Relationships between gene expression and brain wiring in the adult rodent brain

We studied the global relationship between gene expression and neuroanatomical connectivity in the adult rodent brain. We utilized a large data set of the rat brain "connectome" from the Brain Architecture Management System (942 brain regions and over 5000 connections) and used statistical approaches to relate the data to the gene expression signatures of 17,530 genes in 142 anatomical regions from the Allen Brain Atlas. Our analysis shows that adult gene expression signatures have a statistically significant relationship to connectivity. In particular, brain regions that have similar expression profiles tend to have similar connectivity profiles, and this effect is not entirely attributable to spatial correlations. In addition, brain regions which are connected have more similar expression patterns. Using a simple optimization approach, we identified a set of genes most correlated with neuroanatomical connectivity, and find that this set is enriched for genes involved in neuronal development and axon guidance. A number of the genes have been implicated in neurodevelopmental disorders such as autistic spectrum disorder. Our results have the potential to shed light on the role of gene expression patterns in influencing neuronal activity and connectivity, with potential applications to our understanding of brain disorders. Supplementary data are available at http://www.chibi.ubc.ca/ABAMS.     

Conservation of regional gene expression in mouse and human brain

Many neurodegenerative diseases have a hallmark regional and cellular pathology. Gene expression analysis of healthy tissues may provide clues to the differences that distinguish resistant and sensitive tissues and cell types. Comparative analysis of gene expression in healthy mouse and human brain provides a framework to explore the ability of mice to model diseases of the human brain. It may also aid in understanding brain evolution and the basis for higher order cognitive abilities. Here we compare gene expression profiles of human motor cortex, caudate nucleus, and cerebellum to one another and identify genes that are more highly expressed in one region relative to another. We separately perform identical analysis on corresponding brain regions from mice. Within each species, we find that the different brain regions have distinctly different expression profiles. Contrasting between the two species shows that regionally enriched genes in one species are generally regionally enriched genes in the other species. Thus, even when considering thousands of genes, the expression ratios in two regions from one species are significantly correlated with expression ratios in the other species. Finally, genes whose expression is higher in one area of the brain relative to the other areas, in other words genes with patterned expression, tend to have greater conservation of nucleotide sequence than more widely expressed genes. Together these observations suggest that region-specific genes have been conserved in the mammalian brain at both the sequence and gene expression levels. Given the general similarity between patterns of gene expression in healthy human and mouse brains, we believe it is reasonable to expect a high degree of concordance between microarray phenotypes of human neurodegenerative diseases and their mouse models. Finally, these data on very divergent species provide context for studies in more closely related species that address questions such as the origins of cognitive differences.     

Analysis of strain and regional variation in gene expression in mouse brain

Background  

We performed a statistical analysis of a previously published set of gene expression microarray data from six different brain regions in two mouse strains. In the previous analysis, 24 genes showing expression differences between the strains and about 240 genes with regional differences in expression were identified. Like many gene expression studies, that analysis relied primarily on ad hoc 'fold change' and 'absent/present' criteria to select genes. To determine whether statistically motivated methods would give a more sensitive and selective analysis of gene expression patterns in the brain, we decided to use analysis of variance (ANOVA) and feature selection methods designed to select genes showing strain- or region-dependent patterns of expression.     

