Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile

We present a strategy for generating and analyzing comprehensive genetic-interaction maps, termed E-MAPs (epistatic miniarray profiles), comprising quantitative measures of aggravating or alleviating interactions between gene pairs. Crucial to the interpretation of E-MAPs is their high-density nature made possible by focusing on logically connected gene subsets and including essential genes. Described here is the analysis of an E-MAP of genes acting in the yeast early secretory pathway. Hierarchical clustering, together with novel analytical strategies and experimental verification, revealed or clarified the role of many proteins involved in extensively studied processes such as sphingolipid metabolism and retention of HDEL proteins. At a broader level, analysis of the E-MAP delineated pathway organization and components of physical complexes and illustrated the interconnection between the various secretory processes. Extension of this strategy to other logically connected gene subsets in yeast and higher eukaryotes should provide critical insights into the functional/organizational principles of biological systems.     

Network-Free Inference of Knockout Effects in Yeast

Perturbation experiments, in which a certain gene is knocked out and the expression levels of other genes are observed, constitute a fundamental step in uncovering the intricate wiring diagrams in the living cell and elucidating the causal roles of genes in signaling and regulation. Here we present a novel framework for analyzing large cohorts of gene knockout experiments and their genome-wide effects on expression levels. We devise clustering-like algorithms that identify groups of genes that behave similarly with respect to the knockout data, and utilize them to predict knockout effects and to annotate physical interactions between proteins as inhibiting or activating. Differing from previous approaches, our prediction approach does not depend on physical network information; the latter is used only for the annotation task. Consequently, it is both more efficient and of wider applicability than previous methods. We evaluate our approach using a large scale collection of gene knockout experiments in yeast, comparing it to the state-of-the-art SPINE algorithm. In cross validation tests, our algorithm exhibits superior prediction accuracy, while at the same time increasing the coverage by over 25-fold. Significant coverage gains are obtained also in the annotation of the physical network.     

一种融合表达谱相关性信息的激活子网辨识算法

传统表达谱数据分析方法集中于寻找差异表达基因和共表达基因集合,没有考虑基因表达产物之间已知的相互作用.近年来在系统生物学的研究中发展了将基因表达谱与蛋白质相互作用网络进行整合分析的方法.现有方法未能综合考虑基因表达差异性和相关性信息,容易导致辨识结果中重要功能分子缺失且生物学功能相关度不高.提出一种融合表达谱差异性和相关性信息的激活子网辨识算法,能够在蛋白质相互作用网络中辨识高功能相关度的激活子网.应用到人免疫缺陷病毒HIV-1感染过程的研究,结果表明,该算法可以有效避免仅考虑基因表达差异性所引入的偏差,揭示了高相关性低表达差异基因在相关通路中的关键性作用.     

Missing value imputation for epistatic MAPs

Background  

Epistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based), that have been modified to work with symmetric pairwise data.     

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).     

Hierarchical modularity and the evolution of genetic interactomes across species

To date, cross-species comparisons of genetic interactomes have been restricted to small or functionally related gene sets, limiting our ability to infer evolutionary trends. To facilitate a more comprehensive analysis, we constructed a genome-scale epistasis map (E-MAP) for the fission yeast Schizosaccharomyces pombe, providing phenotypic signatures for ~60% of the nonessential genome. Using these signatures, we generated a catalog of 297 functional modules, and we assigned function to 144 previously uncharacterized genes, including mRNA splicing and DNA damage checkpoint factors. Comparison with an integrated genetic interactome from the budding yeast Saccharomyces cerevisiae revealed a hierarchical model for the evolution of genetic interactions, with conservation highest within protein complexes, lower within biological processes, and lowest between distinct biological processes. Despite the large evolutionary distance and extensive rewiring of individual interactions, both networks retain conserved features and display similar levels of functional crosstalk between biological processes, suggesting general design principles of genetic interactomes.     

Class-specific correlations of gene expressions: identification and their effects on clustering analyses

Current microarray studies primarily focus on identifying individual genes with differential expression levels across different conditions or classes. A potential problem is that they may disregard multidimensional information hidden in gene interactions. In this study, we propose an approach to detect gene interactions related to study phenotypes through identifying gene pairs with correlations that appear to be class or condition specific. In addition, we explore the effects of ignoring class-specific correlations (CSC) on correlation-based gene-clustering analyses. Our simulation studies show that ignoring CSC can significantly decrease the accuracy of gene clustering and increase the dissimilarity within clusters. Our results from a DLBCL (distinct types of diffuse large B cell lymphoma) data set illustrate that CSC are clearly present and have great adverse effects on gene-clustering results if ignored. Meanwhile, interesting biological interpretations may be derived from studying gene pairs with CSC. This study demonstrates that our algorithm is simple and computationally efficient and has the ability to detect gene pairs with CSC that are informative for uncovering interesting regulation patterns.     

microRNA target predictions across seven Drosophila species and comparison to mammalian targets

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.     

Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray co-expressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.     

