Ontology highlight
ABSTRACT:
PROVIDER: PRJNA750848 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR15305900_1.fastq.gz | Fastqsanger.gz | |||
| SRR15305900_2.fastq.gz | Fastqsanger.gz | |||
| SRR15306000_1.fastq.gz | Fastqsanger.gz | |||
| SRR15306000_2.fastq.gz | Fastqsanger.gz | |||
| SRR15305901_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 268 |
Similar Datasets
Project description:Global endemic infections, such as leptospirosis, rickettsial diseases, and dengue infections present diagnostic challenges, posing a dilemma for antibiotic stewardship worldwide. The goal of this project was to identify accurate transcriptional classifiers able to discriminate between bacterial and viral illness of global pathogens.
2023-12-19 | GSE211567 | GEO
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:Using Nanopore sequencing, our study has revealed a close correlation between genomic methylation levels and antibiotic resistance rates in Acinetobacter Baumannii. Specifically, the combined genome-wide DNA methylome and transcriptome analysis revealed the first epigenetic-based antibiotic-resistance mechanism in A. baumannii. Our findings suggest that the precise location of methylation sites along the chromosome could provide new diagnostic markers and drug targets to improve the management of multidrug-resistant A. baumannii infections.
2023-09-01 | E-MTAB-13272 | biostudies-arrayexpress
Project description:<p>The study of antimicrobial resistance (AMR) in infectious diarrhea has generally been limited to cultivation, antimicrobial susceptibility testing and targeted PCR assays. When individual strains of significance are identified, whole genome shotgun (WGS) sequencing of important clones and clades is performed. Genes that encode resistance to antibiotics have been detected in environmental, insect, human and animal metagenomes and are known as "resistomes". While metagenomic datasets have been mined to characterize the healthy human gut resistome in the Human Microbiome Project and MetaHIT and in a Yanomani Amerindian cohort, directed metagenomic sequencing has not been used to examine the epidemiology of AMR. Especially in developing countries where sanitation is poor, diarrhea and enteric pathogens likely serve to disseminate antibiotic resistance elements of clinical significance. Unregulated use of antibiotics further exacerbates the problem by selection for acquisition of resistance. This is exemplified by recent reports of multiple antibiotic resistance in Shigella strains in India, in Escherichia coli in India and Pakistan, and in nontyphoidal Salmonella (NTS) in South-East Asia. We propose to use deep metagenomic sequencing and genome level assembly to study the epidemiology of AMR in stools of children suffering from diarrhea. Here the epidemiology component will be surveillance and analysis of the microbial composition (to the bacterial species/strain level where possible) and its constituent antimicrobial resistance genetic elements (such as plasmids, integrons, transposons and other mobile genetic elements, or MGEs) in samples from a cohort where diarrhea is prevalent and antibiotic exposure is endemic. The goal will be to assess whether consortia of specific mobile antimicrobial resistance elements associate with species/strains and whether their presence is enhanced or amplified in diarrheal microbiomes and in the presence of antibiotic exposure. This work could potentially identify clonal complexes of organisms and MGEs with enhanced resistance and the potential to transfer this resistance to other enteric pathogens.</p> <p>We have performed WGS, metagenomic assembly and gene/protein mapping to examine and characterize the types of AMR genes and transfer elements (transposons, integrons, bacteriophage, plasmids) and their distribution in bacterial species and strains assembled from DNA isolated from diarrheal and non-diarrheal stools. The samples were acquired from a cohort of pediatric patients and controls from Colombia, South America where antibiotic use is prevalent. As a control, the distribution and abundance of AMR genes can be compared to published studies where resistome gene lists from healthy cohort sequences were compiled. Our approach is more epidemiologic in nature, as we plan to identify and catalogue antimicrobial elements on MGEs capable of spread through a local population and further we will, where possible, link mobile antimicrobial resistance elements with specific strains within the population.</p>
| phs001260 | dbGaP
Project description:Regulatory RNAs (sRNAs) are now considered as major players in many physiological and adaptive responses in pathogenic bacteria. sRNAs have been extensively studied in Gram-negative bacteria, but less information is available in Gram-positive pathogens. There is a spread of multidrug-resistant (MDR) opportunistic organisms, grouped as “ESKAPE” pathogens, which comprise enterococci, a leading cause of hospital-acquired infections and outbreaks with emergence of MDR isolates, especially vancomycin-resistant Enterococcus faecium (VREF). Note that no information about sRNA expression is known in this major opportunistic pathogen. By transcriptomic and genomic analyses using E. faecium Aus0004 reference strain, 249 transcribed IGRs, including sRNA candidates, were detected and, using a series of cut-offs, this set was lowered down to 54 sRNAs while 7 that were predicted based on comparative sequence analysis. RNA-seq was performed with and without subinhibitory concentrations (SIC) of daptomycin, a cyclic lipopeptide antibiotic used for VREF infections. Under daptomycin SIC exposure, 260 genes (9.1% of the genome) had a significant alteration of expression including 80 upregulated genes and 180 downregulated genes. Among the repressed genes, a large proportion (55%) coded for proteins involved in carbohydrate and transport metabolism. Also, we focused on the 9 sRNAs exhibiting the highest expression, and all of them were confirmed as expressed along bacterial growth by Northern blots and qPCR. Out of these 9 sRNAs, four had significantly lower or higher expression in the presence of daptomycin SIC, and therefore responded to antibiotic exposure. Finally, we also tested the expression of these 9 sRNAs in a collection of isogenic Aus0004 mutants with increasing levels of daptomycin resistance, and we observed by qPCR that some sRNAs had a significantly modified expression in daptomycin resistance mutants. It highlights the significant implication of some of the E. faecium sRNAs in the early steps of the development of daptomycin resistance. This is the first experimental genome-wide sRNA identification in Gram-positive E. faecium, a leading cause of hospital acquired infections.
