Jayavel Sridhar

sRNAscanner: A Computational Tool for Intergenic Small RNA Detection in Bacterial Genomes

Background Bacterial non-coding small RNAs (sRNAs) have attracted considerable attention due to their ubiquitous nature and contribution to numerous cellular processes including survival, adaptation and pathogenesis. Existing computational approaches for identifying bacterial sRNAs demonstrate varying levels of success and there remains considerable room for improvement. Methodology/Principal Findings Here we have proposed a transcriptional signal-based computational method to identify intergenic sRNA transcriptional units (TUs) in completely sequenced bacterial genomes. Our sRNAscanner tool uses position weight matrices derived from experimentally defined E. coli K-12 MG1655 sRNA promoter and rho-independent terminator signals to identify intergenic sRNA TUs through sliding window based genome scans. Analysis of genomes representative of twelve species suggested that sRNAscanner demonstrated equivalent sensitivity to sRNAPredict2, the best performing bioinformatics tool available presently. However, each algorithm yielded substantial numbers of known and uncharacterized hits that were unique to one or the other tool only. sRNAscanner identified 118 novel putative intergenic sRNA genes in Salmonella enterica Typhimurium LT2, none of which were flagged by sRNAPredict2. Candidate sRNA locations were compared with available deep sequencing libraries derived from Hfq-co-immunoprecipitated RNA purified from a second Typhimurium strain (Sittka et al. (2008) PLoS Genetics 4: e1000163). Sixteen potential novel sRNAs computationally predicted and detected in deep sequencing libraries were selected for experimental validation by Northern analysis using total RNA isolated from bacteria grown under eleven different growth conditions. RNA bands of expected sizes were detected in Northern blots for six of the examined candidates. Furthermore, the 5′-ends of these six Northern-supported sRNA candidates were successfully mapped using 5′-RACE analysis. Conclusions/Significance We have developed, computationally examined and experimentally validated the sRNAscanner algorithm. Data derived from this study has successfully identified six novel S. Typhimurium sRNA genes. In addition, the computational specificity analysis we have undertaken suggests that ∼40% of sRNAscanner hits with high cumulative sum of scores represent genuine, undiscovered sRNA genes. Collectively, these data strongly support the utility of sRNAscanner and offer a glimpse of its potential to reveal large numbers of sRNA genes that have to date defied identification. sRNAscanner is available from: http://bicmku.in:8081/sRNAscanner or http://cluster.physics.iisc.ernet.in/sRNAscanner/.

Computational Small RNA Prediction in Bacteria

Bacterial, small RNAs were once regarded as potent regulators of gene expression and are now being considered as essential for their diversified roles. Many small RNAs are now reported to have a wide array of regulatory functions, ranging from environmental sensing to pathogenesis. Traditionally, noncoding transcripts were rarely detected by means of genetic screens. However, the availability of approximately 2200 prokaryotic genome sequences in public databases facilitates the efficient computational search of those molecules, followed by experimental validation. In principle, the following four major computational methods were applied for the prediction of sRNA locations from bacterial genome sequences: (1) comparative genomics, (2) secondary structure and thermodynamic stability, (3) ‘Orphan’ transcriptional signals and (4) ab initio methods regardless of sequence or structure similarity; most of these tools were applied to locate the putative genomic sRNA locations followed by experimental validation of those transcripts. Therefore, computational screening has simplified the sRNA identification process in bacteria. In this review, a plethora of small RNA prediction methods and tools that have been reported in the past decade are discussed comprehensively and assessed based on their attributes, compatibility, and their prediction accuracy.

