Genetic variant in DICER gene is associated with prognosis of hepatocellular carcinoma in a Chinese cohort.

AIM: MicroRNAs (miRNAs) function as gene regulators and play crucial roles in the pathogenesis and prognosis of hepatocellular carcinoma (HCC). Genetic variants in miRNA processing genes may affect miRNA expression and contribute to HCC risk and survival. We hypothesized that single nucleotide polymorphisms (SNPs) in miRNA processing genes may be associated with HCC susceptibility and prognosis. The study aims to verify whether this hypothesis is right or not. METHODS: We first genotyped the selected three SNPs in miRNA processing genes (RAN rs3803012 A>G, HIWI rs10773771 T>C, and DICER rs1057035 T>C) in 312 HCC patients and 320 cancer-free controls using the TaqMan assay, and evaluated the associations of the three SNPs with HCC risk. We also investigated the effect of the three SNPs on the overall survival of 312 HCC patients. RESULTS: There were no significant associations between the three SNPs (RAN rs3803012 A>G, HIWI rs10773771 T>C, and DICER rs1057035 T>C) and HCC risk. However, HCC patients carrying DICER rs1057035 CT + CC genotypes had significantly longer median survival time (log-rank, P = 0.018) and decreased death risk (adjusted hazard ratio = 0.68; 95% confidence interval, 0.49-0.95; P = 0.022) than patients with rs1057035 TT genotypes. The DICER rs1057035 genotype was an independent protective factor for HCC survival (CT + CC vs. TT: hazard ratio = 0.72; 95% confidence interval, 0.55-0.96; P = 0.031). CONCLUSION: This study provides evidence that DICER rs1057035 T>C polymorphism may be a prognostic biomarker for HCC patients.

Model-based clustering for RNA-seq data.

MOTIVATION: RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis. RESULTS: In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models. AVAILABILITY AND IMPLEMENTATION: An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org

Transcriptome-wide changes in Chlamydomonas reinhardtii gene expression regulated by carbon dioxide and the CO2-concentrating mechanism regulator CIA5/CCM1.

We used RNA sequencing to query the Chlamydomonas reinhardtii transcriptome for regulation by CO(2) and by the transcription regulator CIA5 (CCM1). Both CO(2) and CIA5 are known to play roles in acclimation to low CO(2) and in induction of an essential CO(2)-concentrating mechanism (CCM), but less is known about their interaction and impact on the whole transcriptome. Our comparison of the transcriptome of a wild type versus a cia5 mutant strain under three different CO(2) conditions, high CO(2) (5%), low CO(2) (0.03 to 0.05%), and very low CO(2) (<0.02%), provided an entry into global changes in the gene expression patterns occurring in response to the interaction between CO(2) and CIA5. We observed a massive impact of CIA5 and CO(2) on the transcriptome, affecting almost 25% of all Chlamydomonas genes, and we discovered an array of gene clusters with distinctive expression patterns that provide insight into the regulatory interaction between CIA5 and CO(2). Several individual clusters respond primarily to either CIA5 or CO(2), providing access to genes regulated by one factor but decoupled from the other. Three distinct clusters clearly associated with CCM-related genes may represent a rich source of candidates for new CCM components, including a small cluster of genes encoding putative inorganic carbon transporters.

Developmental dynamics of Kranz cell transcriptional specificity in maize leaf reveals early onset of C4-related processes.

The comparison of the cell-specific transcriptomes of bundle sheath (BS) and mesophyll (M) cells from successive developmental stages of maize (Zea mays) leaves reveals that the number of genes preferentially transcribed in one cell type or the other varies considerably from the sink-source transition to mature photosynthetic stages. The number of differentially expressed (DE) genes is maximal at a stage well before full maturity, including those that encode key functions for C4 photosynthesis. The developmental dynamics of BS/M differential expression can be used to identify candidates for other C4-related functions and to simplify the identification of specific pathways members from otherwise complex gene families. A significant portion of the candidates for C4-related transcription factors identified with this developmental DE strategy overlap with those identified in studies using alternative strategies, thus providing independent support for their potential importance.

