Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice.

Mycobacterium tuberculosis infects two billion people across the globe, and results in 8-9 million new tuberculosis (TB) cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. Here, we investigate the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using immune and inflammatory mediators; and clinical, microbiological, and granuloma correlates of disease identified five new loci on mouse chromosomes 1, 2, 4, 16; and three known loci on chromosomes 3 and 17. Further, multiple positively correlated traits shared loci on chromosomes 1, 16, and 17 and had similar patterns of allele effects, suggesting these loci contain critical genetic regulators of inflammatory responses to M. tuberculosis. To narrow the list of candidate genes, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks to generate scores representing functional relationships. The scores were used to rank candidates for each mapped trait, resulting in 11 candidate genes: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Although all candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling, and all contain single nucleotide polymorphisms (SNPs), SNPs in only four genes (S100a8, Itgb5, Fstl1, Zfp318) are predicted to have deleterious effects on protein functions. We performed methodological and candidate validations to (i) assess biological relevance of predicted allele effects by showing that Diversity Outbred mice carrying PWK/PhJ alleles at the H-2 locus on chromosome 17 QTL have shorter survival; (ii) confirm accuracy of predicted allele effects by quantifying S100A8 protein in inbred founder strains; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this body of work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and functionally relevant gene candidates that may be major regulators of complex host-pathogens interactions contributing to granuloma necrosis and acute inflammation in pulmonary TB.

B cells in perivascular and peribronchiolar granuloma-associated lymphoid tissue and B-cell signatures identify asymptomatic Mycobacterium tuberculosis lung infection in Diversity Outbred mice.

Because most humans resist Mycobacterium tuberculosis infection, there is a paucity of lung samples to study. To address this gap, we infected Diversity Outbred mice with M. tuberculosis and studied the lungs of mice in different disease states. After a low-dose aerosol infection, progressors succumbed to acute, inflammatory lung disease within 60 days, while controllers maintained asymptomatic infection for at least 60 days, and then developed chronic pulmonary tuberculosis (TB) lasting months to more than 1 year. Here, we identified features of asymptomatic M. tuberculosis infection by applying computational and statistical approaches to multimodal data sets. Cytokines and anti-M. tuberculosis cell wall antibodies discriminated progressors vs controllers with chronic pulmonary TB but could not classify mice with asymptomatic infection. However, a novel deep-learning neural network trained on lung granuloma images was able to accurately classify asymptomatically infected lungs vs acute pulmonary TB in progressors vs chronic pulmonary TB in controllers, and discrimination was based on perivascular and peribronchiolar lymphocytes. Because the discriminatory lesion was rich in lymphocytes and CD4 T cell-mediated immunity is required for resistance, we expected CD4 T-cell genes would be elevated in asymptomatic infection. However, the significantly different, highly expressed genes were from B-cell pathways (e.g., Bank1, Cd19, Cd79, Fcmr, Ms4a1, Pax5, and H2-Ob), and CD20+ B cells were enriched in the perivascular and peribronchiolar regions of mice with asymptomatic M. tuberculosis infection. Together, these results indicate that genetically controlled B-cell responses are important for establishing asymptomatic M. tuberculosis lung infection.

CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice.

More humans have died of tuberculosis (TB) than any other infectious disease and millions still die each year. Experts advocate for blood-based, serum protein biomarkers to help diagnose TB, which afflicts millions of people in high-burden countries. However, the protein biomarker pipeline is small. Here, we used the Diversity Outbred (DO) mouse population to address this gap, identifying five protein biomarker candidates. One protein biomarker, serum CXCL1, met the World Health Organization's Targeted Product Profile for a triage test to diagnose active TB from latent M.tb infection (LTBI), non-TB lung disease, and normal sera in HIV-negative, adults from South Africa and Vietnam. To find the biomarker candidates, we quantified seven immune cytokines and four inflammatory proteins corresponding to highly expressed genes unique to progressor DO mice. Next, we applied statistical and machine learning methods to the data, i.e., 11 proteins in lungs from 453 infected and 29 non-infected mice. After searching all combinations of five algorithms and 239 protein subsets, validating, and testing the findings on independent data, two combinations accurately diagnosed progressor DO mice: Logistic Regression using MMP8; and Gradient Tree Boosting using a panel of 4: CXCL1, CXCL2, TNF, IL-10. Of those five protein biomarker candidates, two (MMP8 and CXCL1) were crucial for classifying DO mice; were above the limit of detection in most human serum samples; and had not been widely assessed for diagnostic performance in humans before. In patient sera, CXCL1 exceeded the triage diagnostic test criteria (>90% sensitivity; >70% specificity), while MMP8 did not. Using Area Under the Curve analyses, CXCL1 averaged 94.5% sensitivity and 88.8% specificity for active pulmonary TB (ATB) vs LTBI; 90.9% sensitivity and 71.4% specificity for ATB vs non-TB; and 100.0% sensitivity and 98.4% specificity for ATB vs normal sera. Our findings overall show that the DO mouse population can discover diagnostic-quality, serum protein biomarkers of human TB.

Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning.

BACKGROUND: Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversity Outbred (DO) mice reflect human-like genetic diversity and develop human-like lung granulomas when infected with Mycobacterium tuberculosis (M.tb) . METHODS: Following M.tb infection, a "supersusceptible" phenotype develops in approximately one-third of DO mice characterized by rapid morbidity and mortality within 8 weeks. These supersusceptible DO mice develop lung granulomas patterns akin to humans. This led us to utilize deep learning to identify supersusceptibility from hematoxylin & eosin (H&E) lung tissue sections utilizing only clinical outcomes (supersusceptible or not-supersusceptible) as labels. FINDINGS: The proposed machine learning model diagnosed supersusceptibility with high accuracy (91.50 ± 4.68%) compared to two expert pathologists using H&E stained lung sections (94.95% and 94.58%). Two non-experts used the imaging biomarker to diagnose supersusceptibility with high accuracy (88.25% and 87.95%) and agreement (96.00%). A board-certified veterinary pathologist (GB) examined the imaging biomarker and determined the model was making diagnostic decisions using a form of granuloma necrosis (karyorrhectic and pyknotic nuclear debris). This was corroborated by one other board-certified veterinary pathologist. Finally, the imaging biomarker was quantified, providing a novel means to convert visual patterns within granulomas to data suitable for statistical analyses. IMPLICATIONS: Overall, our results have translatable implication to improve our understanding of TB and also to the broader field of computational pathology in which clinical outcomes alone can drive automatic identification of interpretable imaging biomarkers, knowledge discovery, and validation of existing clinical biomarkers. FUNDING: National Institutes of Health and American Lung Association.

Doxorubicin-Induced Cardiotoxicity in Collaborative Cross (CC) Mice Recapitulates Individual Cardiotoxicity in Humans.

Anthracyclines cause progressive cardiotoxicity whose ultimate severity is individual to the patient. Genetic determinants contributing to this variation are difficult to study using current mouse models. Our objective was to determine whether a spectrum of anthracycline induced cardiac disease can be elicited across 10 Collaborative Cross mouse strains given the same dose of doxorubicin. Mice from ten distinct strains were given 5 mg/kg of doxorubicin intravenously once weekly for 5 weeks (total 25 mg/kg). Mice were killed at acute or chronic timepoints. Body weight was assessed weekly, followed by terminal complete blood count, pathology and a panel of biomarkers. Linear models were fit to assess effects of treatment, sex, and sex-by-treatment interactions for each timepoint. Impaired growth and cardiac pathology occurred across all strains. Severity of these varied by strain and sex, with greater severity in males. Cardiac troponin I and myosin light chain 3 demonstrated strain- and sex-specific elevations in the acute phase with subsequent decline despite ongoing progression of cardiac disease. Acute phase cardiac troponin I levels predicted the ultimate severity of cardiac pathology poorly, whereas myosin light chain 3 levels predicted the extent of chronic cardiac injury in males. Strain- and sex-dependent renal toxicity was evident. Regenerative anemia manifested during the acute period. We confirm that variable susceptibility to doxorubicin-induced cardiotoxicity observed in humans can be modeled in a panel of CC strains. In addition, we identified a potential predictive biomarker in males. CC strains provide reproducible models to explore mechanisms contributing to individual susceptibility in humans.

Mouse genome-wide association and systems genetics identifies Lhfp as a regulator of bone mass.

