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PubMed2026

DPAS-Graph: adaptive spatial-feature relation learning for spatial RNA-to-protein prediction and virtual protein profiling.

Paired spatial multi-omics provides a supervised basis for learning RNA-protein correspondence in situ, but predicting protein abundance from spatial transcriptomic data alone remains challenging across tissue contexts and protein panels. Here, we present DPAS-Graph, an adaptive relation-learning framework for spatial RNA-to-protein prediction. Rather than directly merging spatial proximity and transcriptomic similarity as fixed graph priors, DPAS-Graph represents them as two relation channels on a shared edge support and updates their contributions during representation learning for protein prediction. Its Niche-Coupled Field Encoder combines layer-wise edge-relation modeling, intra-branch relation refinement, and cross-branch residual correction to learn spot representations for protein abundance prediction. In a leave-one-dataset-out benchmark across seven paired spatial multi-omics datasets, DPAS-Graph achieved lower aggregate prediction errors and improved spot-level agreement of protein expression profiles, with gains mainly reflected in error-based metrics and PCC-Spot. Spatial autocorrelation and protein-derived domain agreement analyses were further used to characterize the spatial behavior of the predicted protein maps. When applied to external RNA-only spatial sections, DPAS-Graph generated qualitatively interpretable marker-level virtual protein maps, illustrating its use as a complementary tool for protein-level interpretation of transcriptomics-only spatial data.

RNA↗

PubMed2026

Identifying JAK2 and ANXA5 as Key Genes Linking Obstructive Sleep Apnea and Oxidative Stress via Machine Learning and Multilayer Transcriptomic Integration With Functional Validation.

Obstructive sleep apnea (OSA) is a common and severe sleep disorder closely associated with oxidative stress (OS). This study aims to identify and validate potential OS-related genes associated with OSA through bioinformatics methods. We successfully identified OS-related differentially expressed genes (OS-DEGs) by combining the limma test, weighted correlation network analysis (WGCNA), and OS-related genes from the GeneCards database. Key genes and potential biological roles were further identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, protein-protein interaction (PPI) network analysis, Lasso regression analysis, random forest algorithm, and support vector machine recursive feature elimination (SVM-RFE) method. Evaluate and validate the accuracy of key genes through receiver operating characteristic (ROC) curve analysis. The human single-cell RNA sequencing (scRNA-seq) dataset is used for cell classification annotation, analysis of key gene single-cell expression profiles, and virtual gene knockout experiments based on the scTenifoldKnk algorithm. Integrating scRNA-seq sequencing, pseudotime trajectory inference, cell-cell communication analysis, and bulk immune infiltration deconvolution reveals monocyte subtype remodeling in OSA. Finally, the expression levels of key genes in clinical samples were validated using real-time quantitative PCR (RT-qPCR) and Western blotting. A total of 57 common DEGs, indicating significant enrichment in OS, inflammation, and tumor pathways, particularly prominent in the immunometabolism pathway. By integrating DEGs, WGCNA, PPI results, and machine learning methods, key genes Janus kinase 2 (JAK2) and ANXA5 were screened out. JAK2 was significantly upregulated under disease conditions, while ANXA5 was significantly downregulated. ROC curve exhibited high accuracy (area under the curve [AUC] > 0.85). Human scRNA-seq analysis revealed that key genes were predominantly highly expressed in monocytes. Virtual knockout experiments demonstrated that these key genes play a crucial role in regulating immune responses and inflammatory reactions. PPI networks and enrichment analysis verified that downstream genes S100P, ALOX5AP, PROK2, and PADI4 may collaboratively participate in immune response and inflammation regulation. Finally, clinical sample experiment further validated the results of bioinformatics analysis. This study provides new research insights for the diagnosis, mechanism research, and treatment development of OSA in the future by integrating multilayer transcriptomic and machine learning techniques.

Humans↗

PubMed2026

Comparative and Subtractive Genomics Analysis of Multidrug-Resistant Klebsiella pneumoniae Strains for Novel Target Identification and Drug Repurposing Strategies.

