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PubMed2026

SURE-Pipe: a pipeline to compare genomes and extract shared and unique regions.

Identification of unique and shared genomic regions between organisms has substantial translational potential for the development of marker-based diagnostic assays and sequence homology-driven taxonomic classification. An automated pipeline capable of performing genome comparisons at both the intra- and inter-species levels with minimal computational requirements can significantly advance genome-driven translational research. Species-specific genomic regions are particularly valuable for sequence-based species identification and for developing DNA amplification- or hybridization-based diagnostic assays. Here, we present SURE-Pipe, an automated and flexible pipeline for genome comparison and extraction of unique and shared genomic regions (https://github.com/BPaul-bioinfoLAB/SURE-Pipe). Benchmarking of this pipeline using simulated datasets demonstrated high accuracy for shared and unique region identification. Using the pairwise genome comparison module, six genome pairs from diverse microorganisms were analysed, and identified the unique and shared regions. In addition, the multigenome comparison module was applied to 96 genomes representing 24 Bacillus species and identified species-specific genomic regions. These regions were highly conserved among four strains of a species (>98% sequence identity) and exhibit little to no similarity with other species. Species-specific primers designed for all 24 Bacillus species showed no off-target amplification in in-silico polymerase chain reaction analysis, indicating their specificity. Overall, SURE-Pipe provides a robust and multipurpose framework for comparative genomics, and the outcomes can be used for species identification and the development of genome-based diagnostic approaches.

Genome, Bacterial↗

PubMed2026

Uncovering viral protein acquisition events and human-specific folds with pairwise comparisons of predicted protein structures.

Pairwise sequence comparisons are at the center of molecular evolutionary analyses. However, viral pairwise comparisons are challenging because extreme mutation rates and evolutionary pressure cause genomes to diverge rapidly, limiting detectable sequence similarity to fewer than 3% of virus pairs. To overcome these limitations, we compared viruses based on structural similarity, using predicted protein structures from ColabFold and Foldseek to define protein fold clusters. We represented each virus genome by its protein structural content. Pairwise similarities between viruses were then quantified using the Jaccard index based on the presence or absence of protein fold clusters. Using a recently established viral protein fold database, we compared all pairs of eukaryotic viruses in RefSeq. This approach increased the proportion of comparable viral genome pairs from 2.4% to 16.5%. Using this protein-fold representation of viruses, we were able to accurately predict viral families with an average sensitivity of 85.9%. Investigation of viral families showing limited sensitivity with this approach uncovered a laterally transferred structural cluster (Rep/NS1) broadly shared across diverse viral families and found in the avian lineage of adenoviruses. Sequence homology suggests that this Rep was acquired from Parvoviridae, but the protein is mutant in the ATPase active site, indicating possible exaptation toward a purely DNA-binding function. In Gammapapillomaviruses, several E4 clusters were associated with human tropism. In summary, by representing viruses with structural protein clusters, we can classify highly divergent viruses, trace lateral gene transfer, and uncover features associated with viral host range.

Humans↗

PubMed2026

Is a Win-Win possible? Achieving pareto-optimal privacy-utility balance in fine-tuned genome language model embeddings against embedding reconstruction attacks.

