Skip to main content
eLife logoLink to eLife
. 2023 Dec 7;12:e94382. doi: 10.7554/eLife.94382

From mouse to human

Arya Mani 1,
PMCID: PMC10703438  PMID: 38060304

Abstract

A deep analysis of multiple genomic datasets reveals which genetic pathways associated with atherosclerosis and coronary artery disease are shared between mice and humans.

Research organism: Human, Mouse


Related research article Kurt Z, Cheng J, McQuillen CN, Saleem Z, Hsu N, Jiang N, Barrere-Cain R, Pan C, Franzen O, Koplev S, Wang S, Bjorkegren J, Lusis AJ, Blencowe M, Yang X. 2023. Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse. eLife 12:RP88266. doi: 10.7554/eLife.88266.

Over time, various substances that travel through blood – such as cholesterol, inflammatory cells, and cellular debris – can accumulate in the walls of arteries, resulting in their narrowing. This build-up of materials, known as atherosclerosis, can also occur in the blood vessels that supply nutrients and oxygen to the heart, leading to coronary artery disease. Understanding what causes atherosclerosis is crucial for developing effective preventive and therapeutic strategies for coronary artery disease.

Genome-wide association studies – which compare common DNA variations in populations with and without a specific trait or disease – have identified numerous genetic variants linked with an increased risk of atherosclerosis (Khera and Kathiresan, 2017; Tcheandjieu et al., 2022). These variants are either causal or associated with various aspects of atherosclerosis, such as lipid metabolism, inflammation, and endothelial function. Despite significant advances in genetic research, it remains unclear which of these variants drive the condition, and in which genes and/or tissues these variants exert their effects. How other factors that are known to influence atherosclerosis, such as environment, sex and lifestyle, impact gene expression also cannot be inferred from these types of investigations.

To overcome these limitations, researchers use animal models that have been manipulated to develop a certain disease. Mice are the most commonly studied species, and have been used to observe how altering specific genes and controlling various environmental factors affect the way atherosclerosis and coronary artery disease develop. However, mice and humans differ significantly in terms of their physiology and genetics. For instance, their lipid metabolism and immune responses vary, and certain genes implicated in mice might not have direct equivalent functions or effects in humans, making it difficult to translate finding from studies in mice to clinical applications. Now, in eLife, Montgomery Blencowe and Xia Yang from the University of California, Los Angeles (UCLA) and colleagues – including Zeyneb Kurt and Jenny Cheng as joint first authors – report how the genetic pathways and mechanisms associated with atherosclerosis and coronary artery disease compare between these two species (Kurt et al., 2023).

Kurt et al. meticulously analyzed various sources of data, including mouse genomic data from the Hybrid Mouse Diversity Panel, and human genomic data from the CARDIoGRAMplusC4D consortium, GTEx database, and STARNET. In addition to results from genome-wide association studies (GWAS), these datasets include information on which genes are active and which variants alter the expression level of these genes (known as expression quantitative trait loci, or eQTL for short) in specific tissues of interest: the liver and vasculature tissues of humans, and the aorta (which is part of the vasculature) and liver tissues of mice.

First, the team (who are based at institutes in the United States, United Kingdom and Sweden) used the GWAS, gene expression, and eQTL data from mice and humans to determine which genes have similar expression profiles and are therefore likely to be connected, and which genes have a major role in the two conditions. Using these co-expression gene networks, together with another tool known as gene set enrichment analysis, they were able to identify the signaling pathways associated with coronary artery disease and atherosclerosis in humans and mice. Remarkably, this revealed a significant overlap in the pathways linked to coronary artery disease and atherosclerosis, with approximately 75% and 80% of identified pathways being associated with both diseases in the vasculature and liver tissue, respectively. These shared pathways encompass well-known processes, such as lipid metabolism, and introduce novel aspects like the mechanism that breaks down branched chain amino acids.

The analysis also uncovered pathways that were specific to each species, such as the insulin signaling pathway in the aorta of mice, and interferon signaling in the human liver. Kurt et al. then used a probabilistic model known as the Bayesian Network to pinpoint which genes were predominantly driving these species-specific pathways, and identified the subnetwork of genes immediately downstream or neighboring these drivers. The genes that drive the mouse-specific pathways were validated using single-cell RNA sequencing data, which revealed that the subnetwork of genes changed expression in the aortas and livers of mice with coronary artery disease and/or atherosclerosis.