Engineered poly(A)-surrogates for translational regulation and therapeutic biocomputation in mammalian cells.

Here, we present a gene regulation strategy enabling programmable control over eukaryotic translational initiation. By excising the natural poly-adenylation (poly-A) signal of target genes and replacing it with a synthetic control region harboring RNA-binding protein (RBP)-specific aptamers, cap-dependent translation is rendered exclusively dependent on synthetic translation initiation factors (STIFs) containing different RBPs engineered to conditionally associate with different eIF4F-binding proteins (eIFBPs). This modular design framework facilitates the engineering of various gene switches and intracellular sensors responding to many user-defined trigger signals of interest, demonstrating tightly controlled, rapid and reversible regulation of transgene expression in mammalian cells as well as compatibility with various clinically applicable delivery routes of in vivo gene therapy. Therapeutic efficacy was demonstrated in two animal models. To exemplify disease treatments that require on-demand drug secretion, we show that a custom-designed gene switch triggered by the FDA-approved drug grazoprevir can effectively control insulin expression and restore glucose homeostasis in diabetic mice. For diseases that require instantaneous sense-and-response treatment programs, we create highly specific sensors for various subcellularly (mis)localized protein markers (such as cancer-related fusion proteins) and show that translation-based protein sensors can be used either alone or in combination with other cell-state classification strategies to create therapeutic biocomputers driving self-sufficient elimination of tumor cells in mice. This design strategy demonstrates unprecedented flexibility for translational regulation and could form the basis for a novel class of programmable gene therapies in vivo.

An optimal test with maximum average power while controlling FDR with application to RNA-seq data.

The recent RNA-seq technology is an attractive method to study gene expression. One of the most important goals in RNA-seq data analysis is to detect genes differentially expressed across treatments. Although several statistical methods have been published, there are no theoretical justifications for whether these methods are optimal or how to search for the optimal test. Furthermore, most proposed tests are designed for testing whether the mean expression levels are exactly the same or not across treatments, whereas sometimes, biologists are interested in detecting genes with expression changes larger than a certain threshold. Another issue with current methods is that the false discovery rate (FDR) control is not well studied. In this manuscript, we propose a test to address all the above issues. Under model assumptions, we derive an optimal test that achieves the maximum of average power among those that control FDR at the same level. We also provide an approximated version, the approximated most average powerful (AMAP) test, for practical implementation. The proposed method allows for testing null hypotheses that are much more general than the ones most previous studies have considered, and it leads to a natural way of controlling the FDR. Through simulation studies, we show that our test has a higher power than other methods, including the widely-used edgeR, DESeq, and baySeq methods, as well as better FDR control than two other FDR control procedures commonly used in practice. For demonstration, we also apply the proposed method to a real RNA-seq dataset obtained from maize.

A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data.

RNA-Seq technologies are quickly revolutionizing genomic studies, and statistical methods for RNA-seq data are under continuous development. Timely review and comparison of the most recently proposed statistical methods will provide a useful guide for choosing among them for data analysis. Particular interest surrounds the ability to detect differential expression (DE) in genes. Here we compare four recently proposed statistical methods, edgeR, DESeq, baySeq, and a method with a two-stage Poisson model (TSPM), through a variety of simulations that were based on different distribution models or real data. We compared the ability of these methods to detect DE genes in terms of the significance ranking of genes and false discovery rate control. All methods compared are implemented in freely available software. We also discuss the availability and functions of the currently available versions of these software.

Specular Reflections Removal for Endoscopic Image Sequences With Adaptive-RPCA Decomposition.