Using genome-context data to identify specific types of functional associations in pathway/genome databases

BACKGROUND: Hundreds of genes lacking homology to any protein of known function are sequenced every day. Genome-context methods have proved useful in providing clues about functional annotations for many proteins. However, genome-context methods detect many biological types of functional associations, and do not identify which type of functional association they have found. RESULTS: We have developed two new genome-context-based algorithms. Algorithm 1 extends our previous algorithm for identifying missing enzymes in predicted metabolic pathways (pathway holes) to use genome-context features. The new algorithm has significantly improved scope because it can now be applied to pathway reactions to which sequence similarity methods cannot be applied due to an absence of known sequences for enzymes catalyzing the reaction in other organisms. The new method identifies at least one known enzyme in the top ten hits for 58% of EcoCyc reactions that lack enzyme sequences in other organisms. Surprisingly, the addition of genome-context features does not improve the accuracy of the algorithm when sequences for the enzyme do exist in other organisms. Algorithm 2 uses genome-context methods to predict three distinct types of functional relationships between pairs of proteins: pairs that occur in the same protein complex, the same pathway, or the same operon. This algorithm performs with varying degrees of accuracy on each type of relationship, and performs best in predicting pathway and protein complex relationships.     

Identification of retinoblastoma related genes with shortest path in a protein-protein interaction network

This paper presents a new method for identifying retinoblastoma related genes by integrating gene expression profile and shortest path in a functional linkage graph. With the existing protein-protein interaction data from STRING, a weighted functional linkage graph is constructed. 119 consistently differentially expressed genes between retinoblastoma and normal retina were obtained from the overlap of two gene expression studies of retinoblastoma. Then the shortest paths between each pair of these 119 genes were determined with Dijkstra's algorithm. Finally, all the genes present on the shortest paths were extracted and ranked according to their betweenness and the 119 shortest genes with a betweenness greater than 100 and with a p-value less than 0.05 were selected for further analysis. We also identified 53 retinoblastoma related miRNAs from published miRNA array data and most of the 238 (119 consistently differentially expressed genes and 119 shortest path genes) retinoblastoma genes were shown to be target genes of these 53 miRNAs. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network included more cancer genes than did the genes identified from the gene expression profiles alone. In addition, these genes also had greater functional similarity to the reported cancer genes than did the genes identified from the gene expression profiles alone. This study shows promising results and proves the efficiency of the proposed methods.     

Intrinsic Noise of microRNA-Regulated Genes and the ceRNA Hypothesis

MicroRNAs are small noncoding RNAs that regulate genes post-transciptionally by binding and degrading target eukaryotic mRNAs. We use a quantitative model to study gene regulation by inhibitory microRNAs and compare it to gene regulation by prokaryotic small non-coding RNAs (sRNAs). Our model uses a combination of analytic techniques as well as computational simulations to calculate the mean-expression and noise profiles of genes regulated by both microRNAs and sRNAs. We find that despite very different molecular machinery and modes of action (catalytic vs stoichiometric), the mean expression levels and noise profiles of microRNA-regulated genes are almost identical to genes regulated by prokaryotic sRNAs. This behavior is extremely robust and persists across a wide range of biologically relevant parameters. We extend our model to study crosstalk between multiple mRNAs that are regulated by a single microRNA and show that noise is a sensitive measure of microRNA-mediated interaction between mRNAs. We conclude by discussing possible experimental strategies for uncovering the microRNA-mRNA interactions and testing the competing endogenous RNA (ceRNA) hypothesis.     

Empirical evidence of the applicability of functional clustering through gene expression classification

The availability of a great range of prior biological knowledge about the roles and functions of genes and gene-gene interactions allows us to simplify the analysis of gene expression data to make it more robust, compact, and interpretable. Here, we objectively analyze the applicability of functional clustering for the identification of groups of functionally related genes. The analysis is performed in terms of gene expression classification and uses predictive accuracy as an unbiased performance measure. Features of biological samples that originally corresponded to genes are replaced by features that correspond to the centroids of the gene clusters and are then used for classifier learning. Using 10 benchmark data sets, we demonstrate that functional clustering significantly outperforms random clustering without biological relevance. We also show that functional clustering performs comparably to gene expression clustering, which groups genes according to the similarity of their expression profiles. Finally, the suitability of functional clustering as a feature extraction technique is evaluated and discussed.     

Functional Constraints on Insect Immune System Components Govern Their Evolutionary Trajectories

Roles of constraints in shaping evolutionary outcomes are often considered in the contexts of developmental biology and population genetics, in terms of capacities to generate new variants and how selection limits or promotes consequent phenotypic changes. Comparative genomics also recognizes the role of constraints, in terms of shaping evolution of gene and genome architectures, sequence evolutionary rates, and gene gains or losses, as well as on molecular phenotypes. Characterizing patterns of genomic change where putative functions and interactions of system components are relatively well described offers opportunities to explore whether genes with similar roles exhibit similar evolutionary trajectories. Using insect immunity as our test case system, we hypothesize that characterizing gene evolutionary histories can define distinct dynamics associated with different functional roles. We develop metrics that quantify gene evolutionary histories, employ these to characterize evolutionary features of immune gene repertoires, and explore relationships between gene family evolutionary profiles and their roles in immunity to understand how different constraints may relate to distinct dynamics. We identified three main axes of evolutionary trajectories characterized by gene duplication and synteny, maintenance/stability and sequence conservation, and loss and sequence divergence, highlighting similar and contrasting patterns across these axes amongst subsets of immune genes. Our results suggest that where and how genes participate in immune responses limit the range of possible evolutionary scenarios they exhibit. The test case study system of insect immunity highlights the potential of applying comparative genomics approaches to characterize how functional constraints on different components of biological systems govern their evolutionary trajectories.