2017-02-16 | GSE94924 | GEO
Project description:Drug resistance and tolerance eliminate the therapeutic potential of antibiotics against pathogens. Antibiotic tolerance by bacterial biofilms often leads to persistent infections, but its mechanisms are unclear. To uncover antibiotic tolerance mechanisms in biofilms, we applied stable isotope labeling with amino acids (SILAC) proteomics to selectively label and compare proteomes of sensitive and tolerant subpopulations of biofilms formed by Pseudomonas aeruginosa towards colistin, a 'last-resort' antibiotic against multidrug-resistant Gram-negative pathogens. Migration was essential in forming colistin-tolerant biofilm subpopulations, as colistin-tolerant cell-aggregates migrated with type IV pili, onto the top of killed biofilm. The colistin-tolerant cell-aggregates employed quorum sensing (QS) to initiate the formation of fresh colistin-tolerant subpopulations, highlighting multicellular behavior in antibiotic tolerance development. Erythromycin treatment which inhibits motility and QS, boosted biofilm eradication by colistin. This novel ‘-omics’ strategy to study antibiotic tolerant cells provides key insights for designing novel treatments against infections unsuppressed by conventional antimicrobials.
2016-02-16 | PXD002369 | Pride
Project description:With the global increase in the use of carbapenems, several gram-negative bacteria have acquired carbapenem resistance, thereby limiting treatment options. Klebsiella pneumoniae is one of such notorious pathogen that is being widely studied to find novel resistance mechanisms and drug targets. These antibiotic-resistant clinical isolates generally harbor many genetic alterations, and identification of causal mutations will provide insights into the molecular mechanisms of antibiotic resistance. We propose a method to prioritize mutated genes responsible for antibiotic resistance, in which mutated genes that also show significant expression changes among their functionally coupled genes become more likely candidates. For network-based analyses, we developed a genome-scale co-functional network of K. pneumoniae genes, KlebNet (www.inetbio.org/klebnet). Using KlebNet, we could reconstruct functional modules for antibiotic-resistance, and virulence, and retrieved functional association between them. With complementation assays with top candidate genes, we could validate a gene for negative regulation of meropenem resistance and four genes for positive regulation of virulence in Galleria mellonella larvae. Therefore, our study demonstrated the feasibility of network-based identification of genes required for antimicrobial resistance and virulence of human pathogenic bacteria with genomic and transcriptomic profiles from antibiotic-resistant clinical isolates.
2018-06-10 | GSE115539 | GEO
Project description:Chronic Pseudomonas aeruginosa infections evades antibiotic therapy and are associated with mortality in cystic fibrosis (CF) patients. We find that in vitro resistance evolution of P.aeruginosa towards clinically relevant antibiotics leads to phenotypic convergence towards distinct states. These states are associated with collateral sensitivity towards several antibiotic classes and encoded by mutations in antibiotic resistance genes, including transcriptional regulator nfxB. Longitudinal analysis of isolates from CF patients reveals similar and defined phenotypic states, which are associated with extinction of specific sub-lineages in patients. In depth investigation of chronic P.aeruginosa populations in a CF patient during antibiotic therapy revealed dramatic genotypic and phenotypic convergence. Notably, fluoroquinolone-resistant subpopulations harboring nfxB mutations were eradicated by antibiotic therapy as predicted by our in vitro data. This study supports the hypothesis that antibiotic treatment of chronic infections can be optimized by targeting phenotypic states associated with specific mutations to improve treatment success in chronic infections.
2018-10-23 | PXD007972 | Pride
Project description:Opportunistic pathogens and antibiotic resistant genes that causes important urinary tract infections.
| PRJNA946357 | ENA