De novo sequencing and characterization of Picrorhiza kurrooa transcriptome at two temperatures showed major transcriptome adjustments

<p>Abstract</p> <p>Background</p> <p><it>Picrorhiza kurrooa </it>Royle ex Benth. is an endangered plant species of medicinal importance. The medicinal property is attributed to monoterpenoids picroside I and II, which are modulated by temperature. The transcriptome information of this species is limited with the availability of few hundreds of expressed sequence tags (ESTs) in the public databases. In order to gain insight into temperature mediated molecular changes, high throughput <it>de novo </it>transcriptome sequencing and analyses were carried out at 15°C and 25°C, the temperatures known to modulate picrosides content.</p> <p>Results</p> <p>Using paired-end (PE) Illumina sequencing technology, a total of 20,593,412 and 44,229,272 PE reads were obtained after quality filtering for 15°C and 25°C, respectively. Available (e.g., De-Bruijn/Eulerian graph) and in-house developed bioinformatics tools were used for assembly and annotation of transcriptome. A total of 74,336 assembled transcript sequences were obtained, with an average coverage of 76.6 and average length of 439.5. Guanine-cytosine (GC) content was observed to be 44.6%, while the transcriptome exhibited abundance of trinucleotide simple sequence repeat (SSR; 45.63%) markers.</p> <p>Large scale expression profiling through "read per exon kilobase per million (RPKM)", showed changes in several biological processes and metabolic pathways including <it>cytochrome P450s </it>(<it>CYPs</it>), <it>UDP-glycosyltransferases </it>(<it>UGTs</it>) and those associated with picrosides biosynthesis. RPKM data were validated by reverse transcriptase-polymerase chain reaction using a set of 19 genes, wherein 11 genes behaved in accordance with the two expression methods.</p> <p>Conclusions</p> <p>Study generated transcriptome of <it>P. kurrooa </it>at two different temperatures. Large scale expression profiling through RPKM showed major transcriptome changes in response to temperature reflecting alterations in major biological processes and metabolic pathways, and provided insight of GC content and SSR markers. Analysis also identified putative <it>CYPs </it>and <it>UGTs </it>that could help in discovering the hitherto unknown genes associated with picrosides biosynthesis.</p

miR-BAG: bagging based identification of microRNA precursors.

Non-coding elements such as miRNAs play key regulatory roles in living systems. These ultra-short, ∼21 bp long, RNA molecules are derived from their hairpin precursors and usually participate in negative gene regulation by binding the target mRNAs. Discovering miRNA candidate regions across the genome has been a challenging problem. Most of the existing tools work reliably only for limited datasets. Here, we have presented a novel reliable approach, miR-BAG, developed to identify miRNA candidate regions in genomes by scanning sequences as well as by using next generation sequencing (NGS) data. miR-BAG utilizes a bootstrap aggregation based machine learning approach, successfully creating an ensemble of complementary learners to attain high accuracy while balancing sensitivity and specificity. miR-BAG was developed for wide range of species and tested extensively for performance over a wide range of experimentally validated data. Consideration of position-specific variation of triplet structural profiles and mature miRNA anchored structural profiles had a positive impact on performance. miR-BAG's performance was found consistent and the accuracy level was observed to be >90% for most of the species considered in the present study. In a detailed comparative analysis, miR-BAG performed better than six existing tools. Using miR-BAG NGS module, we identified a total of 22 novel miRNA candidate regions in cow genome in addition to a total of 42 cow specific miRNA regions. In practice, discovery of miRNA regions in a genome demands high-throughput data analysis, requiring large amount of processing. Considering this, miR-BAG has been developed in multi-threaded parallel architecture as a web server as well as a user friendly GUI standalone version

Jackknife dendrogram of fungal communities associated with bulk soil, cormosphere and rhizosphere during flowering stage and bulk soil and cormosphere during dormant stage of <i>C</i>. <i>sativus</i>.

<p>The Jackknife tree depicts that the rhizosphere is phylogenetically similar to cormosphere during flowering stage than other samples.</p

Comparison of relative abundance of fungal phyla in bulk soil, rhizosphere and cormosphere during flowering and dormant stage.

<p>The comparison indicates the dominance of <i>Zygomycota</i> in rhizosphere and cormosphere (dormant stage), <i>Basidiomycota</i> in cormosphere (Flowering stage) and <i>Ascomycota</i> in bulk soil during both stages.</p

Relative abundance of fungal genera in the bulk soil, cormosphere and rhizosphere during flowering stage and bulk soil and cormosphere during dormant stage of <i>C</i>. <i>sativus</i>.

<p>During flowering stage, <i>Pseudogymnoascus</i> (30.54%) was dominant in bulk soil, <i>Rhizopus</i> (46.62%) in rhizosphere and yet–to–be–cultivated <i>Basidiomycota</i> fungi (92.6%) in cormosphere. Yet-to-be-cultivated <i>Ascomycota</i> fungi were dominant in Bulk soil = 57.50% and cormosphere = 99.7% during dormant stage.</p

Comparative Metagenomics Reveal Phylum Level Temporal and Spatial Changes in Mycobiome of Belowground Parts of <i>Crocus sativus</i> - Fig 6

<p><b>Venn Diagrams reporting the number of OTUs shared among investigated <i>C</i>. <i>sativus</i> sample types A) bulk soil, cormosphere and rhizosphere during flowering stage. B) bulk soil and cormosphere during dormant stage C) cormosphere during flowering and dormant stage.</b> During flowering stage, out of total 235 OTUs, only 13 OTUs were shared by all the three niches whereas during dormant stage, only 10 OTUs were common out of 115 OTUs. Total of 105 OTUs were catalogued from cormosphere during two growth stages out of which only 7 OTUs were common.</p

Rarefaction curves for fungal OTUs clustering at 97% rRNA sequence similarity from the five niches of <i>C</i>. <i>sativus</i>.

<p>Rarefaction curves represent more diversity in bulk soil during dormant stage as compared to rhizosphere and cormosphere. Curves represent sequences for bulk soil, cormosphere and rhizosphere during flowering and bulk and cormosphere during dormant stage of <i>C</i>. <i>sativus</i>.</p

Dominance pattern of fungal community in each of niche during two growth stages.

<p>During flowering stage, dominance of <i>Rhizopus arrhizus (Zygomycota</i> phylum) in rhizosphere, <i>Pseudogymnoascus roseus (Ascomycota</i> phylum) in bulk soil was observed whereas in the cormosphere, the sequences belonging to dominant <i>Basidiomycota</i> phylum could not be classified upto genera or species level. During dormant stage, <i>Rhizopus arrhizus (Zygomycota</i> phylum) was dominant in cormosphere whereas in the bulk soil the sequences belonging to dominant <i>Ascomycota</i> phylum could not be classified upto genera or species level. In the figure, P represents phylum, G represents genus and S represents species of fungi.</p