Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only ''pair-wise interactions''. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the ''Apriori algorithm'' for deriving ''microbial association rules'' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various ''(subsets of) features/items'' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between ''subsets/subgroups'' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving ''microbial association rules'' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.     

Vikodak - A Modular Framework for Inferring Functional Potential of Microbial Communities from 16S Metagenomic Datasets

Background

The overall metabolic/functional potential of any given environmental niche is a function of the sum total of genes/proteins/enzymes that are encoded and expressed by various interacting microbes residing in that niche. Consequently, prior (collated) information pertaining to genes, enzymes encoded by the resident microbes can aid in indirectly (re)constructing/ inferring the metabolic/ functional potential of a given microbial community (given its taxonomic abundance profile). In this study, we present Vikodak—a multi-modular package that is based on the above assumption and automates inferring and/ or comparing the functional characteristics of an environment using taxonomic abundance generated from one or more environmental sample datasets. With the underlying assumptions of co-metabolism and independent contributions of different microbes in a community, a concerted effort has been made to accommodate microbial co-existence patterns in various modules incorporated in Vikodak.

Results

Validation experiments on over 1400 metagenomic samples have confirmed the utility of Vikodak in (a) deciphering enzyme abundance profiles of any KEGG metabolic pathway, (b) functional resolution of distinct metagenomic environments, (c) inferring patterns of functional interaction between resident microbes, and (d) automating statistical comparison of functional features of studied microbiomes. Novel features incorporated in Vikodak also facilitate automatic removal of false positives and spurious functional predictions.

Conclusions

With novel provisions for comprehensive functional analysis, inclusion of microbial co-existence pattern based algorithms, automated inter-environment comparisons; in-depth analysis of individual metabolic pathways and greater flexibilities at the user end, Vikodak is expected to be an important value addition to the family of existing tools for 16S based function prediction.

Availability and Implementation

A web implementation of Vikodak can be publicly accessed at: http://metagenomics.atc.tcs.com/vikodak. This web service is freely available for all categories of users (academic as well as commercial).     

Design,green synthesis and pharmacological evaluation of novel 5,6-diaryl-1,2,4-triazines bearing 3-morpholinoethylamine moiety as potential antithrombotic agents*

The aim of this research work was to investigate a series of novel 5,6-diaryl-1,2,4-triazines (3a–3q) containing 3-morpholinoethylamine side chain, and to address their antiplatelet activity by in vitro, ex vivo and in vivo methods. All compounds were synthesized by environment benign route and their structures were unambiguously confirmed by spectral data. Compounds (3l) and (3m) were confirmed by their single crystal X-ray structures. Out of all the synthesized compounds, 10 were found to be more potent in vitro than aspirin; six of them were found to be prominent in ex vivo assays and one compound (3d) was found to have the most promising antithrombotic profile in vivo. Moreover, compound (3d) demonstrated less ulcerogenicity in rats as compared to aspirin. The selectivity of the most promising compound (3d) for COX-1 and COX-2 enzymes was determined with the help of molecular docking studies and the results were correlated with the biological activity.     

Identification of conserved residue patterns in small beta-barrel proteins

Our abilities to predict three-dimensional conformation of a polypeptide, given its amino acid sequence, remain limited despite advances in structure analysis. Analysis of structures and sequences of protein families with similar secondary structural elements, but varying topologies, might help in addressing this problem. We have studied the small beta-barrel class of proteins characterized by four strands (n = 4) and a shear number of 8 (S = 8) to understand the principles of barrel formation. Multiple alignments of the various protein sequences were generated for the analysis. Positional entropy, as a measure of residue conservation, indicated conservation of non-polar residues at the core positions. The presence of a type II beta-turn among the various barrel proteins considered was another strikingly invariant feature. A conserved glycyl-aspartyl dipeptide at the beta-turn appeared to be important in guiding the protein sequence into the barrel fold. Molecular dynamics simulations of the type II beta-turn peptide suggested that aspartate is a key residue in the folding of the protein sequence into the barrel. Our study suggests that the conserved type II beta-turn and the non-polar residues in the barrel core are crucial for the folding of the protein's primary sequence into the beta-barrel conformation.     

