs in pathways linked to immunity and diabetes pathogenesis. Interestingly, overexpression of CEBPA demonstrated anti-inflammatory effects under hyperglycemic conditions, while silencing CEBPA expression appeared to worsen inflammation.

CEBPA emerges as a potential hub gene for skin inflammation in T2DM, shedding light on the underlying mechanisms of this condition.

Extraction of meaningful biological insight from gene expression profiling often focuses on the identification of statistically enriched terms or pathways. These methods typically use gene sets as input data, and subsequently return overrepresented terms along with associated statistics describing their enrichment. This approach does not cater to analyses focused on a single gene-of-interest, particularly when the gene lacks prior functional characterization. To address this, we formulated GeneCOCOA, a method which utilizes context-specific gene co-expression and curated functional gene sets, but focuses on a user-supplied gene-of-interest (GOI). The co-expression between the GOI and subsets of genes from functional groups (e.g. pathways, GO terms) is derived using linear regression, and resulting root-mean-square error values are compared against background values obtained from randomly selected genes. The resulting p values provide a statistical ranking of functional gene sets from any collection, along with their associated terms, based on their co-expression with the gene of interest in a manner specific to the context and experiment. GeneCOCOA thereby provides biological insight into both gene function, and putative regulatory mechanisms by which the expression of the GOI is controlled. Despite its relative simplicity, GeneCOCOA outperforms similar methods in the accurate recall of known gene-disease associations. We furthermore include a differential GeneCOCOA mode, thus presenting the first implementation of a gene-focused approach to experiment-specific gene set enrichment analysis. GeneCOCOA is formulated as an R package for ease-of-use, available at https://github.com/si-ze/geneCOCOA.

Over the last two decades archaeal research has expanded into a wide-ranging research field, driven by a fairly small research community. Archaea are now recognized as important players in the One-Health approach and expertise on the biology of archaea has become crucial in the study of a broad range of topics and environments, including the host-associated microbiomes, major nutrient cycles, greenhouse gas metabolism, the cell biology and origin of eukaryotes, adaptation of life to extremes, as well as various biotechnological applications. Here, we summarize existing resources and ongoing efforts in the engaged broader archaeal scientific community to accelerate research and resource sharing guided by FAIR (findable, accessible, interoperable, reusable) data-sharing principles. We highlight ongoing community efforts that: (i) aim to share protocols and best practices for working with archaea (e.g. ARCHAEA.bio), (ii) combine large 'omics datasets for the dissemination of unified, system-wide results (e.g. Archaeal Proteome Project, KBase) and (iii) provide opportunities for scientists to present their work in a supportive environment and to forge connections and collaborations (e.g. Archaea Power Hour). Together, these resources and projects promise to spur and cross-fertilize research, making archaeal research more accessible to a broader and more diverse audience.

Pterygium is a fibrovascular growth associated with chronic inflammation, tissue remodeling, and angiogenesis, which invades the cornea. Circular RNAs (circRNAs) are emerging as pivotal role in many diseases, but their role in pterygium remains unclear. We performed circRNA and miRNA expression profiling on pterygium and conjunctival tissues, then the circRNA-miRNA-mRNA regulatory network was constructed. Bioinformatics was used to predict downstream pathways. Pterygium fibroblasts were used for experiments assessing proliferation (CCK8, EdU), migration (wound healing, transwell), and apoptosis (AnnexinV-FITC/PI). We identified 162 differentially expressed circRNAs and 96 miRNAs. Key pathways involved in pterygium pathogenesis, including focal adhesion and PI3K-Akt signaling, were predicted. Hsa_circ_0081862 was downregulated in pterygium tissues and fibroblasts, inhibiting fibroblast proliferation and migration while promoting apoptosis. This research constructed a ceRNA network and identified hsa_circ_0081682 as the potential diagnostic marker for pterygium. This research contributes to the understanding of biochemical basis of pterygium, which may facilitate the development of targeted strategies for its management and prevention.