Bone mineral density (BMD) is a strong predictor of osteoporotic fracture. It is also one of the most heritable disease-associated quantitative traits. As a result, there has been considerable effort focused on dissecting its genetic basis. Here, we performed a genome-wide association study (GWAS) in a panel of inbred strains to identify associations influencing BMD. This analysis identified a significant (P = 3.1 x 10-12) BMD locus on Chromosome 3@52.5 Mbp that replicated in two separate inbred strain panels and overlapped a BMD quantitative trait locus (QTL) previously identified in a F2 intercross. The association mapped to a 300 Kbp region containing four genes; Gm2447, Gm20750, Cog6, and Lhfp. Further analysis found that Lipoma HMGIC Fusion Partner (Lhfp) was highly expressed in bone and osteoblasts. Furthermore, its expression was regulated by a local expression QTL (eQTL), which overlapped the BMD association. A co-expression network analysis revealed that Lhfp was strongly connected to genes involved in osteoblast differentiation. To directly evaluate its role in bone, Lhfp deficient mice (Lhfp-/-) were created using CRISPR/Cas9. Consistent with genetic and network predictions, bone marrow stromal cells (BMSCs) from Lhfp-/- mice displayed increased osteogenic differentiation. Lhfp-/- mice also had elevated BMD due to increased cortical bone mass. Lastly, we identified SNPs in human LHFP that were associated (P = 1.2 x 10-5) with heel BMD. In conclusion, we used GWAS and systems genetics to identify Lhfp as a regulator of osteoblast activity and bone mass.

Identification of a novel microRNA profile in pediatric patients with cancer treated with anthracycline chemotherapy.

Anthracycline chemotherapy (AC) is associated with decline in left ventricular ejection fraction (LVEF), yet the mechanisms remain unclear. Although changes in microRNAs (miRs) have been identified in adult cardiovascular disease, miR profiles in pediatric patients with AC have not been well studied. The goal of this study was to examine miR profiles (unbiased array) in pediatric patients with AC compared with age-matched referent normal patients. We hypothesize that pediatric patients with AC will express a unique miR profile at the initiation and completion of therapy and will be related to LVEF. Serum was collected in pediatric patients (10-22 yr, n = 12) with newly diagnosed malignancy requiring AC within 24-48 h after the initiation of therapy (30-60 mg/m2) and ~1 yr after completing therapy. A custom microarray of 84 miRs associated with cardiovascular disease was used (quantitative RT-PCR) and indexed to referent normal profiles (13-17 yr, n = 17). LVEF was computed by cardiac MRI. LVEF fell from AC initiation at ~1 yr after AC completion (64.28 ± 1.78% vs. 57.53 ± 0.95%, respectively, P = 0.004). Of the 84 miRs profiled, significant shifts in 17 miRs occurred relative to referent normal ( P ≤ 0.05). Moreover, the functional domain of miRs associated with myocardial differentiation and development fell over threefold at the completion of AC ( P ≤ 0.05). Moreover, eight miRs were significantly downregulated after AC completion in those patients with the greatest decline in LVEF (≥10%, P < 0.05). This study demonstrates, for the first time, that changes in miR expression occur in pediatric patients with AC. These findings suggest that miRs are a potential strategy for the early identification of patients with AC susceptible to left ventricular dysfunction. NEW & NOTEWORTHY Although anthracycline chemotherapy (AC) is effective for a number of pediatric cancers, an all too often consequence of AC is the development of left ventricular failure. The present study identified that specific shifts in the pattern of microRNAs, which regulate myocardial growth, function, and viability, occurred during and after AC in pediatric patients, whereby the magnitude of this shift was associated with the degree of left ventricular failure.

Erratum: "Diversity Outbred Mice Identify Population-Based Exposure Thresholds and Genetic Factors that Influence Benzene-Induced Genotoxicity".

[This corrects the article DOI: http://dx.doi.org/10.1289/ehp.1408202.].

Use of integrated imaging and serum biomarker profiles to identify subclinical dysfunction in pediatric cancer patients treated with anthracyclines.

BACKGROUND: Anthracycline induced cardiomyopathy is a major cause of mortality and morbidity among pediatric cancer survivors. It has been postulated that oxidative stress induction and inflammation may play a role in the pathogenesis of this process. Accordingly, the present study performed an assessment of biomarker profiles and functional imaging parameters focused upon potential early determinants of anthracycline induced cardiomyopathy. METHODS: Patients (10-22 years) were prospectively enrolled between January 2013 and November 2014. Thirteen subjects completed the study and underwent serial cardiac magnetic resonance imaging and plasma biomarker profiling performed 24-48 h after the first anthracycline dose and at set dose intervals. In addition, we collected plasma samples from 62 healthy controls to examine normal plasma biomarker profiles. RESULTS: Left ventricular ejection fraction (LVEF) decreased from 64.3 ± 6.2 at the first visit to 57.5 ± 3.3 (p = 0.004) 1 year after chemotherapy. A decline in longitudinal strain magnitude occurred at lower cumulative doses. A differential inflammatory/matrix signature emerged in anthracycline induced cardiomyopathy patients compared to normal including increased interleukin-8 and MMP levels. With longer periods of anthracycline dosing, MMP-7, a marker of macrophage proteolytic activation, increased by 165 ± 54% whereas interleukin-10 an anti-inflammatory marker decreased by 75 ± 13% (both p < 0.05). MMP7 correlated with time dependent changes in EF. CONCLUSIONS: Asymptomatic pediatric patients exposed to anthracycline therapy develop abnormal strain parameters at lower cumulative doses when compared to changes in EF. A differential biomarker signature containing both inflammatory and matrix domains occur early in anthracycline treatment. Dynamic changes in these domains occur with increased anthracycline doses and progression to anthracycline induced cardiomyopathy. These findings provide potential prognostic and mechanistic insights into the natural history of anthracycline induced cardiomyopathy. TRIAL REGISTRATION NUMBER: NCT03211520 Date of Registration February 13, 2017, retrospectively registered.

