Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Apr;28(3-4):90-99.
doi: 10.1007/s00335-016-9677-0. Epub 2017 Jan 23.

Genetic analysis of a mouse cross implicates an anti-inflammatory gene in control of atherosclerosis susceptibility

Affiliations

Genetic analysis of a mouse cross implicates an anti-inflammatory gene in control of atherosclerosis susceptibility

Norman E Garrett 3rd et al. Mamm Genome. 2017 Apr.

Abstract

Nearly all genetic crosses generated from Apoe-/- or Lldlr-/- mice for genetic analysis of atherosclerosis have used C57BL/6 J (B6) mice as one parental strain, thus limiting their mapping power and coverage of allelic diversity. SM/J-Apoe -/- and BALB/cJ-Apoe -/- mice differ significantly in atherosclerosis susceptibility. 224 male F2 mice were generated from the two Apoe -/- strains to perform quantitative trait locus (QTL) analysis of atherosclerosis. F2 mice were fed 5 weeks of Western diet and analyzed for atherosclerotic lesions in the aortic root. Genome-wide scans with 144 informative SNP markers identified a significant locus near 20.2 Mb on chromosome 10 (LOD score: 6.03), named Ath48, and a suggestive locus near 49.5 Mb on chromosome 9 (LOD: 2.29; Ath29) affecting atherosclerotic lesion sizes. Using bioinformatics tools, we prioritized 12 candidate genes for Ath48. Of them, Tnfaip3, an anti-inflammatory gene, is located precisely underneath the linkage peak and contains two non-synonymous SNPs leading to conservative amino acid substitutions. Thus, this study demonstrates the power of forward genetics involving the use of a different susceptible strain and bioinformatics tools in finding atherosclerosis susceptibility genes.

PubMed Disclaimer

Conflict of interest statement

Conflict of interest

The authors declare no conflicting interests.

Figures

Figure 1
Figure 1
Frequency distributions of atherosclerotic lesion areas in male F2 mice derived from BALB-Apoe−/− and SM-Apoe−/− mice. Left panel: the distribution of untransformed atherosclerotic lesion areas; right panel: the distribution of square-root transformed atherosclerotic lesion areas. Graphs were created using a plotting function of J/qtl software.
Figure 1
Figure 1
Frequency distributions of atherosclerotic lesion areas in male F2 mice derived from BALB-Apoe−/− and SM-Apoe−/− mice. Left panel: the distribution of untransformed atherosclerotic lesion areas; right panel: the distribution of square-root transformed atherosclerotic lesion areas. Graphs were created using a plotting function of J/qtl software.
Figure 2
Figure 2
Genome-wide scans to search for main effect QTLs influencing atherosclerotic lesion areas in male F2 mice. Chromosomes 1 through 20 are represented numerically on the X-axis. Each minor tick on the X axis represents one informative SNP. The Y-axis represents the LOD score. Two horizontal dashed lines denote genome-wide thresholds for suggestive (P=0.63) and significant (P=0.05) linkage. The upper panel (A) shows a genome-wide scan for atherosclerotic lesion areas using the non-parametric mode, and the lower panel (B) shows a genome-wide scan for atherosclerotic lesion areas using the parametric mode. Graphs were created by J/qtl
Figure 2
Figure 2
Genome-wide scans to search for main effect QTLs influencing atherosclerotic lesion areas in male F2 mice. Chromosomes 1 through 20 are represented numerically on the X-axis. Each minor tick on the X axis represents one informative SNP. The Y-axis represents the LOD score. Two horizontal dashed lines denote genome-wide thresholds for suggestive (P=0.63) and significant (P=0.05) linkage. The upper panel (A) shows a genome-wide scan for atherosclerotic lesion areas using the non-parametric mode, and the lower panel (B) shows a genome-wide scan for atherosclerotic lesion areas using the parametric mode. Graphs were created by J/qtl
Figure 3
Figure 3
Interval mapping graphs of chromosomes 10 (left panel) and 9 (right panel) for atherosclerotic lesion areas created by MapManager QTX. The black plot represents LOD scores calculated at 1-cM intervals, the red plot denotes the effect of BALB alleles, and the blue plot represents the effect of SM alleles. The histogram in the plot estimates the confidence interval for a QTL. Two green vertical lines denote genome-wide significance thresholds for suggestive or significant linkage (P=0.63 and P=0.05, respectively).
Figure 3
Figure 3
Interval mapping graphs of chromosomes 10 (left panel) and 9 (right panel) for atherosclerotic lesion areas created by MapManager QTX. The black plot represents LOD scores calculated at 1-cM intervals, the red plot denotes the effect of BALB alleles, and the blue plot represents the effect of SM alleles. The histogram in the plot estimates the confidence interval for a QTL. Two green vertical lines denote genome-wide significance thresholds for suggestive or significant linkage (P=0.63 and P=0.05, respectively).
Figure 4
Figure 4
Comparison of selected Tnfaip3 protein sequences in three mammal species, including rat, human, B6 and BALB mice. Amino acid residues that are different among the species are highlighted.
Figure 5
Figure 5
3D structure of Tnfaip3 protein for B6 (left panel) and BALB (right panel) mice predicted by RaptorX software. The predicted 3D structure is noticeably different between the two mouse strains.
Figure 5
Figure 5
3D structure of Tnfaip3 protein for B6 (left panel) and BALB (right panel) mice predicted by RaptorX software. The predicted 3D structure is noticeably different between the two mouse strains.
Figure 6
Figure 6
Real-time analyses of Tnfaip3 and Gapdh mRNA expression in the liver of B6 and BALB Apoe−/− mice fed a Western diet. Results were expressed as a ratio of Tnfaip3 to Gapdh in real-time PCR cycle threshold (Ct) values of 4 individual mice per strain. * P < 0.05.
Figure 7
Figure 7
Correlations of atherosclerotic lesion sizes with plasma levels of HDL, non-HDL cholesterol, triglyceride, and glucose in the F2 mice. Each point represents values of an individual F2 mouse. The correlation coefficient (R) and significance (P) are shown.

