BackgroundTea is the most popular non-alcoholic health beverage in the world. The tea plant (Camellia sinensis (L.) O. Kuntze) needs to undergo a cold acclimation process to enhance its freezing tolerance in winter. Changes that occur at the molecular level in response to low temperatures are poorly understood in tea plants. To elucidate the molecular mechanisms of cold acclimation, we employed RNA-Seq and digital gene expression (DGE) technologies to the study of genome-wide expression profiles during cold acclimation in tea plants.ResultsUsing the Illumina sequencing platform, we obtained approximately 57.35 million RNA-Seq reads. These reads were assembled into 216,831 transcripts, with an average length of 356 bp and an N50 of 529 bp. In total, 1,770 differentially expressed transcripts were identified, of which 1,168 were up-regulated and 602 down-regulated. These include a group of cold sensor or signal transduction genes, cold-responsive transcription factor genes, plasma membrane stabilization related genes, osmosensing-responsive genes, and detoxification enzyme genes. DGE and quantitative RT-PCR analysis further confirmed the results from RNA-Seq analysis. Pathway analysis indicated that the “carbohydrate metabolism pathway” and the “calcium signaling pathway” might play a vital role in tea plants’ responses to cold stress.ConclusionsOur study presents a global survey of transcriptome profiles of tea plants in response to low, non-freezing temperatures and yields insights into the molecular mechanisms of tea plants during the cold acclimation process. It could also serve as a valuable resource for relevant research on cold-tolerance and help to explore the cold-related genes in improving the understanding of low-temperature tolerance and plant-environment interactions.
SummaryWnt proteins are secreted post-translationally modified proteins that signal locally to regulate development and proliferation. The production of bioactive Wnts requires a number of dedicated factors in the secreting cell whose coordinated functions are not fully understood. A screen for small molecules identified inhibitors of vacuolar acidification as potent inhibitors of Wnt secretion. Inhibition of the V-ATPase or disruption of vacuolar pH gradients by diverse drugs potently inhibited Wnt/-catenin signaling both in cultured human cells and in vivo, and impaired Wnt-regulated convergent extension movements in Xenopus embryos. WNT secretion requires its binding to the carrier protein wntless (WLS); we find that WLS is ER-resident in human cells and WNT3A binding to WLS requires PORCN-dependent lipid modification of WNT3A at serine 209. Inhibition of vacuolar acidification results in accumulation of the WNT3A-WLS complex both in cells and at the plasma membrane. Modeling predictions suggest that WLS has a lipid-binding -barrel that is similar to the lipocalin-family fold. We propose that WLS binds Wnts in part through a lipid-binding domain, and that vacuolar acidification is required to release palmitoylated WNT3A from WLS in secretory vesicles, possibly to facilitate transfer of WNT3A to a soluble carrier protein.
Recent advances in next-generation sequencing technology allow high-throughput cDNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies, in particular for detecting differentially expressed genes between groups. Many software packages have been developed for the identification of differentially expressed genes (DEGs) between treatment groups based on RNA-Seq data. However, there is a lack of consensus on how to approach an optimal study design and choice of suitable software for the analysis. In this comparative study we evaluate the performance of three of the most frequently used software tools: Cufflinks-Cuffdiff2, DESeq and edgeR. A number of important parameters of RNA-Seq technology were taken into consideration, including the number of replicates, sequencing depth, and balanced vs. unbalanced sequencing depth within and between groups. We benchmarked results relative to sets of DEGs identified through either quantitative RT-PCR or microarray. We observed that edgeR performs slightly better than DESeq and Cuffdiff2 in terms of the ability to uncover true positives. Overall, DESeq or taking the intersection of DEGs from two or more tools is recommended if the number of false positives is a major concern in the study. In other circumstances, edgeR is slightly preferable for differential expression analysis at the expense of potentially introducing more false positives.
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