TaSPL6B, a member of the Squamosa promoter binding protein-like family, regulates shoot branching and florescence in Arabidopsis thaliana.

BACKGROUND: Squamosa promoter-binding protein-like (SPL) proteins are essential to plant growth and development as plant-specific transcription factors. However, the functions of SPL proteins in wheat need to be further explored. RESULTS: We cloned and characterized TaSPL6B of wheat in this study. Analysis of physicochemical properties revealed that it contained 961 amino acids and had a molecular weight of 105 kDa. Full-length TaSPL6B transcription activity was not validated in yeast and subcellular localization analysis revealed that TaSPL6B was distributed in the nucleus. Ectopic expression of TaSPL6B in Arabidopsis led to increasing number of branches and early flowering. TaSPL6B was highly transcribed in internodes of transgenic Arabidopsis. The expression of AtSMXL6/AtSMXL7/AtSMXL8 (homologous genes of TaD53) was markedly increased, whereas the expression of AtSPL2 (homologous genes of TaSPL3) and AtBRC1 (homologous genes of TaTB1) was markedly reduced in the internodes of transgenic Arabidopsis. Besides, TaSPL6B, TaSPL3 and TaD53 interacted with one another, as demonstrated by yeast two-hybrid and bimolecular fluorescence complementation assays. Therefore, we speculated that TaSPL6B brought together TaD53 and TaSPL3 and enhanced the inhibition effect of TaD53 on TaSPL3 through integrating light and strigolactone signaling pathways, followed by suppression of TaTB1, a key repressor of tillering. CONCLUSIONS: As a whole, our findings contribute to a better understanding of how SPL genes work in wheat and will be useful for further research into how TaSPL6B affects yield-related traits in wheat.

Disruption of serotonin biosynthesis increases resistance to striped stem borer without changing innate defense response in rice.

Striped stem borer (SSB) is one of the most damaging pests in rice production worldwide. Previously, we preliminarily demonstrated that indica rice Jiazhe LM, an OsT5H (encoding tryptamine-5-hydroxylase) knockout mutant deficient in serotonin, had increased resistance to SSB as compared with its wildtype parent Jiazhe B. However, the full scenario of SSB resistance and the underlying mechanism remain unknown. In this study, we first demonstrated that the OsT5H knockout could generally increase rice resistance to SSB and then proved that the OsT5H knockout does not disrupt the innate defense response of rice plants to SSB infestation, that is, OsT5H knockout mutations neither had significant effect on the transcriptional response of defense genes upon SSB infestation, nor the profile of defense related metabolites and plant hormones, such as lignin, salicylic acid, jasmonic acid, and abscisic acid, nor the activity of reactive oxygen species (ROS) scavenging enzymes and the ROS contents. We then demonstrated that supplementation of serotonin promoted SSB growth and performance in artificial diet feeding experiments. We observed that SSB larvae feeding on Jiazhe B had serotonin 1.72- to 2.30-fold that of those feeding on Jiazhe LM at the whole body level, and more than 3.31 and 1.84 times in the hemolymph and head, respectively. Further studies showed that the expression of genes involved in serotonin biosynthesis and transport was ~88.1% greater in SSB larvae feeding on Jiahze LM than those feeding on Jiazhe B. These observations indicated that SSB increases serotonin synthesis when feeding on serotonin deficient rice but is unable to fully compensate the dietary serotonin deficiency. Put together, the present study strongly suggests that it is the deficiency of serotonin, not the secondary effect of OsT5H knockout on innate defense response confers the SSB resistance in rice, which implies that reducing serotonin level, particularly through inhibition of its inductive synthesis upon SSB damage, could be an efficient strategy for breeding SSB resistant varieties.

Exploring the five different genes associated with PKCα in bladder cancer based on gene expression microarray.

Much progress has been made in understanding the mechanism of bladder cancer (BC) progression. Protein kinase C-α (PKCα) is overexpressed in many kinds of cancers. Additionally, PKCα is considered an oncogene that regulates proliferation, invasion, migration, apoptosis and cell cycle in multiple cancers. However, the mechanism underlying how these cellular processes are regulated by PKCα remains unknown. In the present study, we used PKCα siRNA to knock down PKCα gene expression and found that down-regulation of PKCα could significantly inhibit cell proliferation, migration and invasion and induce apoptosis and G1/S cell cycle arrest in vitro. Overexpression of PKCα promotes tumour growth in vivo. We applied cDNA microarray technology to detect the differential gene expression in J82 cells with PKCα knockdown and found that five key genes (BIRC2, BIRC3, CDK4, TRAF1 and BMP4) were involved in proliferation and apoptosis according to GO analysis and pathway analyses. Correlation analysis revealed a moderate positive correlation between PKCα expression and the expression of five downstream genes. BIRC2 and BIRC3 inhibit apoptosis, whereas CDK4, TRAF1 and BMP4 promote proliferation. Essentially, all five of these target genes participated in proliferation, and apoptosis was regulated by PKCα via the NF-kB signalling pathway.

Variational deep embedding-based active learning for the diagnosis of pneumonia.