Gene expression in the rodent brain is associated with its regional connectivity

The putative link between gene expression of brain regions and their neural connectivity patterns is a fundamental question in neuroscience. Here this question is addressed in the first large scale study of a prototypical mammalian rodent brain, using a combination of rat brain regional connectivity data with gene expression of the mouse brain. Remarkably, even though this study uses data from two different rodent species (due to the data limitations), we still find that the connectivity of the majority of brain regions is highly predictable from their gene expression levels-the outgoing (incoming) connectivity is successfully predicted for 73% (56%) of brain regions, with an overall fairly marked accuracy level of 0.79 (0.83). Many genes are found to play a part in predicting both the incoming and outgoing connectivity (241 out of the 500 top selected genes, p-value<1e-5). Reassuringly, the genes previously known from the literature to be involved in axon guidance do carry significant information about regional brain connectivity. Surveying the genes known to be associated with the pathogenesis of several brain disorders, we find that those associated with schizophrenia, autism and attention deficit disorder are the most highly enriched in the connectivity-related genes identified here. Finally, we find that the profile of functional annotation groups that are associated with regional connectivity in the rodent is significantly correlated with the annotation profile of genes previously found to determine neural connectivity in C. elegans (Pearson correlation of 0.24, p<1e-6 for the outgoing connections and 0.27, p<1e-5 for the incoming). Overall, the association between connectivity and gene expression in a specific extant rodent species' brain is likely to be even stronger than found here, given the limitations of current data.     

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.     

Gene microarray analysis using angular distribution decomposition.

Clustering techniques have been widely used in the analysis of microarray data to group genes with similar expression profiles. The similarity of expression profiles and hence the results of clustering greatly depend on how the data has been transformed. We present a method that uses the relative expression changes between pairs of conditions and an angular transformation to define the similarity of gene expression patterns. The pairwise comparisons of experimental conditions can be chosen to reflect the purpose of clustering allowing control the definition of similarity between genes. A variational Bayes mixture modeling approach is then used to find clusters within the transformed data. The purpose of microarray data analysis is often to locate groups genes showing particular patterns of expression change and within these groups to locate specific target genes that may warrant further experimental investigation. We show that the angular transformation maps data to a representation from which information, in terms of relative regulation changes, can be automatically mined. This information can be then be used to understand the "features" of expression change important to different clusters allowing potentially interesting clusters to be easily located. Finally, we show how the genes within a cluster can be visualized in terms of their expression pattern and intensity change, allowing potential target genes to be highlighted within the clusters of interest.     

Over 1000 genes are involved in the DNA damage response of Escherichia coli

Changes in gene expression after treatment of Escherichia coli cultures with mitomycin C were assessed using gene array technology. Unexpectedly, a large number of genes (nearly 30% of all genes) displayed significant changes in their expression level. Analysis and classification of expression profiles of the corresponding genes allowed us to assign this large number of genes into one or two dozen small clusters of genes with similar expression profiles. This assignment allowed us to describe systematically the changes in the level of gene expression in response to DNA damage. Among the damage-induced genes, more than 100 are novel. From those genes involved in DNA metabolism that have not previously been shown to be induced by DNA damage, the mutS gene involved in mismatch repair is especially noteworthy. In addition to the SOS response, we observed the induction of other stress response pathways, such as those of oxidative stress and osmotic protection. Among the genes that are downregulated in response to DNA damage are numerous protein biosynthesis genes. Analysis of the gene expression data highlighted the essential involvement of sigma(s)-regulated genes and the general stress response network in the response to DNA damage.     

Temporal analysis of gene expression in a field population of the Scleractinian coral Montastraea faveolata

Organisms maintain homeostasis and abate cellular damage by altering gene expression. Coral colonies have been shown to produce unique gene expression patterns in response to different environmental stimuli. In order to understand these induced changes, the natural variation in expression of genetic biomarkers needs to be determined. In this study, an array of genes isolated from Scleractinian coral was used to track changes in gene expression within a population of Montastraea faveolata from April to October 2001 in the Florida Keys. The profiles of genes observed in this study can be divided into two groups based on expression over this time period. In spring and early summer, May through July, most of the genes show little deviation from their average level of expression. In August and September, several genes show large deviations from their average level of expression. The physiological and environmental triggers for the observed changes in gene expression have not yet been identified, but the results show that our coral stress gene array can be used to track temporal changes in gene expression in a natural coral population.     