Specular reflections (i.e., highlight) always exist in endoscopic images, and they can severely disturb surgeons' observation and judgment. In an augmented reality (AR)-based surgery navigation system, the highlight may also lead to the failure of feature extraction or registration. In this paper, we propose an adaptive robust principal component analysis (Adaptive-RPCA) method to remove the specular reflections in endoscopic image sequences. It can iteratively optimize the sparse part parameter during RPCA decomposition. In this new approach, we first adaptively detect the highlight image based on pixels. With the proposed distance metric algorithm, it then automatically measures the similarity distance between the sparse result image and the detected highlight image. Finally, the low-rank and sparse results are obtained by enforcing the similarity distance between the two types of images to fall within a certain range. Our method has been verified by multiple different types of endoscopic image sequences in minimally invasive surgery (MIS). The experiments and clinical blind tests demonstrate that the new Adaptive-RPCA method can obtain the optimal sparse decomposition parameters directly and can generate robust highlight removal results. Compared with the state-of-the-art approaches, the proposed method not only achieves the better highlight removal results but also can adaptively process image sequences.

Quercetin inhibits the growth of human gastric cancer stem cells by inducing mitochondrial-dependent apoptosis through the inhibition of PI3K/Akt signaling.

Cancer stem cells (CSCs) have recently been linked to new treatment strategies for gastric cancer due to the critical role which they play as the 'heartbeat' of cancer. In the present study, we explored the effects of quercetin, an anti-inflammatory and antiviral compound, on gastric CSCs (GCSCs). We noted that quercetin exerted pronounced inhibitory effects on GCSC survival. Moreover, quercetin induced cell apoptosis in a mitochondrial-dependent manner, as shown by the reduction in mitochondrial membrane potential, the activation of caspase-3 and -9, and the downregulation of Bcl-2, as well as the upregulation of Bax and cytochrome c (Cyt-c). Additionally, a marked decrease in Akt phosphorylation levels was observed following treatment with quercetin, whereas pre-treatment with fumonisin B1 (FB1, Akt activator) significantly attenuated the inhibitory effects of quercetin on cell growth and its promoting effects on mitochondrial-dependent apoptosis. Notably, FB1 enhanced the expression of Bcl-2, which was inhibited by quercetin, and prevented the decrease in mitochondrial membrane potential induced by quercetin. However, the increase in the levels of caspases, Bax and Cyt-c induced by quercetin was also attenuated by the addition of FB1 to the GCSCs. Therefore, our results demonstrate that quercetin triggers mitochondrial apoptotic-dependent growth inhibition via the blockade of phosphoinositide 3-kinase (PI3K)-Akt signaling in GCSCs, indicating a potential target for the treatment of gastric cancer.

MicroRNA-542-3p suppresses cell growth of gastric cancer cells via targeting oncogene astrocyte-elevated gene-1.

MicroRNAs (miRNAs) have been suggested to play critical roles in tumorigenesis as well as in the development of therapies for the treatment of cancers. However, the tumor-associated miRNAs in gastric cancers remain poorly understood. Here, we report on miR-542-3p in gastric cancers, which has been widely studied in other cancers as a tumor suppressor. Real-time quantitative PCR analysis demonstrated that miR-542-3p was significantly down-regulated in gastric cancer tissues (p < 0.0001) and cell lines (p < 0.001). Overexpression of miR-542-3p significantly inhibited cell growth of gastric cancer cells both in vitro (p < 0.01) and in vivo (p < 0.01). Notably, overexpression of miR-542-3p apparently reduced the protein expression of astrocyte-elevated gene-1 (AEG-1) (p < 0.01). The dual-luciferase reporter assay validated that miR-542-3p directly bound the 3'-untranslated region (UTR) of AEG-1, which could be abolished by mutation of the predicted miR-542-3p binding site. Furthermore, overexpression of miR-542-3p markedly inhibited the activation of oncogenic signaling pathways including the Akt, ß-catenin and nuclear factor-κB pathways. Additionally, overexpression of AEG-1 without the 3'-UTR partially reversed the cell growth arrest induced by miR-542-3p overexpression in gastric cancer cells (p < 0.05). Taken together, these data suggest that miR-542-3p might function as a tumor suppressor in gastric cancer, potentially by targeting the oncogene AEG-1, implying a potential role for miR-542-3p in the development of therapeutic methods for gastric cancer.

Intensive Versus Conventional Glycemic Control in Patients with Diabetes During Enteral Nutrition After Gastrectomy.

