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:We analyzed the extracellular proteome of colistin-resistant Korean Acinetobacter baumannii (KAB) strains to identify proteome profiles that can be used to characterize extensively drug-resistant KAB strains.
Project description:Colistin is a crucial last-line drug used for the treatment of life-threatening infections caused by multi-drug resistant strains of the Gram-negative bacteria, Acinetobacter baumannii. However, colistin resistant A. baumannii isolates can be isolated following failed colistin therapy. Resistance is most often mediated by the addition of phosphoethanolamine (pEtN) to lipid A by PmrC, following missense mutations in the pmrCAB operon encoding PmrC and the two-component signal transduction system PmrA/PmrB. We recovered an isogenic pair of A. baumannii isolates from a single patient before (6009-1) and after (6009-2) failed colistin treatment that displayed low/intermediate and high levels of colistin resistance, respectively. To understand how increased colistin-resistance arose, we genome sequenced each isolate which revealed that 6009-2 had an extra copy of the insertion sequence element ISAba125 within a gene encoding an H-NS-family transcriptional regulator. Consequently, transcriptomic analysis of the clinical isolates identified was performed and more than 150 genes as differentially expressed in the colistin-resistant, hns mutant, 6009-2. Importantly, the expression of eptA, encoding a second lipid A-specific pEtN transferase, but not pmrC, was significantly increased in the hns mutant. This is the first time an H-NS-family transcriptional regulator has been associated with a pEtN transferase and colistin resistance.
Project description:The bacterial pathogen, Acinetobacter baumannii, is a leading cause of drug-resistant infections. Here, we investigated the potential of developing nanobodies that specifically recognize A. baumannii over other Gram-negative bacteria. Through generation and panning of a synthetic nanobody library, we identified several potential lead candidates. We demonstrate how incorporation of next generation sequencing analysis can aid in selection of lead candidates for further characterization. Using monoclonal phage display, we validated the binding of several lead nanobodies to A. baumannii. Subsequent purification and biochemical characterization revealed one particularly robust nanobody that broadly and specifically bound A. baumannii compared to other common drug resistant pathogens. These findings support the potentially for nanobodies to selectively target A. baumannii and the identification of lead candidates for possible future diagnostic and therapeutic development.
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: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: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.
Project description:Background: Acinetobacter baumannii is one of the most dangerous multidrug-resistant pathogens worldwide. Currently, 50-70% of clinical isolates of A. baumannii are extensively drug-resistant (XDR) and available antibiotic options against A. baumannii infections are limited. There are still needs to discover specific de facto bacterial antigenic proteins that could be effective vaccine candidates in human infection. With the growth of research in recent years, several candidate molecules have been identified for vaccine development. So far, there is no public health authorities approved vaccine against A. baumannii. Methods: The purpose of this study was to identify immunodominant vaccine candidate proteins that can be immunoprecipitated specifically with patients’ IgGs. Relaying on hypothesis that IgGs of infected person have capacity to capture immunodominant bacterial proteins. Herein, outer membrane and secreted proteins of sensitive and drug resistant A. baumannii were captured by using IgGs obtained from patient and healthy control sera and were identified by LC-MS/MS analysis. Results: By using subtractive proteomic approach, we determined 34 unique proteins which were captured only in drug-resistant A. baumannii strain via patient sera. After extensive evaluation of predicted epitope regions, solubility, membrane transverse characteristics, and structural properties, we selected several notable vaccine candidates. Conclusion: We identified vaccine candidate proteins that triggered de facto response of human immune system against the antibiotic-resistant A. baumannii. Precipitation of bacterial proteins via patient immunoglobulins was a novel approach to identify the proteins which have potential to trigger to response in patient immune system.
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.