Metagenomics study
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB12719 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Periapical abscesses, radicular cysts, and periapical granulomas are among the most frequently identified pathological lesions in the alveolar bone. Although many studies have investigated bacterial metagenomics in periapical abscesses, little is known about the genome mining of abundant bacteria of periapical lesions and its correlation to human transcriptome. This study aims to explore the enriched metabolic environment of periapical lesions associated with microbial diversity and their role in lesion progression. Bacterial DNA and human RNA were isolated from periapical lesions and healthy pulp tissue and sequenced using next-generation sequencing. Bacterial sequences were then analyzed to identify secondary metabolites, pathogenic proteins, and their associated metabolic pathways. Integrated bacterial and human metabolic pathways indicated similar pathways in each lesion. Among these pathways, inflammatory response, humoral immune response, and hemopoiesis were enriched in abscesses. NABA matrisome associated, neutrophil degranulation, and P73 pathway were enriched in cysts. Meanwhile, response to bacterium, regulation of immune effector process, and positive regulation of response to external stimulus were enriched in granulomas. In conclusion, this study is the first to elucidate the interplay between microbial and human metabolic activity. These findings have significant clinical implications for the early diagnosis, prevention, and treatment of periapical lesions.
2025-06-25 | GSE299006 | GEO
Project description:Monitoring microbial communities can aid in understanding the state of these habitats. Environmental DNA (eDNA) techniques provide efficient and comprehensive monitoring by capturing broader diversity. Besides structural profiling, eDNA methods allow the study of functional profiles, encompassing the genes within the microbial community. In this study, three methodologies were compared for functional profiling of microbial communities in estuarine and coastal sites in the Bay of Biscay. The methodologies included inference from 16S metabarcoding data using Tax4Fun, GeoChip microarrays, and shotgun metagenomics.
2024-04-14 | E-MTAB-13075 | biostudies-arrayexpress
Project description:Metagenomics study
| PRJEB31740 | ENA
Project description:Understanding and quantifying the effects of environmental factors influencing the variation of abundance and diversity of microbial communities was a key theme of ecology. For microbial communities, there were two factors proposed in explaining the variation in current theory, which were contemporary environmental heterogeneity and historical events. Here, we report a study to profile soil microbial structure, which infers functional roles of microbial communities, along the latitudinal gradient from the north to the south in China mainland, aiming to explore potential microbial responses to external condition, especially for global climate changes via a strategy of space-for-time substitution. Using a microarray-based metagenomics tool named GeoChip 5.0, we showed that microbial communities were distinct for most but not all of the sites. Using substantial statistical analyses, exploring the dominant factor in influencing the soil microbial communities along the latitudinal gradient. Substantial variations were apparent in nutrient cycling genes, but they were in line with the functional roles of these genes. 300 samples were collected from 30 sites along the latitudinal gradient, with 10 replicates in every site
2015-05-23 | E-GEOD-69171 | biostudies-arrayexpress
Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
2024-01-08 | GSE252118 | GEO
Project description:Freshwater metagenomics study of Pune City, India
| PRJEB83103 | ENA