Santasabuj Das

Bacterial exotoxins downregulate cathelicidin (hCAP‐18/LL‐37) and human β‐defensin 1 (HBD‐1) expression in the intestinal epithelial cells

Cathelicidin (hCAP‐18/LL‐37) and β‐defensin 1 (HBD‐1) are human antimicrobial peptides (AMPs) with high basal expression levels, which form the first line of host defence against infections over the epithelial surfaces. The antimicrobial functions owe to their direct microbicidal effects as well as the immunomodulatory role. Pathogenic microorganisms have developed multiple modalities including transcriptional repression to combat this arm of the host immune response. The precise mechanisms and the pathogen‐derived molecules responsible for transcriptional downregulation remain unknown. Here, we have shown that enteric pathogens suppress LL‐37 and HBD‐1 expression in the intestinal epithelial cells (IECs) with Vibrio cholerae and enterotoxigenic Escherichia coli (ETEC) exerting the most dramatic effects. Cholera toxin (CT) and labile toxin (LT), the major virulence proteins of V.u2003cholerae and ETEC, respectively, are predominantly responsible for these effects, both in vitro and in vivo. CT transcriptionally downregulates the AMPs by activating several intracellular signalling pathways involving protein kinase A (PKA), ERK MAPKinase and Cox‐2 downstream of cAMP accumulation and inducible cAMP early repressor (ICER) may mediate this role of CT, at least in part. This is the first report to show transcriptional repression of the AMPs through the activation of cellular signal transduction pathways by well‐known virulence proteins of pathogenic microorganisms.

Metagenomic Surveys of Gut Microbiota

Gut microbiota of higher vertebrates is host-specific. The number and diversity of the organisms residing within the gut ecosystem are defined by physiological and environmental factors, such as host genotype, habitat, and diet. Recently, culture-independent sequencing techniques have added a new dimension to the study of gut microbiota and the challenge to analyze the large volume of sequencing data is increasingly addressed by the development of novel computational tools and methods. Interestingly, gut microbiota maintains a constant relative abundance at operational taxonomic unit (OTU) levels and altered bacterial abundance has been associated with complex diseases such as symptomatic atherosclerosis, type 2 diabetes, obesity, and colorectal cancer. Therefore, the study of gut microbial population has emerged as an important field of research in order to ultimately achieve better health. In addition, there is a spontaneous, non-linear, and dynamic interaction among different bacterial species residing in the gut. Thus, predicting the influence of perturbed microbe–microbe interaction network on health can aid in developing novel therapeutics. Here, we summarize the population abundance of gut microbiota and its variation in different clinical states, computational tools available to analyze the pyrosequencing data, and gut microbe–microbe interaction networks.

Live and heat-killed probiotic Lactobacillus casei Lbs2 protects from experimental colitis through Toll-like receptor 2-dependent induction of T-regulatory response.

Inflammatory bowel disease (IBD) is a group of inflammatory disorders of the intestine caused by dysregulated T-cell mediated immune response against commensal microflora. Probiotics are reported as therapeutically effective against IBD. However, variable efficacy of the live probiotic strains, difference in survival and persistence in the gut between the strains and the lack of insight into the mechanisms of probiotic action limit optimal therapeutic efficacy. Our aims were to evaluate the lactobacillus strains isolated from the North Indian population for the generation of regulatory cells and cytokines in the intestine, to study their effects on pro-inflammatory mediators in the mouse model of inflammatory bowel disease and to explore the underlying mechanisms of their actions. Among the selected lactobacillus strains, Lactobacillus casei Lbs2 (MTCC5953) significantly suppressed lipopolysaccharide-induced pro-inflammatory cytokine (TNF-alpha, IL-6) secretion. Both live and heat-killed Lbs2 polarized Th0 cells to T-regulatory (Treg) cells in vitro, increased the frequency of FoxP3(+) Treg cells in the mesenteric lymph nodes (MLNs) and alleviated macroscopic and histopathological features of colitis in probiotic-fed mice. Moreover, the levels of IL-12, TNF-alpha and IL-17A were suppressed, while IL-10 and TGF-beta levels were augmented in the colonic tissues of Lbs2-treated mice. The induced Treg (iTreg) cells secreted IL-10 and TGF-beta and exerted suppressive effects on the proliferation of effector T-cells. Adoptive transfer of iTreg cells ameliorated the disease manifestations of murine colitis and suppressed the levels of TNF-alpha and IL-17A. Finally, Lbs2 effects were mediated by Toll-like receptor 2 (TLR2) activation on the dendritic cells. This study identified live and heat-killed Lbs2 as putative therapeutic candidates against IBD and highlighted their Toll-like receptor 2-dependent immunomodulatory and regulatory function.

