Functional characterization of a putative Glycine max ELF4 in transgenic arabidopsis and its role during flowering control.

Flowering is an important trait in major crops like soybean due to its direct relation to grain production. The circadian clock mediates the perception of seasonal changes in day length and temperature to modulate flowering time. The circadian clock gene EARLY FLOWERING 4 (ELF4) was identified in Arabidopsis thaliana and is believed to play a key role in the integration of photoperiod, circadian regulation, and flowering. The molecular circuitry that comprises the circadian clock and flowering control in soybeans is just beginning to be understood. To date, insufficient information regarding the soybean negative flowering regulators exist, and the biological function of the soybean ELF4 (GmELF4) remains unknown. Here, we investigate the ELF4 family members in soybean and functionally characterize a GmELF4 homologous gene. The constitutive overexpression of GmELF4 delayed flowering in Arabidopsis, showing the ELF4 functional conservation among plants as part of the flowering control machinery. We also show that GmELF4 alters the expression of Arabidopsis key flowering time genes (AtCO and AtFT), and this down-regulation is the likely cause of flowering delay phenotypes. Furthermore, we identified the GmELF4 network genes to infer the participation of GmELF4 in soybeans. The data generated in this study provide original insights for comprehending the role of the soybean circadian clock ELF4 gene as a negative flowering controller

Análise de transcriptoma de experimentos de RNA- Seq com e sem repetições biológicas: revisão.

The discovery of nucleic acids opened new frontiers of knowledge, enablingresearchers to access an enormous amount of data, through large-scale sequencing methodologiesand bioinformatics tools. Amongst these new possibilities, RNA-Seq has been used to identify andquantify RNA molecules. To obtain more accurate biological responses from RNA-Seq data somequestions should be considered such as experimental design, type ofsynthesized library, size ofthefragments generated, number ofbiological replicates, depth, and coverage ofthe sequencing, speciesgenome availability and, the choice of software to properly perform the computational analyzes.Accurate bioinformatics analyzes allow the selection ofgenes with a lower error rate, increasing thevalidation assertiveness via RT-qPCR and thus, reducing costs. The objective of this review was topresent the analysis stages of RNA-Seq data, from experimental design to systems biology,considering relevant points, as well as to pointed out some software currently available to carry theseanalyzes out. Besides, with this review, we aimed to help the academic community to understand allsteps and biases involved in RNA-Seq data analysis, from experiments with or without biologicalreplicates.A descoberta de ácidos nucléicos abriu novas fronteiras de conhecimento, permitindoque os pesquisadores acessassem uma enorme quantidade de dados, através de metodologias desequenciamento em larga escala e ferramentas de bioinformática. Entre essas novas possibilidades,o RNA-Seq (sequenciamento de RNA) tem sido usado para identificar e quantificar moléculas deRNA. Para obter respostas biológicas mais precisas a partir dos dados de RNA-Seq, algumasquestões devem ser consideradas, como o desenho experimental, o tipo de biblioteca sintetizada, otamanho dos fragmentos gerados, o número de repetições biológicas, a profundidade e cobertura dosequenciamento, a disponibilidade do genoma da espécie e, a escolha dos softwares para executaradequadamente as análises computacionais. Análises bioinformáticas precisas permitem a seleçãode genes com menor taxa de erro, aumentando a assertividade da validação via RT-qPCR e, assim,reduzindo custos. O objetivo desta revisão foi apresentar as etapas de análise de dados de RNA-Seq,desde o projeto experimental até a biologia dos sistemas, considerando pontos relevantes, bemcomo apontar alguns softwares atualmente disponíveis para realizar essas análises. Além disso, comesta revisão, objetivamos ajudar a comunidade acadêmica a compreender todas as etapas e viesesenvolvidos na análise de dados de RNA-Seq, a partir de experimentos com ou sem réplicasbiológicas

Introduction of the rd29A: AtDREB2A CA gene into soybean (Glycine max L. Merril) and its molecular characterization in leaves and roots during dehydration

