Array-based comparative genomic hybridization and copy number variation in cancer research

Array-based comparative genomic hybridization (aCGH) is a molecular cytogenetic technique used in detecting and mapping DNA copy number alterations. aCGH is able to interrogate the entire genome at a previously unattainable, high resolution and has directly led to the recent appreciation of a novel class of genomic variation: copy number variation (CNV) in mammalian genomes. All forms of DNA variation/polymorphism are important for studying the basis of phenotypic diversity among individuals. CNV research is still at its infancy, requiring careful collation and annotation of accumulating CNV data that will undoubtedly be useful for accurate interpretation of genomic imbalances identified during cancer research.     

A broadly tuned mouse odorant receptor that detects nitrotoluenes

Serine hydroxymethyltransferase (SHMT) catalyzes the transfer of a β-carbon from serine to tetrahydrofolate to form glycine and 5,10-methylene-tetrahydrofolate. This reaction plays an important role in neurotransmitter synthesis and metabolism. We set out to resequence SHMT1 and SHMT2, followed by functional genomic studies. We identified 87 and 60 polymorphisms in SHMT1 and SHMT2, respectively. We observed no significant functional effect of the 13 non-synonymous single-nucleotide polymorphism (SNPs) in these genes, either on catalytic activity or protein quantity. We imputed additional variants across the two genes using '1000 Genomes' data, and identified 14 variants that were significantly associated (p<1.0E-10) with SHMT1 messenger RNA expression in lymphoblastoid cell lines. Many of these SNPs were also significantly correlated with basal SHMT1 protein expression in 268 human liver biopsy samples. Reporter gene assays suggested that the SHMT1 promoter SNP, rs669340, contributed to this variation. Finally, SHMT1 and SHMT2 expression were significantly correlated with those of other Folate and Methionine Cycle genes at both the messenger RNA and protein levels. These experiments represent a comprehensive study of SHMT1 and SHMT2 gene sequence variation and its functional implications. In addition, we obtained preliminary indications that these genes may be co-regulated with other Folate and Methionine Cycle genes.     

Development of bioinformatics resources for display and analysis of copy number and other structural variants in the human genome

The discovery of an abundance of copy number variants (CNVs; gains and losses of DNA sequences >1 kb) and other structural variants in the human genome is influencing the way research and diagnostic analyses are being designed and interpreted. As such, comprehensive databases with the most relevant information will be critical to fully understand the results and have impact in a diverse range of disciplines ranging from molecular biology to clinical genetics. Here, we describe the development of bioinformatics resources to facilitate these studies. The Database of Genomic Variants (http://projects.tcag.ca/variation/) is a comprehensive catalogue of structural variation in the human genome. The database currently contains 1,267 regions reported to contain copy number variation or inversions in apparently healthy human cases. We describe the current contents of the database and how it can serve as a resource for interpretation of array comparative genomic hybridization (array CGH) and other DNA copy imbalance data. We also present the structure of the database, which was built using a new data modeling methodology termed Cross-Referenced Tables (XRT). This is a generic and easy-to-use platform, which is strong in handling textual data and complex relationships. Web-based presentation tools have been built allowing publication of XRT data to the web immediately along with rapid sharing of files with other databases and genome browsers. We also describe a novel tool named eFISH (electronic fluorescence in situ hybridization) (http://projects.tcag.ca/efish/), a BLAST-based program that was developed to facilitate the choice of appropriate clones for FISH and CGH experiments, as well as interpretation of results in which genomic DNA probes are used in hybridization-based experiments.     

Genetics of the human metabolome,what is next?

Increases in throughput and decreases in costs have facilitated large scale metabolomics studies, the simultaneous measurement of large numbers of biochemical components in biological samples. Initial large scale studies focused on biomarker discovery for disease or disease progression and helped to understand biochemical pathways underlying disease. The first population-based studies that combined metabolomics and genome wide association studies (mGWAS) have increased our understanding of the (genetic) regulation of biochemical conversions. Measurements of metabolites as intermediate phenotypes are a potentially very powerful approach to uncover how genetic variation affects disease susceptibility and progression. However, we still face many hurdles in the interpretation of mGWAS data. Due to the composite nature of many metabolites, single enzymes may affect the levels of multiple metabolites and, conversely, levels of single metabolites may be affected by multiple enzymes. Here, we will provide a global review of the current status of mGWAS. We will specifically discuss the application of prior biological knowledge present in databases to the interpretation of mGWAS results and discuss the potential of mathematical models. As the technology continuously improves to detect metabolites and to measure genetic variation, it is clear that comprehensive systems biology based approaches are required to further our insight in the association between genes, metabolites and disease. This article is part of a Special Issue entitled: From Genome to Function.     

Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance

The abundance of different SSU rRNA (“16S”) gene sequences in environmental samples is widely used in studies of microbial ecology as a measure of microbial community structure and diversity. However, the genomic copy number of the 16S gene varies greatly – from one in many species to up to 15 in some bacteria and to hundreds in some microbial eukaryotes. As a result of this variation the relative abundance of 16S genes in environmental samples can be attributed both to variation in the relative abundance of different organisms, and to variation in genomic 16S copy number among those organisms. Despite this fact, many studies assume that the abundance of 16S gene sequences is a surrogate measure of the relative abundance of the organisms containing those sequences. Here we present a method that uses data on sequences and genomic copy number of 16S genes along with phylogenetic placement and ancestral state estimation to estimate organismal abundances from environmental DNA sequence data. We use theory and simulations to demonstrate that 16S genomic copy number can be accurately estimated from the short reads typically obtained from high-throughput environmental sequencing of the 16S gene, and that organismal abundances in microbial communities are more strongly correlated with estimated abundances obtained from our method than with gene abundances. We re-analyze several published empirical data sets and demonstrate that the use of gene abundance versus estimated organismal abundance can lead to different inferences about community diversity and structure and the identity of the dominant taxa in microbial communities. Our approach will allow microbial ecologists to make more accurate inferences about microbial diversity and abundance based on 16S sequence data.     

