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. 2023 Dec 7:12:RP88266.
doi: 10.7554/eLife.88266.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

Zeyneb Kurt #  1   2 , Jenny Cheng #  1   3 , Rio Barrere-Cain  1 , Caden N McQuillen  1 , Zara Saleem  1 , Neil Hsu  1 , Nuoya Jiang  1 , Calvin Pan  4 , Oscar Franzén  5 , Simon Koplev  5 , Susanna Wang  1 , Johan Björkegren  5   6 , Aldons J Lusis  4   7   8 , Montgomery Blencowe #  1   3 , Xia Yang  1   3   9   10
Affiliations

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

Zeyneb Kurt et al. Elife. .

Abstract

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Keywords: atherosclerosis; computational biology; coronary artery disease; cross-species comparison; gene regulatory networks; human; mouse; multiomics; systems biology.

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Conflict of interest statement

ZK, JC, RB, CM, ZS, NH, NJ, CP, OF, SK, SW, JB, AL, MB, XY No competing interests declared

Figures

Figure 1.
Figure 1.. Overview of Study.
Figure 2.
Figure 2.. Shared and species-specific biological pathways.
(A) Bar plot highlighting the number of significant pathways shared and unique between mouse and human in vascular tissue (FDR<0.05). (B) Venn diagram highlighting the top shared and unique significant pathways between mouse and human in vascular tissue. (C) Bar plot highlighting the number of significant pathways shared and unique between mouse and human in non-vascular tissue (liver) (FDR<0.05). (D) Venn diagram highlighting the top shared and unique significant pathways between mouse and human in non-vascular tissue (liver).
Figure 3.
Figure 3.. Shared networks between mice and humans.
(A) Vascular tissue gene regulatory network shared between mice and humans (B) Non-vascular gene regulatory network shared between mice and humans. Each node is color coded based on the pathway/module that the genes are derived from with larger nodes signifying key driver genes. Red border diamonds represent CAD GWAS hits uncovered after the CARDIOGRAM+C4D GWAS (2016 onwards) and pink border diamonds represent CAD GWAS hits prior to the CARDIOGRAM+C4D GWAS.
Figure 4.
Figure 4.. Species-specific networks.
(A) Human vascular gene regulatory network, (B) Human non-vascular gene regulatory network, (C) Mouse vascular gene regulatory network, (D) Mouse non-vascular gene regulatory network. Each node is color coded based on the pathway/module that the genes are derived from with larger nodes signifying key driver genes. Red border diamonds represent CAD GWAS hits uncovered after the CARDIOGRAM+C4D GWAS (2016 onwards) and pink border diamonds represent CAD GWAS hits prior to the CARDIOGRAM+C4D GWAS.
Figure 5.
Figure 5.. In silico validation of select KDs using single cell RNA-sequencing data.
(A-D) Aorta KDs and subnetwork genes, where AvgLog2(FC) is representing gene expression change between atherogenic diet and chow diet groups. (E-H) Liver OIT3 KD and subnetwork genes, where AvgLog2(FC) is representing gene expression changes between NASH and control. The last 5 genes in each plot are randomly selected negative control genes. We utilized a Wilcoxon rank sum test with bonferroni correction to derive significance for each gene. * represents a p<0.05.
See this image and copyright information in PMC

Update of

Comment in

  • From mouse to human.
    Mani A. Mani A. Elife. 2023 Dec 7;12:e94382. doi: 10.7554/eLife.94382. Elife. 2023. PMID: 38060304 Free PMC article.

References

    1. Alpaydin E. Introduction to Machine Learning Adaptive Computation and Machine Learning. The MIT Press; 2010.
    1. Aragam KG, Jiang T, Goel A, Kanoni S, Wolford BN, Atri DS, Weeks EM, Wang M, Hindy G, Zhou W, Grace C, Roselli C, Marston NA, Kamanu FK, Surakka I, Venegas LM, Sherliker P, Koyama S, Ishigaki K, Åsvold BO, Brown MR, Brumpton B, de Vries PS, Giannakopoulou O, Giardoglou P, Gudbjartsson DF, Güldener U, Haider SMI, Helgadottir A, Ibrahim M, Kastrati A, Kessler T, Kyriakou T, Konopka T, Li L, Ma L, Meitinger T, Mucha S, Munz M, Murgia F, Nielsen JB, Nöthen MM, Pang S, Reinberger T, Schnitzler G, Smedley D, Thorleifsson G, von Scheidt M, Ulirsch JC, Biobank Japan. EPIC-CVD. Arnar DO, Burtt NP, Costanzo MC, Flannick J, Ito K, Jang D-K, Kamatani Y, Khera AV, Komuro I, Kullo IJ, Lotta LA, Nelson CP, Roberts R, Thorgeirsson G, Thorsteinsdottir U, Webb TR, Baras A, Björkegren JLM, Boerwinkle E, Dedoussis G, Holm H, Hveem K, Melander O, Morrison AC, Orho-Melander M, Rallidis LS, Ruusalepp A, Sabatine MS, Stefansson K, Zalloua P, Ellinor PT, Farrall M, Danesh J, Ruff CT, Finucane HK, Hopewell JC, Clarke R, Gupta RM, Erdmann J, Samani NJ, Schunkert H, Watkins H, Willer CJ, Deloukas P, Kathiresan S, Butterworth AS, CARDIoGRAMplusC4D Consortium Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nature Genetics. 2022;54:1803–1815. doi: 10.1038/s41588-022-01233-6. - DOI - PMC - PubMed
    1. Arneson D, Bhattacharya A, Shu L, Mäkinen VP, Yang X. Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration. BMC Genomics. 2016;17:722. doi: 10.1186/s12864-016-3057-8. - DOI - PMC - PubMed
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. 1995;57:289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x. - DOI
    1. Bennett BJ, Farber CR, Orozco L, Kang HM, Ghazalpour A, Siemers N, Neubauer M, Neuhaus I, Yordanova R, Guan B, Truong A, Yang W, He A, Kayne P, Gargalovic P, Kirchgessner T, Pan C, Castellani LW, Kostem E, Furlotte N, Drake TA, Eskin E, Lusis AJ. A high-resolution association mapping panel for the dissection of complex traits in mice. Genome Research. 2010;20:281–290. doi: 10.1101/gr.099234.109. - DOI - PMC - PubMed

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