Further analysis revealed that some of these previously unknown key driver genes were also hits in a recent GWAS of coronary artery disease, suggesting they have a crucial role in the disease. This included a key driver of coronary artery disease in both humans and mice, the ARNTL gene (also known as BMAL1) which is a transcriptional activator that forms a core component of the circadian clock and negatively regulates adipogenesis (Guo et al., 2012).

Interestingly, a common variant in the ARNTL gene has been associated with coronary artery disease and other factors linked to this condition and atherosclerosis, such as body mass index, diastolic blood pressure, triglyceride levels, and type 2 diabetes (van der Harst and Verweij, 2018; Pulit et al., 2019; Sakaue et al., 2021; de Vries et al., 2019, Vujkovic et al., 2020). Furthermore, values derived from the GTEx dataset suggest that the alternative variant reduces the expression of the gene in whole blood. Deletion of ARTNL in certain blood cells has also been shown to predispose mice to acute and chronic inflammation (Nguyen et al., 2013). Use of functional genomics, particularly in the context of sex differences, will likely establish the causality of ARNTL and other predicted key driver genes (Gunawardhana et al., 2023).

The findings of Kurt et al. are a pivotal contribution to our understanding of coronary artery disease and atherosclerosis in mice and humans. The integrative genomic study also creates avenues for further research, such as applying the same approach to larger GWAS datasets and incorporating variants that impact the splicing or quantity of protein produced into the analysis. Employing additional mouse models of atherosclerosis and coronary artery disease, and analyzing other relevant tissues, could also help identify additional cross-species similarities and differences. These future studies, together with the work by Kurt et al., will help researchers to determine how well findings in mice relate to human coronary artery disease and atherosclerosis, and whether these results could translate to clinical applications.

Biography

Arya Mani is in the Department of Internal Medicine and Genetics, Yale University School of Medicine, New Haven, United States

Competing interests

No competing interests declared.

