Insect small nuclear RNA gene promoters evolve rapidly yet retain conserved features involved in determining promoter activity and RNA polymerase specificity

In animals, most small nuclear RNAs (snRNAs) are synthesized by RNA polymerase II (Pol II), but U6 snRNA is synthesized by RNA polymerase III (Pol III). In Drosophila melanogaster, the promoters for the Pol II-transcribed snRNA genes consist of approximately 21 bp PSEA and approximately 8 bp PSEB. U6 genes utilize a PSEA but have a TATA box instead of the PSEB. The PSEAs of the two classes of genes bind the same protein complex, DmSNAPc. However, the PSEAs that recruit Pol II and Pol III differ in sequence at a few nucleotide positions that play an important role in determining RNA polymerase specificity. We have now performed a bioinformatic analysis to examine the conservation and divergence of the snRNA gene promoter elements in other species of insects. The 5' half of the PSEA is well-conserved, but the 3' half is divergent. Moreover, within each species positions exist where the PSEAs of the Pol III-transcribed genes differ from those of the Pol II-transcribed genes. Interestingly, the specific positions vary among species. Nevertheless, we speculate that these nucleotide differences within the 3' half of the PSEA act similarly to induce conformational alterations in DNA-bound SNAPc that result in RNA polymerase specificity.     

Correspondence regarding Zhong et al., BMC Bioinformatics 2013 Mar 7;14:89

Computational expression deconvolution aims to estimate the contribution of individual cell populations to expression profiles measured in samples of heterogeneous composition. Zhong et al. recently proposed Digital Sorting Algorithm (BMC Bioinformatics 2013 Mar 7;14:89) and showed that they could accurately estimate population-specific expression levels and expression differences between two populations. They compared DSA with Population-Specific Expression Analysis (PSEA), a previous deconvolution method that we developed to detect expression changes occurring within the same population between two conditions (e.g. disease versus non-disease). However, Zhong et al. compared PSEA-derived specific expression levels across different cell populations. Specific expression levels obtained with PSEA cannot be directly compared across different populations as they are on a relative scale. They are accurate as we demonstrate by deconvolving the same dataset used by Zhong et al. and, importantly, allow for comparison of population-specific expression across conditions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0347-5) contains supplementary material, which is available to authorized users.     

Molecular Predictors of Long-Term Survival in Glioblastoma Multiforme Patients

Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with <10% of patients surviving for more than 3 years. Demographic and clinical factors (e.g. age) and individual molecular biomarkers have been associated with prolonged survival in GBM patients. However, comprehensive systems-level analyses of molecular profiles associated with long-term survival (LTS) in GBM patients are still lacking. We present an integrative study of molecular data and clinical variables in these long-term survivors (LTSs, patients surviving >3 years) to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients.     

Gene expression sigantures, cancer cell evolution and metastatic progression

Global gene expression profiling and molecular cytogenetics for single cells provide new opportunities to resolve important questions, such as the mechanisms of tumor cell selection during cancer progression. While gene expression analyses show that metastatic progression correlates with the deregulation of certain gene sets in the primary tumor, the direct analysis of disseminated cancer cells, the putative precursors of metachronous metastases, suggests that dissemination is a very early event in the genetic development of human cancers.     

Identification and molecular characterization of novel anther-specific genes in Oryza sativa L. by using cDNA microarray

The complicated genetic pathway regulates the developmental programs of male reproductive organ, anther tissues. To understand these molecular mechanisms, we performed cDNA microarray analyses and in situ hybridization to monitor gene expression patterns during anther development in rice. Microarray analysis of 4,304 cDNA clones revealed that the hybridization signal of 396 cDNA clones (271 non-redundant groups) increased more than six-fold in every stage of the anthers compared with that of leaves. Cluster analysis with the expression data showed that 259 cDNA clones (156 non redundant groups) were specifically or predominantly expressed in anther tissues and were regulated by developmental stage-specific manners in the anther tissues. These co-regulated genes would be important for development of functional anther tissues. Furthermore, we selected several clones for RNA in situ hybridization analysis. From these analyses, we found several novel genes that show temporal and spatial expression patterns during anther development in addition to anther-specific genes reported so far. These results indicate that the genes identified in this experiment are controlled by different programs and are specialized in their developmental and cell types.     

