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SRX17724312: GSM6603485: AB Abaucin treated 4.5h; Acinetobacter baumannii ATCC 17978; RNA-Seq
2 ILLUMINA (Illumina HiSeq 3000) runs: 23.6M spots, 7.1G bases, 2.1Gb downloads

External Id: GSM6603485_r1
Submitted by: Stokes Lab, Institute for Infectious Disease Research, McMaster University
Study: Deep learning-guided discovery of a narrow-spectrum antibiotic against Acinetobacter baumannii
show Abstracthide Abstract
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. Overall design: Transcriptomic analysis of A. baumannii ATCC 17978 when treated with novel antibiotic compound
Sample: AB Abaucin treated 4.5h
SAMN31042090 • SRS15254263 • All experiments • All runs
Library:
Name: GSM6603485
Instrument: Illumina HiSeq 3000
Strategy: RNA-Seq
Source: TRANSCRIPTOMIC
Selection: cDNA
Layout: PAIRED
Construction protocol: After the required durations of incubation post-treatment, 2 ml samples were harvested and flash-frozen on liquid nitrogen. Extractions were performed on fresh frozen cell pellets using Qiagen Rneasy Plus Universal mini kit following manufacturer's instructions (Qiagen, Hilden, Germany). rRNA depletion sequencing library was prepared by using QIAGEN FastSelect rRNA HMR Kit (Qiagen, Hilden, Germany). RNA sequencing library preparation uses NEBNext Ultra II RNA Library Preparation Kit for Illumina by following the manufacturer's recommendations (NEB, Ipswich, MA, USA). Briefly, enriched RNAs are fragmented for 15 minutes at 94 °C. First strand and second strand cDNA are subsequently synthesized. cDNA fragments are end repaired and adenylated at 3'ends, and universal adapters are ligated to cDNA fragments, followed by index addition and library enrichment with limited cycle PCR. Sequencing libraries were validated using the Agilent Tapestation 4200 (Agilent Technologies, Palo Alto, CA, USA), and quantified using Qubit 2.0 Fluorometer (ThermoFisher Scientific, Waltham, MA, USA) as well as by quantitative PCR (KAPA Biosystems, Wilmington, MA, USA).
Runs: 2 runs, 23.6M spots, 7.1G bases, 2.1Gb
Run# of Spots# of BasesSizePublished
SRR2172853011,894,9693.6G1.1Gb2023-02-01
SRR2172853111,754,5233.5G1.1Gb2023-02-01

ID:
24628853

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