Andy D. Perkins

Extracting gene networks for low-dose radiation using graph theoretical algorithms.

Genes with common functions often exhibit correlated expression levels, which can be used to identify sets of interacting genes from microarray data. Microarrays typically measure expression across genomic space, creating a massive matrix of co-expression that must be mined to extract only the most relevant gene interactions. We describe a graph theoretical approach to extracting co-expressed sets of genes, based on the computation of cliques. Unlike the results of traditional clustering algorithms, cliques are not disjoint and allow genes to be assigned to multiple sets of interacting partners, consistent with biological reality. A graph is created by thresholding the correlation matrix to include only the correlations most likely to signify functional relationships. Cliques computed from the graph correspond to sets of genes for which significant edges are present between all members of the set, representing potential members of common or interacting pathways. Clique membership can be used to infer function about poorly annotated genes, based on the known functions of better-annotated genes with which they share clique membership (i.e., “guilt-by-association”). We illustrate our method by applying it to microarray data collected from the spleens of mice exposed to low-dose ionizing radiation. Differential analysis is used to identify sets of genes whose interactions are impacted by radiation exposure. The correlation graph is also queried independently of clique to extract edges that are impacted by radiation. We present several examples of multiple gene interactions that are altered by radiation exposure and thus represent potential molecular pathways that mediate the radiation response.

Genome-level analysis of genetic regulation of liver gene expression networks

The liver is the primary site for the metabolism of nutrients, drugs, and chemical agents. Although metabolic pathways are complex and tightly regulated, genetic variation among individuals, reflected in variations in gene expression levels, introduces complexity into research on liver disease. This study dissected genetic networks that control liver gene expression through the combination of large‐scale quantitative mRNA expression analysis with genetic mapping in a reference population of BXD recombinant inbred mouse strains for which extensive single‐nucleotide polymorphism, haplotype, and phenotypic data are publicly available. We profiled gene expression in livers of naive mice of both sexes from C57BL/6J, DBA/2J, B6D2F1, and 37 BXD strains using Agilent oligonucleotide microarrays. These data were used to map quantitative trait loci (QTLs) responsible for variations in the expression of about 19,000 transcripts. We identified polymorphic local and distant QTLs, including several loci that control the expression of large numbers of genes in liver, by comparing the physical transcript position with the location of the controlling QTL. Conclusion: The data are available through a public web‐based resource (www.genenetwork.org) that allows custom data mining, identification of coregulated transcripts and correlated phenotypes, cross‐tissue, and cross‐species comparisons, as well as testing of a broad array of hypotheses. (HEPATOLOGY 2007.)

Clustering of high throughput gene expression data

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics - clustering gene expression data - to the operations research community.

Threshold selection in gene co-expression networks using spectral graph theory techniques

BackgroundGene co-expression networks are often constructed by computing some measure of similarity between expression levels of gene transcripts and subsequently applying a high-pass filter to remove all but the most likely biologically-significant relationships. The selection of this expression threshold necessarily has a significant effect on any conclusions derived from the resulting network. Many approaches have been taken to choose an appropriate threshold, among them computing levels of statistical significance, accepting only the top one percent of relationships, and selecting an arbitrary expression cutoff.ResultsWe apply spectral graph theory methods to develop a systematic method for threshold selection. Eigenvalues and eigenvectors are computed for a transformation of the adjacency matrix of the network constructed at various threshold values. From these, we use a basic spectral clustering method to examine the set of gene-gene relationships and select a threshold dependent upon the community structure of the data. This approach is applied to two well-studied microarray data sets from Homo sapiens and Saccharomyces cerevisiae.ConclusionThis method presents a systematic, data-based alternative to using more artificial cutoff values and results in a more conservative approach to threshold selection than some other popular techniques such as retaining only statistically-significant relationships or setting a cutoff to include a percentage of the highest correlations.

Dynamics of microRNAs in bull spermatozoa

BackgroundMicroRNAs are small non-coding RNAs that regulate gene expression and thus play important roles in mammalian development. However, the comprehensive lists of microRNAs, as well as, molecular mechanisms by which microRNAs regulate gene expression during gamete and embryo development are poorly defined. The objectives of this study were to determine microRNAs in bull sperm and predict their functions.MethodsTo accomplish our objectives we isolated miRNAs from sperm of high and low fertility bulls, conducted microRNA microarray experiments and validated expression of a panel of microRNAs using real time RT-PCR. Bioinformatic approaches were carried out to identify regulated targets.ResultsWe demonstrated that an abundance of microRNAs were present in bovine spermatozoa, however, only seven were differentially expressed; hsa-aga-3155, -8197, -6727, -11796, -14189, -6125, -13659. The abundance of miRNAs in the spermatozoa and the differential expression in sperm from high vs. low fertility bulls suggests that the miRNAs possibly play important functions in the regulating mechanisms of bovine spermatozoa.ConclusionIdentification of specific microRNAs expressed in spermatozoa of bulls with different fertility phenotypes will help better understand mammalian gametogenesis and early development.