Adults can develop ulcerative colitis (UC), a chronic inflammatory illness of the colon, while atherosclerosis (AA) is a chronic inflammatory disease of the blood vessels caused by a range of risk factors. Prior research has demonstrated that UC increases the risk of AA, although the underlying pathological mechanisms are not entirely understood. The purpose of this work was to discover differentially expressed genes (DEGs) in UC and AA and investigate their molecular processes using a bioinformatics method. The UC (GSE36807) and AA (GSE28829) datasets were obtained from the Gene Expression Omnibus (GEO) database. Following the identification of genes that are differentially expressed in common with UC and AA, functional annotation, the construction of protein-protein interaction (PPI) networks and modules, the identification of hub genes, and co-expression analysis were carried out. A total of 105 (including 92 up-regulated and 13 down-regulated genes) DEGs were selected for correlation analysis in the above two datasets, and after Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analysis immune responses, cytokines, and chemokines were found to play crucial roles in both diseases. Finally, a total of 16 hub genes were identified by CytoHubba and MCODE plugins in Cytoscape, including Chemokine (C-C motif) ligand 4(CCL4), Toll-like receptor 2 (TLR2), Integrin Beta 2(ITGB2), Chemokine (C-C motif) Receptor 1(CCR1), Toll-Like Receptor 8 (TLR8), Fc Fragment of IgG Receptor IIa (FCGR2A), Neutrophil Cytosolic Factor 2(NCF2), Leukocyte immunoglobulin-like receptor B2(LILRB2), FGR proto-oncogene, Src family tyrosine kinase(FGR), Intercellular Adhesion Molecule 1 (ICAM1), Caspase 1(CASP1), Matrix Metallopeptidase 9(MMP9), Cluster of Differentiation 163(CD163), Complement Component 5a Receptor 1 (C5AR1), Neutrophil Cytosolic Factor 4 (NCF4), Selectin P (SELP). This study discovered a link between UC and AA, as well as shared hub genes and pathways, which may bring new insights into the processes of UC and AA.

Gene network models provide a foundation for graph theory approaches, aiding in the novel discovery of drug targets, disease genes, and genetic mechanisms for various biological functions. Disease genetics must be interpreted within the cellular context of disease-associated cell types, which cannot be achieved with datasets consisting solely of organism-level samples. Single-cell RNA sequencing (scRNA-seq) technology allows computational distinction of cell states which provides a unique opportunity to understand cellular biology that drives disease processes. Importantly, the abundance of cell samples with their transcriptome-wide profile allows the modeling of systemic cell-type-specific gene networks (CGNs), offering insights into gene-cell-disease relationships. In this review, we present reference-based and de novo inference of gene functional interaction networks that we have recently developed using scRNA-seq datasets. We also introduce a compendium of CGNs as a useful resource for cell-type-resolved disease genetics. By leveraging these advances, we envision single-cell network biology as the key approach for mapping the gene-cell-disease axis.

Synechococcus elongatus PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO2-to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully revealed the organizational principles of circadian regulation. Our analysis identified distinct regulatory modules coordinating day-night metabolic transitions, with photosynthesis and carbon/nitrogen metabolism controlled by day-phase regulators, while nighttime modules orchestrate glycogen mobilization and redox metabolism. Through network centrality analysis, we identified potentially significant but previously understudied transcriptional regulators: HimA as a putative DNA architecture regulator, and TetR and SrrB as potential coordinators of nighttime metabolism, working alongside established global regulators RpaA and RpaB. This work demonstrates how network-level analysis can extract biologically meaningful insights despite limitations in predicting direct regulatory interactions. The regulatory principles uncovered here advance our understanding of how cyanobacteria coordinate complex metabolic transitions and may inform metabolic engineering strategies for enhanced photosynthetic bioproduction from CO2.

Neurodegeneration in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) results from both gain of toxicity and loss of normal function of the RNA-binding protein TDP-43, but their mechanistic connection remains unclear. Increasing evidence suggests that TDP-43 aggregates act as self-templating seeds, propagating pathology through the central nervous system via a prion-like cascade. We developed a robust TDP-43-seeding platform for quantitative assessment of TDP-43 aggregate uptake, cell-to-cell spreading, and loss of function within living cells, while they progress toward pathology. We show that both patient-derived and recombinant TDP-43 pathological aggregates were abundantly internalized by human neuron-like cells, efficiently recruited endogenous TDP-43, and formed cytoplasmic inclusions reminiscent of ALS/FTD pathology. Combining a fluorescent reporter of TDP-43 function with RNA sequencing and proteomics, we demonstrated aberrant cryptic splicing and a loss-of-function profile resulting from TDP-43-templated aggregation. Our data highlight known and novel pathological signatures in the context of seed-induced TDP-43 loss of function.

