Search Results (2,885)

Generative artificial intelligence (AI) is transforming biological and medical research and data analysis. Beyond analyzing existing information, these models can learn complex patterns and generate new data such as realistic protein sequences, genetic variants, or clinical notes. In molecular biology, language-like sequence models can read and generate DNA, RNA, and amino acid sequences to predict genetic variant effects, design new proteins, and explore molecular functions. In medicine, large language models (LLMs) trained on biomedical literature and electronic health records (EHRs) can summarize clinical findings, identify patterns, and provide decision support for clinicians and healthcare providers. Additionally, synthetic data generation can help protect patient privacy and augment existing disease datasets. While these advances make tasks that were previously impractical possible at scale, they also carry major risks, including producing convincing but incorrect results, reflecting hidden biases in the training data, and underperforming when real-world conditions change. Full article

Jute (Corchorus spp.) is the most important bast fiber crop, providing raw materials for textiles, bio-composites, and papermaking. This study analyzed the chloroplast genomes of two wild long-fruited jute species: Qiaojianyehuangma (QJYHM) and Maliyehuangma (MLYHM). The chloroplast genomes exhibited typical circular quadripartite structures (LSC, SSC, IRa/IRb), containing 129 genes (37 tRNA, 8 rRNA, 84 mRNA). Overall GC content was 36.76%, indicating high genetic conservation. Compared with cultivated varieties, wild varieties exhibit differences in LSC region length, IR boundary positions, and repetitive sequences, reflecting minor sequence variations in the chloroplast genome that occurred during domestication. Codon preference analysis showed both wild species favor A/U-ending synonymous codons, with a strong preference for methionine’s AUG codon. Repetitive sequence analysis revealed 280 and 252 dispersed repeats in Qiaojianyehuangma and Maliyehuangma, respectively, primarily mononucleotide SSRs. Based on Ka/Ks analysis, it was discovered that most chloroplast genes were under purifying selection. In contrast, positive selection signals were detected in rpl23, ycf1, and ycf2, implying their involvement in adaptive evolution. We identified 161 polymorphic sites (97 SNPs, 64 InDels), with ycf1 as a mutation hotspot. Phylogenetic analysis clustered both wild species with Corchorus capsularis with a 100% bootstrap value, forming a well-supported sister group. This study provides basic chloroplast genome data for two wild Corchorus olitorius accessions, revealing their conserved genomic features and minor sequence variations. Full article

In addition to its role in energy storage, adipose tissue contributes substantially to energy metabolism, endocrine regulation, and inflammatory processes. Sujiang pigs, a hybrid breed approved by the National Livestock and Poultry Genetic Resources Committee of China as a new national breed in 2013, possess a genetic predisposition for substantial fat deposition, making them an ideal model for investigating the mechanisms underlying adipose tissue accumulation. In this study, back fat (BF; subcutaneous adipose tissue), greater omentum (GOM; visceral adipose tissue), and mesenteric adipose tissue (MAD; visceral adipose tissue) were collected from three 6-month-old male Sujiang pigs for RNA-seq analysis. Comparative analyses identified 3005 differentially expressed genes (DEGs) between BF and GOM, 975 DEGs between BF and MAD, and 892 DEGs between GOM and MAD. To validate the reliability of the sequencing data, five DEGs were randomly selected for RT-qPCR verification. The DEGs were further subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. By integrating protein–protein interaction (PPI) networks with bioinformatics analyses, we identified candidate genes potentially associated with lipid metabolism (e.g., WNT9A, WNT5A, and PDGFRA) and inflammatory responses in adipose tissue (e.g., CSF1R, C1QB, and CD4). These findings indicate potential molecular differences between porcine visceral and subcutaneous adipose tissues and may serve as a reference for further studies on the molecular regulation of adipose tissue metabolism. Full article