BACKGROUND: This study compared intensive and conventional glycemic management strategies in diabetic patients receiving enteral nutrition after gastrectomy. METHODS: Diabetic patients (n = 212) who underwent gastrectomy between September 2006 and March 2014 were randomized to intensive glycemic (IG) management with continuous insulin infusion (target glucose 4.4-6.1 mmol/l (80-110 mg/dl)) or conventional glycemic (CG) management with intermittent bolus insulin (target glucose <11.1 mmol/l (<200 mg/dl)). Outcomes included blood glucose concentrations, insulin administration, and postoperative morbidity and mortality. RESULTS: Blood glucose levels were lower (5.4 ± 1.2 vs. 9.5 ± 1.8 mmol/l, P < 0.001) and mean insulin dose was higher (55 ± 15 vs.32 ± 16 units/day, P < 0.001) in the IG than in the CG group. Rates of severe hypoglycemia (7.5 vs. 0.9%, P = 0.035) and achievement of target blood glucose (86.3 vs. 72.6%, P = 0.023) were higher, while severe hyperglycemia rate was lower (1.9 vs. 11.3%, P = 0.010), in the IG group. Surgical site infection rate was lower in the IG group (4.7 vs. 13.2%, P < 0.030). Rates of other infective complications, bleeding, delayed gastric emptying, obstruction, hepatic dysfunction, renal dysfunction, and circulatory insufficiency were similar in the two groups. CONCLUSIONS: Intensive glycemic control in diabetic patients receiving enteral nutrition after gastrectomy was associated with a lower surgical site infection rate but a higher hypoglycemia rate.

The unfolded protein response potentiates epithelial-to-mesenchymal transition (EMT) of gastric cancer cells under severe hypoxic conditions.

The hypoxic condition occurs in most types of solid tumors and has been shown to be associated with the metastatic ability of gastric cancer. A previous study has demonstrated that hypoxia might stimulate epithelial-to-mesenchymal transition (EMT) of gastric cancer cells. Nevertheless, the mechanism has not yet been completely understood. In the current study, the human gastric cancer cell lines HGC27 and MGC803 were presented to normoxic (21 % O2), hypoxic (1 % O2) or severe hypoxic (0.1 % O2) conditions for 24 h. We found that hypoxia exposure induced EMT of gastric cancer cells, which was promoted by severe hypoxia condition. Meanwhile, expressions of PERK, ATF4 and ATF6 proteins were elevated in cells under conditions of severe hypoxia but not by normoxia or hypoxia. Knockdown of PERK, ATF4 or ATF6 impeded EMT of gastric cancer cells induced by severe hypoxia. Furthermore, severe hypoxia exposure extremely boosted the expression of TGF-ß, which was blocked by the knockdown of PERK, ATF4 or ATF6 expression. Additionally, we found that TGF-ß release caused by hypoxia is facilitated by elevated UPR proteins and led to the activation of Smad2/3 and PI3K/Akt signaling. Our data suggest that UPR potentiates the EMT of gastric cancer cells under conditions of severe hypoxia.

DNA methyltransferase 3b silencing affects locus-specific DNA methylation and inhibits proliferation, migration and invasion in human hepatocellular carcinoma SMMC-7721 and BEL-7402 cells.

DNA methylation is an important regulator of gene transcription, and its role in carcinogenesis has been a topic of considerable interest in previous years. The present study examined the influence of DNA methyltransferase 3b (DNMT3b) on cell proliferation, migration and invasion, and the methylation status of identified tumor suppressor genes in hepatoma SMMC-7721 and BEL-7402 cells. DNMT3b was silenced by small interfering RNA (siRNA) in human hepatocellular carcinoma cell lines. Transfection efficiency was verified using a fluorescent imaging system, reverse transcription polymerase chain reaction (RT-PCR) and western blotting. A cell proliferation assay was performed to evaluate cell viability. Cell cycle distribution and apoptosis were analyzed by flow cytometry. The migratory and invasive ability of cells was measured using a Transwell assay. Methylation-specific PCR (MSP) was performed to assess methylation in the promoter region of genes. The present data revealed that DNMT3b siRNA successfully inhibited expression of the DNMT3b gene in these two liver cancer cell lines and therefore inhibited the proliferation of the transfected cells, stimulated apoptosis in the cells, led to an accumulation of cells in the G2/M phase and decreased cell migration and invasion. It was also found that silencing DNMT3b expression results in hypomethylation of specific sets of gene promoters and increases the expression of distinct set of genes in HCC cell lines. The present study is therefore useful for assessing the specificity of emerging action based on the altered expression of associated regulatory genes, particularly in methylation-silenced genes.

