Onto-Tools,the toolkit of the modern biologist: Onto-Express,Onto-Compare,Onto-Design and Onto-Translate

Onto-Tools is a set of four seamlessly integrated databases: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate. Onto-Express is able to automatically translate lists of genes found to be differentially regulated in a given condition into functional profiles characterizing the impact of the condition studied upon various biological processes and pathways. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function and chromosome location. Statistical significance values are calculated for each category. Once the initial exploratory analysis identified a number of relevant biological processes, specific mechanisms of interactions can be hypothesized for the conditions studied. Currently, many commercial arrays are available for the investigation of specific mechanisms. Each such array is characterized by a biological bias determined by the extent to which the genes present on the array represent specific pathways. Onto-Compare is a tool that allows efficient comparisons of any sets of commercial or custom arrays. Using Onto-Compare, a researcher can determine quickly which array, or set of arrays, covers best the hypotheses studied. In many situations, no commercial arrays are available for specific biological mechanisms. Onto-Design is a tool that allows the user to select genes that represent given functional categories. Onto-Translate allows the user to translate easily lists of accession numbers, UniGene clusters and Affymetrix probes into one another. All tools above are seamlessly integrated. The Onto-Tools are available online at http://vortex.cs.wayne.edu/Projects.html.     

Express path analysis identifies a tyrosine kinase Src-centric network regulating divergent host responses to Mycobacterium tuberculosis infection

Global gene expression profiling has emerged as a major tool in understanding complex response patterns of biological systems to perturbations. However, a lack of unbiased analytical approaches has restricted the utility of complex microarray data to gain novel system level insights. Here we report a strategy, express path analysis (EPA), that helps to establish various pathways differentially recruited to achieve specific cellular responses under contrasting environmental conditions in an unbiased manner. The analysis superimposes differentially regulated genes between contrasting environments onto the network of functional protein associations followed by a series of iterative enrichments and network analysis. To test the utility of the approach, we infected THP1 macrophage cells with a virulent Mycobacterium tuberculosis strain (H37Rv) or the attenuated non-virulent strain H37Ra as contrasting perturbations and generated the temporal global expression profiles. EPA of the results provided details of response-specific and time-dependent host molecular network perturbations. Further analysis identified tyrosine kinase Src as the major regulatory hub discriminating the responses between wild-type and attenuated Mtb infection. We were then able to verify this novel role of Src experimentally and show that Src executes its role through regulating two vital antimicrobial processes of the host cells (i.e. autophagy and acidification of phagolysosome). These results bear significant potential for developing novel anti-tuberculosis therapy. We propose that EPA could prove extremely useful in understanding complex cellular responses for a variety of perturbations, including pathogenic infections.     

转录组学对转基因杨树表型变化的代谢调控机制解析北大核心CSCD

D型细胞周期蛋白(D-type cyclin)调控着细胞周期G1/S的转变,在植物生长发育过程中发挥重要作用。转基因杨树PtoCYCD2;1(OE-PtoCYCD2;1)植株出现明显的表型变化,株高降低,茎粗变细且叶片发生卷曲。该研究以转基因杨树OE-PtoCYCD2;1为研究材料,通过转录组学测序和生理指标变化并结合植株表型特征分析PtoCYCD2;1在植物生长发育中的功能,为研究木本植物D型细胞周期蛋白功能提供理论基础。结果表明:(1)在OE-PtoCYCD2;1中共鉴定得到1269个差异表达基因,其中有700个上调表达,569个下调表达。分析发现,有26个属于AP2/ERF转录因子的基因上调表达;有8个下调的差异表达基因富集在木质部合成通路中;在碳代谢通路中共富集27个下调差异表达基因,其中有8个基因富集到卡尔文循环通路中。(2)qRT-PCR实验结果显示,9个差异表达基因的qRT-PCR结果与RNA-seq测定的表达水平变化趋势一致,表明所用RNA-seq结果可靠。(3)生理指标分析发现,与野生型(WT)相比,转基因杨树OE-PtoCYCD2;1的幼叶和成熟叶的总叶绿素含量分别增加57.36%和78.22%;成熟叶的可溶性糖含量下降了12.72%;幼叶和成熟叶中的木质素含量分别下降了4.48%和8.03%;幼茎和成熟茎中的木质素含量分别下降了20.03%和31.63%。研究认为,转基因杨树OE-PtoCYCD2;1通过影响杨树碳代谢和木质素合成过程中相关基因的表达,从而造成转基因植株相应代谢物含量减少,最终导致植株表型改变,总体生物量降低。     

Profiling gene expression using onto-express

Gene expression profiles obtained through microarray or data mining analyses often exist as vast data strings. To interpret the biology of these genetic profiles, investigators must analyze this data in the context of other information such as the biological, biochemical, or molecular function of the translated proteins. This is particularly challenging for a human analyst because large quantities of less than relevant data often bury such information. To address this need we implemented an automated routine, called Onto-Express (http://vortex.cs.wayne.edu:8080), to systematically translate genetic fingerprints into functional profiles. Using strings of accession or cluster identification numbers, Onto-Express searches the public databases and returns tables that correlate expression profiles with the cytogenetic locations, biochemical and molecular functions, biological processes, cellular components, and cellular roles of the translated proteins. The profiles created by Onto-Express fundamentally increase the value of gene expression analyses by facilitating the translation of quantitative value sets to records that contain biological implications.     