Background/Objectives: Malnutrition and diabetes mellitus (DM) have been linked to gut microbial perturbations, yet data are scarce for the aging population, especially in a nursing-home setting. As this group is generally at risk for malnutrition, we aimed to investigate the link between DM and gut microbial patterns in interaction with nutritional risk status in nursing-home residents. Methods: Stool samples were collected from 173 nursing-home residents (77.5% female, mean age 86 years) and were analyzed via 16S rRNA sequencing. Furthermore, the Geriatric Nutritional Risk Index (GNRI) was assessed and data on comorbidity status, anthropometric measurements, and medication were acquired. Results: Fifty-one residents had DM (mean HbA1c 6%). There were no DM-related differences in alpha diversity (observed richness: p = 0.733; Shannon index: p = 0.747). PERMANOVA revealed slight differences in beta diversity according to GNRI (R² = 0.009, p = 0.032), but no significant differences when adding DM status. Differential abundance analysis showed Clostridium_Clostridiaceae, Haemophilus, Actinomycetaceae and Micrococcaceae as significantly decreased with DM, independent of age, sex, and BMI. No interaction between DM and the GNRI in microbial diversity or composition was found. Conclusions: We report malnutrition-related differences in beta diversity and diabetes-related microbial taxa differences in nursing-home residents. DM status did not influence the relationship between the GNRI and gut microbiota in this population. Full article

Phormia regina (Meigen, 1826) (Diptera: Calliphoridae), a necrophagous blow fly of significant forensic value, plays a crucial role in postmortem interval (PMI) estimation. The intra-puparial period of this species constitutes approximately half of its entire immature developmental duration. Therefore, precise aging of intra-puparial specimens is essential to improve the accuracy of PMI estimation. This study was performed at a constant 25 °C, examining intra-puparial morphology, histological sections, and body weight across different developmental stages. Additionally, full-length transcriptome profiling was conducted via a hybrid sequencing strategy combining PacBio single-molecule real-time (SMRT) sequencing and Illumina short-read next-generation sequencing (NGS). Morphological studies revealed the intra-puparial morphological characteristics, the reconstruction process of tissues and organs, and the continuous body weight loss during the intra-puparial period. Transcriptomic research identified a total of 425,349 full-length non-chimeric (FLNC) sequences. After redundancy removal, 84,852 transcript sequences were obtained, of which 46,325 transcripts were annotated. Using day-0 puparia (D0) as the control, differentially expressed gene (DEG) analysis was performed on samples from subsequent developmental stages (D1–D5), identifying 4242, 7964, 9509, 10,526, and 10,011 DEGs, respectively. Results from GO enrichment and KEGG pathway analyses provided reasonable explanations for the behavioral traits of different developmental stages. Eight candidate genes for intra-puparial development were screened, most of which were highly expressed at different time points during the intra-puparial period. Quantitative real-time PCR (qRT-PCR) results showed consistent gene expression trends with the RNA-seq data, confirming the reliability of the RNA-seq results. This study provides key morphological and molecular markers for P. regina in forensic PMI estimation. Full article

Background: The upper gastrointestinal (GI) tract is a complex environment characterized by sharp physicochemical gradients. While the oral microbiome is a major source of microbial seeding for downstream organs, it remains unclear how these communities correlate and diverge across different anatomical sites. This study provides a high-resolution re-analysis of a comprehensive multi-site dataset to delineate the microbial architecture and ecological signatures along the oral–upper GI axis. Method: Human oral, esophageal, gastric mucosal, and gastric juice microbiome sequencing data were retrieved from the publicly available National Center for Biotechnology Information (NCBI) BioProject PRJNA1049979 database. Using these publicly available 16S rRNA sequencing data, we performed an integrated ecological analysis. Microbial diversity, taxonomic composition, and niche-specific community structures were evaluated using Quantitative Insights Into Microbial Ecology 2 (QIIME2) and R-based tools, including linear discriminant analysis effect size (LEfSe) and phylogenetic mapping. Results: The esophageal microbiome showed significantly greater richness and evenness than the oral cavity and stomach. Beta diversity analysis demonstrated clear compositional separation between oral and downstream upper GI communities, whereas gastric samples, particularly gastric juice, showed greater heterogeneity. Although major phyla were shared across sites, their relative abundances differed markedly. Oral samples were enriched with periodontal-associated taxa, including Porphyromonas, Prevotella, Alloprevotella, and Fusobacterium. In contrast, gastric mucosal samples were enriched with Akkermansia muciniphila and Helicobacter pylori, whereas gastric juice was characterized by Sarcina ventriculi, Fusobacterium periodonticum, and Clostridium perfringens. These findings indicate both taxonomic continuity and pronounced site-specific ecological divergence along the oral–upper GI axis. Conclusion: The oral cavity, esophagus, stomach, and gastric juice share a common microbial framework but exhibit distinct community restructuring driven by local environmental selection. This study provides a detailed ecological view of the oral–upper GI microbiome and highlights the importance of site-specific microbial organization in upper GI health and disease. Full article