A module-based analytical strategy to identify novel disease-associated genes shows an inhibitory role for interleukin 7 Receptor in allergic inflammation

BackgroundThe identification of novel genes by high-throughput studies of complex diseases is complicated by the large number of potential genes. However, since disease-associated genes tend to interact, one solution is to arrange them in modules based on co-expression data and known gene interactions. The hypothesis of this study was that such a module could be a) found and validated in allergic disease and b) used to find and validate one ore more novel disease-associated genes.ResultsTo test these hypotheses integrated analysis of a large number of gene expression microarray experiments from different forms of allergy was performed. This led to the identification of an experimentally validated reference gene that was used to construct a module of co-expressed and interacting genes. This module was validated in an independent material, by replicating the expression changes in allergen-challenged CD4+ cells. Moreover, the changes were reversed following treatment with corticosteroids. The module contained several novel disease-associated genes, of which the one with the highest number of interactions with known disease genes, IL7R, was selected for further validation. The expression levels of IL7R in allergen challenged CD4+ cells decreased following challenge but increased after treatment. This suggested an inhibitory role, which was confirmed by functional studies.ConclusionWe propose that a module-based analytical strategy is generally applicable to find novel genes in complex diseases.

A systematic comparison of genome scale clustering algorithms

BackgroundA wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae.MethodsFor each clustering method under study, a variety of parameters were tested. Jaccard similarity was used to measure each clusters agreement with every GO and KEGG annotation set, and the highest Jaccard score was assigned to the cluster. Clusters were grouped into small, medium, and large bins, and the Jaccard score of the top five scoring clusters in each bin were averaged and reported as the best average top 5 (BAT5) score for the particular method.ResultsClusters produced by each method were evaluated based upon the positive match to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Methods were also tested to determine whether they were able to identify clusters consistent with those identified by other clustering methods.ConclusionsValidation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that further development and application of combinatorial strategies is warranted.

Short-Read Sequencing for Genomic Analysis of the Brown Rot Fungus Fibroporia radiculosa

ABSTRACT The feasibility of short-read sequencing for genomic analysis was demonstrated for Fibroporia radiculosa, a copper-tolerant fungus that causes brown rot decay of wood. The effect of read quality on genomic assembly was assessed by filtering Illumina GAIIx reads from a single run of a paired-end library (75-nucleotide read length and 300-bp fragment size) at three different stringency levels and then assembling each data set with Velvet. A simple approach was devised to determine which filter stringency was “best.” Venn diagrams identified the regions containing reads that were used in an assembly but were of a low-enough quality to be removed by a filter. By plotting base quality histograms of reads in this region, we judged whether a filter was too stringent or not stringent enough. Our best assembly had a genome size of 33.6 Mb, an N50 of 65.8 kb for a k-mer of 51, and a maximum contig length of 347 kb. Using GeneMark, 9,262 genes were predicted. TargetP and SignalP analyses showed that among the 1,213 genes with secreted products, 986 had motifs for signal peptides and 227 had motifs for signal anchors. Blast2GO analysis provided functional annotation for 5,407 genes. We identified 29 genes with putative roles in copper tolerance and 73 genes for lignocellulose degradation. A search for homologs of these 102 genes showed that F. radiculosa exhibited more similarity to Postia placenta than Serpula lacrymans. Notable differences were found, however, and their involvements in copper tolerance and wood decay are discussed.

Molecular Physiognomies and Applications of Adipose-Derived Stem Cells

Adipose-derived stromal/stem cells (ASC) are multipotent with abilities to differentiate into multiple lineages including connective tissue and neural cells. Despite unlimited opportunity and needs for human and veterinary regenerative medicine, applications of adipose-derived stromal/stem cells are at present very limited. Furthermore, the fundamental biological factors regulating stemness in ASC and their stable differentiation into other tissue cells are not fully understood. The objective of this review was to provide an update on the current knowledge of the nature and isolation, molecular and epigenetic determinants of the potency, and applications of adipose-derived stromal/stem cells, as well as challenges and future directions. The first quarter of the review focuses on the nature of ASC, namely their definition, origin, isolation and sorting methods and multilineage differentiation potential, often with a comparison to mesenchymal stem cells of bone marrow. Due to the indisputable role of epigenetic regulation on cell identities, epigenetic modifications (DNA methylation, chromatin remodeling and microRNAs) are described broadly in stem cells but with a focus on ASC. The final sections provide insights into the current and potential applications of ASC in human and veterinary regenerative medicine.

Gene Expression Analysis of Copper Tolerance and Wood Decay in the Brown Rot Fungus Fibroporia radiculosa

ABSTRACT High-throughput transcriptomics was used to identify Fibroporia radiculosa genes that were differentially regulated during colonization of wood treated with a copper-based preservative. The transcriptome was profiled at two time points while the fungus was growing on wood treated with micronized copper quat (MCQ). A total of 917 transcripts were differentially expressed. Fifty-eight of these genes were more highly expressed when the MCQ was protecting the wood from strength loss and had putative functions related to oxalate production/degradation, laccase activity, quinone biosynthesis, pectin degradation, ATP production, cytochrome P450 activity, signal transduction, and transcriptional regulation. Sixty-one genes were more highly expressed when the MCQ lost its effectiveness (>50% strength loss) and had functions related to oxalate degradation; cytochrome P450 activity; H2O2 production and degradation; degradation of cellulose, hemicellulose, and pectin; hexose transport; membrane glycerophospholipid metabolism; and cell wall chemistry. Ten of these differentially regulated genes were quantified by reverse transcriptase PCR for a more in-depth study (4 time points on wood with or without MCQ treatment). Our results showed that MCQ induced higher than normal levels of expression for four genes (putative annotations for isocitrate lyase, glyoxylate dehydrogenase, laccase, and oxalate decarboxylase 1), while four other genes (putative annotations for oxalate decarboxylase 2, aryl alcohol oxidase, glycoside hydrolase 5, and glycoside hydrolase 10) were repressed. The significance of these results is that we have identified several genes that appear to be coregulated, with putative functions related to copper tolerance and/or wood decay.