GRAS transcription factors are known to play important roles in plant signal transduction and development. A comprehensive study was conducted to explore the GRAS family in the Brassica juncea genome. A total of 88 GRAS genes were identified which were categorized into nine groups according to the phylogenetic analysis. Gene structure analysis showed a high group-specificity, which corroborated the gene grouping results. The chromosome distribution and sequence analysis suggested that gene duplication events are vital for the expansion of GRAS genes in the B. juncea genome. The changes in evolution rates and amino acid properties among groups might be responsible for their functional divergence. Interaction networks and cis-regulatory elements were analyzed including DELLA and eight interaction proteins (including four GID1, two SLY1, and two PIF3 proteins) that are primarily involved in light and hormone signaling. To understand their regulatory role in growth and development, the expression profiles of BjuGRASs and interaction genes were examined based on transcriptome data and qRT-PCR, and selected genes (BjuGRAS3, 5, 7, 8, 10, BjuB006276, BjuB037910, and BjuA021658) had distinct temporal expression patterns during stem swelling, indicating that they possessed diverse regulatory functions during the developmental process. These results contribute to our understanding on the GRAS gene family and provide the basis for further investigations on the evolution and functional characterization of GRAS genes.

Accurate analysis of gene expression requires selection of appropriate reference genes. In this study, we report analysis of eight candidate reference genes (ACTIN, UBQ, EF-1α, UBC, IF-4α, TUB, PP2A, and HIS), which were screened from the genome and transcriptome data in Brassica juncea. Four statistical analysis softwares geNorm, NormFinder, BestKeeper, and RefFinder were used to test the reliability and stability of gene expression of the reference genes. To further validate the stability of reference genes, the expression levels of two CYCD3 genes (BjuB045330 and BjuA003219) were studied. In addition, all genes in the xyloglucan endotransglucosylase/hydrolase (XTH) family were identified in B. juncea and their patterns at different periods of stem enlargement were analyzed. Results indicated that UBC and TUB genes showed stable levels of expression and are recommended for future research. In addition, XTH genes were involved in regulation of stem enlargement expression. These results provide new insights for future research aiming at exploring important functional genes, their expression patterns and regulatory mechanisms for mustard development.

Bladder cancer is a common malignant tumour with high recurrence rate. Cytokeratin 19 fragments (Cyfra21-1) in urine has been regarded as a promising biomarker for the prognosis and diagnosis of bladder cancer due to the relevance of its high urinary level to the bladder cancer patients. However, currently detection methods of Cyfra21-1 have their limits, such as complicated steps, limited sensitivity or unsatisfying specificity. In this study, we developed a novel time-resolved fluoroimmuno test strip by using europium chelate microparticle (Eu-CM). Detection was performed in simple steps by carrying drops of sample into the well of the test strip, waiting for 15 min and inserting the strip into a fluorescence strip reader for quantitation. The standard curve equation of the test strip was y ¼ 0.0177x þ 0.01 (R 2 ¼ .9993). In the analysis of human urine samples (n ¼ 115), it demonstrated a good performance (accuracy: CV < 10%, AUC: 0.989). With the cutoff value of 81 ng/mL, the sensitivity and specificity for bladder cancer were 92.86 and 100%, respectively. In comparison to ELISA and electrochemiluminescence methods, the Eu-CM based time-resolved fluoroimmuno test strip provided a rapid, sensitive and reliable method for monitoring bladder cancer. It may be applied as a non-invasive approach for in point-of-care for bladder cancer detection.

Heat shock transcription factor (HSF) is ubiquitous in the whole biological world and plays an important role in regulating growth and development and responses to environment stress. In this study, a total of 60 HSF transcription factors in Brassica juncea genome were identified and analyzed. Phylogenetic analysis showed that HSF genes were divided into three groups namely: A, B, and C, of which group A was further divided into nine subgroups (A1-A9). The analysis of gene structure and conserved motifs showed that some homologous genes are highly conserved. There was strong conservative microcollinearity among Brassica rapa, B. juncea, and Brassica oleracea, which provides a basis for studying the replication of gene families. Moreover, the results revealed that the promoter regions of BjuHSF genes were rich in cis-elements related to growth and development, hormone signal, and stress response. The prediction of protein interaction results showed that HSFs could interact with multiple transcription factors and proteins in the genome, while functional annotation revealed that BjuHSF genes were involved in many biological processes. The expression patterns of BjuHSF genes were analyzed by qPCR, and the results showed that these genes were closely linked to stress response, hormones, and development process. These results are a foundation for further analysis of the regulation mechanism of HSF gene family.

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Stem mustard is a stem variety of mustard, an important Brassica vegetable. The formation and development of the tumorous stem, which is the key organ for the direct yield and quality, is a complex biological process involving morphogenesis, material accumulation and gene regulation. In this study, we demonstrated through anatomical studies that stem swelling is mainly dependent on the increase in the number of cells and the volume of parenchyma cells in the cortex and pith. To further understand transcript and metabolic changes during stem swelling, we obtained 27,901 differentially expressed genes, of which 671 were specifically detected using transcriptome sequencing technology in all four stages of stem swelling. Functional annotation identified enrichment for genes involved in photosynthesis, energy metabolism, cell growth, sulfur metabolism and glucosinolate biosynthesis. Glucosinolates are a group of nitrogen- and sulfur-containing secondary metabolites, which largely exist in the Cruciferous vegetables. HPLC analysis of the contents and components of glucosinolates in four different stem development stages revealed eight glucosinolates, namely, three aliphatic glucosinolates (sinigrin, glucoalyssin and gluconapin), four indole glucosinolates (4-hydroxyglucobrassicin, glucobrassicin, 4-methoxyglucobrassicin and neoglucobrassicin) and one aromatic glucosinolate (gluconasturtiin). All these types of glucosinolates showed a significant downward trend during the stem swelling period. The content of aliphatic glucosinolates was the highest, with sinigrin being the main component. In addition, qPCR was used to validate the expression of nine genes involved in glucosinolate biosynthesis. Most of these genes were down-regulated during stem swelling in qPCR, which is consistent with transcriptome data. These data provide a basic resource for further molecular and genetic research on Brassica juncea.

No abstract

Detecting quantitative trait loci (QTL) and estimating QTL variances (represented by the squared QTL effects) are two main goals of QTL mapping and genome-wide association studies (GWAS). However, there are issues associated with estimated QTL variances and such issues have not attracted much attention from the QTL mapping community. Estimated QTL variances are usually biased upwards due to estimation being associated with significance tests. The phenomenon is called the Beavis effect. However, estimated variances of QTL without significance tests can also be biased upwards, which cannot be explained by the Beavis effect; rather, this bias is due to the fact that QTL variances are often estimated as the squares of the estimated QTL effects. The parameters are the QTL effects and the estimated QTL variances are obtained by squaring the estimated QTL effects. This square transformation failed to incorporate the errors of estimated QTL effects into the transformation. The consequence is biases in estimated QTL variances. To correct the biases, we can either reformulate the QTL model by treating the QTL effect as random and directly estimate the QTL variance (as a variance component) or adjust the bias by taking into account the error of the estimated QTL effect. A moment method of estimation has been proposed to correct the bias. The method has been validated via Monte Carlo simulation studies. The method has been applied to QTL mapping for the 10-week-body-weight trait from an F2 mouse population.