MOTIVATION: Genomic data is among the most sensitive categories of personal information, and the growing adoption of language models for sequence analysis raises significant privacy concerns. Prior work demonstrated that embeddings from general-purpose language models adapted for genomic sequences leak substantial single-nucleotide information under reconstruction attacks, and that fine-tuning embeddings can reduce this vulnerability at certain positions. However, three critical questions remain unaddressed: (i) whether privacy-utility tradeoffs are inherent constraints or configuration-dependent phenomena; (ii) whether genomic-specialized models such as DNABERT-base and Nucleotide Transformer exhibit different vulnerabilities than adapted general-purpose models; and (iii) how to statistically validate whether observed privacy improvements represent meaningful gains. Addressing these gaps is essential for guiding model selection in privacy-sensitive genomic applications. RESULTS: We systematically evaluated 13 transformer architectures, 9 general-purpose and 4 genomic-specialized, under position-specific embedding reconstruction attacks. We assessed the vulnerabilities of both pre-trained and fine-tuned models to the single-nucleotide inference-reconstruction attack using our new metrics, including error-based privacy gain and Pareto dominance scores, and statistically validated the results via paired t-tests. XLNet-Large achieved the best observed privacy protection among all evaluated models (+19.5% mean privacy gain) while maintaining competitive prediction performance. General-purpose models outperformed genomic-specialized models in 56% of pairwise comparisons. Tokenization strategy, rather than domain specialization, emerged as the primary determinant of the privacy-utility balance. These findings provide evidence-based guidance for selecting models in privacy-sensitive short-window genomic applications. All privacy claims in this work are specific to position-wise embedding reconstruction attacks and do not extend to other privacy risks, such as membership inference or training data extraction, which may respond differently to fine-tuning. AVAILABILITY AND IMPLEMENTATION: The code is publicly available at https://github.com/AnonymousISCBConf/Win-Win-Privacy-Utility-Analysis.

Genomics↗

PubMed2025

Pairwise graph edit distance characterizes the impact of the construction method on pangenome graphs.

MOTIVATION: Pangenome variation graphs are an increasingly used tool to perform genome analysis, aiming to replace a linear reference in a wide variety of genomic analyses. The construction of a variation graph from a collection of chromosome-size genome sequences is a difficult task that is generally addressed using a number of heuristics. The question that arises is to what extent the construction method influences the resulting graph, and the characterization of variability. RESULTS: We aim to characterize the differences between variation graphs derived from the same set of genomes with a metric which expresses and pinpoint differences. We designed a pairwise variation graph comparison algorithm, which establishes an edit distance between variation graphs, threading the genomes through both graphs. We applied our method to pangenome graphs built from yeast and human chromosome collections, and demonstrate that our method effectively characterizes discordances between pangenome graph construction methods and scales to real datasets. AVAILABILITY AND IMPLEMENTATION: pancat compare is published as free Rust software under the AGPL3.0 open source license. Source code and documentation are available at https://github.com/dubssieg/rs-pancat-compare. Snapshot available on Software Heritage at swh:1:dir:61acda8ba3dac1709ed60530147d3871831be629.

Algorithms↗

PubMed2026

Rawsamble: overlapping raw nanopore signals using a hash-based seeding mechanism.

MOTIVATION: Raw nanopore signal analysis is a common approach in genomics to provide fast and resource-efficient analysis without translating the signals to bases (i.e. without basecalling). However, existing solutions cannot interpret raw signals directly if a reference genome is unknown due to a lack of accurate mechanisms to handle increased noise in pairwise raw signal comparison. Our goal is to enable the direct analysis of raw signals without a reference genome. To this end, we propose Rawsamble, the first mechanism that can identify regions of similarity between all raw signal pairs, known as all-vs-all overlapping, using a hash-based search mechanism. RESULTS: We use these overlaps to construct de novo assembly graphs with an existing assembler, miniasm, off-the-shelf. To our knowledge, these are the first de novo assemblies ever constructed directly from raw signals without basecalling. Our extensive evaluations across multiple genomes of varying sizes show that Rawsamble provides a significant speedup (on average by 5.01× and up to 23.10×) and reduces peak memory usage (on average by 5.74× and up to by 22.00×) compared to a conventional genome assembly pipeline using the state-of-the-art tools for basecalling (Dorado's fastest mode) and overlapping (minimap2) on a CPU. We find that around one-third of Rawsamble's overlapping pairs are also found by minimap2. We find that when we use overlapping reads from Rawsamble, we can construct unitigs that are (i) as accurate as those built from minimap2's overlaps and (ii) up to half a chromosome in length (e.g. 2.3 million bases for E. coli). AVAILABILITY AND IMPLEMENTATION: Rawsamble is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.

Nanopores↗

PubMed2026

Comparative genomics reveals population structure and functional differentiation in Limosilactobacillus fermentum.

