NCBI GEO: archive for functional genomics data sets--update

Identification of genes involved in energy metabolism in preeclampsia and discovery of early biomarkers

Background

Preeclampsia is a complex pregnancy condition marked by hypertension and organ dysfunction, posing significant risks to maternal and fetal health. This study investigates the role of energy metabolism-associated genes in preeclampsia development and identifies potential early diagnostic biomarkers.

Methods

Preeclampsia datasets from Gene Expression Omnibus were analyzed for batch correction, normalization, and differential expression. Enrichment analyses using gene ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment were performed. Protein-protein interaction networks were constructed to identify key genes, and regulatory networks involving transcription factors, miRNAs, and RNA-binding proteins were established. Differential expression was validated with receiver operating characteristic curve analyses, and immune infiltration was assessed.

Results

Six energy metabolism-related genes were identified. Enrichment analyses revealed their involvement in glycolysis, gluconeogenesis, lipid transport, bone remodeling, and glucagon secretion. Key differentially expressed genes included CRH(Corticotropin-Releasing Hormone), LEP(Leptin), PDK4(Pyruvate Dehydrogenase Kinase Isozyme 4), SPP1(Secreted Phosphoprotein 1), and SST(Somatostatin). PDK4 exhibited moderate accuracy in receiver operating characteristic analysis. Immune infiltration analysis indicated significant differences between preeclampsia and control samples. qRT-PCR confirmed LEP and CRH increased, while SPP1 expression in preeclampsia samples.

Conclusion

Dysregulated energy metabolism-related genes may contribute to preeclampsia through metabolic and immune changes. Identifying these genes aids in understanding preeclampsia's molecular basis and early diagnosis. Future studies should validate these markers in larger cohorts and explore targeted treatments.

Writers, readers, and erasers of N6-Methyladenosine (m6A) methylomes in oilseed rape: identification, molecular evolution, and expression profiling

BACKGROUND

m6A RNA modifications are the most prevalent internal modifications in eukaryotic mRNAs and are crucial for plant growth and development, as well as for responses to biotic or abiotic stresses. The modification is catalyzed by writers, removed by erasers, and decoded by various m6A-binding proteins, which are readers. Brassica napus is a major oilseed crop. The dynamic regulation of m6A modifications by writers, erasers, and readers offers potential targets for improving the quality of this crop.

RESULTS

In this study, we identified 92 m6A-regulatory genes in B. napus, including 13 writers, 29 erasers, and 50 readers. A phylogenetic analysis revealed that they could be further divided into four, three, and two clades, respectively. The distribution of protein motifs and gene structures among members of the same clade exhibited notable similarity. During the course of evolution, whole genome duplication (WGD) and segmental duplication were the primary drivers of the expansion of m6A-related gene families. The genes were subjected to rigorous purification selection. Additionally, several sites under positive selection were identified in the proteins. RNA-seq and quantitative real-time PCR (qRT-PCR) expression analyses revealed that the identified Bnam6As exhibit tissue-specific expression patterns, as well as their expression patterns in response to various abiotic and biotic stresses. The 2000 bp sequence upstream of Bnam6As contained a number of cis-acting elements that regulate plant growth and environmental response. Furthermore, the protein interaction network revealed their interactions with a number of proteins of significant functional importance.

CONCLUSION

The identification of m6A modifiers in oilseed rape and their molecular evolution and expression profiling have revealed potential functions and molecular mechanisms of m6A, thus establishing a foundation for further functional validation and molecular breeding.

Machine Learning-Enabled Drug-Induced Toxicity Prediction

Unexpected toxicity has become a significant obstacle to drug candidate development, accounting for 30% of drug discovery failures. Traditional toxicity assessment through animal testing is costly and time-consuming. Big data and artificial intelligence (AI), especially machine learning (ML), are robustly contributing to innovation and progress in toxicology research. However, the optimal AI model for different types of toxicity usually varies, making it essential to conduct comparative analyses of AI methods across toxicity domains. The diverse data sources also pose challenges for researchers focusing on specific toxicity studies. In this review, 10 categories of drug-induced toxicity is examined, summarizing the characteristics and applicable ML models, including both predictive and interpretable algorithms, striking a balance between breadth and depth. Key databases and tools used in toxicity prediction are also highlighted, including toxicology, chemical, multi-omics, and benchmark databases, organized by their focus and function to clarify their roles in drug-induced toxicity prediction. Finally, strategies to turn challenges into opportunities are analyzed and discussed. This review may provide researchers with a valuable reference for understanding and utilizing the available resources to bridge prediction and mechanistic insights, and further advance the application of ML in drugs-induced toxicity prediction.

Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics

The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes such as the regulation of gene transcription. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR's coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use.

Areca nut-induced AREG promote oral epithelial cell proliferation, migration, and EMT

OBJECTIVE

To analyze the biological effect and mechanism of areca nut extract (ANE) on human oral keratinocyte (HOK) cells.

MATERIALS AND METHODS

The effect of gradient concentration of ANE on the proliferation activity of HOK cells was analyzed by cell counting kit-8 (CCK-8) assays. The differentially expressed genes between the ANE group and control group HOK cells were analyzed by second-generation transcriptome sequencing. Real-time PCR and western blot were, respectively, used to analyze the expression of AREG gene and protein in HOK cells. After AREG gene overexpression or knockdown, the proliferation, migration, and expression of proteins related to epithelial-mesenchymal transformation (EMT), MAPK signal pathway in HOK cells were, respectively, detected by CCK-8, wound healing, transwell, and western blot assays.

RESULTS

ANE (500 μg/mL) promoted the proliferation and migration of HOK cells, ANE (2 mg/mL) promoted the EMT of HOK cells, and ANE (50 mg/mL) inhibited the proliferation of HOK cells. AREG knockdown inhibited ANE-induced proliferation and migration of HOK cells, while AREG overexpression promoted the proliferation and migration of HOK cells. Western blot assay showed that ANE activated MAPK signal pathway by upregulating AREG protein in HOK cells.

CONCLUSIONS

ANE promoted HOK cell proliferation, migration, and EMT by mediating AREG-MAPK signaling pathway.

Biological aging of different blood cell types

Biological age (BA) captures detrimental age-related changes. The best-known and most-used BA indicators include DNA methylation-based epigenetic clocks and telomere length (TL). The most common biological sample material for epidemiological aging studies, whole blood, is composed of different cell types. We aimed to compare differences in BAs between blood cell types and assessed the BA indicators' cell type-specific associations with chronological age (CA). An analysis of DNA methylation-based BA indicators, including TL, methylation level at cg16867657 in ELOVL2, as well as the Hannum, Horvath, DNAmPhenoAge, and DunedinPACE epigenetic clocks, was performed on 428 biological samples of 12 blood cell types. BA values were different in the majority of the pairwise comparisons between cell types, as well as in comparison to whole blood (p < 0.05). DNAmPhenoAge showed the largest cell type differences, up to 44.5 years and DNA methylation-based TL showed the lowest differences. T cells generally had the "youngest" BA values, with differences across subsets, whereas monocytes had the "oldest" values. All BA indicators, except DunedinPACE, strongly correlated with CA within a cell type. Some differences such as DNAmPhenoAge-difference between naïve CD4 + T cells and monocytes were constant regardless of the blood donor's CA (range 20-80 years), while for DunedinPACE they were not. In conclusion, DNA methylation-based indicators of BA exhibit cell type-specific characteristics. Our results have implications for understanding the molecular mechanisms underlying epigenetic clocks and underscore the importance of considering cell composition when utilizing them as indicators for the success of aging interventions.

Panaxadiol Attenuates Neuronal Oxidative Stress and Apoptosis in Cerebral Ischemia/Reperfusion Injury via Regulation of the JAK3/STAT3/HIF-1α Signaling Pathway

BACKGROUND

Cerebral ischemic stroke (CIS) is a debilitating neurological condition lacking specific treatments. Cerebral ischemia/reperfusion injury (CIRI) is a critical pathological process in CIS.

PURPOSE

This study aimed to explore the protective effects of panaxadiol (PD) against oxidative stress-induced neuronal apoptosis in CIS/CIRI and its underlying mechanisms.

METHOD

An MCAO mouse model was established to investigate the therapeutic effects of PD in vivo. Network pharmacology and molecular docking techniques were used to predict PD's anti-CIS targets. The protective effects of PD were further validated in vitro using oxygen-glucose deprivation/reoxygenation (OGD/R)-treated HT22 cells. Finally, core targets were verified through combined in vivo and in vitro experiments to elucidate the mechanisms of PD in treating CIS.

RESULT

PD exhibited significant neuroprotective activity, demonstrated by restoration of behavioral performance, reduced infarct volume, and decreased neuronal apoptosis in mice. Network pharmacology analysis identified 24 overlapping target genes between PD and CIS-related targets. The hub genes, PTGS2, SERPINE1, ICAM-1, STAT3, MMP3, HMOX1, and NOS3, were associated with the HIF-1α pathway, which may play a crucial role in PD's anti-CIS effects. Molecular docking confirmed the stable binding of PD to these hub genes. Both in vitro and in vivo experiments further confirmed that PD significantly mitigates neuronal apoptosis and oxidative stress induced by CIS/CIRI.

CONCLUSION

PD significantly counteracts CIS/CIRI by modulating the JAK3/STAT3/HIF-1α signaling pathway, making it a promising therapeutic agent for treating CIS/CIRI.

Identification and Validation of Biomarkers in Metabolic Dysfunction-Associated Steatohepatitis Using Machine Learning and Bioinformatics

BACKGROUND

The incidence of metabolic dysfunction-associated steatohepatitis (MASH) is increasing annually. MASH can progress to cirrhosis and hepatocellular carcinoma. However, the early diagnosis of MASH is challenging.

