Duplicate gene enrichment and expression pattern diversification in multicellularity

The enrichment of duplicate genes, and therefore paralogs (proteins coded by duplicate genes), in multicellular versus unicellular organisms enhances genomic functional innovation. This study quantitatively examined relationships among paralog enrichment, expression pattern diversification and multicellularity, aiming to better understand genomic basis of multicellularity. Paralog abundance in specific cells was compared with those in unicellular proteomes and the whole proteomes of multicellular organisms. The budding yeast, Saccharomyces cerevisiae and the nematode, Caenorhabditis elegans, for which the gene sets expressed in specific cells are available, were used as uni and multicellular models, respectively. Paralog count (K) distributions [P((k))] follow a power-law relationship [P((k)) ∝ k(-α)] in the whole proteomes of both species and in specific C. elegans cells. The value of the constant α can be used as a gauge of paralog abundance; the higher the value, the lower the paralog abundance. The α-value is indeed lower in the whole proteome of C. elegans (1.74) than in S. cerevisiae (2.34), quantifying the enrichment of paralogs in multicellular species. We also found that the power-law relationship applies to the proteomes of specific C. elegans cells. Strikingly, values of α in specific cells are higher and comparable to that in S. cerevisiae. Thus, paralog abundance in specific cells is lower and comparable to that in unicellular species. Furthermore, how much the expression level of a gene fluctuates across different C. elegans cells correlates positively with its paralog count, which is further confirmed by human gene-expression patterns across different tissues. Taken together, these results quantitatively and mechanistically establish enrichment of paralogs with diversifying expression patterns as genomic and evolutionary basis of multicellularity.     

Estimating genomic coexpression networks using first-order conditional independence

We describe a computationally efficient statistical framework for estimating networks of coexpressed genes. This framework exploits first-order conditional independence relationships among gene-expression measurements to estimate patterns of association. We use this approach to estimate a coexpression network from microarray gene-expression measurements from Saccharomyces cerevisiae. We demonstrate the biological utility of this approach by showing that a large number of metabolic pathways are coherently represented in the estimated network. We describe a complementary unsupervised graph search algorithm for discovering locally distinct subgraphs of a large weighted graph. We apply this algorithm to our coexpression network model and show that subgraphs found using this approach correspond to particular biological processes or contain representatives of distinct gene families.     

Specificity and complexity of the Caenorhabditis elegans innate immune response

In response to infection, Caenorhabditis elegans produces an array of antimicrobial proteins. To understand the C. elegans immune response, we have investigated the regulation of a large, representative sample of candidate antimicrobial genes. We found that all these putative antimicrobial genes are expressed in tissues exposed to the environment, a position from which they can ward off infection. Using RNA interference to inhibit the function of immune signaling pathways in C. elegans, we found that different immune response pathways regulate expression of distinct but overlapping sets of antimicrobial genes. We also show that different bacterial pathogens regulate distinct but overlapping sets of antimicrobial genes. The patterns of genes induced by pathogens do not coincide with any single immune signaling pathway. Thus, even in this simple model system for innate immunity, striking specificity and complexity exist in the immune response. The unique patterns of antimicrobial gene expression observed when C. elegans is exposed to different pathogens or when different immune signaling pathways are perturbed suggest that a large set of yet to be identified pathogen recognition receptors (PRRs) exist in the nematode. These PRRs must interact in a complicated fashion to induce a unique set of antimicrobial genes. We also propose the existence of an "antimicrobial fingerprint," which will aid in assigning newly identified C. elegans innate immunity genes to known immune signaling pathways.     

Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions.

The complexity of biological systems provides for a great diversity of relationships between genes. The current analysis of whole-genome expression data focuses on relationships based on global correlation over a whole time-course, identifying clusters of genes whose expression levels simultaneously rise and fall. There are, of course, other potential relationships between genes, which are missed by such global clustering. These include activation, where one expects a time-delay between related expression profiles, and inhibition, where one expects an inverted relationship. Here, we propose a new method, which we call local clustering, for identifying these time-delayed and inverted relationships. It is related to conventional gene-expression clustering in a fashion analogous to the way local sequence alignment (the Smith-Waterman algorithm) is derived from global alignment (Needleman-Wunsch). An integral part of our method is the use of random score distributions to assess the statistical significance of each cluster. We applied our method to the yeast cell-cycle expression dataset and were able to detect a considerable number of additional biological relationships between genes, beyond those resulting from conventional correlation. We related these new relationships between genes to their similarity in function (as determined from the MIPS scheme) or their having known protein-protein interactions (as determined from the large-scale two-hybrid experiment); we found that genes strongly related by local clustering were considerably more likely than random to have a known interaction or a similar cellular role. This suggests that local clustering may be useful in functional annotation of uncharacterized genes. We examined many of the new relationships in detail. Some of them were already well-documented examples of inhibition or activation, which provide corroboration for our results. For instance, we found an inverted expression profile relationship between genes YME1 and YNT20, where the latter has been experimentally documented as a bypass suppressor of the former. We also found new relationships involving uncharacterized yeast genes and were able to suggest functions for many of them. In particular, we found a time-delayed expression relationship between J0544 (which has not yet been functionally characterized) and four genes associated with the mitochondria. This suggests that J0544 may be involved in the control or activation of mitochondrial genes. We have also looked at other, less extensive datasets than the yeast cell-cycle and found further interesting relationships. Our clustering program and a detailed website of clustering results is available at http://www.bioinfo.mbb.yale.edu/expression/cluster (or http://www.genecensus.org/expression/cluster).     