Limosilactobacillus fermentum is a widely distributed lactic acid bacterium frequently detected in fermented foods and host-associated microbiota, yet its global genomic diversity and functional variability remain insufficiently characterized. Here, we performed a large-scale comparative genomic analysis of 336 high-quality L. fermentum genomes curated from public databases. Species identity was validated using average nucleotide identity (ANI), and population structure was examined using pairwise ANI comparisons together with Mash-based phylogenetic reconstruction. Clustering at ≥ 99% ANI resolved the dataset into 15 genomic clusters, with four dominant lineages comprising the majority of genomes. Pangenome reconstruction identified 5,853 gene clusters, including 1,325 core genes (22.6%) and a large accessory component dominated by low-frequency genes. Heap's law modeling (λ = 0.19) indicated a weakly open pangenome, suggesting ongoing gene acquisition as additional genomes are sampled. Functional annotation revealed that core genes were primarily associated with essential cellular processes, whereas accessory genes were enriched in carbohydrate metabolism, membrane-associated functions, and defense-related systems. Variation in carbohydrate-active enzymes (CAZymes), transport systems, and stress-response genes was observed across lineages, indicating strain-level functional diversity. Although genomes from human and food sources were broadly distributed across phylogenetic lineages, multivariate analysis showed that gene-content variation was more strongly associated with genomic lineage than with isolation source. These results provide a population genomic framework for understanding genomic diversity and functional potential in L. fermentum.

Phylogeny↗

PubMed2026

Molecular characterization of a new emaravirus infecting Clerodendrum thomsoniae plants.

High-throughput sequencing revealed a new emaravirus named clerodendrum yellow blotch virus (ClYBV), infecting Clerodendrum thomsoniae plants in São Paulo State, Brazil. Four genomic RNA segments (RNA1, RNA3, RNA4, and RNA5) were recovered, whereas RNA2 remained undetected. Phylogenetic analysis and pairwise comparisons based on amino acid sequences showed ClYBV is most closely related to redbud yellow ringspot-associated virus (RYRaV, Emaravirus cercidis). No ClYBV particles were observed by transmission electron microscopy in leaf tissues from infected plants. These results support the identification of ClYBV as a new Emaravirus member, though the undetectable RNA2 suggests the isolate may be defective, lacking this segment and unable to produce virus particles. The binomial species name "Emaravirus clerodendri" is proposed.

Phylogeny↗

PubMed2026

Homologous recombination-deficient high-grade serous ovarian cancers exhibit distinct morphological features.

OBJECTIVE: Access to homologous recombination testing remains limited in many centers. We aim to correlate the morphology and immunophenotype of high-grade serous ovarian carcinoma with homologous recombination statuses. METHODS: A retrospective analysis of a high-grade serous ovarian carcinoma tumors with known homologous recombination status. A pathological review of morphology was performed for each tumor, along with immunohistochemical profiling. Tumor morphology was classified as (1) solid, pseudo-endometrioid, or transitional (2) micropapillary or nested. RESULTS: Overall, 81 tumors were included. The median age was 62 (interquartile range; 52-71). Of those, 27 (33.3%) tumors were BRCA1mut, 19 (23.5%) were BRCA2mut, 15 (18.5%) tumors had no BRCA1 or BRCA2 mutations but exhibited a genomic instability score ≥42 and were classified as BRCA1/2-wild-type with homologous recombinant deficient. The remainder 20 (24.7%) cases were homologous recombinant proficient. The proportion of tumors with solid transitional-like morphology was higher in BRCA1 (12/21, 57%) and BRCA2 (12/18, 67%) compared to the tumors with homologous recombinant proficient (3/17, 18%), p =.019. When stratified by genomic instability score, tumors with low score (genomic instability score <26) exhibited 0% solid transitional-like morphology versus 43% solid transitional-like morphology in high-score (genomic instability score >26), p =.03. PAX8 diffuse expression was detected in 71% of BRCA1, 65% of BRCA2, 92% of BRCA-wild-type homologous recombinant deficient tumors, and 100% of homologous recombinant proficient tumors, p =.071. The proportion of diffuse expression was higher in homologous recombinant proficient (100%) versus BRCA2 (65%) (Bonferroni-adjusted pairwise comparisons). CONCLUSIONS: Homologous recombinant deficient tumors are associated with the solid transitional-like morphology, with the BRCA1/2-mutated homologous recombinant deficient cases showing the strongest correlation. Genomic instability score alone may not fully capture the spectrum of homologous recombinant deficient-related phenotypes. The variation in solid transitional-like morphology features among BRCA1- or BRCA2-mutated, BRCA1/2- wild-type with homologous recombinant deficient, and homologous recombinant proficient cases may reflect the diverse biological spectrum of different homologous recombination alterations.

