Biomedical subjects
Publications and source records attributed to Thomas A Drake.
Proteins play a central role in a systems view of biologic processes. This review provides an overview of proteomics from a systems perspective. We survey the key tools and methodologies used, present examples of how these are currently being used in the systems biology context, and discuss future directions.
We report a comprehensive analysis of gene expression differences between sexes in multiple somatic tissues of 334 mice derived from an intercross between inbred mouse strains C57BL/6J and C3H/HeJ. The analysis of a large number of individuals provided the power to detect relatively small differences in expression between sexes, and the use of an intercross allowed analysis of the genetic control of sexually dimorphic gene expression. Microarray analysis of 23,574 transcripts revealed that the extent of sexual dimorphism in gene expression was much greater than previously recognized. Thus, thousands of genes showed sexual dimorphism in liver, adipose, and muscle, and hundreds of genes were sexually dimorphic in brain. These genes exhibited highly tissue-specific patterns of expression and were enriched for distinct pathways represented in the Gene Ontology database. They also showed evidence of chromosomal enrichment, not only on the sex chromosomes, but also on several autosomes. Genetic analyses provided evidence of the global regulation of subsets of the sexually dimorphic genes, as the transcript levels of a large number of these genes were controlled by several expression quantitative trait loci (eQTL) hotspots that exhibited tissue-specific control. Moreover, many tissue-specific transcription factor binding sites were found to be enriched in the sexually dimorphic genes.
Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight-related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest.
The millions of common DNA variations that occur in the human population, or among inbred strains of mice and rats, perturb the expression (transcript levels) of a large fraction of the genes expressed in a particular tissue. The hundreds or thousands of common cis-acting variations that occur in the population may in turn affect the expression of thousands of other genes by affecting transcription factors, signaling molecules, RNA processing, and other processes that act in trans. The levels of transcripts are conveniently quantitated using expression arrays, and the cis- and trans-acting loci can be mapped using quantitative trait locus (QTL) analysis, in the same manner as loci for physiologic or clinical traits. Thousands of such expression QTL (eQTL) have been mapped in various crosses in mice, as well as other experimental organisms, and less detailed maps have been produced in studies of cells from human pedigrees. Such an integrative genetics approach (sometimes referred to as "genetical genomics") is proving useful for identifying genes and pathways that contribute to complex clinical traits. The coincidence of clinical trait QTL and eQTL can help in the prioritization of positional candidate genes. More importantly, mathematical modeling of correlations between levels of transcripts and clinical traits in genetic crosses can allow prediction of causal interactions and the identification of "key driver" genes. An important objective of such studies will be to model biological networks in physiologic processes. When combined with high-density single nucleotide polymorphism (SNP) mapping, it should be feasible to identify genes that contribute to transcript levels using association analysis in outbred populations. In this review we discuss the basic concepts and applications of this integrative genomic approach to cardiovascular and metabolic diseases.
The integration of expression profiling with linkage analysis has increasingly been used to identify genes underlying complex phenotypes. The effects of gender on the regulation of many physiological traits are well documented; however, "genetical genomic" analyses have not yet addressed the degree to which their conclusions are affected by sex. We constructed and densely genotyped a large F2 intercross derived from the inbred mouse strains C57BL/6J and C3H/HeJ on an apolipoprotein E null (ApoE-/-) background. This BXH.ApoE-/- population recapitulates several "metabolic syndrome" phenotypes. The cross consists of 334 animals of both sexes, allowing us to specifically test for the dependence of linkage on sex. We detected several thousand liver gene expression quantitative trait loci, a significant proportion of which are sex-biased. We used these analyses to dissect the genetics of gonadal fat mass, a complex trait with sex-specific regulation. We present evidence for a remarkably high degree of sex-dependence on both the cis and trans regulation of gene expression. We demonstrate how these analyses can be applied to the study of the genetics underlying gonadal fat mass, a complex trait showing significantly female-biased heritability. These data have implications on the potential effects of sex on the genetic regulation of other complex traits.
Formative assessments are systematically designed instructional interventions to assess and provide feedback on students' strengths and weaknesses in the course of teaching and learning. Despite their known benefits to student attitudes and learning, medical school curricula have been slow to integrate such assessments into the curriculum. This study investigates how performance on two different modes of formative assessment relate to each other and to performance on summative assessments in an integrated, medical-school environment. Two types of formative assessment were administered to 146 first-year medical students each week over 8 weeks: a timed, closed-book component to assess factual recall and image recognition, and an un-timed, open-book component to assess higher order reasoning including the ability to identify and access appropriate resources and to integrate and apply knowledge. Analogous summative assessments were administered in the ninth week. Models relating formative and summative assessment performance were tested using Structural Equation Modeling. Two latent variables underlying achievement on formative and summative assessments could be identified; a "formative-assessment factor" and a "summative-assessment factor," with the former predicting the latter. A latent variable underlying achievement on open-book formative assessments was highly predictive of achievement on both open- and closed-book summative assessments, whereas a latent variable underlying closed-book assessments only predicted performance on the closed-book summative assessment. Formative assessments can be used as effective predictive tools of summative performance in medical school. Open-book, un-timed assessments of higher order processes appeared to be better predictors of overall summative performance than closed-book, timed assessments of factual recall and image recognition.