Analysis of Gene Coexpression by B-Spline Based CoD Estimation

The gene coexpression study has emerged as a novel holistic approach for microarray data analysis. Different indices have been used in exploring coexpression relationship, but each is associated with certain pitfalls. The Pearson's correlation coefficient, for example, is not capable of uncovering nonlinear pattern and directionality of coexpression. Mutual information can detect nonlinearity but fails to show directionality. The coefficient of determination (CoD) is unique in exploring different patterns of gene coexpression, but so far only applied to discrete data and the conversion of continuous microarray data to the discrete format could lead to information loss. Here, we proposed an effective algorithm, CoexPro, for gene coexpression analysis. The new algorithm is based on B-spline approximation of coexpression between a pair of genes, followed by CoD estimation. The algorithm was justified by simulation studies and by functional semantic similarity analysis. The proposed algorithm is capable of uncovering both linear and a specific class of nonlinear relationships from continuous microarray data. It can also provide suggestions for possible directionality of coexpression to the researchers. The new algorithm presents a novel model for gene coexpression and will be a valuable tool for a variety of gene expression and network studies. The application of the algorithm was demonstrated by an analysis on ligand-receptor coexpression in cancerous and noncancerous cells. The software implementing the algorithm is available upon request to the authors.     

Digital three-dimensional models of Drosophila development

Digital models of organs, cells and subcellular structures have become important tools in biological and medical research. Reaching far beyond their traditional widespread use as didactic tools, computer-generated models serve as electronic atlases to identify specific elements in complex patterns, and as analytical tools that reveal relationships between such pattern elements that would remain obscure in two-dimensional sections. Digital models also offer the unique opportunity to store and display gene-expression patterns, and pilot studies have been made in several genetic model organisms, including mouse, Drosophila and Caenorhabditis elegans, to construct digital graphic databases intended as repositories for gene-expression data.     

A reverse genetic analysis of components of the Toll signaling pathway in Caenorhabditis elegans

BACKGROUND: Both animals and plants respond rapidly to pathogens by inducing the expression of defense-related genes. Whether such an inducible system of innate immunity is present in the model nematode Caenorhabditis elegans is currently an open question. Among conserved signaling pathways important for innate immunity, the Toll pathway is the best characterized. In Drosophila, this pathway also has an essential developmental role. C. elegans possesses structural homologs of components of this pathway, and this observation raises the possibility that a Toll pathway might also function in nematodes to trigger defense mechanisms or to control development. RESULTS: We have generated and characterized deletion mutants for four genes supposed to function in a nematode Toll signaling pathway. These genes are tol-1, trf-1, pik-1, and ikb-1 and are homologous to the Drosophila melanogaster Toll, dTraf, pelle, and cactus genes, respectively. Of these four genes, only tol-1 is required for nematode development. None of them are important for the resistance of C. elegans to a number of pathogens. On the other hand, C. elegans is capable of distinguishing different bacterial species and has a tendency to avoid certain pathogens, including Serratia marcescens. The tol-1 mutants are defective in their avoidance of pathogenic S. marcescens, although other chemosensory behaviors are wild type. CONCLUSIONS: In C. elegans, tol-1 is important for development and pathogen recognition, as is Toll in Drosophila, but remarkably for the latter r?le, it functions in the context of a behavioral mechanism that keeps worms away from potential danger.     

Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders

In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene–gene expression correlations are a common source of molecular networks because they can be extracted from high‐dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming ‘hub genes’ direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi‐omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease‐related prioritization of genes and molecular systems and network meta‐analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality–lethality relationship, integration with machine learning approaches and network pharmacology .     

Comparative network analysis reveals that tissue specificity and gene function are important factors influencing the mode of expression evolution in Arabidopsis and rice

Microarray experiments have yielded massive amounts of expression information measured under various conditions for the model species Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa). Expression compendia grouping multiple experiments make it possible to define correlated gene expression patterns within one species and to study how expression has evolved between species. We developed a robust framework to measure expression context conservation (ECC) and found, by analyzing 4,630 pairs of orthologous Arabidopsis and rice genes, that 77% showed conserved coexpression. Examples of nonconserved ECC categories suggested a link between regulatory evolution and environmental adaptations and included genes involved in signal transduction, response to different abiotic stresses, and hormone stimuli. To identify genomic features that influence expression evolution, we analyzed the relationship between ECC, tissue specificity, and protein evolution. Tissue-specific genes showed higher expression conservation compared with broadly expressed genes but were fast evolving at the protein level. No significant correlation was found between protein and expression evolution, implying that both modes of gene evolution are not strongly coupled in plants. By integration of cis-regulatory elements, many ECC conserved genes were significantly enriched for shared DNA motifs, hinting at the conservation of ancestral regulatory interactions in both model species. Surprisingly, for several tissue-specific genes, patterns of concerted network evolution were observed, unveiling conserved coexpression in the absence of conservation of tissue specificity. These findings demonstrate that orthologs inferred through sequence similarity in many cases do not share similar biological functions and highlight the importance of incorporating expression information when comparing genes across species.     

