Organization of human cardiovascular-expressed genes on chromosomes 21 and 22

Macroarray analysis in the hypertrophic left ventricle of renin-dependent hypertensive rats: identification of target genes for renin

Introduction The aim of this work was to identify new renin target genes in left ventricular hypertrophy during hypertension.

Materials and methods We compared left ventricle gene expression from four transgenic TGR(mRen2)27 (TG+/-) rats and four non-transgenic littermates (TG-/-) using cDNA macroarray. Hybridisation signals were quantified with a phosphorimager, and normalised to an external scale. Data analysis was performed with Statistical Analysis for Microarrays (SAM 1.21) software. The mRNA levels of candidate genes were determined by semi-quantitative RT-PCR in three different hypertensive rats: TG+/-, spontaneously hypertensive (SHR) and genetically Lyon hypertensive (LH) rats, compared to their respective controls (TG-/-, Wistar-Kyoto, Lyon low blood pressure rats).

Results Out of 1,200 genes present on the macroarray, 233 were reliably measured and only three were overexpressed (Biglycan, β1-adenosine monophosphate-activated protein kinase [AMPK] and amyloid precursor like protein 2 [APLP2]) and 19 were underexpressed in the left ventricle of TG+/compared with TG-/-. APLP2 is a member of the amyloid precursor protein (APP) family. APLP2 and APP mRNA levels were increased in TGR(mRen2)27 but significantly decreased in LH rats, while only APP was increased in SHR rats.

Conclusions We report new genes associated with renin-dependent left ventricular hypertrophy. Moreover, this work shows for the first time that the APP family gene expression could be altered in response to high renin activity and this effect is independent of cardiac remodelling and hypertension.

Positive correlation between gene coexpression and positional clustering in the zebrafish genome

Background

Co-expressing genes tend to cluster in eukaryotic genomes. This paper analyzes correlation between the proximity of eukaryotic genes and their transcriptional expression pattern in the zebrafish (Danio rerio) genome using available microarray data and gene annotation.

Results

The analyses show that neighbouring genes are significantly coexpressed in the zebrafish genome, and the coexpression level is influenced by the intergenic distance and transcription orientation. This fact is further supported by examining the coexpression level of genes within positional clusters in the neighbourhood model. There is a positive correlation between gene coexpression and positional clustering in the zebrafish genome.

Conclusion

The study provides another piece of evidence for the hypothesis that coexpressed genes do cluster in the eukaryotic genomes.

A method for similarity search of genomic positional expression using CAGE

With the advancement of genome research, it is becoming clear that genes are not distributed on the genome in random order. Clusters of genes distributed at localized genome positions have been reported in several eukaryotes. Various correlations have been observed between the expressions of genes in adjacent or nearby positions along the chromosomes depending on tissue type and developmental stage. Moreover, in several cases, their transcripts, which control epigenetic transcription via processes such as transcriptional interference and genomic imprinting, occur in clusters. It is reasonable that genomic regions that have similar mechanisms show similar expression patterns and that the characteristics of expression in the same genomic regions differ depending on tissue type and developmental stage. In this study, we analyzed gene expression patterns using the cap analysis gene expression (CAGE) method for exploring systematic views of the mouse transcriptome. Counting the number of mapped CAGE tags for fixed-length regions allowed us to determine genomic expression levels. These expression levels were normalized, quantified, and converted into four types of descriptors, allowing the expression patterns along the genome to be represented by character strings. We analyzed them using dynamic programming in the same manner as for sequence analysis. We have developed a novel algorithm that provides a novel view of the genome from the perspective of genomic positional expression. In a similarity search of expression patterns across chromosomes and tissues, we found regions that had clusters of genes that showed expression patterns similar to each other depending on tissue type. Our results suggest the possibility that the regions that have sense–antisense transcription show similar expression patterns between forward and reverse strands.

Through the advancement of genome research, it is becoming clear that genes are not distributed on the genome in random order. Clusters of genes distributed at localized genome positions have been reported in several eukaryotes. Various correlations have been observed between the expressions of genes in adjacent or nearby positions along the chromosomes depending on tissue type and developmental stage. It is reasonable that genomic regions that have similar mechanisms show similar expression patterns. In this study, the authors analyzed gene expression patterns using the computational algorithm of similarity search for exploring systematic views of the mouse transcriptome. They found regions that had clusters of highly expressed genes in certain tissue types whose expression patterns showed strong similarity to each other. This work aims to provide additional insight into genome-wide mechanisms of transcription.

Clusters of adjacent and similarly expressed genes across normal human tissues complicate comparative transcriptomic discovery

Transcriptomic techniques are valuable tools with which to validate genetic and biological hypotheses and are now widely available for research. However, with the exception of tumor biology, comparative genomics analyses have been difficult to use as discovery engines to describe biologically relevant expression changes. We propose that physical proximity of human genes correlates with similar mRNA expression, so that increased expression might include a disease-relevant gene and many other genes in the adjacent region. To increase the efficiency of combining susceptibility gene mapping and interpretation of transcriptomics, we developed a method to identify clusters of adjacent and similarly expressed genes. Gene expression profiles for 28,945 genes across 101 normal human tissues were obtained from the Gene Logic BioExpress system. The expression similarity for genes in sliding-windows was measured using average pair-wise Pearson correlation coefficients. We identified 187 clusters (p < 10e-4) of co-regulated genes, including 2648 genes, or 9.1% of all genes considered and termed these "clusters of adjacent and similarly expressed genes" (CASEGs). Genes in 15 (8.2%) of these clusters demonstrate a significant co-expression enrichment (p < 10e-10). This study demonstrates the coordinate expression of neighboring genes and provides a comprehensive view of expression-based compartmentalization of the human genome, which can be overlaid on genetic susceptibility gene maps.

Chromosomal clustering of a human transcriptome reveals regulatory background

Background

There has been much evidence recently for a link between transcriptional regulation and chromosomal gene order, but the relationship between genomic organization, regulation and gene function in higher eukaryotes remains to be precisely defined.

Results

Here, we present evidence for organization of a large proportion of a human transcriptome into gene clusters throughout the genome, which are partly regulated by the same transcription factors, share biological functions and are characterized by non-housekeeping genes. This analysis was based on the cardiac transcriptome identified by our genome-wide array analysis of 55 human heart samples. We found 37% of these genes to be arranged mainly in adjacent pairs or triplets. A significant number of pairs of adjacent genes are putatively regulated by common transcription factors (p = 0.02). Furthermore, these gene pairs share a significant number of GO functional classification terms. We show that the human cardiac transcriptome is organized into many small clusters across the whole genome, rather than being concentrated in a few larger clusters.

Conclusion

Our findings suggest that genes expressed in concert are organized in a linear arrangement for coordinated regulation. Determining the relationship between gene arrangement, regulation and nuclear organization as well as gene function will have broad biological implications.

Genome-scale analysis of positional clustering of mouse testis-specific genes

BACKGROUND

Genes are not randomly distributed on a chromosome as they were thought even after removal of tandem repeats. The positional clustering of co-expressed genes is known in prokaryotes and recently reported in several eukaryotic organisms such as Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens. In order to further investigate the mode of tissue-specific gene clustering in higher eukaryotes, we have performed a genome-scale analysis of positional clustering of the mouse testis-specific genes.

RESULTS

Our computational analysis shows that a large proportion of testis-specific genes are clustered in groups of 2 to 5 genes in the mouse genome. The number of clusters is much higher than expected by chance even after removal of tandem repeats.

CONCLUSION

Our result suggests that testis-specific genes tend to cluster on the mouse chromosomes. This provides another piece of evidence for the hypothesis that clusters of tissue-specific genes do exist.

Expressed genome molecular signatures of heart failure

Traditional gene expression studies typically focus on one or a few genes of interest. An important limitation of single-gene studies is that they present a portrait of disease that is essentially static. However, disease is a dynamic process, driven by a combination of genetic, epigenetic and environmental factors. Recently, genomic technologies have permitted better characterization of the dynamic aspect of disease progression. Genome-wide expression profiles of cardiovascular diseases, heart failure in particular, using microarrays have been published and are providing new insights into this complex disease. Tissue biopsies required for traditional microarray studies, however, are often invasive and not readily available. By contrast, blood samples are relatively non-invasive and are readily available. In a number of recent studies, blood cells appear to be a viable substitute for tissue biopsy. Blood cells have the ability to mirror the body's tissues and organs in health and disease; thus, we hypothesize that blood cells can indicate at the molecular level the presence of disease. Here we review microarray gene expression profiling of blood RNA for a number of different diseases. Sieving through gene expression molecular signatures has identified groups of genes characteristic of each and has identified biomarkers associated with specific diseases.

First comprehensive mapping of cartilage transcripts to the human genome

We present the first comprehensive transcriptome-to-genome mapping for human cartilage. First, we determined that the cartilage transcriptome represents between 13,200 and 15,800 unique genes. Next, a subset of approximately 10,000 of the best characterized cartilage-expressed transcripts (CETs) was selected and mapped to the human genome. The distribution of CETs across the genome was found to be significantly different compared to the expected distribution. Furthermore, clusters of adjacent coordinately transcribed genes, as well as numerous "hot spots" and "cold spots" for transcription in cartilage, were identified. We propose that transcriptional control in cartilage can be exerted over genomic domains containing as few as four neighboring genes. Our findings, which are consistent with recent "chromatin domain" models of transcription, are further supported by our identification of CETs that putatively encode components of the HDAC- and Swi/SNF-mediated chromatin remodeling pathways. Our study illustrates the value of comprehensive high-resolution scans to detect transcription patterns within the human genome.

