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        <title>Frontiers in Bioinformatics | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/bioinformatics</link>
        <description>RSS Feed for Frontiers in Bioinformatics | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-08-13T05:10:28.457+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1926188</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1926188</link>
        <title><![CDATA[RLNSF-MDA: reliability-guided graph-regularized matrix factorization for immune-related miRNA–disease association prediction]]></title>
        <pubdate>2026-08-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xin Li</author><author>Yaoyu Liu</author><author>Ming Xu</author>
        <description><![CDATA[IntroductionMicroRNAs (miRNAs) regulate gene expression and are closely linked to the onset and progression of immune‑related diseases. Experimental discovery of disease‑associated miRNAs remains costly and time‑consuming, motivating computational prioritization. However, existing matrix‑completion and deep graph‑learning methods either underuse local biological neighborhood evidence or require complex multi‑view neural architectures.MethodsIn this study, we present RLNSF‑MDA, a reliability‑guided graph‑regularized logistic matrix factorization framework for predicting potential miRNA–disease associations, with an emphasis on immune disease analysis. The method integrates disease semantic similarity; miRNA functional, semantic, and sequence similarities; and training‑only Gaussian interaction profile similarities through data‑driven reliability weights. It then combines multi‑scale neighborhood evidence, diffusion scores, low‑rank reconstruction features, and contrastive graph‑regularized latent factors.ResultsOn the HMDD v3.2 benchmark containing 788 miRNAs and 374 diseases, RLNSF‑MDA achieved an average accuracy of 0.8525, an AUC of 0.9194, and an AUPR of 0.9055 in five‑fold cross‑validation, and an average accuracy of 0.8569, an AUC of 0.9266, and an AUPR of 0.9197 in ten‑fold cross‑validation. Ablation experiments showed that the full model outperformed reduced feature combinations and training strategies, supporting the contribution of reliability‑guided fusion, side‑score construction, graph regularization, and contrastive ranking. Evidence from immune‑related case studies in dbDEMC V2.0 and miR2Disease further covered lupus nephritis, lymphoma, and leukemia, with 45, 47, and 47 confirmed miRNAs, respectively.DiscussionThese results suggest that RLNSF‑MDA provides an effective and interpretable framework for prioritizing candidate miRNAs associated with immune‑related diseases.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1833416</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1833416</link>
        <title><![CDATA[Single cell mapping of B and T cell dynamics in breast cancer lymph node metastasis]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sajid Khan</author><author>Sabahat Jamil</author><author>Muhammad Hamza</author><author>Zarlish Attique</author><author>Suping Zhang</author>
        <description><![CDATA[Metastatic breast cancer remains difficult to cure, and the way B and T lymphocytes adapt across metastatic niches especially under therapy remains insufficiently defined. Clarifying compartment specific immune remodeling may help explain resistance to PD-1/PD-L1 blockade and identify actionable targets. We performed an integrated meta-analysis of single cell RNA-seq datasets from normal breast tissue, primary tumors, tumor-draining lymph nodes (TLNs), and peripheral blood mononuclear cells (PBMCs), focusing on B and Tcell states. Immune composition differed notably by compartment. Tumors were enriched for effector CD8 states (CD8 cytotoxic 20.1%; CD8 activated 13.5%), whereas TLNs preserved larger naïve and memory reservoirs (CD4 naïve 40.7%; B naïve 11.4%; B memory 12.0%) and contained a higher B cell fraction than tumors (39.6% vs. 19.5%). Post therapy, PBMCs and TLNs showed increased BTLA-HVEM (TNFRSF14) checkpoint signaling and enhanced MIF-CD74 interactions with a shift from CD44 toward CXCR4, consistent with CXCR4 driven migratory and survival programs. In TLNs, TNFRSF14 signaling was unidirectional (B→T), absent in the reverse direction, and not detected in tumors. Clinically, higher tumor CXCR4 combined with lower TNFRSF14 was associated with shorter progression free survival in TCGA-BRCA, most evident in node positive, early stage disease. To target the BTLA-HVEM checkpoint axis, we performed structure guided de novo peptide design using the native HVEM (23-39) peptide as an active structural template, followed by docking and molecular dynamics simulations. The optimized De novo-P2 peptide showed stable and favorable interactions at the BTLA interface, supporting its potential as a competitive modulator of BTLA-HVEM signaling. These data define niche specific lymphocyte remodeling and implicate BTLA-HVEM and CXCL12-CXCR4 as candidate biomarkers and therapeutic targets linked to PD-1/PD-L1 resistance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1856011</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1856011</link>
        <title><![CDATA[Detecting poliovirus sequences in the sequence read archive database using bioinformatics tools]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Katie Farrell</author><author>Kevin Tang</author><author>Melchizedek Mashiku</author><author>Dawit Abay</author><author>Joseph Longo</author><author>Edward Ramos</author><author>Gabriel Leventhal-Douglas</author><author>Margaret Rohrbaugh</author><author>Paul Chenoweth</author><author>Cara C. Burns</author><author>Kun Zhao</author>