Differential Expression of Proteins Associated with the Hair Follicle Cycle - Proteomics and Bioinformatics Analyses

Hair follicle cycling can be divided into the following three stages: anagen, catagen, and telogen. The molecular signals that orchestrate the follicular transition between phases are still unknown. To better understand the detailed protein networks controlling this process, proteomics and bioinformatics analyses were performed to construct comparative protein profiles of mouse skin at specific time points (0, 8, and 20 days). Ninety-five differentially expressed protein spots were identified by MALDI-TOF/TOF as 44 proteins, which were found to change during hair follicle cycle transition. Proteomics analysis revealed that these changes in protein expression are involved in Ca²⁺-regulated biological processes, migration, and regulation of signal transduction, among other processes. Subsequently, three proteins were selected to validate the reliability of expression patterns using western blotting. Cluster analysis revealed three expression patterns, and each pattern correlated with specific cell processes that occur during the hair cycle. Furthermore, bioinformatics analysis indicated that the differentially expressed proteins impacted multiple biological networks, after which detailed functional analyses were performed. Taken together, the above data may provide insight into the three stages of mouse hair follicle morphogenesis and provide a solid basis for potential therapeutic molecular targets for this hair disease.     

Functional analysis of pathogenicity genes in a genomics world

Genome-wide mutational and expression analyses have been performed in yeast and provide a model for large-scale analysis of gene function in filamentous fungi. The recent completion of the Neurospora crassa genome offers a resource for comparative analysis with plant pathogenic filamentous fungi. These advances have important implications for molecular genetic studies of pathogenicity genes.     

The Influence of the Global Gene Expression Shift on Downstream Analyses

The assumption that total abundance of RNAs in a cell is roughly the same in different cells is underlying most studies based on gene expression analyses. But experiments have shown that changes in the expression of some master regulators such as c-MYC can cause global shift in the expression of almost all genes in some cell types like cancers. Such shift will violate this assumption and can cause wrong or biased conclusions for standard data analysis practices, such as detection of differentially expressed (DE) genes and molecular classification of tumors based on gene expression. Most existing gene expression data were generated without considering this possibility, and are therefore at the risk of having produced unreliable results if such global shift effect exists in the data. To evaluate this risk, we conducted a systematic study on the possible influence of the global gene expression shift effect on differential expression analysis and on molecular classification analysis. We collected data with known global shift effect and also generated data to simulate different situations of the effect based on a wide collection of real gene expression data, and conducted comparative studies on representative existing methods. We observed that some DE analysis methods are more tolerant to the global shift while others are very sensitive to it. Classification accuracy is not sensitive to the shift and actually can benefit from it, but genes selected for the classification can be greatly affected.     

De-differentiation-derived mesenchymal stem cells demonstrate selective repression in H19 bioregulatory RNA gene expression

Cellular de-differentiation can induce anticancer activity that makes cells resistant to carcinogenesis, but the molecular mechanism of this phenomenon has not been defined. To determine whether stable molecular changes develop in association with the process of de-differentiation, DNA microarray analyses were performed. These analyses compared control undifferentiated cells with three carcinogenesis-resistant clones of de-differentiated cells that were derived from mature adipocytes. The results of analysis of 6,000 genes and 6,000 ESTs establish that relative to control cells, all three de-differentiation-derived cell clones demonstrate that only one gene shows a consistent difference in expression. The expression of the H19 bioregulatory RNA is repressed an average of >fourfold in all de-differentiated cell clones. Real-time PCR analyses confirm these findings. This suggests that decreased H19 expression may account, at least in part, for the anticancer activity observed in de-differentiated cell clones.     

Molecular differences in Alzheimer's disease between male and female patients determined by integrative network analysis

Alzheimer's disease (AD) is a complex neurodegenerative disease and the most common cause of dementia among the elderly. There has been increasing recognition of sex differences in AD prevalence, clinical manifestation, disease course and prognosis. However, there have been few studies on the molecular mechanism underlying these differences. To address this issue, we carried out global gene expression and integrative network analyses based on expression profiles (GSE84422) across 17 cortical regions of 125 individuals with AD. There were few genes that were differentially expressed across the 17 regions between the two sexes, with only four (encoding glutamate metabotropic receptor 2, oestrogen‐related receptor beta, kinesin family member 26B, and aspartoacylase) that were differentially expressed in three regions. A pan‐cortical brain region co‐expression network analysis identified pathways and genes (eg, glycogen synthase kinase 3β) that were significantly associated with clinical characteristics of AD (such as neurofibrillary score) in males only. Similarity analyses between region‐specific networks indicated that male patients exhibited greater variability, especially in the superior parietal lobule, dorsolateral prefrontal cortex and occipital visual cortex. A network module analysis revealed an association between clinical traits and crosstalk of sex‐specific modules. An examination of temporal and spatial patterns of sex differences in AD showed that molecular networks were more conserved in females than in males in different cortical regions and at different AD stages. These findings provide insight into critical molecular pathways governing sex differences in AD pathology.     