Host genetic susceptibility to urogenital Chlamydia trachomatis (Ct) reinfection remains poorly understood. Coding variants identified in prior genome-wide association studies (GWAS) explained only a small fraction of the risk of reinfection. Our goal in this study was to characterize whether more risk would be captured by sequence variation that traditional GWAS insufficiently captures. Specifically, we evaluated the risk attributable to SNPs present in regulatory, non-coding regions; post-transcriptional regulation by microRNAs (miRNAs) that may depend on sequence variation in either the miRNA or the target mRNA; and copy number variants (CNVs). We analyzed GWAS data from African American women with or without documented urogenital Ct reinfection. Fine mapping and independent association analyses identified 30 unique index single-nucleotide polymorphisms (iSNPs), which were expanded to variants in linkage disequilibrium. Regulatory annotation was performed using HaploReg, RegulomeDB, FORGEdb, rSNPBase, and GTEx. We examined whether genes identified in the Ct reinfection GWAS are targeted by known Ct infection–associated microRNAs using curated databases. Genome-wide CNV calling was conducted using SNP intensity data, followed by stringent quality control and gene-level association testing. Functional annotation prioritized 7 SNPs with strong regulatory evidence, with stringent criteria for regulatory relevance, using HaploReg, RegulomeDB, FORGEdb, and rSNPBase. The strongest signals were observed at the CHIT1 locus, where multiple intronic variants (including rs2486963 and rs2244385) overlapped regulatory chromatin, altered transcription factor binding motifs, and acted as cis-expression quantitative trait loci for CHIT1 in whole blood. Additional regulatory variants were identified near TDRP, ERICH1, and DLGAP1, showing tissue-specific regulatory effects. MicroRNA analysis revealed extensive post-transcriptional targeting of SOCS6 and SULF1, while CHIT1 showed no curated Ct-associated miRNA interactions. CNV analysis identified 5775 high-confidence events, with nominal gene-level associations observed for ATAD3A, CARD14, TMEM240, and ZNF140. These results indicate that a greater fraction of the susceptibility to urogenital Ct reinfection may be driven by genetic variation affecting immune and epithelial pathways rather than protein-coding changes. Full article

Giardia duodenalis (synonyms G. lamblia and G. intestinalis) and Blastocystis sp. are prevalent zoonotic intestinal protozoans with significant public health and economic importance. Both parasites infect various hosts, including humans and most livestock. G. duodenalis can cause infections ranging from asymptomatic carriage to clinical manifestations such as acute diarrheal disease, growth stunting and malabsorption, whereas Blastocystsi sp. infection is often asymptomatic and its pathogenicity remains debated. Following the rapid expansion of modern equine industries (horse racing, equestrian sports, production, and tourism), concern about the spread of parasitic diseases caused by human–horse contact has gradually increased. In this study, 631 horse fecal specimens were collected from three geographical localities in Shanxi Province, North China. The prevalence and genetic characteristics of the two parasites were determined by amplification of multiple genetic loci (tpi, triosephosphate isomerase; gdh, glutamate dehydrogenase; bg, beta-giardin) for G. duodenalis and the small subunit ribosomal RNA (SSU rRNA) gene for Blastocystis sp. Overall, the prevalence of G. duodenalis and Blastocystis sp. in horses in Shanxi Province was 7.9% and 0.8%, respectively. Sequence analysis identified three assemblages (A, B, and E) of G. duodenalis and two subtypes (ST1 and ST5) of Blastocystis sp. in horses; assemblage B and subtype ST1 were detected more frequently than the other types (subtypes) in this data set. This study is the first to report G. duodenalis and Blastocystis sp. infections in horses in Shanxi Province, and these findings provide baseline molecular epidemiological data for horses in Shanxi Province and support continued hygiene management to reduce potential zoonotic transmission. Full article