        <description><![CDATA[One approach to find possible poliovirus sources that have not been detected by the Global Polio Eradication Initiative’s (GPEI) surveillance systems is to scan reads in the Sequence Read Archive (SRA) for potential poliovirus sequences. In the post-eradication era, the identification of poliovirus sequences in the SRA database could signal a potential biosafety risk which may set back the enormous achievements of the GPEI. To advance the use of the SRA database for detecting poliovirus, we hypothesized that bioinformatics alignment tools like Bowtie2, BLASTn, Magic-BLAST, MegaBLAST, STAT, and ElasticBLAST could distinguish between non-poliovirus and poliovirus reads from samples represented in the SRA database. Short poliovirus sequencing reads were simulated using poliovirus Sabin strain genomes. Simulation was also done for sequences other than poliovirus (referred here as “non-poliovirus reads”). Simulated reads were aligned to reference poliovirus genomes using different alignment tools to benchmark the accuracy and computing time of each tool. Parameters were also established to identify previously unknown poliovirus reads using percent identity and alignment length from BLASTn results. Bowtie2 was the most accurate and efficient tool, correctly identifying all simulated poliovirus reads. The STAT tool detected 99.6% of known control poliovirus accessions using the enterovirus query but only 77.6% using the poliovirus query, demonstrating strong but incomplete detection capability. This study demonstrates the feasibility of screening the SRA for poliovirus sequences as a tool to strengthen poliovirus containment and mitigate post-eradication risks, which can serve as an additional safety net for the GPEI.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1871436</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1871436</link>
        <title><![CDATA[A microbe–drug association prediction model based on graph attention networks and rotation forest]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jing Li</author><author>Juncai Li</author><author>Qijia Chen</author><author>Zhong Wang</author><author>Xianzhi Liu</author><author>Mingmin Liang</author><author>Junzhuang Wang</author><author>Hongyuan Ding</author><author>Bin Zeng</author><author>Lei Wang</author>
        <description><![CDATA[BackgroundIn recent years, with the diversification and expansion of drug research in the medical field, the widespread use of drugs, particularly antibiotics, has led to increased microbial resistance. Consequently, exploring potential associations between drugs and microbes has become critically important. However, traditional biological experiments are extremely expensive and time-consuming. Therefore, developing more effective computational models for predicting potential associations between microbes and drugs is both essential and challenging.ResultsWe proposed GATROF, a hybrid heterogeneous graph-based framework for microbe–drug association prediction. In GATROF, by integrating multiple microbe–drug–disease similarity measures, we first constructed two distinct microbe–drug networks. In addition, based on different features of microbes and drugs, we further constructed two novel microbe–drug feature matrices. On this basis, the microbe–drug networks and the constructed feature matrices were further used in a Graph Attention Network to learn complementary topology-aware representations of microbes and drugs. These GAT-derived representations were then integrated with the constructed drug-side and microbe-side feature matrices and input into a Rotation Forest classifier for final association prediction. Experimental results and case studies demonstrated that GATROF predicts microbe–drug associations more accurately than existing state-of-the-art methods.ConclusionGATROF provides a new integrated predictive framework for predicting potential microbe–drug associations. By combining heterogeneous biological information, GAT-based topological representation learning, and Rotation Forest classification, GATROF may help prioritize candidate drug–microbe associations for further biological validation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1903746</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1903746</link>
        <title><![CDATA[Agentic AI for trustworthy synthetic microbial genomics: a perspective on generation, validation, and governance]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Fahim Sufi</author>
        <description><![CDATA[Synthetic microbial genomic data are becoming increasingly important for benchmarking microbial genome analysis pipelines, simulating rare taxa, evaluating metagenomic workflows, and supporting reproducible computational biology. Recent genomic foundation models demonstrate that biological sequences can be modelled at unprecedented scale, with emerging capacity for genome-level interpretation, generation, and design. However, the scientific value of synthetic microbial genomic data depends not only on whether sequences can be generated, but whether they are biologically plausible, computationally useful, reproducible, and responsibly governed. This Perspective argues that agentic AI can provide the missing orchestration layer for trustworthy synthetic microbial genomics. Rather than treating synthetic data generation as a single model output, agentic workflows can coordinate specialised roles for sequence generation, biological plausibility assessment, taxonomic validation, functional annotation, contamination detection, downstream benchmarking, provenance logging, and governance review. I propose a validation-first agentic framework in which synthetic microbial genomes, plasmids, phages, and metagenomic profiles are iteratively generated, evaluated, revised, and documented before release or downstream use. Such a framework can help transform synthetic microbial genomic data from computational artefacts into auditable scientific infrastructure with explicit validation gates, escalation criteria, and machine-readable provenance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1896572</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1896572</link>
        <title><![CDATA[rMAP-Candida: a modular Dockerized WDL/Cromwell workflow for reproducible Candida species typing, assembly-contiguity assessment, antifungal-resistance marker screening, and phylogenomic surveillance]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Gerald Mboowa</author><author>Ivan Sserwadda</author><author>Stephen Kanyerezi</author><author>Benson R. Kidenya</author><author>Jonani Bwambale</author><author>Benson Musinguzi</author>