Chronic obstructive pulmonary disease (COPD) is among the three leading causes of death worldwide, with its prevalence, morbidity, and mortality rates increasing annually. Oxidative stress (OS) is a key mechanism in COPD development, making the identification of OS-related biomarkers beneficial for improving its diagnosis and treatment.

The genetic data from patients with COPD and controls were obtained from the Gene Expression Omnibus database to identify OS-related genes (OSRGs). Functional enrichment analysis was conducted using the Kyoto encyclopedia of genes and genomes signaling pathway and gene ontology (GO). Protein-protein interaction networks were constructed to identify the core genes, which were further evaluated using receiver operating characteristic (ROC) curves. Diagnostic models were developed based on the core genes. Besides, the correlation between the expression of the core genes and the immune cells was analyzed using single-sample gene set enrichment analysis. Drug-gene interactions were explored to predict target drugs, and related microribonucleic acid (miRNA) and transcription factors (TFs) were identified using miRNet.

In this study, we identified 299 differential genes, including 16 OSRGs. Among these, five core genes-heat shock protein family A (Hsp70) member 1A (HSPA1A), glutamate-cysteine ligase modifier subunit, interleukin-1 beta (IL-1β), intercellular adhesion molecule 1 (ICAM1), and glutamate-cysteine ligase catalytic subunit (GCLC)-were screened and validated using ROC curve analysis. The results of GO enrichment analysis were mainly focused on the OS response, the negative regulation of the exogenous apoptosis signaling pathway, and the regulation of the apoptosis signaling pathway. Additionally, 33 target drugs were predicted, including ofloxacin, cisplatin, and pegolimumab, among others. Meanwhile, the regulatory networks comprising 33 miRNAs related to the core genes and 38 TFs associated with HSPA1A, IL-1β, ICAM1, and GCLC were constructed. A diagnostic model based on the five genes was constructed and validated with an area under the curve of 0.981 (95% confidence interval: 0.941-1.000).

This study identifies potential biomarkers for diagnosing COPD, new potential targets, and new directions for drug development and treatment.

Long noncoding RNAs (lncRNAs) are a newer class of noncoding transcripts identified as key regulators of biological processes. Here, we aimed to identify novel lncRNA targets that play critical roles in major human respiratory viral infections by systematically mining large-scale transcriptomic data sets. Using bulk RNA-sequencing (RNA-seq) analysis, we identified a previously uncharacterized lncRNA, named virus-inducible lncRNA modulator of interferon response (VILMIR), that was consistently upregulated after in vitro influenza infection across multiple human epithelial cell lines and influenza A virus subtypes. VILMIR was also upregulated after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and respiratory syncytial virus (RSV) infections in vitro. We experimentally confirmed the response of VILMIR to influenza infection and interferon-beta (IFN-β) treatment in the A549 human epithelial cell line and found the expression of VILMIR was robustly induced by IFN-β treatment in a dose- and time-specific manner. Single-cell RNA-seq analysis of bronchoalveolar lavage fluid samples from coronavirus disease 2019 (COVID-19) patients uncovered that VILMIR was upregulated across various cell types, including at least five immune cells. The upregulation of VILMIR in immune cells was further confirmed in the human T cell and monocyte cell lines, SUP-T1 and THP-1, after IFN-β treatment. Finally, we found that knockdown of VILMIR expression reduced the magnitude of host transcriptional responses to both IFN-β treatment and influenza A virus infection in A549 cells. Together, our results show that VILMIR is a novel interferon-stimulated gene (ISG) that regulates the host interferon response and may be a potential therapeutic target for human respiratory viral infections upon further mechanistic investigation.IMPORTANCEIdentifying host factors that regulate the immune response to human respiratory viral infection is critical to developing new therapeutics. Human long noncoding RNAs (lncRNAs) have been found to play key regulatory roles during biological processes; however, the majority of lncRNA functions within the host antiviral response remain unknown. In this study, we identified that a previously uncharacterized lncRNA, virus-inducible lncRNA modulator of interferon response (VILMIR), is upregulated after major respiratory viral infections including influenza, severe acute respiratory syndrome coronavirus 2, and respiratory syncytial virus. We demonstrated that VILMIR is an interferon-stimulated gene that is upregulated after interferon-beta (IFN-β) in several human cell types. We also found that knockdown of VILMIR reduced the magnitude of host transcriptional responses to IFN-β treatment and influenza A infection in human epithelial cells. Our results reveal that VILMIR regulates the host interferon response and may present a new therapeutic target during human respiratory viral infections.