CloudNeo: a cloud pipeline for identifying patient-specific tumor neoantigens.

SUMMARY: We present CloudNeo, a cloud-based computational workflow for identifying patient-specific tumor neoantigens from next generation sequencing data. Tumor-specific mutant peptides can be detected by the immune system through their interactions with the human leukocyte antigen complex, and neoantigen presence has recently been shown to correlate with anti T-cell immunity and efficacy of checkpoint inhibitor therapy. However computing capabilities to identify neoantigens from genomic sequencing data are a limiting factor for understanding their role. This challenge has grown as cancer datasets become increasingly abundant, making them cumbersome to store and analyze on local servers. Our cloud-based pipeline provides scalable computation capabilities for neoantigen identification while eliminating the need to invest in local infrastructure for data transfer, storage or compute. The pipeline is a Common Workflow Language (CWL) implementation of human leukocyte antigen (HLA) typing using Polysolver or HLAminer combined with custom scripts for mutant peptide identification and NetMHCpan for neoantigen prediction. We have demonstrated the efficacy of these pipelines on Amazon cloud instances through the Seven Bridges Genomics implementation of the NCI Cancer Genomics Cloud, which provides graphical interfaces for running and editing, infrastructure for workflow sharing and version tracking, and access to TCGA data. AVAILABILITY AND IMPLEMENTATION: The CWL implementation is at: https://github.com/TheJacksonLaboratory/CloudNeo. For users who have obtained licenses for all internal software, integrated versions in CWL and on the Seven Bridges Cancer Genomics Cloud platform (https://cgc.sbgenomics.com/, recommended version) can be obtained by contacting the authors. CONTACT: jeff.chuang@jax.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mapping Complex Traits in a Diversity Outbred F1 Mouse Population Identifies Germline Modifiers of Metastasis in Human Prostate Cancer.

It is unclear how standing genetic variation affects the prognosis of prostate cancer patients. To provide one controlled answer to this problem, we crossed a dominant, penetrant mouse model of prostate cancer to Diversity Outbred mice, a collection of animals that carries over 40 million SNPs. Integration of disease phenotype and SNP variation data in 493 F1 males identified a metastasis modifier locus on Chromosome 8 (LOD = 8.42); further analysis identified the genes Rwdd4, Cenpu, and Casp3 as functional effectors of this locus. Accordingly, analysis of over 5,300 prostate cancer patient samples revealed correlations between the presence of genetic variants at these loci, their expression levels, cancer aggressiveness, and patient survival. We also observed that ectopic overexpression of RWDD4 and CENPU increased the aggressiveness of two human prostate cancer cell lines. In aggregate, our approach demonstrates how well-characterized genetic variation in mice can be harnessed in conjunction with systems genetics approaches to identify and characterize germline modifiers of human disease processes.

Genetic Architectures of Quantitative Variation in RNA Editing Pathways.

RNA editing refers to post-transcriptional processes that alter the base sequence of RNA. Recently, hundreds of new RNA editing targets have been reported. However, the mechanisms that determine the specificity and degree of editing are not well understood. We examined quantitative variation of site-specific editing in a genetically diverse multiparent population, Diversity Outbred mice, and mapped polymorphic loci that alter editing ratios globally for C-to-U editing and at specific sites for A-to-I editing. An allelic series in the C-to-U editing enzyme Apobec1 influences the editing efficiency of Apob and 58 additional C-to-U editing targets. We identified 49 A-to-I editing sites with polymorphisms in the edited transcript that alter editing efficiency. In contrast to the shared genetic control of C-to-U editing, most of the variable A-to-I editing sites were determined by local nucleotide polymorphisms in proximity to the editing site in the RNA secondary structure. Our results indicate that RNA editing is a quantitative trait subject to genetic variation and that evolutionary constraints have given rise to distinct genetic architectures in the two canonical types of RNA editing.

