Abstract Background Beyond viral infection alone, emerging evidence suggests that the respiratory microbiome plays a key role in influencing any disease severity and immune modulation (1,2). While genomic studies of SARS-CoV-2 are abundant, host-microbiome interactions at the transcriptomic level remain underexplored, particularly in diverse, underrepresented populations. This study investigated the metatranscriptomic profiles of nasopharyngeal samples from Bangladeshi cohorts, aiming to identify microbial signatures, antimicrobial resistance gene (ARG) activity, and host immune responses associated with variable COVID-19 outcomes. Methodology Forty nasopharyngeal samples representing four groups-severe, mild, asymptomatic, and negative, were sequenced using the Illumina NextSeq550 platform. Reads underwent preprocessing, host genome subtraction, and taxonomic and functional annotation. The reads that mapped with human genome were used for host differential expression, while unmapped reads were subjected to microbial taxonomic and functional analyses. This approach minimized cross-alignment errors and enabled integrated assessment of microbial diversity, antimicrobial resistance gene expression, and host pathway responses. Microbial diversity (α and β) was calculated using Shannon, Simpson, and Bray–Curtis metrics. ARG profiles were identified via functional annotation, while differential gene expression and pathway enrichment of host transcripts were analyzed with DESeq2 and KEGG/GO tools. Results Microbial diversity and taxonomic profiles Alpha diversity analysis showed significant prokaryotic diversity across severity groups, with asymptomatic samples clustering separately in β-diversity (PCoA) for both prokaryotes and eukaryotes. Severe cases showed widespread eukaryotic diversity, consistent with opportunistic fungal colonization. Relative abundance profiling revealed enrichment of pathogenic and multidrug-resistant bacteria (e.g., Acinetobacter, Pseudomonas) in COVID-19 positive patients, alongside increased fungal dominance beyond SARS-CoV-2 infection. Functional activity and AMR signatures of the microbiome Metatranscriptomic profiling indicated upregulation of translation machinery, ribosomal proteins, and cold shock proteins in asymptomatic patients, suggesting active microbial function with reduced immune triggering. In contrast, mild patients displayed upregulated viral gene expression (e.g., replicase polyprotein, spike protein), while severe cases showed elevated stress response proteins and β-lactamase activity. GO and KEGG enrichment highlighted functional divergence in metabolic and immune-modulatory pathways across groups. ARG analysis revealed asymptomatic patients harbored a distinct resistome enriched in aminoglycoside-, trimethoprim-, and rifamycin-resistance genes. TEM1-D β-lactamase was consistently abundant across all groups, suggesting widespread resistance potential. Host gene expression and immune signaling Differential expression analysis identified 23 upregulated immune genes in COVID-19 positive patients, including IFIT1, IFIT2, and CXCL10, associated with cytokine-mediated signaling and type I interferon response. Severe and mild cases showed strong pro-inflammatory signaling, while asymptomatic patients uniquely displayed downregulation of TLR4 and reduced cytokine expression (IL-4, IL-13), potentially explaining their lack of clinical symptoms. Pathway analysis confirmed modulation of cytokine-cytokine receptor and Toll-like receptor signaling pathways depending on disease severity. Conclusion This study reveals distinct microbial and host transcriptional signatures across COVID-19 severity groups. Asymptomatic patients exhibited unique microbial diversity, enriched antimicrobial resistance genes, and reduced innate immune activation, particularly through downregulated TLR4 signaling. In contrast, severe cases were marked by increased pathogenic load, stress responses, and heightened pro-inflammatory cytokine activity. Together, these findings underscore the interplay between the respiratory microbiome, ARGs, and host immune responses in shaping COVID-19 outcomes. These insights highlight the potential of integrated metatranscriptomic approaches for understanding infectious disease heterogeneity, and emphasize the need for validation in larger, ethnically diverse cohorts with detailed clinical metadata. References 1. Chen J, Liu X, Liu W, Yang C, Jia R, Ke Y, Guo J, Jia L, Wang C, Chen Y. ‘Comparison of the respiratory tract microbiome in hospitalized COVID-19 patients with different disease severity.’ Journal of Medical Virology. 2022 Nov;94(11):5284–93. 2. Garcia-Nuñez M, Millares L, Pomares X, Ferrari R, Pérez-Brocal V, Gallego M, Espasa M, Moya A, Monsó E. ‘Severity-related changes of bronchial microbiome in chronic obstructive pulmonary disease.’ Journal of clinical microbiology. 2014 Dec;52(12):4217–23.

Abstract Background Long non-coding RNAs (lncRNAs) are crucial regulators in cancer, yet their epigenetic control in Lung Adenocarcinoma (LUAD) remains underexplored. Aim This study aimed to integrate multi-omics data to identify novel prognostic biomarkers and elucidate their potential mechanism. Methods We analyzed RNA-Seq and Illumina 450k methylation data from 473 LUAD and 32 normal The Cancer Genome Atlas (TCGA) samples. We performed differential expression and methylation analysis to identify candidate lncRNAs whose expression was negatively correlated with promoter methylation. The prognostic value was evaluated using Kaplan-Meier curves and multivariate Cox regression. A lncRNA-miRNA-mRNA regulatory network was constructed to investigate potential mechanisms. For validation, FAM83A-AS2 expression was quantified in a preliminary set of clinical blood samples using RT-qPCR. Results Our multi-omics analysis identified two lncRNAs, FAM83A-AS2 and AC012213.1, whose high expression was driven by significant promoter hypomethylation and correlated with significantly lower patient survival. Differential analysis and correlation revealed promoter hypomethylation of specific CpG sites (cg19924352 for FAM83A-AS2; cg16648062 and cg20129213 for AC012213.1) drives their upregulation. Multivariate Cox regression confirmed their status as independent prognostic markers after adjusting for clinical covariates (HR=1.55, p<0.01 for FAM83A-AS2; HR=1.30, p<0.05 for AC012213.1), with strong diagnostic potential (AUC=0.72). A regulatory network analysis implicated these lncRNAs in modulating key LUAD-associated genes like RALGPS2, HOXA13 via miRNA MIR126 and MIR34C. Gene set enrichment analysis further linked these lncRNAs to fundamental molecular processes like chromatin modification and DNA methylation. Importantly, the pilot wet-lab validation on an initial set of clinical blood samples supported these findings, demonstrating a marked upregulation of FAM83A-AS2 in patients compared to healthy controls. Conclusion This study presents FAM83A-AS2 and AC012213.1 as promising biomarkers for risk stratification and potential therapeutic targets in LUAD.