Genes that determine flower color: the role of regulatory changes in the evolution of phenotypic adaptations

A central goal of evolutionary genetics is to trace the causal pathway between mutations at particular genes and adaptation at the phenotypic level. The proximate objective is to identify adaptations through the analysis of molecular sequence data from specific candidate genes or their regulatory elements. In this paper, we consider the molecular evolution of floral color in the morning glory genus (Ipomoea) as a model for relating molecular and phenotypic evolution. To begin, flower color variation usually conforms to simple Mendelian transmission, thus facilitating genetic and molecular analyses. Population genetic studies of flower color polymorphisms in the common morning glory (Ipomoea purpurea) have shown that some morphs are subject to complex patterns of selection. Striking differences in floral color and morphology are also associated with speciation in the genus Ipomoea. The molecular bases for these adaptive shifts can be dissected because the biosynthetic pathways that determine floral pigmentation are well understood and many of the genes of flavonoid biosynthesis have been isolated and extensively studied. We present a comparative analysis of the level of gene expression in Ipomoea for several key genes in flavonoid biosynthesis. Specifically we ask: how frequently are adaptive shifts in flower color phenotypes associated with changes in regulation of gene expression versus mutations in structural genes? The results of this study show that most species differences in this crucial phenotype are associated with changes in the regulation of gene expression.     

Isolation and characterisation of an <Emphasis Type="Italic">HpSHP</Emphasis> gene from <Emphasis Type="Italic">Hosta plantaginea</Emphasis>

Based on genetic and molecular analyses, the ABC model has been proposed to explain the genetic control of floral development. C-class MADS-box genes play crucial roles in Arabidopsis thaliana development by regulating the organ identities of stamens and gynoecium. The present research reports for the first time the cloning of an HpSHP gene from Hosta plantaginea (Lam.) Aschers. Phylogenetic analysis shows that HpSHP is a member of the C-class MADS-box genes that is closely related to C-lineage SHP homologues from monocot species. Semi-quantitative and real-time polymerase chain reaction analyses show that HpSHP expression is stamen and gynoecium specific. HpSHP also has spatial and temporal expression patterns in the reproductive organs of H. plantaginea. A functional analysis is carried out in Arabidopsis by overexpression of HpSHP. Homeotic transformations of sepals into carpelloid organs, bent ovaries, and prematurely shattering fruits are observed in 35S::HpSHP transgenic plants. All these results show that HpSHP plays a crucial role in gynoecium development.     

种子休眠与萌发的分子生物学的研究进展

休眠与萌发是植物种子对环境变化的适应特征,受许多基因调控和环境因子的影响.利用数量遗传学方法(如QTL分析)和突变等手段已对休眠和萌发特性进行了深入的遗传学研究.近些年来,随着分子生物学的快速发展,种子休眠和萌发研究已经深入到分子水平.分子生物学技术的运用,特别是基因表达、基因组测序和以双向凝胶电泳及质谱分析为技术基础的蛋白质组学分析,已成为研究种子休眠和萌发的新工具和新方向.本文主要就利用分子生物学方法研究种子休眠与萌发的进展给予简要综述.     

SWATH‐based proteomics reveals processes associated with immune evasion and metastasis in poor prognosis colorectal tumours

Newly emerged proteomic methodologies, particularly data‐independent acquisition (DIA) analysis–related approaches, would improve current gene expression–based classifications of colorectal cancer (CRC). Therefore, this study was aimed to identify protein expression signatures using SWATH‐MS DIA and targeted data extraction, to aid in the classification of molecular subtypes of CRC and advance in the diagnosis and development of new drugs. For this purpose, 40 human CRC samples and 7 samples of healthy tissue were subjected to proteomic and bioinformatic analysis. The proteomic analysis identified three different molecular CRC subtypes: P1, P2 and P3. Significantly, P3 subtype showed high agreement with the mesenchymal/stem‐like subtype defined by gene expression signatures and characterized by poor prognosis and survival. The P3 subtype was characterized by decreased expression of ribosomal proteins, the spliceosome, and histone deacetylase 2, as well as increased expression of osteopontin, SERPINA 1 and SERPINA 3, and proteins involved in wound healing, acute inflammation and complement pathway. This was also confirmed by immunodetection and gene expression analyses. Our results show that these tumours are characterized by altered expression of proteins involved in biological processes associated with immune evasion and metastasis, suggesting new therapeutic options in the treatment of this aggressive type of CRC.