Structure–sequence features based prediction of phosphosites of serine/threonine protein kinases of Mycobacterium tuberculosis

Elucidation of signaling events in a pathogen is potentially important to tackle the infection caused by it. Such events mediated by protein phosphorylation play important roles in infection, and therefore, to predict the phosphosites and substrates of the serine/threonine protein kinases, we have developed a Machine learning-based approach for Mycobacterium tuberculosis serine/threonine protein kinases using kinase-peptide structure–sequence data. This approach utilizes features derived from kinase three-dimensional-structure environment and known phosphosite sequences to generate support vector machine (SVM)-based kinase-specific predictions of phosphosites of serine/threonine protein kinases (STPKs) with no or scarce data of their substrates. SVM outperformed the four machine learning algorithms we tried (random forest, logistic regression, SVM, and k-nearest neighbors) with an area under the curve receiver-operating characteristic value of 0.88 on the independent testing dataset and a 10-fold cross-validation accuracy of ~81.6% for the final model. Our predicted phosphosites of M. tuberculosis STPKs form a useful resource for experimental biologists enabling elucidation of STPK mediated posttranslational regulation of important cellular processes.     

Multiple chaperonins in bacteria—novel functions and non-canonical behaviors

Chaperonins are a class of molecular chaperones that assemble into a large double ring architecture with each ring constituting seven to nine subunits and enclosing a cavity for substrate encapsulation. The well-studied Escherichia coli chaperonin GroEL binds non-native substrates and encapsulates them in the cavity thereby sequestering the substrates from unfavorable conditions and allowing the substrates to fold. Using this mechanism, GroEL assists folding of about 10–15 % of cellular proteins. Surprisingly, about 30 % of the bacteria express multiple chaperonin genes. The presence of multiple chaperonins raises questions on whether they increase general chaperoning ability in the cell or have developed specific novel cellular roles. Although the latter view is widely supported, evidence for the former is beginning to appear. Some of these chaperonins can functionally replace GroEL in E. coli and are generally indispensable, while others are ineffective and likewise are dispensable. Additionally, moonlighting functions for several chaperonins have been demonstrated, indicating a functional diversity among the chaperonins. Furthermore, proteomic studies have identified diverse substrate pools for multiple chaperonins. We review the current perception on multiple chaperonins and their physiological and functional specificities.     

ProViDE: A software tool for accurate estimation of viral diversity in metagenomic samples

Given the absence of universal marker genes in the viral kingdom, researchers typically use BLAST (with stringent E-values) for taxonomic classification of viral metagenomic sequences. Since majority of metagenomic sequences originate from hitherto unknown viral groups, using stringent e-values results in most sequences remaining unclassified. Furthermore, using less stringent e-values results in a high number of incorrect taxonomic assignments. The SOrt-ITEMS algorithm provides an approach to address the above issues. Based on alignment parameters, SOrt-ITEMS follows an elaborate work-flow for assigning reads originating from hitherto unknown archaeal/bacterial genomes. In SOrt-ITEMS, alignment parameter thresholds were generated by observing patterns of sequence divergence within and across various taxonomic groups belonging to bacterial and archaeal kingdoms. However, many taxonomic groups within the viral kingdom lack a typical Linnean-like taxonomic hierarchy. In this paper, we present ProViDE (Program for Viral Diversity Estimation), an algorithm that uses a customized set of alignment parameter thresholds, specifically suited for viral metagenomic sequences. These thresholds capture the pattern of sequence divergence and the non-uniform taxonomic hierarchy observed within/across various taxonomic groups of the viral kingdom. Validation results indicate that the percentage of 'correct' assignments by ProViDE is around 1.7 to 3 times higher than that by the widely used similarity based method MEGAN. The misclassification rate of ProViDE is around 3 to 19% (as compared to 5 to 42% by MEGAN) indicating significantly better assignment accuracy. ProViDE software and a supplementary file (containing supplementary figures and tables referred to in this article) is available for download from http://metagenomics.atc.tcs.com/binning/ProViDE/     

The population structure of African cultivated rice oryza glaberrima (Steud.): evidence for elevated levels of linkage disequilibrium caused by admixture with O. sativa and ecological adaptation

Genome-wide linkage disequilibrium (LD) was investigated for 198 accessions of Oryza glaberrima using 93 nuclear microsatellite markers. Significantly elevated levels of LD were detected, even among distantly located markers. Free recombination among loci at the population genetic level was shown (1) by a lack of decay in LD among markers on the same chromosome and (2) by a strictly increasing composite likelihood function for the recombination parameter. This suggested that the elevation in LD was due not to physical linkage but to other factors, such as population structure. A Bayesian clustering analysis confirmed this hypothesis, indicating that the sample of O. glaberrima in this study was subdivided into at least five cryptic subpopulations. Two of these subpopulations clustered with control samples of O. sativa, subspecies indica and japonica, indicating that some O. glaberrima accessions represent admixtures. The remaining three O. glaberrima subpopulations were significantly associated with specific combinations of phenotypic traits-possibly reflecting ecological adaptation to different growing environments.