AIM

To screen prospective biomarkers for MASH and verify their effectiveness through in vitro and in vivo experiments.

METHODS

Microarray datasets (GSE89632, GSE48452, and GSE63067) from the Gene Expression Omnibus database were used to identify differentially expressed genes (DEGs) between patients with MASH and healthy controls. Machine learning methods such as support vector machine recursive feature elimination and least absolute shrinkage and selection operator were utilized to identify optimum feature genes (OFGs). OFGs were validated using the GSE66676 dataset. CIBERSORT was utilized to illustrate the variations in immune cell abundance between patients with MASH and healthy controls. The correlation between OFGs and immune cell populations was evaluated. The OFGs were validated at both transcriptional and protein levels.

RESULTS

Initially, 37 DEGs were identified in patients with MASH compared with healthy controls. In the enrichment analysis, the DEGs were mainly related to inflammatory responses and immune signal-related pathways. Subsequently, using machine learning algorithms, five genes (FMO1, PEG10, TP53I3, ME1, and TRHDE) were identified as OFGs. The candidate biomarkers were validated in the testing dataset and through experiments with animal and cell models. The malic enzyme (ME1) gene (HGNC:6983) expression was significantly upregulated in MASH samples compared to controls (0.4353 ± 0.2262 vs. -0.06968 ± 0.3222, p = 0.00076). Immune infiltration analysis revealed a negative correlation between ME1 expression and plasma cells (R = -0.77, p = 0.0033).

CONCLUSION

This study found that ME1 plays a regulatory role in early MASH, which may affect disease progression by mediating plasma cells and T cells gamma delta to regulate immune microenvironment. This finding provides a new idea for the early diagnosis, monitoring and potential therapeutic intervention of MASH.

Nicotinamide n-methyltransferase inhibitor synergizes with sodium-glucose cotransporter 2 inhibitor to protect renal tubular epithelium in experimental models of type 2 diabetes mellitus

AIMS

We aim to explore the potential of nicotinamide n-methyltransferase (NNMT) as a sensitive marker of renal tubular injury and the possibility of an NNMT inhibitor to combine with sodium-glucose cotransporter 2 (SGLT2) inhibitor to protect proximal tubular epithelium in vivo and in vitro model of Type 2 diabetes mellitus (T2DM), respectively.

METHODS

In vivo, immunohistochemical staining, Masson's trichrome staining and Sirius red staining were used to observe the changes of NNMT expression, renal tubular injury and interstitial fibrosis in renal tissue from the db/db mice. Bioinformatic analysis was also conducted to broaden the range of data validation. In vitro, Western Blot and quantitative RT-PCR were used to measure the degree of damage of HK-2 cells.

RESULTS

Our in vivo data showed upregulation of NNMT expression paralleled renal tubular damage and interstitial fibrosis. Our in vitro data revealed both NNMT inhibitors and SGLT2 inhibitors can protect against the injury as assessed by extracellular matrix (ECM) synthesis and profibrotic phenotype transition of HK-2 cells, and the combination of these two agents can further reduce these injuries.

CONCLUSIONS

The present study is the first to show that NNMT is a promising marker of renal tubular injury in diabetic nephropathy (DN) and NNMT inhibitors can synergize with SGLT2 inhibitors to protect HK-2 better. Our findings will provide the insight and pave the way of developing novel therapeutic strategies for chronic renal tubular injury associated with T2DM.

Prognostic markers and molecular pathways in primary colorectal cancer with a high potential of liver metastases: a systems biology approach

Background and purpose

Colorectal cancer (CRC) holds the position of being the third most prevalent cancer and the second primary cause of cancer-related fatalities on a global scale. Approximately 65% of CRC patients survive for 5 years following diagnosis. Metastasis and recurrence frequently occur in half of CRC patients diagnosed at the late stage. This study used bioinformatics analysis to identify key signaling pathways, hub genes, transcription factors, and protein kinases involved in transforming primary CRC with liver metastasis potential. Prognostic markers in CRC were also identified.

Experimental approach

The GSE81582 dataset was re-analyzed to identify differentially expressed genes (DEGs) in early CRC compared to non-tumoral tissues. A protein interaction network (PIN) was constructed, revealing significant modules and hub genes. Prognostic markers, transcription factors, and protein kinases were determined. Boxplot and gene set enrichment analyses were performed.

Findings/Results

This study identified 1113 DEGs in primary CRC compared to healthy controls. PIN analysis revealed 75 hub genes and 8 significant clusters associated with early CRC. The down-regulation of SUCLG2 and KPNA2 correlated with poor prognosis. SIN3A and CDK6 played crucial roles in early CRC transformation, affecting rRNA processing pathways.

Conclusion and implications

This study demonstrated several pathways, biological processes, and genes mediating the malignant transformation of healthy colorectal tissues to primary CRC and may help the prognosis and treatment of patients with early CRC.

Targeting CDK2 Confers Vulnerability to Lenvatinib Via Driving Senescence in Anaplastic Thyroid Cancer

Anaplastic thyroid cancer (ATC) is the most lethal tumor arising from thyroid follicular epithelium. Lenvatinib is an off-label use option for ATC patients in many countries but an approved prescription in Japan. However, lenvatinib resistance is a substantial clinical challenge. Clinical ATC samples including lenvatinib-resistant tumors are used to build patient-derived cells and patient-derived xenografts. High-throughput drug screening and synergy analyses are performed to identify an effective combination partner for lenvatinib. Cellular functions are detected by cell senescence, apoptosis, cell cycle, cell viability and colony formation assays. CDK2 inhibition showed the significant synthetic lethality with lenvatinib via inhibiting G1/S transition and inducing cell senescence in ATC. High expression of CDK2 is associated with lenvatinib resistance and poor clinical outcomes of ATC patients. Lenvatinib increased protein expression of CDK2 in lenvatinib-resistant ATC cells. Mechanistically, lenvatinib inhibited protein degradation of CDK2 via reducing CDK2's interaction with the RACK1-FBW7 complex, which is involved in ubiquitination and subsequent proteasomal degradation of CDK2. Combination of CDK2 inhibitors in clinical trials (Dinaciclib or PF-07104091) and lenvatinib markedly suppressed growth of xenograft tumors from the lenvatinib-resistant patient. The findings support the combination therapy strategy of lenvatinib and CDK2 inhibitor for lenvatinib-resistant ATC patients with high CDK2 expression.

Clinical Significance of Acyl-CoA Dehydrogenase Short Chain and Its Anti-tumor Role in Hepatocellular Carcinoma by Inhibiting Canonical Wnt/β-Catenin Pathway

BACKGROUND

The pathogenesis of hepatocellular carcinoma (HCC) emphasizes metabolic disorders. HCC patients showed abnormally low expression of Acyl-CoA dehydrogenase short chain (ACADS).

OBJECTIVES

This study aimed to elucidate the clinical significance and mechanistic role of ACADS in HCC.

METHODS

We investigated the expression patterns and significance of ACADS in HCC by analyzing multiple public databases and clinical samples (Chip data). Immunohistochemistry was employed to observe the expression levels of ACADS in HCC tissues. In vitro experiments involved silencing or overexpressing ACADS in HCC cell lines, with protein expression levels determined by Western blotting. Functional validation included CCK-8, Transwell, and scratch wound healing assays. TOPFlash and FOPFlash reporter gene assays, co-immunoprecipitation, and immunofluorescence were used to explore the interaction between ACADS and β-catenin.

RESULTS

ACADS was low expressed in HCC and was clinically associated with vascular invasion, TNM stage, and AFP levels. The low ACADS expression in HCC patients was negatively correlated with their survival. Overexpression of ACADS significantly suppressed the viability, migration, and invasive capacity of HCC cells, whereas silencing ACADS had the opposite effect. Mechanistically, co-immunoprecipitation experiments indicated that there was an interaction between ACADS and β-catenin. Overexpression of ACADS inhibited β-catenin activity and resulted in decreased nuclear β-catenin translocation and increased its cytoplasmic level. Immunofluorescence results also showed a decrease in β-catenin nuclear import following ACADS overexpression, whereas silencing ACADS led to an enhancement of its nuclear translocation.

CONCLUSION

ACADS emerges as a potentially valuable biomarker for HCC prognosis, exhibiting tumor-suppressive functions in HCC by participating in the regulation of β-catenin activity.

Identification of regulatory genes associated with POAG by integrating expression and sequencing data

BACKGROUND

Primary open-angle glaucoma (POAG) is a subtype of glaucoma that accounts for 60%~70% of all cases. Its pathogenic mechanism is intricate and its pathogenic process is concealed. Numerous significant biological processes associated with POAG continue to be elucidated.

METHODS

In this study, by exploring the expression data of POAG tissues and normal tissues, we mined the regulatory lncRNAs and mRNAs closely associated with the pathogenesis and progression of POAG by exploring a regulatory network of competing endogenous RNA (ceRNA), established by integrating gene expression data with the known lncRNA-miRNA and miRNA-mRNA-regulatory interactions. The key regulatory pathways and regulatory elements of POAG were identified by topological analysis. Simultaneously, the exome data of 28 cases with POAG and healthy controls were analyzed to identify high-frequency mutations and genes.

RESULTS

A total of 2712 differentially expressed genes were identified, including 1828 mRNAs and 884 lncRNAs. Network analysis suggested that lncRNAs such as HAGLR, HOTAIR and MIR29B2CHG, and mRNAs such as PPP6R3, BMPR2 and CFL2, may be involved in the onset and progression of POAG. In addition, 55 mutations with potential pathogenicity were identified.