References

  1. de Vries PS, Brown MR, Bentley AR, Sung YJ, Winkler TW, Ntalla I, Schwander K, Kraja AT, Guo X, Franceschini N, Cheng CY, Sim X, Vojinovic D, Huffman JE, Musani SK, Li C, Feitosa MF, Richard MA, Noordam R, Aschard H, Bartz TM, Bielak LF, Deng X, Dorajoo R, Lohman KK, Manning AK, Rankinen T, Smith AV, Tajuddin SM, Evangelou E, Graff M, Alver M, Boissel M, Chai JF, Chen X, Divers J, Gandin I, Gao C, Goel A, Hagemeijer Y, Harris SE, Hartwig FP, He M, Horimoto A, Hsu FC, Jackson AU, Kasturiratne A, Komulainen P, Kühnel B, Laguzzi F, Lee JH, Luan J, Lyytikäinen LP, Matoba N, Nolte IM, Pietzner M, Riaz M, Said MA, Scott RA, Sofer T, Stančáková A, Takeuchi F, Tayo BO, van der Most PJ, Varga TV, Wang Y, Ware EB, Wen W, Yanek LR, Zhang W, Zhao JH, Afaq S, Amin N, Amini M, Arking DE, Aung T, Ballantyne C, Boerwinkle E, Broeckel U, Campbell A, Canouil M, Charumathi S, Chen YDI, Connell JM, de Faire U, de Las Fuentes L, de Mutsert R, de Silva HJ, Ding J, Dominiczak AF, Duan Q, Eaton CB, Eppinga RN, Faul JD, Fisher V, Forrester T, Franco OH, Friedlander Y, Ghanbari M, Giulianini F, Grabe HJ, Grove ML, Gu CC, Harris TB, Heikkinen S, Heng CK, Hirata M, Hixson JE, Howard BV, Ikram MA, InterAct Consortium. Jacobs DR, Johnson C, Jonas JB, Kammerer CM, Katsuya T, Khor CC, Kilpeläinen TO, Koh WP, Koistinen HA, Kolcic I, Kooperberg C, Krieger JE, Kritchevsky SB, Kubo M, Kuusisto J, Lakka TA, Langefeld CD, Langenberg C, Launer LJ, Lehne B, Lemaitre RN, Li Y, Liang J, Liu J, Liu K, Loh M, Louie T, Mägi R, Manichaikul AW, McKenzie CA, Meitinger T, Metspalu A, Milaneschi Y, Milani L, Mohlke KL, Mosley TH, Mukamal KJ, Nalls MA, Nauck M, Nelson CP, Sotoodehnia N, O’Connell JR, Palmer ND, Pazoki R, Pedersen NL, Peters A, Peyser PA, Polasek O, Poulter N, Raffel LJ, Raitakari OT, Reiner AP, Rice TK, Rich SS, Robino A, Robinson JG, Rose LM, Rudan I, Schmidt CO, Schreiner PJ, Scott WR, Sever P, Shi Y, Sidney S, Sims M, Smith BH, Smith JA, Snieder H, Starr JM, Strauch K, Tan N, Taylor KD, Teo YY, Tham YC, Uitterlinden AG, van Heemst D, Vuckovic D, Waldenberger M, Wang L, Wang Y, Wang Z, Wei WB, Williams C, Wilson G, Wojczynski MK, Yao J, Yu B, Yu C, Yuan JM, Zhao W, Zonderman AB, Becker DM, Boehnke M, Bowden DW, Chambers JC, Deary IJ, Esko T, Farrall M, Franks PW, Freedman BI, Froguel P, Gasparini P, Gieger C, Horta BL, Kamatani Y, Kato N, Kooner JS, Laakso M, Leander K, Lehtimäki T, Lifelines Cohort, Groningen, The Netherlands (Lifelines Cohort Study) Magnusson PKE, Penninx B, Pereira AC, Rauramaa R, Samani NJ, Scott J, Shu XO, van der Harst P, Wagenknecht LE, Wang YX, Wareham NJ, Watkins H, Weir DR, Wickremasinghe AR, Zheng W, Elliott P, North KE, Bouchard C, Evans MK, Gudnason V, Liu CT, Liu Y, Psaty BM, Ridker PM, van Dam RM, Kardia SLR, Zhu X, Rotimi CN, Mook-Kanamori DO, Fornage M, Kelly TN, Fox ER, Hayward C, van Duijn CM, Tai ES, Wong TY, Liu J, Rotter JI, Gauderman WJ, Province MA, Munroe PB, Rice K, Chasman DI, Cupples LA, Rao DC, Morrison AC. Multiancestry genome-wide association study of lipid levels incorporating gene-alcohol interactions. American Journal of Epidemiology. 2019;188:1033–1054. doi: 10.1093/aje/kwz005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Gunawardhana KL, Hong L, Rugira T, Uebbing S, Kucharczak J, Mehta S, Karunamuni DR, Cabera-Mendoza B, Gandotra N, Scharfe C, Polimanti R, Noonan JP, Mani A. A systems biology approach identifies the role of dysregulated PRDM6 in the development of hypertension. The Journal of Clinical Investigation. 2023;133:e160036. doi: 10.1172/JCI160036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Guo B, Chatterjee S, Li L, Kim JM, Lee J, Yechoor VK, Minze LJ, Hsueh W, Ma K. The clock gene, brain and muscle Arnt-like 1, regulates adipogenesis via Wnt signaling pathway. FASEB Journal. 2012;26:3453–3463. doi: 10.1096/fj.12-205781. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Khera AV, Kathiresan S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nature Reviews Genetics. 