BMC Genomics最新文献

Background: Programmed cell death (or apoptosis) is a fundamental process in metazoans, extensively characterised in mammals and other vertebrates but much less so in invertebrates beyond the free-living nematode Caenorhabditis elegans and the vinegar fly - Drosophila melanogaster. Here, we present the first reconstruction of the complete intrinsic apoptosis pathway in the parasitic nematode Haemonchus contortus, a blood-feeding pathogen of ruminants and a major cause of global production losses.

Results: Using C. elegans proteins as references, we combined genome-wide homology searches, structural modelling, and developmental transcriptomic and proteomic analysis to identify and characterise apoptosis regulators in H. contortus. Homologues of all canonical C. elegans components were found, including CEP-1, EGL-1, CED-9, CED-4 and CED-3, together with modulators such as DRE-1 and PUF-8. Structural models revealed conservation of the CED-9:CED-4 and CED-4:CED-3 complexes, while EGL-1 and CEP-1 retained key structural domains despite significant sequence divergence. Transcriptomic data showed that the genes Hc-ced-9 and Hc-ced-3 are constitutively expressed across developmental stages, whereas Hc-cep-1 and Hc-egl-1 display stage-specific transcription. Proteomic data confirmed the presence of Hc-CED-9, Hc-CED-4 and Hc-CED-3 in at least one developmental stage, while Hc-EGL-1 and Hc-DRE-1 were not detected. Discordances between RNA and protein profiles, particularly for Hc-EGL-1, suggest tight post-transcriptional control. These findings demonstrate that, while the core architecture of apoptosis is conserved in H. contortus, regulatory divergence has occurred, reflecting lineage-specific adaptations.

Conclusion: This molecular framework highlights conserved structural features and developmental regulation of apoptosis in a parasitic nematode and provides a basis for functional studies to evaluate apoptotic regulators as potential targets for anthelmintic development.

Differential methylation is a key epigenetic process contributing to cancer development. Most DNA methylation prediction methods rely on DNA sequences from the background reference genome, neglecting individual genetic variation, which limits their ability to capture methylation differences. To address this, we propose CMC-WDTK, a deep learning framework that combines a weight-sharing dual-branch Transformer with a Kolmogorov‒Arnold network (KAN) to integrate sequences flanking CpG sites and adjacent single nucleotide variation (SNV) information to predict methylation changes between DNA sequences. CMC-WDTK captures global and local features of both reference and variant sequences and models high-dimensional relationships, offering accurate predictions of methylation changes. CMC-WDTK accurately predicted DNA methylation changes in eight real datasets (AUC greater than 0.8 for all datasets), with strong generalizability across datasets. Method comparison and ablation analyses further confirm that CMC-WDTK outperforms existing approaches and that its full architectural design is essential for achieving robust and accurate methylation-change prediction across datasets. Additionally, it identified a repeated cytosine and guanine sequence motif that promotes increased methylation. CMC-WDTK is the first computational tool used to predict methylation changes between sequences, offering significant advancements in understanding and comparing DNA methylation across diverse datasets and biological conditions.

Staphylococcus aureus, particularly methicillin-resistant S. aureus (MRSA), poses a persistent global health challenge. Genomic surveillance is essential but often hindered by the bioinformatics complexity of integrating multiple, disparate analysis tools. To address this, we developed StaphScope, a specialized computational pipeline for the comprehensive genotyping of S. aureus. Distributed as a single-install Conda package, StaphScope integrates six core analyses-Multi-Locus Sequence typing (MLST), staphylococcal protein A (spa) typing, staphylococcal cassette chromosome mec (SCCmec) characterization, antimicrobial resistance (AMR) profiling, virulence factor screening, and plasmid detection-within a unified workflow. It features intelligent resource management via the Python psutil library for efficient parallel execution. Validation using reference strains showed complete concordance with established types. Analysis of 24 S. aureus genomes identified prevalent lineages (e.g., ST5, ST9), diverse resistance mechanisms, and key virulence determinants, with the pipeline completing all analyses in estimated 10-14 min on a system with 16 CPU cores and 16 GB RAM. StaphScope generates consolidated, interactive HTML reports alongside structured data files (TSV, JSON). By streamlining access to integrated genomic analysis, it supports enhanced surveillance and outbreak response. The tool is available at: https://github.com/bbeckley-hub/staphscope-typing-tool.

Background: Heat stress (HS) is a growing environmental factor impacting the growth and medicinal value of plateau medicinal plants due to global climate change. Plant heat shock factors (HSFs) are key transcriptional regulators in HS responses, yet the mechanisms of HSFs in plateau medicinal plants remain largely unexplored.

Results: In this study, we identified 17 HSF genes from the plateau medicinal plant Fritillaria cirrhosa D.Don. All FcHSF members were divided into two different phylogenetic groups. Moreover, the distribution of conserved motifs among these genes reveals subfamily-specific divergence. PCR-based cloning was further used to amplify two transcript variants of FcHSFA1, designated as FcHSFA1a and FcHSFA1b, which display distinct tandem repeat configurations at their C-termini regions. Both variants were upregulated under HS, with FcHSFA1b showing higher expression. Subcellular localization showed both variants in the nucleus and cytoplasm of tobacco epidermal cells. FcHSFA1b exhibited stronger transcriptional activation activity than FcHSFA1a in yeast cells. Overexpression of both variants in tobacco enhanced HS-related gene expression, increased peroxidase activity and chlorophyll content, and thereby improved thermotolerance.

Conclusions: These findings suggest that FcHSFA1 variants contribute to heat tolerance, with distinct transcriptional responses, offering strategies to enhance basal thermotolerance in F. cirrhosa.

The rapid evolution of viral antigens poses a major challenge to infectious disease control, particularly for pathogens like influenza that undergo frequent antigenic changes. While deep mutational scanning and platforms such as Nextstrain have advanced our understanding of mutation effects and population-level viral dynamics, they often rely on strain-level analyses that may overlook key within-strain antigenic changes. In this study, we adopted a site-based approach to systematically identify and analyze hemagglutinin (HA) mutations in influenza viruses that differed from vaccine strains, using publicly available genomic data. We found that nonsynonymous mutations exhibiting vaccine-associated allele frequency changes were significantly enriched in epitope regions in both pH1N1 and H3N2, and that pH1N1 showed a higher proportion of rapid allele-replacement events occurring within a single influenza season, whereas H3N2 substitutions more often occurred across multiple seasons. Geographically, several mutations displayed allele frequency changes correlated with local vaccination coverage. Phylogenetic analyses further revealed that five nonsynonymous mutations in H3N2 arose independently across multiple clades. Serological assays confirmed reduced neutralization for multiple pH1N1 mutations, and computational protein stability analyses indicated that observed mutations tended to increase protein stability in both subtypes, and that in pH1N1, potential epitope mutations were more stabilizing than those in non-epitope regions. By integrating bioinformatics with experimental validation, our approach provides a refined understanding of how selective pressures shape antigenic evolution at the site level, which could aid future studies on vaccine effectiveness and epidemic trends.