Genetics: From mouse to human

A deep analysis of multiple genomic datasets reveals which genetic pathways associated with atherosclerosis and coronary artery disease are shared between mice and humans.
  1. Arya Mani  Is a corresponding author
  1. Department of Internal Medicine and Genetics, Yale University School of Medicine, United States

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.

References

    1. de Vries PS
    2. Brown MR
    3. Bentley AR
    4. Sung YJ
    5. Winkler TW
    6. Ntalla I
    7. Schwander K
    8. Kraja AT
    9. Guo X
    10. Franceschini N
    11. Cheng CY
    12. Sim X
    13. Vojinovic D
    14. Huffman JE
    15. Musani SK
    16. Li C
    17. Feitosa MF
    18. Richard MA
    19. Noordam R
    20. Aschard H
    21. Bartz TM
    22. Bielak LF
    23. Deng X
    24. Dorajoo R
    25. Lohman KK
    26. Manning AK
    27. Rankinen T
    28. Smith AV
    29. Tajuddin SM
    30. Evangelou E
    31. Graff M
    32. Alver M
    33. Boissel M
    34. Chai JF
    35. Chen X
    36. Divers J
    37. Gandin I
    38. Gao C
    39. Goel A
    40. Hagemeijer Y
    41. Harris SE
    42. Hartwig FP
    43. He M
    44. Horimoto A
    45. Hsu FC
    46. Jackson AU
    47. Kasturiratne A
    48. Komulainen P
    49. Kühnel B
    50. Laguzzi F
    51. Lee JH
    52. Luan J
    53. Lyytikäinen LP
    54. Matoba N
    55. Nolte IM
    56. Pietzner M
    57. Riaz M
    58. Said MA
    59. Scott RA
    60. Sofer T
    61. Stančáková A
    62. Takeuchi F
    63. Tayo BO
    64. van der Most PJ
    65. Varga TV
    66. Wang Y
    67. Ware EB
    68. Wen W
    69. Yanek LR
    70. Zhang W
    71. Zhao JH
    72. Afaq S
    73. Amin N
    74. Amini M
    75. Arking DE
    76. Aung T
    77. Ballantyne C
    78. Boerwinkle E
    79. Broeckel U
    80. Campbell A
    81. Canouil M
    82. Charumathi S
    83. Chen YDI
    84. Connell JM
    85. de Faire U
    86. de Las Fuentes L
    87. de Mutsert R
    88. de Silva HJ
    89. Ding J
    90. Dominiczak AF
    91. Duan Q
    92. Eaton CB
    93. Eppinga RN
    94. Faul JD
    95. Fisher V
    96. Forrester T
    97. Franco OH
    98. Friedlander Y
    99. Ghanbari M
    100. Giulianini F
    101. Grabe HJ
    102. Grove ML
    103. Gu CC
    104. Harris TB
    105. Heikkinen S
    106. Heng CK
    107. Hirata M
    108. Hixson JE
    109. Howard BV
    110. Ikram MA
    111. InterAct Consortium
    112. Jacobs DR
    113. Johnson C
    114. Jonas JB
    115. Kammerer CM
    116. Katsuya T
    117. Khor CC
    118. Kilpeläinen TO
    119. Koh WP
    120. Koistinen HA
    121. Kolcic I
    122. Kooperberg C
    123. Krieger JE
    124. Kritchevsky SB
    125. Kubo M
    126. Kuusisto J
    127. Lakka TA
    128. Langefeld CD
    129. Langenberg C
    130. Launer LJ
    131. Lehne B
    132. Lemaitre RN
    133. Li Y
    134. Liang J
    135. Liu J
    136. Liu K
    137. Loh M
    138. Louie T
    139. Mägi R
    140. Manichaikul AW
    141. McKenzie CA
    142. Meitinger T
    143. Metspalu A
    144. Milaneschi Y
    145. Milani L
    146. Mohlke KL
    147. Mosley TH
    148. Mukamal KJ
    149. Nalls MA
    150. Nauck M
    151. Nelson CP
    152. Sotoodehnia N
    153. O’Connell JR
    154. Palmer ND
    155. Pazoki R
    156. Pedersen NL
    157. Peters A
    158. Peyser PA
    159. Polasek O
    160. Poulter N
    161. Raffel LJ
    162. Raitakari OT
    163. Reiner AP
    164. Rice TK
    165. Rich SS
    166. Robino A
    167. Robinson JG
    168. Rose LM
    169. Rudan I
    170. Schmidt CO
    171. Schreiner PJ
    172. Scott WR
    173. Sever P
    174. Shi Y
    175. Sidney S
    176. Sims M
    177. Smith BH
    178. Smith JA
    179. Snieder H
    180. Starr JM
    181. Strauch K
    182. Tan N
    183. Taylor KD
    184. Teo YY
    185. Tham YC
    186. Uitterlinden AG
    187. van Heemst D
    188. Vuckovic D
    189. Waldenberger M
    190. Wang L
    191. Wang Y
    192. Wang Z
    193. Wei WB
    194. Williams C
    195. Wilson G
    196. Wojczynski MK
    197. Yao J
    198. Yu B
    199. Yu C
    200. Yuan JM
    201. Zhao W
    202. Zonderman AB
    203. Becker DM
    204. Boehnke M
    205. Bowden DW
    206. Chambers JC
    207. Deary IJ
    208. Esko T
    209. Farrall M
    210. Franks PW
    211. Freedman BI
    212. Froguel P
    213. Gasparini P
    214. Gieger C
    215. Horta BL
    216. Kamatani Y
    217. Kato N
    218. Kooner JS
    219. Laakso M
    220. Leander K
    221. Lehtimäki T
    222. Lifelines Cohort, Groningen, The Netherlands (Lifelines Cohort Study)
    223. Magnusson PKE
    224. Penninx B
    225. Pereira AC
    226. Rauramaa R
    227. Samani NJ
    228. Scott J
    229. Shu XO
    230. van der Harst P
    231. Wagenknecht LE
    232. Wang YX
    233. Wareham NJ
    234. Watkins H
    235. Weir DR
    236. Wickremasinghe AR
    237. Zheng W
    238. Elliott P
    239. North KE
    240. Bouchard C
    241. Evans MK
    242. Gudnason V
    243. Liu CT
    244. Liu Y
    245. Psaty BM
    246. Ridker PM
    247. van Dam RM
    248. Kardia SLR
    249. Zhu X
    250. Rotimi CN
    251. Mook-Kanamori DO
    252. Fornage M
    253. Kelly TN
    254. Fox ER
    255. Hayward C
    256. van Duijn CM
    257. Tai ES
    258. Wong TY
    259. Liu J
    260. Rotter JI
    261. Gauderman WJ
    262. Province MA
    263. Munroe PB
    264. Rice K
    265. Chasman DI
    266. Cupples LA
    267. Rao DC
    268. Morrison AC
    (2019) Multiancestry genome-wide association study of lipid levels incorporating gene-alcohol interactions
    American Journal of Epidemiology 188:1033–1054.
    https://doi.org/10.1093/aje/kwz005