Chromosomal distribution of the human cardiovascular transcriptome

On the basis of previous observations in chromosomes 21 and 22, we hypothesize that there is a tissue-specific organization of cardiovascular gene transcripts in the human genome. To examine the distribution of heart-derived transcripts, we assigned a nonredundant set of 4628 fetal and 3574 adult known and uncharacterized cardiovascular expressed-sequence tags (cvESTs) to 5-Mb chromosomal 'windows' on the basis of publicly available sequence mapping data. On a whole-genome level (36,617 genes), chromosome 17 (19.2% in fetal, 16.5% in adult) contained the highest proportion of cvESTs, whereas chromosome Y (2.0% in fetal and adult) contained the lowest. In total, 50 of the 639 windows contained a significantly higher proportion of cvESTs (P < 0.003) compared with the genome-wide cvEST gene density, particularly on gene-dense chromosomes (that is, 17, 19, 22) as opposed to gene-rich chromosomes (for example, 1, 2, 11). This report provides insight into a possible role for complex tissue-specific gene regulation in the human genome.

In silico identification of metazoan transcriptional regulatory regions

Transcriptional regulation remains one of the most intriguing and challenging subjects in biomedical research. The catalysis of transcription is a clear example of multiple proteins interacting to orchestrate a biological process, offering a starting point for the study of biological systems. Transcriptional regulation is viewed as one of the principal mechanisms governing the spatial and temporal distribution of gene expression, thus the field of transcriptional regulation provides a natural stage for quantitative studies of multiple gene systems. Building on the body of focused experimental studies and new genomics-driven data, computational biologists are making significant strides in accelerating our understanding of the transcriptional regulatory process in metazoan cells. Recent advances in the computational analysis of the interplay between factors have been fueled by well-defined computational methods for the modeling of the binding of individual transcription factors. We present here an overview of advances in the analysis of regulatory systems and the fundamental methods that underlie the recent developments.

Positional clustering of differentially expressed genes on human chromosomes 20, 21 and 22

BACKGROUND

Clusters of genes co-expressed are known in prokaryotes (operons) and were recently described in several eukaryote organisms, including Human. According to some studies, these clusters consist of housekeeping genes, whereas other studies suggest that these clustered genes exhibit similar tissue specificity. Here we further explore the relationship between co-expression and chromosomal co-localization in the human genome by analyzing the expression status of the genes along the best-annotated chromosomes 20, 21 and 22.

METHODS

Gene expression levels were estimated according to their publicly available ESTs and gene differential expressions were assessed using a previously described and validated statistical test. Gene sequences for chromosomes 20, 21 and 22 were taken from the Ensembl annotation.

RESULTS

We identified clusters of genes specifically expressed in similar tissues along chromosomes 20, 21 and 22. These co-expression clusters occurred more frequently than expected by chance and may thus be biologically significant.

CONCLUSION

The co-expression of co-localized genes might be due to higher chromatin structures influencing the gene availability for transcription in a given tissue or cell type.

Microarray gene expression profiles in dilated and hypertrophic cardiomyopathic end-stage heart failure

Despite similar clinical endpoints, heart failure resulting from dilated cardiomyopathy (DCM) or hypertrophic cardiomyopathy (HCM) appears to develop through different remodeling and molecular pathways. Current understanding of heart failure has been facilitated by microarray technology. We constructed an in-house spotted cDNA microarray using 10,272 unique clones from various cardiovascular cDNA libraries sequenced and annotated in our laboratory. RNA samples were obtained from left ventricular tissues of precardiac transplantation DCM and HCM patients and were hybridized against normal adult heart reference RNA. After filtering, differentially expressed genes were determined using novel analyzing software. We demonstrated that normalization for cDNA microarray data is slide-dependent and nonlinear. The feasibility of this model was validated by quantitative real-time reverse transcription-PCR, and the accuracy rate depended on the fold change and statistical significance level. Our results showed that 192 genes were highly expressed in both DCM and HCM (e.g., atrial natriuretic peptide, CD59, decorin, elongation factor 2, and heat shock protein 90), and 51 genes were downregulated in both conditions (e.g., elastin, sarcoplasmic/endoplasmic reticulum Ca2+-ATPase). We also identified several genes differentially expressed between DCM and HCM (e.g., alphaB-crystallin, antagonizer of myc transcriptional activity, beta-dystrobrevin, calsequestrin, lipocortin, and lumican). Microarray technology provides us with a genomic approach to explore the genetic markers and molecular mechanisms leading to heart failure.