        <description><![CDATA[BackgroundCandida spp. infections are an increasing public health concern, particularly in settings where laboratory mycology, genomic surveillance infrastructure, and antifungal susceptibility testing remain limited. Accurate species identification, reproducible assembly assessment, conservative genomic screening for antifungal-resistance markers, and interpretable phylogenomic outputs are essential for surveillance and outbreak preparedness. However, fungal whole-genome sequencing workflows remain fragmented, difficult to reproduce across computing environments, and insufficiently adapted for implementation in low-resource public health genomics settings.MethodsWe developed rMAP-Candida, a modular, Dockerized WDL/Cromwell workflow for paired-end Candida spp. whole-genome sequencing analysis. The workflow performs read quality control and trimming with fastp, Candida-focused species typing using Kraken2/Bracken, de novo assembly with MEGAHIT, assembly-contiguity assessment with QUAST, optional genome-completeness assessment using Compleasm or BUSCO, antifungal-resistance marker screening using ChroQueTas/FungAMR-derived outputs, species-aware core-SNP phylogenomics, pairwise SNP-distance summarization, closest-neighbor analysis, and integrated HTML surveillance reporting. To improve independent reproducibility, the repository includes a quick-start local Cromwell test, a corrected two-sample input JSON, documented checks for public container and database access, and a two-sample reproducibility run.ResultsrMAP-Candida generated reproducible species assignments, assembly-contiguity metrics, optional completeness summaries, antifungal-resistance marker outputs, species-aware phylogenomic summaries, pairwise SNP-distance tables, closest-neighbor summaries, and integrated HTML reports. In the Ugandan validation dataset, the workflow identified six principal species groups, dominated by Candida albicans, followed by Candida tropicalis, Pichia kudriavzevii, Nakaseomyces glabratus, Clavispora lusitaniae, and Candida parapsilosis. The integrated report summarized 24 antifungal-resistance marker hits and a median assembly N50 of 35,946 bp. Species-aware phylogenomics was performed for eligible species groups, while ineligible or skipped groups were explicitly reported with reasons. The report also distinguished “no curated genomic antifungal-resistance marker detected” from phenotypic susceptibility, supporting conservative interpretation of resistance-screening outputs.ConclusionrMAP-Candida provides a portable, reproducible, modular, and surveillance-oriented WDL/Cromwell workflow for Candida spp. genomic analysis. By integrating species identification, assembly-contiguity assessment, optional completeness evaluation, antifungal-resistance marker screening, species-aware phylogenomics, SNP-distance summarization, closest-neighbor reporting, and HTML reporting, the workflow supports training, research, and applied fungal genomic surveillance in low-resource and other implementation settings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1953979</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1953979</link>
        <title><![CDATA[Correction: Transcriptome-informed metabolic modeling reveals astrocyte-specific vulnerabilities in mild cognitive impairment and Alzheimer’s disease progression]]></title>
        <pubdate>2026-08-11T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Andrea Angarita-Rodríguez</author><author>Viviana Vargas-López</author><author>Andrés Pinzón</author><author>Adrián Sandoval-Hernandez</author><author>Kai Kang</author><author>Leping Li</author><author>Jason Papin</author><author>Pedro Puentes-Rozo</author><author>Andrés Felipe Aristizábal</author><author>Janneth González</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1873262</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1873262</link>
        <title><![CDATA[Design and in silico evaluation of Thiazole–Isoxazole hybrids as PPARγ-Targeted antidiabetic agents]]></title>
        <pubdate>2026-08-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abhirami PV</author><author>Gupta Dheeraj Rajesh</author><author>N. V. L. Sirisha Mulukuri</author><author>Ranjitha A</author><author>Niyas Rehman</author><author>Shiv Basant Kumar</author><author>Dileep Kumar</author><author>Pankaj Kumar</author>
        <description><![CDATA[IntroductionDiabetes mellitus is a chronic metabolic disorder characterised by persistent hyperglycemia resulting from impaired insulin secretion, action, or both. Prolonged hyperglycemia disrupts metabolic homeostasis and increases the risk of complications, including cardiovascular disease, neuropathy, nephropathy, and retinopathy. Current therapies target multiple pathways but are limited by reduced efficacy and adverse effects. Peroxisome proliferator-activated receptor gamma (PPARγ) is a key regulator of glucose and lipid metabolism and an important therapeutic target. Thiazole and isoxazole scaffolds possess antidiabetic potential, and their hybridisation may improve efficacy and safety.ObjectiveTo design and evaluate a novel series of thiazole-linked isoxazole derivatives targeting the alternate ligand-binding domain of PPARγ for potential antidiabetic activity.ResultThe designed compounds (C1–C210) were docked into PPARγ (Protein Data Bank: 5 GTN), yielding nine candidates with binding affinities of −11.11 to −9.97 kcal/mol. Reference ligands included pioglitazone, Q-35, and S-35, with C139 showing the highest affinity. Molecular dynamics simulations (200 ns) confirmed stability. Advanced Trajectory analyses indicate favourable conformational sampling, correlated residue motions, and a relatively confined conformational space. In silico drug-likeness profiling further supported their activity profile.ConclusionIn this computational study, the thiazole-linked isoxazole compound C139 demonstrated probable antidiabetic activity against PPARγ. However, these findings remain predictive and require further validation through comprehensive in vitro and in vivo studies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1909327</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1909327</link>
        <title><![CDATA[PUDU (pipeline for universal diversity unveiling): an accessible end-to-end workflow for taxonomic profiling and ecological visualization of environmental microbiomes across amplicon, shotgun, and long-read sequencing]]></title>