Abstract Background Long non-coding RNAs (lncRNAs) are crucial regulators in cancer, yet their epigenetic control in Lung Adenocarcinoma (LUAD) remains underexplored. Aim This study aimed to integrate multi-omics data to identify novel prognostic biomarkers and elucidate their potential mechanism. Methods We analyzed RNA-Seq and Illumina 450 k methylation data from 473 LUAD and 32 normal The Cancer Genome Atlas (TCGA) samples. We performed differential expression and methylation analysis to identify candidate lncRNAs whose expression was negatively correlated with promoter methylation. The prognostic value was evaluated using Kaplan–Meier curves and multivariate Cox regression. A lncRNA-miRNA-mRNA regulatory network was constructed to investigate potential mechanisms. For validation, FAM83A-AS2 expression was quantified in a preliminary set of clinical blood samples using RT-qPCR. Results Our multi-omics analysis identified two lncRNAs, FAM83A-AS2 and AC012213.1, whose high expression was driven by significant promoter hypomethylation and correlated with significantly lower patient survival. Differential analysis and correlation revealed promoter hypomethylation of specific CpG sites (cg19924352 for FAM83A-AS2; cg16648062 and cg20129213 for AC012213.1) drives their upregulation. Multivariate Cox regression confirmed their status as independent prognostic markers after adjusting for clinical covariates (HR = 1.55, p < 0.01 for FAM83A-AS2; HR = 1.30, p < 0.05 for AC012213.1), with strong diagnostic potential (AUC = 0.72). A regulatory network analysis implicated these lncRNAs in modulating key LUAD-associated genes like RALGPS2, HOXA13 via miRNA MIR126 and MIR34C. Gene set enrichment analysis further linked these lncRNAs to fundamental molecular processes like chromatin modification and DNA methylation. Importantly, the pilot wet-lab validation on an initial set of clinical blood samples supported these findings, demonstrating a marked upregulation of FAM83A-AS2 in patients compared to healthy controls. Conclusion This study presents FAM83A-AS2 and AC012213.1 as promising biomarkers for risk stratification and potential therapeutic targets in LUAD.

The COVID-19 pandemic led to devastating physical, psychological, and financial impacts on millions of people across the world. Amidst a rapidly evolving research landscape, the global scientific community was forced to swiftly adapt to novel working methods, including remote collaboration tools, virtual conferences, and online research platforms. Surveys of life sciences researchers have indicated that computational biologists experienced less disruption and a smoother transition to remote working than experimental biologists, due to their reduced reliance on laboratory equipment. Despite this adaptability, the sudden shift to remote work, compounded by stress and social isolation, has posed significant mental health challenges for these workers. However, remote work has also facilitated opportunities for more flexible work arrangements and increased collaboration across geographical boundaries. To investigate these impacts, we conducted surveys of computational biologists during the Intelligent Systems for Molecular Biology (ISMB) conferences in 2020 and 2021, which were held virtually due to the COVID-19 lockdowns. This study implements a thorough statistical analysis of the survey results to offer insights into the repercussions of the lockdowns on researchers and their work. Key areas of investigation include the effects of institutional support (or lack thereof), the difference in productivity compared to pre-lockdown levels, and the significance of gender in determining these impacts. Notably, a lack of institutional support with regard to mental health and finances was shown to have a significant negative effect on early-career researchers. Although limited by a small sample size, our study sets the stage for a more robust exploration of these trends in future research. Importantly, by illuminating the challenges and opportunities arising from the COVID-19 pandemic and lockdowns, our study offers hope for potential solutions supporting the well-being of early-career researchers in unprecedented circumstances.

MOTIVATION: Wikipedia is one of the most important channels for the public communication of science and is frequently accessed as an educational resource in computational biology. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further improvement in quality, especially when compared to related scientific fields such as genetics and medicine. Facilitating involvement of members from ISCB Communities of Special Interest (COSIs) would improve a vital open education resource in computational biology, additionally allowing COSIs to provide a quality educational resource highly specific to their subfield. RESULTS: We generate a list of around 1500 English Wikipedia articles relating to computational biology and describe the development of a binary COSI-Article matrix, linking COSIs to relevant articles and thereby defining domain-specific open educational resources. Our analysis of the COSI-Article matrix data provides a quantitative assessment of computational biology representation on Wikipedia against other fields and at a COSI-specific level. Furthermore, we conducted similarity analysis and subsequent clustering of COSI-Article data to provide insight into potential relationships between COSIs. Finally, based on our analysis, we suggest courses of action to improve the quality of computational biology representation on Wikipedia.