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Microbes modulate their metabolic and physiological mechanisms in response to changing environmental conditions. It is our interest to identify small regulatory RNAs using microarray expression data (GSE26043) obtained from B. cereus ATCC 14579 in AgNO3 stress. By definition, expression of transcripts from the Intergenic Regions (IGR) with >=2 fold under silver stress is predicted as novel small RNAs. Computational analysis of the IGR expression levels extracted from the available microarray data help in the identification of stress responsive sRNAs with rare promoters (Sigma 24, 28, 32, 54 and 70) followed by terminator signals predicted using the sRNAscanner tool. We predicted 1512 sRNA specific regions on both positive and negative strands collectively. Thus, a non-redundant high scoring unique 860 sRNAs with distinct promoter (S24: 83, S28: 86, S32: 31, S54: 57, S70: 223, sRNA_specific_S70: 380) and terminator signals are reported. These unique computationally predicted sRNA regions were verified with the highly expressing IGRs from the microarray data. It should be noted that 14 sRNAs reported in earlier studies were also found in this dataset. This study has reported 71 additional sRNAs from the transcriptome under metal stress response. Hence, we use global transcriptomics data for the identification of novel sRNAs in B. cereus. We described a general model using a procedure for the identification of small regulatory RNAs using microarray expression data with appropriate cross validation modules. It is found that some sRNAs reported in this study were found to have multiple rare promoters. This opens the possibility of sRNA activation under multiple stress condition. These sRNA data reported in this study should be characterized for their mRNA targets and molecular functional networks in future investigations.

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.

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.

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.