The stationary phase-specific sRNA FimR2 is a multifunctional regulator of bacterial motility, biofilm formation and virulence

Bacterial pathogens employ a plethora of virulence factors for host invasion, and their use is tightly regulated to maximize infection efficiency and manage resources in a nutrient-limited environment. Here we show that during Escherichia coli stationary phase the 3' UTR-derived small non-coding RNA FimR2 regulates fimbrial and flagellar biosynthesis at the post-transcriptional level, leading to biofilm formation as the dominant mode of survival under conditions of nutrient depletion. FimR2 interacts with the translational regulator CsrA, antagonizing its functions and firmly tightening control over motility and biofilm formation. Generated through RNase E cleavage, FimR2 regulates stationary phase biology by fine-tuning target mRNA levels independently of the chaperones Hfq and ProQ. The Salmonella enterica orthologue of FimR2 induces effector protein secretion by the type III secretion system and stimulates infection, thus linking the sRNA to virulence. This work reveals the importance of bacterial sRNAs in modulating various aspects of bacterial physiology including stationary phase and virulence

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

<div><p>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: <a href="http://metagenomics.atc.tcs.com/arm" target="_blank">http://metagenomics.atc.tcs.com/arm</a>.</p></div

Number of association rules generated from the prebiotics dataset with various run-time thresholds.

<p>Number of association rules generated using the Apriori rule mining approach on the prebiotics dataset at various values of support count and confidence thresholds. Table also depicts variations in number of rules due to adoption of various strategies that define the minimum abundance threshold for individual taxa to be considered for rule mining.</p

Comparison of results generated using correlation approach and the Apriori approach.

<p>A comparison of results generated using (i) correlation approach and (ii) the Apriori approach. The abundance values indicated in part A represent the actual abundances of 4 genera in various samples constituting the prebiotic datasets [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154493#pone.0154493.ref002" target="_blank">2</a>]. Table shown in Part B indicates Spearman correlation values computed between various taxa pairs. The taxon pair that generated a significant correlation is indicated in green font. Part C depicts association rules generated using the Apriori approach.</p

Minimalist graphical representation of associative rules involving 3 or more genera.

<p>A 'minimalist' graphical representation of associative rules (involving 3 or more genera) generated from an example dataset containing 26 genera named alphabetically (A to Z). Rules indicated in this example involve only 13 out of 26 genera. It is pertinent to note here that genera (and/ or groups of genera) constituting an individual rule share an all-to-all associative relationship. For examples rule 3 (involving 5 genera viz. X, Y, Z, H, and O) not only indicates an associative relationship between all possible genera pairs, but also between all possible combinations of genera. For the purpose of clarity, an exhaustive list of such combinations (possible from rule 3) is provided in the table depicted in Fig 3. As indicated, rule 3 (for instance) indicates an association between the abundances of genera pair (X, Y) and the genera group (Z, H, and O). Given that Fig 3 illustrates a 'minimalist' graphical representation of all associative rules, genera X, Y, and Z (common to rules 3 and 4) are shown only once in the circled portion of the illustrated figure. The table depicted in Fig 3 also provides an exhaustive list of taxa and combinations of taxa generated from rule 4.</p

Associative rules (involving 3 or more genera) generated from the HMP datasets.

<p>A graphic representation of associative rules (involving 3 or more genera) generated from the HMP datasets [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154493#pone.0154493.ref004" target="_blank">4</a>]. Parts A and B depict association rules generated from samples corresponding to male and female subjects respectively.</p

Binpairs: utilization of Illumina paired-end information for improving efficiency of taxonomic binning of metagenomic sequences.

MOTIVATION:Paired-end sequencing protocols, offered by next generation sequencing (NGS) platforms like Illumia, generate a pair of reads for every DNA fragment in a sample. Although this protocol has been utilized for several metagenomics studies, most taxonomic binning approaches classify each of the reads (forming a pair), independently. The present work explores some simple but effective strategies of utilizing pairing-information of Illumina short reads for improving the accuracy of taxonomic binning of metagenomic datasets. The strategies proposed can be used in conjunction with all genres of existing binning methods. RESULTS:Validation results suggest that employment of these "Binpairs" strategies can provide significant improvements in the binning outcome. The quality of the taxonomic assignments thus obtained are often comparable to those that can only be achieved with relatively longer reads obtained using other NGS platforms (such as Roche). AVAILABILITY:An implementation of the proposed strategies of utilizing pairing information is freely available for academic users at https://metagenomics.atc.tcs.com/binning/binpairs

Number of association rules generated from the HMP (full) dataset with various run-time thresholds.

<p>Number of association rules generated using the Apriori rule mining approach on the HMP (full) dataset at various values of support count and confidence thresholds. Table also depicts variations in number of rules due to adoption of various strategies that define the minimum abundance threshold for individual taxa to be considered for rule mining.</p

Number of association rules generated from the HMP (male) dataset with various run-time thresholds.

<p>Number of association rules generated using the Apriori rule mining approach on the HMP (male) dataset at various values of support count and confidence thresholds. Table also depicts variations in number of rules due to adoption of various strategies that define the minimum abundance threshold for individual taxa to be considered for rule mining.</p