Systematic analysis of HD-ZIP transcription factors in sesame genome and gene expression profiling of SiHD-ZIP class I entailing drought stress responses at early seedling stage

BACKGROUND

Sesame is an ancient oilseed crop, known for its high oil content and quality. Its sensitivity to drought at early seedling stage is one of the limiting factors affecting its world-wide growth and productivity. Among plant specific transcription factors, the association of HD-ZIPs with sesame drought responses at early seedling stage is not well-established yet and is very important to develop our molecular understanding on sesame drought tolerance.

METHODS AND RESULTS

In this study, total 61 sesame HD-ZIP proteins were identified, based on their protein sequence homology with Arabidopsis and protein domain(s) architecture prediction, followed by their phylogenetic, conserved domain(s) motifs and gene structure analyses to classify them into four classes (HD-ZIP Class I-IV). HD-ZIP Class I was also subdivided into four subgroups: α (SiHZ25, SiHZ43, SiHZ9 and SiHZ16), β1 (SiHZ10, SiHZ30, SiHZ32 and SiHZ26), β2 (SiHZ42 and SiHZ45) and γ (SiHZ17, SiHZ7 and SiHZ35) by a comparative phylogenetic analysis of sesame with Arabidopsis and maize. Afterwards, twenty-one days old sesame seedlings were exposed to drought stress by withholding water for 7 days (when soil moisture content reduced to ~16%) and gene expression of HD-ZIP Class I (13 members) was performed in well- watered (control) and drought stressed seedlings. The gene expression analysis showed that the expressions of SiHZ7 (6.8 fold) and SiHZ35 (2.6 fold) from γ subgroup were significantly high in drought seedlings.

CONCLUSIONS

This study is useful in demonstrating the role of SiHD-ZIP Class I in sesame drought responses at early seedling stage and to develop its novel drought tolerant varieties.

Shaping the nebulous enhancer in the era of high-throughput assays and genome editing

Since the 1st discovery of transcriptional enhancers in 1981, their textbook definition has remained largely unchanged in the past 37 years. With the emergence of high-throughput assays and genome editing, which are switching the paradigm from bottom-up discovery and testing of individual enhancers to top-down profiling of enhancer activities genome-wide, it has become increasingly evidenced that this classical definition has left substantial gray areas in different aspects. Here we survey a representative set of recent research articles and report the definitions of enhancers they have adopted. The results reveal that a wide spectrum of definitions is used usually without the definition stated explicitly, which could lead to difficulties in data interpretation and downstream analyses. Based on these findings, we discuss the practical implications and suggestions for future studies.

Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions

BACKGROUND

Structure profiling experiments provide single-nucleotide information on RNA structure. Recent advances in chemistry combined with application of high-throughput sequencing have enabled structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. Propelled by these experimental advances, massive data with ever-increasing diversity and complexity have been generated, which give rise to new challenges in interpreting and analyzing these data.

RESULTS

We review current practices in analysis of structure profiling data with emphasis on comparative and integrative analysis as well as highlight emerging questions. Comparative analysis has revealed structural patterns across transcriptomes and has become an integral component of recent profiling studies. Additionally, profiling data can be integrated into traditional structure prediction algorithms to improve prediction accuracy.

CONCLUSIONS

To keep pace with experimental developments, methods to facilitate, enhance and refine such analyses are needed. Parallel advances in analysis methodology will complement profiling technologies and help them reach their full potential.

RNAex: an RNA secondary structure prediction server enhanced by high-throughput structure-probing data

Several high-throughput technologies have been developed to probe RNA base pairs and loops at the transcriptome level in multiple species. However, to obtain the final RNA secondary structure, extensive effort and considerable expertise is required to statistically process the probing data and combine them with free energy models. Therefore, we developed an RNA secondary structure prediction server that is enhanced by experimental data (RNAex). RNAex is a web interface that enables non-specialists to easily access cutting-edge structure-probing data and predict RNA secondary structures enhanced by in vivo and in vitro data. RNAex annotates the RNA editing, RNA modification and SNP sites on the predicted structures. It provides four structure-folding methods, restrained MaxExpect, SeqFold, RNAstructure (Fold) and RNAfold that can be selected by the user. The performance of these four folding methods has been verified by previous publications on known structures. We re-mapped the raw sequencing data of the probing experiments to the whole genome for each species. RNAex thus enables users to predict secondary structures for both known and novel RNA transcripts in human, mouse, yeast and Arabidopsis The RNAex web server is available at http://RNAex.ncrnalab.org/.