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5 recordsLinked to original sources
PubMed2025

Metaproteomic Analysis to Assess the Impact of Storage Media on Human Gut Microbiome in Fecal Samples.

The human gut microbiome is a diverse community of microorganisms residing in the gastrointestinal tract. The storage condition of fecal samples may impact the taxonomic and protein compositions of microbiomes in these samples. Here, we performed a mass spectrometry-based metaproteomic study to assess the impact of storage media on human gut microbiome in fecal samples. We evaluated FDA-authorized OMNIgene·GUT (OG), phosphate-buffered saline (PBS), and RNALater (RNAL) buffers and identified 38,185 microbial peptides corresponding to 7348 microbial proteins, which matched 16 phyla, 20 classes, 50 orders, 104 families, 332 genera, and 453 species. We found a high similarity among the fecal microbiomes preserved in OG, PBS, and RNAL in terms of the identification of proteins, taxa, and functional annotations. Both alpha and beta diversity suggested the high similarity among samples stored in the three media. Nonetheless, we also found some notable differences among buffers regarding the abundances of a few taxon groups. A partial human proteome (over 400 proteins) was identified in the fecal samples, with most of these proteins associated with the membrane and extracellular regions. The findings indicate the similarity among microbiomes in the fecal samples stored in OG, PBS, and RNAL regarding proteome profile, taxa, and functional capacity. SUMMARY: This study thoroughly analyzed and compared the metaproteomes of fecal samples preserved at -80°C in PBS, RNALater, and OMNIgene·GUT Dx buffers, offering novel insights into the effectiveness of these buffers in maintaining the stability and composition of the human gut microbiome. We found a high similarity in the identification and quantification of proteins, taxa, and functional annotations across the three buffers, with notable quantitative differences highlighting subtle yet important variations in preservation efficacy. The unique datasets and findings could offer valuable revelations into the impact of fecal sample preservation on translational and clinical analyses of the human gut microbiome.

Humans↗
PubMed2024

Effectiveness of stabilization methods for the immediate and short-term preservation of bovine fecal and upper respiratory tract genomic DNA.

Previous research on stabilization methods for microbiome investigations has largely focused on human fecal samples. There are a few studies using feces from other species, but no published studies investigating preservation of samples collected from cattle. Given that microbial taxa are differentially impacted during storage it is warranted to study impacts of preservation methods on microbial communities found in samples outside of human fecal samples. Here we tested methods of preserving bovine fecal respiratory specimens for up to 2 weeks at four temperatures (room temperature, 4°C, -20°C, and -80°C) by comparing microbial diversity and community composition to samples extracted immediately after collection. Importantly, fecal specimens preserved and analyzed were technical replicates, providing a look at the effects of preservation method in the absence of biological variation. We found that preservation with the OMNIgene®•GUT kit resulted in community structure most like that of fresh samples extracted immediately, even when stored at room temperature (~20°C). Samples that were flash-frozen without added preservation solution were the next most representative of original communities, while samples preserved with ethanol were the least representative. These results contradict previous reports that ethanol is effective in preserving fecal communities and suggest for studies investigating cattle either flash-freezing of samples without preservative or preservation with OMNIgene®•GUT will yield more representative microbial communities.

Cattle↗
PubMed2026

Simulation of CRISPR/Cas9-mediated gene editing for the Vitellogenin gene in Apis mellifera.

CRISPR/Cas9 genome editing provides a powerful framework for interrogating gene function in Apis mellifera. Yet, empirical application remains challenging due to biological constraints, including haplodiploid genetics, narrow embryonic injection window, and the social rearing requirements that complicate functional validation. These constraints necessitate in silico pre-screening to maximize editing success before resource-intensive wet-lab implementation. Within the omnigenic framework, which distinguishes core regulatory genes from peripheral loci buffered by network effects, vitellogenin (Vg) represents an optimal target which is ancestrally dedicated to yolk provisioning; it has been co-opted to orchestrate diverse non-reproductive functions including longevity, stress resistance, immunity, and social behavior. We developed a computational pipeline to design a list of 57 and 56 candidate guide RNAs (gRNA) for targeted Vg knockout, evaluating candidate sites in both functional exons 2 and 3 based on structural accessibility and frameshift efficiency. Comparative analysis revealed complementary strengths in two top-best candidates from initial target pool of predicted gRNAs. The gRNA targeting exon 2 exhibits weaker secondary structure (ΔG = -0.25 kcal/mol versus -2.10 kcal/mol for exon 3), aligning with empirical evidence that sites with ΔG > -1.0 kcal/mol achieve 2-5 × higher Cas9 binding efficiency. This site yielded moderate frameshift frequency (77.8%; 61.9 percentile). Conversely, the predicted editing outcome for the gRNA targeting exon 3, despite stronger structural constraints, demonstrated superior functional disruption metrics demonstrating very high frameshift frequency (88.3%; 95.2 percentile), high in silico editing precision, minimal microhomology-mediated repair bias, and reproducible outcomes wherein nearly all predicted indels disrupt the coding sequence. Protein structure and domain analyses further predict that frameshift edits will generate a truncated protein missing all downstream functional domains. We recommend parallel empirical validation of both exon 2 and exon 3 targets to resolve the trade-off between structural accessibility (favoring higher editing rates) and frameshift efficacy (favoring complete loss-of-function). This dual-target strategy accommodates uncertainty in in vivo performance while maximizing the probability of generating informative phenotypes. Our in silico framework enables rational CRISPR design in non-model organisms by computationally balancing biophysical accessibility with functional impact, accelerating functional genomics in species where empirical optimization faces substantial biological constraints.

Animals↗
PubMed2025

Genome-Wide Aggregated Trans Effects Analysis Identifies Genes Encoding Immune Checkpoints as Core Genes for Rheumatoid Arthritis.

OBJECTIVE: The sparse effector "omnigenic" hypothesis postulates that the polygenic effects of common single nucleotide polymorphisms (SNPs) on a typical complex trait are mediated by trans effects that coalesce on expression of a relatively sparse set of core genes. The objective of this study was to identify core genes for rheumatoid arthritis by testing for association of rheumatoid arthritis with genome-wide aggregated trans effects (GATE) scores for expression of each gene as transcript in whole blood or as circulating protein levels. METHODS: GATE scores were calculated for 5,400 cases and 453,705 non-cases of primary rheumatoid arthritis in UK Biobank participants of European ancestry. RESULTS: Testing for association with GATE scores identified 16 putative core genes for rheumatoid arthritis outside the HLA region, of which six-TP53BP1, PDCD1, TNFRSF14, LAIR1, LILRA4, and IDO1-were supported by Mendelian randomization analysis based on the marginal likelihood of the causal effect parameter. Five of these 16 genes were validated by a reported association of rheumatoid arthritis with SNPs within 200 kb of the transcription site, eight by association of the measured protein level with rheumatoid arthritis in UK Biobank, 10 by experimental perturbation in mouse models of inflammatory arthritis, and two-CTLA4 and PDCD1-by evidence that drugs targeting the gene cause or ameliorate inflammatory arthritis in humans. Fourteen of these 16 genes are in pathways affecting immunity or inflammation, and six-CD5, CTLA4, TIGIT, LAIR1, TNFRSF14, and PDCD1-encode receptors that have been characterized as immune checkpoints exploited by cancer cells to escape the immune response. CONCLUSION: These results highlight the key role of immune checkpoints in rheumatoid arthritis and identify possible therapeutic targets.

Humans↗
PubMed2024

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes.

BACKGROUND: The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. RESULTS: We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin. CONCLUSION: We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Humans↗