Background: Osteoarthritis (OA) is a heterogeneous joint disease characterized by cartilage degeneration. The interplay between extracellular matrix (ECM) remodeling, endoplasmic reticulum (ER) stress, and inflammatory signaling in OA pathogenesis remains incompletely understood. This study aimed to identify robust diagnostic biomarkers and explore the mechanistic convergence of key genes in OA cartilage through an integrated transcriptomic framework. Methods: Three independent cartilage transcriptomic datasets (GSE285234, GSE287861, GSE289464) were integrated after ComBat batch correction. Differentially expressed genes (DEGs) were identified using limma, followed by ORA and GSEA for functional enrichment. LASSO logistic regression identified hub genes for a diagnostic model and nomogram, validated by leave-one-out cross-validation (LOOCV). Consensus clustering stratified OA samples into molecular subtypes. Single-cell RNA-sequencing (scRNA-seq) data (GSE169454, GSE220243) were used to validate cell-type-specific expression. Virtual gene knockout (scTenifoldKnk) and pathway analysis inferred downstream functional consequences. Results: Fifty-eight DEGs (predominantly downregulated) were enriched in ECM and ER protein processing pathways. Six hub genes (EIF2S1, GANAB, STT3A, XBP1, MGP, PMP22) showed robust selection stability. The diagnostic model achieved a LOOCV AUC of 0.769, a well-calibrated nomogram, and superior net benefit. Unsupervised clustering revealed two OA subtypes with divergent unfolded protein response (UPR) and TGF-β pathway activities. scRNA-seq confirmed hub gene expression in chondrocytes and other joint microenvironment cells. Notably, virtual knockout of five hub genes convergently perturbed IL-17, NF-κB, and chemokine signaling pathways. Conclusions: This study identified and validated a six-gene signature reflecting ECM-ER-inflammatory crosstalk in OA cartilage. The convergent perturbation of inflammatory pathways by functionally distinct hub genes reveals a mechanistic core that may serve as a diagnostic panel and a platform for targeted therapeutic investigation in OA. Full article

Background/Objectives: Whether anesthetic maintenance influences tumor biology in cervical cancer remains unsettled. We examined whether plasma extracellular vesicles (EVs) collected during sevoflurane or propofol anesthesia differentially affect HeLa cell behavior and explored lipidomic alterations associated with the biologically active EV condition. Methods: In a single-center prospective observational cohort, paired plasma samples were collected before anesthesia induction and before wound closure from 53 patients with stage II cervical cancer undergoing radical surgery under sevoflurane (n = 28) or propofol (n = 25) anesthesia. EV preparations were characterized by transmission electron microscopy, nanoparticle tracking analysis, and immunoblotting for EV markers. Their effects on HeLa cell proliferation, invasion, and wound closure, as well as HUVEC tube formation, were examined in vitro. EV miRNA profiles were analyzed by small-RNA sequencing. Lipidomic profiling by LC-MS and immunoblot analysis of EGFR/PKCα/NF-κB signaling were performed in recipient HeLa cells exposed to sevoflurane-associated EVs. Results: EVs collected after sevoflurane anesthesia increased HeLa cell proliferation, invasion, and wound closure and enhanced endothelial branching in HUVEC tube-formation assays, whereas post-propofol EVs showed no comparable phenotype. Small-RNA sequencing identified distinct anesthesia-associated EV miRNA changes, with the sevoflurane-related signature enriched in glycerolipid metabolism, glycerophospholipid metabolism, glycosylphosphatidylinositol-anchor biosynthesis, phosphatidylinositol signaling, and inositol phosphate metabolism. In HeLa cells treated with post-sevoflurane EVs, lipidomic analysis showed clear separation from pre-sevoflurane EV-treated cells and identified increased diacylglycerol (DG) species, including DG (16:1/18:2), DG (16:0/16:1), DG (18:2/18:2), DG (18:2/20:4), and DG (16:0/18:2). These changes were accompanied by higher p-EGFR, PKCα, and p-NF-κB p65 levels. Several DG species correlated positively with proliferation and invasion readouts and inversely with residual wound area. Conclusions: Plasma EVs collected after sevoflurane anesthesia were associated with a more aggressive phenotype in recipient cervical cancer cells and with lipid remodeling characterized by DG accumulation and altered EGFR/PKCα/NF-κB signaling. The data support an exploratory mechanistic model linking sevoflurane-associated EV cargo to metabolic reprogramming in cervical cancer cells. Full article