CONCLUSION

These genes and mutations provide novel potential genetic heterogeneity and genetic susceptibility of POAG, as well as fresh suggestions for elucidating the molecular mechanism underlying the pathogenesis of POAG.

IL7 as a Risk Factor for Prostate Cancer: Implications for T Cell Apoptosis and Infiltration in the Tumor Microenvironment

BACKGROUND

Prostate cancer's complex interplay with the immune microenvironment prompted an investigation into immune-related pathogenic mechanisms and potential therapeutic targets.

METHODS

Within the GSE176031 data set, Seurat meticulously dissected single-cell profiles from radical prostatectomy patients. Leveraging CellMarker and SingleR cell identities were precisely annotated. Then, monocle traced pseudotime trajectories, illuminating cellular paths, complemented by CellChat's insights into intricate intercellular communications. Furthermore, mendelian randomization (MR) robustly substantiated causal associations within prostate cancer contexts.

RESULTS

Employing single-cell analysis on intraoperative tumor and normal tissue, we identified 15 distinct cell types, notably observing a significant T cell reduction in tumor samples. Intercellular communication analysis revealed multiple pathways between epithelial cells and T cells, highlighting interleukin (IL)-IL7R-IL2RG interactions. IL7R, crucial in T cell apoptosis, showed differential expression across T cell development stages. Patients with IL7 amplification had poorer outcomes (p < 0.05), supported by MR in two cohorts (ieu-b-4809 cohort: odds ratio [OR] = 1.005, p = 0.002, 95% confidence interval [CI] [1.002-1.008]; ebi-a-GCST90018905: OR = 1.063, p = 0.032, 95% CI [1.005-1.125]), confirming IL7 as a prostate cancer risk factor.

CONCLUSIONS

These findings suggest T cell depletion via IL7-IL7R signaling may drive prostate cancer progression, offering novel therapeutic insights.

Integrative mapping of human CD8+ T cells in inflammation and cancer

CD8+ T cells exhibit remarkable phenotypic diversity in inflammation and cancer. However, a comprehensive understanding of their clonal landscape and dynamics remains elusive. Here we introduce scAtlasVAE, a deep-learning-based model for the integration of large-scale single-cell RNA sequencing data and cross-atlas comparisons. scAtlasVAE has enabled us to construct an extensive human CD8+ T cell atlas, comprising 1,151,678 cells from 961 samples across 68 studies and 42 disease conditions, with paired T cell receptor information. Through incorporating information in T cell receptor clonal expansion and sharing, we have successfully established connections between distinct cell subtypes and shed light on their phenotypic and functional transitions. Notably, our approach characterizes three distinct exhausted T cell subtypes and reveals diverse transcriptome and clonal sharing patterns in autoimmune and immune-related adverse event inflammation. Furthermore, scAtlasVAE facilitates the automatic annotation of CD8+ T cell subtypes in query single-cell RNA sequencing datasets, enabling unbiased and scalable analyses. In conclusion, our work presents a comprehensive single-cell reference and computational framework for CD8+ T cell research.

Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer

Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non-cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co-expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co-expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR-1290, miR-1246, and miR-451a among the top up-regulated, and miR-6875-5p, miR-6784-5p, miR-1228-5p, and miR-6765-5p among the top down-regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA-target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR-187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR-187 expression. In summary, the integration of differential gene expression, co-expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

Cross-cellular analysis of chromatin accessibility markers H3K4me3 and DNase in the context of detecting cell-identity genes: An "all-or-nothing" approach

Cell identity is often associated to a subset of highly-expressed genes that define the cell processes, as opposed to essential genes that are always active. Cell-specific genes may be defined in opposition to essential genes, or via experimental means. Detection of said cell-specific genes is often a primary goal in the study of novel biosamples. Chromatin accessibility markers (such as DNase and H3K4me3) help identify actively transcribed genes, but data can be difficult to come by for entirely novel biosamples. In this study, we investigate the possibility of associating the cell-specificity status of genes with chromatin accessibility markers from different cell lines, and we suggest that the number of cell lines in which a gene is found to be marked by DNase/H3K4me3 is predictive of the essentiality status itself. We define a measure called the Cross-cellular Chromatin Openness (CCO) level, and show that it is associated with the essentiality status using two differentiation experiments. We then compare the CCO-level predictive power to existing scRNA-Seq and bulk RNA-Seq methods, showing it has good concordance when applicable.

Hsa_circ_0009910 knockdown in HeLa cells increases miR‑198 expression levels and decreases c‑Met expression levels and cell viability

Cervical cancer (CC) is considered a public health problem. Circular RNAs (circRNAs) serve important roles in different types of cancer, including CC. However, the mechanisms used by circRNAs to facilitate CC progression are currently unclear. The present study analyzed the effects of hsa_circ_0009910 knockdown on microRNA (miRNA/miR)-198 and mesenchymal-epithelial transition factor (c-Met) expression levels and its impact on apoptosis and the viability of HeLa cells. Differentially expressed circRNAs in CC were identified using analysis of circRNA microarray data. Bioinformatics analysis was performed to predict circRNA-microRNA (miRNA) and miRNA-mRNA interactions. The knockdown of hsa_circ_0009910 in HeLa cells was performed using small interfering RNA and the expression levels of hsa_circ_0009910, miR-198 and c-Met were assessed using reverse transcription-quantitative PCR. The viability and apoptosis of HeLa cells were evaluated using MTT, neutral red uptake and ApoLive-Glo™ multiplex assays. Hsa_circ_0009910 was significantly upregulated in HeLa cells and the knockdown of hsa_circ_0009910 increased miRNA-198 expression levels, reduced c-Met expression levels and decreased cellular viability, but not apoptosis, in HeLa cells. Overall, these results indicated that hsa_circ_0009910 could act as a molecular sponge of miRNA-198 and contribute to the upregulation of c-Met expression levels. The hsa_circ_0009910/miRNA-198/c-Met interaction network affects the viability, but not apoptosis, of HeLa cells. Based on this mechanism, the present study suggests that hsa_circ_0009910 may be a promising biomarker for CC.

Fragile X mental retardation protein modulates translation of proteins with predicted tendencies for liquid-liquid phase separation

The Fragile X Mental Retardation Protein (FMRP) is an RNA-binding protein and a key regulator of translation in neurons, hence crucial for neural development and plasticity. FMRP loss, resulting from mutations in the Fmr1 gene, leads to Fragile X Syndrome (FXS) and Autism Spectrum Disorder (ASD), the most common inherited intellectual disabilities. Ribosome profiling in neurons consistently reveals that FMRP-knockout (FK) significantly down-regulates the translation of numerous lengthy genes, many of which are FMRP-binding targets and associated with ASD. Despite these findings, the functional explanation for FMRP's translation regulation of large neuronal proteins remains elusive. Our present study compiles data from published ribosome profiling studies, to identify genes with significantly decreased translation in FK neurons. Using bioinformatic analysis and machine-learning sequence-based tools, PSPredictor and FuzDrop, we found that the proteins encoded by these genes are predicted to be enriched in intrinsically disordered regions and are prone to liquid-liquid phase separation. These findings suggest that FMRP modulates the translation of proteins involved in the formation of biomolecular condensates. Our results can have significant implications for understanding the molecular mechanisms of FXS and ASD, adding complexity to FMRP's regulatory functions, thus offering avenues for further exploration and targeted therapeutic interventions in intellectual disability disorders.

Single-cell multiomics to advance cell therapy

Single-cell RNA-sequencing (scRNAseq) was first introduced in 2009 and has evolved with many technological advancements over the last decade. Not only are there several scRNAseq platforms differing in many aspects, but there are also a large number of computational pipelines available for downstream analyses which are being developed at an exponential rate. Such computational data appear in many scientific publications in virtually every field of study; thus, investigators should be able to understand and interpret data in this rapidly evolving field. Here, we discuss key differences in scRNAseq platforms, crucial steps in scRNAseq experiments, standard downstream analyses and introduce newly developed multimodal approaches. We then discuss how single-cell omics has been applied to advance the field of cell therapy.

Transcriptomic analysis of coxsackievirus B3 infection in induced pluripotent stem cell-derived brain-like endothelial cells

Viral aseptic meningitis is a neuroinflammatory condition that occurs when viruses gain access to the central nervous system (CNS) and induce inflammation. The blood-brain barrier (BBB) is comprised of brain endothelial cells (BECs) that stringently regulate the passage of molecules, toxins, and pathogens from the circulation into the CNS. Through their unique properties, such as complex tight junctions, reduced rates of endocytosis, expression of efflux transporters, and restricted expression of leukocyte adhesion molecules, the BBB is often able to limit pathogen entry into the brain; however, certain neurotropic pathogens, such as coxsackievirus B3 (CVB3) are able to infect the CNS. We have previously demonstrated that CVB3 can infect and disrupt induced pluripotent stem cell-derived brain-like endothelial cells (iBECs), but the host response to this infection remains unknown. Here, we investigate global host transcriptional changes during CVB3 infection of iBECs using RNA sequencing. We validated our data set by exploring pathways altered by CVB3 using quantitative real-time PCR (qPCR) and enzyme-linked immunosorbent assay of upregulated cytokines and interferon signaling molecules.

