Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of E<10(-5)) are included in 27 clusters. Five clusters are associated with metabolism, containing P450 genes restricted to the Brassica family and predicted to be involved in secondary metabolism. Operon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system.     

The computational analysis of scientific literature to define and recognize gene expression clusters

A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present a computational method that leverages the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in the analysis of gene expression data offers an opportunity to incorporate functional information about the genes when defining expression clusters. We have created a method that associates gene expression profiles with known biological functions. Our method has two steps. First, we apply hierarchical clustering to the given gene expression data set. Secondly, we use text from abstracts about genes to (i) resolve hierarchical cluster boundaries to optimize the functional coherence of the clusters and (ii) recognize those clusters that are most functionally coherent. In the case where a gene has not been investigated and therefore lacks primary literature, articles about well-studied homologous genes are added as references. We apply our method to two large gene expression data sets with different properties. The first contains measurements for a subset of well-studied Saccharomyces cerevisiae genes with multiple literature references, and the second contains newly discovered genes in Drosophila melanogaster; many have no literature references at all. In both cases, we are able to rapidly define and identify the biologically relevant gene expression profiles without manual intervention. In both cases, we identified novel clusters that were not noted by the original investigators.     

Construction of a full-length cDNA library from young spikelets of hexaploid wheat and its characterization by large-scale sequencing of expressed sequence tags

The polyploid nature of wheat is a key characteristic of the plant. Full-length complementary DNAs (cDNAs) provide essential information that can be used to annotate the genes and provide a functional analysis of these genes and their products. We constructed a full-length cDNA library derived from young spikelets of common wheat, and obtained 24056 expressed sequence tags (ESTs) from both ends of the cDNA clones. These ESTs were grouped into 3605 contigs using the phrap method, representing expressed loci from each of the three genomes. Using BLAST, 3605 contigs were grouped into 1902 gene clusters, showing that loci of the three genomes are not always expressed. A homology search of these gene clusters against a wheat EST database (15964 gene clusters) and a rice full-length cDNA database (21447 gene clusters) revealed that a quarter of the wheat full-length cDNAs were novel. A protein database of Arabidopsis was used to examine the functional classification of these gene clusters. The GC-content in the 5 -UTR region of wheat cDNAs was compared to that of rice. Forty-three genes (3.5% of wheat cDNAs homologous to those of rice) possessed distinct GC-content in the 5 -UTR region, suggesting different breeding behaviors of wheat and rice.     

EST analysis of gene expression in testis of the ascidian Ciona intestinalis

To explore the gene expression underlying spermatogenesis, a large-scale analysis has been done on the cDNAs from testis of the ascidian, Ciona intestinalis. A set of 5,461 expressed sequence tags was analyzed and grouped into 2,806 independent clusters. Approximately 30% of the clusters showed significant sequence matches to the proteins reported in DDBJ/GenBank/EMBL database including a set of proteins closely related to the gene regulation during spermatogenesis, functional and morphological changes of spermatogenic cells during spermiogenesis, and physiological functions of sperm, as well as those with housekeeping functions commonly expressed in other cells. Some clones show similarities to the proteins present in vertebrate lymphocytes, suggesting a primitive immune system in ascidians. We have also found some genes that are known to participate in hormonal regulation of spermatogenesis in vertebrates. The large majority of the genes expressed in Ciona testis show no significant matches to known proteins and the further analysis of these genes may shed new light on the molecular mechanism of spermatogenesis and sperm functions.     

Production and Functional Characterization of Murine Osteoclasts Differentiated from ER-Hoxb8-Immortalized Myeloid Progenitor Cells