Forward genetic approaches to identify genes involved in complex traits such as common human diseases have met with limited success. Fine mapping of linkage regions and validation of positional candidates are time-consuming and not always successful. Here we detail a hybrid procedure to map loci involved in complex traits that leverages the strengths of forward and reverse genetic approaches. By integrating genotypic and expression data in a segregating mouse population, we show how clusters of expression quantitative trait loci linking to regions of the genome accurately reflect the underlying perturbation to the transcriptional network induced by DNA variations in genes that control the complex traits. By matching patterns of gene expression in a segregating population with expression responses induced by single-gene perturbation experiments, we show how genes controlling clusters of expression and clinical quantitative trait loci can be mapped directly. We demonstrate the utility of this approach by identifying 5-lipoxygenase as underlying previously identified quantitative trait loci in an F(2) cross between strains C57BL/6J and DBA/2J and showing that it has pleiotropic effects on body fat, lipid levels and bone density.
BACKGROUND: A segregating population of (C57BL/6J x DBA/2J)F2 intercross mice was studied for obesity-related traits and for global gene expression in liver. Quantitative trait locus analyses were applied to the subcutaneous fat-mass trait and all gene-expression data. These data were then used to identify gene sets that are differentially perturbed in lean and obese mice. RESULTS: We integrated global gene-expression data with phenotypic and genetic segregation analyses to evaluate metabolic pathways associated with obesity. Using two approaches we identified 13 metabolic pathways whose genes are coordinately regulated in association with obesity. Four genomic regions on chromosomes 3, 6, 16, and 19 were found to control the coordinated expression of these pathways. Using criteria that included trait correlation, differential gene expression, and linkage to genomic regions, we identified novel genes potentially associated with the identified pathways. CONCLUSION: This study demonstrates that genetic and gene-expression data can be integrated to identify pathways associated with clinical traits and their underlying genetic determinants.
A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.
We previously reported the analysis of genome-wide expression profiles and various diabetes-related traits in a segregating cross between inbred mouse strains C57BL/6J (B6) and DBA/2J (DBA). By considering transcript levels as quantitative traits, we identified several thousand expression quantitative trait loci (eQTL) with LOD score >4.3. We now experimentally address the problem of multiple comparisons by estimating the fraction of false-positive eQTL that are under cis-acting regulation. For this, we have utilized a classic cis-trans test with (B6 x DBA)F(1) mice to determine the relative levels of transcripts from the B6 and DBA alleles. The results suggest that at least 64% of cis-acting eQTL with LOD >4.3 are true positives, while the remaining 36% could not be confirmed as truly cis-acting. Moreover, we find that >96% of apparent cis-acting eQTL occur in regions that do not share SNP haplotypes. Cis-acting eQTL serve as an important new resource for the identification of positional candidates in QTL studies in mice. Also, we use the analysis of the correlation structures between genotypes, gene expression traits, and phenotypic traits to further characterize genes expressed in liver that are under cis-acting control, and highlight the advantages and disadvantages of integrating genetics and gene expression data in segregating populations.
The goal of systems biology is to define all of the elements present in a given system and to create an interaction network between these components so that the behavior of the system, as a whole and in parts, can be explained under specified conditions. The elements constituting the network that influences the development of atherosclerosis could be genes, pathways, transcript levels, proteins, or physiologic traits. In this review, we discuss how the integration of genetics and technologies such as transcriptomics and proteomics, combined with mathematical modeling, may lead to an understanding of such networks.
OBJECTIVE: Destruction of the elastic media is the most striking histologic feature of atherosclerotic aortic aneurysms. Apolipoprotein E-deficient (apoE-/-) mice fed a Western diet develop advanced atherosclerotic lesions in the aorta. We sought to assess the integrity of atherosclerotic aortic walls in 2 apoE-/- strains, C57BL/6 (B6) and C3H/HeJ (C3H) that differ markedly in atherosclerosis susceptibility. METHODS AND RESULTS: C3H.apoE-/- mice developed much smaller atherosclerotic lesions than did B6.apoE-/- mice after being fed a Western diet for 16 weeks, but the C3H.apoE-/- mice exhibited destruction of the elastic media, including erosion, fragmentation, and focal dilatation beneath plaques. Gelatin and casein zymography showed proteolytic activity of matrix metalloproteinases (MMPs) -9, -2, and -12 in aortic tissues and of MMP-9 and -12 in macrophages from both strains. However, C3H.apoE-/- mice showed significantly increased MMP-2 and -12 activity in aortas and macrophages compared with those from B6.apoE-/- mice. MMP-9 activity was comparable in aortic tissues of the 2 strains, but it was significantly higher in macrophages from C3H.apoE-/- than from B6.apoE-/- mice. CONCLUSIONS: Data indicate that genetic backgrounds but not sizes of atherosclerotic lesions determine medial destruction in the aortic root of apoE-/- mice and that an increase in MMP proteolytic activity might contribute to the medial destruction of aortic walls in C3H.apoE-/- mice.
Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci.
Genetic factors independent of those affecting plasma lipid levels are a major contributor to risk for atherosclerosis in humans, yet the basis for these is poorly understood. This study examined plasma lipids and diet-induced atherosclerosis in 16-month-old female mice of strains C56BL/6J and DBA/2J. Mice of the parental strains, from recombinant inbred strains derived from these (BXD RI), and F(2) progeny were fed an atherogenic diet for 16 weeks, beginning at 1 year of age. This induced atherosclerotic lesion formation in both parental strains, accompanied by increased plasma LDL levels. However, individual BXD RI strains and the BXD F(2) mice demonstrated a range of atherosclerotic lesion formation that was not or at best weakly correlated with plasma lipid levels. Quantitative trait locus (QTL) analysis of the BXD F(2) mice identified a locus with significant linkage (lod 4.5) for aortic lesion size on Chromosome (Chr) 10 that was independent of plasma lipids. Other loci with suggestive or significant linkage for various plasma lipid measures were identified on Chr 2, 3, 4, 5, 6, 7, 11, and 17. In this intercross, the genes primarily influencing atherosclerosis are distinct from those controlling plasma lipid levels.
C57BL/6J mice develop genetically determined age-related hippocampal granular deposits that have some similarities to lesions seen in the brains of human patients with tau protein related neurodegenerative disorders ("tauopathies"). We sought to identify the genetic loci responsible for these in an F2 intercross of inbred mouse strains C57BL/6J and DBA/2J, using quantitative trait locus (QTL) analysis. Hippocampal lesions were shown to be PAS positive, H and E negative, and immunoreactive for tau protein and alpha synuclein, but not to Abeta 1-40 or Abeta 1-42, or for ubiquitin. These were quantitated by histomorphometry, and QTL analysis revealed a locus on chromosome 7 with a lod score of 6.5 as well as two suggestive loci on chromosome 10. The genomic data indicate that the genetic basis is complex, but with one locus playing a major role in lesion formation. These lesions may represent a useful model for investigating dysregulation of tau protein in the hippocampus.
Discrepancies in size and shape of the jaws are the underlying etiology in many orthodontic and orthognathic surgery patients. Genetic factors combined with environmental interactions have been postulated to play a causal or contributory role in these craniofacial abnormalities. Along with the soon-to-be-available complete human and mouse genomic sequence data, mouse mutants have become a valuable tool in the functional mapping of genes involved in the development of human maxillofacial dysmorphologies. We review two powerful methods in such efforts: N-ethyl-N-nitrosourea (ENU) large-scale mutagenesis and quantitative trait linkage (QTL) analysis. The former aims at producing a plethora of novel variants of particular trait(s), and ultimately mapping the point mutations responsible for the appearance of these new traits. In contrast, the latter applies intensive breeding and mapping techniques to identify multiple loci (and, subsequently, genes) contributing to the phenotypic difference between the tested strains. A prerequisite for either approach to studying variations in the traits of interest is the application of effective mouse cephalometric phenotype analysis and rapid DNA mapping techniques. These approaches will produce a wealth of new data on critical genes that influence the size and shape of the human face.
Calcification occurs frequently in the development of atherosclerotic lesions, and studies in mice have indicated a genetic contribution. We now show that one genetic factor contributing to aortic calcification is the Dyscalc locus, previously shown to contribute to myocardial calcification. Thus, the Dyscalc locus, on proximal mouse Chromosome (Chr) 7, segregated with vascular calcification in a large cross between susceptible strain DBA/2J and resistant strain C57BL/6J. Further evidence was observed by analysis of recombinant inbred strains derived from various susceptible and resistant parental strains. Myocardial and vascular calcifications are importantly influenced by multiple modifier loci as well as the Dyscalc gene, making fine mapping of Dyscalc difficult. In order to allow more detailed genetic and biochemical characterization of Dyscalc, we have identified congenic strains containing the Dyscalc locus from resistant strain C57BL/10 on the background of susceptible strain C3H/DiSnA. The congenic strains exhibit little or no myocardial or vascular calcification, unlike the background HcB C3H strain, and the calcification segregated as a Mendelian factor, allowing finer mapping of Dyscalc.
Increasingly, the mouse is becoming the standard model for mammalian physiology and disease. It can be genetically analyzed and manipulated with relative ease. Moreover, the endogenous genetic variation that exists among inbred mouse strains can be exploited to identify genetic control of complex physiologic processes involved in diabetes and the metabolic syndrome, among other conditions relevant to human disease. Recent advances in genetics and gene expression technology have greatly increased the knowledge to be derived from this approach when applied to traditional genetic studies.