Lung necrosis and neutrophils reflect common pathways of susceptibility to Mycobacterium tuberculosis in genetically diverse, immune-competent mice.

Pulmonary tuberculosis (TB) is caused by Mycobacterium tuberculosis in susceptible humans. Here, we infected Diversity Outbred (DO) mice with ∼100 bacilli by aerosol to model responses in a highly heterogeneous population. Following infection, 'supersusceptible', 'susceptible' and 'resistant' phenotypes emerged. TB disease (reduced survival, weight loss, high bacterial load) correlated strongly with neutrophils, neutrophil chemokines, tumor necrosis factor (TNF) and cell death. By contrast, immune cytokines were weak correlates of disease. We next applied statistical and machine learning approaches to our dataset of cytokines and chemokines from lungs and blood. Six molecules from the lung: TNF, CXCL1, CXCL2, CXCL5, interferon-γ (IFN-γ), interleukin 12 (IL-12); and two molecules from blood - IL-2 and TNF - were identified as being important by applying both statistical and machine learning methods. Using molecular features to generate tree classifiers, CXCL1, CXCL2 and CXCL5 distinguished four classes (supersusceptible, susceptible, resistant and non-infected) from each other with approximately 77% accuracy using completely independent experimental data. By contrast, models based on other molecules were less accurate. Low to no IFN-γ, IL-12, IL-2 and IL-10 successfully discriminated non-infected mice from infected mice but failed to discriminate disease status amongst supersusceptible, susceptible and resistant M.-tuberculosis-infected DO mice. Additional analyses identified CXCL1 as a promising peripheral biomarker of disease and of CXCL1 production in the lungs. From these results, we conclude that: (1) DO mice respond variably to M. tuberculosis infection and will be useful to identify pathways involving necrosis and neutrophils; (2) data from DO mice is suited for machine learning methods to build, validate and test models with independent data based solely on molecular biomarkers; (3) low levels of immunological cytokines best indicate a lack of exposure to M. tuberculosis but cannot distinguish infection from disease.

Sensitivity to hepatotoxicity due to epigallocatechin gallate is affected by genetic background in diversity outbred mice.

Consumer use of herbal and dietary supplements has recently grown in the United States and, with increased use, reports of rare adverse reactions have emerged. One such supplement is green tea extract, containing the polyphenol epigallocatechin gallate (EGCG), which has been shown to be hepatotoxic at high doses in animal models. The Drug-Induced Liver Injury Network has identified multiple patients who have experienced liver injury ascribed to green tea extract consumption and the relationship to dose has not been straightforward, indicating that differences in sensitivity may contribute to the adverse response in susceptible people. The Diversity Outbred (DO), a genetically heterogeneous mouse population, provides a potential platform for study of interindividual toxicity responses to green tea extract. Within the DO population, an equal exposure to EGCG (50 mg/kg; daily for three days) was found to be tolerated in the majority of mice; however, a small fraction of the animals (16%; 43/272) exhibited severe hepatotoxicity (10-86.8% liver necrosis) that is analogous to the clinical cases. The data indicate that the DO mice may provide a platform for informing risk of rare, adverse reactions that may occur in consumer populations upon ingestion of concentrated herbal products.

Diversity Outbred Mice Identify Population-Based Exposure Thresholds and Genetic Factors that Influence Benzene-Induced Genotoxicity.

BACKGROUND: Inhalation of benzene at levels below the current exposure limit values leads to hematotoxicity in occupationally exposed workers. OBJECTIVE: We sought to evaluate Diversity Outbred (DO) mice as a tool for exposure threshold assessment and to identify genetic factors that influence benzene-induced genotoxicity. METHODS: We exposed male DO mice to benzene (0, 1, 10, or 100 ppm; 75 mice/exposure group) via inhalation for 28 days (6 hr/day for 5 days/week). The study was repeated using two independent cohorts of 300 animals each. We measured micronuclei frequency in reticulocytes from peripheral blood and bone marrow and applied benchmark concentration modeling to estimate exposure thresholds. We genotyped the mice and performed linkage analysis. RESULTS: We observed a dose-dependent increase in benzene-induced chromosomal damage and estimated a benchmark concentration limit of 0.205 ppm benzene using DO mice. This estimate is an order of magnitude below the value estimated using B6C3F1 mice. We identified a locus on Chr 10 (31.87 Mb) that contained a pair of overexpressed sulfotransferases that were inversely correlated with genotoxicity. CONCLUSIONS: The genetically diverse DO mice provided a reproducible response to benzene exposure. The DO mice display interindividual variation in toxicity response and, as such, may more accurately reflect the range of response that is observed in human populations. Studies using DO mice can localize genetic associations with high precision. The identification of sulfotransferases as candidate genes suggests that DO mice may provide additional insight into benzene-induced genotoxicity.