In vitro differentiation into functional osteoclasts is routinely achieved by incubation of embryonic stem cells, induced pluripotent stem cells, or primary as well as cryopreserved spleen and bone marrow-derived cells with soluble receptor activator of nuclear factor kappa-B ligand and macrophage colony-stimulating factor. Additionally, osteoclasts can be derived from co-cultures with osteoblasts or by direct administration of soluble receptor activator of nuclear factor kappa-B ligand to RAW 264.7 macrophage lineage cells. However, despite their benefits for osteoclast-associated research, these different methods have several drawbacks with respect to differentiation yields, time and animal consumption, storage life of progenitor cells or the limited potential for genetic manipulation of osteoclast precursors. In the present study, we therefore established a novel protocol for the differentiation of osteoclasts from murine ER-Hoxb8-immortalized myeloid stem cells. We isolated and immortalized bone marrow cells from wild type and genetically manipulated mouse lines, optimized protocols for osteoclast differentiation and compared these cells to osteoclasts derived from conventional sources. In vitro generated ER-Hoxb8 osteoclasts displayed typical osteoclast characteristics such as multi-nucleation, tartrate-resistant acid phosphatase staining of supernatants and cells, F-actin ring formation and bone resorption activity. Furthermore, the osteoclast differentiation time course was traced on a gene expression level. Increased expression of osteoclast-specific genes and decreased expression of stem cell marker genes during differentiation of osteoclasts from ER-Hoxb8-immortalized myeloid progenitor cells were detected by gene array and confirmed by semi-quantitative and quantitative RT-PCR approaches. In summary, we established a novel method for the quantitative production of murine bona fide osteoclasts from ER-Hoxb8 stem cells generated from wild type or genetically manipulated mouse lines. These cells represent a standardized and theoretically unlimited source for osteoclast-associated research projects.     

Gene expression during skeletal development in three osteopetrotic rat mutations. Evidence for osteoblast abnormalities

Osteopetrosis is a group of metabolic bone diseases characterized by reductions in osteoclast development and/or function. These aspects of osteoclast biology are known to be influenced by osteoblasts and their products. To ascertain whether osteoblast dysfunction contributes to aberrations in the structural and functional properties of osteoclasts in osteopetrosis, we systematically examined gene expression as reflected by mRNA levels for a series of cell growth- and tissue-related genes associated with the osteoblast phenotype during skeletal development in normal and mutant rats of three different osteopetrotic stocks. We show that the methods used permit the reproducible isolation of undegraded total cellular RNA from bone and that mRNA levels can be reliably quantitated in these preparations. Each osteopetrotic mutation exhibits a distinct aberrant pattern of osteoblast gene expression that may be correlated with and explain some abnormalities in extracellular matrix composition, mineralization, osteoclast development, and effects of elevated serum levels of 1 alpha,25-dihydroxyvitamin D3, depending upon the mutation. Normal rats show minor variations in gene expression that reflect the genetic background (stock). This, the first comprehensive molecular analysis of osteoblast gene expression in osteopetrosis, suggests that some osteopetroses, particularly in the toothless rat, are associated with and potentially related to mechanisms associated with aberrations in osteoblast function. More generally, the present studies demonstrate alterations in gene expression as reflected by mRNA levels that are associated with functional properties of the osteoblast, particularly those contributing to the recruitment and/or differentiation of osteoclasts, thereby influencing skeletal modeling.     

Connected gene neighborhoods in prokaryotic genomes

A computational method was developed for delineating connected gene neighborhoods in bacterial and archaeal genomes. These gene neighborhoods are not typically present, in their entirety, in any single genome, but are held together by overlapping, partially conserved gene arrays. The procedure was applied to comparing the orders of orthologous genes, which were extracted from the database of Clusters of Orthologous Groups of proteins (COGs), in 31 prokaryotic genomes and resulted in the identification of 188 clusters of gene arrays, which included 1001 of 2890 COGs. These clusters were projected onto actual genomes to produce extended neighborhoods including additional genes, which are adjacent to the genes from the clusters and are transcribed in the same direction, which resulted in a total of 2387 COGs being included in the neighborhoods. Most of the neighborhoods consist predominantly of genes united by a coherent functional theme, but also include a minority of genes without an obvious functional connection to the main theme. We hypothesize that although some of the latter genes might have unsuspected roles, others are maintained within gene arrays because of the advantage of expression at a level that is typical of the given neighborhood. We designate this phenomenon ‘genomic hitchhiking’. The largest neighborhood includes 79 genes (COGs) and consists of overlapping, rearranged ribosomal protein superoperons; apparent genome hitchhiking is particularly typical of this neighborhood and other neighborhoods that consist of genes coding for translation machinery components. Several neighborhoods involve previously undetected connections between genes, allowing new functional predictions. Gene neighborhoods appear to evolve via complex rearrangement, with different combinations of genes from a neighborhood fixed in different lineages.     

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.     

