Integrated Co-functional Network Analysis on the Resistance and Virulence Features in Acinetobacter baumannii.

Acinetobacter baumannii is one of the most troublesome bacterial pathogens that pose major public health threats due to its rapidly increasing drug resistance property. It is not only derived from clinic setting but also emerges from aquaculture as a fish pathogen, which could pass the resistant genes in the food chain. Understanding the mechanism of antibiotic resistance development and pathogenesis will aid our battle with the infections caused by A. baumannii. In this study, we constructed a co-functional network by integrating multiple sources of data from A. baumannii and then used the k-shell decomposition to analyze the co-functional network. We found that genes involving in basic cellular physiological function, including genes for antibiotic resistance, tended to have high k-shell values and locate in the internal layer of our network. In contrast, the non-essential genes, such as genes associated with virulence, tended to have lower k-shell values and locate in the external layer. This finding allows us to fish out the potential antibiotic resistance factors and virulence factors. In addition, we constructed an online platform ABviresDB (https://acba.shinyapps.io/ABviresDB/) for visualization of the network and features of each gene in A. baumannii. The network analysis in this study will not only aid the study on A. baumannii but also could be referenced for the research of antibiotic resistance and pathogenesis in other bacteria.

Non-walled spherical Acinetobacter baumannii is an important type of persister upon ß-lactam antibiotic treatment.

Bacterial persistence is one of the major causes of antibiotic treatment failure and the step stone for antibiotic resistance. However, the mechanism by which persisters arise has not been well understood. Maintaining a dormant state to prevent antibiotics from taking effect is believed to be the fundamental mechanistic basis, and persisters normally maintain an intact cellular structure. Here we examined the morphologies of persisters in Acinetobacter baumannii survived from the treatment by three major classes of antibiotics (i.e. ß-lactam, aminoglycoside, and fluoroquinolone) with microcopy and found that a fraction of enlarged spherical bacteria constitutes a major sub-population of bacterial survivors from ß-lactam antibiotic treatment, whereas survivors from the treatment of aminoglycoside and fluoroquinolone were less changed morphologically. Further studies showed that these spherical bacteria had completely lost their cell wall structures but could survive without any osmoprotective reagent. The spherical bacteria were not the viable-but-non-culturable cells and they could revive upon the removal of ß-lactam antibiotics. Importantly, these non-walled spherical bacteria also persisted during antibiotic therapy in vivo using Galleria mellonella as the infection model. Additionally, the combinational treatment on A. baumannii by ß-lactam and membrane-targeting antibiotic significantly enhanced the killing efficacy. Our results indicate that in addition to the dormant, structure intact persisters, the non-wall spherical bacterium is another important type of persister in A. baumannii. The finding suggests that targeting the bacterial cell membrane during ß-lactam chemotherapy could enhance therapeutic efficacy on A. baumannii infection, which might also help to reduce the resistance development of A. baumannii.

Organism dual RNA-seq reveals the importance of BarA/UvrY in Vibrio parahaemolyticus virulence.

Elucidation of host-pathogen interaction is essential for developing effective strategies to combat bacterial infection. Dual RNA-Seq using cultured cells or tissues/organs as the host of pathogen has emerged as a novel strategy to understand the responses concurrently from both pathogen and host at cellular level. However, bacterial infection mostly causes systematic responses from the host at organism level where the interplay is urgently to be understood but inevitably being neglected by the current practice. Here, we developed an approach that simultaneously monitor the genome-wide infection-linked transcriptional alterations in both pathogenic Vibrio parahaemolyticus and the infection host nematode Caenorhabditis elegans. Besides the dynamic alterations in transcriptomes of both C. elegans and V. parahaemolyticus during infection, we identify a two-component system, BarA/UvrY, that is important for virulence in host. BarA/UvrY not only controls the virulence factors in V. parahaemolyticus including Type III and Type VI secretion systems, but also attenuates innate immune responses in C. elegans, including repression on the MAP kinase-mediated cascades. Thus, our study exemplifies the use of dual RNA-Seq at organism level to uncover previously unrecognized interplay between host and pathogen.

Alarmone Ap4A is elevated by aminoglycoside antibiotics and enhances their bactericidal activity.

Second messenger molecules play important roles in the responses to various stimuli that can determine a cell's fate under stress conditions. Here, we report that lethal concentrations of aminoglycoside antibiotics result in the production of a dinucleotide alarmone metabolite-diadenosine tetraphosphate (Ap4A), which promotes bacterial cell killing by this class of antibiotics. We show that the treatment of Escherichia coli with lethal concentrations of kanamycin (Kan) dramatically increases the production of Ap4A. This elevation of Ap4A is dependent on the production of a hydroxyl radical and involves the induction of the Ap4A synthetase lysyl-tRNA synthetase (LysU). Ectopic alteration of intracellular Ap4A concentration via the elimination of the Ap4A phosphatase diadenosine tetraphosphatase (ApaH) and the overexpression of LysU causes over a 5,000-fold increase in bacterial killing by aminoglycosides. This increased susceptibility to aminoglycosides correlates with bacterial membrane disruption. Our findings provide a role for the alarmone Ap4A and suggest that blocking Ap4A degradation or increasing its synthesis might constitute an approach to enhance aminoglycoside killing potency by broadening their therapeutic index and thereby allowing lower nontoxic dosages of these antibiotics to be used in the treatment of multidrug-resistant infections.

Retraction Note: Vibrio parahaemolyticus RhsP represents a widespread group of pro-effectors for type VI secretion systems.

This paper has been retracted.

Vibrio parahaemolyticus RhsP represents a widespread group of pro-effectors for type VI secretion systems.

Type VI secretion systems (T6SSs) translocate effector proteins, such as Rhs toxins, to eukaryotic cells or prokaryotic competitors. All T6SS Rhs-type effectors characterized thus far contain a PAAR motif or a similar structure. Here, we describe a T6SS-dependent delivery mechanism for a subset of Rhs proteins that lack a PAAR motif. We show that the N-terminal Rhs domain of protein RhsP (or VP1517) from Vibrio parahaemolyticus inhibits the activity of the C-terminal DNase domain. Upon auto-proteolysis, the Rhs fragment remains inside the cells, and the C-terminal region interacts with PAAR2 and is secreted by T6SS2; therefore, RhsP acts as a pro-effector. Furthermore, we show that RhsP contributes to the control of certain "social cheaters" (opaR mutants). Genes encoding proteins with similar Rhs and PAAR-interacting domains, but diverse C-terminal regions, are widely distributed among Vibrio species.

Analysis of global prevalence of antibiotic resistance in Acinetobacter baumannii infections disclosed a faster increase in OECD countries.

Acinetobacter baumannii is one of the most challenging nosocomial pathogens due to the emergence and widespread of antibiotic resistance. We aimed to provide the first analysis of global prevalence of antibiotic resistance in A. baumannii infections, by synthesizing data and knowledge through a systematic review. We searched studies reporting antibiotic resistance in A. baumannii infections using the Medline, Embase, Web of Science, and Cochrane databases from January 2000 to December 2016. Studies were eligible if they investigated and reported antibiotic resistance in A. baumannii infections with inpatients or outpatients in hospital. Our investigation showed a high prevalence of resistance to the common prescribed antibiotics in A. baumannii infections in both OECD (Organization for Economic Co-operation and Development) and non-OECD countries. Strikingly, though OECD countries have substantially lower pooled prevalence of resistance compared to non-OECD countries based on the data during 2006-2016, a further investigation in a time scale disclosed a faster increase in OECD countries during the past 11 years, and currently both of them have a comparable prevalence of resistance (2011-2016). Tigecycline and colistin are still active but their resistances are expected to become common if the preventative measures are not taken. Antibiotic resistance in A. baumannii infection developed fast and is a crisis for both OECD and non-OECD countries. A "post-antibiotic era" for A. baumannii infection is expected in the next 10-20 years without immediate actions from pharmaceutical companies and governments.

Identification of potential drug targets based on a computational biology algorithm for venous thromboembolism.

Venous thromboembolism (VTE) is a common, fatal and frequently recurrent disease. Changes in the activity of different coagulation factors serve as a pathophysiological basis for the recurrent risk of VTE. Systems biology approaches provide a better understanding of the pathological mechanisms responsible for recurrent VTE. In this study, a novel computational method was presented to identify the recurrent risk modules (RRMs) based on the integration of expression profiles and human signaling network, which hold promise for achieving new and deeper insights into the mechanisms responsible for VTE. The results revealed that the RRMs had good classification performance to discriminate patients with recurrent VTE. The functional annotation analysis demonstrated that the RRMs played a crucial role in the pathogenesis of VTE. Furthermore, a variety of approved drug targets in the RRM M5 were related to VTE. Thus, the M5 may be applied to select potential drug targets for combination therapy and the extended treatment of VTE.

Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network.

Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biological background, 1 PPDT-Module and 22 PCOS potential drug targets were identified, 21 of which were verified by literatures to be associated with the pathogenesis of PCOS. 42 drugs targeting to 13 PCOS potential drug targets were investigated experimentally or clinically for PCOS. Evaluated by independent datasets, the whole PPDT-Module and 22 PCOS potential drug targets could not only reveal the drug response, but also distinguish the statuses between normal and disease. Our identified PPDT-Module and PCOS potential drug targets would shed light on the treatment of PCOS. And our approach would provide valuable insights to research on the pathogenesis and drug response of other diseases.

A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism-Related Genes.

Identifying the genes involved in venous thromboembolism (VTE) recurrence is important not only for understanding the pathogenesis but also for discovering the therapeutic targets. We proposed a novel prioritization method called Function-Interaction-Pearson (FIP) by creating gene-disease similarity scores to prioritize candidate genes underling VTE. The scores were calculated by integrating and optimizing three types of resources including gene expression, gene ontology and protein-protein interaction. As a result, 124 out of top 200 prioritized candidate genes had been confirmed in literature, among which there were 34 antithrombotic drug targets. Compared with two well-known gene prioritization tools Endeavour and ToppNet, FIP was shown to have better performance. The approach provides a valuable alternative for drug targets discovery and disease therapy.

Identification of potential COPD genes based on multi-omics data at the functional level.

Chronic obstructive pulmonary disease (COPD) is a complex disease, which involves dysfunctions in multi-omics. The changes in biological processes, such as adhesion junction, signaling transduction, transcriptional regulation, and cell proliferation, will lead to the occurrence of COPD. A novel systematic approach MMMG (Methylation-MicroRNA-MRNA-GO) was proposed to identify potential COPD genes by integrating function information with a methylation profile, a microRNA expression profile and an mRNA expression profile. 8 co-functional classes and 102 potential COPD genes were identified. These genes displayed a high performance in classifying COPD patients and normal samples, revealed COPD-related pathways, and have been confirmed to be associated with COPD by Matthews correlation coefficient (MCC)-values, literature, an independent data set, and pathways. The MMMG method that analyzed multi-omics data at the functional level could effectively identify potential COPD genes. These potential COPD genes would provide in-depth insights into understanding the complexity of COPD genome landscapes, improve the early diagnostics, and guide new efforts to develop therapeutics in the future.

Identifying progression related disease risk modules based on the human subcellular signaling networks.

Many studies have shown that the structure and dynamics of the human signaling network are disturbed in complex diseases such as coronary artery disease, and gene expression profiles can distinguish variations in diseases since they can accurately reflect the status of cells. Integration of subcellular localization and the human signaling network holds promise for providing insight into human diseases. In this study, we performed a novel algorithm to identify progression-related-disease-risk modules (PRDRMs) among patients of different disease states within eleven subcellular sub-networks from a human signaling network. The functional annotation and literature retrieval showed that the PRDRMs were strongly associated with disease pathogenesis. The results indicated that the PRDRM expression values as classification features had a good classification performance to distinguish patients of different disease states. Our approach compared with the method PageRank had a better classification performance. The identification of the PRDRMs in response to the dynamic gene expression change could facilitate our understanding of the pathological basis of complex diseases. Our strategy could provide new insights into the potential use of prognostic biomarkers and the effective guidance of clinical therapy from the human subcellular signaling network perspective.

Identifying colon cancer risk modules with better classification performance based on human signaling network.

Identifying differences between normal and tumor samples from a modular perspective may help to improve our understanding of the mechanisms responsible for colon cancer. Many cancer studies have shown that signaling transduction and biological pathways are disturbed in disease states, and expression profiles can distinguish variations in diseases. In this study, we integrated a weighted human signaling network and gene expression profiles to select risk modules associated with tumor conditions. Risk modules as classification features by our method had a better classification performance than other methods, and one risk module for colon cancer had a good classification performance for distinguishing between normal/tumor samples and between tumor stages. All genes in the module were annotated to the biological process of positive regulation of cell proliferation, and were highly associated with colon cancer. These results suggested that these genes might be the potential risk genes for colon cancer.