Similar articles

Cited by

References

    1. Bachmann JM, Willis BL, Ayers CR, Khera A, Berry JD. Association between family history and coronary heart disease death across long-term follow-up in men: the Cooper Center Longitudinal Study. Circulation. 2012;125:3092–3098. - PMC - PubMed
    1. CARDIoGRAMplusC4D Consortium. Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, Ingelsson E, Saleheen D, Erdmann J, Goldstein BA, Stirrups K, Konig IR, Cazier JB, Johansson A, Hall AS, Lee JY, Willer CJ, Chambers JC, Esko T, Folkersen L, Goel A, Grundberg E, Havulinna AS, Ho WK, Hopewell JC, Eriksson N, Kleber ME, Kristiansson K, Lundmark P, Lyytikainen LP, Rafelt S, Shungin D, Strawbridge RJ, Thorleifsson G, Tikkanen E, Van Zuydam N, Voight BF, Waite LL, Zhang W, Ziegler A, Absher D, Altshuler D, Balmforth AJ, Barroso I, Braund PS, Burgdorf C, Claudi-Boehm S, Cox D, Dimitriou M, Do R, DIAGRAM Consortium CARDIOGENICS Consortium Doney AS, El Mokhtari N, Eriksson P, Fischer K, Fontanillas P, Franco-Cereceda A, Gigante B, Groop L, Gustafsson S, Hager J, Hallmans G, Han BG, Hunt SE, Kang HM, Illig T, Kessler T, Knowles JW, Kolovou G, Kuusisto J, Langenberg C, Langford C, Leander K, Lokki ML, Lundmark A, McCarthy MI, Meisinger C, Melander O, Mihailov E, Maouche S, Morris AD, Muller-Nurasyid M, MuTHER Consortium Nikus K, Peden JF, Rayner NW, Rasheed A, Rosinger S, Rubin D, Rumpf MP, Schafer A, Sivananthan M, Song C, Stewart AF, Tan ST, Thorgeirsson G, van der Schoot CE, Wagner PJ, Wellcome Trust Case Control Consortium Wells GA, Wild PS, Yang TP, Amouyel P, Arveiler D, Basart H, Boehnke M, Boerwinkle E, Brambilla P, Cambien F, Cupples AL, de Faire U, Dehghan A, Diemert P, Epstein SE, Evans A, Ferrario MM, Ferrieres J, Gauguier D, Go AS, Goodall AH, Gudnason V, Hazen SL, Holm H, Iribarren C, Jang Y, Kahonen M, Kee F, Kim HS, Klopp N, Koenig W, Kratzer W, Kuulasmaa K, Laakso M, Laaksonen R, Lee JY, Lind L, Ouwehand WH, Parish S, Park JE, Pedersen NL, Peters A, Quertermous T, Rader DJ, Salomaa V, Schadt E, Shah SH, Sinisalo J, Stark K, Stefansson K, Tregouet DA, Virtamo J, Wallentin L, Wareham N, Zimmermann ME, Nieminen MS, Hengstenberg C, Sandhu MS, Pastinen T, Syvanen AC, Hovingh GK, Dedoussis G, Franks PW, Lehtimaki T, Metspalu A, Zalloua PA, Siegbahn A, Schreiber S, Ripatti S, Blankenberg SS, Perola M, Clarke R, Boehm BO, O’Donnell C, Reilly MP, Marz W, Collins R, Kathiresan S, Hamsten A, Kooner JS, Thorsteinsdottir U, Danesh J, Palmer CN, Roberts R, Watkins H, Schunkert H, Samani NJ. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45:25–33. - PMC - PubMed
    1. Cooper JT, Stroka DM, Brostjan C, Palmetshofer A, Bach FH, Ferran C. A20 blocks endothelial cell activation through a NF-kappaB-dependent mechanism. J Biol Chem. 1996;271:18068–18073. - PubMed
    1. Dansky HM, Shu P, Donavan M, Montagno J, Nagle DL, Smutko JS, Roy N, Whiteing S, Barrios J, McBride TJ, Smith JD, Duyk G, Breslow JL, Moore KJ. A phenotype-sensitizing Apoe-deficient genetic background reveals novel atherosclerosis predisposition loci in the mouse. Genetics. 2002;160:1599–1608. - PMC - PubMed
