AimTPD52 (tumor protein D52) and TPD52L2 (tumor protein D52-like 2), members of the TPD52 gene family, have been implicated in multiple malignancies. However, their roles in gastric cancer (GC) remain elusive. Herein, we integrated multiomics analyses and experimental validation to elucidate their prognostic and functional significance in GC.MethodsUtilizing The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and tissue microarray datasets, we analyzed TPD52/TPD52L2 expression patterns in patients with GC. Survival analysis, Cox regression, and nomogram construction were performed to assess prognostic value. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis and immune infiltration evaluation (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts‌/Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data) (CIBERSORTx/ESTIMATE) were conducted to explore the molecular mechanisms involved. In vitro experiments (cell proliferation, migration, invasion, and apoptosis assays) were performed via lentivirus-mediated gene knockdown in gastric cancer cell lines AGS and MKN45 cells.ResultsTPD52 and TPD52L2 were significantly overexpressed in GC tissues compared with their normal counterparts. Elevated TPD52L2 expression was significantly associated with advanced Tumor, Node, Metastasis (TNM) stage and independently predicted reduced overall survival according to multivariate Cox regression. Multivariate analysis identified TPD52L2 as an independent prognostic factor. Diagnostic Receiver Operating Characteristic (ROC) curves yielded area under the curve values of 0.813 (TPD52) and 0.807 (TPD52L2). The results of functional experiments suggested that TPD52/TPD52L2 knockdown inhibited proliferation, migration, G0/G1 arrest, and induced apoptosis. Mechanistically, TPD52/TPD52L2 silencing suppressed PI3K/Akt serine/threonine kinase (AKT)/mammalian target of rapamycin (mTOR) signaling and epithelial–mesenchymal transition marker expression.ConclusionTPD52 and TPD52L2 are promising prognostic biomarkers in GC, with TPD52L2 exhibiting greater clinical relevance. Targeting these proteins may disrupt oncogenic signaling pathways and enhance immunotherapy efficacy, warranting further investigation in clinical trials.

BackgroundComorbidities and genetic correlations between gastrointestinal tract diseases and psychiatric disorders have been widely reported, but the underlying intrinsic link between Alzheimer’s disease (AD) and inflammatory bowel disease (IBD) is not adequately understood.MethodsTo identify pathogenic cell types of AD and IBD and explore their shared genetic architecture, we developed Pathogenic Cell types and shared Genetic Loci (PCGL) framework, which studied AD and IBD and its two subtypes of ulcerative colitis (UC) and Crohn’s disease (CD).ResultsWe found that monocytes and CD8 T cells were the enriched pathogenic cell types of AD and IBDs, respectively. By PCGL framework, there was a significant global genetic correlation between AD and each of IBD, UC, and CD. Especially, local genetic correlations between AD and IBD showed strong signals in chr6. Bidirectional two-sample MR Analyses also validated these. Cross-trait meta-analysis identified two key genetic loci rs660895 (on chr6) and rs917117 (on chr7), which have not been previously reported. Two loci are located on the genes HLA-DRB1 and JAZF1, respectively. MAGMA genome-wide gene-based analysis identified six overlapping genes including HLA-DRB1. Subsequently, for one thing, SMR analyses further validated six shared genes in specific tissues and monocytes. For another, pathway enrichment analysis revealed shared genes were enriched in several natural killer cell mediated cytotoxicity and chemokine signaling pathways.ConclusionsPCGL not only revealed the significant genetic correlations underlying AD and IBDs but also identified enriched pathogenic cell types and new shared loci and genes. We highlighted the mediation of HLA-DRB1 effects in the comorbidity mechanisms.

Multi-omics characterization of individual cells offers remarkable potential for analyzing the dynamics and relationships of gene regulatory states across millions of cells. How to integrate multimodal data is an open problem, existing integration methods struggle with accuracy and modality-specific biological variation retention. In this paper, we present scHyper (scalable, interpretable machine learning for single cell integration), a low-code and data-efficient deep transfer model designed for integrating paired and unpaired single-cell multimodal data. We benchmark scHyper against datasets from different multimodal data. ScHyper learns a low-dimensional representation and aligns the covariance matrices of the measured modalities, achieving high accuracy even with large scale atlas-level datasets with low memory and computational time across different cell lines, shedding light on regulatory relationships between different types of omics. Altogether, we show that scHyper is a versatile and robust tool for cell-type label transfer and integration from multimodal single-cell datasets.