Prediction of Interactions between Viral and Host Proteins Using Supervised Machine Learning Methods

Background Viral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs) has great implication for therapeutics. Methods In this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques. Results Out of 44 descriptors, best features were found to be domain-domain association and methionine, serine and valine amino acid composition of viral proteins. In this study, SVM-based method achieved better sensitivity of 67% over Naïve Bayes (37.49%) and Random Forest (55.66%). However the specificity of Naïve Bayes was the highest (99.52%) as compared with SVM (74%) and Random Forest (89.08%). Overall, the SVM and Random Forest achieved accuracy of 71% and 72.41%, respectively. The proposed SVM-based method was evaluated on blind dataset and attained a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown potential targets of hepatitis B virus-human and hepatitis E virus-human PPIs have been predicted through proposed SVM model and validated by gene ontology enrichment analysis. Our proposed model shows that, hepatitis B virus “C protein” binds to membrane docking protein, while “X protein” and “P protein” interacts with cell-killing and metabolic process proteins, respectively. Conclusion The proposed method can predict large scale interspecies viral-human PPIs. The nature and function of unknown viral proteins (HBV and HEV), interacting partners of host protein were identified using optimised SVM model.

Caveolin-1 is transcribed from a hypermethylated promoter to mediate colonocyte differentiation and apoptosis.

Caveolin-1(CAV1) is a tyrosine-phosphorylated scaffold protein of caveolae with multiple interacting partners. It functions both as an oncogene and a tumour suppressor depending upon the cellular contexts. In the early stage of colorectal cancers (CRC), CAV1 suppresses tumour progression, while over-expression of CAV1 reduced the tumourigenicity of colon carcinoma cells. In contrast, elevated level of CAV1 was reported in stage III CRC. To address this ambiguity, we studied the functional role and the regulation of CAV1 expression during colonocyte differentiation and apoptosis. Here, we reported for the first time that CAV1 expression was increased during colonocyte differentiation and mediated butyrate-induced differentiation and apoptosis of HT29 cells. CAV1 expression was silenced by promoter hypermethylation in HT-29 cells and reactivated by prolonged histone hyperacetylation of the promoter upon treatment of the cells with butyrate. However, the methylation status was unaltered by butyrate. We for the first time showed that HDAC inhibitor-mediated transactivation of CAV1 was regulated by methylation density of the promoter. Our study further explains the underlying mechanisms of the anti-cancer property of butyrate in CRC.

Antibiotic binding of STY3178, a yfdX protein from Salmonella Typhi.

The yfdX family proteins are known for long time to occur in various virulent bacteria including their multidrug resistant (MDR) strains, without any direct assigned function for them. However, yfdX protein along with other proteins involved in acid tolerance response is reported to be up regulated by the multidrug response regulatory system in E. coli. Hence, molecular and functional characterization of this protein is important for understanding of key cellular processes in bacterial cells. Here we study STY3178, a yfdX protein from a MDR strain of typhoid fever causing Salmonella Typhi. Our experimental results indicate that STY3178 is a helical protein existing in a trimeric oligomerization state in solution. We also observe many small antibiotics, like ciprofloxacin, rifampin and ampicillin viably interact with this protein. The dissociation constants from the quenching of steady state fluorescence and isothermal titration calorimetry show that ciprofloxacin binding is stronger than rifampin followed by ampicillin.

Ribavirin suppresses bacterial virulence by targeting LysR-type transcriptional regulators.

Targeting bacterial virulence mechanisms without compromising bacterial growth is a promising strategy to prevent drug resistance. LysR-type transcriptional regulators (LTTRs) possess structural conservation across bacterial species and regulate virulence in numerous pathogens, making them attractive targets for antimicrobial agents. We targeted AphB, a Vibrio cholerae LTTR, which regulates the expression of genes encoding cholera toxin and toxin-co-regulated pilus for inhibitor designing. Since AphB ligand is unknown, we followed a molecular fragment-based approach for ligand designing using FDA-approved drugs and subsequent screen to identify molecules that exhibited high-affinity binding to AphB ligand-binding pocket. Among the identified compounds, ribavirin, an anti-viral drug, antagonized AphB functions. Ribavirin perturbed Vibrio cholerae pathogenesis in animal models. The inhibitory effects of the drug was limited to the bacteria expressing wild type AphB, but not its constitutively active mutant (AphBN100E), which represents the ligand-bound state, suggesting that ribavirin binds to the active site of AphB to exert its inhibitory role and there exists no AphB-independent mechanism of its action. Similarly, ribavirin suppressed the functions of Salmonella Typhi LTTR Hrg, indicating its broad spectrum efficacy. Moreover, ribavirin did not affect the bacterial viability in culture. This study cites an example of drug repurposing for anti-infective therapy.

