Mitochondrial functions play a major role in the pathophysiology of several metabolic syndrome traits including obesity, insulin resistance and fatty liver disease (Yin et al., 2014; Chouchani et al., 2014; Begriche et al., 2006; Sanyal et al., 2001; Kim et al., 2008). There is now substantial evidence showing that genetic variation in mitochondrial functions contribute importantly to ‘complex’ diseases such as cardiovascular diseases (CVD) (Wallace, 2018). A scientific statement from the American Heart Association summarizes the central role of mitochondria in heart disease (Murphy et al., 2016). Mitochondrial bioenergetics and other functions impinge on virtually all aspects of the cell, including energy, cellular redox, apoptosis, and substrates for epigenetic modifications. Mitochondrial dysfunction also contributes to the development of heart failure (Brown et al., 2017; Zhou and Tian, 2018). The primary putative mechanism linking mitochondrial dysfunction to heart failure is decreased oxidative respiration leading to contractile failure. However, many other mechanisms have been postulated in recent years to implicate mitochondrial dysfunction in heart failure. They include excessive oxidative stress leading to inflammation, cell damage, and cell death; disturbed calcium homeostasis that triggers the opening of the mitochondrial permeability transition pore (mPTP), leading to loss of membrane potential, and eventual cell death. Therefore, mitochondria are an attractive target for heart failure therapy (Brown et al., 2017). Despite evidence showing that genetic variation in mitochondrial proteins is linked to disease, most studies tend to overlook the role of genetic variation in exploring the link between mitochondrial function and relevant phenotypes. To this end, we employ a ‘systems genetics’ approach to address this issue.

Our laboratory uses a combination of genetics, molecular biology, and informatics to investigate pathways underlying common cardiovascular and metabolic disorders. We exploit natural genetic variation among inbred strains of mice (and among human populations where possible) to identify novel targets and formulate hypotheses, taking advantage of unbiased global multi-omics technologies, such as transcriptomics, metabolomics, and proteomics, to help decipher causal mechanisms that drive complex traits. This ‘systems genetics’ approach integrates natural genetic variation with omics-level data (such as global protein abundance levels) to examine complex interactions that are difficult to address directly in humans. It shares with systems biology a holistic, global perspective (Civelek and Lusis, 2014; Seldin et al., 2019). Typically, a genetic reference population is examined for relevant clinical traits as well as global molecular traits, such as proteomics, and the data are integrated using correlation structure, genetic mapping, and mathematical modeling (Civelek and Lusis, 2014; Seldin et al., 2019).

Our systems genetics approach utilizes a well-characterized genetic reference population of 100 inbred mice strains, termed the Hybrid Mouse Diversity Panel (HMDP) (Lusis et al., 2016). The design of the HMDP resource, consisting of a panel of permanent inbred strains of mice that can be examined for many phenotypes, has proved invaluable for studying metabolic syndrome traits. Major advantages of this approach are that the mapping resolution for complex traits is superior to traditional genetic crosses and the use of inbred strains affords replication of biological measures. It also facilitates studies of gene-by-environment and gene-by-sex interactions, which are difficult to address in human populations. Using this resource, we have characterized several cardiometabolic traits in both sexes over the last 10 years (Parks et al., 2013; Parks et al., 2015; Rau et al., 2015; Org et al., 2016; Wang et al., 2016; Seldin et al., 2017; Rau et al., 2017; Norheim et al., 2017; Norheim et al., 2018; Hui et al., 2018; Seldin et al., 2018; Chella Krishnan et al., 2018; Chella Krishnan et al., 2019; Norheim et al., 2019; Chella Krishnan et al., 2021).

In the present study, we have explored the genetic regulation of mitochondrial pathways and their contribution to heart function in the HMDP. Using an integrative proteomics approach, we now report the identification of three independent genetic loci that control distinct classes of mitochondrial proteins as well as heart hypertrophy. Each locus contains proteins previously shown to affect heart pathophysiology but by unknown mechanisms. Our results show that genetic diversity in the mitochondrial proteome plays a central role in heart pathophysiology.

We have previously used systems genetics analyses in HMDP and discovered a central role for adipose mitochondrial function in the sex differences observed in cardiometabolic traits, including obesity, insulin resistance and plasma lipids (Norheim et al., 2019; Chella Krishnan et al., 2021), and liver mitochondrial function in non-alcoholic fatty liver disease (Chella Krishnan et al., 2018). In the present study, an integrative proteomics approach was utilized in HMDP to investigate the effects of genetic regulation of mitochondrial pathways on heart function. Several conclusions have emerged. First, we identified three distinct and independent trans-regulating genetic loci, on chr13, chr17 and chr7, respectively. Second, our enrichment analyses suggested mechanisms perturbed by each of these loci. Thus, the chr13 locus was enriched for mitochondrial complex I, chr17 for mitoribosomes, and chr7 was enriched for ubiquinone biosynthesis. Third, our regional mapping identified NDUFS4, LRPPRC and COQ7 as candidate genes for chr13, chr17, and chr7 loci, respectively. Finally, we performed both experimental and statistical analyses to support their causal roles. Each of these points is discussed in detail below.

Trans-acting human pQTLs, mostly using blood/plasma proteome (Yao et al., 2018; Ruffieux et al., 2020; Zhong et al., 2020; Yang et al., 2021; Folkersen et al., 2020; He et al., 2020), and trans-acting human eQTLs (Aguet, 2017; Brynedal et al., 2017; Yao et al., 2017; Small et al., 2018; GTEx Consortium, 2020), have been reported, but exploring trans-regulatory landscapes in human studies is limited by sample size, tissue accessibility, and environmental factors. In the current study, mapping genetic loci controlling protein abundance levels in heart tissues from an inbred mouse population revealed that ~9% of available nuclear-encoded mitochondrial proteins (74/848) are significantly controlled (p<1E-6) by three genetic loci, on chr13 (26 proteins), chr17 (22 proteins) and chr7 (26 proteins), respectively. Each of these three trans regulating hotspots were specific for the proteome, as they were not identified in our heart transcriptome. Interestingly, two of these hotspots independently controlled mitochondrial complex I, one at the mRNA (chr17 via LRPPRC/SLIRP) and the other at the protein level (chr13 via miR-27b/NDUFS4). Finally, the chr7 locus was found to control ubiquinone metabolism via COQ7 protein.