    1. global Lipids Genetics Consortium Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Do R, Donnelly LA, Ehret GB, Esko T, Feitosa MF, Ferreira T, Fischer K, Fontanillas P, Fraser RM, Freitag DF, Gurdasani D, Heikkila K, Hypponen E, Isaacs A, Jackson AU, Johansson A, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikainen LP, Magnusson PK, Mangino M, Mihailov E, Montasser ME, Muller-Nurasyid M, Nolte IM, O’Connell JR, Palmer CD, Perola M, Petersen AK, Sanna S, Saxena R, Service SK, Shah S, Shungin D, Sidore C, Song C, Strawbridge RJ, Surakka I, Tanaka T, Teslovich TM, Thorleifsson G, Van den Herik EG, Voight BF, Volcik KA, Waite LL, Wong A, Wu Y, Zhang W, Absher D, Asiki G, Barroso I, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, Cesana G, Dimitriou M, Doney AS, Doring A, Elliott P, Epstein SE, Eyjolfsson GI, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen AL, Hayward C, Hernandez D, Hicks AA, Holm H, Hung YJ, Illig T, Jones MR, Kaleebu P, Kastelein JJ, Khaw KT, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimaki T, Lin SY, Lindstrom J, Loos RJ, Mach F, McArdle WL, Meisinger C, Mitchell BD, Muller G, Nagaraja R, Narisu N, Nieminen TV, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Rader DJ, Reilly MP, Ridker PM, Rivadeneira F, Rudan I, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stancakova A, Stirrups K, Swift AJ, Tiret L, Uitterlinden AG, van Pelt LJ, Vedantam S, Wainwright N, Wijmenga C, Wild SH, Willemsen G, Wilsgaard T, Wilson JF, Young EH, Zhao JH, Adair LS, Arveiler D, Assimes TL, Bandinelli S, Bennett F, Bochud M, Boehm BO, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Campbell H, Chakravarti A, Chambers JC, Chen YD, Collins FS, Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrieres J, Ferrucci L, Freimer NB, Gieger C, Groop LC, Gudnason V, Gyllensten U, Hamsten A, Harris TB, Hingorani A, Hirschhorn JN, Hofman A, Hovingh GK, Hsiung CA, Humphries SE, Hunt SC, Hveem K, Iribarren C, Jarvelin MR, Jula A, Kahonen M, Kaprio J, Kesaniemi A, Kivimaki M, Kooner JS, Koudstaal PJ, Krauss RM, Kuh D, Kuusisto J, Kyvik KO, Laakso M, Lakka TA, Lind L, Lindgren CM, Martin NG, Marz W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A, Moilanen L, Morris AD, Munroe PB, Njolstad I, Pedersen NL, Power C, Pramstaller PP, Price JF, Psaty BM, Quertermous T, Rauramaa R, Saleheen D, Salomaa V, Sanghera DK, Saramies J, Schwarz PE, Sheu WH, Shuldiner AR, Siegbahn A, Spector TD, Stefansson K, Strachan DP, Tayo BO, Tremoli E, Tuomilehto J, Uusitupa M, van Duijn CM, Vollenweider P, Wallentin L, Wareham NJ, Whitfield JB, Wolffenbuttel BH, Ordovas JM, Boerwinkle E, Palmer CN, Thorsteinsdottir U, Chasman DI, Rotter JI, Franks PW, Ripatti S, Cupples LA, Sandhu MS, Rich SS, Boehnke M, Deloukas P, Kathiresan S, Mohlke KL, Ingelsson E, Abecasis GR. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–1283. - PMC - PubMed

Publication types

MeSH terms

Substances

LinkOut - more resources