An Inducible and Secreted Eukaryote-Like Serine/Threonine Kinase of Salmonella enterica Serovar Typhi Promotes Intracellular Survival and Pathogenesis

ABSTRACT Eukaryote-like serine/threonine kinases (eSTKs) constitute an important family of bacterial virulence factors. Genome analysis had predicted putative eSTKs in Salmonella enterica serovar Typhi, although their functional characterization and the elucidation of their role in pathogenesis are still awaited. We show here that the primary sequence and secondary structure of the t4519 locus of Salmonella Typhi Ty2 have all the signatures of eukaryotic superfamily kinases. t4519 encodes a ∼39-kDa protein (T4519), which shows serine/threonine kinase activities in vitro. Recombinant T4519 (rT4519) is autophosphorylated and phosphorylates the universal substrate myelin basic protein. Infection of macrophages results in decreased viability of the mutant (Ty2Δt4519) strain, which is reversed by gene complementation. Moreover, reactive oxygen species produced by the macrophages signal to the bacteria to induce T4519, which is translocated to the host cell cytoplasm. That T4519 may target a host substrate(s) is further supported by the activation of host cellular signaling pathways and the induction of cytokines/chemokines. Finally, the role of T4519 in the pathogenesis of Salmonella Typhi is underscored by the significantly decreased mortality of mice infected with the Ty2Δt4519 strain and the fact that the competitive index of this strain for causing systemic infection is 0.25% that of the wild-type strain. This study characterizes the first eSTK of Salmonella Typhi and demonstrates its role in promoting phagosomal survival of the bacteria within macrophages, which is a key determinant of pathogenesis. This, to the best of our knowledge, is the first study to describe the essential role of eSTKs in the in vivo pathogenesis of Salmonella spp.

An improved method for identification of small non-coding RNAs in bacteria using support vector machine

Bacterial small non-coding RNAs (sRNAs) are not translated into proteins, but act as functional RNAs. They are involved in diverse biological processes like virulence, stress response and quorum sensing. Several high-throughput techniques have enabled identification of sRNAs in bacteria, but experimental detection remains a challenge and grossly incomplete for most species. Thus, there is a need to develop computational tools to predict bacterial sRNAs. Here, we propose a computational method to identify sRNAs in bacteria using support vector machine (SVM) classifier. The primary sequence and secondary structure features of experimentally-validated sRNAs of Salmonella Typhimurium LT2 (SLT2) was used to build the optimal SVM model. We found that a tri-nucleotide composition feature of sRNAs achieved an accuracy of 88.35% for SLT2. We validated the SVM model also on the experimentally-detected sRNAs of E. coli and Salmonella Typhi. The proposed model had robustly attained an accuracy of 81.25% and 88.82% for E. coli K-12 and S. Typhi Ty2, respectively. We confirmed that this method significantly improved the identification of sRNAs in bacteria. Furthermore, we used a sliding window-based method and identified sRNAs from complete genomes of SLT2, S. Typhi Ty2 and E. coli K-12 with sensitivities of 89.09%, 83.33% and 67.39%, respectively.

Prediction of Intra-Species Protein-Protein Interactions in Enteropathogens Facilitating Systems Biology Study

Protein-protein interactions in Escherichia coli (E. coli) has been studied extensively using high throughput methods such as tandem affinity purification followed by mass spectrometry and yeast two-hybrid method. This can in turn be used to understand the mechanisms of bacterial cellular processes. However, experimental characterization of such huge amount of interactions data is not available for other important enteropathogens. Here, we propose a support vector machine (SVM)-based prediction model using the known PPIs data of E. coli that can be used to predict PPIs in other enteropathogens, such as Vibrio cholerae, Salmonella Typhi, Shigella flexneri and Yersinia entrocolitica. Different features such as domain-domain association (DDA), network topology, and sequence information were used in developing the SVM model. The proposed model using DDA, degree and amino acid composition features has achieved an accuracy of 82% and 62% on 5-fold cross validation and blind E. coli datasets, respectively. The predicted interactions were validated by Gene Ontology (GO) semantic similarity measure and String PPIs database (experimental PPIs only). Finally, we have developed a user-friendly webserver named EnPPIpred to predict intra-species PPIs in enteropathogens, which will be of great help for the experimental biologists. The webserver EnPPIpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/EnPPIpred/.