Mitochondrial complex I is the first and largest (~1 MDa) protein complex of the mitochondrial electron transport chain comprised of 45 subunits, seven encoded in the mtDNA (Zhu et al., 2016; Fiedorczuk et al., 2016; Hirst, 2013). NDUFS4 is an 18 kDa accessory subunit that assembles the catalytic N-module (where NADH oxidation occurs) with the rest of the complex I. Mutations leading to the loss of NDUFS4 result in complex I deficiency (Zhu et al., 2016; Stroud et al., 2016; Gu et al., 2016; Kahlhöfer et al., 2017; Scacco et al., 2003) and are involved in cardiomyopathies, both in protective (ischemia/reperfusion injury) and detrimental roles (pressure overload) (Chouchani et al., 2014; Karamanlidis et al., 2013; Zhang et al., 2018). It was also reported that loss of NDUFS4 causes reduced complex I mediated ROS generation (ischemia/reperfusion injury) and increased protein acetylation (pressure overload), leading to their respective pathologies. Here, we demonstrate that heart-specific NDUFS4 KO mice have higher levels of NDUFAF2, which is reported to partly stabilize complex I in the absence of NDUFS4, although the resultant complex I is unstable and has reduced activity (Adjobo-Hermans et al., 2020). Taken together, we report that natural variation in NDUFS4 protein levels affect other complex I proteins resulting in reduced complex I activity and cardiac hypertrophy. On the other hand, the role of miR-27b on heart function is unclear. Cardiac-specific overexpression of miR-27b has been reported to increase pressure overload-induced cardiac hypertrophy via PPARG (Wang et al., 2012) but attenuated angiotensin II-induced atrial fibrosis via ALK5 (Wang et al., 2018). Also, whole body knockout of miR-27b was found to attenuate pressure overload-induced cardiac hypertrophy via FGF1 (Li et al., 2022), but this could be due to extracardiac effects mediated by miR-27b-3p such as adipocyte browning (Yu et al., 2018; Sun and Trajkovski, 2014; Kong et al., 2015). Using 100 HMDP inbred strains of mice, here we found that cardiac miR-27b expression was inversely correlated with heart weights and that mature miR-27b-3p appears to have a protective role in reducing cardiac hypertrophy via increasing NDUFS4 protein levels.

Mitochondria are unique organelles having their own genome that encodes for 13 protein coding genes, which are essential subunits of complexes I, III, IV, and V. Nuclear-encoded LRPPRC protein working together with the SLIRP protein helps to enable mitochondrial mRNA translation via promoting its polyadenylation and stability (Siira et al., 2017; Chujo et al., 2012; Ruzzenente et al., 2012). Loss-of-function mutations in LRPPRC causes Leigh syndrome, French-Canadian type that are associated with complex IV deficiency, impaired mitochondrial respiration and in some cases, neonatal cardiomyopathy, and congenital cardiac abnormalities (Mootha et al., 2003; Oláhová et al., 2015). Here, we show that natural genetic variations in a panel of inbred mouse strains at a chr17 locus (TT allele) result in higher protein levels of the LRPPRC/SLIRP complex as well as lower heart weights. This phenomenon is most likely mediated by increased expression levels of mitochondrial mRNA that coincide with reduced protein levels of mitoribosomes. We propose that since these mitochondrial mRNAs are protected by LRPPRC/SLIRP complex, fewer mitoribosomes are necessary for translation. Interestingly, although LRPPRC mutations are often associated with complex IV deficiency (Mootha et al., 2003; Oláhová et al., 2015), we observed that complex I mRNAs were also strongly regulated by the LRPPRC/SLIRP complex, in addition to complex IV mRNAs. Thus, further research on the bioenergetic consequences of LRPPRC/SLIRP loss-of-function mutations beyond complex IV deficiency are warranted.

Mitochondrial coenzyme Q, or ubiquinone or CoQ, is essential for mitochondrial electron transport chain function as it shuttles electrons from both complexes I and II to complex III. The enzyme COQ7 is responsible for catalyzing the penultimate step in CoQ biosynthesis. Here, we report that the chr7 locus (GG allele) is associated with reduced levels of COQ7 protein, which is in turn associated with increased heart weights. Interestingly, when we measured CoQ levels in these hearts, we found no differences in the mitochondrial fractions but an increase in both CoQ9 and CoQ10 levels in the lysates. Mice with a complete loss of Coq7 expression have embryonic lethality (Nakai et al., 2001) but survive for several months after a knockout is induced in the adult (Wang et al., 2015). Interestingly, Coq7 heterozygous knockout mice are long-lived but exhibit dysfunctional mitochondria (such as reduced respiration, reduced ATP levels and increased ROS generation) with no differences in CoQ levels despite significantly reduced COQ7 protein levels (Lapointe and Hekimi, 2008; Lapointe et al., 2009; Liu et al., 2005). Furthermore, these Coq7 heterozygous knockout mice were found to harbor a varying distribution of CoQ9 levels in their liver mitochondria with the outer membrane having increased CoQ9 while the inner membrane had reduced CoQ9 levels (Lapointe et al., 2012). It was postulated that higher CoQ9 levels in the liver mitochondrial outer membrane may be a protective response to increased ROS levels generated in Coq7 heterozygous knockout mice (Lapointe et al., 2012). Thus, reduced COQ7 protein levels do not necessarily associate with reduced CoQ levels but have heterogeneous sub-mitochondrial and possibly subcellular CoQ distributions. Taken together, we propose that increased heart weights in mice harboring chr7 locus (GG allele) is a result of increased ROS generation caused by reduced COQ7 protein levels. Also, the increased CoQ levels in the heart lysates of these mice might be a protective response against the oxidative stress generated by reduced COQ7 protein in these mice. Further research is warranted in understanding the purported role of COQ7 in submitochondrial and subcellular CoQ heterogeneous distribution focusing on heart pathophysiology.

In conclusion, our unbiased systems genetics analyses identified three loci regulating mitochondrial function in the heart. None of these loci were observed when transcript levels were examined, providing justification for proteomic rather than transcriptomic studies. All three loci are associated with heart mass in two independent heart stress models and/or negatively correlated with known hypertrophic markers (Supplementary file 1j). Nevertheless, the percent change in heart mass between the three loci were only of modest value. However, both heart failure and hypertrophy being complex traits are influenced by a large number of genetic factors, each exerting a small to large effect. The resulting phenotypes are therefore a sum of all impacts. So, we believe it is important to characterize modest as well as large effects. In summary, our results provide mechanistic information about the roles of previously studied genes namely, NDUFS4, LRPPRC, and COQ7, in heart failure.