Identifying clusters of functionally related genes in genomes

MOTIVATION: An increasing body of literature shows that genomes of eukaryotes can contain clusters of functionally related genes. Most approaches to identify gene clusters utilize microarray data or metabolic pathway databases to find groups of genes on chromosomes that are linked by common attributes. A generalized method that can find gene clusters regardless of the mechanism of origin would provide researchers with an unbiased method for finding clusters and studying the evolutionary forces that give rise to them. RESULTS: We present an algorithm to identify gene clusters in eukaryotic genomes that utilizes functional categories defined in graph-based vocabularies such as the Gene Ontology (GO). Clusters identified in this manner need only have a common function and are not constrained by gene expression or other properties. We tested the algorithm by analyzing genomes of a representative set of species. We identified species-specific variation in percentage of clustered genes as well as in properties of gene clusters including size distribution and functional annotation. These properties may be diagnostic of the evolutionary forces that lead to the formation of gene clusters. AVAILABILITY: A software implementation of the algorithm and example output files are available at http://fcg.tamu.edu/C_Hunter/.     

Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters

MOTIVATION: Cluster analysis of genome-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and samples. In the present paper, we focus on several important issues related to clustering algorithms that have not yet been fully studied. RESULTS: We describe a simple and robust algorithm for the clustering of temporal gene expression profiles that is based on the simulated annealing procedure. In general, this algorithm guarantees to eventually find the globally optimal distribution of genes over clusters. We introduce an iterative scheme that serves to evaluate quantitatively the optimal number of clusters for each specific data set. The scheme is based on standard approaches used in regular statistical tests. The basic idea is to organize the search of the optimal number of clusters simultaneously with the optimization of the distribution of genes over clusters. The efficiency of the proposed algorithm has been evaluated by means of a reverse engineering experiment, that is, a situation in which the correct distribution of genes over clusters is known a priori. The employment of this statistically rigorous test has shown that our algorithm places greater than 90% genes into correct clusters. Finally, the algorithm has been tested on real gene expression data (expression changes during yeast cell cycle) for which the fundamental patterns of gene expression and the assignment of genes to clusters are well understood from numerous previous studies.     

Assignment of orthologous relationships among mammalian alpha-globin genes by examining flanking regions reveals a rapid rate of evolution

In order to study the relationships among mammalian alpha-globin genes, we have determined the sequence of the 3' flanking region of the human alpha 1 globin gene and have made pairwise comparisons between sequenced alpha-globin genes. The flanking regions were examined in detail because sequence matches in these regions could be interpreted with the least complication from the gene duplications and conversions that have occurred frequently in mammalian alpha-like globin gene clusters. We found good matches between the flanking regions of human alpha 1 and rabbit alpha 1, human psi alpha 1 and goat I alpha, human alpha 2 and goat II alpha, and horse alpha 1 and goat II alpha. These matches were used to align the alpha-globin genes in gene clusters from different mammals. This alignment shows that genes at equivalent positions in the gene clusters of different mammals can be functional or nonfunctional, depending on whether they corrected against a functional alpha-globin gene in recent evolutionary history. The number of alpha-globin genes (including pseudogenes) appears to differ among species, although highly divergent pseudogenes may not have been detected in all species examined. Although matching sequences could be found in interspecies comparisons of the flanking regions of alpha- globin genes, these matches are not as extensive as those found in the flanking regions of mammalian beta-like globin genes. This observation suggests that the noncoding sequences in the mammalian alpha-globin gene clusters are evolving at a faster rate than those in the beta-like globin gene clusters. The proposed faster rate of evolution fits with the poor conservation of the genetic linkage map around alpha-globin gene clusters when compared to that of the beta-like globin gene clusters. Analysis of the 3' flanking regions of alpha-globin genes has revealed a conserved sequence approximately 100-150 bp 3' to the polyadenylation site; this sequence may be involved in the expression or regulation of alpha-globin genes.      

Tests for gene clustering.

