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:Acinetobacter baumannii, a robust Gram-negative bacterium known for inducing nosocomial infections and displaying multidrug resistance, remains a formidable challenge to combat. The limited arsenal of antimicrobial peptides targeting this pathogen underscores the need for innovative strategies. Here, we report a pioneering few-shot learning-based pipeline to identify potent antimicrobial peptides targeting A. baumannii. This pipeline effectively scans through the entire libraries of hexapeptides, heptapeptides, and octapeptides, encompassing tens of billions of candidates, despite the extreme scarcity of available training data. Comprising classification, ranking, and regression modules as an integration, each module is trained using a few-shot learning strategy involving pre-training and multiple fine-tuning steps while incorporating similar and true data fine-tuning. This methodology mitigates the potential overfitting concerns due to the small size of the training samples and then enhances the predictive capability of the pipeline. The results highlight the robustness and versatility of the methodology, demonstrating its effectiveness in discovering AMPs against A. baumannii and C. albicans, while demonstrating low off-target toxicity and negligible susceptibility to drug resistance. Additionally, the EME7(7) exhibits efficacy in controlling A. baumannii infections within a mouse pneumonia model, notably without inducing kidney injury — a contrast to the observed effects of polymyxin B. This work provides a paradigm for addressing the challenges posed by limited data availability.
2025-09-01 | GSE306268 | 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:Carbapenem-resistant Acinetobacter baumannii (CRAB) is a critical nosocomial pathogen with limited treatment options. Although antibiotic resistance in CRAB is well-characterized, its interactions with host immunity and the contribution of outer membrane vesicles (OMVs) to pathogenesis remain poorly understood. We examined a clinical CRAB isolate and compared it with the reference strain A19606. Antimicrobial susceptibility testing revealed complete resistance of CRAB to commonly used antibiotics in clinical practice, while A19606 remained susceptible to most agents. In murine intranasal infection models and bone marrow-derived macrophages, CRAB induced significantly stronger activation of inflammatory signaling pathways and elevated levels of pro-inflammatory cytokines relative to A19606. Transcriptomic analysis of infected lung tissue identified differentially expressed genes, enriched for inflammatory response pathways. proteomics showed upregulated proteins in CRAB related to secretion systems. OMVs characterization revealed that CRAB-derived OMVs highly enriched in proteins associated with periplasmic and outer membrane spaces, and more potent in triggering macrophage inflammatory signaling. CRAB displays expansive antibiotic resistance and enhanced pro-inflammatory potential mediated in part by unique OMVs properties. Targeting OMVs formation or host immune modulation may represent effective strategies for combating CRAB infections.
2026-01-19 | GSE313682 | GEO
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:Asymptomatic gut colonization increases the risk of clinical infection and transmission by the multidrug-resistant pathogen Acinetobacter baumannii. Ornithine utilization was shown to be critical for A. baumannii competition with the resident microbiota to persist in gut colonization, but the regulatory mechanisms and cues are unknown. Here, we identify a transcriptional regulator, AstR, that specifically activates the expression of the A. baumannii ornithine utilization operon astNOP. Phylogenetic analysis suggests that AstR was co-opted from the Acinetobacter arginine utilization ast(G)CADBE locus and is specialized to regulate ornithine utilization in A. baumannii. Reporter assays showed that astN promoter expression was activated by ornithine but inhibited by glutamate and other preferred amino acids. astN promoter expression was similarly activated by incubation with fecal samples from conventional mice but not germ-free mice, suggesting AstR-dependent activation of the astN promoter responds to intermicrobial competition for amino acids. Finally, AstR was required for A. baumannii to colonize the gut in a mouse model. Together, these results suggest that pathogenic Acinetobacter species evolved AstR to regulate ornithine catabolism, which is required to compete with the microbiota during gut colonization.
2025-09-19 | GSE308136 | 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