All sequencing raw data can be accessed at the Gene Expression Omnibus under accession GSE194198 (HF/HS heart HMDP RNA-seq data) and GSE207142 (HF/HS heart HMDP miRNA-seq data). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE Perez-Riverol et al., 2022 partner repository with the dataset identifiers PXD036398 (HMDP hearts) and PXD036399 (NDUFS4 KO hearts).

1. 1. Lusis AJ
   2. Chella Krishnan K
   3. Kaczor-Urbanowicz KE

   (2022) NCBI Gene Expression Omnibus

   ID GSE207142. Small RNA sequencing of hearts from female mice on high fat diet, from 85 strains.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE207142
2. 1. Lusis AJ
   2. Chella Krishnan K
   3. Parker BL

   (2022) ProteomeXchange

   ID PXD036398. HMDP hearts.

   https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD036398
3. 1. Lusis AJ
   2. Chella Krishnan K
   3. Parker BL

   (2022) ProteomeXchange

   ID PXD036399. NDUFS4 KO hearts.

   https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD036399

1. 1. Lusis AJ
   2. Cao Y

   (2022) NCBI Gene Expression Omnibus

   ID GSE194198. Heart profiling in HMDP (high-fat/high-sucrose diet).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194198

1. 1. Adjobo-Hermans MJW
   2. de Haas R
   3. Willems P
   4. Wojtala A
   5. van Emst-de Vries SE
   6. Wagenaars JA
   7. van den Brand M
   8. Rodenburg RJ
   9. Smeitink JAM
   10. Nijtmans LG
   11. Sazanov LA
   12. Wieckowski MR
   13. Koopman WJH

   (2020) Ndufs4 deletion triggers loss of Ndufa12 in Ndufs4(-/-) mice and Leigh syndrome patients: A stabilizing role for Ndufaf2

   Biochimica et Biophysica Acta. Bioenergetics 1861:148213.

   https://doi.org/10.1016/j.bbabio.2020.148213

   • PubMed
   • Google Scholar
2. 1. Aguet F

   (2017) Genetic effects on gene expression across human tissues

   Nature 550:204–213.

   https://doi.org/10.1038/nature24277

   • Google Scholar
3. 1. Ali AT
   2. Boehme L
   3. Carbajosa G
   4. Seitan VC
   5. Small KS
   6. Hodgkinson A

   (2019) Nuclear genetic regulation of the human mitochondrial transcriptome

   eLife 8:e41927.

   https://doi.org/10.7554/eLife.41927

   • PubMed
   • Google Scholar
4. 1. Begriche K
   2. Igoudjil A
   3. Pessayre D
   4. Fromenty B

   (2006) Mitochondrial dysfunction in NASH: causes, consequences and possible means to prevent it

   Mitochondrion 6:1–28.

   https://doi.org/10.1016/j.mito.2005.10.004

   • PubMed
   • Google Scholar
5. 1. Bennett BJ
   2. Farber CR
   3. Orozco L
   4. Kang HM
   5. Ghazalpour A
   6. Siemers N
   7. Neubauer M
   8. Neuhaus I
   9. Yordanova R
   10. Guan B
   11. Truong A
   12. Yang W
   13. He A
   14. Kayne P
   15. Gargalovic P
   16. Kirchgessner T
   17. Pan C
   18. Castellani LW
   19. Kostem E
   20. Furlotte N
   21. Drake TA
   22. Eskin E
   23. Lusis AJ

   (2010) A high-resolution Association mapping panel for the dissection of complex traits in mice

   Genome Research 20:281–290.

   https://doi.org/10.1101/gr.099234.109

   • PubMed
   • Google Scholar
6. 1. Brown DA
   2. Perry JB
   3. Allen ME
   4. Sabbah HN
   5. Stauffer BL
   6. Shaikh SR
   7. Cleland JGF
   8. Colucci WS
   9. Butler J
   10. Voors AA
   11. Anker SD
   12. Pitt B
   13. Pieske B
   14. Filippatos G
   15. Greene SJ
   16. Gheorghiade M

   (2017) Expert consensus document: mitochondrial function as a therapeutic target in heart failure

   Nature Reviews Cardiology 14:238–250.

   https://doi.org/10.1038/nrcardio.2016.203

   • Google Scholar
7. 1. Brynedal B
   2. Choi J
   3. Raj T
   4. Bjornson R
   5. Stranger BE
   6. Neale BM
   7. Voight BF
   8. Cotsapas C

   (2017) Large-scale Trans-eQTLs affect hundreds of transcripts and mediate patterns of transcriptional Co-regulation

   American Journal of Human Genetics 100:581–591.

   https://doi.org/10.1016/j.ajhg.2017.02.004

   • PubMed
   • Google Scholar
8. 1. Burger N
   2. Logan A
   3. Prime TA
   4. Mottahedin A
   5. Caldwell ST
   6. Krieg T
   7. Hartley RC
   8. James AM
   9. Murphy MP

   (2020) A sensitive mass spectrometric assay for mitochondrial Coq pool redox state in vivo

   Free Radical Biology & Medicine 147:37–47.

   https://doi.org/10.1016/j.freeradbiomed.2019.11.028

   • PubMed
   • Google Scholar
9. 1. Cao Y
   2. Vergnes L
   3. Wang YC
   4. Pan C
   5. Chella Krishnan K
   6. Moore TM
   7. Rosa-Garrido M
   8. Kimball TH
   9. Zhou Z
   10. Charugundla S
   11. Rau CD
   12. Seldin MM
   13. Wang J
   14. Wang Y
   15. Vondriska TM
   16. Reue K
   17. Lusis AJ

   (2022) Sex differences in heart mitochondria regulate diastolic dysfunction

   Nature Communications 13:3850.

   https://doi.org/10.1038/s41467-022-31544-5

   • PubMed
   • Google Scholar
10. 1. Chella Krishnan K
    2. Kurt Z
    3. Barrere-Cain R
    4. Sabir S
    5. Das A
    6. Floyd R
    7. Vergnes L
    8. Zhao Y
    9. Che N
    10. Charugundla S
    11. Qi H
    12. Zhou Z
    13. Meng Y
    14. Pan C
    15. Seldin MM
    16. Norheim F
    17. Hui S
    18. Reue K
    19. Lusis AJ
    20. Yang X

    (2018) Integration of multi-Omics data from mouse diversity panel highlights mitochondrial dysfunction in non-alcoholic fatty liver disease

    Cell Systems 6:103–115.