Sepsis, which causes systemic inflammation and organ failure, is one of the leading causes of death in the intensive care unit (ICU) and an urgent social health problem. However, the pathogenesis and molecular mechanism of sepsis are unclear. Therefore, this study aimed to identify candidate Hub genes during sepsis progression and the candidate target genes for sepsis diagnosis and treatment.

GSE54514, GSE57065, GSE69528, GSE95233, and GSE131761 datasets were downloaded from public databases, and the differentially expressed genes (DEGs) between healthy and septic patients in each dataset were screened at adjusted P-value < 0.05 and| log2FC| ≥ 0.58. Subsequently, the obtained DEGs in each dataset were intersected to obtain the Hub genes. In addition, the DEGs between patients with better and poor prognoses in datasets GSE54514 and GSE95233 were analyzed after 28 days. The differential expression of Hub genes in septic patients with good and poor prognoses was detected at adjusted P-value < 0.05 and| log2FC| ≥ 0.58. Finally, real-time quantitative polymerase chain reaction (qRT-PCR) was used to verify the bioinformatics results.

In datasets GSE54514, GSE57065, GSE69528, GSE95233 and GSE131761, RNASE2, RNASE3, CTSG, SLPI, TNFAIP6, PGLYRP1 and BLOC1S1 were up-regulated in septic patients, and RPL10A and KLRB1 were down-regulated compared to healthy controls. qRT-PCR confirmed the expression trend of the hub genes except CTSG (which was not differentially expressed). Compared to septic patients with good prognoses, the differential expression of RNASE3 was higher in patients with poor prognoses. Furthermore, qRT-PCR revealed that KLRB1 was the only differentially expressed hub gene with down-regulated expression in sepsis patients with poor prognosis.

The candidate Hub genes closely related to sepsis include KLRB1, RNASE2, RNASE3, CTSG, SLPI, TNFAIP6, PGLYRP1, BLOC1S1, and RPL10A. KLRB1 is the most relevant candidate hub gene among these hub genes in the molecular underpinnings of sepsis, which could be targeted for sepsis detection and treatment.

Multi-omic data-driven studies are at the forefront of precision medicine by characterizing complex disease signaling systems across multiple views and levels. The integration and interpretation of multi-omic data are critical for identifying disease targets and deciphering disease signaling pathways. However, it remains an open problem due to the complex signaling interactions among many proteins. Herein, we propose a multi-scale multi-hop multi-omic network flow model, M3NetFlow, to facilitate both hypothesis-guided and generic multi-omic data analysis tasks. We evaluated M3NetFlow using two independent case studies: (1) uncovering mechanisms of synergy of drug combinations (hypothesis/anchor-target guided multi-omic analysis) and (2) identifying biomarkers of Alzheimer's disease (generic multi-omic analysis). The evaluation and comparison results showed that M3NetFlow achieved the best prediction accuracy and identified a set of drug combination synergy- and disease-associated targets. The model can be directly applied to other multi-omic data-driven studies.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder characterized by the progressive loss of nigrostriatal dopaminergic neurons (DA) which can be caused by environmental and genetic factors. lncRNAs have emerged as an important regulatory layer in neurodegenerative disorders, including PD. In this study, we investigated and validated lncRNAs that may serve as diagnostic or therapeutic targets for PD. Key genes associated with midbrain and DA cells were screened by differential gene expression analysis on GSE213100 dataset and candidate lncRNAs were selected for further examination. P19 cells were differentiated into DA cells and received treatment with MPP+ to induce PD-like cytotoxic events, which were confirmed by light microscopy, RT-qPCR, immunofluorescence and flow cytometry. Then, the cells were used to investigate the changes of lncRNAs Malat1, Norad, Snhg1 and Meg3. Here we found that the neuronal phenotype was mainly observed on the 12th day of differentiation and the number of DA markers significantly decreased in PD model cells compared with the control group. Moreover, the expression levels of Meg3, Norad, and Snhg1 were decreased by MPP+ whereas Malat1 level was noticeably higher in MPP+ cells compared to DA cells and the control group. In conclusion, the expression level of lncRNAs was able to show a significant difference between differentiated dopaminergic cells and their Parkinsonian model, thereby improving our understanding of the molecular pathogenesis of PD.