A multivariate screening strategy for investigating metabolic effects of strenuous physical exercise in human serum

A novel hypothesis-free multivariate screening methodology for the study of human exercise metabolism in blood serum is presented. Serum gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) data was processed using hierarchical multivariate curve resolution (H-MCR), and orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to model the systematic variation related to the acute effect of strenuous exercise. Potential metabolic biomarkers were identified using data base comparisons. Extensive validation was carried out including predictive H-MCR, 7-fold full cross-validation, and predictions for the OPLS-DA model, variable permutation for highlighting interesting metabolites, and pairwise t tests for examining the significance of metabolites. The concentration changes of potential biomarkers were verified in the raw GC/TOFMS data. In total, 420 potential metabolites were resolved in the serum samples. On the basis of the relative concentrations of the 420 resolved metabolites, a valid multivariate model for the difference between pre- and post-exercise subjects was obtained. A total of 34 metabolites were highlighted as potential biomarkers, all statistically significant (p < 8.1E-05). As an example, two potential markers were identified as glycerol and asparagine. The concentration changes for these two metabolites were also verified in the raw GC/TOFMS data.The strategy was shown to facilitate interpretation and validation of metabolic interactions in human serum as well as revealing the identity of potential markers for known or novel mechanisms of human exercise physiology. The multivariate way of addressing metabolism studies can help to increase the understanding of the integrative biology behind, as well as unravel new mechanistic explanations in relation to, exercise physiology.     

A case for evolutionary genomics and the comprehensive examination of sequence biodiversity

Comparative analysis is one of the most powerful methods available for understanding the diverse and complex systems found in biology, but it is often limited by a lack of comprehensive taxonomic sampling. Despite the recent development of powerful genome technologies capable of producing sequence data in large quantities (witness the recently completed first draft of the human genome), there has been relatively little change in how evolutionary studies are conducted. The application of genomic methods to evolutionary biology is a challenge, in part because gene segments from different organisms are manipulated separately, requiring individual purification, cloning, and sequencing. We suggest that a feasible approach to collecting genome-scale data sets for evolutionary biology (i.e., evolutionary genomics) may consist of combination of DNA samples prior to cloning and sequencing, followed by computational reconstruction of the original sequences. This approach will allow the full benefit of automated protocols developed by genome projects to be realized; taxon sampling levels can easily increase to thousands for targeted genomes and genomic regions. Sequence diversity at this level will dramatically improve the quality and accuracy of phylogenetic inference, as well as the accuracy and resolution of comparative evolutionary studies. In particular, it will be possible to make accurate estimates of normal evolution in the context of constant structural and functional constraints (i.e., site-specific substitution probabilities), along with accurate estimates of changes in evolutionary patterns, including pairwise coevolution between sites, adaptive bursts, and changes in selective constraints. These estimates can then be used to understand and predict the effects of protein structure and function on sequence evolution and to predict unknown details of protein structure, function, and functional divergence. In order to demonstrate the practicality of these ideas and the potential benefit for functional genomic analysis, we describe a pilot project we are conducting to simultaneously sequence large numbers of vertebrate mitochondrial genomes.     

Identification and functional characterization of copy number variations in diverse chicken breeds

Background

The detection and functional characterization of genomic structural variations are important for understanding the landscape of genetic variation in the chicken. A recently recognized aspect of genomic structural variation, called copy number variation (CNV), is gaining interest in chicken genomic studies. The aim of the present study was to investigate the pattern and functional characterization of CNVs in five characteristic chicken breeds, which will be important for future studies associating phenotype with chicken genome architecture.

Results

Using a commercial 385 K array-based comparative genomic hybridization (aCGH) genome array, we performed CNV discovery using 10 chicken samples from four local Chinese breeds and the French breed Houdan chicken. The female Anka broiler was used as a reference. A total of 281 copy number variation regions (CNVR) were identified, covering 12.8 Mb of polymorphic sequences or 1.07% of the entire chicken genome. The functional annotation of CNVRs indicated that these regions completely or partially overlapped with 231 genes and 1032 quantitative traits loci, suggesting these CNVs have important functions and might be promising resources for exploring differences among various breeds. In addition, we employed quantitative PCR (qPCR) to further validate several copy number variable genes, such as prolactin receptor, endothelin 3 (EDN3), suppressor of cytokine signaling 2, CD8a molecule, with important functions, and the results suggested that EDN3 might be a molecular marker for the selection of dark skin color in poultry production. Moreover, we also identified a new CNVR (chr24: 3484617–3512275), encoding the sortilin-related receptor gene, with copy number changes in only black-bone chicken.

Conclusions

Here, we report a genome-wide analysis of the CNVs in five chicken breeds using aCGH. The association between EDN3 and melanoblast proliferation was further confirmed using qPCR. These results provide additional information for understanding genomic variation and related phenotypic characteristics.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-934) contains supplementary material, which is available to authorized users.