IMPORTANCE

Coxsackievirus B3 (CVB3) is a leading cause of viral aseptic meningitis that can produce severe disease in susceptible individuals. To gain access to the central nervous system, CVB3 must cross central nervous system barriers, such as the blood-brain barrier. Previously, we have shown that CVB3 infects a human stem cell-derived brain-like endothelial cell model. Here, we report the global transcriptome of stem cell-derived brain-like endothelial cells to CVB3 infection and provide proof-of-concept validation of the dataset using molecular biology techniques. These data could inform novel mechanisms of CVB3-mediated blood-brain barrier dysfunction.

Prognostic development and validation of a prediction model based on major histocompatibility complex-related differentially expressed genes in stomach adenocarcinoma

Background

Stomach adenocarcinoma (STAD) is a common malignant tumor with high morbidity and mortality. Major histocompatibility complex (MHC) is an important component of the immune system responsible for antigen presentation. However, no studies have yet reported on the relationship between major histocompatibility complex-related differentially expressed genes (MHCRDEGs) and the survival prognosis of STAD. The aim of this study is to explore the relationship between MHCRDEGs and survival prognosis in STAD patients.

Methods

Using The Cancer Genome Atlas (TCGA) database, we screened for differentially expressed MHCRDEGs, and a survival prognosis model was constructed based on these genes. We generated training and validation samples from the TCGA and Gene Expression Omnibus (GEO) datasets to enhance the robustness of our findings. The predictive effects of the model were assessed using Kaplan-Meier (KM) survival curve analysis, receiver operating characteristic (ROC) curve analysis, calibration analysis and decision curve analysis (DCA), with statistical significance reported as P values. The differences in the expression of key MHCRDEGs between different subgroups of TCGA and GEO databases were analyzed. Finally, a multifactorial survival prognostic model was constructed by combining MHC score (MHCs), and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to verify the expression of key genes.

Results

We identified five key MHCRDEGs: MKI67, MYB, SERPINE1, TRIM31, and HAVCR1. In the first prognostic model, the KM curves demonstrated a highly statistically significant difference in predicting overall survival (OS) in patients (P<0.001). The ROC curves indicated that the model showed relatively low accuracy in predicting 1-year [area under curve (AUC) =0.616], 3-year (AUC =0.644), and 5-year (AUC =0.619) occurrence. Furthermore, calibration analysis and DCA suggested that the model's predictions of OS were consistent with the actual patient survival, with the 5-year prognostic model exhibiting the best clinical utility. In the TCGA and GEO datasets, most of the key genes showed significant expression differences between the STAD/GEO and normal groups (P<0.001). Finally, the predictive model constructed by combining MHCs with clinicopathological staging demonstrated good predictive accuracy with optimal clinical utility at 5 years, with specific accuracy metrics provided as part of our results, and validated their expression via qRT-PCR in cell lines (MKI67: P=0.01, MYB: P=0.02, SERPINE1: P=0.02, TRIM31: P=0.02, HAVCR1: P<0.0001).

Conclusions

In this study, the expression and distribution of MHCRDEGs in STAD were analyzed by various methods, and a clinical prediction model of STAD was constructed using MHCRDEGs. The validity of this model confirms the feasibility of MHCRDEGs as prognostic markers for STAD, elucidating their potential clinical implications in guiding treatment strategies for this disease.

Bioinformatics combining machine learning and single-cell sequencing analysis to identify common mechanisms and biomarkers of rheumatoid arthritis and ischemic heart failure

Patients with rheumatoid arthritis (RA) have an increased risk of ischemic heart failure (IHF), but the shared mechanisms are unclear. This study analyzed RNA sequencing data from five RA and IHF datasets to identify common biological mechanisms and significant biomarkers. One hundred and seventy-seven common differentially expressed genes (CDEGs) were identified, with enrichment analysis highlighting pathways related to sarcomere organization, ventricular myocardial tissue morphogenesis, chondrocyte differentiation, prolactin signaling, hematopoietic cell lineage, and protein methyltransferases. Five hub genes (CD2, CD3D, CCL5, IL7R, and SPATA18) were identified through protein-protein interaction (PPI) network analysis and machine learning. Co-expression and immune cell infiltration analyses underscored the importance of the inflammatory immune response, with hub genes showing significant correlations with plasma cells, activated CD4+ T memory cells, monocytes, and T regulatory cells. Single-cell RNA sequencing (scRNA-seq) confirmed hub gene expression primarily in T cells, activated T cells, monocytes, and NK cells. The findings underscore the critical roles of sarcomere organization, prolactin signaling, protein methyltransferase activity, and immune responses in the progression of IHF in RA patients. These insights not only identify valuable biomarkers and therapeutic targets but also offer promising directions for early diagnosis, personalized treatments, and preventive strategies for IHF in the context of RA. Moreover, the results highlight opportunities for repurposing existing drugs and developing new therapeutic interventions, which could reduce the risk of IHF in RA patients and improve their overall prognosis.

Integrating multiomics sequencing analyses uncover the key mechanisms related to oxidative stress, mitochondria, and immune cells in keloid

BACKGROUND

This study aimed to investigate the key molecular mechanisms underlying keloid pathogenesis by integrating oxidative stress, mitochondria, and immune cells.

METHODS

Transcriptome sequencing (mRNA, lncRNA, and circRNA expression data), proteomic sequencing, and small RNA sequencing analyses of lesional and non-lesional skin of patients with keloids and healthy control (normal) skin were conducted. By integrating mRNA and publicly available gene expression data (GSE158395), differentially expressed genes related to oxidative stress and mitochondrial function in keloids were identified. Hub genes were identified using various bioinformatics analyses such as immune infiltration analysis, weighted gene co-expression network analysis, machine learning, and expression validation using proteomics sequencing data. Moreover, a competing endogenous RNA (ceRNA) network of hub genes was constructed by combining miRNA, lncRNA, and circRNA expression data. Five hub genes were identified: MGST1, DHCR24, ALDH3A2, ADH1B, and FKBP5.

RESULTS

These hub genes had a high diagnostic value for keloids, with an AUC value > 0.8 each. In addition, five hub genes were associated with the infiltration of multiple immune cells. The immune cells with the strongest positive and negative correlations with hub genes were M0 and M1 macrophages. A ceRNA network was constructed, and several ceRNAs, such as AC005062.1/miR-134-5p/FKBP5 and BASP1-AS1/miR-503-5p/ADH1B, were identified. These five hub genes may contribute to keloid pathogenesis.

CONCLUSION

These genes and their related ceRNAs may serve as diagnostic biomarkers and therapeutic targets for keloids.

Cross-disease drug discovery based on bioinformatics and virtual screening: Study of key genes in Alzheimer's disease and ovarian cancer

BACKGROUND

Alzheimer's disease (AD) and cancer, both age-related diseases, are characterized by abnormal cellular behavior. Epidemiological data indicate an inverse relationship between AD and various cancers. Accordingly, this study seeks to analyze the negatively correlated genes between AD and ovarian cancer and identify closely related compounds through virtual screening technology to explore potential therapeutic drugs.

METHODS

Microarray data were downloaded from the Gene Expression Omnibus database, and negatively correlated genes between AD and ovarian cancer were identified using bioinformatics analysis. Clinical prognostic and survival analyses were performed to identify genes most negatively associated with these diseases. The top ten compounds with the strongest binding to the target genes were screened from the ChemDiv database using virtual screening technology, considering the blood-brain barrier. Molecular dynamics simulations were used to identify potential sites for the binding of these compounds to the target protein MX1. Additionally, point mutation analysis of the target protein was performed. Finally, the binding site was verified in vitro.

RESULTS

The MX1 gene was most significantly negatively associated with AD and ovarian cancer. Molecular dynamics simulations revealed intersection sites at Glu-227 and Gly-188, where MX1 binds tightly to the head compound.

CONCLUSION

This study successfully identified MX1 as being negatively associated with AD and ovarian cancer and assessed the potential drug compounds that bind most closely to it. Our findings provide important rationale and candidate targets for the development of novel therapeutic strategies for AD and ovarian cancer.

Sotorasib resistance triggers epithelial-mesenchymal transition and activates AKT and P38-mediated signaling

Background

The molecular non-genetic changes of resistance to sotorasib are currently uncertain. The aim of this study was to generate a sotorasib-resistant cell line via selective pressure and systematically examine the molecular and phenotypic alterations caused by resistance.

Methods

Mutant NCI-H358 (KRASG12C) were exposed to incremental doses (2-512 nM) of sotorasib. Then, resistant clones were separated by single-cell sorting. Proliferation was analyzed in real-time by xCELLigence; protein profiles were quantified by protein arrays; and mRNA expression profile was measured using the PanCancer Pathways panel by NanoString. In silico analyses were conducted from a database comprising patient-derived xenograft (PDX) models and cell lines resistant to sotorasib. AKT and p38. The synergistic effect of combining AKT, p38, and EGFR inhibitors was assessed using the SynergyFinder platform. Additionally, AKT and p38 genes were silenced using esiRNA.

Results

Sotorasib-resistant H358-R cell line displayed markers of the mesenchymal-epithelial transition and loss of cell adhesion. Were identified 30 overexpressed genes in the resistance model, implicating in signaling pathways that leads to AKT activation and heightened protein expression levels of phosphorylated AKT and p38. To identify potential therapeutic strategies for overcoming sotorasib resistance, we investigated the combination of AKT and p38 inhibitors. Notably, combined inhibition of AKT (MK2206) and p38 (adezmapimod) restored sensitivity to sotorasib in resistant cell lines, as did silencing AKT expression.

Conclusion

These findings underscore the importance of adaptive mechanisms in sotorasib resistance in NSCLC cells contributing by EMT activation and demonstrates synergic combination with AKT and p38 inhibitors to restore sotorasib sensitivity in KRASG12C cells.

