Phylogenomic Analysis of R2R3 MYB Transcription Factors in Sorghum and their Role in Conditioning Biofuel Syndrome.

BACKGROUND: Large scale cultivation of sorghum for food, feed, and biofuel requires concerted efforts for engineering multipurpose cultivars with optimised agronomic traits. Due to their vital role in regulating the biosynthesis of phenylpropanoid-derived compounds, biomass composition, biotic, and abiotic stress response, R2R3-MYB family transcription factors are ideal targets for improving environmental resilience and economic value of sorghum. METHODS: We used diverse computational biology tools to survey the sorghum genome to identify R2R3-MYB transcription factors followed by their structural and phylogenomic analysis. We used in-house generated as well as publicly available high throughput expression data to analyse the R2R3 expression patterns in various sorghum tissue types. RESULTS: We have identified a total of 134 R2R3-MYB genes from sorghum and developed a framework to predict gene functions. Collating information from the physical location, duplication, structural analysis, orthologous sequences, phylogeny, and expression patterns revealed the role of duplications in clade-wise expansion of the R2R3-MYB family as well as intra-clade functional diversification. Using publicly available and in-house generated RNA sequencing data, we provide MYB candidates for conditioning biofuel syndrome by engineering phenylpropanoid biosynthesis and sugar signalling pathways in sorghum. CONCLUSION: The results presented here are pivotal to prioritize MYB genes for functional validation and optimize agronomic traits in sorghum.

Genome-Wide Transcript Profiling Reveals an Auxin-Responsive Transcription Factor, OsAP2/ERF-40, Promoting Rice Adventitious Root Development.

Unlike dicots, the robust root system in grass species largely originates from stem base during postembryonic development. The mechanisms by which plant hormone signaling pathways control the architecture of adventitious root remain largely unknown. Here, we studied the modulations in global genes activity in developing rice adventitious root by genome-wide RNA sequencing in response to external auxin and cytokinin signaling cues. We further analyzed spatiotemporal regulations of key developmental regulators emerged from our global transcriptome analysis. Interestingly, some of the key cell fate determinants such as homeodomain transcription factor (TF), OsHOX12, no apical meristem protein, OsNAC39, APETALA2/ethylene response factor, OsAP2/ERF-40 and WUSCHEL-related homeobox, OsWOX6.1 and OsWOX6.2, specifically expressed in adventitious root primordia. Functional analysis of one of these regulators, an auxin-induced TF containing AP2/ERF domain, OsAP2/ERF-40, demonstrates its sufficiency to confer the adventitious root fate. The ability to trigger the root developmental program is largely attributed to OsAP2/ERF-40-mediated dose-dependent transcriptional activation of genes that can facilitate generating effective auxin response, and OsERF3-OsWOX11-OsRR2 pathway. Our studies reveal gene regulatory network operating in response to hormone signaling pathways and identify a novel TF regulating adventitious root developmental program, a key agronomically important quantitative trait, upstream of OsERF3-OsWOX11-OsRR2 pathway.

Machine Learning-Based Annotation of Long Noncoding RNAs Using PLncPRO.

Long noncoding RNAs (lncRNAs) are noncoding RNAs with transcript length more than 200 nucleotides. Although poorly conserved, lncRNAs are expressed across diverse species, including plants and animals, and are known to be involved in regulation of various biological processes. To understand their biological significance, we first need to identify the lncRNAs accurately. However, distinguishing lncRNAs from coding transcripts is still a challenging task. Here, we describe a machine learning-based approach to accurately identify the plant lncRNAs. We describe the usage of plant long noncoding RNA prediction by random forests (PLncPRO), which employs machine learning-based random forest algorithm to recognize the lncRNAs from the set of given transcript sequences. Stepwise instructions have been provided to use PLncPRO to annotate the lncRNA sequences.