The intricate involvement of the histaminergic system, encompassing histamine and histamine receptors, in the progression of diverse neoplasias has attracted considerable scrutiny. Histamine receptor H1 (HRH1) was reported to be overexpressed in several cancer types, but its specific functional implications in oral squamous cell carcinoma (OSCC) predominantly remain unexplored. Our findings indicate that dysregulated high levels of HRH1 were correlated with lymph node (LN) metastasis and poor prognoses in OSCC patients. We identified a disintegrin and metalloprotease 9 (ADAM9) as a critical downstream target of HRH1, promoting protumorigenic and prometastatic characteristics both in vitro and in vivo. Molecular investigations revealed that the cyclic increase in the HRH1-ADAM9-Snail/Slug axis promoted progression of the epithelial-to-mesenchymal transition (EMT). Clinical analyses demonstrated significant correlations of HRH1 expression with ADAM9 and with EMT-related markers, with elevated ADAM9 also associated with LN metastasis in OSCC patients. Regarding therapeutic aspects, we discovered that activated STAT3 acts as a compensatory pathway for the long-term HRH1 signaling blockade in OSCC cells. Combining inhibition of HRH1 and STAT3 using their respective inhibitors or short hairpin (sh)RNAs enhanced the tumor-suppressive effects compared to HRH1 inhibition/depletion alone in OSCC cells and a xenograft model. In summary, HRH1 has emerged as a valuable biomarker for predicting OSCC progression, and combined targeting of HRH1 and STAT3 may represent a promising strategy for preventing OSCC progression.

Congenital disorders of glycosylation (CDG) comprise a diverse group of genetic diseases characterized by aberrant glycosylation that leads to severe multi-systematic effects. Despite advancements in understanding the underlying molecular mechanisms, curative options remain limited. This study employed computational methods to identify key molecular biomarkers for CDG-I and examine the pharmacological effects of Ginkgolide A (GA), a potent bioactive natural compound.

We analyzed the GSE8440 microarray dataset to discover differentially expressed genes (DEGs) in patients compared to healthy individuals with CDG-I utilizing GEO2R. Functional enrichments, including gene ontologies (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses, were conducted to contextualize the biological mechanisms and molecular signatures involved in CDG-I (Congenital Disorders of Glycosylation Type-1). The protein-protein interaction (PPI) network for DEGs was constructed using the STRING database, and the central hub genes within the PPI network were identified using Cytohubba. Furthermore, the 3D structure of the top hub gene (P4HB) was predicted by using the Robetta server. The CASTp was employed to evaluate the active sites. Molecular docking of P4HB with GA was carried out to investigate the binding affinity using the PyRx tool, and the stability of the docked complex was validated through MD simulation. The pharmacokinetics, toxicity, and bioactivity score of GA were comprehensively assessed using SwissADME, ProTox-II, and Molinspiration.

Our findings indicated 247 significant DEGs, including 146 up-regulated and 101 down-regulated genes. GO and KEGG pathway analyses confirmed that the up-regulated and hub genes were strongly associated with protein folding, glycoprotein processing in the endoplasmic reticulum, and endoplasmic reticulum stress (ER) pathways. P4HB emerged as the top hub gene in CDG-I, playing a significant role in protein folding and ER stress. The 3D structure of P4HB was refined and validated, achieving 95.8 % residues in the most favored region of the Ramachandran plot, with an overall quality of 92.97 %. The CASTp server predicted the largest active site with an area of 2243.660 Å2 and a volume of 3236.584 Å3. Molecular docking revealed that GA has a strong binding affinity with P4HB (-8.9 kcal/mol). The ADME (Absorption, Distribution, Metabolism, Excretion) and toxicity assessments confirmed promising drug-like characteristics, excellent bioavailability, and minimal toxicity risk.

This study emphasizes GA as a potential treatment possibility option to alleviated CDG-I pathology by targeting protein misfolding and ER stress, which are fundamental aspects of the disease. Additionally, our findings indicate that P4HB is a critical molecular target in CDG-I. These results pave the way for future preclinical and clinical investigations aimed at advancing the targeted and tailored treatments for CDG.