特征代谢通路上的基因表达相关性及共调控表达模式

利用随机森林-通路分析法,通过袋外样本OOB的分类错误率筛选特征代谢通路,在特征通路上作基因表达相关性研究并对通路上的基因采用MAP(Mining attribute profile)算法挖掘不同实验条件下基因的共调控表达模式,对共调控表达模式进行聚类.分析结果显示同一特征代谢通路上的基因表达倾向相似,有2条特征代谢通路存在共表达模式.其中一条通路含108个表达模式,对这些模式进行聚类,其最低聚类的相似系数仍高达0.623.说明同一特征代谢通路上的基因共表达模式在不同实验条件下仍具有高度的相似性.对以通路作为基因模块进行复杂疾病的研究具有借鉴意义.     

Microarray analysis of host gene-expression during intracellular nests formation of Trypanosoma cruzi amastigotes

The intracellular pathogens utilize numerous cellular components of host cells to advance the infection as well as to enter the host cell. Analyzing the host cellular response enables us to get a better understanding of the pathogenesis, and subsequently indicate possible therapeutic targets. We therefore analyzed gene-expression profile of NIH3T3 fibroblast cells infected by Trypanosoma, a representative intracellular pathogen similar to Leishmania, using custom-designed cDNA microarray consisting of 1,701 mKIAA cDNAs. Focusing on intracellular nest formation of Trypanosoma cruzi amastigotes, we profiled the host gene-expression at 8 days post-infection and found several degrees of change in 16 mKIAA genes. Among these genes, 10 were up-regulated and 6 were down-regulated. Assuming that these genes had important roles in the infection's progression, we performed semi-quantitative RT-PCR analysis and con-firmed the gene expression change of 4 genes. Furthermore, 5 genes were mapped on cadherin signaling pathway using pathway analysis software. These results indicate significance of the host cellular pathway in the proliferative stage of Trypanosoma cruzi amastigotes.     

Growth factor-specific signaling pathway stimulation and gene expression mediated by ErbB receptors

The mechanisms by which receptor tyrosine kinases (RTKs) utilize intracellular signaling pathways to direct gene expression and cellular response remain unclear. A current question is whether different RTKs within a single cell target similar or different sets of genes. In this study we have used the ErbB receptor network to explore the relationship between RTK activation and gene expression. We profiled growth factor-stimulated signaling pathway usage and broad gene expression patterns in two human mammary tumor cell lines expressing different complements of ErbB receptors. Although the growth factors epidermal growth factor (EGF) and neuregulin (NRG) 1 similarly stimulated Erk1/2 in MDA-MB-361 cells, EGF acting through an EGF receptor/ErbB2 heterodimer preferentially stimulated protein kinase C, and NRG1beta acting through an ErbB2/ErbB3 heterodimer preferentially stimulated Akt. The two growth factors regulated partially overlapping yet distinct sets of genes in these cells. In MDA-MB-453 cells, NRG1beta acting through an ErbB2/ErbB3 heterodimer stimulated prolonged signaling of all pathways examined relative to NRG2beta acting through the same heterodimeric receptor species. Surprisingly, NRG1beta and NRG2beta also regulated partially overlapping but distinct sets of genes in these cells. These results demonstrate that the activation of different RTKs, or activation of the same RTKs with different ligands, can lead to distinct profiles of gene regulation within a single cell type. Our observations also suggest that the identity and kinetics of signaling pathway usage by RTKs may play a role in the selection of regulated genes.     

Identifying interventional and pathogenic mechanisms by generative inverse modeling of gene expression profiles.

MOTIVATION: The characterization of genetic mechanisms underlying normal cellular function, cancer development, pathogenesis, and the effect of drug treatment is one of the most challenging topics for cancer research and molecular biology. Existing methods for inferring genetic regulatory networks from genome-wide expression profiles provide important information about gene interactions and regulatory relationships. However, these methods do not provide information about the impact of possible interventions or changes on such regulatory networks to study cause-effect relationships at a systems-biology level. RESULTS: We present a data-driven method called generative inverse modeling, which simulates the effect of local genetic changes on the global cellular state, as reflected by an altered genome-wide expression profile. For each genetic change we define a pathogenic score by calculating to what extent it transforms the simulated expression patterns into patterns measured for pathologically altered tissues. The method can be used to estimate the relevance of genes for disease-specific genetic mechanisms, e.g., as presented here for pathogenesis. Generative inverse modeling is based on a Bayesian probability density estimation from a set of measured gene-expression patterns.