Humans↗

PubMed2026

Temporal shifts in K-locus composition and expansion of dual-carbapenemase-producing ST11-KL62 Klebsiella pneumoniae: a retrospective genomic surveillance study.

OBJECTIVES: To characterize longitudinal changes in carbapenem-resistant Klebsiella pneumoniae (CRKP) and investigate the recent increase in dual-carbapenemase-producing ST11-KL62 isolates. METHODS: We retrospectively analysed 1,239 non-duplicate CRKP isolates recovered at a tertiary hospital in China during 2018-2025. Antimicrobial susceptibility testing, whole-genome sequencing, K-locus and resistance/virulence gene profiling, core-genome single-nucleotide polymorphism analysis, reference-guided plasmid comparison, conjugation and stability assays, and a murine lethality model were used. RESULTS: ST11 accounted for 936/1,239 isolates (75.5%). KL47 declined from 39/151 (25.8%) in 2018-2019 to 45/755 (6.0%) in 2024-2025, whereas KL62 increased from 3/151 (2.0%) to 147/755 (19.5%). Among 148 ST11-KL62 isolates, 13/148 (8.8%) co-harboured blaKPC-2 and blaNDM-1, of which 12/13 (92.3%) met the study's molecular definition of hypervirulent CRKP. Pairwise single-nucleotide polymorphism distances among local ST11-KL62 isolates ranged from 0 to 43 (median, 14), suggesting that clonal expansion may have contributed to their increase. Complete genome analysis of ZD872 located blaKPC-2, blaNDM-1, and major virulence-associated genes on distinct plasmids; related plasmid backbones were predicted in other isolates using short-read comparisons. ZD872 exhibited a hypervirulent phenotype in the murine model. CONCLUSIONS: The ST11 CRKP population underwent temporal shifts in K-locus composition, including expansion of a closely related ST11-KL62 subset carrying dual carbapenemases and hypervirulence-associated markers. These findings support integrating longitudinal genomic surveillance with local transmission analysis.

Carbapenem-resistant Klebsiella pneumoniae↗

PubMed2025

Shared Genetic Architecture Between Atopic Dermatitis and Autoimmune Diseases.

Atopic dermatitis (AD) and autoimmune diseases exhibit epidemiological comorbidity, yet the shared genetic architecture remains incompletely understood. We investigated the genetic overlap between AD and three autoimmune disorders including inflammatory bowel disease (IBD), rheumatoid arthritis (RA), and vitiligo, leveraging genome-wide association data. Despite modest evidence for global genetic correlations, we found 113 independent pleiotropic loci shared among AD and autoimmune diseases, with 11 displaying a concordant effect across all 3 pairwise comparisons. Gene-set and tissue enrichment analyses evidenced the inflammatory background of pleiotropic associations. Multi-trait colocalization analysis prioritized 22 loci, linking the tissue-specific expression of DOK2, GPR132, RERE, RERE-AS1, SUOX, TNFRSF11A, and TRAF1 pleiotropic genes with AD risk. Mendelian randomization revealed no causal effect of genetic liability to AD on autoimmune diseases. Nevertheless, genetic liability to IBD increased AD risk, while vitiligo exhibited a protective effect post outlier correction. Our findings provide mechanistic insights into the multimorbidity of atopic dermatitis (AD) and autoimmune diseases, offering additional evidence for the pleiotropic genetic architecture of AD that contributes to systemic immune dysregulation across multiple organ systems.

Humans↗

PubMed2026

Validation of a national genetic evaluation for methane emission in Holstein cattle.