RNA sequence analysis landscape: A comprehensive review of task types, databases, datasets, word embedding methods, and language models

Deciphering information of RNA sequences reveals their diverse roles in living organisms, including gene regulation and protein synthesis. Aberrations in RNA sequence such as dysregulation and mutations can drive a diverse spectrum of diseases including cancers, genetic disorders, and neurodegenerative conditions. Furthermore, researchers are harnessing RNA's therapeutic potential for transforming traditional treatment paradigms into personalized therapies through the development of RNA-based drugs and gene therapies. To gain insights of biological functions and to detect diseases at early stages and develop potent therapeutics, researchers are performing diverse types RNA sequence analysis tasks. RNA sequence analysis through conventional wet-lab methods is expensive, time-consuming and error prone. To enable large-scale RNA sequence analysis, empowerment of wet-lab experimental methods with Artificial Intelligence (AI) applications necessitates scientists to have a comprehensive knowledge of both DNA and AI fields. While molecular biologists encounter challenges in understanding AI methods, computer scientists often lack basic foundations of RNA sequence analysis tasks. Considering the absence of a comprehensive literature that bridges this research gap and promotes the development of AI-driven RNA sequence analysis applications, the contributions of this manuscript are manifold: It equips AI researchers with biological foundations of 47 distinct RNA sequence analysis tasks. It sets a stage for development of benchmark datasets related to 47 distinct RNA sequence analysis tasks by facilitating cruxes of 64 different biological databases. It presents word embeddings and language models applications across 47 distinct RNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 58 word embeddings and 70 language models based predictive pipelines performance values as well as top performing traditional sequence encoding based predictors and their performances across 47 RNA sequence analysis tasks.

Identification and validation of depression-associated genetic variants in the UK Biobank cohort with transcriptome and DNA methylation analyses in independent cohorts

Depression is one of the most common psychiatric conditions resulting from a complex interaction of genetic, epigenetic and environmental factors. The present study aimed to identify independent genetic variants in the protein-coding genes that associate with depression and to analyze their transcriptomic and methylation profile. Data from the GWAS Catalogue was used to identify independent genetic variants for depression. The identified genetic variants were validated in the UK Biobank cohort and used to calculate a genetic risk score for depression. Data was also used from publicly available cohorts to conduct transcriptome and methylation analyses. Eight SNPs corresponding to six protein-coding genes (TNXB, NCAM1, LTBP3, BTN3A2, DAG1, FHIT) were identified that were highly associated with depression. These validated genetic variants for depression were used to calculate a genetic risk score that showed a significant association with depression (p < 0.05) but not with co-morbid traits. The transcriptome and methylation analyses suggested nominal significance for some gene probes (TNXB- and NCAM1) with depressed phenotype. The present study identified six protein-coding genes associated with depression and primarily involved in inflammation (TNXB), neuroplasticity (NCAM1 and LTBP3), immune response (BTN3A2), cell survival (DAG1) and circadian clock modification (FHIT). Our findings confirmed previous evidence for TNXB- and NCAM1 in the pathophysiology of depression and suggested new potential candidate genes (LTBP3, BTN3A2, DAG1 and FHIT) that warrant further investigation.

Comprehensive computational analysis of differentially expressed miRNAs and their influence on transcriptomic signatures in prostate cancer

Prostate cancer presents a major health issue, with its progression influenced by intricate molecular factors. Notably, the interplay between miRNAs and changes in transcriptomic patterns is not fully understood. Our study seeks to bridge this knowledge gap, employing computational techniques to explore how miRNAs and transcriptomic alterations jointly regulate the development of prostate cancer. The study involved retrieving miRNA expression data from the GEO database specific to prostate cancer. Identification of DEMs was conducted using the 'limma' package in R. Integration of these DEMs with mRNA interactions was done using the MiRTarBase database. Finally, a network depicting miRNA-mRNA interactions was constructed using Cytoscape software to analyze the regulatory network of prostate cancer. The study pinpointed seven pivotal differentially expressed microRNAs (DEmiRNAs) in prostate cancer: hsa-miR-185-5p, hsa-miR-153-3p, hsa-miR-198, hsa-miR-182-5p, hsa-miR-223-3p, hsa-miR-372-3p, and hsa-miR-188-5p. These miRNAs influence key genes, including FOXO3, NFAT3, PTEN, RHOA, VEGFA, SMAD7, and CDK2, playing significant roles in both tumor suppression and oncogenesis. The analysis revealed a complex network of miRNA-mRNA interactions, comprising 1849 nodes and 3604 edges. Functional Enrichment Analysis through ClueGO highlighted 74 GO terms associated with these mRNA targets. This analysis uncovered their substantial impact on critical biological processes and molecular functions, such as cyclin-dependent protein kinase activity, mitotic DNA damage checkpoint signalling, stress-activated MAPK cascade, regulation of extrinsic apoptotic signalling pathway, and positive regulation of cell adhesion. Our analysis of miRNAs and DEGs genes revealed an intriguing mix of established and potentially novel regulators in prostate cancer development. These findings both reinforce our current understanding of prostate cancer's molecular landscape and point to unexplored pathways that could lead to novel therapeutic strategies. By mapping these regulatory relationships, our work contributes to the growing knowledge base needed for developing more targeted and effective treatments.

Elucidating the role of FBXW4 in osteoporosis: integrating bioinformatics and machine learning for advanced insight

BACKGROUND

Osteoporosis (OP), often termed the "silent epidemic," poses a substantial public health burden. Emerging insights into the molecular functions of FBXW4 have spurred interest in its potential roles across various diseases.

METHODS

This study explored FBXW4 by integrating DEGs from GEO datasets GSE2208, GSE7158, GSE56815, and GSE35956 with immune-related gene compilations from the ImmPort repository. Gene selection was refined using advanced approaches, including LASSO regression and SVM-RFE. Functional enrichment of FBXW4-associated genes was assessed via GSEA and GSVA, identifying significant immune pathway involvement. Immune-related biological processes linked to FBXW4 expression were further evaluated using CIBERSORT and ESTIMATE algorithms. Validation of FBXW4 expression was performed using GSE35956.

RESULTS

A total of 13 hub genes were selected through LASSO and SVM-RFE analyses. Functional assays implicated FBXW4 in antiviral defense, cytokine production, and immune response modulation. Notably, FBXW4 expression correlated positively with several immune cell subsets, including memory B cells, activated memory CD4+ T cells, naive B cells, gamma delta T cells, M0 macrophages, follicular helper T cells, and naive CD4+ T cells, while showing a negative association with neutrophils.

CONCLUSIONS

This study uncovers a complex interplay between FBXW4 and immune processes in osteoporosis, suggesting its potential utility as a biomarker for OP diagnosis and monitoring. These findings lay the groundwork for future investigations into the therapeutic and diagnostic potential of FBXW4 in OP.

Effective Components of Panax notoginseng-Salvia miltiorrhiza in the Treatment of Melasma and Its Experimental Study

PURPOSE

The main purpose of this study was to predict and verify the active ingredients of Panax notoginseng-Salvia miltiorrhiza in melasma based on network pharmacology analysis and experimental verification.

MATERIALS AND METHODS

Panax notoginseng-Salviae miltiorrhizae was investigated by network pharmacology, GEO database analysis, and molecular docking techniques to screen its active ingredients. The active components of Panax notoginseng-Salviae miltiorrhizae were further validated by an in vitro α-melanin-induced B16F10 melanoma cell model and an in vivo UV irradiation combined with a progesterone injection-induced melasma rat model.

RESULTS

Network pharmacology analysis and molecular docking showed that salvianolic acid B might be the key active ingredient. In vitro cellular experiments revealed that salvianolic acid B inhibits tyrosinase activity in B16F10 cells at concentrations of 60-90 nmol/mL. In vivo animal experiments found that TYR, MDA, and TNF-α were decreased in the skin and serum of rats in the group of the low-, medium-, and high-dose groups of salvianolic acid B, and the expression of GSH-Px and SOD was increased. The high-dose groups of salvianolic acid B showed the best therapeutic effect.

CONCLUSION

In this study, experiments collectively show that salvianolic acid B in Panax notoginseng-Salvia miltiorrhiza slows down the process of melasma by inhibiting lipid peroxidation in the organism, increasing the antioxidant capacity of the skin, decreasing the activity of tyrosinase, and providing anti-inflammation. This highlights the successful application of network pharmacology and provides a scientific basis for the clinical citation of Panax notoginseng-Salvia miltiorrhiza in treating melasma.

Interferon-γ Signaling in Eosinophilic Esophagitis Affects Epithelial Barrier Function and Programmed Cell Death

BACKGROUND & AIMS

Eosinophilic esophagitis (EoE) is a chronic esophageal inflammatory disorder characterized by eosinophil-rich mucosal inflammation and tissue remodeling. Prior research has revealed the upregulation of interferon (IFN) response signature genes (ISGs) in biopsy tissue from patients with EoE, but the specific cell types that contribute to this IFN response and the effect of interferons on the esophageal epithelium remain incompletely understood. Here, we use single-cell RNA sequencing (scRNA-seq) to examine the expression of IFN and ISGs during EoE and explore how IFN-α and IFN-γ treatments affect epithelial function.

METHODS

Epithelial gene expression from patients with EoE was examined using scRNA-seq and a confirmatory bulk RNA-seq experiment of isolated epithelial cells. The functional impact of IFN-α and IFN-γ on epithelial cells was investigated using organoid models.

