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.
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.
Project description:In recent years, the Gram-negative bacterium Acinetobacter baumannii has garnered considerable attention for its unprecedented capacity to rapidly develop resistance to antibacterial therapeutics. This is coupled with the seemingly epidemic emergence of new hyper-virulent strains. Although strain-specific differences for A. baumannii isolates have been well described, these studies have primarily focused on proteinaceous factors. At present, only limited publications have investigated the presence and role of small regulatory RNA (sRNA) transcripts. Herein, we perform such an analysis, describing the RNA-seq-based identification of 78 A. baumannii sRNAs in the AB5075 background. Together with six previously identified elements, we include each of these in a new genome annotation file, which will serve as a tool to investigate regulatory events in this organism. Our work reveals that the sRNAs display high expression, accounting for >50 % of the 20 most strongly expressed genes. Through conservation analysis we identified six classes of similar sRNAs, with one found to be particularly abundant and homologous to regulatory, C4 antisense RNAs found in bacteriophages. These elements appear to be processed from larger transcripts in an analogous manner to the phage C4 molecule and are putatively controlled by two further sRNAs that are strongly antisense to them. Collectively, this study offers a detailed view of the sRNA content of A. baumannii, exposing sequence and structural conservation amongst these elements, and provides novel insight into the potential evolution, and role, of these understudied regulatory molecules. This study is based on the annotation of novel sRNAs on basis of an Acinetobacter baumannii RNA sequencing dataset. Each sample was generated by pooling three independent biological replicate RNA preps
Project description:Purpose: The goal of this study was to elucidate the collateral effects associated with OXA-23 overexpression on the Acinetobacter baumannii global transcriptome. Results: Besides the 99.73-fold increase in blaOXA-23 transcript upon IPTG induction, no other transcripts showed more than a 2-fold change compared to the wildtype control. This suggests that OXA-23 over expression to levels similarly observed in multi drug resistant A. baumannii clinical isolates does not effect the transcriptome.
Project description:A major reservoir for spread of the emerging pathogen Acinetobacter baumannii is hopsital surfaces, where bacteria persist in a desiccated state. To identify gene products influencing desiccation survival, a transposon sequencing (Tn-seq) screen was performed. Using this approach, we identified genes both positively and negatively impacting the desiccation tolerance of A. baumannii.
Project description:Desiccation tolerance has been implicated as an important characteristic that potentiates the spread of the bacterial pathogen Acinetobacter baumannii through hospitals on dry surfaces. Despite the potential importance of this stress response, scarce information is available describing the underlying mechanisms of A. baumannii desiccation tolerance. Here we characterize the factors influencing desiccation survival of A. baumannii. At the macroscale level, we find that desiccation tolerance is influenced by cell density, growth phase, and desiccation medium. Our transcriptome analysis indicates that desiccation represents a unique state for A. baumannii compared to commonly studied growth conditions and strongly influences pathways responsible for proteostasis. Remarkably, we find that an increase in total cellular protein aggregates, which is often considered deleterious, correlates positively with the ability of A. baumannii to survive desiccation. We show that artificially inducing protein aggregate formation increases desiccation survival, and more importantly, that proteins incorporated into cellular aggregates can retain activity. Our results suggest that protein aggregates may promote desiccation tolerance in A. baumannii through preserving and protecting proteins from damage during desiccation until rehydration occurs.
Project description:Cefiderocol (CFDC) is a novel chlorocatechol-substituted siderophore approved to treat complicated urinary tract infections and for hospital-acquired and ventilator-acquired pneumonia. In previous work, human fluids, were shown to increase the minimum inhibitory concentration (MICs) of Acinetobacter baumannii against CFDC and reduce the expression of genes related to iron uptake systems, which could explain the need for higher concentrations of CFDC to exert inhibitory action. Herein, we analyzed the impact of human urine (HU), which contains low albumin concentrations, on the expression of iron-uptake related genes and MIC values of two carbapenem-resistant A. baumannii. Levels of resistance to CFDC were not modified by HU in strain AMA40 but were reduced in the case of strain AB5075. Testing other carbapenem-resistant A. baumannii isolates showed that the CFDC MICs were unmodified or reduced in the presence of HU. The expression of piuA, pirA, bauA, and bfnH determined by qRT-PCR was enhanced in both strains when HU was present in the culture medium. All four tested genes are involved in recognizing ferric siderophore complexes or internalization into the cell’s cytosol. In contrast, the effect of HU on genes associated with resistance to β-lactams, antibiotics commonly used to treat urinary tract infections caused by A. baumannii, was variable; the transcriptional analysis of pbp1, pbp3, blaOXA-51-like, blaADC, and blaNDM-1 showed significant variation. In summary, HU, probably due to the albumin and free iron content, does not adversely impact or slightly improves the activity of CFDC when tested against A. baumannii in urine in contrast to other human bodily fluids.
Project description:Acinetobacter baumannii is a Gram-negative opportunistic pathogen that causes multiple infections, including pneumonia, bacteremia, and wound infections. Due to multiple intrinsic and acquired drug-resistance mechanisms, A. baumannii isolates are commonly multi-drug resistant and infections are notoriously difficult to treat. Therefore, it is important to identify mechanisms used by A. baumannii to survive stresses encountered during infection as a means of identifying new drug targets. In this study, we determined the transcriptional response of A. baumannii to hydrogen peroxide stress using RNASequencing. Upon exposure to hydrogen peroxide, A. baumannii differentially transcribes several hundred genes. In this study, we also determined the transcriptional profile of A. baumannii strains with the transcriptional regulators mumR or oxyR genetically inactivated and identified transcriptional differences between these strains and wild-type A. baumannii in response to hydrogen peroxide stress. In doing this, the function of A. baumannii OxyR in hydrogen peroxide stress resistance and regulation of genes required for hydrogen peroxide detoxification was defined. Moreover, the contribution of the uncharacterized regulator MumR to hydrogen peroxide stress resistance was also explored. This work reveals the transcriptome of an important human pathogen in the presence of hydrogen peroxide stress.