Quantitative trait transcripts for nicotine resistance in Drosophila melanogaster

Insight into the genetics of hypertension, a core component of the metabolic syndrome

PURPOSE OF REVIEW

To provide insight into genetics of essential hypertension, including discussion of methods used both in human and animal experimental studies and interpretation of results.

RECENT FINDINGS

On the basis of recent progress in sequencing of human genome, detection of millions of single nucleotide polymorphism markers, determination of the extend of linkage disequilibrium (haplotypes), efficient genotyping technology, collection of DNA from thousands of rigorously phenotyped patients and controls and designing sound statistical methods, genome-wide associations studies were widely applied to analyses of common diseases including essential hypertension for the first time in 2007. Concurrently, new experimental approaches combined gene expression profiling with linkage and correlation analyses to identify quantitative trait loci underlying complex traits at the molecular level.

SUMMARY

These new approaches yielded new exciting results but also posed questions regarding data analyses, interpretation and clinical significance.

Identification of renal Cd36 as a determinant of blood pressure and risk for hypertension

To identify renally expressed genes that influence risk for hypertension, we integrated expression quantitative trait locus (QTL) analysis of the kidney with genome-wide correlation analysis of renal expression profiles and blood pressure in recombinant inbred strains derived from the spontaneously hypertensive rat (SHR). This strategy, together with renal transplantation studies in SHR progenitor, transgenic and congenic strains, identified deficient renal expression of Cd36 encoding fatty acid translocase as a genetically determined risk factor for spontaneous hypertension.

RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays

Ultra-high-throughput sequencing is emerging as an attractive alternative to microarrays for genotyping, analysis of methylation patterns, and identification of transcription factor binding sites. Here, we describe an application of the Illumina sequencing (formerly Solexa sequencing) platform to study mRNA expression levels. Our goals were to estimate technical variance associated with Illumina sequencing in this context and to compare its ability to identify differentially expressed genes with existing array technologies. To do so, we estimated gene expression differences between liver and kidney RNA samples using multiple sequencing replicates, and compared the sequencing data to results obtained from Affymetrix arrays using the same RNA samples. We find that the Illumina sequencing data are highly replicable, with relatively little technical variation, and thus, for many purposes, it may suffice to sequence each mRNA sample only once (i.e., using one lane). The information in a single lane of Illumina sequencing data appears comparable to that in a single array in enabling identification of differentially expressed genes, while allowing for additional analyses such as detection of low-expressed genes, alternative splice variants, and novel transcripts. Based on our observations, we propose an empirical protocol and a statistical framework for the analysis of gene expression using ultra-high-throughput sequencing technology.

Harnessing naturally randomized transcription to infer regulatory relationships among genes

An approach is developed that utilizes randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level. The approach is applied to an experiment in yeast, yielding new insights into the topology of the yeast transcriptional regulatory network.

We develop an approach utilizing randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level, based on experiments in which genotyping and expression profiling are performed. This approach can be used to build transcriptional regulatory networks and to identify putative regulators of genes. We apply the method to an experiment in yeast, in which genes known to be in the same processes and functions are recovered in the resulting transcriptional regulatory network.

Integrated genomic approaches implicate osteoglycin (Ogn) in the regulation of left ventricular mass

Left ventricular mass (LVM) and cardiac gene expression are complex traits regulated by factors both intrinsic and extrinsic to the heart. To dissect the major determinants of LVM, we combined expression quantitative trait locus1 and quantitative trait transcript (QTT) analyses of the cardiac transcriptome in the rat. Using these methods and in vitro functional assays, we identified osteoglycin (Ogn) as a major candidate regulator of rat LVM, with increased Ogn protein expression associated with elevated LVM. We also applied genome-wide QTT analysis to the human heart and observed that, out of 22,000 transcripts, OGN transcript abundance had the highest correlation with LVM. We further confirmed a role for Ogn in the in vivo regulation of LVM in Ogn knockout mice. Taken together, these data implicate Ogn as a key regulator of LVM in rats, mice and humans, and suggest that Ogn modifies the hypertrophic response to extrinsic factors such as hypertension and aortic stenosis.

Mixture modeling of transcript abundance classes in natural populations

Expression profiling of Drosophila melanogaster adult female heads for 108 nearly isogenic lines from two different populations, and of CEPH lymphoblastoid lines, shows that differential expression of transcripts among individuals is due to a complex interplay of cis- and trans-acting factors.

Background

Populations diverge in genotype and phenotype under the influence of such evolutionary processes as genetic drift, mutation accumulation, and natural selection. Because genotype maps onto phenotype by way of transcription, it is of interest to evaluate how these evolutionary factors influence the structure of variation at the level of transcription. Here, we explore the distributions of cis-acting and trans-acting factors and their relative contributions to expression of transcripts that exhibit two or more classes of abundance among individuals within populations.

Results

Expression profiling using cDNA microarrays was conducted in Drosophila melanogaster adult female heads for 58 nearly isogenic lines from a North Carolina population and 50 from a California population. Using a mixture modeling approach, transcripts were identified that exhibit more than one mode of transcript abundance across the samples. Power studies indicate that sample sizes of 50 individuals will generally be sufficient to detect divergent transcript abundance classes. The distribution of transcript abundance classes is skewed toward low frequency minor classes, which is reminiscent of the typical skew in genotype frequencies. Similar results are observed in reported data on gene expression in human lymphoblast cell lines, in which analysis of association with linked polymorphisms implies that cis-acting single nucleotide polymorphisms make only a modest contribution to bimodal distributions of transcript abundance.

Conclusion

Population surveys of gene expression may complement genetical genomics as a general approach to quantifying sources of transcriptional variation. Differential expression of transcripts among individuals is due to a complex interplay of cis-acting and trans-acting factors.

The influence of genetic variation on gene expression

The view that changes to the control of gene expression rather than alterations to protein sequence are central to the evolution of organisms has become something of a truism in molecular biology. In reality, the direct evidence for this is limited, and only recently have we had the ability to look more globally at how genetic variation influences gene expression, focusing upon inter-individual variation in gene expression and using microarrays to test for differences in mRNA levels. Here, we review the scope of these experimental analyses, what they are designed to tell us about genetic variation, and what are their limitations from both a technical and a conceptual viewpoint. We conclude that while we are starting to understand the impact of this class of genetic variation upon steady-state mRNA levels, we are still far from identifying the potential phenotypic and evolutionary outcomes.

Brain gene expression correlates with changes in behavior in the R6/1 mouse model of Huntington's disease

Huntington's disease (HD) is an inherited neurodegeneration that causes a severe progressive illness and early death. Several animal models of the disease have been generated carrying the causative mutation and these have shown that one of the earliest molecular signs of the disease process is a substantial transcriptional deficit. We examined the alterations in brain gene expression in the R6/1 mouse line over the course of the development of phenotypic signs from 18 to 27 weeks. Changes in R6/1 mice were similar to those previously reported in R6/2 mice, and gene ontology analysis shows that pathways related to intracellular and electrical signaling are altered among downregulated genes and lipid biosynthesis and RNA processes among upregulated genes. The R6/1 mice showed deficits in rotarod performance, locomotor activity and exploratory behavior over the time-course. We have correlated the alterations in gene expression with changes in behavior seen in the mice and find that few alterations in gene expression correlate with all behavioral changes but rather that different subsets of the changes are uniquely correlated with one behavior only. This indicates that multiple behavioral tasks assessing different behavioral domains are likely to be necessary in therapeutic trials in mouse models of HD.