        <pubdate>2026-08-11T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Alejandro Medaglia-Mata</author><author>Pablo Rojas-Rodríguez</author><author>Vojtěch Bystrý</author><author>Rossy Guillén-Watson</author><author>Olman Gómez-Espinoza</author><author>Kattia Núñez-Montero</author>
        <description><![CDATA[BackgroundEnvironmental microbiome research has advanced through three complementary sequencing modalities, targeted 16S rRNA amplicon sequencing, whole-genome shotgun (WGS) metagenomics, and long-read full-length 16S rRNA profiling, each supported by distinct toolsets with heterogeneous outputs, variable configurations, and different levels of reproducibility documentation. Existing pipelines are typically modality-specific, require substantial configuration expertise, or produce outputs that need further custom scripting before standard ecological analyses can begin. This analytical fragmentation introduces avoidable technical variability and complicates cross-study reproducibility and comparability. PUDU addresses this by integrating all three modalities into a single reproducible workflow with simplified configuration, harmonized outputs across classifiers, and direct compatibility with downstream ecological analysis frameworks.ResultsWe present PUDU (Pipeline for Universal Diversity Unveiling), a modular Snakemake workflow that supports amplicon (short-read 16S), shotgun metagenomics (WGS), and long-read 16S analyses from raw reads to standardized outputs for downstream microbial ecology. PUDU performs technology-aware preprocessing and centralized quality control, and integrates established taxonomic approaches, including DADA2 for amplicons, Emu for full-length 16S long reads, and Kraken2/Bracken and Centrifuger for WGS. Across methods, PUDU produces harmonized count and relative-abundance tables at user-defined taxonomic ranks, Krona files, and a standardized Phyloseq-compatible R object to streamline diversity analyses and statistical workflows. PUDU also provides an integrated Shiny interface for metadata-aware alpha/beta diversity, ordination, community composition, and shared-taxa exploration with exportable figures and taxa tables. We demonstrate PUDU on two publicly available environmental datasets spanning rhizosphere WGS and long-read marine sediment 16S, yielding broadly consistent community-level patterns across classifiers (Spearman ρ = 0.936 at phylum level; PERMANOVA R2 = 0.87–0.95) with peak memory below 45 GB on a standard Linux workstation.ConclusionPUDU is an end-to-end, reproducible, and extensible framework that enables standardized taxonomic profiling and ecology-oriented analysis across sequencing modalities. By combining harmonized outputs, Phyloseq interoperability, and an integrated visualization layer, PUDU facilitates reproducible, standardized, and comparable environmental microbiome analysis from raw reads to interpretable ecological insights.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1906036</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1906036</link>
        <title><![CDATA[LungMicroHostR: an R package for integrated host–microbiome analysis of bronchoalveolar lavage fluid metagenomic sequencing data]]></title>
        <pubdate>2026-08-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nan Li</author><author>Jing Hu</author><author>Wanning Tong</author><author>Chengdong Liu</author><author>Yun Ding</author><author>Ning Li</author><author>Zhigang Cai</author>
        <description><![CDATA[IntroductionBronchoalveolar lavage fluid metagenomic next-generation sequencing captures microbial profiles and host-derived molecular measurements from the same respiratory specimen, but downstream analysis requires coordinated handling of low-biomass microbial signals, negative-control information and multiple feature tables.MethodsWe developed LungMicroHostR, an R package for downstream host–microbiome analysis of bronchoalveolar lavage fluid metagenomic sequencing data. The package brings processed microbial profiles, host-derived molecular measurements, sample metadata and negative-control information into a unified R workflow for feature filtering, comparative model evaluation, visualization and reproducible reporting.ResultsUsing the public GSE252118 resource comprising 402 samples from patients with lung cancer or pulmonary infections, LungMicroHostR assembled matched microbial, host and clinical feature tables, estimated prevalence in negative controls and compared host transcriptomic, microbial-profile and combined host–microbial models. In the test set, the 10-feature host transcriptome nearest-centroid model achieved an AUC of 0.772 (95% confidence interval, 0.680–0.860), the five-feature RNA microbial logistic model achieved an AUC of 0.745 (0.655–0.832), and the combined host transcriptome–RNA microbial logistic model achieved an AUC of 0.765 (0.655–0.866) with balanced accuracy of 0.720. An external PRJNA714488 BALF shotgun metagenomic dataset was additionally analysed at the mOTU level; LungMicroHostR matched the resulting feature table with phenotype metadata and generated a 388-feature by 26-sample microbial abundance matrix.DiscussionLungMicroHostR provides documented functions for respiratory metagenomic analyses that require joint evaluation of microbial profiles, host-derived measurements, negative-control information and external microbial feature tables.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1893303</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1893303</link>
        <title><![CDATA[Multiple binding modes underlie Cannabis sativa cannabinoids recognition by peroxisome proliferator-activated receptor gamma]]></title>
        <pubdate>2026-08-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>N. R. Carina Alves</author><author>Justina Miranda</author><author>Lautaro D. Alvarez</author>