    https://doi.org/10.1016/j.cels.2017.12.006

    • PubMed
    • Google Scholar
11. 1. Chella Krishnan K
    2. Sabir S
    3. Shum M
    4. Meng Y
    5. Acín-Pérez R
    6. Lang JM
    7. Floyd RR
    8. Vergnes L
    9. Seldin MM
    10. Fuqua BK
    11. Jayasekera DW
    12. Nand SK
    13. Anum DC
    14. Pan C
    15. Stiles L
    16. Péterfy M
    17. Reue K
    18. Liesa M
    19. Lusis AJ

    (2019) Sex-specific metabolic functions of Adipose Lipocalin-2

    Molecular Metabolism 30:30–47.

    https://doi.org/10.1016/j.molmet.2019.09.009

    • PubMed
    • Google Scholar
12. 1. Chella Krishnan K
    2. Vergnes L
    3. Acín-Pérez R
    4. Stiles L
    5. Shum M
    6. Ma L
    7. Mouisel E
    8. Pan C
    9. Moore TM
    10. Péterfy M
    11. Romanoski CE
    12. Reue K
    13. Björkegren JLM
    14. Laakso M
    15. Liesa M
    16. Lusis AJ

    (2021) Sex-specific genetic regulation of Adipose mitochondria and metabolic syndrome by Ndufv2

    Nature Metabolism 3:1552–1568.

    https://doi.org/10.1038/s42255-021-00481-w

    • PubMed
    • Google Scholar
13. 1. Chouchani ET
    2. Methner C
    3. Buonincontri G
    4. Hu C-H
    5. Logan A
    6. Sawiak SJ
    7. Murphy MP
    8. Krieg T

    (2014) Complex I deficiency due to selective loss of Ndufs4 in the mouse heart results in severe hypertrophic cardiomyopathy

    PLOS ONE 9:e94157.

    https://doi.org/10.1371/journal.pone.0094157

    • PubMed
    • Google Scholar
14. 1. Chujo T
    2. Ohira T
    3. Sakaguchi Y
    4. Goshima N
    5. Nomura N
    6. Nagao A
    7. Suzuki T

    (2012) LRPPRC/SLIRP suppresses Pnpase-mediated mRNA decay and promotes Polyadenylation in human mitochondria

    Nucleic Acids Research 40:8033–8047.

    https://doi.org/10.1093/nar/gks506

    • PubMed
    • Google Scholar
15. 1. Civelek M
    2. Lusis AJ

    (2014) Systems Genetics approaches to understand complex traits

    Nature Reviews. Genetics 15:34–48.

    https://doi.org/10.1038/nrg3575

    • PubMed
    • Google Scholar
16. 1. Eng JK
    2. McCormack AL
    3. Yates JR

    (1994) An approach to correlate Tandem mass spectral data of peptides with amino acid sequences in a protein database

    Journal of the American Society for Mass Spectrometry 5:976–989.

    https://doi.org/10.1016/1044-0305(94)80016-2

    • PubMed
    • Google Scholar
17. 1. Fiedorczuk K
    2. Letts JA
    3. Degliesposti G
    4. Kaszuba K
    5. Skehel M
    6. Sazanov LA

    (2016) Atomic structure of the entire mammalian mitochondrial complex I

    Nature 538:406–410.

    https://doi.org/10.1038/nature19794

    • PubMed
    • Google Scholar
18. 1. Folkersen L
    2. Gustafsson S
    3. Wang Q
    4. Hansen DH
    5. Hedman ÅK
    6. Schork A
    7. Page K
    8. Zhernakova DV
    9. Wu Y
    10. Peters J
    11. Eriksson N
    12. Bergen SE
    13. Boutin TS
    14. Bretherick AD
    15. Enroth S
    16. Kalnapenkis A
    17. Gådin JR
    18. Suur BE
    19. Chen Y
    20. Matic L
    21. Gale JD
    22. Lee J
    23. Zhang W
    24. Quazi A
    25. Ala-Korpela M
    26. Choi SH
    27. Claringbould A
    28. Danesh J
    29. Davey Smith G
    30. de Masi F
    31. Elmståhl S
    32. Engström G
    33. Fauman E
    34. Fernandez C
    35. Franke L
    36. Franks PW
    37. Giedraitis V
    38. Haley C
    39. Hamsten A
    40. Ingason A
    41. Johansson Å
    42. Joshi PK
    43. Lind L
    44. Lindgren CM
    45. Lubitz S
    46. Palmer T
    47. Macdonald-Dunlop E
    48. Magnusson M
    49. Melander O
    50. Michaelsson K
    51. Morris AP
    52. Mägi R
    53. Nagle MW
    54. Nilsson PM
    55. Nilsson J
    56. Orho-Melander M
    57. Polasek O
    58. Prins B
    59. Pålsson E
    60. Qi T
    61. Sjögren M
    62. Sundström J
    63. Surendran P
    64. Võsa U
    65. Werge T
    66. Wernersson R
    67. Westra H-J
    68. Yang J
    69. Zhernakova A
    70. Ärnlöv J
    71. Fu J
    72. Smith JG
    73. Esko T
    74. Hayward C
    75. Gyllensten U
    76. Landen M
    77. Siegbahn A
    78. Wilson JF
    79. Wallentin L
    80. Butterworth AS
    81. Holmes MV
    82. Ingelsson E
    83. Mälarstig A

    (2020) Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals

    Nature Metabolism 2:1135–1148.

    https://doi.org/10.1038/s42255-020-00287-2

    • PubMed
    • Google Scholar
19. 1. GTEx Consortium

    (2020) The Gtex consortium Atlas of genetic regulatory effects across human tissues

    Science 369:1318–1330.

    https://doi.org/10.1126/science.aaz1776

    • PubMed
    • Google Scholar
20. 1. Gu J
    2. Wu M
    3. Guo R
    4. Yan K
    5. Lei J
    6. Gao N
    7. Yang M

    (2016) The architecture of the mammalian Respirasome

    Nature 537:639–643.

    https://doi.org/10.1038/nature19359

    • PubMed
    • Google Scholar
21. 1. He B
    2. Shi J
    3. Wang X
    4. Jiang H
    5. Zhu HJ

    (2020) Genome-wide pQTL analysis of protein expression regulatory networks in the human liver

    BMC Biology 18:97.

    https://doi.org/10.1186/s12915-020-00830-3

    • PubMed
    • Google Scholar
22. 1. Hirst J

    (2013) Mitochondrial complex I

    Annual Review of Biochemistry 82:551–575.

