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

Winter, Jean M.; Gildea, Derek; Andreas, Jonathan; Gatti, Daniel M.; Williams, Kendra A.; Lee, Minnkyong; Hu, Ying; Zhang, Suiyuan; Mullikin, James C.; Wolfsberg, Tyra G.; McDonnell, Shannon K.; Fogarty, Zachary C.; Larson, Melissa C.; French, Amy J.; Schaid, Daniel J.; Thibodeau, Stephen N.; Churchill, Gary A.; Crawford, Nigel P.S.

doi:10.1016/j.cels.2016.10.018

Cited by 46 publications

(39 citation statements)

References 56 publications

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“…The structured genetic diversity in the DO allows for the association of founder haplotypes with the trait of interest, which can then be connected with the DO founder strain’s genome. The DO has been used to identify quantitative trait loci (QTL) for a wide variety of traits, including behavior, prostate cancer, cocaine self-administration, pain sensitivity, and atherosclerosis (Logan et al 2013; Recla et al 2014; Smallwood et al 2014; Dickson et al 2015; Tyler et al 2017; Winter et al 2016). The structured genetic diversity, and previous mapping successes make the DO research population ideal for exploring the genetic architecture of complex health-related traits such as heart size.…”

Section: Introductionmentioning

confidence: 99%

Quantitative trait mapping in Diversity Outbred mice identifies two genomic regions associated with heart size

et al. 2017

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Heart size is an important factor in cardiac health and disease. In particular, increased heart weight is predictive of adverse cardiovascular outcomes in multiple large community-based studies. We use two cohorts of Diversity Outbred (DO) mice to investigate the role of genetics, sex, age, and diet on heart size. DO mice (n = 289) of both sexes from generation 10 were fed a standard chow diet, and analyzed at 12-15 weeks of age. Another cohort of female DO mice (n = 258) from generation 11 were fed either a high-fat, cholesterol-containing (HFC) diet or a low-fat, high-protein diet, and analyzed at 24-25 weeks. We did not observe an effect of diet on body or heart weight in generation 11 mice, although we previously reported an effect on other cardiovascular risk factors, including cholesterol, triglycerides, and insulin. We do observe a significant genetic effect on heart weight in this population. We identified two quantitative trait loci for heart weight, one (Hwtf1) at a genome-wide significance level of p ≤ 0.05 on MMU15 and one (Hwtf2) at a genome-wide suggestive level of p ≤ 0.1 on MMU10, that together explain 13.3% of the phenotypic variance. Hwtf1 contained collagen type XXII alpha 1 chain (Col22a1), and the NZO/HlLtJ and WSB/EiJ haplotypes were associated with larger hearts. This is consistent with heart tissue Col22a1 expression in DO founders and SNP patterns within Hwtf1 for Col22a1. Col22a1 has been previously associated with cardiac fibrosis in mice, suggesting that Col22a1 may be involved in pathological cardiac hypertrophy.

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Section: Introductionmentioning

confidence: 99%

Quantitative trait mapping in Diversity Outbred mice identifies two genomic regions associated with heart size

et al. 2017

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“…Hunter and colleagues showed nuclear quantitative trait loci that affected the efficiency of mammary carcinoma metastasis (9–12), establishing that an individual's underlying genetic background could affect metastasis efficiency. They have since identified several metastasis efficiency modifier loci within the nuclear genome for breast and prostate cancers (13–15). …”

Section: Introductionmentioning

confidence: 99%

“…Several studies have described changes in transgene behavior on different mouse strains (4, 15, 30). Nuclear modifiers appear to be combination-dependent, i.e., nuclear modifiers are driver-dependent or vice versa .…”