RESULTS

Using scRNA-seq, the highest number of differentially regulated ISGs was found in the epithelial cells of patients with active EoE, and ISGs in transitional epithelial cells correlated significantly with eosinophil counts and endoscopic reference scores. IFN-γ and IFN-α treatments reduced organoid formation rate and size in a dose-dependent manner, with IFN-γ showing a more pronounced impact on measures of epithelial barrier formation and induction of caspase activity. We identify high IFNG expression in a cluster of majority CD8+ T cells with high expression of CD69 and FOS.

CONCLUSIONS

These findings reveal that interferon, especially IFN-γ, plays a central role in epithelial cell dysfunction, significantly affecting gene expression, cellular differentiation, and barrier integrity. Clarifying the contribution of varied cytokine signals in EoE may help explain the heterogeneity in patient presentation and therapeutic response.

Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation

Background

Type 2 diabetes (T2DM) combined with fatty liver is a subtype of metabolic fatty liver disease (MAFLD), and the relationship between T2DM and MAFLD is close and mutually influential. However, the connection and mechanisms between the two are still unclear. Therefore, we aimed to identify potential biomarkers for diagnosing both conditions.

Methods

We performed differential expression analysis and weighted gene correlation network analysis (WGCNA) on publicly available data on the two diseases in the Gene Expression Omnibus database to find genes related to both conditions. We utilised protein-protein interactions (PPIs), Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes to identify T2DM-associated MAFLD genes and potential mechanisms. Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. Finally, we collected whole blood from patients with T2DM-related MAFLD, MAFLD patients and healthy individuals, and used high-fat, high-glucose combined with high-fat cell models to verify the expression of hub genes.

Results

Differential expression analysis and WGCNA identified 354 genes in the MAFLD dataset. The differential expression analysis of the T2DM-peripheral blood mononuclear cells/liver dataset screened 91 T2DM-associated secreted proteins. PPI analysis revealed two important modules of T2DM-related pathogenic genes in MAFLD, which contained 49 nodes, suggesting their involvement in cell interaction, inflammation, and other processes. TNFSF10, SERPINB2, and TNFRSF1A were the only coexisting genes shared between MAFLD key genes and T2DM-related secreted proteins, enabling the construction of highly accurate diagnostic models for both disorders. Additionally, high-fat, high-glucose combined with high-fat cell models were successfully produced. The expression patterns of TNFRSF1A and SERPINB2 were verified in patient blood and our cellular model. Immune dysregulation was observed in MAFLD, with TNFRSF1A and SERPINB2 strongly linked to immune regulation.

Conclusion

The sensitivity and accuracy in diagnosing and predicting T2DM-associated MAFLD can be greatly improved using SERPINB2 and TNFRSF1A. These genes may significantly influence the development of T2DM-associated MAFLD, offering new diagnostic options for patients with T2DM combined with MAFLD.

Age-dependent differences in breast tumor microenvironment: challenges and opportunities for efficacy studies in preclinical models

Immunity suffers a function deficit during aging, and the incidence of cancer is increased in the elderly. However, most cancer models employ young mice, which are poorly representative of adult cancer patients. We have previously reported that Triple-Therapy (TT), involving antigen-presenting-cell activation by vinorelbine and generation of TCF1+-stem-cell-like T cells (scTs) by cyclophosphamide significantly improved anti-PD-1 efficacy in anti-PD1-resistant models like Triple-Negative Breast Cancer (TNBC) and Non-Hodgkin's Lymphoma (NHL), due to T-cell-mediated tumor killing. Here, we describe the effect of TT on TNBC growth and on tumor-microenvironment (TME) of young (6-8w, representative of human puberty) versus adult (12 m, representative of 40y-humans) mice. TT-efficacy was similar in young and adults, as CD8+ scTs were only marginally reduced in adults. However, single-cell analyses revealed major differences in the TME: adults had fewer CD4+ scTs, B-naïve and NK-cells, and more memory-B-cells. Cancer-associated-fibroblasts (CAF) with an Extracellular Matrix (ECM) deposition-signature (Matrix-CAFs) were more common in young mice, while pro-inflammatory stromal populations and myofibroblasts were more represented in adults. Matrix-CAFs in adult mice displayed decreased ECM-remodeling abilities, reduced collagen deposition, and a different pattern of interactions with the other cells of the TME. Taken together, our results suggest that age-dependent differences in the TME should be considered when designing preclinical studies.

Luteolin Alleviates Inflammation Induced by Staphylococcus aureus in Bovine Mammary Epithelial Cells by Attenuating NF-κB and MAPK Activation

The internalization of S. aureus in bMECs is a major pathogenic mechanism leading to mastitis, causing significant economic losses in the dairy industry. Numerous plants contain Lut, a natural flavonoid with anti-inflammatory and antioxidant properties. However, little is known about Lut's ability to reduce inflammation caused by S. aureus in bMECs. This research aimed to evaluate the mechanism by which Lut reduces S. aureus-induced inflammation in bMECs. Through GO and KEGG enrichment analysis, researchers analyzed the differentially expressed genes in bMECs infected with S. aureus in NCBI GEO (GSE139612) and also analyzed the targets of Lut predicted by various online platforms. These studies identified two overlapping signaling pathways, the NF-κB and the MAPK pathways. We stimulated bMECs with S. aureus for two hours and then added Lut for ten hours, with a total duration of twelve hours. The expression levels of TLR2-MyD88-TRAF6 components, inflammatory cytokines, and protein phosphorylation associated with the MAPK and NF-κB signaling pathways were then assessed. Based on all of the results, Lut inhibited the generation of inflammatory cytokines in bMECs that were induced by S. aureus through the TLR2, NF-κB, and MAPK signaling pathways. This process might account for the anti-inflammatory properties of Lut.

Genomic clustering tendency of transcription factors reflects phase-separated transcriptional condensates at super-enhancers

Many transcription factors (TFs) have been shown to bind to super-enhancers, forming transcriptional condensates to activate transcription in various cellular systems. However, the genomic and epigenomic determinants of phase-separated transcriptional condensate formation remain poorly understood. Questions regarding which TFs tend to associate with transcriptional condensates and what factors influence their association are largely unanswered. Here we systematically analyzed 571 DNA sequence motifs across the human genome and 6650 TF binding profiles across different cell types to identify the molecular features contributing to the formation of transcriptional condensates. We found that the genomic distributions of sequence motifs for different TFs exhibit distinct clustering tendencies. Notably, TF motifs with a high genomic clustering tendency are significantly associated with super-enhancers. TF binding profiles showing a high genomic clustering tendency are further enriched at cell-type-specific super-enhancers. TFs with a high binding clustering tendency also possess high liquid-liquid phase separation abilities. Compared to nonclustered TF binding, densely clustered TF binding sites are more enriched at cell-type-specific super-enhancers with higher chromatin accessibility, elevated chromatin interaction and stronger association with cancer outcomes. Our results indicate that the clustered genomic binding patterns and the phase separation properties of TFs collectively contribute to the formation of transcriptional condensates.

Network Pharmacology and Bioinformatics Study of Six Medicinal Food Homologous Plants Against Colorectal Cancer

Integrating network pharmacological analysis and bioinformatic techniques, this study systematically investigated the molecular mechanisms of six medicinal food homologous plants (Astragalus membranaceus, Ganoderma lucidum, Dioscorea opposite, Curcuma longa, Glycyrrhiza uralensis, and Pueraria lobata) against colorectal cancer. Through screening the TCMSP database, 303 active compounds and 453 drug targets were identified. By integrating differential expression gene analysis with WGCNA on the GSE41258 dataset from the GEO database, 49 potential therapeutic targets were identified. GO and KEGG enrichment analyses demonstrated that these targets are primarily involved in drug response, fatty acid metabolism, and key cancer-related pathways. Cross-validation using three machine learning algorithms-LASSO regression, SVM-RFE, and Random Forest-pinpointed four critical target genes: CA1, CCND1, CXCL2, and EIF6. Further, CIBERSORT immune infiltration analysis revealed strong associations between these core genes and the tumor immune microenvironment in colorectal cancer patients, notably in modulating M0 macrophage infiltration and mast cell activity. Molecular docking analyses confirmed robust binding interactions between active compounds and core target proteins. This study systematically elucidated the molecular mechanisms of six medicinal food homologous plants against colorectal cancer, providing scientific evidence for their rational clinical application.

Rdh10-mediated Retinoic Acid Signaling Regulates the Neural Crest Cell Microenvironment During ENS Formation

The enteric nervous system (ENS) is formed from vagal neural crest cells (NCC), which generate most of the neurons and glia that regulate gastrointestinal function. Defects in the migration or differentiation of NCC in the gut can result in gastrointestinal disorders such as Hirschsprung disease (HSCR). Although mutations in many genes have been associated with the etiology of HSCR, a significant proportion of affected individuals have an undetermined genetic diagnosis. Therefore, it's important to identify new genes, modifiers and environmental factors that regulate ENS development and disease. Rdh10 catalyzes the first oxidative step in the metabolism of vitamin A to its active metabolite, RA, and is therefore a central regulator of vitamin A metabolism and retinoic acid (RA) synthesis during embryogenesis. We discovered that retinol dehydrogenase 10 (Rdh10) loss-of-function mouse embryos exhibit intestinal aganglionosis, characteristic of HSCR. Vagal NCC form and migrate in Rdh10 mutant embryos but fail to invade the foregut. Rdh10 is highly expressed in the mesenchyme surrounding the entrance to the foregut and is essential between E7.5-E9.5 for NCC invasion into the gut. Comparative RNA-sequencing revealed downregulation of the Ret-Gdnf-Gfrα1 gene signaling network in Rdh10 mutants, which is critical for vagal NCC chemotaxis. Furthermore, the composition of the extracellular matrix through which NCC migrate is also altered, in part by increased collagen deposition. Collectively this restricts NCC entry into the gut, demonstrating that Rdh10-mediated vitamin A metabolism and RA signaling pleiotropically regulates the NCC microenvironment during ENS formation and in the pathogenesis of intestinal aganglionosis.