Machine learning works similar to the way humans train their brains. In general, previous experiences prepared the brain by firing specific nerve cells in the brain and increasing the weight of the links between them. Machine learning also completes the classification task by constantly changing the weights in the model through training on the training set. It can conduct a much more significant amount of training and achieve higher recognition accuracy in specific fields than the human brain. In this paper, we proposed an active learning framework called variational deep embedding-based active learning (VaDEAL) as a human-centric computing method to improve the accuracy of diagnosing pneumonia. Because active learning (AL) realizes label-efficient learning by labeling the most valuable queries, we propose a new AL strategy that incorporates clustering to improve the sampling quality. Our framework consists of a VaDE module, a task learner, and a sampling calculator. First, the VaDE performs unsupervised reduction and clustering of dimension over the entire data set. The end-to-end task learner obtains the embedding representations of the VaDE-processed sample while training the target classifier of the model. The sampling calculator will calculate the representativeness of the samples by VaDE, the uncertainty of the samples through task learning, and ensure the overall diversity of the samples by calculating the similarity constraints between the current and previous samples. With our novel design, the combination of uncertainty, representativeness, and diversity scores allows us to select the most informative samples for labeling, thus improving overall performance. With extensive experiments and evaluations performed on a large dataset, we demonstrate that our proposed method is superior to the state-of-the-art methods and has the highest accuracy in the diagnosis of pneumonia.

Long non-coding RNA LINC02446 suppresses the proliferation and metastasis of bladder cancer cells by binding with EIF3G and regulating the mTOR signalling pathway.

Accumulating evidence has been obtained to understand the mechanisms of long non-coding RNAs (lncRNAs) in bladder cancer (BC). However, due to the recurrence and metastasis of BC, searching for lncRNAs that are related to prognosis and metastasis and exploring the pathogenesis of BC might provide new insights for the treatment of BC. In the present study, we used the TCGA and GEO databases and identified LINC02446 as associated with prognosis and differentially expressed in bladder cancer tissues and para-cancer tissues. Then, we found that LINC02446 could affect the proliferation, migration and invasion of BC cells. Additionally, we found that LINC02446 could bind to the EIF3G protein and regulate the protein stability of EIF3G and then inhibit the mTOR signalling pathway. In summary, all these findings show that LINC02446 might serve as a promising therapeutic target for BC intervention.

Characterization and Mutational Analysis of a Monogalactosyldiacylglycerol Synthase Gene OsMGD2 in Rice.

Monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG) are the two predominant galactolipids present in the photosynthetic membrane in many photosynthetic organisms, including algae and higher plants. These galactolipids are the main constituents of thylakoid membrane and are essential for chloroplast biogenesis and photoautotrophic growth. In silico analysis revealed that rice (Oryza sativa L.) genome has three genes encoding MGDG synthase (OsMGD1, 2, and 3). Although subcellular localization analysis demonstrated that OsMGD2 is localized to chloroplast, its expression was observed mainly in anther and endosperm, suggesting that MGDG might have an important role in the development of flower and grain in rice. Knock-out mutants of OsMGD2 were generated employing the CRISPR/Cas9 system and their morphology, yield and grain quality related traits were studied. The leaf of osmgd2 mutants showed reduced MGDG (∼11.6%) and DGDG (∼9.5%) content with chlorophyll a content decreased by ∼23%, consequently affecting the photosynthesis. The mutants also exhibited poor agronomic performance with plant height and panicle length decreased by ∼12.2 and ∼7.3%, respectively. Similarly, the number of filled grains per panicle was reduced by 43.8%, while the 1000 grain weight was increased by ∼6.3% in the mutants. The milled rice of mutants also had altered pasting properties and decreased linoleic acid content (∼26.6%). Put together, the present study demonstrated that OsMGD2 is the predominantly expressed gene encoding MGDG synthase in anther and grain and plays important roles in plant growth and development, as well as in grain quality.

A long non-coding RNA signature to improve prognostic prediction in clear cell renal cell carcinoma.

BACKGROUND: Accumulating research reports have indicated that long non-coding RNAs (lncRNAs) are abnormally expressed in many types of cancers. However, few lncRNA signatures for predicting cancer prognosis have been established. Our goal is to establish a lncRNA signature for predicting the prognosis of clear cell renal cell carcinoma (ccRCC). METHODS: We downloaded KIRC lncRNA FPKM (Fragments Per Kilobase of transcript per Million Fragments) standardized expression data from The Cancer Genome Atlas (TCGA) by using the TANRIC tool. We established an 11-lncRNA signature that was clearly linked to the overall survival (OS) rates in the training and test sets. RESULTS: The training set was divided into the high-risk and low-risk subgroups, between which the OS was disparate (HR = 1.51, 95%CI = 1.39-1.64, P < 0.0001). The accuracy of the 11-lncRNA signature for predicting prognosis was confirmed in the test set. Further analysis revealed that the prognostic value of this signature was independent of the neoplasm grade and TNM stage. Gene set enrichment analysis (GSEA) was performed, and a summary of 4 gene sets related to canonical pathway, biological process, molecular function and cellular component was obtained. We demonstrated the biological function of these lncRNAs in ccRCC cell lines and found that LINC00488 and HOTTIP promoted tumour proliferation and inhibited apoptosis. However, LINC-PINT had the opposite effect. CONCLUSIONS: The establishment of the 11-lncRNA signature indicated the underlying biochemical functional roles of the selected lncRNAs in ccRCC. Our results may provide a reliable theoretical basis for clinical evaluation of ccRCC prognosis.