        <description><![CDATA[IntroductionPeroxisome proliferator-activated receptor gamma (PPARγ) is a ligand-activated nuclear receptor with broad therapeutic relevance across various pathologies, including type 2 diabetes, obesity, cancer, and inflammatory disorders. Cannabinoids are a class of terpene-phenolic compounds from Cannabis sativa L. that have been shown to act as partial agonists of PPARγ. Among them, the acidic forms Δ9-tetrahydrocannabinolic acid (THCA) and cannabidiolic acid (CBDA) display higher potency than their decarboxylated counterp arts Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD). Despite experimental evidence supporting direct PPARγ-cannabinoid interaction, the molecular determinants governing ligand recognition within the binding pocket have not yet been comprehensively investigated.MethodsA combination of molecular docking and molecular dynamics simulations was employed to characterize the binding modes of THC, CBD, THCA, and CBDA within the PPARγ ligand-binding domain. Docking calculations were performed on a curated set of 70 PPARγ crystal structures co-crystallized with structurally diverse ligands, exploiting thus the conformational variability of the binding pocket. The best-ranked solutions were subjected to 500 ns MD simulations and evaluated on the basis of ligand stability, persistence of polar and aromatic-aromatic interactions with the receptor, and energetic contributions estimated by MM/GBSA. Three candidate binding modes per ligand were selected and their trajectories extended to 1,000 ns.ResultsAll four cannabinoids yielded at least one stable binding mode at the microsecond timescale. The cannabinoids THCA and CBDA displayed a greater number of stable binding modes than THC and CBD, a result consistent with the higher potency previously reported for these compounds in experimental studies. This behavior may be attributable to the formation of salt bridges with basic residues in the binding pocket.ConclusionOur findings provide a structural framework for understanding cannabinoid recognition by PPARγ. The ability of these compounds to adopt multiple binding modes may contribute to their partial agonist profile, opening new avenues for the rational design of selective PPARγ modulators with improved therapeutic properties.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1911554</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1911554</link>
        <title><![CDATA[Integrated analysis of enzymes, mRNAs, and miRNAs provides insight into the regulatory potential of extracellular vesicles in recipient cell glucose metabolism]]></title>
        <pubdate>2026-08-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Namita N. Kashyap</author><author>Sharath Mohan Bhat</author><author>Padmanabha Udupa E. G.</author><author>Kavitha S. Shettigar</author><author>Vinutha R. Bhat</author><author>Dinesh Upadhya</author>
        <description><![CDATA[IntroductionExtracellular vesicles (EVs) are increasingly recognized as active coordinators of metabolic processes rather than mere messengers. By carrying unique subsets of enzymes, metabolites, lipids, and nucleic acids, EVs can directly deliver functional metabolic machinery or dynamically alter intracellular metabolic fluxes in recipient cells. However, their role in regulating specific biochemical pathways remains largely unknown.MethodologyIn the current in silico analysis, we explored the dominant metabolic role of EV cargo using publicly available multi-omics data. For this, the top 500 mRNAs and proteins, along with miRNAs reported at least 10 times in humans across independent studies, as catalogued in the EVpedia database are considered and curated into a comprehensive dataset.ResultsEnrichment analysis of these mRNAs and proteins revealed that carbohydrate metabolic pathways, including glycolysis, the pentose phosphate pathway and the TCA cycle, were over-represented in EVs. Further, to investigate whether EVs carry miRNAs that regulate these pathways, we analyzed the miRNA targets. Enrichment analysis of EV miRNA targets mapped glycolytic regulatory genes, including HK1, HK2, PFKP and PKM. Interestingly, we also found miRNAs targeting genes encoding glucose transporters (SLC2A1, SLC2A3, SLC2A4, and SLC2A14) reported in EVs. Genomic annotation of these miRNAs revealed them to form clusters, including the miR-17-92 cluster, a well-known regulator of glycolysis.DiscussionWhile the study has limitations—mainly due to the biological heterogeneity of EVs and the difficulty of standardizing cargo—the results potentially suggest that, by delivering enzymes, their mRNAs, regulatory miRNAs or combinations thereof, EVs could potentially mediate recipient cell glucose metabolism.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1887419</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1887419</link>
        <title><![CDATA[AI-enhanced virtual screening identifies a potent small-molecule modulator of ClC-3 for cervical cancer drug discovery]]></title>
        <pubdate>2026-08-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chao Liu</author><author>Chongxing Ji</author>
        <description><![CDATA[ClC-3 chloride channels play essential roles in cervical cancer progression by regulating lysosomal acidification, cell volume homeostasis, and chemoresistance. However, no highly selective small-molecule modulators of ClC-3 have been reported to date. Motivated by the urgent clinical need to reverse ClC-3-mediated chemoresistance and the challenge of processing massive chemical libraries under limited computational hardware resources, we propose a novel AI-driven Drug Discovery (AIDD) pipeline. Here, we present an integrated virtual drug discovery framework that transitions from traditional Computer-Aided Drug Design (CADD) by combining large-scale molecular docking, deep-learning-based rescoring, pharmacokinetic filtering, and atomistic molecular dynamics (MD) simulations. The primary advantage of this proposed scheme lies in the integration of GNINA 3D-convolutional neural network (CNN) rescoring, which significantly reduces the false-positive rates inherent in empirical scoring functions for membrane proteins. A library of ∼180,000 ZINC15 compounds was initially screened using AutoDock Vina, followed by GNINA convolutional neural network rescoring to refine predicted binding affinity and pose confidence. ADMET profiling further narrowed the candidates, providing computational proof of drug-likeness and toxicity criteria rather than experimental validation. Ultimately, only