Phylogenetic reconstruction of endophytic fungal isolates using internal transcribed spacer 2 (ITS2) region

Endophytic fungi are inhabitants of plants, living most part of their lifecycle asymptomatically which mainly confer protection and ecological advantages to the host plant. In this present study, 48 endophytic fungi were isolated from the leaves of three medicinal plants and characterized based on ITS2 sequence – secondary structure analysis. ITS2 secondary structures were elucidated with minimum free energy method (MFOLD version 3.1) and consensus structure of each genus was generated by 4SALE. ProfDistS was used to generate ITS2 sequence structure based phylogenetic tree respectively. Our elucidated isolates were belonging to Ascomycetes family, representing 5 orders and 6 genera. Colletotrichum/Glomerella spp., Diaporthae/Phomopsis spp., and Alternaria spp., were predominantly observed while Cochliobolus sp., Cladosporium sp., and Emericella sp., were represented by singletons. The constructed phylogenetic tree has well resolved monophyletic groups with >50% bootstrap value support. Secondary structures based fungal systematics improves not only the stability; it also increases the precision of phylogenetic inference. Above ITS2 based phylogenetic analysis was performed for our 48 isolates along with sequences of known ex-types taken from GenBank which confirms the efficiency of the proposed method. Further, we propose it as superlative marker for reconstructing phylogenetic relationships at different taxonomic levels due to their lesser length.

Modeling and structural analysis of cellulases using Clostridium thermocellum as template

Cellulase is one of the most widely distributed enzymes with wide application. They are involved in conversion of biomass into simpler sugars. Cellulase of Trichoderma longibrachiatum, a known cellulolytic fungus was compared with Clostridium thermocellum [AAA23226.1] cellulase. Blastp was performed with AAA23226.1 as query sequence to obtain nine similar sequences from NCBI protein data bank. The physicochemical properties of cellulase were analyzed using ExPASy’s ProtParam tool namely ProtParam, SOPMA and GOR IV. Homology modeling was done using SWISS MODEL and checked quality by RMSD values using VMD1.9.1. Active sites of each model were predicted using automated active site prediction server of SCFBio. Study revealed instability of cellulase of two eukaryotic strains namely Trichoderma longibrachiatum [CAA43059.1] and Melanocarpus albomyces [CAD56665.1]. The negative GRAVY score value of cellulases ensured better interaction and activity in aqueous phase. It was found that molecular weight (M. Wt) ranges between 25-127.56 kDa. Iso-electric point (pI) of cellulases was found to be acidic in nature. GOR IV and SOPMA were used to predict secondary structure of cellulase, which showed that random coil, was dominated. Neighbor joining tree with C. thermocellum [AAA23226.1] cellulase as root showed that cellulases of Thermoaerobacter subterraneus [ZP_07835928] and C. thermocellum [CAA4305.1] were more similar to eukaryotic cellulases supported by least boot strap values. Pseudoalteromonas haloplanktis cellulase was found to be the ideal model supported by least RMSD score among the predicted structures. Trichoderma longibrachiatum cellulase was found to be the best compared to other cellulases, which possess high number of active sites with ASN and THR rich active sites. CYS residues were also present ensuring stable interaction and better bonding. Hydrophilic residues were found high in active sites of all analyzed models and template.

BrucellaBase: Genome information resource

Brucella sp. causes a major zoonotic disease, brucellosis. Brucella belongs to the family Brucellaceae under the order Rhizobiales of Alphaproteobacteria. We present BrucellaBase, a web-based platform, providing features of a genome database together with unique analysis tools. We have developed a web version of the multilocus sequence typing (MLST) (Whatmore et al., 2007) and phylogenetic analysis of Brucella spp. BrucellaBase currently contains genome data of 510 Brucella strains along with the user interfaces for BLAST, VFDB, CARD, pairwise genome alignment and MLST typing. Availability of these tools will enable the researchers interested in Brucella to get meaningful information from Brucella genome sequences. BrucellaBase will regularly be updated with new genome sequences, new features along with improvements in genome annotations. BrucellaBase is available online at http://www.dbtbrucellosis.in/brucellabase.html or http://59.99.226.203/brucellabase/homepage.html.