    https://doi.org/10.1146/annurev-biochem-070511-103700

    • PubMed
    • Google Scholar
23. 1. Huang DW
    2. Sherman BT
    3. Lempicki RA

    (2009) Systematic and integrative analysis of large Gene lists using DAVID Bioinformatics resources

    Nature Protocols 4:44–57.

    https://doi.org/10.1038/nprot.2008.211

    • Google Scholar
24. 1. Hui ST
    2. Kurt Z
    3. Tuominen I
    4. Norheim F
    5. Davis RC
    6. Pan C
    7. Dirks DL
    8. Magyar CE
    9. French SW
    10. Chella Krishnan K
    11. Sabir S
    12. Campos-Pérez F
    13. Méndez-Sánchez N
    14. Macías-Kauffer L
    15. León-Mimila P
    16. Canizales-Quinteros S
    17. Yang X
    18. Beaven SW
    19. Huertas-Vazquez A
    20. Lusis AJ

    (2018) The genetic architecture of diet-induced hepatic fibrosis in mice

    Hepatology 68:2182–2196.

    https://doi.org/10.1002/hep.30113

    • PubMed
    • Google Scholar
25. 1. Kahlhöfer F
    2. Kmita K
    3. Wittig I
    4. Zwicker K
    5. Zickermann V

    (2017) Accessory subunit NUYM (Ndufs4) is required for stability of the electron input Module and activity of mitochondrial complex I

    Biochimica et Biophysica Acta - Bioenergetics 1858:175–181.

    https://doi.org/10.1016/j.bbabio.2016.11.010

    • Google Scholar
26. 1. Käll L
    2. Canterbury JD
    3. Weston J
    4. Noble WS
    5. MacCoss MJ

    (2007) Semi-supervised learning for peptide identification from shotgun proteomics datasets

    Nature Methods 4:923–925.

    https://doi.org/10.1038/nmeth1113

    • PubMed
    • Google Scholar
27. 1. Käll L
    2. Storey JD
    3. Noble WS

    (2009) QVALITY: non-parametric estimation of Q-values and posterior error probabilities

    Bioinformatics 25:964–966.

    https://doi.org/10.1093/bioinformatics/btp021

    • PubMed
    • Google Scholar
28. 1. Karamanlidis G
    2. Lee CF
    3. Garcia-Menendez L
    4. Kolwicz SC
    5. Suthammarak W
    6. Gong G
    7. Sedensky MM
    8. Morgan PG
    9. Wang W
    10. Tian R

    (2013) Mitochondrial complex I deficiency increases protein Acetylation and accelerates heart failure

    Cell Metabolism 18:239–250.

    https://doi.org/10.1016/j.cmet.2013.07.002

    • PubMed
    • Google Scholar
29. 1. Keller MP
    2. Paul PK
    3. Rabaglia ME
    4. Stapleton DS
    5. Schueler KL
    6. Broman AT
    7. Ye SI
    8. Leng N
    9. Brandon CJ
    10. Neto EC
    11. Plaisier CL
    12. Simonett SP
    13. Kebede MA
    14. Sheynkman GM
    15. Klein MA
    16. Baliga NS
    17. Smith LM
    18. Broman KW
    19. Yandell BS
    20. Kendziorski C
    21. Attie AD

    (2016) The transcription factor Nfatc2 regulates beta-cell proliferation and genes associated with type 2 diabetes in mouse and human Islets

    PLOS Genetics 12:e1006466.

    https://doi.org/10.1371/journal.pgen.1006466

    • PubMed
    • Google Scholar
30. 1. Keller MP
    2. Gatti DM
    3. Schueler KL
    4. Rabaglia ME
    5. Stapleton DS
    6. Simecek P
    7. Vincent M
    8. Allen S
    9. Broman AT
    10. Bacher R
    11. Kendziorski C
    12. Broman KW
    13. Yandell BS
    14. Churchill GA
    15. Attie AD

    (2018) Genetic drivers of Pancreatic Islet function

    Genetics 209:335–356.

    https://doi.org/10.1534/genetics.118.300864

    • PubMed
    • Google Scholar
31. 1. Keller MP
    2. Rabaglia ME
    3. Schueler KL
    4. Stapleton DS
    5. Gatti DM
    6. Vincent M
    7. Mitok KA
    8. Wang Z
    9. Ishimura T
    10. Simonett SP
    11. Emfinger CH
    12. Das R
    13. Beck T
    14. Kendziorski C
    15. Broman KW
    16. Yandell BS
    17. Churchill GA
    18. Attie AD

    (2019) Gene Loci associated with insulin secretion in Islets from non-diabetic mice

    The Journal of Clinical Investigation 129:4419–4432.

    https://doi.org/10.1172/JCI129143

    • PubMed
    • Google Scholar
32. 1. Kim JA
    2. Wei Y
    3. Sowers JR

    (2008) Role of mitochondrial dysfunction in insulin resistance

    Circulation Research 102:401–414.

    https://doi.org/10.1161/CIRCRESAHA.107.165472

    • PubMed
    • Google Scholar
33. 1. Kong X
    2. Yu J
    3. Bi J
    4. Qi H
    5. Di W
    6. Wu L
    7. Wang L
    8. Zha J
    9. Lv S
    10. Zhang F
    11. Li Y
    12. Hu F
    13. Liu F
    14. Zhou H
    15. Liu J
    16. Ding G

    (2015) Glucocorticoids Transcriptionally regulate miR-27B expression promoting body fat accumulation via suppressing the Browning of white adipose tissue

    Diabetes 64:393–404.

    https://doi.org/10.2337/db14-0395

    • PubMed
    • Google Scholar
34. 1. Kozomara A
    2. Griffiths-Jones S

    (2011) miRBase: integrating microRNA Annotation and deep-sequencing data

    Nucleic Acids Research 39:D152–D157.

    https://doi.org/10.1093/nar/gkq1027

    • PubMed
    • Google Scholar
35. 1. Langfelder P
    2. Horvath S

    (2008) WGCNA: an R package for weighted correlation network analysis

    BMC Bioinformatics 9:559.

    https://doi.org/10.1186/1471-2105-9-559

    • PubMed
    • Google Scholar
36. 1. Lapointe J
    2. Hekimi S

    (2008) Early mitochondrial dysfunction in long-lived Mclk1+/- mice

    The Journal of Biological Chemistry 283:26217–26227.