Systematic identification of common functional modules related to heart failure with different etiologies

The development of heart failure (HF) is a complex process that can be initiated by multiple etiologies. Identifying common functional modules associated with HF is a challenging task. Here, we developed a systems method to identify these common functional modules by integrating multiple expression profiles, protein interactions from four species, gene function annotations, and text information. We identified 1439 consistently differentially expressed genes (CDEGs) across HF with different etiologies by applying three meta-analysis methods to multiple HF-related expression profiles. Using a weighted human interaction network constructed by combining interaction data from multiple species, we extracted 60 candidate CDEG modules. We further evaluated the functional relevance of each module by using expression, interaction network, functional annotations, and text information together. Finally, five functional modules with significant biological relevance were identified. We found that almost half of the genes in these modules are hubs in the weighted network, and that these modules can accurately classify HF patients from healthy subjects. We also identified many significantly enriched biological processes that contribute to the pathophysiology of HF, including two new ones, RNA splicing and vesicle-mediated protein transport. In summary, we proposed a novel framework to analyze common functional modules related to HF with different etiologies. Our findings provide important insights into the complex mechanism of HF. Further biological experimentations should be required to validate these novel biological processes.     

基因芯片技术筛选转SlNAC1基因拟南芥差异表达基因

研究已表明植物特有的一些NAC(NAM,ATAF1/2,CUC2)转录因子可提高植物抗逆性,利用基因芯片技术筛选转SlNAC1基因拟南芥与野生型拟南芥间差异表达基因,能够为研究转基因拟南芥非生物胁迫抗性相关基因提供依据。结果显示,在转SlNAC1基因拟南芥43 604个基因中有3 046个差异表达2倍以上的基因。对差异表达5倍以上基因经过GO富集度统计学分析表明,细胞组分相关基因占33.05%;分子功能相关基因占33.95%;生物学过程相关基因占33.00%。对差异表达2倍以上基因进行KEGG信号通路分析,结果表明有2 431个基因涉及到88个不同的信号通路。通过筛选获得转基因拟南芥非生物胁迫抗性相关候选基因,为后续研究NAC转录因子的下游基因及其调控网络的构建提供方向和理论支撑。     

Overlapping but distinct gene regulation profiles by glucocorticoids and progestins in human breast cancer cells

Glucocorticoids and progestins bind to receptors that share many structural and functional similarities, including virtually identical DNA recognition specificity. Nonetheless, the two hormones mediate very distinct biological functions. For example, progestins are associated with the incidence and progression of breast cancer, whereas glucocorticoids are growth suppressive in mammary cancer cells. To understand the mechanisms that engender biological specificity, it is necessary to identify genes that are differentially regulated by the two receptors. Here we employ Affymetrix oligonucleotide arrays to compare glucocorticoid- and progestin-regulated gene expression in a human breast cancer cell line. This global analysis reveals that the two hormones regulate overlapping but distinct sets of genes, including 31 genes that are differentially regulated. Surprisingly, the set of differentially regulated genes was almost as large as the set of genes regulated by both hormones. Examination of the set of differentially regulated genes suggests mechanisms behind the distinct growth effects of the two hormones in breast cancer. The differential regulation of four genes representing different regulatory patterns was confirmed by RT-PCR and Northern blot analyses. Treatment with cycloheximide or RU486 indicates that the regulation is a primary, receptor-mediated event. Detailed analyses of genes identified in these studies will furnish a mechanistic understanding of differential regulation by glucocorticoids and progestins.     