Patterns of evolution in the unique tRNA gene arrays of the genus Entamoeba

Genome sequencing of the protistan parasite Entamoeba histolytica HM-1:IMSS revealed that almost all the tRNA genes are organized into tandem arrays that make up over 10% of the genome. The 25 distinct array units contain up to 5 tRNA genes each and some also encode the 5S RNA. Between adjacent genes in array units are complex short tandem repeats (STRs) resembling microsatellites. To investigate the origins and evolution of this unique gene organization, we have undertaken a genome survey to determine the array unit organization in 4 other species of Entamoeba-Entamoeba dispar, Entamoeba moshkovskii, Entamoeba terrapinae, and Entamoeba invadens-and have explored the STR structure in other isolates of E. histolytica. The genome surveys revealed that E. dispar has the same array unit organization as E. histolytica, including the presence and numerical variation of STRs between adjacent genes. However, the individual repeat sequences are completely different to those in E. histolytica. All other species of Entamoeba studied also have tandem arrays of clustered tRNA genes, but the gene composition of the array units often differs from that in E. histolytica/E. dispar. None of the other species' arrays exhibit the complex STRs between adjacent genes although simple tandem duplications are occasionally seen. The degree of similarity in organization reflects the phylogenetic relationships among the species studied. Within individual isolates of E. histolytica most copies of the array unit are uniform in sequence with only minor variation in the number and organization of the STRs. Between isolates, however, substantial differences in STR number and organization can exist although the individual repeat sequences tend to be conserved. The origin of this unique gene organization in the genus Entamoeba clearly predates the common ancestor of the species investigated to date and their function remains unclear.     

DISTRIBUTION OF CHOLINE ACETYLTRANSFERASE AND GLUTAMATE DECARBOXYLASE WITHIN THE SUBSTANTIA NIGRA AND IN OTHER BRAIN REGIONS FROM CONTROL AND PARKINSONIAN PATIENTS

—The distribution of choline acetyltransferase (ChAc, EC 2.3.1.6) and l -glutamate 1-carboxylyase (glutamate decarboxylase, GAD, EC 4.1.1.15) was studied in serial frontal slices of the substantia nigra (SN) (pars compacta, PC; pars reticulata, PR; an intermediate region, IR) as well as in other brain areas from post mortem tissue of control and Parkinsonian patients. Within the SN from control brain ChAc and GAD activities showed a distinctive distribution: ChAc activity in PC was higher than in PR and IR by 427% and 253% respectively and within PC the enzyme activity in the rostral part exceeded that in the control part by 353%. The GAD activity in PC was higher by 41% than that in PR and within PC seemed to be higher in the caudal than in the rostral part. For both enzyme activities there were no significant differences between PR and IR or within these regions. In Parkinsonian brain both ChAc and GAD activities were reduced to 15-25% of controls in all 3 regions of the SN. The distinctive distribution of ChAc and GAD activity found in the SN of control brain was abolished: no difference was observed between the 3 regions. However, within PC the ChAc activity was lower in the medial than in the rostral part. Since nigral ChAc is possibly located in interneurons, the decrease in enzyme activity may be connected with the cell loss observed in the SN of Parkinsonian brain. By contrast, nigral GAD is probably contained in terminals of strio-nigral neurons and the decrease in enzyme activity in Parkinson's disease in the absence of striatal cell loss, may reflect a change in the functional state of these GABA neurons. Among various areas of control brains ChAc activity was highest in caudate nucleus and putamen while GAD was highest in SN. caudate nucleus, putamen and cerebral cortex. In Parkinsonian brain the most severe reduction in ChAc and GAD activities was found in the SN.     

Transcriptomic analyses support the similarity of gene expression between brain and testis in human as well as mouse

We previously revealed similarity in gene expression patterns between human brain and testis, based on digital differential display analyses of 760 human Unigenes. In the present work, we reanalyzed the gene expression data in many tissues of human and mouse for a large number of genes almost covering the respective whole genomes. The results indicated that both in human and in mouse, the gene expression profiles exhibited by brain, cerebellum and testis are most similar to each other compared with other tissues.     