Comparative analyses of C4 and C3 photosynthesis in developing leaves of maize and rice.

C4 and C3 photosynthesis differ in the efficiency with which they consume water and nitrogen. Engineering traits of the more efficient C4 photosynthesis into C3 crops could substantially increase crop yields in hot, arid conditions. To identify differences between C4 and C3 photosynthetic mechanisms, we profiled metabolites and gene expression in the developing leaves of Zea mays (maize), a C4 plant, and Oryza sativa (rice), a C3 plant, using a statistical method named the unified developmental model (UDM). Candidate cis-regulatory elements and transcription factors that might regulate photosynthesis were identified, together with differences between C4 and C3 nitrogen and carbon metabolism. The UDM algorithms could be applied to analyze and compare development in other species. These data sets together with community viewers to access and mine them provide a resource for photosynthetic research that will inform efforts to engineer improvements in carbon fixation in economically valuable grass crops.

A low-cost library construction protocol and data analysis pipeline for Illumina-based strand-specific multiplex RNA-seq.

The emergence of NextGen sequencing technology has generated much interest in the exploration of transcriptomes. Currently, Illumina Inc. (San Diego, CA) provides one of the most widely utilized sequencing platforms for gene expression analysis. While Illumina reagents and protocols perform adequately in RNA-sequencing (RNA-seq), alternative reagents and protocols promise a higher throughput at a much lower cost. We have developed a low-cost and robust protocol to produce Illumina-compatible (GAIIx and HiSeq2000 platforms) RNA-seq libraries by combining several recent improvements. First, we designed balanced adapter sequences for multiplexing of samples; second, dUTP incorporation in 2(nd) strand synthesis was used to enforce strand-specificity; third, we simplified RNA purification, fragmentation and library size-selection steps thus drastically reducing the time and increasing throughput of library construction; fourth, we included an RNA spike-in control for validation and normalization purposes. To streamline informatics analysis for the community, we established a pipeline within the iPlant Collaborative. These scripts are easily customized to meet specific research needs and improve on existing informatics and statistical treatments of RNA-seq data. In particular, we apply significance tests for determining differential gene expression and intron retention events. To demonstrate the potential of both the library-construction protocol and data-analysis pipeline, we characterized the transcriptome of the rice leaf. Our data supports novel gene models and can be used to improve current rice genome annotation. Additionally, using the rice transcriptome data, we compared different methods of calculating gene expression and discuss the advantages of a strand-specific approach to detect bona-fide anti-sense transcripts and to detect intron retention events. Our results demonstrate the potential of this low cost and robust method for RNA-seq library construction and data analysis.

The developmental dynamics of the maize leaf transcriptome.

We have analyzed the maize leaf transcriptome using Illumina sequencing. We mapped more than 120 million reads to define gene structure and alternative splicing events and to quantify transcript abundance along a leaf developmental gradient and in mature bundle sheath and mesophyll cells. We detected differential mRNA processing events for most maize genes. We found that 64% and 21% of genes were differentially expressed along the developmental gradient and between bundle sheath and mesophyll cells, respectively. We implemented Gbrowse, an electronic fluorescent pictograph browser, and created a two-cell biochemical pathway viewer to visualize datasets. Cluster analysis of the data revealed a dynamic transcriptome, with transcripts for primary cell wall and basic cellular metabolism at the leaf base transitioning to transcripts for secondary cell wall biosynthesis and C(4) photosynthetic development toward the tip. This dataset will serve as the foundation for a systems biology approach to the understanding of photosynthetic development.