Modeling gene interactions in polygenic prediction via geometric deep learning

Polygenic risk score (PRS) is a widely used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning-based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genome-wide PRS at the single-gene resolution and then explicitly encapsulates gene-gene interactions leveraging a graph neural network (GNN) for genetic risk prediction, enabling a systematic characterization of molecular interplay underpinning diseases. An attentive readout module is introduced to facilitate model interpretation. Extensive tests across multiple complex traits and diseases demonstrate the superior prediction performance of PRS-Net compared with a wide range of conventional PRS methods. The interpretability of PRS-Net further enhances the identification of disease-relevant genes and gene programs. PRS-Net provides a potent tool for concurrent genetic risk prediction and biological discovery for complex diseases.

Deciphering the potential ability of DExD/H-box helicase 60 (DDX60) on the proliferation, diagnostic and prognostic biomarker in pancreatic cancer: a research based on silico, RNA-seq and molecular biology experiment

BACKGROUND

Pancreatic cancer is one of the most malignant abdominal tumors. DDX60 has been shown to be associated with a variety of tumor biological processes. However, DDX60 in pancreatic cancer has not been reported. Our study confirmed that DDX60 can serve as a novel biomarker for diagnosis and treatment of pancreatic cancer.

MATERIALS AND METHODS

We downloaded pancreatic cancer datasets from GEO and TCGA databases, respectively. To investigate the relationship between DDX60 expression and prognosis in pancreatic cancer. GSEA analysis was performed on DDX60. We performed RNA-seq to further explore the downstream biological targets of DDX60 and the signaling pathways that may be involved in pancreatic cancer. Finally, we tested it through molecular biology experiments. First, we constructed the plasmid and tested the plasmid effect by WB. Then MTT assay was performed to explore the effect of DDX60 knockout on the proliferation of pancreatic cancer cells. LDH assay was performed to explore the effect of DDX60 on the release of lactate dehydrogenase from tumor cells. The effect of DDX60 on cell proliferation was further explored by clonal formation experiment. Continue to explore clinical therapeutic drugs sensitive to DDX60 targets.

RESULTS

By analyzing the GSE71729, GSE183795, GSE16515, GSE28735 and GSE62452 data sets, we found that DDX60 was highly expressed in pancreatic cancer. And is associated with poorer outcomes for pancreatic patients. The mRNA expression level of DDX60 was correlated with lymph node metastasis and grade in clinical pancreatic patients. Through the results of RNA-seq analysis, GO and KEGG analysis showed that DDX60 may be associated with cell cycle in pancreatic cancer. Through molecular biology experiments (MTT, LDH, and clonal formation experiment), we found that When DDX60 is knocked down in pancreatic cancer cells, the proliferation ability of tumor cells is significantly decreased. Several drugs targeting about DDX60 have been found, such as JW-7-52-1, this could provide direction for drug therapy against the DDX60 target.

CONCLUSION

In summary, DDX60 can be used as a novel biomarker related to the diagnosis and treatment of pancreatic cancer, participate in tumor proliferation, and is associated with poor prognosis in patients.

Aberrant FGF signaling promotes granule neuron precursor expansion in SHH subgroup infantile medulloblastoma

Mutations in Sonic Hedgehog (SHH) signaling pathway genes, for example, Suppressor of Fused (SUFU), drive granule neuron precursors (GNP) to form medulloblastomas (MBSHH). However, how different molecular lesions in the Shh pathway drive transformation is frequently unclear, and SUFU mutations in the cerebellum seem distinct. In this study, we show that fibroblast growth factor 5 (FGF5) signaling is integral for many infantile MBSHH cases and that FGF5 expression is uniquely upregulated in infantile MBSHH tumors. Similarly, mice lacking SUFU (Sufu-cKO) ectopically express Fgf5 specifically along the secondary fissure where GNPs harbor preneoplastic lesions and show that FGFR signaling is also ectopically activated in this region. Treatment with an FGFR antagonist rescues the severe GNP hyperplasia and restores cerebellar architecture. Thus, direct inhibition of FGF signaling may be a promising and novel therapeutic candidate for infantile MBSHH.

Identification of circadian rhythm-related biomarkers and development of diagnostic models for Crohn's disease using machine learning algorithms

The global rise in Crohn's Disease (CD) incidence has intensified diagnostic challenges. This study identified circadian rhythm-related biomarkers for CD using datasets from the GEO database. Differentially expressed genes underwent Weighted Gene Co-Expression Network Analysis, with 49 hub genes intersected from GeneCards data. Diagnostic models were constructed using machine learning algorithms, and biologic therapy efficacy was predicted with advanced regression techniques. Single-cell sequencing showed high gene expression in stem cells, immune, and endothelial cells, with validation confirming significant differences between CD patients and controls. These findings suggest circadian rhythm-related genes as promising diagnostic biomarkers for CD.

Investigation of Scutellaria Barbata's immunological mechanism against thyroid cancer using network pharmacology and experimental validation

Thyroid cancer (TC) is the most common endocrine malignancy, with a rapidly increasing global incidence. Scutellariae Barbatae Herba (SBH) exhibits significant antitumor activity; however, its mechanism against TC remains unclear. This study aims to explore the immunotherapeutic mechanism of SBH in treating TC through network pharmacology, bioinformatics analysis, and experimental validation. In the TCMSP database, the active components and potential targets of SBH were screened to construct a drug-component-target-disease network. TC targets were then filtered, and common targets were selected to build a protein-protein interaction network. GO and KEGG enrichment analyses were performed. The expression, prognosis, and immunotherapeutic roles of core genes were validated using TCGA databases. Molecular docking demonstrated the binding interactions between core components and targets. Finally, in vitro experiments were conducted to validate the results of the network pharmacology analysis. 14 active components and 29 potential targets of SBH in treating TC were identified from the TCMSP database. PPI network analysis highlighted SPP1 as a key target. GO enrichment analysis involved 722 biological processes, 24 cellular components, and 73 molecular functions. KEGG enrichment analysis suggested that the anticancer effect of SBH might be mediated through signaling pathways such as AGE-RAGE and PI3K-Akt. TCGA data indicated that SPP1 is highly expressed in TC and is associated with diagnosis, pathological stage, N stage, and gender of TC patients. Additionally, SPP1 expression correlated with the infiltration of 24 types of immune cells, with the highest correlation observed with macrophages. Molecular docking demonstrated that SPP1 has high binding stability with quercetin, Rhamnazin, and Salvigenin, with binding energies of -8.117, -7.494, and - 7.202 kJ∙mol - 1, respectively. Experimental validation showed that quercetin inhibited the growth of TC cells in a dose-dependent manner. Protein results indicated that quercetin downregulated SPP1 mRNA and protein expression. This study combines database predictions with experimental validation to reveal the potential mechanisms of SBH against TC, providing effective strategies for the immunotherapy of TC.

Germline alteration analysis reveals EPHB4R91H mutation as a key player in multiple primary lung tumors

Multiple primary lung tumors are garnering attention from clinicians, with adenocarcinoma emerging as the predominant histological type. Because of the heterogeneity and diffuse distribution of lesions in the same patient, the treatment of multiple primary lung adenocarcinoma (MPLA) is a significant challenge. As a kind of variation unaffected by tumor heterogeneity, germline alterations may play a key role in the development of MPLA. Here, whole-exome sequencing of peripheral blood was employed to obtain germline alteration data. Intergroup comparative analyses on rare and deleterious alterations of MPLA, solitary lung adenocarcinoma, and healthy individuals in an MPLA family were performed to clarify the candidate alterations. Whole-exome sequencing and targeted Sanger sequencing were performed in 27 disseminated MPLA patients to detect the mutation site that had been screened. A rare and deleterious germline alteration, EPHB4R91H, was found in all of the patients of an MPLA family and a patient with disseminated MPLA. It was revealed that EPHB4R91H was able to enhance the proliferation, migration, and invasion ability of A549 cells through increased binding affinity to ephrinB2, which in turn activated the EPHB4/ERK/JNK/MAPK pathway. Our findings corroborate that germline alterations are involved in the development of MPLA. And it was found for the first time that the EPHB4R91H mutation promotes the development of MPLA by enhancing its affinity for ephrinB2 and thereby active EPHB4/ERK/JNK/MAPK pathway.

CXCL12 is a potential therapeutic target for type 2 diabetes mellitus complicated by chronic obstructive pulmonary disease

OBJECTIVES

To identify the key genes and immunological pathways shared by type 2 diabetes mellitus (T2DM) and chronic obstructive pulmonary disease (COPD) and explore the potential therapeutic targets of T2DM complicated by COPD.

METHODS

GEO database was used for analyzing the gene expression profiles in T2DM and COPD to identify the common differentially expressed genes (DEGs) in the two diseases. A protein-protein interaction network was constructed to identify the candidate hub genes, which were validated in datasets and disease sets to obtain the target genes. The diagnostic accuracy of these target genes was assessed with ROC analysis, and their expression levels and association with pulmonary functions were investigated using clinical data and blood samples of patients with T2DM and COPD. The abundance of 22 immune cells was analyzed with CIBERSORT algorithm, and their relationship with the target genes was examined using correlation analysis. DGIdb database was used for analyzing the drug-gene interactions and the druggable genes followed by gene set enrichment analysis.