Transcription factor (TF) chromatin immunoprecipitation followed by sequencing (ChIP-seq) is essential for identifying genome-wide TF-binding sites (TFBSs), and the collected datasets offer a variety of opportunities for downstream analyses such as inference of gene regulatory network and prediction for effects of single-nucleotide polymorphisms (SNPs) on TFBSs. Although TF ChIP-seq data continue to accumulate in public databases, comprehensive coverage of biologically relevant TF-sample pairs (i.e. combination of targeted TF and cell type) remains elusive. This is due to the need for TF-specific antibodies and large cell numbers, limiting feasible TF–cell type combinations. Moreover, ChIP-seq is measurable when the TF is expressed in the target cell type. Thus, defining the full space of biologically relevant TF–sample pairs—including both measured and unmeasured—is essential to assess and improve dataset comprehensiveness. Here, we investigated publicly available human TF ChIP-seq datasets and introduced the concept of unmeasured TF-sample pairs, defined as biologically relevant TF–sample combinations for which ChIP-seq experiments have not yet been performed. Notably, many expressed TFs in specific cell types remain unmeasured by ChIP-seq, affecting the coverage of regulatory regions revealed by TF ChIP-seq and genome-wide association study–SNP analyses. Furthermore, we propose practical strategies to efficiently supplement currently unmeasured data and discuss how these approaches can significantly enhance data-driven research. The database of unmeasured human TF–sample pairs is publicly accessible at https://moccs-db.shinyapps.io/Unmeasured_shiny_v1/, facilitating the systematic expansion of TF ChIP-seq datasets and thereby enhancing our comprehension of gene regulatory mechanisms.

Recently, the mRNA presence of pregnancy-specific glycoproteins (PSGs) in cancer biopsies has been shown to be associated with poor survival. Given the pregnancy-related function of PSGs, we hypothesized that PSGs might act in a sex-dependent behavior in cancer patients. A differential sex effect of PSG genes with respect to tumor immune landscape and cancer outcomes was investigated using statistical, bioinformatic, and machine learning analyses in The Cancer Genome Atlas (TCGA) data. The resulting findings were then validated in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data. In a pan-cancer TCGA data analysis, the strongest PSG-related sex difference for the prognostic association was found in lung adenocarcinoma (LUAD). Kaplan-Meier analysis revealed that expression of PSG genes is strongly associated with overall survival rate in the female group on the TCGA, but not in the male group. This sex-specific association was validated in an independent dataset from the CPTAC study. A combination of PSG3, PSG7, and PSG8 expression was most significantly linked to poor prognosis in females (P = 8.67E-06 in TCGA and P = .0382 in CPTAC). Pathway analysis revealed enrichment of the 'KRAS Signaling Down' pathway in the high-risk female group. A predictive model showed good predictive performance for the female group (validated C-index = 0.78 in CPTAC), but poor predictive performance for the male group. These findings suggest that PSGs may have a sex-specific negative impact on survival in female LUAD patients, and the mechanism may be related to KRAS signaling pathway modulation.

The viruses threats provoke concerns regarding their sustained epidemic transmission, making the development of vaccines particularly important. In the prolonged and costly process of vaccine development, the most important initial step is to identify protective immunogens. Machine learning (ML) approaches are productive in analyzing big data such as microbial proteomes, and can remarkably reduce the cost of experimental work in developing novel vaccine candidates. We intensively evaluated the B cell epitope immunogenicity prediction power of eight commonly-used ML methods by random sampling cross validation on a large dataset consisting of known viral immunogens and non-immunogens we manually curated from the public domain. Extreme Gradient Boosting, K Nearest Neighbours, and Random Forest) showed the strongest predictive power. We then proposed a novel soft-voting based ensemble approach (VirusImmu), which demonstrated a powerful and stable capability for viral immunogenicity prediction across the test set and external test set irrespective of protein sequence length. VirusImmu was successfully applied to facilitate identifying linear B cell epitopes against African Swine Fever Virus as confirmed by indirect ELISA in vitro. In short, VirusImmu exhibited tremendous potentials in predicting immunogenicity of viral protein segments. It is freely accessible at https://github.com/zhangjbig/VirusImmu.

Growth of the common bean plant Phaseolus vulgaris is tightly linked to its symbiotic relationship with diverse rhizobial species, particularly Rhizobium phaseoli, an alphaproteobacterium that forms root nodules and provides high levels of nitrogen to the plant. Molecular cross-talk is known to happen through plant-derived metabolites, but only flavonoids have been identified as nodulation signals, which act through the activation of the NodD Transcription Factor (TF). The identification of signals that mediate nodulation via TFs can aid in the rational design of biofertilizers that promote plant-bacteria symbiosis. Here, we identified 57 TFs in the R. phaseoli genome through sequence conservation from Escherichia coli, and predicted a transcriptional regulatory network comprising 16 TFs, and 1,371 target genes. We identified the regulatory interactions relevant to nodulation via transcriptome analysis, and hypothesize that PuuR is a TF involved in nodulation, potentially acting via its known binding metabolite putrescine. Sequence and structural evidence predict a model where putrescine acts as a signaling metabolite in nodulation via the TF PuuR, and the regulation of the nodI gene.