The rapid rise of multidrug-resistant (MDR) Klebsiella pneumoniae has created a major global health challenge due to the limited availability of conserved therapeutic targets effective across diverse resistant strains. In this study, an integrative computational target-discovery and drug-repurposing framework was applied to six clinically relevant K. pneumoniae strains. Comparative genomic analysis identified 3012 conserved genes, which were subsequently filtered to nine essential, non-host homologous proteins. Among these, three conserved cytoplasmic proteins (accD, cpxR, and mraZ) were prioritized for functional analysis, with acetyl-CoA carboxylase subunit beta (accD) emerging as the most promising therapeutic target based on sequence conservation, predicted essentiality, subcellular localization, and pathway association. Structural assessment supported the reliability of the predicted accD model, whereas consensus binding-site analysis identified key residues suitable for ligand interaction. Virtual screening of FDA-approved drugs followed by molecular docking identified several compounds with favorable binding profiles toward accD. Subsequent molecular dynamics simulations, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), hydrogen-bond occupancy, principal component analysis (PCA), and PCA-based free energy landscape (FEL) analyses, consistently identified tenapanor, micafungin, deferoxamine, and cobicistat as the most stable protein-ligand complexes, with tenapanor exhibiting the most favorable overall structural and thermodynamic stability profile. These findings identify accD as a promising therapeutic target in MDR K. pneumoniae and suggest several FDA-approved compounds as potential candidates for drug repurposing. Although experimental validation is needed to confirm their biological activity and therapeutic potential, this study demonstrates the potential of integrating comparative genomics with molecular dynamics analyses to support antimicrobial target identification and drug repurposing against MDR bacterial pathogens.

Klebsiella pneumoniae↗

PubMed2021

RP-REP Ribosomal Profiling Reports: an open-source cloud-enabled framework for reproducible ribosomal profiling data processing, analysis, and result reporting.

Ribosomal profiling is an emerging experimental technology to measure protein synthesis by sequencing short mRNA fragments undergoing translation in ribosomes. Applied on the genome wide scale, this is a powerful tool to profile global protein synthesis within cell populations of interest. Such information can be utilized for biomarker discovery and detection of treatment-responsive genes. However, analysis of ribosomal profiling data requires careful preprocessing to reduce the impact of artifacts and dedicated statistical methods for visualizing and modeling the high-dimensional discrete read count data. Here we present Ribosomal Profiling Reports (RP-REP), a new open-source cloud-enabled software that allows users to execute start-to-end gene-level ribosomal profiling and RNA-Seq analysis on a pre-configured Amazon Virtual Machine Image (AMI) hosted on AWS or on the user's own Ubuntu Linux server. The software works with FASTQ files stored locally, on AWS S3, or at the Sequence Read Archive (SRA). RP-REP automatically executes a series of customizable steps including filtering of contaminant RNA, enrichment of true ribosomal footprints, reference alignment and gene translation quantification, gene body coverage, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially translated genes, and generation of heatmaps, co-translated gene clusters, enriched pathways, and other custom visualizations. RP-REP provides functionality to contrast RNA-SEQ and ribosomal profiling results, and calculates translational efficiency per gene. The software outputs a PDF report and publication-ready table and figure files. As a use case, we provide RP-REP results for a dengue virus study that tested cytosol and endoplasmic reticulum cellular fractions of human Huh7 cells pre-infection and at 6 h, 12 h, 24 h, and 40 h post-infection. Case study results, Ubuntu installation scripts, and the most recent RP-REP source code are accessible at GitHub. The cloud-ready AMI is available at AWS (AMI ID: RPREP RSEQREP (Ribosome Profiling and RNA-Seq Reports) v2.1 (ami-00b92f52d763145d3)).

AMI↗

PubMed2025

Histology-Based Virtual RNA Inference Identifies Pathways Associated With Metastasis Risk in Colorectal Cancer.

Colorectal cancer (CRC) remains a major health concern, with >150,000 new diagnoses and >50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (eg, Visium and Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented virtual RNA inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST data set to date, comprising 45 patients and >300,000 Visium spots from primary tumors. Using state-of-the-art deep learning models (UNI, ResNet-50, Vision Transformer, and Vision Mamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, and inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

Humans↗

PubMed2026

Multistage Genetic, Transcriptomic, and Single-Cell Evidence Prioritizes MAP1LC3A among Ferroptosis-Related Genes in Glioblastoma.