Background: Previous work from our group demonstrated an association between immunohistochemical detection of Human cytomegalovirus (HCMV) late antigen and poor event-free survival (EFS) in pediatric medulloblastoma. Whole-genome sequencing (WGS) further identified increased abundance of HCMV-aligned reads at the UL88 locus, particularly in Group 3 tumors, a molecular subgroup associated with aggressive clinical behavior and poor prognosis. Methods: We performed an integrated multi-omics analysis of pediatric medulloblastoma using WGS (n = 39) and RNA sequencing (RNA-seq; n = 28) datasets. RNA-seq data were filtered using stringent alignment criteria (MAPQ ≥ 20) and compared with fetal brain (n = 12), adult brain (n = 12), and HCMV-infected cell culture controls (n = 3). Only high-confidence uniquely aligned reads were retained to reduce nonspecific and multi-mapped viral alignments. Sequencing reads were aligned to the HCMV Merlin reference genome (NC_006273.2) using a standardized analytical pipeline. A subset of 28 cases with matched tumor WGS, tumor RNA-seq, and germline WGS data was used for integrated multi-omics analyses. Orthogonal validation analyses were performed in Group 3 tumors using independent genomic and transcriptomic approaches. Exploratory survival analyses were conducted in a combined cohort (n = 84) integrating genomic and immunohistochemical datasets. Results: Recurrent low-level HCMV-aligned molecular signals were identified across medulloblastoma datasets. Reads aligning to UL76, UL88, and UL99 were the most consistently detected HCMV-associated late-gene signals across RNA-seq and WGS datasets. A composite HCMV late-gene signature (UL76–UL88–UL99) showed higher levels in Group 3 tumors than in other molecular subgroups (p < 0.05 in WGS analyses). Orthogonal analyses demonstrated concordant low-level HCMV-associated genomic and transcriptomic signals enriched in tumors with MYC-associated activation and chromosome 17 imbalance. In the combined cohort (n = 84), elevated HCMV-associated signal assessed by immunohistochemistry and genomic profiling was associated with reduced EFS (median 55 vs. 147 months; log-rank p < 0.001). The subgroup classified as HCMV-high Group 3 demonstrated the strongest association with adverse outcome in exploratory multivariable analyses (HR = 6.43, p = 0.002). Conclusions: This study identifies recurrent low-level HCMV-associated genomic and transcriptomic signals across pediatric medulloblastoma datasets, with preferential enrichment in biologically aggressive Group 3 tumors. Although the extremely low abundance of viral-aligned reads precludes definitive evidence of productive viral infection, the reproducible detection of HCMV-associated molecular signatures across independent sequencing platforms supports further investigation into a potential oncomodulatory association in pediatric medulloblastoma. Additional validation using optimized viral detection methodologies, independent cohorts, and mechanistic studies will be necessary to clarify the biological and clinical significance of these findings. Full article

Quantum dots such as CdS QDs have been extensively studied using human cells, plants, and unicellular eukaryotes such as Saccharomyces cerevisiae, whereas ZnS QDs—considered low-toxicity alternatives to cadmium-based nanomaterials—remain comparatively underexplored. Following preliminary analyses of ZnS QDs’ effects on wild-type S. cerevisiae BY4742 growth, the Yeast Knock-Out collection, comprising ~4600 haploid mutants deleted in non-essential genes, was screened in the presence of ZnS QDs. Sensitive mutants were predominantly associated with mitochondrial functions, prompting further characterization of sod1Δ, glr1Δ, and of the hypersensitive mutant pos5Δ. This last mutant, which lacks a mitochondrial NADH kinase, showed hypersensitivity specific to ZnS QDs but not to CdS QDs or zinc sulfate (ZnSO₄). Flow cytometry analysis of the wild-type strain and the pos5Δ mutant detected no significant increase in reactive oxygen species after ZnS QD treatment. RNA-sequencing analyses of the wild-type strain and the pos5Δ mutant exposed to ZnS QDs (or ZnSO₄) revealed that ZnS QD exposure selectively modulated genes encoding mitochondrial proteins, metal-binding factors, and intracellular trafficking components. Comparison with published data on CdS QDs identified specific mechanisms involving protein synthesis and degradation. Saccharomyces cerevisiae once again proved its versatility for studying engineered nanomaterial interactions with biological systems. Full article