Enhancer RNA (eRNA), a type of non-coding RNA transcribed from enhancer regions, serves as a class of critical regulatory elements in gene expression. In cancer biology, eRNAs exhibit profound roles in tumorigenesis, metastasis, and therapeutic response modulation. In this review, we outline eRNA identification methods utilizing enhancer region prediction, histone H3 lysine 4 monomethyl chromatin signatures, and nucleosome positioning analysis. We quantitate eRNA expression through RNA-seq, single-cell transcriptomics, and epigenomic integration approaches. Functionally, eRNAs regulate gene expression, protein function modulation, and chromatin modification. Key databases detailing eRNA annotations and interactions are highlighted. Furthermore, we analyze the connection of eRNA with immune cells and its potential in immunotherapy. Emerging evidence demonstrates eRNA's critical involvement in immune cell crosstalk and tumor microenvironment reprogramming. Notably, eRNA signatures show promise as predictive biomarkers for immunotherapy response and chemoresistance monitoring in multiple malignancies. This review underscores eRNA's transformative potential in precision oncology, advocating for integrated multiomics approaches to fully realize their clinical applicability.

The evolutionarily conserved Integrator complex, which is composed of over 10 subunits, orchestrates diverse RNA-processing events such as 3'-end maturation of small nuclear RNAs (snRNAs), transcription termination of RNA Polymerase II, and DNA damage response signaling pathways; however, the functional roles of individual Integrator complex subunits in lung adenocarcinoma (LUAD) remain poorly characterized, and this study aimed to systematically investigate the potential oncogenic functions and prognostic values of these subunits in LUAD. To achieve this goal, the expression profiles of Integrator complex subunits were profiled using transcriptomic data from the The Cancer Genome Atlas (TCGA) database, survival analyses (including Kaplan-Meier and Cox regression models) were performed to evaluate the correlations between subunit expression levels and patient survival outcomes (overall survival (OS) and disease-free survival (DFS)), co-expression network analysis was conducted to annotate the potential biological functions of key subunits, and functional validation was performed using CCK-8 assays and flow cytometry to assess the impact of INTS7 depletion on cell proliferation and cycle progression in LUAD cell lines. The findings of this study showed that Integrator complex subunits were significantly overexpressed in LUAD tissues compared to normal lung parenchyma; among these subunits, INTS7 expression was most strongly associated with shortened OS and DFS, indicating its pivotal role in LUAD pathogenesis, while bioinformatics analyses revealed that INTS7 is involved in regulating critical biological processes including cell cycle progression, transcriptional regulation, and RNA metabolism, and loss-of-function experiments demonstrated that genetic silencing of INTS7 significantly inhibited cell proliferation and induced cell cycle arrest in LUAD cells. Ultimately, this study provides the first evidence that INTS7, a core component of the Integrator complex, serves as a functional and prognostic regulator in LUAD, highlighting its potential as a therapeutic target for this malignancy.

Molecular epidemiology of Foot-and-mouth disease (FMD) is crucial to implement its control strategies including vaccination and containment, which primarily deals with knowing serotype, topotype, and lineage of the virus. The existing approaches including serotyping are biological in nature, which are time-consuming and risky due to live virus handling. Thus, novel computational tools are highly required for large-scale molecular epidemiology of the FMD virus. This study reported a comprehensive computational tool for FMD molecular epidemiology. Ten learning algorithms were initially evaluated on cross-validated and ten independent secondary datasets for serotype prediction using sequence-based features through accuracy, sensitivity and 14 other metrics. Next, best performing algorithms, with higher serotype predictive accuracies, were evaluated for topotype and lineage prediction using cross-validation. These algorithms are implemented in the computational tool. Then, performance of the developed approach was assessed on five independent secondary datasets, never seen before, and primary experimental data. Our cross-validated and independent evaluation of learning algorithms for serotype prediction revealed that support vector machine, random forest, XGBoost, and AdaBoost algorithms outperformed others. Then, these four algorithms were evaluated for topotype and lineage prediction, which achieved accuracy ≥96% and precision ≥95% on cross-validated data. These algorithms are implemented in the web-server (https://nifmd-bbf.icar.gov.in/MolEpidPred), which allows rapid molecular epidemiology of FMD virus. The independent validation of the MolEpidPred observed accuracies ≥98%, ≥90%, and ≥ 80% for serotype, topotype, and lineage prediction, respectively. On wet-lab data, the MolEpidPred tool provided results in fewer seconds and achieved accuracies of 100%, 100%, and 96% for serotype, topotype, and lineage prediction, respectively, when benchmarked with phylogenetic analysis. MolEpidPred tool provides an innovative platform for large-scale molecular epidemiology of FMD virus, which is crucial for tracking FMD virus infection and implementing control program.