Ziauddin Ahamed Rafi

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/.

Molecular modeling of the additional inhibitor site located in secretory phospholipase A(2).

Abstract Phospholipase A2 belongs to a super family of enzymes that is massively over expressed in a variety of severe inflammatory diseases, which degrades membrane phospholipids. It has also been reported that this activity leads to loss of tissue, organ integrity and function. This enzyme is an important target for anti-inflammatory drugs. Unsaturated aldehyde terpenoids (non-specific inhibitors) are also being reported, however, they are known to irreversibly modify the enzyme and its action through covalent bond formation. Conformational analysis of secretory phospholipase A2 indicates that the enzymes known active site (hydrophobic site) is highly flexible. The studies revealed an additional inhibitor interaction site at the interfacial allosteric binding region of the enzyme. This study unequivocally establishes that non-specific inhibitors like aldehyde terpenoids can simultaneously interact with the enzyme at dual active sites and hence they are reported to be very effective for their inhibitory action.

Molecular Modeling of Sialyloligosaccharide Fragments into the Active site of Influenza Virus N9 Neuraminidase

Abstract Molecular modeling studies have been carried out to investigate the interactions between substrate sialyloligosaccharide (SOS) fragments bearing different glycosidic linkages and influenza virus N9 neuraminidase, a surface glycoprotein of influenza virus subtype N9. The studies revealed that the allowed orientation for sialic acid (SA) is less than 1% in the Eulerian space at the active site. The active site of this enzyme has enough space to accommodate various SOS fragments, NeuNAcα(2–3)Gal, NeuNAcα (2–6)Gal, NeuNAcα (2–8)NeuNAc and NeuNAcα (2–9)NeuNAc, but on specific conformations. In the bound conformation, among these substrates there exists a conformational similarity leading to a structural similarity, which may be an essential requirement for the cleavage activity of the neuraminidases irrespective of the type of glycosidic linkage.

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/.

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.

Intergenics: A tool for extraction of intergenicregions

For the past one decade, there has been considerable explosion of interest in searching novel regulatory elements in the intergenic region between the protein coding regions. The microbial genomes are the most exploited in terms of intergenic (noncoding) regions due to its less complexity. We think, the increasing pace of genome sequencing calls for a tool which will be useful for the extraction of intergenic regions. IntergenicS (Intergenic Sequence) is a tool which can extract the intergenic regions of microbial genomes at NCBI. All the unannotated regions between annotated protein coding genes and noncoding RNA genes can be extracted. It also deals with the calculation of GC base composition of the intergenic regions. This will be a useful tool for the analysis of noncoding regions of both bacterial and archael genomes.

sRNAbase: A Web Resource for Small RNAs in Enterobacteriaceae\m{1}

Bacterial small RNAs (sRNAs) have gained considerable attention due to their multivalent roles in the survival and pathogenesis of bacteria and mostly identified through bio-computational methods. A manually curated web-resource, sRNAbase has been constructed to give comprehensive and exhaustive information on non-coding small RNAs excluding tRNAs and rRNAs in Enterobacteriaceae family. The sRNA entries curated in sRNAbase contain experimentally verified small RNAs available in the literature and their partial/non-homologs reported within the related genomes from our earlier studies. The sRNAbase aims to facilitate the scientific community by providing information on the physical genomic location of the non-coding small RNAs, its alias names, sequences, strand orientation, gene identification numbers of the conserved genes that sandwiches the particular sRNA with possible functional role and a link to the PubMed literatures. Currently, sRNAbase holding information on 1986 entries belongs to 80 sRNA families spread over 45 Enterobacteriaceae genomes. The sRNAbase is accessible on the web at http://bicmku.in:8081/srnabase/.

PsRNA: A Computing Engine for the Comparative Identification of Putative Small RNA Locations within Intergenic Regions

Small RNAs (sRNAs) are non-coding transcripts exerting their functions in the cells directly. Identification of sRNAs is a difficult task due to the lack of clear sequence and structural biases. Most sRNAs are identified within genus specific intergenic regions in related genomes. However, several of these regions remain un-annotated due to lack of sequence homology and/or potent statistical identification tools. A computational engine has been built to search within the intergenic regions to identify and roughly annotate new putative sRNA regions in Enterobacteriaceae genomes. It utilizes experimentally known sRNA data and their flanking genes/KEGG Orthology (KO) numbers as templates to identify similar sRNA regions in related query genomes. The search engine not only has the capability to locate putative intergenic regions for specific sRNAs, but also has the potency to locate conserved, shuffled or deleted gene clusters in query genomes. Because it uses the KO terms for locating functionally important regions such as sRNAs, any further KO number assignment to additional genes will increase the sensitivity. The PsRNA server is used for the identification of putative sRNA regions through the information retrieved from the sRNA of interest. The computing engine is available online at http://bioserver1.physics.iisc.ernet.in/psrna/ and http://bicmku.in:8081/psrna/.

3-benzoyl-2-hydroxy-2-methylchromene.

In the title compound, C17H14O3, the conformation of the pyran ring is intermediate between sofa and half-chair. In the crystal structure, the hydroxyl and carbonyl O atoms are involved in O—H⋯O intermolecular hydrogen bonding, forming chains along the a axis.