ZINC000001556308 (Lig8) satisfied all in silico criteria. To validate binding stability, we performed 100-ns all-atom MD simulations of the ClC-3–Lig8 complex embedded in a lipid bilayer. Lig8 induced reduced RMSD fluctuations, lower RMSF values across key transmembrane helices, and a more compact radius of gyration, indicating enhanced structural stabilization of ClC-3. MM/PBSA calculations confirmed favorable binding energetics dominated by van der Waals interactions, while per-residue decomposition identified PHE527, GLY283, and GLY584 as major contributors to ligand recognition. These results reveal a previously uncharacterized binding pocket within the ClC-3 transmembrane domain and highlight Lig8 as a promising lead compound for targeting ClC-3-mediated oncogenic signaling. Overall, this study establishes the first comprehensive computational framework for ClC-3 modulator discovery and provides a validated chemical scaffold for future therapeutic development against cervical cancer.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1832826</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1832826</link>
        <title><![CDATA[Spatial transcriptomics reveal heterogeneous cell‒cell interactions among brain regions in cuprizone model consistent with multiple sclerosis lesions]]></title>
        <pubdate>2026-08-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hui-Hsin Tsai</author><author>Sarbottam Piya</author><author>Jing Wang</author><author>Jing Zhu</author><author>Wenxing Hu</author><author>Andrew R. Gehrke</author><author>Shaolong Cao</author><author>Amanda J. Guise</author><author>Su Jing Chan</author><author>Mark Sheehan</author><author>Jenhwa Chu</author><author>Zhengyu Ouyang</author><author>Matthew Ryals</author><author>Michelle Lee</author><author>Wanli Wang</author><author>Edward Zhao</author><author>Patrick Cullen</author><author>Ravi Challa</author><author>Eric Marshall</author><author>Wanyong Zeng</author><author>Yea Jin Kaeser-Woo</author><author>Chris Ehrenfels</author><author>Luke Jandreski</author><author>Helen McLaughlin</author><author>Thomas M. Carlile</author><author>Jake Gagnon</author><author>Taylor L. Reynolds</author><author>Mingyao Li</author><author>Kejie Li</author><author>Baohong Zhang</author>
        <description><![CDATA[The cuprizone (CPZ) model is widely used for modeling demyelination in multiple sclerosis (MS) and for testing potential remyelination therapies. To better understand the underlying pathology of the CPZ model and evaluate its translatability, we integrated single-cell and spatial transcriptomics (ST) to investigate spatial cellular and molecular interactions during de- and remyelination in multiple brain regions. ST revealed global demyelination and neuroinflammation in the brain beyond the corpus callosum (CC), with region-specific differences. We identified oligodendroglia and microglia as two major cell types with significant transcriptomic changes in the model. CPZ-associated subclusters of oligodendroglia (marker genes Arap2, Dock10, Tenm4, Pex5l and Dock1) and microglia (marker genes ApoE, Axl, Cd9 and Lpl) were mapped to the CC by ST. During remyelination, while mature oligodendrocytes (MOL) nearly reversed their phenotype back to the control state, microglia remained associated with the demyelination phenotype. Ligand‒receptor (LR) pairing analyses predicted growth factor and phagocytic pathway enrichment during demyelination, which is consistent with changes in MS lesions, and microglia were predicted to be the major sender cells. LR pairing also predicted a high likelihood of interaction between oligodendroglia and microglia, and a novel interaction between MOL and oligodendrocyte precursor cells (OPC), underscoring their roles during de- and remyelination. Finally, astrocytes in the CPZ model had the greatest preservation of disease-associated modules in MS lesions, while MOL, OPC, and microglia showed moderate to low preservation, which overall suggests that the CPZ model has moderate translatability to chronically active MS lesions.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1827877</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1827877</link>
        <title><![CDATA[SiaRNA: a siamese neural network with bidirectional cross-attention for pairwise siRNA-mRNA efficacy prediction]]></title>
        <pubdate>2026-08-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Vaishnavi Sapireddy</author><author>Rajkumar Nathi</author><author>Venkata Harshit Meruva</author><author>Varun Raju Nannapuraju</author><author>Bhargava Chary Basangari</author><author>Vani Kondaparthi</author>
        <description><![CDATA[IntroductionSmall interfering RNA (siRNA) therapeutics have extraordinary potential for targeted gene silencing. They mediate post-transcriptional gene regulation by binding to complementary messenger RNA (mRNA) sequences and degrading them, thereby preventing the production of unwanted proteins. Recent machine learning and deep learning frameworks for predicting siRNA efficacy have only achieved moderate success as these models solely rely either on handcrafted features or on sequential relations and therefore cannot capture the full complexity of siRNA-mRNA interactions.MethodsIn the above context, we propose SiaRNA, which uses a Siamese Neural Network for feature-derived representations and a bidirectional cross-attention mechanism for sequence-level relationships. It uniquely identifies mRNAs and their corresponding siRNAs as paired entities, allowing unified and context-aware modeling. Unlike previous models, which discard 2-nucleotide (2-nt) overhangs at the 3′ end while using 21-nt efficacy labels, SiaRNA both trains and tests on 21-nt sequences to ensure biologically consistent predictions.ResultsOur model sets a new performance benchmark, outperforming previous state-of-the-art models. SiaRNA is trained on the HUVK dataset, achieving an accuracy of 0.881, and its generalization is confirmed by testing on the independent Simone dataset.DiscussionThe results prove SiaRNA’s potential as a reliable and biologically accurate framework to guide siRNA design and improve therapeutic outcomes. On performing a case study using Patisiran siRNA and its target transthyretin (TTR) mRNA, an efficacy value of 0.7134 was observed indicating that our model can successfully identify therapeutically effective targets.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1894439</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1894439</link>
