Genome sequencing and analysis reveals possible determinants of Staphylococcus aureus nasal carriage

<p>Abstract</p> <p>Background</p> <p>Nasal carriage of <it>Staphylococcus aureus </it>is a major risk factor in clinical and community settings due to the range of etiologies caused by the organism. We have identified unique immunological and ultrastructural properties associated with nasal carriage isolates denoting a role for bacterial factors in nasal carriage. However, despite extensive molecular level characterizations by several groups suggesting factors necessary for colonization on nasal epithelium, genetic determinants of nasal carriage are unknown. Herein, we have set a genomic foundation for unraveling the bacterial determinants of nasal carriage in <it>S. aureus.</it></p> <p>Results</p> <p>MLST analysis revealed no lineage specific differences between carrier and non-carrier strains suggesting a role for mobile genetic elements. We completely sequenced a model carrier isolate (D30) and a model non-carrier strain (930918-3) to identify differential gene content. Comparison revealed the presence of 84 genes unique to the carrier strain and strongly suggests a role for Type VII secretion systems in nasal carriage. These genes, along with a putative pathogenicity island (SaPIBov) present uniquely in the carrier strains are likely important in affecting carriage. Further, PCR-based genotyping of other clinical isolates for a specific subset of these 84 genes raise the possibility of nasal carriage being caused by multiple gene sets.</p> <p>Conclusion</p> <p>Our data suggest that carriage is likely a heterogeneic phenotypic trait and implies a role for nucleotide level polymorphism in carriage. Complete genome level analyses of multiple carriage strains of <it>S. aureus </it>will be important in clarifying molecular determinants of <it>S. aureus </it>nasal carriage.</p

Promoter addresses: revelations from oligonucleotide profiling applied to the Escherichia coli genome

BACKGROUND: Transcription is the first step in cellular information processing. It is regulated by cis-acting elements such as promoters and operators in the DNA, and trans-acting elements such as transcription factors and sigma factors. Identification of cis-acting regulatory elements on a genomic scale requires computational analysis. RESULTS: We have used oligonucleotide profiling to predict regulatory regions in a bacterial genome. The method has been applied to the Escherichia coli K12 genome and the results analyzed. The information content of the putative regulatory oligonucleotides so predicted is validated through intra-genomic analyses, correlations with experimental data and inter-genome comparisons. Based on the results we have proposed a model for the bacterial promoter. The results show that the method is capable of identifying, in the E.coli genome, cis-acting elements such as TATAAT (sigma70 binding site), CCCTAT (1 base relative of sigma32 binding site), CTATNN (LexA binding site), AGGA-containing hexanucleotides (Shine Dalgarno consensus) and CTAG-containing hexanucleotides (core binding sites for Trp and Met repressors). CONCLUSION: The method adopted is simple yet effective in predicting upstream regulatory elements in bacteria. It does not need any prior experimental data except the sequence itself. This method should be applicable to most known genomes. Profiling, as applied to the E.coli genome, picks up known cis-acting and regulatory elements. Based on the profile results, we propose a model for the bacterial promoter that is extensible even to eukaryotes. The model is that the core promoter lies within a plateau of bent AT-rich DNA. This bent DNA acts as a homing segment for the sigma factor to recognize the promoter. The model thus suggests an important role for local landscapes in prokaryotic and eukaryotic gene regulation

Non-coding and Coding Transcriptional Profiles Are Significantly Altered in Pediatric Retinoblastoma Tumors.

Retinoblastoma is a rare pediatric tumor of the retina, caused by the homozygous loss of the Retinoblastoma 1 (RB1) tumor suppressor gene. Previous microarray studies have identified changes in the expression profiles of coding genes; however, our understanding of how non-coding genes change in this tumor is absent. This is an important area of research, as in many adult malignancies, non-coding genes including LNC-RNAs are used as biomarkers to predict outcome and/or relapse. To establish a complete and in-depth RNA profile, of both coding and non-coding genes, in Retinoblastoma tumors, we conducted RNA-seq from a cohort of tumors and normal retina controls. This analysis identified widespread transcriptional changes in the levels of both coding and non-coding genes. Unexpectedly, we also found rare RNA fusion products resulting from genomic alterations, specific to Retinoblastoma tumor samples. We then determined whether these gene expression changes, of both coding and non-coding genes, were also found in a completely independent Retinoblastoma cohort. Using our dataset, we then profiled the potential effects of deregulated LNC-RNAs on the expression of neighboring genes, the entire genome, and on mRNAs that contain a putative area of homology. This analysis showed that most deregulated LNC-RNAs do not act locally to change the transcriptional environment, but potentially function to modulate genes at distant sites. From this analysis, we selected a strongly down-regulated LNC-RNA in Retinoblastoma, DRAIC, and found that restoring DRAIC RNA levels significantly slowed the growth of the Y79 Retinoblastoma cell line. Collectively, our work has generated the first non-coding RNA profile of Retinoblastoma tumors and has found that these tumors show widespread transcriptional deregulation

Codon choice in genes depends on flanking sequence information—implications for theoretical reverse translation

Algorithms for theoretical reverse translation have direct applications in degenerate PCR. The conventional practice is to create several degenerate primers each of which variably encode the peptide region of interest. In the current work, for each codon we have analyzed the flanking residues in proteins and determined their influence on codon choice. From this, we created a method for theoretical reverse translation that includes information from flanking residues of the protein in question. Our method, named the neighbor correlation method (NCM) and its enhancement, the consensus-NCM (c-NCM) performed significantly better than the conventional codon-usage statistic method (CSM). Using the methods NCM and c-NCM, we were able to increase the average sequence identity from 77% up to 81%. Furthermore, we revealed a significant increase in coverage, at 80% identity, from < 20% (CSM) to > 75% (c-NCM). The algorithms, their applications and implications are discussed herein

Non-coding and Coding Transcriptional Profiles Are Significantly Altered in Pediatric Retinoblastoma Tumors

Retinoblastoma is a rare pediatric tumor of the retina, caused by the homozygous loss of the Retinoblastoma 1 (RB1) tumor suppressor gene. Previous microarray studies have identified changes in the expression profiles of coding genes; however, our understanding of how non-coding genes change in this tumor is absent. This is an important area of research, as in many adult malignancies, non-coding genes including LNC-RNAs are used as biomarkers to predict outcome and/or relapse. To establish a complete and in-depth RNA profile, of both coding and non-coding genes, in Retinoblastoma tumors, we conducted RNA-seq from a cohort of tumors and normal retina controls. This analysis identified widespread transcriptional changes in the levels of both coding and non-coding genes. Unexpectedly, we also found rare RNA fusion products resulting from genomic alterations, specific to Retinoblastoma tumor samples. We then determined whether these gene expression changes, of both coding and non-coding genes, were also found in a completely independent Retinoblastoma cohort. Using our dataset, we then profiled the potential effects of deregulated LNC-RNAs on the expression of neighboring genes, the entire genome, and on mRNAs that contain a putative area of homology. This analysis showed that most deregulated LNC-RNAs do not act locally to change the transcriptional environment, but potentially function to modulate genes at distant sites. From this analysis, we selected a strongly down-regulated LNC-RNA in Retinoblastoma, DRAIC, and found that restoring DRAIC RNA levels significantly slowed the growth of the Y79 Retinoblastoma cell line. Collectively, our work has generated the first non-coding RNA profile of Retinoblastoma tumors and has found that these tumors show widespread transcriptional deregulation

Characterization of the yehUT Two-Component Regulatory System of Salmonella enterica Serovar Typhi and Typhimurium

10.1371/journal.pone.0084567PLoS ONE812-POLN

Sensitive Identification and Quantification of Microbial Species in Metagenomes

Shotgun Metagenomics has emerged as a popular approach for understanding the underlying microbial community composition and metabolic potential in the recent years. Many computational methods have been developed in order decipher the taxonomic composition based on de novo assembly, marker genes, read assignment and a combination of approaches. However, the performance of some of the methods in terms of time and memory requirement hinders their usage. This work presents a method developed to deduce the taxonomic composition of the underlying community with an emphasis on the time reduction. The quality checked reads are initially subjected to Human DNA contamination removal by mapping it to HG19 reference genome. The unaligned reads are then mapped to bacterial reference genomes followed by viral and fungal genomes using bowtie2. The remaining reads are then subjected to 3 cycles of de novo assembly, contigs gap closure and ORF prediction followed by the annotation. The mapped reads and the contigs are used to infer the taxonomic profile of the metagenome. The method is validated by comparing these taxonomic profiles with ones generated using simulated reads. The method has been broadly tested on a set of 36 oral samples. The metagenomics analysis approach taken by us typically gives a taxonomic profile covering 60 -70% of the data including bacteria and viruses with a short runtime (6 hrs)

FACTOR CAUSING STRESS TO ASSISTANT LIBRARIANS OF SELF FINANCING EDUCATIONAL INSTITUTIONS IN TIRUCHIRAPPALLI DISTRICT, TAMILNADU - AN EMPIRICAL INVESTIGATION

<p>Executive Summary : The factors causing stress in a person are called stressors. The<br>common stressors on employees may generate action from individuals, groups and<br>organizational sources. Individual, group and organizational stressors constitute work stressors,<br>whereas extra – organizational stressors refer to non-work stressors. In this research major<br>factors that causes stress to Assistant Librarians in private educational institutions were<br>identified.</p> <p> </p