Genomewide analysis of nucleosome density histone acetylation and HDAC function in fission yeast

We have conducted a genomewide investigation into the enzymatic specificity, expression profiles, and binding locations of four histone deacetylases (HDACs), representing the three different phylogenetic classes in fission yeast (Schizosaccharomyces pombe). By directly comparing nucleosome density, histone acetylation patterns and HDAC binding in both intergenic and coding regions with gene expression profiles, we found that Sir2 (class III) and Hos2 (class I) have a role in preventing histone loss; Clr6 (class I) is the principal enzyme in promoter-localized repression. Hos2 has an unexpected role in promoting high expression of growth-related genes by deacetylating H4K16Ac in their open reading frames. Clr3 (class II) acts cooperatively with Sir2 throughout the genome, including the silent regions: rDNA, centromeres, mat2/3 and telomeres. The most significant acetylation sites are H3K14Ac for Clr3 and H3K9Ac for Sir2 at their genomic targets. Clr3 also affects subtelomeric regions which contain clustered stress- and meiosis-induced genes. Thus, this combined genomic approach has uncovered different roles for fission yeast HDACs at the silent regions in repression and activation of gene expression.     

Prediction of drug efficacy for cancer treatment based on comparative analysis of chemosensitivity and gene expression data

The NCI60 database is the largest available collection of compounds with measured anti-cancer activity. The strengths and limitations for using the NCI60 database as a source of new anti-cancer agents are explored and discussed in relation to previous studies. We selected a sub-set of 2333 compounds with reliable experimental half maximum growth inhibitions (GI(50)) values for 30 cell lines from the NCI60 data set and evaluated their growth inhibitory effect (chemosensitivity) with respect to tissue of origin. This was done by identifying natural clusters in the chemosensitivity data set and in a data set of expression profiles of 1901 genes for the corresponding tumor cell lines. Five clusters were identified based on the gene expression data using self-organizing maps (SOM), comprising leukemia, melanoma, ovarian and prostate, basal breast, and luminal breast cancer cells, respectively. The strong difference in gene expression between basal and luminal breast cancer cells was reflected clearly in the chemosensitivity data. Although most compounds in the data set were of low potency, high efficacy compounds that showed specificity with respect to tissue of origin could be found. Furthermore, eight potential topoisomerase II inhibitors were identified using a structural similarity search. Finally, a set of genes with expression profiles that were significantly correlated with anti-cancer drug activity was identified. Our study demonstrates that the combined data sets, which provide comprehensive information on drug activity and gene expression profiles of tumor cell lines studied, are useful for identifying potential new active compounds.     

Selection and validation of microarray candidate genes from subregions and subnuclei of the brain

DNA array technology now allows an enormous amount of expression data to be obtained. For large-scale gene profiling enterprises, this is of course welcome. However, the scientist interested in follow-up studies of a handful of differentially expressed genes may find it hard to sift through the vast datasets to pinpoint genes with the most desirable and reliable behaviors. Here, we present the methodology we have employed to discover genes differentially expressed in the adult mouse brain. We first used Affymetrix microarrays to compare gene expression from five different brain regions: the amygdala, cerebellum, hippocampus, olfactory bulb, and periaqueductal gray. Second, we identified genes differentially expressed within three distinct amygdala subnuclei. In this case, the tissue was microdissected by laser-capture to minimize contamination from adjacent subnuclei, and extracted RNA was subjected to three rounds of linear amplification prior to hybridization to the microarrays. To select candidate genes, we developed a custom algorithm to identify those genes with the most robust changes in expression across different replicate samples. Confirmation of expression patterns with in situ hybridization uncovered further criteria to consider in the selection process.     

Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network

One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING (Search Tool for the Retrieval of Interacting Genes), a weighted functional protein association network was constructed. By means of the mRMR (Maximum Relevance Minimum Redundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well.