    https://doi.org/10.1074/jbc.M803287200

    • PubMed
    • Google Scholar
37. 1. Lapointe J
    2. Stepanyan Z
    3. Bigras E
    4. Hekimi S

    (2009) Reversal of the mitochondrial phenotype and slow development of oxidative biomarkers of aging in long-lived Mclk1+/- mice

    The Journal of Biological Chemistry 284:20364–20374.

    https://doi.org/10.1074/jbc.M109.006569

    • PubMed
    • Google Scholar
38. 1. Lapointe J
    2. Wang Y
    3. Bigras E
    4. Hekimi S

    (2012) The Submitochondrial distribution of Ubiquinone affects respiration in long-lived Mclk1+/- mice

    The Journal of Cell Biology 199:215–224.

    https://doi.org/10.1083/jcb.201203090

    • PubMed
    • Google Scholar
39. 1. Leong DW
    2. Komen JC
    3. Hewitt CA
    4. Arnaud E
    5. McKenzie M
    6. Phipson B
    7. Bahlo M
    8. Laskowski A
    9. Kinkel SA
    10. Davey GM
    11. Heath WR
    12. Voss AK
    13. Zahedi RP
    14. Pitt JJ
    15. Chrast R
    16. Sickmann A
    17. Ryan MT
    18. Smyth GK
    19. Thorburn DR
    20. Scott HS

    (2012) Proteomic and Metabolomic analyses of mitochondrial complex I-deficient mouse model generated by spontaneous B2 short interspersed nuclear element (SINE) insertion into NADH dehydrogenase (Ubiquinone) Fe-S protein 4 (Ndufs4) Gene

    The Journal of Biological Chemistry 287:20652–20663.

    https://doi.org/10.1074/jbc.M111.327601

    • PubMed
    • Google Scholar
40. 1. Leshinsky-Silver E
    2. Lebre A-S
    3. Minai L
    4. Saada A
    5. Steffann J
    6. Cohen S
    7. Rötig A
    8. Munnich A
    9. Lev D
    10. Lerman-Sagie T

    (2009) Ndufs4 mutations cause Leigh syndrome with predominant brainstem involvement

    Molecular Genetics and Metabolism 97:185–189.

    https://doi.org/10.1016/j.ymgme.2009.03.002

    • PubMed
    • Google Scholar
41. 1. Li G
    2. Shao Y
    3. Guo HC
    4. Zhi Y
    5. Qiao B
    6. Ma K
    7. Du J
    8. Lai YQ
    9. Li Y

    (2022) Microrna-27B-3P down-regulates Fgf1 and aggravates pathological cardiac remodelling

    Cardiovascular Research 118:2139–2151.

    https://doi.org/10.1093/cvr/cvab248

    • PubMed
    • Google Scholar
42. 1. Lippert C
    2. Listgarten J
    3. Liu Y
    4. Kadie CM
    5. Davidson RI
    6. Heckerman D

    (2011) Fast linear mixed models for genome-wide Association studies

    Nature Methods 8:833–835.

    https://doi.org/10.1038/nmeth.1681

    • PubMed
    • Google Scholar
43. 1. Liu X
    2. Jiang N
    3. Hughes B
    4. Bigras E
    5. Shoubridge E
    6. Hekimi S

    (2005) Evolutionary conservation of the Clk-1-dependent mechanism of longevity: loss of Mclk1 increases cellular fitness and lifespan in mice

    Genes & Development 19:2424–2434.

    https://doi.org/10.1101/gad.1352905

    • PubMed
    • Google Scholar
44. 1. Lusis AJ
    2. Seldin MM
    3. Allayee H
    4. Bennett BJ
    5. Civelek M
    6. Davis RC
    7. Eskin E
    8. Farber CR
    9. Hui S
    10. Mehrabian M
    11. Norheim F
    12. Pan C
    13. Parks B
    14. Rau CD
    15. Smith DJ
    16. Vallim T
    17. Wang Y
    18. Wang J

    (2016) The hybrid mouse diversity panel: a resource for systems Genetics analyses of metabolic and cardiovascular traits

    Journal of Lipid Research 57:925–942.

    https://doi.org/10.1194/jlr.R066944

    • Google Scholar
45. 1. McAlister GC
    2. Nusinow DP
    3. Jedrychowski MP
    4. Wühr M
    5. Huttlin EL
    6. Erickson BK
    7. Rad R
    8. Haas W
    9. Gygi SP

    (2014) Multinotch Ms3 enables accurate, sensitive, and Multiplexed detection of differential expression across cancer cell line Proteomes

    Analytical Chemistry 86:7150–7158.

    https://doi.org/10.1021/ac502040v

    • PubMed
    • Google Scholar
46. 1. Mootha VK
    2. Lepage P
    3. Miller K
    4. Bunkenborg J
    5. Reich M
    6. Hjerrild M
    7. Delmonte T
    8. Villeneuve A
    9. Sladek R
    10. Xu F
    11. Mitchell GA
    12. Morin C
    13. Mann M
    14. Hudson TJ
    15. Robinson B
    16. Rioux JD
    17. Lander ES

    (2003) Identification of a gene causing human cytochrome C oxidase deficiency by integrative Genomics

    PNAS 100:605–610.

    https://doi.org/10.1073/pnas.242716699

    • PubMed
    • Google Scholar
47. 1. Morgan AP
    2. Fu CP
    3. Kao CY
    4. Welsh CE
    5. Didion JP
    6. Yadgary L
    7. Hyacinth L
    8. Ferris MT
    9. Bell TA
    10. Miller DR
    11. Giusti-Rodriguez P
    12. Nonneman RJ
    13. Cook KD
    14. Whitmire JK
    15. Gralinski LE
    16. Keller M
    17. Attie AD
    18. Churchill GA
    19. Petkov P
    20. Sullivan PF
    21. Brennan JR
    22. McMillan L
    23. Pardo-Manuel de Villena F

    (2015) The mouse universal Genotyping array: from Substrains to subspecies

    G3: Genes, Genomes, Genetics 6:263–279.

    https://doi.org/10.1534/g3.115.022087

    • PubMed
    • Google Scholar
48. 1. Murphy E
    2. Ardehali H
    3. Balaban RS
    4. DiLisa F
    5. Dorn GW
    6. Kitsis RN
    7. Otsu K
    8. Ping P
    9. Rizzuto R
    10. Sack MN
    11. Wallace D
    12. Youle RJ
    13. American Heart Association Council on Basic Cardiovascular Sciences, Council on Clinical Cardiology, and Council on Functional Genomics and Translational Biology