RESULTS

We identified a total of 175 common DEGs in T2DM and COPD, mainly enriched in immune- and inflammation-related pathways. Among these genes, CXCL12 was identified as the final target gene, whose expression was elevated in both T2DM and COPD (P<0.05) and showed good diagnostic efficacy. Immune cell infiltration correlation analysis showed significant correlations of CXCL12 with various immune cells (P<0.01). GESA analysis showed that high CXCL12 expression was significantly correlated with "cytokine-cytokine receptor interaction". Drug-gene analysis showed that most of CXCL12-related drugs were not targeted drugs with significant cytotoxicity.

CONCLUSIONS

CXCL12 is a potential common key pathogenic gene of COPD and T2DM, and small-molecule targeted drugs against CXCL12 can provide a new strategy for treatment T2DM complicated by COPD.

The Single-Cell Landscape of Peripheral and Tumor-infiltrating Immune Cells in HPV- HNSCC

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. HPV-negative HNSCC, which arises in the upper airway mucosa, is particularly aggressive, with nearly half of patients succumbing to the disease within five years and limited response to immune checkpoint inhibitors compared to other cancers. There is a need to further explore the complex immune landscape in HPV-negative HNSCC to identify potential therapeutic targets. Here, we integrated two single-cell RNA sequencing datasets from 29 samples and nearly 300,000 immune cells to investigate immune cell dynamics across tumor progression and lymph node metastasis. Notable shifts toward adaptative immune cell populations were observed in the 14 distinct HNSCC-associated peripheral blood mononuclear (PBMCs) and 21 tumor-infiltrating immune cells (TICs) considering disease stages. All PBMCs and TICs revealed unique molecular signatures correlating with lymph node involvement; however, broadly, TICs increased ligand expression among effector cytokines, growth factors, and interferon-related genes. Pathway analysis comparing PBMCs and TICs further confirmed active cell signaling among Monocyte-Macrophage, Dendritic cell, Natural Killer (NK), and T cell populations. Receptor-ligand analysis revealed significant communication patterns shifts among TICs, between CD8+ T cells and NK cells, showing heightened immunosuppressive signaling that correlated with disease progression. In locally invasive HPV-negative HNSCC samples, highly multiplexed immunofluorescence assays highlighted peri-tumoral clustering of exhausted CD8+ T and NK cells, alongside their exclusion from intra-tumoral niches. These findings emphasize cytotoxic immune cells as valuable biomarkers and therapeutic targets, shedding light on the mechanisms by which the HNSCC sustainably evades immune responses.

Human umbilical cord mesenchymal stem cells small extracellular vesicles-derived miR-370-3p inhibits cervical precancerous lesions by targeting DHCR24

BACKGROUND

Cervical cancer is often caused by persistent high-risk human papillomavirus (HPV) infection, causing precancerous lesions. Human umbilical cord mesenchymal stem cells-derived small extracellular vesicles (hucMSC-sEV) exhibit diverse effects on tumors. This study investigates hucMSC-sEV, the impact and mechanisms on HPV-positive cervical precancerous lesion cells to provide new treatment insights.

MATERIALS AND METHODS

We previously obtained hucMSC and hucMSC-sEV. In vitro experiments evaluated hucMSC-sEV effects on the proliferation and migration of S12 cells (derived from cervical precancerous lesions). Bioinformatics identified key microRNA components, and their impact on S12 cell proliferation and migration was investigated. The target gene of the microRNA component was predicted and confirmed via bioinformatics and dual-luciferase reporter assays. Lentiviral systems overexpressed target gene in S12 cells to examine the effects on microRNA impacts. SH-42 inhibitor was used to investigate target gene treatment potential. Immunohistochemistry assessed target gene expression in cervical precancerous lesions tissue.

RESULTS

hucMSC-sEV significantly inhibited S12 cell proliferation and migration. Bioinformatics identified miR-370-3p as an effective cargo, which also suppressed S12 cell proliferation and migration. miR-370-3p was confirmed targeting DHCR24 (24-Dehydrocholesterol Reductase). DHCR24 overexpression reversed miR-370-3p's inhibitory effects, while SH-42 counteracted DHCR24 overexpression's promoting effects. Clinical specimen analysis supported these findings, demonstrating a positive correlation between DHCR24 protein expression and cervical precancerous lesions' progression.

CONCLUSIONS

hucMSC-sEV inhibits S12 cell proliferation and migration, mediated by miR-370-3p targeting DHCR24 to regulate cellular cholesterol content. DHCR24 inhibition reduces the cholesterol level and cell functions, suggesting its potential as a therapeutic target in cervical precancerous lesions.

Screening for transcriptomic associations with Swine Inflammation and Necrosis Syndrome

BACKGROUND

The recently identified swine inflammation and necrosis syndrome (SINS) affects tail, ears, teats, coronary bands, claws and heels of affected individuals. The primarily endogenous syndrome is based on vasculitis, thrombosis, and intimal proliferation, involving defence cells, interleukins, chemokines, and acute phase proteins and accompanied by alterations in clinical chemistry, metabolome, and liver transcriptome. The complexity of metabolic alterations and the influence of the boar led to hypothesize a polygenic architecture of SINS. This should be investigated by a transcriptome study. For this purpose, the three to five least affected (SINS-low) and most SINS affected (SINS-high) 3d-old piglets, each of three boars, a relatively SINS stable Duroc boar (DU), a relatively stable Pietrain boar (PI+) and a highly susceptible Pietrain boar (PI-) were selected from 27 litters of mixed semen to minimize environmental effects.

RESULTS

A genome-wide expression experiment revealed a huge set of differentially expressed genes that are involved in vasculitis, inflammation and necrosis, keratinization and erythrocyte epitopes. Among them were CRP, GYPA, S100A12, and LIPK. The results confirm and complement previous studies to this topic.

CONCLUSIONS

The results confirm the outstanding importance of defence in the context of SINS. At the same time, for the first time, there is evidence for a direct involvement of the keratinisation capacity of the skin and various epitopes of the erythrocyte membrane, which seem to be associated with the severity of SINS. These genes could serve to clarify the pathogenesis of the syndrome and to develop diagnostic tools in future studies.

Rare Variant Analyses in Ancestrally Diverse Cohorts Reveal Novel ADHD Risk Genes

Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder, but its genetic architecture remains incompletely characterized. Rare coding variants, which can profoundly impact gene function, represent an underexplored dimension of ADHD risk. In this study, we analyzed large-scale DNA sequencing datasets from ancestrally diverse cohorts and observed significant enrichment of rare protein-truncating and deleterious missense variants in highly evolutionarily constrained genes. This analysis identified 15 high-confidence ADHD risk genes, including the previously implicated KDM5B. Integrating these findings with genome-wide association study (GWAS) data revealed nine enriched pathways, with strong involvement in synapse organization, neuronal development, and chromatin regulation. Protein-protein interaction analyses identified chromatin regulators as central network hubs, and single-cell transcriptomic profiling confirmed their expression in neurons and glial cells, with distinct patterns in oligodendrocyte subtypes. These findings advance our understanding of the genetic architecture of ADHD, uncover core molecular mechanisms, and provide promising directions for future therapeutic development.

Exploring the Anti-PANoptosis Mechanism of Dachaihu Decoction Against Sepsis-Induced Acute Lung Injury: Network Pharmacology, Bioinformatics, and Experimental Validation

Background

Dachaihu decoction (DCHD) is a common Chinese medicine formula against sepsis-induced acute lung injury (SALI). PANoptosis is a novel type of programmed cell death. Nevertheless, The mechanisms of DCHD against SALI via anti-PANoptosis remains unknown.

Methods

First, we identified the intersecting targets among DCHD, SALI, and PANoptosis using relevant databases and published literature. Then, protein-protein interaction (PPI) network, molecular docking, and functional enrichment analysis were conducted. In vivo, cecal ligation and puncture (CLP) was used to construct a sepsis mouse model, and the therapeutic effects of DCHD on SALI were evaluated using hematoxylin and eosin (H&E) staining, quantitative real-time PCR (qRT-PCR), and ELISA. Finally, qRT-PCR, immunofluorescence staining, and Western blotting were used to verify the effect of DCHD-containing serum (DCHD-DS) on LPS-induced RAW 264.7 macrophages in vitro.

Results

82 intersecting targets were identified by mapping the targets of DCHD, SALI, and PANoptosis. Enrichment analysis showed that DCHD against SALI via anti-PANoptosis by modulating tumor necrosis factor (TNF), AGE-RAGE, phosphoinositide 3-kinase (PI3K)-AKT, and Toll-like receptor signaling pathways by targeting Casp3, cellular tumor antigen p53 (TP53), B-cell lymphoma 2 (Bcl2), toll-like receptor-4 (TLR4), STAT3, STAT1, RELA, NF-κB1, myeloid cell leukemia-1 (MCL1), JUN, IL-1β, HSP90AA1, Casp9, Casp8, and Bcl2l1. Molecular docking analysis revealed that the key components of DCHD have a high binding affinity to the core targets. In vivo, DCHD improved lung histopathological injury, reduced inflammatory factor expression, and alleviated oxidative stress injury in lung tissues. In vitro, DCHD-DS alleviated cell morphology changes, the release of pro-inflammatory factors, and p65 nucleus aggregation. Furthermore, we verified that DCHD-DS inhibited PANoptosis by downregulating the PI3K/AKT/NF-κB signalling pathway.

Conclusion

DCHD attenuates SALI by inhibiting PANoptosis via control of the PI3K/AKT/NF-κB pathway. Our study provides a solid foundation for investigating the mechanisms of DCHD and its clinical application in the treatment of SALI.