2017;18:331–344. doi: 10.1038/nrg.2016.160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Kurt Z, Cheng J, McQuillen CN, Saleem Z, Hsu N, Jiang N, Barrere-Cain R, Pan C, Franzen O, Koplev S, Wang S, Bjorkegren J, Lusis AJ, Blencowe M, Yang X. Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse. eLife. 2023;12:RP88266. doi: 10.7554/eLife.88266.2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Nguyen KD, Fentress SJ, Qiu Y, Yun K, Cox JS, Chawla A. Circadian gene Bmal1 regulates diurnal oscillations of Ly6C(hi) inflammatory monocytes. Science. 2013;341:1483–1488. doi: 10.1126/science.1240636. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, Yengo L, Ferreira T, Marouli E, Ji Y, Yang J, Jones S, Beaumont R, Croteau-Chonka DC, Winkler TW, GIANT Consortium. Hattersley AT, Loos RJF, Hirschhorn JN, Visscher PM, Frayling TM, Yaghootkar H, Lindgren CM. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Human Molecular Genetics. 2019;28:166–174. doi: 10.1093/hmg/ddy327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, Narita A, Konuma T, Yamamoto K, Akiyama M, Ishigaki K, Suzuki A, Suzuki K, Obara W, Yamaji K, Takahashi K, Asai S, Takahashi Y, Suzuki T, Shinozaki N, Yamaguchi H, Minami S, Murayama S, Yoshimori K, Nagayama S, Obata D, Higashiyama M, Masumoto A, Koretsune Y, FinnGen. Ito K, Terao C, Yamauchi T, Komuro I, Kadowaki T, Tamiya G, Yamamoto M, Nakamura Y, Kubo M, Murakami Y, Yamamoto K, Kamatani Y, Palotie A, Rivas MA, Daly MJ, Matsuda K, Okada Y. A cross-population atlas of genetic associations for 220 human phenotypes. Nature Genetics. 2021;53:1415–1424. doi: 10.1038/s41588-021-00931-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Tcheandjieu C, Zhu X, Hilliard AT, Clarke SL, Napolioni V, Ma S, Lee KM, Fang H, Chen F, Lu Y, Tsao NL, Raghavan S, Koyama S, Gorman BR, Vujkovic M, Klarin D, Levin MG, Sinnott-Armstrong N, Wojcik GL, Plomondon ME, Maddox TM, Waldo SW, Bick AG, Pyarajan S, Huang J, Song R, Ho YL, Buyske S, Kooperberg C, Haessler J, Loos RJF, Do R, Verbanck M, Chaudhary K, North KE, Avery CL, Graff M, Haiman CA, Le Marchand L, Wilkens LR, Bis JC, Leonard H, Shen B, Lange LA, Giri A, Dikilitas O, Kullo IJ, Stanaway IB, Jarvik GP, Gordon AS, Hebbring S, Namjou B, Kaufman KM, Ito K, Ishigaki K, Kamatani Y, Verma SS, Ritchie MD, Kember RL, Baras A, Lotta LA, Regeneron Genetics Center. CARDIoGRAMplusC4D Consortium. Biobank Japan. Million Veteran Program. Kathiresan S, Hauser ER, Miller DR, Lee JS, Saleheen D, Reaven PD, Cho K, Gaziano JM, Natarajan P, Huffman JE, Voight BF, Rader DJ, Chang KM, Lynch JA, Damrauer SM, Wilson PWF, Tang H, Sun YV, Tsao PS, O’Donnell CJ, Assimes TL. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nature Medicine. 2022;28:1679–1692. doi: 10.1038/s41591-022-01891-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. van der Harst P, Verweij N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circulation Research. 2018;122:433–443. doi: 10.1161/CIRCRESAHA.117.312086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Vujkovic M, Keaton JM, Lynch JA, Miller DR, Zhou J, Tcheandjieu C, Huffman JE, Assimes TL, Lorenz K, Zhu X, Hilliard AT, Judy RL, Huang J, Lee KM, Klarin D, Pyarajan S, Danesh J, Melander O, Rasheed A, Mallick NH, Hameed S, Qureshi IH, Afzal MN, Malik U, Jalal A, Abbas S, Sheng X, Gao L, Kaestner KH, Susztak K, Sun YV, DuVall SL, Cho K, Lee JS, Gaziano JM, Phillips LS, Meigs JB, Reaven PD, Wilson PW, Edwards TL, Rader DJ, Damrauer SM, O’Donnell CJ, Tsao PS, HPAP Consortium. Regeneron Genetics Center. VA Million Veteran Program. Chang KM, Voight BF, Saleheen D. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nature Genetics. 2020;52:680–691. doi: 10.1038/s41588-020-0637-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from eLife are provided here courtesy of eLife Sciences Publications, Ltd

RESOURCES