Lactanet Canada launched a genomic evaluation for methane efficiency for Holsteins in April 2023, utilizing milk mid-infrared-predicted methane (CH4) emissions (CH4MIR) as a proxy. This study validated the methane efficiency genomic evaluation using genotyped cows with CH4MIR and CH4 records from GreenFeed systems (CH4GF), along with relative breeding values (RBV) for methane production and methane efficiency from the April 2023 evaluation. In Lactanet's methane efficiency evaluation, a higher RBV indicates more desirable, lower-emitting animals. For the validation, RBV were categorized into quintiles for the CH4MIR dataset and tertiles for the CH4GF dataset to evaluate trends across the RBV distribution. Mean CH4MIR decreased progressively across RBV quintiles for both traits, with all pairwise comparisons among quintiles significantly different. Similarly, CH4GF emissions declined across RBV tertiles, with significant differences observed between the lowest and highest tertiles. Additional analyses using RBV threshold categories confirmed that cows with the highest RBV consistently exhibited lower methane emissions. Linear regression analyses further demonstrated a negative relationship between RBV and methane emissions, supporting the predictive ability of the genomic evaluation. These findings confirm that Canada's genomic evaluation for methane efficiency effectively differentiates cows by methane emission potential, reinforcing its potential as a tool for genetic selection to reduce methane emissions in dairy cattle.

Journal Article↗

PubMed2026

Pesci: fast and user-friendly software to compare single-cell gene expression across species.

SUMMARY: Recent technological advances have propelled comparative functional genomics into the single-cell era, spurring a rapid development of methods to analyse these complex datasets. However, comparing single-cell gene expression across species to quantify expression similarity and ultimately identify homologous cell types remains an open problem. The ICC algorithm (Iterative Correlation of Coexpression) has been recently proposed as an attractive approach to tackle this challenge, but, to date, no software implementation is available. Here, we introduce Pesci (Pretty Easy Single-cell Comparisons using ICC), an efficient and user-friendly implementation of the ICC algorithm applied to pairwise comparisons of single-cell gene expression atlases across species. AVAILABILITY: Pesci is implemented in Python 3 (≥3.7). It is available for download on Linux, macOS and Windows via pip, conda and GitHub at https://github.com/eparey/pesci. The source code is permanently archived on Zenodo (https://doi.org/10.5281/zenodo.21477543).

Software↗

PubMed2026

Integrative Multi-Omics Analysis of Stem Growth Habit Divergence in Wild Soybean (Glycine soja).

Stem architecture is a major determinant of lodging resistance, biomass accumulation, and harvest efficiency in soybean. However, the molecular features associated with contrasting stem growth habits in wild soybean remain incompletely characterised. Here, we performed an integrated transcriptomic, metabolomic, and epigenomic analysis of stem growth-habit divergence in wild soybean, comparing the wild-type accession ZYD7068 with contrasting vining and erect mutant lines derived from carbon-ion beam mutagenesis. Pairwise transcriptomic comparisons identified between 20 311 and 28 705 differentially expressed genes per contrast, with a core set of 2672 genes consistently altered across the comparisons. Functional enrichment, gene set variation analysis, and gene set enrichment analysis converged on xylem and phloem pattern formation as a prominent molecular pathway associated with growth-habit divergence. Random forest analysis identified BBR-BPC and ARF transcription factor families as major molecular discriminators, while metabolomic profiling revealed distinct metabolic profiles involving amino-acid-derived and lipid-associated metabolites. Whole-genome bisulfite sequencing revealed context-specific DNA methylation differences, including substantial variation in CHG methylation among erect mutant lines. Integrated network and in silico perturbation analyses prioritised four candidate genes associated with vascular development for future functional validation. Together, these results provide a multi-layer molecular resource for investigating stem growth-habit divergence in G. soja and establish testable candidate pathways and genes for subsequent functional studies and soybean improvement.

glycine soja↗

PubMed2025

Parallel algorithms for phylogenetic inference under a structured coalescent approximation.