Biological and functional analysis of statistically significant pathways deregulated in colon cancer by using gene expression profiles

Gene expression profiling offers a great opportunity for studying multi-factor diseases and for understanding the key role of genes in mechanisms which drive a normal cell to a cancer state. Single gene analysis is insufficient to describe the complex perturbations responsible for cancer onset, progression and invasion. A deeper understanding of the mechanisms of tumorigenesis can be reached focusing on deregulation of gene sets or pathways rather than on individual genes. We apply two known and statistically well founded methods for finding pathways and biological processes deregulated in pathological conditions by analyzing gene expression profiles. In particular, we measure the amount of deregulation and assess the statistical significance of predefined pathways belonging to a curated collection (Molecular Signature Database) in a colon cancer data set. We find that pathways strongly involved in different tumors are strictly connected with colon cancer. Moreover, our experimental results show that the study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. Our study shows the importance of using gene sets rather than single genes for understanding the main biological processes and pathways involved in colorectal cancer. Our analysis evidences that many of the genes involved in these pathways are strongly associated to colorectal tumorigenesis. In this new perspective, the focus shifts from finding differentially expressed genes to identifying biological processes, cellular functions and pathways perturbed in the phenotypic conditions by analyzing genes co-expressed in a given pathway as a whole, taking into account the possible interactions among them and, more importantly, the correlation of their expression with the phenotypical conditions.     

Macrophage-mediated neuroprotection and neurogenesis in the olfactory epithelium.

Resident and recruited olfactory epithelial macrophages participate in the regulation of the survival, degeneration, and replacement of olfactory sensory neurons (OSNs). We have reported that liposome-encapsulated clodronate (Lip-C) induced selective and statistically significant depletion of macrophages in the OE of sham and 48 h OBX mice (38 and 35%, respectively) that resulted in increased OSN apoptosis and decreased numbers of mature OSNs and proliferating basal cells compared to controls (Lip-O). The aim of this study was to identify molecular mechanisms by which the selective depletion of macrophages in the OE resulted in these cellular changes by using a microarray expression pattern analysis. A 2x2 ANOVA identified 4,085 overall significantly (P < 0.01) regulated genes in the OE of Lip-O and Lip-C sham and 48 h OBX mice, and further statistical analysis using pairwise comparisons identified 4,024 genes that had either a significant (P < 0.01) treatment main effect (n = 2,680), group main effect (n = 778), or interaction effect (n = 980). The mean hybridization signals of immune response genes, e.g., Cxcr4, and genes encoding growth factors and neurogenesis regulators, e.g., Hdgf and Neurod1, respectively, were primarily lower in Lip-C mice compared with Lip-O mice. Apoptosis genes, e.g., Bak1, were also differentially regulated in Lip-C and/or OBX mice. Expression patterns of selected genes were validated with real-time RT-PCR; immunohistochemistry was used to localize selected gene products. These results identified the differential regulation of several novel genes through which alternatively activated macrophages regulate OSN progenitor cell proliferation, differentiation, and maturation, and the survival of OSNs.     

Differential responses of normal human coronary artery endothelial cells against multiple cytokines comparatively assessed by gene expression profiles

Endothelial cells play an important role in terms of biological functions by responding to a variety of stimuli in the blood. However, little is known about the molecular mechanism involved in rendering the variety in the cellular response. To investigate the variety of the cellular responses against exogenous stimuli at the gene expression level, we attempted to describe the cellular responses with comprehensive gene expression profiles, dissect them into multiple response patterns, and characterize the response patterns according to the information accumulated so far on the genes included in the patterns. We comparatively analyzed in parallel the gene expression profiles obtained with DNA microarrays from normal human coronary artery endothelial cells (HCAECs) stimulated with multiple cytokines, interleukin-1β, tumor necrosis factor-, interferon-β, interferon-γ, and oncostatin M, which are profoundly involved in various functional responses of endothelial cells. These analyses revealed that the cellular responses of HCAECs against these cytokines included at least 15 response patterns specific to a single cytokine or common to multiple cytokines. Moreover, we statistically extracted genes contained within the individual response patterns and characterized the response patterns with the genes referring to the previously accumulated findings including the biological process defined by the Gene Ontology Consortium (GO). Out of the 15 response patterns in which at least one gene was successfully extracted through the statistical approach, 11 response patterns were differentially characterized by representing the number of genes contained in individual criteria of the biological process in the GO only. The approach to dissect cellular responses into response patterns and to characterize the pattern at the gene expression level may contribute to the gaining of insight for untangling the diversity of cellular functions.     

Meta-Analysis of Gene Expression Signatures Reveals Hidden Links among Diverse Biological Processes in Arabidopsis

The model plant Arabidopsis has been well-studied using high-throughput genomics technologies, which usually generate lists of differentially expressed genes under various conditions. Our group recently collected 1065 gene lists from 397 gene expression studies as a knowledgebase for pathway analysis. Here we systematically analyzed these gene lists by computing overlaps in all-vs.-all comparisons. We identified 16,261 statistically significant overlaps, represented by an undirected network in which nodes correspond to gene lists and edges indicate significant overlaps. The network highlights the correlation across the gene expression signatures of the diverse biological processes. We also partitioned the main network into 20 sub-networks, representing groups of highly similar expression signatures. These are common sets of genes that were co-regulated under different treatments or conditions and are often related to specific biological themes. Overall, our result suggests that diverse gene expression signatures are highly interconnected in a modular fashion.