    (2016) Mitochondrial function, biology, and role in disease: A scientific statement from the American heart Association

    Circulation Research 118:1960–1991.

    https://doi.org/10.1161/RES.0000000000000104

    • PubMed
    • Google Scholar
49. 1. Nakai D
    2. Yuasa S
    3. Takahashi M
    4. Shimizu T
    5. Asaumi S
    6. Isono K
    7. Takao T
    8. Suzuki Y
    9. Kuroyanagi H
    10. Hirokawa K
    11. Koseki H
    12. Shirsawa T

    (2001) Mouse Homologue of Coq7/Clk-1, longevity Gene in Caenorhabditis elegans, is essential for coenzyme Q synthesis, maintenance of mitochondrial integrity, and neurogenesis

    Biochemical and Biophysical Research Communications 289:463–471.

    https://doi.org/10.1006/bbrc.2001.5977

    • PubMed
    • Google Scholar
50. 1. Norheim F
    2. Hui ST
    3. Kulahcioglu E
    4. Mehrabian M
    5. Cantor RM
    6. Pan C
    7. Parks BW
    8. Lusis AJ

    (2017) Genetic and hormonal control of hepatic steatosis in female and male mice

    Journal of Lipid Research 58:178–187.

    https://doi.org/10.1194/jlr.M071522

    • PubMed
    • Google Scholar
51. 1. Norheim F
    2. Bjellaas T
    3. Hui ST
    4. Chella Krishnan K
    5. Lee J
    6. Gupta S
    7. Pan C
    8. Hasin-Brumshtein Y
    9. Parks BW
    10. Li DY
    11. Bui HH
    12. Mosier M
    13. Wu Y
    14. Huertas-Vazquez A
    15. Hazen SL
    16. Gundersen TE
    17. Mehrabian M
    18. Tang WHW
    19. Hevener AL
    20. Drevon CA
    21. Lusis AJ

    (2018) Genetic, dietary, and sex-specific regulation of hepatic Ceramides and the relationship between hepatic Ceramides and IR

    Journal of Lipid Research 59:1164–1174.

    https://doi.org/10.1194/jlr.M081398

    • PubMed
    • Google Scholar
52. 1. Norheim F
    2. Hasin-Brumshtein Y
    3. Vergnes L
    4. Chella Krishnan K
    5. Pan C
    6. Seldin MM
    7. Hui ST
    8. Mehrabian M
    9. Zhou Z
    10. Gupta S
    11. Parks BW
    12. Walch A
    13. Reue K
    14. Hofmann SM
    15. Arnold AP
    16. Lusis AJ

    (2019) Gene-by-sex interactions in mitochondrial functions and Cardio-metabolic traits

    Cell Metabolism 29:932–949.

    https://doi.org/10.1016/j.cmet.2018.12.013

    • PubMed
    • Google Scholar
53. 1. Oláhová M
    2. Hardy SA
    3. Hall J
    4. Yarham JW
    5. Haack TB
    6. Wilson WC
    7. Alston CL
    8. He L
    9. Aznauryan E
    10. Brown RM
    11. Brown GK
    12. Morris AAM
    13. Mundy H
    14. Broomfield A
    15. Barbosa IA
    16. Simpson MA
    17. Deshpande C
    18. Moeslinger D
    19. Koch J
    20. Stettner GM
    21. Bonnen PE
    22. Prokisch H
    23. Lightowlers RN
    24. McFarland R
    25. Chrzanowska-Lightowlers ZMA
    26. Taylor RW

    (2015) LRPPRC mutations cause early-onset Multisystem mitochondrial disease outside of the French-Canadian population

    Brain 138:3503–3519.

    https://doi.org/10.1093/brain/awv291

    • PubMed
    • Google Scholar
54. 1. Org E
    2. Mehrabian M
    3. Parks BW
    4. Shipkova P
    5. Liu X
    6. Drake TA
    7. Lusis AJ

    (2016) Sex differences and hormonal effects on gut Microbiota composition in mice

    Gut Microbes 7:313–322.

    https://doi.org/10.1080/19490976.2016.1203502

    • Google Scholar
55. 1. Parks BW
    2. Nam E
    3. Org E
    4. Kostem E
    5. Norheim F
    6. Hui ST
    7. Pan C
    8. Civelek M
    9. Rau CD
    10. Bennett BJ
    11. Mehrabian M
    12. Ursell LK
    13. He A
    14. Castellani LW
    15. Zinker B
    16. Kirby M
    17. Drake TA
    18. Drevon CA
    19. Knight R
    20. Gargalovic P
    21. Kirchgessner T
    22. Eskin E
    23. Lusis AJ

    (2013) Genetic control of obesity and gut Microbiota composition in response to high-fat, high-Sucrose diet in mice

    Cell Metabolism 17:141–152.

    https://doi.org/10.1016/j.cmet.2012.12.007

    • PubMed
    • Google Scholar
56. 1. Parks BW
    2. Sallam T
    3. Mehrabian M
    4. Psychogios N
    5. Hui ST
    6. Norheim F
    7. Castellani LW
    8. Rau CD
    9. Pan C
    10. Phun J
    11. Zhou Z
    12. Yang W-P
    13. Neuhaus I
    14. Gargalovic PS
    15. Kirchgessner TG
    16. Graham M
    17. Lee R
    18. Tontonoz P
    19. Gerszten RE
    20. Hevener AL
    21. Lusis AJ

    (2015) Genetic architecture of insulin resistance in the mouse

    Cell Metabolism 21:334–347.

    https://doi.org/10.1016/j.cmet.2015.01.002

    • PubMed
    • Google Scholar
57. 1. Perez-Riverol Y
    2. Bai J
    3. Bandla C
    4. García-Seisdedos D
    5. Hewapathirana S
    6. Kamatchinathan S
    7. Kundu DJ
    8. Prakash A
    9. Frericks-Zipper A
    10. Eisenacher M
    11. Walzer M
    12. Wang S
    13. Brazma A
    14. Vizcaíno JA

    (2022) The PRIDE database resources in 2022: a Hub for mass Spectrometry-based Proteomics evidences

    Nucleic Acids Research 50:D543–D552.