Glioblastoma (GBM) remains a highly aggressive malignancy, and the contribution of ferroptosis-related genes to disease susceptibility remains incompletely understood. A genetically anchored, multistage framework was applied to prioritize ferroptosis-related genes associated with GBM. Among 483 genes curated from FerrDb V2, 315 had candidate cis-expression quantitative trait loci (cis-eQTLs) in eQTLGen, 250 retained at least three independent instruments after linkage disequilibrium clumping, and 226 yielded valid inverse-variance weighted (IVW) Mendelian randomization estimates using a GBM genome-wide association study comprising 6,183 cases and 18,169 controls. Thirty-four genes met the exploratory discovery criteria of P < 0.05 and a Benjamini-Hochberg false discovery rate (BH-FDR) < 0.20, with directionally concordant Bayesian weighted Mendelian randomization (BWMR) estimates. Replication-stage Mendelian randomization using GTEx V10 whole-blood cis-eQTLs supported four genes: ATG7, RPTOR, MAP1LC3A, and CHMP6. Evaluation across three independent tumor-control transcriptomic cohorts demonstrated that MAP1LC3A was consistently downregulated in tumor tissue and showed a significant random-effects pooled estimate (log₂ fold change, -1.273; 95% confidence interval, -1.625 to -0.920; false discovery rate = 0.016), whereas the other three genes lacked comparable cross-cohort statistical support. Single-cell virtual knockout analysis was subsequently performed in a patient-balanced subset of 2,400 malignant cells selected from 4,916 eligible cells across 20 adult IDH-wild-type GBM tumors. Across five independently seeded runs, 3, 15, 4, and 7 robust downstream genes were identified for ATG7, RPTOR, MAP1LC3A, and CHMP6, respectively. The resulting consensus sets comprised 17 unique genes, with RND3 shared across all four targets. Gene Ontology analysis indicated enrichment of cell-adhesion and cell-surface processes, whereas no KEGG or Reactome pathways remained significant after multiple-testing correction. Collectively, these findings prioritize MAP1LC3A for future experimental investigation while distinguishing genetic association, tumor-expression concordance, and computational perturbation from definitive evidence of causality or mechanism.

Humans↗

PubMed2026

Genomic characterization of KPC-2 and NDM coproducing carbapenem-resistant Klebsiella pneumoniae in a hospital: discovery of ST1869 clone and a novel hybrid plasmid.

UNLABELLED: To characterize the plasmid architecture and molecular background of KPC-NDM coproducing carbapenem-resistant Klebsiella pneumoniae (KN-CRKP) in a South China hospital. Five KN-CRKP isolates were collected, including three from one patient. All underwent Illumina sequencing; two (ST11 and ST1869) additionally had Nanopore sequencing. Antimicrobial susceptibility testing strain sequence types, conjugation assays, resistance gene profiling, plasmid typing, genetic structure comparison, core-genome single nucleotide polymorphisms (SNPs) analysis, and plasmid clustering were performed. All isolates exhibited an imipenem minimum inhibitory concentration (MIC) of ≥128 µg/mL and harbored multiple resistance genes. One isolate (1/5) belonged to ST1869 and co-harbored blaKPC-2 and blaNDM-5. The blaNDM-5-carrying plasmid was a novel IncI1/X3 fusion plasmid that also carried blaCMY-42. Unlike several IncX3 plasmids carrying blaNDM in publicly available KN-CRKP genomes from South China, this IncI1/X3 hybrid lacked a complete conjugative transfer system. ST11 was the predominant clone (4/5), co-harboring blaKPC-2 and blaNDM-1. A rare genetic structure, ΔISKpn6-blaKPC-2-ISKpn28, was identified on IncFII plasmids carrying blaKPC-2. Plasmid clustering analysis of 126 comparative KN-CRKP genomes showed diverse sequence types and plasmid backgrounds associated with the KPC/NDM co-production pattern. The observed plasmid diversity and structural variation in KN-CRKP support continued genomic surveillance, with particular attention to the ST1869 clone, the novel IncI1/X3 hybrid plasmid harboring blaNDM-5 and blaCMY-42, and the rare "ΔISKpn6-blaKPC-2-ISKpn28" genetic structure. Expanded genomic data on KN-CRKP are needed to further elucidate its resistance mechanisms and plasmid evolutionary trajectories. IMPORTANCE: The co-production of KPC and NDM carbapenemases in Klebsiella pneumoniae poses a formidable threat to clinical antimicrobial therapy, as these enzymes confer resistance to virtually all β-lactam agents, including carbapenems. Here, we report novel genomic features of KN-CRKP in South China, including the emergence of the ST1869 clone, a unique IncI1/X3 hybrid plasmid harboring blaNDM-5 and blaCMY-42, and the rare ΔISKpn6-blaKPC-2-ISKpn28 genetic structure. These findings substantially expand current understanding of plasmid evolution and resistance gene dissemination in this region. The identification of diverse resistance mechanisms and clonal backgrounds supports enhanced genomic surveillance and infection-control awareness for pan-resistant Enterobacterales.

Plasmids↗