While advances in molecular epidemiology and computational modeling have enhanced our capacity to track pathogen evolution, the accurate reconstruction of spatiotemporal transmission dynamics remains essential for developing epidemic preparedness frameworks and implementing outbreak response measures. Structured coalescent models offer a phylogeographic framework by restricting lineage coalescence events to geographically proximate host populations. Although the Bayesian structured coalescent approximation (BASTA) provides a tractable approach, contemporary phylogeographic analyses involving dozens of geographic localities and hundreds to thousands of viral genomes substantially exceed the computational capacity of existing implementations. The BASTA likelihood scales cubically with deme count and quadratically with sequence count due to matrix exponentiation and pairwise coalescent probability calculations. Here, we introduce a comprehensive algorithmic restructuring of the structured coalescent likelihood that eliminates redundancies, optimizes memory access, and exposes parallelization opportunities. Our approach reorganizes computations along three dimensions: (i) independent calculation of deme-transition probability matrices across time intervals; (ii) simultaneous evaluation of partial likelihood vectors within temporal slices; and (iii) concurrent aggregation of coalescent probabilities. Algorithmic restructuring cuts average coalescent likelihood computation by 7-8 fold, and parallelization further boosts performance to 10-26 fold, enabling joint phylogeographic analyses of dengue virus across 10 South American countries and H5N1 avian influenza across 20 Eurasian regions to finish in a fraction of prior time. This computational efficiency also enables comparison between backward-in-time structured coalescent approximations and forward-in-time phylogeographic methods, revealing that the former provides appropriately conservative posterior estimates, particularly at intermediate phylogenetic depths. We integrate our implementation into the popular BEAST X and BEAGLE software packages, with an accompanying interface in BEAUti X to easily set up the analyses, providing researchers with an accessible and scalable tool for real-time phylogeographic surveillance of rapidly evolving pathogens.

Journal Article↗

PubMed2026

MultiDMPcaller: a one-stop software for detection and visualization of differentially methylated positions and regions.

MOTIVATION: Whole-genome bisulfite sequencing (WGBS/BS-Seq) is the gold standard for single-base resolution DNA methylome profiling. However, the diverse statistical models of existing computational methods lead to limited overlap between their results, highlighting the need for novel methods to detect differentially methylated positions (DMPs) and differentially methylated regions (DMRs). RESULTS: We developed MultiDMPcaller, an automated downstream methylome analysis software. It processes upstream outputs to profile DMPs, non-DMPs, DMRs, and context-specific (CpG/CHG/CHH) methylation status, alongside visualizing their chromosomal distribution and enrichment. The software features two key innovations: (i) an adaptive two-step P-value adjustment strategy based on organism-specific methylation patterns, with raw P-value ≤0.05 pre-filtering followed by false discovery rate (FDR) correction, to recover potential DMPs usually missed by standard FDR correction in plant CHG/CHH and animal CpG contexts; and (ii) a multiple pairwise comparison approach, which performs m × n pairwise comparisons for m control and n experimental replicates, followed by a voting system supporting both user-defined majority thresholds and model-based adaptive thresholds, to identify robust and reliable DMPs (with a stricter voting threshold exclusively for loci with low methylation differences) and DMRs. On real datasets from Arabidopsis, apple, and mouse, as well as simulated human datasets, MultiDMPcaller's results showed good agreement with those of other software, exhibiting high conservativeness and superior precision, which suggested a low false discovery proportion. AVAILABILITY AND IMPLEMENTATION: MultiDMPcaller is available at GitHub (https://github.com/jiantaoyuNWAFU/MultiDMPcaller) and via a web server (https://ciebioinfo.nwafu.edu.cn).

Software↗

PubMed2025

Exploration of the genetic diversity of Avena Fatua L. (wild oat) through genotyping-by-sequencing and SDS-PAGE.