Comparing chromosomal gene order in two or more related species is an important approach to studying the forces that guide genome organization and evolution. Linked clusters of similar genes found in related genomes are often used to support arguments of evolutionary relatedness or functional selection. However, as the gene order and the gene complement of sister genomes diverge progressively due to large scale rearrangements, horizontal gene transfer, gene duplication and gene loss, it becomes increasingly difficult to determine whether observed similarities in local genomic structure are indeed remnants of common ancestral gene order, or are merely coincidences. A rigorous comparative genomics requires principled methods for distinguishing chance commonalities, within or between genomes, from genuine historical or functional relationships. In this paper, we construct tests for significant groupings against null hypotheses of random gene order, taking incomplete clusters, multiple genomes, and gene families into account. We consider both the significance of individual clusters of prespecified genes and the overall degree of clustering in whole genomes.     

Pichia stipitis genomics, transcriptomics, and gene clusters

Genome sequencing and subsequent global gene expression studies have advanced our understanding of the lignocellulose-fermenting yeast Pichia stipitis . These studies have provided an insight into its central carbon metabolism, and analysis of its genome has revealed numerous functional gene clusters and tandem repeats. Specialized physiological traits are often the result of several gene products acting together. When coinheritance is necessary for the overall physiological function, recombination and selection favor colocation of these genes in a cluster. These are particularly evident in strongly conserved and idiomatic traits. In some cases, the functional clusters consist of multiple gene families. Phylogenetic analyses of the members in each family show that once formed, functional clusters undergo duplication and differentiation. Genome-wide expression analysis reveals that regulatory patterns of clusters are similar after they have duplicated and that the expression profiles evolve along with functional differentiation of the clusters. Orthologous gene families appear to arise through tandem gene duplication, followed by differentiation in the regulatory and coding regions of the gene. Genome-wide expression analysis combined with cross-species comparisons of functional gene clusters should reveal many more aspects of eukaryotic physiology.     

Identification of two novel clusters of ultrahigh-sulfur keratin-associated protein genes on human chromosome 11

We analyzed two novel clusters of keratin-associated protein (KAP) genes on human chromosome 11 (11p15.5 and 11q13.5) in which we identified two known human KRTAP5 genes, KerA (=KRN1) and KerB, and nine novel KRTAP5 family genes. RT-PCR analysis of these KAP genes showed preferential expression in human hair root, suggesting these gene products are required for hair formation. Based on the deduced amino acid sequences, all these KAP proteins were classified into an ultrahigh-sulfur (UHS) type KAP with high cysteine content (> 30 mol%). These KAPs also showed high glycine and serine contents (average 24.30 and 21.13 mol%, respectively), distinguishing from other UHS/HS KAP families located on human chromosomes 17 and 21. Dot-matrix analysis revealed a significant similarity between these two KAP gene clusters. We postulated a mechanism by which these two KAP gene clusters are generated via genomic duplication of a primordial gene cluster followed by genetic modification during evolution.     

Mouse genome-wide association and systems genetics identify Asxl2 as a regulator of bone mineral density and osteoclastogenesis

Significant advances have been made in the discovery of genes affecting bone mineral density (BMD); however, our understanding of its genetic basis remains incomplete. In the current study, genome-wide association (GWA) and co-expression network analysis were used in the recently described Hybrid Mouse Diversity Panel (HMDP) to identify and functionally characterize novel BMD genes. In the HMDP, a GWA of total body, spinal, and femoral BMD revealed four significant associations (-log10P>5.39) affecting at least one BMD trait on chromosomes (Chrs.) 7, 11, 12, and 17. The associations implicated a total of 163 genes with each association harboring between 14 and 112 genes. This list was reduced to 26 functional candidates by identifying those genes that were regulated by local eQTL in bone or harbored potentially functional non-synonymous (NS) SNPs. This analysis revealed that the most significant BMD SNP on Chr. 12 was a NS SNP in the additional sex combs like-2 (Asxl2) gene that was predicted to be functional. The involvement of Asxl2 in the regulation of bone mass was confirmed by the observation that Asxl2 knockout mice had reduced BMD. To begin to unravel the mechanism through which Asxl2 influenced BMD, a gene co-expression network was created using cortical bone gene expression microarray data from the HMDP strains. Asxl2 was identified as a member of a co-expression module enriched for genes involved in the differentiation of myeloid cells. In bone, osteoclasts are bone-resorbing cells of myeloid origin, suggesting that Asxl2 may play a role in osteoclast differentiation. In agreement, the knockdown of Asxl2 in bone marrow macrophages impaired their ability to form osteoclasts. This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits.