The Triton X-114-based solubilization and temperature-dependent phase separation of proteins is used for subcellular fractionation where, aqueous, detergent, and pellet fractions represents cytoplasmic, outer membrane (OM), and inner membrane proteins, respectively. Mass spectrometry-based proteomic analysis of Triton X-114 fractions of proteomic analysis of Leptospira interrogans identified 2957 unique proteins distributed across the fractions. The results are compared with bioinformatics predictions on their subcellular localization and pathogenic nature. Analysis of the distribution of proteins across the Triton X-114 fractions with the predicted characteristics is performed based on "number" of unique type of proteins, and "quantity" which represents the amount of unique protein. The highest number of predicted outer membrane proteins (OMPs) and pathogenic proteins are found in aqueous and pellet fractions, whereas detergent fraction representing the OM has the highest quantity of OMPs and pathogenic proteins though lower in number than the aqueous and pellet fractions. This leaves the possibility of an upsurge in pathogenic proteins and OMPs on the OM under pathogenic conditions suggesting their potential use to combat leptospirosis. Further, the Triton X-114 subcellular fractions are more correlated to enrichment of pathogenic proteins predicted by MP3 software than predicted localization.
Background: Small non-coding RNAs (sRNAs) are regulatory molecules, present in all forms of life, known to regulate various biological processes in response to the different environmental signals. In recent years, deep sequencing and various other computational prediction methods have been employed to identify and analyze sRNAs. Results: In the present study, we have applied an improved sRNA scanner method to predict sRNAs from the genome of Rhizobium etli, based on PWM matrix of conditional sigma factor 32. sRNAs predicted from the genome are integrated with the available stress specific transcriptome data to predict putative conditional specific sRNAs. A total of 271 sRNAs from the genome and 173 sRNAs from the transcriptome are computationally predicted. Of these, 25 sRNAs are found in both genome and transcriptome data. Putative targets for these sRNAs are predicted using TargetRNA2 and these targets are involved in a wide array of cellular functions such as cell division, transport and metabolism of amino acids, carbohydrates, energy production and conversion, translation, cell wall/membrane biogenesis, posttranslation modification, protein turnover and chaperones. Predicted targets are functionally classified based on COG analysis and GO annotations.
Conclusion:sRNAs predicted from the genome, using PWM matrices for conditional sigma factor 32 could be a better method to identify the conditional specific sRNAs which expand the list of putative sRNAs from the intergenic regions (IgRs) of R. etli and closely related α-proteobacteria. sRNAs identified in this study would be helpful to explore their regulatory role in biological cellular process during the stress.
A survey was conducted to study the biological and genetic diversity of Cardamom mosaic virus (CdMV) that causes the most widespread disease in the cardamom growing area in the Western Ghats of south India. Six distinct subgroups were derived based on their symptomatology and host range from the sixty isolates collected. The serological variability between the virus isolates was analysed by ELISA and Western blotting. The 3 terminal region consisting of the coat protein (CP) coding sequence and 3 untranslated region (3 UTR) was cloned and sequenced from seven isolates. Sequence comparisons revealed considerable genetic diversity among the isolates in their CP and 3 UTR, making CdMV one of the highly variable members of Potyviridae. The possible occurrence of recombination between the isolates and the movement of the virus in the cardamom tract of south India are discussed.
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