BACKGROUND: The consumption of oats has rapidly increased due to their exceptional nutritional value. However, concerns over genetic erosion have emerged as oat breeding programs rely on a highly limited genetic pool. This study aimed to expand the genetic diversity pool of oats by collecting wild oat (Avena fatua L.) populations in South Korea and assessing their genetic diversity and seed storage protein patterns. RESULTS: A total of 237 A. fatua individuals were collected in 2022 from eight regions in the southwestern coastal areas of South Korea. Genetic diversity and seed storage protein patterns were analyzed using genotyping-by-sequencing (GBS) and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The GBS analysis identified 20,836 single-nucleotide polymorphisms (SNPs). An analysis of molecular variance (AMOVA) based on regional populations revealed that 40.9% of the genetic variation was attributed to differences among populations, while 59.1% was within populations, indicating high genetic differentiation within regional populations. Subsequent population structure analysis and discriminant analysis of principal components (DAPC) both stated the formation of two distinct genetic groups, with an AMOVA value of 70.9% between the groups, suggesting a high level of genetic variation. Pairwise FST analysis was conducted to compare the genetic differentiation between two populations, revealing that Jindo and Jangheung exhibited the highest level of genetic differentiation (FST = 0.795) among the geographic groups. Seed storage proteins were analyzed using SDS-PAGE, and the patterns were grouped using k-means clustering. A comparison between the groups based on protein patterns and those based on genetic variation revealed no significant correlation. CONCLUSION: This study provides data on the genetic diversity of A. fatua, a wild relative of cultivated oats, aimed at expanding the genetic pool of oats for future breeding programs. These findings are expected to be a foundational resource for oat breeding and genetic improvement efforts.

Genetic Variation↗

PubMed2026

The prognostic value and molecular mechanisms of Porphyromonas gingivalis infection-associated differentially expressed genes in oral squamous cell carcinoma.

BACKGROUND: Increasing evidence suggests that Porphyromonas gingivalis (Pg) is associated with oral squamous cell carcinoma (OSCC) development and progression. This study aimed to identify Pg-associated genes with prognostic relevance in OSCC through integrated bioinformatics analysis. METHODS: OSCC-related differentially expressed genes (DEGs) were identified from the The Cancer Genome Atlas (TCGA)-OSCC cohort and intersected with Pg supernatant-associated DEGs from GSE192887. Raw count data were analyzed with DESeq2, whereas transcripts per million (TPM)-transformed expression values were used for downstream visualization and model construction. Weighted gene co-expression network analysis (WGCNA), univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariable Cox modeling were used to develop a seven-gene prognostic signature, which was externally evaluated in GSE41613. Additional analyses examined treatment-associated expression changes in the seven model genes, pairwise correlations among the model genes, and correlations between Pg supernatant-associated differentially expressed gene (PgSDEG)-derived module eigengenes and immune-cell fractions. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was performed in eight paired OSCC and adjacent non-tumor tissues and in supplemented-brain heart infusion (BHI) vehicle-control and Pg culture-supernatant-treated HOK, HSC-3, and CAL-27 cells. RESULTS: A prognostic signature comprising CXCL8, GAST, HBQ1, PADI3, STC1, TEX19, and TMEM92 was established. The signature showed limited-to-moderate discrimination in the TCGA training cohort, with 1-, 3-, and 5-year areas under the curve (AUCs) of 0.68, 0.69, and 0.69, respectively, and limited discrimination in the GSE41613 external cohort (AUCs: 0.66, 0.67, and 0.61). Kaplan-Meier analysis showed poorer survival in the high-risk group in both cohorts. The GSE192887 analysis showed significant treatment-associated expression changes in all seven genes after Pg culture-supernatant exposure. In paired tissues, CXCL8 and TMEM92 were significantly higher in OSCC tissues, whereas STC1 was not significant after Holm correction. In CAL-27 cells, CXCL8, STC1, and TMEM92 increased significantly after culture-supernatant treatment, whereas the corresponding comparisons were not significant in HOK or HSC-3 cells after adjustment. CONCLUSIONS: This study developed a seven-gene Pg-associated prognostic signature for OSCC and provided complementary transcriptomic, immune-correlation, tissue, and cell-based evidence that placed the signature in biological context. The model showed limited-to-moderate discrimination and is not ready for clinical use. The enrichment, gene-correlation, and immune-correlation findings are hypothesis-generating rather than mechanistic evidence. Further independent validation and dedicated functional studies are required.

Oral squamous cell carcinoma (OSCC)↗