A novel stress-related gene in developing pepper anthers

To study gene expression patterns and to find genes related with microspore embryogenesis during pepper (Capsicum annuum L.) anther development, mRNA expression patterns were investigated at four developmental stages distinguished according to the size of flower bud, the color of anthers, and the cytological feature of microspores. Through GeneFishing using 120 random primers, 81 genes were found to be differentially expressed as anthers develop. We directly sequenced seven of them, which were either up- or down-regulated at stage 2, since microspores at stage 2 are known to be responsive to the induction signals for microspore embryogenesis. Nucleotide sequence analysis of the isolated differentially expressed genes (DEGs) and the comparison of these sequences with the GenBank data indicate that DEG13 is a novel gene, which is highly homologous to a stress-related gene of potato, POACT88 (≈91%) and to alcohol dehydrogenase gene of Arabidopsis (≈70%), whose expression is also tightly related to stresses. In vitro data also showed that DEG13 was more abundantly expressed in heat-treated microspores than in untreated microspores. Here, we report developmental stage-specific gene expression patterns during anther development and a novel stress-related gene, DEG13, which may be involved in microspore embryogenesis in response to heat treatment.     

Annealing control primer system for identification of differentially expressed genes on agarose gels

We developed GeneFishing technology, an improved method for the identification of differentially expressed genes (DEGs) using our novel annealing control primer (ACP) system. Because of high annealing specificity during PCR using the ACP system, the application of the ACP to DEG discovery generates reproducible, authentic, and long (100 bp to 2 kb) PCR products that are detectable on agarose gels. To demonstrate this method for gene expression profiling, Gene-Fishing technology was used to detect genes that are differentially expressed during development using total RNAs isolated from mouse conceptus tissues at 4.5-18.5 days of gestation. Ten DEGs (DEG1-10) were isolated and confirmed by Northern blot hybridization. The sequence analysis of these DEGs showed that DEG6 and DEG10 are unknown genes.     

Identification of a five-gene signature with prognostic value in colorectal cancer

Colorectal cancer (CRC) ranks as one of the most commonly diagnosed malignancies worldwide. Although mortality rates have been decreasing, the prognosis of CRC patients is still highly dependent on the individual. Therefore, identifying and understanding novel biomarkers for CRC prognosis remains crucial. The gene expression profiles of five-gene expression omnibus (GEO) data sets of CRC were first downloaded. A total of 352 consistent differentially expressed genes (DEGs) were identified for CRC and paired with normal tissues. Functional analysis including gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment revealed that these DEGs were related to metabolic pathways, tight junctions, and the cell cycle. Ten hub DEGs were identified based on the search tool for the retrieval of interacting genes database and protein–protein interaction networks. By using univariate Cox proportional hazard regression analysis, we found 11 survival-related genes among these DEGs. We finally established a five-gene signature (kinesin family member 15, N-acetyltransferase 2, glutathione peroxidase 3, secretogranin II, and chloride channel accessory 1) with prognostic value in CRC by step multivariate Cox regression analysis. Based on this risk scoring system, patients in the high-risk group had significantly poorer survival results compared with those in the low-risk group (log-rank test, p < 0.0001). Finally, we validated our gene signature scoring system in two independent GEO cohorts (GSE17536 and GSE33113). We found all five of the signature genes to be DEGs in The Cancer Genome Atlas database. In conclusion, our findings suggest that our five DEG-based signature can provide a novel biomarker with useful applications in CRC prognosis.     

Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis

Multiple myeloma (MM) is a common hematologic malignancy for which the underlying molecular mechanisms remain largely unclear. This study aimed to elucidate key candidate genes and pathways in MM by integrated bioinformatics analysis. Expression profiles GSE6477 and GSE47552 were obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) with p < .05 and [logFC] > 1 were identified. Functional enrichment, protein–protein interaction network construction and survival analyses were then performed. First, 51 upregulated and 78 downregulated DEGs shared between the two GSE datasets were identified. Second, functional enrichment analysis showed that these DEGs are mainly involved in the B cell receptor signaling pathway, hematopoietic cell lineage, and NF-kappa B pathway. Moreover, interrelation analysis of immune system processes showed enrichment of the downregulated DEGs mainly in B cell differentiation, positive regulation of monocyte chemotaxis and positive regulation of T cell proliferation. Finally, the correlation between DEG expression and survival in MM was evaluated using the PrognoScan database. In conclusion, we identified key candidate genes that affect the outcomes of patients with MM, and these genes might serve as potential therapeutic targets.     