Article and author information

Author details

  1. Arya Mani

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

    For correspondence
    arya.mani@yale.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6699-259X

Publication history

  1. Version of Record published: December 7, 2023 (version 1)

Copyright

© 2023, Mani

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 340
    views
  • 36
    downloads
  • 0
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Arya Mani
(2023)
Genetics: From mouse to human
eLife 12:e94382.
https://doi.org/10.7554/eLife.94382

Further reading

    1. Computational and Systems Biology
    Iván Plaza-Menacho
    Insight

    A study of two enzymes in the brain reveals new insights into how redox reactions regulate the activity of protein kinases.

    1. Computational and Systems Biology
    2. Epidemiology and Global Health
    Javier I Ottaviani, Virag Sagi-Kiss ... Gunter GC Kuhnle
    Research Article

    The chemical composition of foods is complex, variable, and dependent on many factors. This has a major impact on nutrition research as it foundationally affects our ability to adequately assess the actual intake of nutrients and other compounds. In spite of this, accurate data on nutrient intake are key for investigating the associations and causal relationships between intake, health, and disease risk at the service of developing evidence-based dietary guidance that enables improvements in population health. Here, we exemplify the importance of this challenge by investigating the impact of food content variability on nutrition research using three bioactives as model: flavan-3-ols, (–)-epicatechin, and nitrate. Our results show that common approaches aimed at addressing the high compositional variability of even the same foods impede the accurate assessment of nutrient intake generally. This suggests that the results of many nutrition studies using food composition data are potentially unreliable and carry greater limitations than commonly appreciated, consequently resulting in dietary recommendations with significant limitations and unreliable impact on public health. Thus, current challenges related to nutrient intake assessments need to be addressed and mitigated by the development of improved dietary assessment methods involving the use of nutritional biomarkers.