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One of the fascinating outcomes of human microbiome studies adopting multi-omics technology is its ability to decipher millions of microbial encoded functions in the most complex and crowded microbial ecosystem, including the human gastrointestinal (GI) tract without cultivating the microbes. It is well established that several functions that modulate the human metabolism, nutrient assimilation, immunity, infections, disease severity and therapeutic efficacy of drugs are mostly of microbial origins. In addition, these microbial functions are dynamic and can disseminate between microbial taxa residing in the same ecosystem or other microbial ecosystems through horizontal gene transfer. For clinicians and researchers alike, understanding the toxins, virulence factors and drug resistance traits encoded by the microbes associated with the human body is of utmost importance. Nevertheless, when such traits are genetically linked with mobile genetic elements (MGEs) that make them transmissible, it creates an additional burden to public health. This review mainly focuses on the functions of gut commensals and the dynamics and crosstalk between commensal and pathogenic bacteria in the gut. Also, the review summarises the plethora of MGEs linked with virulence genes present in the genomes of various enteric bacterial pathogens, which are transmissible among other pathogens and commensals.

Ventilator Associated Pneumonia (VAP) is a nosocomial infection contracted by patients on ventilators, where bacteria colonize the upper digestive tract and contaminated secretions are released into the lower airway. This nosocomial infection increases the morbidity and mortality of the patients as well as the cost of treatment. Of late Probiotic formulations are postulated to prevent the colonization of these pathogenic bacteria. In this prospective observational study, we aimed to investigate the effects of probiotics on gut microbiota and their relation to clinical outcomes in mechanically ventilated patients. For this study, 30 patients were recruited (20 probiotic treated and 10 without probiotic treatment) screened from a cohort of 169 patients. Patients in the probiotic group were given a dose of 6 capsules of commercially available probiotic (VSL#3®:112.5 billion CFU/cap) in three divided doses for 10 days. Sampling was carried out after each dose to monitor the temporal change in the gut microbiota composition. To profile the microbiota, we used a 16S rRNA based targeted metagenomic approach and differences among the groups were computed using multivariate statistical analyses. Differences in gut microbial diversity (Bray Curtis & Jaccard distance, p-value < 0.05) among the probiotic treated group and the control group were observed. Furthermore, treatment with probiotics resulted in the enrichment of Lactobacillus and Streptococcus in the gut microbiota of the probiotic-treated groups. Our results demonstrated that probiotics may lead to favourable alteration in gut microbiome characteristics. Future studies should focus on appropriate dosages and frequency of probiotics, which can lead to improved clinical outcomes.

To investigate the tidal hydrodynamics, sediment transport and morphology variation in the Hooghly Estuary, India, a two-dimensional model was developed in the CMS Flow platform. Application of numerical modeling techniques to sediment transport problems in rivers require detailed study of the river reach characteristics and development of the appropriate mathematical model. The characteristics of the river vary considerably starting from its origin to its mouth where it joins the sea, the flow and sediment transport processes also vary accordingly. This makes modeling of sediment transport for the whole reach very complex. Flow and sediment transport processes in estuaries are more complicated because of factors like two-way tidal flow, and widely varying morphology. Numerical models for estuaries are usually extensive and require high computational time because of the complexities in the solution algorithms and large time-space solution domain of the problem which also require a wide variety of data. It becomes very time-consuming to perform these simulations repeatedly, for analyzing changes in sediment transport for variations in boundary conditions, local changes in bathymetry, presence of structures or for long-term simulations. Artificial Neural Networks models when used in combination with numerical models, to simulate flow and sediment transport reduces the computational time considerably. In this work a combined ANN-numerical model is developed for simulation sediment transport and bathymetry change in the Hooghly estuary.

Hydrodynamic models for morphodynamic studies in estuaries require continuous tidal water level data as boundary conditions. However, for the Hooghly estuary in India, measurement of continuous tidal water elevation data at the most downstream point is a very difficult task because of the remote location and the confluence with the deep sea. The tidal water level data at this station are measured for a half tidal cycle which is not useful for hydrodynamic modeling. However, at other upstream stations, tide water level data are measured continuously. Accordingly, in this study, an attempt is made to generate continuous tidal water level data at the remote station, using the data of the neighboring stations as input to an artificial neural network (ANN) model. A three-layered feed-forward backpropagation (FFBP) network with two hidden layers is selected and five different combinations of input vectors are used. Simulated water level data obtained from each model are compared with the observed data graphically as well as by estimating the standard error parameters. The best model suitable for prediction of continuous tidal elevation during any time of the tidal cycle and applicable throughout the year is then identified. It is found that tidal data from the nearest neighboring station are more suitable for training.

Helicobacter pylori is a ubiquitous bacterium and contributes significantly to the burden of chronic gastritis, peptic ulcers, and gastric cancer across the world. Adaptive phenotypes and virulence factors in H. pylori are heterogeneous and dynamic. However, limited information is available about the molecular nature of antimicrobial resistance phenotypes and virulence factors of H. pylori strains circulating in India. In the present study, we analyzed the whole genome sequences of 143 H. pylori strains, of which 32 are isolated from two different regions (eastern and southern) of India. Genomic repertoires of individual strains show distinct region-specific signatures. We observed lower resistance phenotypes and genotypes in the East Indian (Kolkata) H. pylori isolates against amoxicillin and furazolidone antibiotics, whereas higher resistance phenotypes to metronidazole and clarithromycin. Also, at molecular level, a greater number of AMR genes were observed in the east Indian H. pylori isolates as compared to the southern Indian isolates. From our findings, we suggest that metronidazole and clarithromycin antibiotics should be used judicially in the eastern India. However, no horizontally acquired antimicrobial resistance gene was observed in the current H. pylori strains. The comparative genome analysis shows that the number of genes involved in virulence, disease and resistance of H. pylori isolated from two different regions of India is significantly different. Single-nucleotide polymorphisms (SNPs) based phylogenetic analysis distinguished H. pylori strains into different clades according to their geographical locations. Conditionally beneficial functions including antibiotic resistance phenotypes that are linked with faster evolution rates in the Indian isolates.