High-resolution genetic mapping in the diversity outbred mouse population identifies Apobec1 as a candidate gene for atherosclerosis.

Inbred mice exhibit strain-specific variation in susceptibility to atherosclerosis and dyslipidemia that renders them useful in dissecting the genetic architecture of these complex diseases. Traditional quantitative trait locus (QTL) mapping studies using inbred strains often identify large genomic regions, containing many genes, due to limited recombination and/or sample size. This hampers candidate gene identification and translation of these results into possible risk factors and therapeutic targets. An alternative approach is the use of multiparental outbred lines for genetic mapping, such as the Diversity Outbred (DO) mouse panel, which can be more informative than traditional two-parent crosses and can aid in the identification of causal genes and variants associated with QTL. We fed 292 female DO mice either a high-fat, cholesterol-containing (HFCA) diet, to induce atherosclerosis, or a low-fat, high-protein diet for 18 wk and measured plasma lipid levels before and after diet treatment. We measured markers of atherosclerosis in the mice fed the HFCA diet. The mice were genotyped on a medium-density single-nucleotide polymorphism array and founder haplotypes were reconstructed using a hidden Markov model. The reconstructed haplotypes were then used to perform linkage mapping of atherosclerotic lesion size as well as plasma total cholesterol, triglycerides, insulin, and glucose. Among our highly significant QTL we detected a ~100 kb QTL interval for atherosclerosis on Chromosome 6, as well as a 1.4 Mb QTL interval on Chromosome 9 for triglyceride levels at baseline and a coincident 22.2 Mb QTL interval on Chromosome 9 for total cholesterol after dietary treatment. One candidate gene within the Chromosome 6 peak region associated with atherosclerosis is Apobec1, the apolipoprotein B (ApoB) mRNA-editing enzyme, which plays a role in the regulation of ApoB, a critical component of low-density lipoprotein, by editing ApoB mRNA. This study demonstrates the value of the DO population to improve mapping resolution and to aid in the identification of potential therapeutic targets for cardiovascular disease. Using a DO mouse population fed an HFCA diet, we were able to identify an A/J-specific isoform of Apobec1 that contributes to atherosclerosis.

Time-course comparison of xenobiotic activators of CAR and PPARalpha in mouse liver.

Constitutive androstane receptor (CAR) and peroxisome proliferator activated receptor (PPAR)alpha are transcription factors known to be primary mediators of liver effects, including carcinogenesis, by phenobarbital-like compounds and peroxisome proliferators, respectively, in rodents. Many similarities exist in the phenotypes elicited by these two classes of agents in rodent liver, and we hypothesized that the initial transcriptional responses to the xenobiotic activators of CAR and PPARalpha will exhibit distinct patterns, but at later time-points these biological pathways will converge. In order to capture the global transcriptional changes that result from activation of these nuclear receptors over a time-course in the mouse liver, microarray technology was used. First, differences in basal expression of liver genes between C57Bl/6J wild-type and Car-null mice were examined and 14 significantly differentially expressed genes were identified. Next, mice were treated with phenobarbital (100 mg/kg by gavage for 24 h, or 0.085% w/w diet for 7 or 28 days), and liver gene expression changes with regards to both time and treatment were identified. While several pathways related to cellular proliferation and metabolism were affected by phenobarbital in wild-type mice, no significant changes in gene expression were found over time in the Car-nulls. Next, we determined commonalities and differences in the temporal response to phenobarbital and WY-14,643, a prototypical activator of PPAR alpha. Gene expression signatures from livers of wild-type mice C57Bl6/J mice treated with PB or WY-14,643 were compared. Similar pathways were affected by both compounds; however, considerable time-related differences were present. This study establishes common gene expression fingerprints of exposure to activators of CAR and PPARalpha in rodent liver and demonstrates that despite similar phenotypic changes, molecular pathways differ between classes of chemical carcinogens.