        <title><![CDATA[Decoding the hypoxic injury landscape and hypoxic risk model construction in diabetic kidney disease: a multi-omics study]]></title>
        <pubdate>2026-08-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Li Jiang</author><author>Chien Chieh</author><author>Haojun Zhang</author><author>Tingting Zhao</author><author>Xiai Wu</author>
        <description><![CDATA[ObjectivesTo identify the core hypoxic injury pattern of DKD, construct a DKD risk model based on hypoxic injury-related (HIR) score, and explore the potential therapeutic targets of DKD.MethodsDKD-related microarray-based transcriptomic analyses, single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics were retrieved from the Gene Expression Omnibus (GEO) database. Seven HIR gene sets were obtained from various public databases. Core hypoxic genes were identified using different machine-learning algorithm. LASSO and nomogram were applied to construct a HIR risk score for cellular hypoxic damage. The detailed expression of hub gene would be showed in the single cell and kidney region. The prognostic value of the HIR score was externally validated using plasma proteomics from the United Kingdom Biobank.ResultsFive core hypoxic injury pathways in DKD were identified: Hypoxia, Autophagy, Ferroptosis, Endoplasmic Reticulum (ER) Stress, and Apoptosis. The HIR risk score was constructed based on three hub genes: CASP3, DUSP1, and ZFP36. The HIR score demonstrated high diagnostic efficiency for DKD patients. Higher HIR scores were associated with significantly infiltrated immune cells and poorer kidney function. In United Kingdom Biobank validation, the HIR score significantly improved the prediction of kidney outcomes, renal death, and secondary endpoints beyond demographic and metabolic variables (AUC increments 0.04–0.06), and correlated negatively with eGFR and positively with lipoprotein(a). The calculated tissue-level HIR scores also showed a highly significant and robust increase in the renal microenvironment of BTBR ob/ob mice.ConclusionThese results provided a predictive model for clinical evaluation in patients with DKD and also a new insight into the role of HIR genes in the pathogenesis of DKD.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1883130</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1883130</link>
        <title><![CDATA[MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference]]></title>
        <pubdate>2026-08-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Noor Jamal Alkhateeb</author><author>Mamoun Awad</author>
        <description><![CDATA[IntroductionExisting methods for gene regulatory network (GRN) inference rely primarily on gene expression data alone or on lower-resolution bulk sequencing data. Despite recent advances in integrating chromatin accessibility and RNA sequencing, inferring GRNs from paired single-cell multi-omics data remains challenging due to noise, sparsity, and complex nonlinear regulatory relationships.MethodsWe present MultiCausGRN, a graph attention network (GAT)-based framework for GRN inference from paired scRNA-seq and scATAC-seq data. The model incorporates directed prior-guided graph attention learning to capture biologically grounded regulatory directionality by integrating curated directed regulatory edges into graph representation learning. MultiCausGRN performs supervised transcription factor–target link prediction using integrated multi-omics features within a two-layer graph attention architecture.ResultsOn the human PBMC multi-omics dataset, prior knowledge integration improved predictive stability and achieved a mean test AUPRC of 0.743 ± 0.049 and a mean AUROC of 0.682 ± 0.026 across five independent random seeds.DiscussionThese results demonstrate that directed prior-guided graph learning can improve the robustness and biological interpretability of GRN inference in data-limited settings. MultiCausGRN is publicly available at: https://github.com/nrr-90/MultiCausGRN.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1882476</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1882476</link>
        <title><![CDATA[LNMGAT: a laplacian regularized pseudo-negative mining graph attention network for robust drug–target interaction prediction under multi-scenario cold-start settings]]></title>
        <pubdate>2026-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shuai Guo</author><author>Weichi Liu</author><author>Jie Zou</author><author>Tao Ban</author><author>Gaifang Dong</author>
        <description><![CDATA[Computational drug–target interaction (DTI) prediction provides a scalable alternative to costly and time-consuming experimental screening, but its reliability is limited by the scarcity of experimentally verified negative interactions. In public DTI databases, most unobserved drug–target pairs are unlabeled rather than true non-interactions. Randomly treating these unlabeled pairs as negatives can introduce label noise and reduce model reliability, particularly in cold-start scenarios involving unseen drugs or targets. To address this issue, we propose LNMGAT, a LapRLS-guided reliable pseudo-negative mining framework coupled with dual graph attention encoders. Instead of relying on experimentally confirmed negative labels or randomly sampled negatives, LNMGAT first applies Laplacian regularized least squares to drug and target similarity graphs to identify low-confidence unlabeled pairs as reliable pseudo-negatives. Drug and target representations are then learned separately on similarity-based k-nearest-neighbor graphs using graph attention networks, and their embeddings are concatenated for MLP-based interaction prediction. Across Yamanishi, Davis, KIBA, and BindingDB benchmarks, LNMGAT achieved the best AUPR in 10 of 16 evaluation settings and ranked within the top two in 14 of 16 settings. In the 12 cold-start settings, LNMGAT obtained the best AUPR in 9 cases, with absolute AUPR gains over the strongest baseline of up to 0.016 in pair cold-start prediction. External evaluation on DrugBank positive interactions and SwissDock-based molecular docking further provided database-level and in silico support for the plausibility of high-ranked predictions. Nevertheless, the biological validation in this study remains computational and database-based; no wet-lab binding assay was performed. LNMGAT therefore provides a competitive and interpretable framework for DTI prediction under negative-label uncertainty, while further experimental validation is required for its top-ranked candidates.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1821527</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1821527</link>