Section: Introductionmentioning

confidence: 99%

Mitochondrial Haplotype Alters Mammary Cancer Tumorigenicity and Metastasis in an Oncogenic Driver–Dependent Manner

et al. 2017

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Using a novel mouse model, a mitochondrial-nuclear exchange model termed MNX, we tested the hypothesis that inherited mitochondrial haplotypes alter primary tumor latency and metastatic efficiency. Male FVB/N-Tg(MMTVneu)202Mul/J (Her2) transgenic mice were bred to female MNX mice having FVB/NJ nuclear DNA with either FVB/NJ, C57BL/6J, or BALB/cJ mtDNA. Pups receiving the C57BL/6J or BALB/cJ mitochondrial genome (i.e., females crossed with Her2 males) showed significantly (P < 0.001) longer tumor latency (262 vs. 293 vs. 225 days), fewer pulmonary metastases (5 vs. 7 vs. 15), and differences in size of lung metastases (1.2 vs. 1.4 vs. 1.0 mm diameter) compared with FVB/NJ mtDNA. Although polyoma virus middle T–driven tumors showed altered primary and metastatic profiles in previous studies, depending upon nuclear and mtDNA haplotype, the magnitude and direction of changes were not the same in the HER2-driven mammary carcinomas. Collectively, these results establish mitochondrial polymorphisms as quantitative trait loci in mammary carcinogenesis, and they implicate distinct interactions between tumor drivers and mitochondria as critical modifiers of tumorigenicity and metastasis.

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“…RWDD4 (RWD domain-containing protein 4) is a member of the RWD domain protein superfamily DEGs differentially expressed genes [33]. RWDD4 contains a RWD domain that is involved in protein-protein interactions [34]. The knockdown of RWDD4 inhibits transitional cell carcinoma (TCC), cell proliferation, migration, and invasion [35].…”

Section: Discussionmentioning

confidence: 99%

Identification of key differentially expressed genes and gene mutations in breast ductal carcinoma in situ using RNA-seq analysis

Zhu

et al. 2020

World J Surg Onc

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Background: The aim of this study was to identify the key differentially expressed genes (DEGs) and high-risk gene mutations in breast ductal carcinoma in situ (DCIS). Methods: Raw data (GSE36863) were downloaded from the database of Gene Expression Omnibus (GEO), including three DCIS samples (DCIS cell lines MCF10.DCIS, Sum102, and Sum225) and one normal control sample (normal mammary epithelial cell line MCF10A). The DEGs were analyzed using NOIseq and annotated via DAVID. Motif scanning in the promoter region of DEGs was performed via SeqPos. Additionally, single nucleotide variations (SNVs) were identified via GenomeAnalysisTK and SNV risk was assessed via VarioWatch. Mutant genes with a high frequency and risk were validated by RT-PCR analyses. Results: Finally, 5391, 7073, and 7944 DEGs were identified in DCIS, Sum102, and Sum22 cell lines, respectively, when compared with MCF10A. VENN analysis of the three cell lines revealed 603 upregulated and 1043 downregulated DEGs, including 16 upregulated and 36 downregulated transcription factor (TF) genes. In addition, six TFs each (e.g., E2F1 and CREB1) were found to regulate the core up-and downregulated DEGs, respectively. Furthermore, SNV detection results revealed 1104 (MCF10.DCIS), 2833 (Sum102), and 1132 (Sum22) mutation sites. Four mutant genes (RWDD4, SDHC, SEPT7, and SFN) with high frequency and risk were identified. The results of RT-PCR analysis as well as bioinformatics analysis consistently demonstrated that the expression of RWDD4, SDHC, SEPT7, and SFN was downregulated in the tumor tissues as compared with that in adjacent non-tumor tissues. Conclusions: The differentially expressed TFs, TFs regulating DEGs (e.g., E2F1 and CREB1), and high-frequency mutant genes (RWDD4, SDHC, SEPT7, and SFN) might play key roles in the pathogenesis of DCIS.

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

Cited by 46 publications

References 56 publications

Quantitative trait mapping in Diversity Outbred mice identifies two genomic regions associated with heart size

Quantitative trait mapping in Diversity Outbred mice identifies two genomic regions associated with heart size

Mitochondrial Haplotype Alters Mammary Cancer Tumorigenicity and Metastasis in an Oncogenic Driver–Dependent Manner

Identification of key differentially expressed genes and gene mutations in breast ductal carcinoma in situ using RNA-seq analysis

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