Junker: An Intergenic Explorer for Bacterial Genomes

In the past few decades, scientists from all over the world have taken a keen interest in novel functional units such as small regulatory RNAs, small open reading frames, pseudogenes, transposons, integrase binding attB/attP sites, repeat elements within the bacterial intergenic regions (IGRs) and in the analysis of those “junk” regions for genomic complexity. Here we have developed a web server, named Junker, to facilitate the in-depth analysis of IGRs for examining their length distribution, four-quadrant plots, GC percentage and repeat details. Upon selection of a particular bacterial genome, the physical genome map is displayed as a multiple loci with options to view any loci of interest in detail. In addition, an IGR statistics module has been created and implemented in the web server to analyze the length distribution of the IGRs and to understand the disordered grouping of IGRs across the genome by generating the four-quadrant plots. The proposed web server is freely available at the URL http://pranag.physics.iisc.ernet.in/junker/.

Draft Genome Sequence of Brucella melitensis Strain ADMAS-G1, Isolated from Placental Fluids of an Aborted Goat

ABSTRACT Here, we report the draft genome sequence and annotation of the Brucella melitensis strain designated ADMAS-G1, isolated from placental fluids of an aborted goat. The length of the genome is 3,284,982 bp, with a 57.3% GC content. A total of 3,325 protein-coding genes and 63 RNA genes were predicted.

CsrA interacting small RNAs in Haemophilus spp genomes: a theoretical analysis

The csrA is a carbon storage regulator gene that encodes a protein with multiple RNA interaction sites. Bacterial non-coding small RNAs like csrB, csrC and their counterparts in diverse bacterial genus are identified to control the regulatory activities of CsrA and its orthologs. An attempt has been made in this study to identify ‘novel’ non-coding small RNAs that are involved in the regulatory activities of csrA gene. All CsrA-interacting small RNAs are computationally fingerprinted to have multiple occurrence of 7-nucleotide CsrA interacting repeats [CAGGA(U/A/C)G] along with a 18-nucleotide upstream binding site. However, in several of the genomes like Haemophilus spp, the upstream binding site is not identified. The current methodology overcomes this difficulty by identifying small RNA-specific orphan transcriptional units within the intergenic regions of the genome. The results could identify all known CsrA-interacting small RNAs in E. coli, Vibrio cholerae and Pseudomonas aeruginosa genomes and additionally has picked six new possible CsrA-interacting small RNA regions in E. coli. Our computational analysis indicates that known rygD and rprA sRNAs in E. coli could possibly interact with CsrA proteins. The study is extended to three of the Haemophilus genomes that could identify seven new possible CsrA interacting small RNAs.

Functional annotations in bacterial genomes based on small RNA signatures.

One of the key challenges in computational genomics is annotating coding genes and identification of regulatory RNAs in complete genomes. An attempt is made in this study which uses the regulatory RNA locations and their conserved flanking genes identified within the genomic backbone of template genome to search for similar RNA locations in query genomes. The search is based on recently reported coexistence of small RNAs and their conserved flanking genes in related genomes. Based on our study, 54 additional sRNA locations and functions of 96 uncharacterized genes are predicted in two draft genomes viz., Serratia marcesens Db1 and Yersinia enterocolitica 8081. Although most of the identified additional small RNA regions and their corresponding flanking genes are homologous in nature, the proposed anchoring technique could successfully identify four non-homologous small RNA regions in Y. enterocolitica genome also. The KEGG Orthology (KO) based automated functional predictions confirms the predicted functions of 65 flanking genes having defined KO numbers, out of the total 96 predictions made by this method. This coexistence based method shows more sensitivity than controlled vocabularies in locating orthologous gene pairs even in the absence of defined Orthology numbers. All functional predictions made by this study in Y. enterocolitica 8081 were confirmed by the recently published complete genome sequence and annotations. This study also reports the possible regions of gene rearrangements in these two genomes and further characterization of such RNA regions could shed more light on their possible role in genome evolution.

Draft Genome Sequence of Brucella abortus Virulent Strain 544

ABSTRACT Here, we present the draft genome sequence and annotation of Brucella abortus virulent strain 544. The genome of this strain is 3,289,405 bp long, with 57.2% G+C content. A total of 3,259 protein-coding genes and 60 RNA genes were predicted.