Plant U-box E3 ubiquitin ligases (PUBs) are involved in various biological processes in response to abiotic stress. Recent studies have shown that E3 ubiquitin ligases can regulate the production of important compounds in medicinal plants by targeted degradation of transcription factors. Abscisic acid (ABA), a plant stress response hormone, can cause changes in the content of the medically important monoterpene indole alkaloids (MIAs) in U. rhynchophylla. In this study, we explored the relationship between UrPUB gene expression and MIA content. ABA was applied to tissue-cultured seedlings of U. rhynchophylla, resulting in consistent changes in the content of four MIAs. Seventy-three UrPUB genes were identified and bioinformatically characterized from the genome sequence of U. rhynchophylla. The expression levels of transcription factors involved in regulating the biosynthesis pathway of MIAs and UrPUB genes with higher RNA transcript levels in the roots were determined. Co-expression association analysis revealed that UrPUB17, UrPUB40, UrPUB41, UrPUB44 and UrPUB55 negatively correlate with UrGATA8 and UrWRKY37 under ABA stimulation. Based solely on these correlation data, we hypothesize that these UrPUBs might regulate MIA biosynthesis via ubiquitination of UrGATA8 and UrWRKY37, but direct evidence (protein interaction, ubiquitination, degradation, or genetic manipulation) is lacking. This study identified the UrPUB gene family in the U. rhynchophylla genome and proposes this ubiquitination model as a testable hypothesis, not a demonstrated mechanism. These findings provide new insights into the biological function of the PUB family in response to ABA. Full article

Imaging-based spatial transcriptomics has advanced from low-plex single-molecule fluorescence in situ hybridization to a diverse set of highly multiplexed platforms, with recent multimodal and pathology-compatible capabilities. Despite major differences in chemistry, coding, and imaging strategies across different platforms, their biological interpretation often converges on a few notable computational biology problems. This review examines imaging-based spatial transcriptomics through the lens of data interpretation and applications, focusing on the analytical framework that converts raw fluorescence signals or accompanying in situ sequencing data into molecule-, cell-, and tissue-level representations. We discuss the key challenges in preprocessing, registration, restoration, feature detection, barcode decoding, molecule calling, cell segmentation, transcript assignment, probabilistic cell typing, spatial-domain inference, and atlas integration. We also highlight how optical crowding, tissue thickness, panel bias, and multimodal complexity increase computational difficulty. Finally, we summarize applications of imaging-based spatial transcriptomics techniques, ranging from subcellular RNA localization to atlas-scale and pathology-aware spatial analysis. Full article

Understanding biological communities is essential for elucidating ecosystem structure and function. Metabarcoding based on ribosomal RNA (rRNA) genes, particularly 16S and 23S, is widely used to characterise bacterial and microalgal communities. However, analysing high-throughput sequencing data generated by platforms such as the Illumina MiSeq remains challenging due to fragmented bioinformatics tools, complex parameterisation, and limited accessibility for non-specialist users. In this study, a comprehensive and user-friendly bioinformatics pipeline is proposed for the analysis of 16S and 23S paired-end metabarcoding data. The workflow integrates all critical processing steps, including read merging, primer and adapter trimming, quality filtering, dereplication, chimaera removal, and clustering into Operational Taxonomic Units (OTUs). Taxonomic assignment is performed using curated reference databases, namely EZBioCloud for bacterial communities and µgreen for microalgae. The pipeline was developed in Python 3.11 and incorporates validated tools such as VSEARCH and Cutadapt, ensuring robustness and computational efficiency. Additionally, modules for alpha and beta diversity analysis are included to support comprehensive ecological interpretation. The main novelty of this work lies in providing a unified, GUI-based framework that enables the standardised processing of dual-marker (16S/23S) metabarcoding data within a single environment. In its current implementation, SOMBA supports the analysis of each marker through separate but harmonised workflows, ensuring consistency in parameterisation, processing steps, and output structure. This approach provides an accessible and standardised solution that bridges the gap between raw sequencing data and reliable biological insights, supporting applications in environmental microbiology and biotechnology. Full article