Classification and characterisation of extracellular vesicles-related tuberculosis subgroups and immune cell profiles

Around the world, tuberculosis (TB) remains one of the most common causes of morbidity and mortality. The molecular mechanism of Mycobacterium tuberculosis (Mtb) infection is still unclear. Extracellular vesicles (EVs) play a key role in the onset and progression of many disease states and can serve as effective biomarkers or therapeutic targets for the identification and treatment of TB patients. We analysed the expression profile to better clarify the EVs characteristics of TB and explored potential diagnostic markers to distinguish TB from healthy control (HC). Twenty EVs-related differentially expressed genes (DEGs) were identified, and 17 EVs-related DEGs were up-regulated and three DEGs were down-regulated in TB samples, which were related to immune cells. Using machine learning, a nine EVs-related gene signature was identified and two EVs-related subclusters were defined. The single-cell RNA sequence (scRNA-seq) analysis further confirmed that these hub genes might play important roles in TB pathogenesis. The nine EVs-related hub genes had excellent diagnostic values and accurately estimated TB progression. TB's high-risk group had significantly enriched immune-related pathways, and there were substantial variations in immunity across different groups. Furthermore, five potential drugs were predicted for TB using CMap database. Based on the EVs-related gene signature, the TB risk model was established through a comprehensive analysis of different EV patterns, which can accurately predict TB. These genes could be used as novel biomarkers to distinguish TB from HC. These findings lay the foundation for further research and design of new therapeutic interventions aimed at treating this deadly infectious disease.     

Harnessing Gene Expression Networks to Prioritize Candidate Epileptic Encephalopathy Genes

We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.     

三种植物线粒体基因低温差异表达比较分析

线粒体作为植物细胞的能量代谢中心,在植物响应逆境胁迫中有重要的作用。该研究基于雪莲(Saussurea involucrata)、番茄(Lycopersicon esculentum)和拟南芥(Arabidopsis thaliana)三种不同低温耐受性植物的低温转录组。通过blast比对和数据库检索筛选相关物种的线粒体基因,使用PlantCARE在线网站分析启动子,使用mega7软件对系统发育树构建分析。结果表明:通过差异表达基因筛选,分别在雪莲、拟南芥、番茄中筛选出2、24、15个显著差异表达基因,主要包括线粒体核糖体亚基和电子传递链各复合体亚基,其中部分基因的低温差异表达情况如NAD1和NAD5,可能与植物的低温适应性有关; 通过表达模式的聚类分析,雪莲与拟南芥在基因的表达模式上相对于番茄更为相近,且不同类别的基因表达模式在不同物种间差异较大; 雪莲与其他菊科植物的呼吸链相关基因的蛋白序列具有很大差异,与万年藓(Climacium dendroides)、牛舌藓(Anomodon minor)等高山植物的进化距离较近。在整体上拟南芥、番茄和雪莲三种植物线粒体基因在低温响应上具有很大差异,表明线粒体基因及其表达调控与植物的低温耐受性之间存在一定的关联性。     

Identification and validation of key genes associated with non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is one of the main causes of death induced by cancer globally. However, the molecular aberrations in NSCLC patients remain unclearly. In the present study, four messenger RNA microarray datasets (GSE18842, GSE40275, GSE43458, and GSE102287) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between NSCLC tissues and adjacent lung tissues were obtained from GEO2R and the overlapping DEGs were identified. Moreover, functional and pathway enrichment were performed by Funrich, while the protein–protein interaction (PPI) network construction were obtained from STRING and hub genes were visualized and identified by Cytoscape software. Furthermore, validation, overall survival (OS) and tumor staging analysis of selected hub genes were performed by GEPIA. A total of 367 DEGs (95 upregulated and 272 downregulated) were obtained through gene integration analysis. The PPI network consisted of 94 nodes and 1036 edges in the upregulated DEGs and 272 nodes and 464 edges in the downregulated DEGs, respectively. The PPI network identified 46 upregulated and 27 downregulated hub genes among the DEGs, and six (such as CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M) of that have not been identified to be associated with NSCLC so far. Moreover, the expression differences of the mentioned hub genes were consistent with that in lung adenocarcinoma and lung squamous cell carcinoma in the TCGA database. Further analysis showed that all the six hub genes were associated with tumor staging except MYH11, while only the upregulated DEG CENPE was associated with the worse OS of patients with NSCLC. In conclusion, the current study showed that CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M might be the key genes contributed to tumorigenesis or tumor progression in NSCLC, further functional study is needed to explore the involved mechanisms.     