        <title><![CDATA[Molecular docking and dynamics simulation studies uncover the host–pathogen protein–protein interactions in soybean (Glycine max (L.) Merr.) and Groundnut bud necrosis virus: first report]]></title>
        <pubdate>2026-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rayees Ahmad Bhat</author><author>Ayyagari Ramlal</author><author>Amooru Harika</author><author>Ashutosh Sharma</author><author>Ambika Rajendran</author><author>Iten M. Fawzy</author><author>Sreeramanan Subramaniam</author><author>Dhandapani Raju</author><author>S. K. Lal</author><author>Sonu Krishankumar</author><author>Shyam S. Kurup</author>
        <description><![CDATA[BackgroundSoybean is a widely consumed oilseed crop with economic importance. It is enriched with numerous bioactive compounds that have health-promoting properties. Groundnut bud necrosis virus (GBNV) affects many agriculturally important crops, such as soybean, resulting in crop losses. GBNV is a single-stranded RNA virus from the genus Orthotospovirus and family Bunyaviridae. It is transmitted by insect vectors (thrips) and is further aggravated by secondary transmission from infected plants to others in the same field. There are no commercially available drugs against GBNV; thus, there is a need to explore phytomolecules that control the infection. This study utilizes soybean proteins (CAM, CDK1, Cul1, GSK3, HSP70, LOX, PCNA, and TCP) and GBNV proteins (CP, EGP, MP, NSP, NS, and RDRP) for the analysis of their inhibitory interactions. Physicochemical properties for both protein groups were examined. The molecular basis for the selective predictive association of these protein interactions was evaluated using in silico molecular docking approaches, dynamics simulations analysis, and post-simulation computational analyses.ResultsThe results indicate that among these 50 protein–protein interactions, heat shock protein 70 (HSP70) exhibited potential inhibitory action against the RNA-dependent RNA polymerase (RDRP) with −12.12 kcal/mol binding affinity (z-score: 0.0). RDRP is an essential enzyme required for the transcription and replication of the viral genome. HSPs function in both abiotic and biotic stresses. Although further studies are required, these preliminary findings will be useful for developing new therapeutic agents against the virus, thus paving the way for researchers to find better alternatives.ConclusionThis is the first study to combine prediction, structural validation, and interface analysis of the interaction between soybean and GBNV proteins.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1902380</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1902380</link>
        <title><![CDATA[A linked independent component analysis framework for characterizing site-effect patterns in multi-site structural and functional MRI]]></title>
        <pubdate>2026-07-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Huashuai Xu</author><author>Yuge Xing</author><author>Weiya Guo</author>
        <description><![CDATA[IntroductionLarge-scale multi-site magnetic resonance imaging (MRI) improves population coverage and statistical power, but scanner- and protocol-related variability can obscure biological effects. Most harmonization methods aim to reduce site-related variance for downstream analysis, whereas less attention has been paid to where site effects are spatially expressed, whether they are reproducible across site compositions, and which acquisition parameters contribute to them.MethodsWe developed a modality-wise Linked Independent Component Analysis (LICA) framework to identify and interpret site-effect patterns in structural and resting-state functional MRI. Grey matter (GM) volume, amplitude of low-frequency fluctuation (ALFF), and regional homogeneity (ReHo) maps were analyzed separately. For each imaging measure, LICA decomposed voxel-wise maps into spatial components and subject-level loadings. Components were classified according to their associations with site labels and biological covariates, their spatial reproducibility was assessed using stepwise site-inclusion analyses, and their technical attribution was evaluated using cross-validated models based on site labels and recorded acquisition parameters. The framework was applied to ABIDE II GM maps from 913 participants across 18 sites and ALFF and ReHo maps from 795 participants across 16 sites.ResultsLICA identified site-related components across all three imaging measures. Site effects were not limited to uniform global shifts, but formed modality-specific spatial patterns. GM volume showed a dominant and highly stable whole-brain site-effect pattern, together with site-specific and regional components. In contrast, ALFF and ReHo showed more heterogeneous functional patterns, including global, focal, and scattered configurations. Site labels explained the largest proportion of loading variance, whereas recorded acquisition parameters showed modality-dependent contributions: TR and TE were more prominent for structural site effects, while FA, voxel size, TR, and scanner model contributed more strongly to functional site effects.DiscussionThe proposed framework provides a component-level diagnostic approach for multi-site MRI analysis. By mapping, stabilizing, and technically interpreting site-effect patterns, it complements conventional harmonization methods and may improve the transparency and reproducibility of multi-site structural and functional MRI studies.]]></description>
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