Ontology highlight
ABSTRACT:
PROVIDER: PRJNA942016 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug-resistance due to its robust outer membrane and its ability to acquire and retain extracellular DNA. Moreover, it can survive for prolonged durations on surfaces and is resistant to desiccation. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new chemical matter with antibacterial activity against this burdensome pathogen. Here, we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a deep neural network with this growth inhibition dataset and performed predictions on the Drug Repurposing Hub for structurally novel molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii, which could overcome intrinsic and acquired resistance mechanisms in clinical isolates. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE, a functionally conserved protein that contributes to shuttling lipoproteins from the inner membrane to the outer membrane. Moreover, abaucin was able to control an A. baumannii infection in a murine wound model. Together, this work highlights the utility of machine learning in discovering new antibiotics and describes a promising lead with narrow-spectrum activity against a challenging Gram-negative pathogen.
2023-02-01 | GSE214305 | 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:Two Acinetobacter baumannii strains with low susceptibility to fosmidomycin and two reference with high susceptibility to fosmidomycin were DNA-sequenced to investigate the genomic determinants of fosmidomycin resistance.
2022-12-21 | E-MTAB-12357 | biostudies-arrayexpress
Project description:RNA sequencing was used to analyze the transcriptome of a thioredoxin-A knockout of a clinical isolate of Acinetobacter baumannii
2019-01-14 | GSE125017 | GEO
Project description:Transcriptomics by RNA-seq provides unparalleled insight into bacterial gene expression networks, enabling a deeper understanding of the regulation of pathogenicity, mechanisms of antimicrobial resistance, metabolism, and other cellular processes. Here we present the transcriptome architecture of Acinetobacter baumannii ATCC 17978, a species emerging as a leading cause of antimicrobial resistant nosocomial infections. Differential RNA-seq (dRNA-seq) examination of model strain ATCC 17978 in 16 laboratory conditions identified 3731 transcriptional start sites (TSS), and 110 small RNAs, including the first identification of 22 sRNA encoded at the 3′ end of mRNA.
2018-06-25 | GSE103244 | GEO
Project description:Transcriptomic comparison of a multidrug resistant Acinetobacter baumannii strain and a susceptible reference strain.
2009-11-20 | E-MEXP-2254 | biostudies-arrayexpress
Project description:Acinetobacter baumannii is an important pathogen of nosocomial infection worldwide, which can primarily cause pneumonia, bloodstream infection, and urinary tract infection. The increasing drug resistance rate of A. baumannii and the slow development of new antibacterial drugs brought great challenges for clinical treatment. Host immunity is crucial to the defense of A. baumannii infection, and understanding the mechanisms of immune response can facilitate the development of new therapeutic strategies. To obtain the system-level changes of host proteome in immune response, we used TMT labeling quantitative proteomics to compare the proteome changes of lungs from A. baumannii infected mice with control mice six hours after infection. A total of 6218 proteins were identified in which 6172 could be quantified. With threshold p < 0.05 and fold change > 1.2, we found 120 differentially expressed proteins. Bioinformatics analysis showed that differentially expressed proteins after infection were associated with receptor recognition, NADPH oxidase (NOX) activation and antimicrobial peptides. These differentially expressed proteins were involved in the pathways including leukocyte transendothelial migration, phagocyte, neutrophil degranulation, and antimicrobial peptides. In conclusion, our study showed proteome changes in mouse lung tissue due to A. baumannii infection and suggested the important roles of NOX, neutrophils, and antimicrobial peptides in host response. Our results provide a potential list of proteins candidates for the further study of host-bacteria interaction in A. baumannii infection.
2021-03-16 | PXD020640 | Pride
Project description:Comparative Genomic and Transcriptomic Characterization of Colistin-Resistant Acinetobacter baumannii
2019-01-23 | GSE109951 | GEO
Project description:Transcriptome profiling of quorum sensing regulated genes in Acinetobacter baumannii 863
2019-06-21 | GSE120346 | GEO