Understanding the genetic basis of invasiveness

Invasive species provide excellent study systems to evaluate the ecological and evolutionary processes that contribute to the colonization of novel environments. While the ecological processes that contribute to the successful establishment of invasive plants have been studied in detail, investigation of the evolutionary processes involved in successful invasions has only recently received attention. In particular, studies investigating the genomic and gene expression differences between native and introduced populations of invasive species are just beginning and are required if we are to understand how plants become invasive. In the current issue of Molecular Ecology, Hodgins et al. ( 2013 ) tackle this unresolved question, by examining gene expression differences between native and introduced populations of annual ragweed, Ambrosia artemisiifolia. The study identifies a number of potential candidate genes based on gene expression differences that may be responsible for the success of annual ragweed in its introduced range. Furthermore, genes involved in stress response are over‐represented in the differentially expressed gene set. Future experiments could use functional studies to test whether changes in gene expression at these candidate genes do in fact underlie changes in growth characteristics and reproductive output observed in this and other invasive species.     

Prioritization and evaluation of depression candidate genes by combining multidimensional data resources

Background

Large scale and individual genetic studies have suggested numerous susceptible genes for depression in the past decade without conclusive results. There is a strong need to review and integrate multi-dimensional data for follow up validation. The present study aimed to apply prioritization procedures to build-up an evidence-based candidate genes dataset for depression.

Methods

Depression candidate genes were collected in human and animal studies across various data resources. Each gene was scored according to its magnitude of evidence related to depression and was multiplied by a source-specific weight to form a combined score measure. All genes were evaluated through a prioritization system to obtain an optimal weight matrix to rank their relative importance with depression using the combined scores. The resulting candidate gene list for depression (DEPgenes) was further evaluated by a genome-wide association (GWA) dataset and microarray gene expression in human tissues.

Results

A total of 5,055 candidate genes (4,850 genes from human and 387 genes from animal studies with 182 being overlapped) were included from seven data sources. Through the prioritization procedures, we identified 169 DEPgenes, which exhibited high chance to be associated with depression in GWA dataset (Wilcoxon rank-sum test, p = 0.00005). Additionally, the DEPgenes had a higher percentage to express in human brain or nerve related tissues than non-DEPgenes, supporting the neurotransmitter and neuroplasticity theories in depression.

Conclusions

With comprehensive data collection and curation and an application of integrative approach, we successfully generated DEPgenes through an effective gene prioritization system. The prioritized DEPgenes are promising for future biological experiments or replication efforts to discoverthe underlying molecular mechanisms for depression.     

Identification of differentially expressed genes in primary Sjögren's syndrome

Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease that affects exocrine glands. To study the molecular mechanism and identify crucial genes/pathways in pSS pathogenesis, the microarray-based whole-genome gene expression profiles from salivary glands of patients with pSS and non-sicca controls were retrieved. After normalization and subsequent batch effect adjustment, significance analysis of microarrays method was applied to five available datasets, and 379 differentially expressed genes (DEGs) were identified. The 300 upregulated DEGs were enriched in Gene Ontology terms of immune and inflammatory responses, including antigen processing and presentation, interferon-mediated signaling pathway, and chemotaxis. Previously reported pSS-associated genes, including HLA-DRA, TAP2, PRDM1, and IFI16, were found to be significantly upregulated. The downregulated DEGs were enriched in pathways of salivary secretion, carbohydrate digestion and absorption, and starch and sucrose metabolism, implying dysfunction of salivary glands during pathogenesis. Next, a protein-protein interaction network was constructed, and B2M, an upregulated DEG, was shown to be a hub, suggesting its potential involvement in pSS development. In summary, we found the activation of pSS-associated genes in pathogenesis, and provide clues for salivary glands dysfunction. Experimental investigation on the identified DEGs in this study will deepen our understanding on pSS.     

Parallel multiplicity and error discovery rate (EDR) in microarray experiments

Background  

In microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statistical tests. It is important to control the number of false positives or errors that are present in the resultant DEG list. To date, more than 20 different multiple test methods have been reported that compute overall Type I error rates in microarray experiments. However, these methods share the following dilemma: they have low power in cases where only a small number of DEGs exist among a large number of total genes on the array.     

A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism-Related Genes

Identifying the genes involved in venous thromboembolism (VTE) recurrence is important not only for understanding the pathogenesis but also for discovering the therapeutic targets. We proposed a novel prioritization method called Function-Interaction-Pearson (FIP) by creating gene-disease similarity scores to prioritize candidate genes underling VTE. The scores were calculated by integrating and optimizing three types of resources including gene expression, gene ontology and protein-protein interaction. As a result, 124 out of top 200 prioritized candidate genes had been confirmed in literature, among which there were 34 antithrombotic drug targets. Compared with two well-known gene prioritization tools Endeavour and ToppNet, FIP was shown to have better performance. The approach provides a valuable alternative for drug targets discovery and disease therapy.     

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.