The loss of soybean yield to Brazilian producers because of a water deficit in the 2011-2012 season was 12.9%. To reduce such losses, molecular biology techniques, including plant transformation, can be used to insert genes of interest into conventional soybean cultivars to produce lines that are more tolerant to drought. The abscisic acid (ABA)-independent Dehydration Responsive Element Binding (DREB) gene family has been used to obtain plants with increased tolerance to abiotic stresses. In the present study, the rd29A:AtDREB2A CA gene from Arabidopsis thaliana was inserted into soybean using biolistics. Seventy-eight genetically modified (GM) soybean lines containing 2-17 copies of the AtDREB2A CA gene were produced. Two GM soybean lines (P1397 and P2193) were analyzed to assess the differential expression of the AtDREB2A CA transgene in leaves and roots submitted to various dehydration treatments. Both GM lines exhibited high expression of the transgene, with the roots of P2193 showing the highest expression levels during water deficit. Physiological parameters examined during water deficit confirmed the induction of stress. This analysis of AtDREB2A CA expression in GM soybean indicated that line P2193 had the greatest stability and highest expression in roots during water deficit-induced stress

Transcriptome-Wide Identification of Reference Genes for Expression Analysis of Soybean Responses to Drought Stress along the Day.

The soybean transcriptome displays strong variation along the day in optimal growth conditions and also in response to adverse circumstances, like drought stress. However, no study conducted to date has presented suitable reference genes, with stable expression along the day, for relative gene expression quantification in combined studies on drought stress and diurnal oscillations. Recently, water deficit responses have been associated with circadian clock oscillations at the transcription level, revealing the existence of hitherto unknown processes and increasing the demand for studies on plant responses to drought stress and its oscillation during the day. We performed data mining from a transcriptome-wide background using microarrays and RNA-seq databases to select an unpublished set of candidate reference genes, specifically chosen for the normalization of gene expression in studies on soybean under both drought stress and diurnal oscillations. Experimental validation and stability analysis in soybean plants submitted to drought stress and sampled during a 24 h timecourse showed that four of these newer reference genes (FYVE, NUDIX, Golgin-84 and CYST) indeed exhibited greater expression stability than the conventionally used housekeeping genes (ELF1-β and β-actin) under these conditions. We also demonstrated the effect of using reference candidate genes with different stability values to normalize the relative expression data from a drought-inducible soybean gene (DREB5) evaluated in different periods of the day

The diurnal oscillation of drought-responsive genes in soybean leaves during moderate stress is confirmed by RNA-seq analysis.

<p>Collect time points are represented by ZT (Zeitgeiber Time) 0 to 20, starting from the time the lights came on ( ZT0) and proceeding with 4 h intervals until ZT20. The error bars represent the standard error. The ANOVA and the Tukey HSD (95% family-wise confidence level) multiple comparison tests can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086402#pone.0086402.s007" target="_blank">Table S4</a>.</p

Drought-responsive genes in soybean exhibit diurnal regulation, and the expression pattern is modified under drought stress.

<p>Gene expression data regards qPCR analysis of soybean leaves during moderate (A) and severe (B) drought. Expression axis represents normalized expression (NE) = 2∧^{-(Ct experimental – Ctn)}. Collect time points are represented by ZT (Zeitgeiber Time) 0 to 20, starting from the time the lights came on ( ZT0) and proceeding with 4 h intervals until ZT20. For easy viewing, asterisks represent significant differences between control and stressed plants in each time point (Duncan’ test 5%, <i>time -treatment</i> interaction). The ANOVA and the complete Duncan’s tests results can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086402#pone.0086402.s006" target="_blank">Table S3</a>.</p

ABA treatment affects the regulation of drought-responsive and some circadian clock genes.

<p>Gene expression data regards qPCR analysis of circadian clock (A) and drought-responsive (B) genes. Expression axis represents normalized expression (NE) = 2∧^{-(Ct experimental – Ctn)}. Collect time points are represented by ZT (Zeitgeiber Time) 4 to 24, starting 4h after the lights came on ( ZT4) and proceeding with 4 h intervals until ZT24. For easy viewing, asterisks represent significant differences between control and stressed plants in each time point (Duncan’s test 5%, <i>time -treatment</i> interaction). The ANOVA and the complete Duncan’s tests results can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086402#pone.0086402.s008" target="_blank">Table S5</a>.</p

<i>Arabidopsis</i> and soybean ortholog genes.

<p>The <b>Arabidopsis gene</b> identification using the TAIR database and the <b>soybean</b><b>orthologous</b> gene identification using the Phytozome database are shown.</p><p>The <b>forward</b> and <b>reverse primer</b> sequences correspond to the oligonucleotides used to amplify the soybean orthologs.</p><p><b>The TBLASTN e-value</b> and <b>Identity</b> correrspond to the local alignament between the Arabidospsis protein and the soybean translated genome at Phytozome database.</p

Information on reference and target genes.

<p>^a New reference candidate genes</p><p>^b Commonly used reference genes</p><p>^c Target gene</p><p>Information on reference and target genes.</p