    https://doi.org/10.1093/nar/gkab1038

    • Google Scholar
58. 1. Rath S
    2. Sharma R
    3. Gupta R
    4. Ast T
    5. Chan C
    6. Durham TJ
    7. Goodman RP
    8. Grabarek Z
    9. Haas ME
    10. Hung WHW
    11. Joshi PR
    12. Jourdain AA
    13. Kim SH
    14. Kotrys AV
    15. Lam SS
    16. McCoy JG
    17. Meisel JD
    18. Miranda M
    19. Panda A
    20. Patgiri A
    21. Rogers R
    22. Sadre S
    23. Shah H
    24. Skinner OS
    25. To T-L
    26. Walker MA
    27. Wang H
    28. Ward PS
    29. Wengrod J
    30. Yuan C-C
    31. Calvo SE
    32. Mootha VK

    (2021) Mitocarta3.0: an updated mitochondrial Proteome now with sub-Organelle localization and pathway annotations

    Nucleic Acids Research 49:D1541–D1547.

    https://doi.org/10.1093/nar/gkaa1011

    • PubMed
    • Google Scholar
59. 1. Rau CD
    2. Wang J
    3. Avetisyan R
    4. Romay MC
    5. Martin L
    6. Ren S
    7. Wang Y
    8. Lusis AJ

    (2015) Mapping genetic contributions to cardiac pathology induced by beta-adrenergic stimulation in mice

    Circulation. Cardiovascular Genetics 8:40–49.

    https://doi.org/10.1161/CIRCGENETICS.113.000732

    • PubMed
    • Google Scholar
60. 1. Rau CD
    2. Romay MC
    3. Tuteryan M
    4. Wang JJC
    5. Santolini M
    6. Ren S
    7. Karma A
    8. Weiss JN
    9. Wang Y
    10. Lusis AJ

    (2017) Systems Genetics approach identifies Gene pathways and Adamts2 as drivers of isoproterenol-induced cardiac hypertrophy and cardiomyopathy in mice

    Cell Systems 4:121–128.

    https://doi.org/10.1016/j.cels.2016.10.016

    • PubMed
    • Google Scholar
61. 1. Ruffieux H
    2. Carayol J
    3. Popescu R
    4. Harper ME
    5. Dent R
    6. Saris WHM
    7. Astrup A
    8. Hager J
    9. Davison AC
    10. Valsesia A

    (2020) A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma

    PLOS Computational Biology 16:e1007882.

    https://doi.org/10.1371/journal.pcbi.1007882

    • PubMed
    • Google Scholar
62. 1. Ruzzenente B
    2. Metodiev MD
    3. Wredenberg A
    4. Bratic A
    5. Park CB
    6. Cámara Y
    7. Milenkovic D
    8. Zickermann V
    9. Wibom R
    10. Hultenby K
    11. Erdjument-Bromage H
    12. Tempst P
    13. Brandt U
    14. Stewart JB
    15. Gustafsson CM
    16. Larsson N-G

    (2012) LRPPRC is necessary for Polyadenylation and coordination of translation of mitochondrial mRNAs

    The EMBO Journal 31:443–456.

    https://doi.org/10.1038/emboj.2011.392

    • PubMed
    • Google Scholar
63. 1. Sanyal AJ
    2. Campbell–Sargent C
    3. Mirshahi F
    4. Rizzo WB
    5. Contos MJ
    6. Sterling RK
    7. Luketic VA
    8. Shiffman ML
    9. Clore JN

    (2001) Nonalcoholic Steatohepatitis: Association of insulin resistance and mitochondrial abnormalities

    Gastroenterology 120:1183–1192.

    https://doi.org/10.1053/gast.2001.23256

    • Google Scholar
64. 1. Scacco S
    2. Petruzzella V
    3. Budde S
    4. Vergari R
    5. Tamborra R
    6. Panelli D
    7. van den Heuvel LP
    8. Smeitink JA
    9. Papa S

    (2003) Pathological mutations of the human Ndufs4 Gene of the 18-kDa (AQDQ) subunit of complex I affect the expression of the protein and the assembly and function of the complex

    The Journal of Biological Chemistry 278:44161–44167.

    https://doi.org/10.1074/jbc.M307615200

    • PubMed
    • Google Scholar
65. 1. Seldin MM
    2. Kim ED
    3. Romay MC
    4. Li S
    5. Rau CD
    6. Wang JJ
    7. Krishnan KC
    8. Wang Y
    9. Deb A
    10. Lusis AJ

    (2017) A systems Genetics approach identifies Trp53Inp2 as a link between cardiomyocyte glucose utilization and hypertrophic response

    American Journal of Physiology. Heart and Circulatory Physiology 312:H728–H741.

    https://doi.org/10.1152/ajpheart.00068.2016

    • PubMed
    • Google Scholar
66. 1. Seldin MM
    2. Koplev S
    3. Rajbhandari P
    4. Vergnes L
    5. Rosenberg GM
    6. Meng Y
    7. Pan C
    8. Phuong TMN
    9. Gharakhanian R
    10. Che N
    11. Mäkinen S
    12. Shih DM
    13. Civelek M
    14. Parks BW
    15. Kim ED
    16. Norheim F
    17. Chella Krishnan K
    18. Hasin-Brumshtein Y
    19. Mehrabian M
    20. Laakso M
    21. Drevon CA
    22. Koistinen HA
    23. Tontonoz P
    24. Reue K
    25. Cantor RM
    26. Björkegren JLM
    27. Lusis AJ

    (2018) A strategy for discovery of endocrine interactions with application to whole-body metabolism

    Cell Metabolism 27:1138–1155.

    https://doi.org/10.1016/j.cmet.2018.03.015

    • PubMed
    • Google Scholar
67. 1. Seldin M
    2. Yang X
    3. Lusis AJ

    (2019) Systems Genetics applications in metabolism research

    Nature Metabolism 1:1038–1050.

    https://doi.org/10.1038/s42255-019-0132-x

    • PubMed
    • Google Scholar
68. 1. Sharma A
    2. Fonarow GC
    3. Butler J
    4. Ezekowitz JA
    5. Felker GM

    (2016) Coenzyme Q10 and heart failure: A state-of-the-art review

    Circulation. Heart Failure 9:e002639.

    https://doi.org/10.1161/CIRCHEARTFAILURE.115.002639

    • PubMed
    • Google Scholar
69. 1. Siira SJ
    2. Spåhr H
    3. Shearwood A-MJ
    4. Ruzzenente B
    5. Larsson N-G
    6. Rackham O
    7. Filipovska A

    (2017) LRPPRC-mediated folding of the mitochondrial transcriptome

    Nature Communications 8:1532.

    https://doi.org/10.1038/s41467-017-01221-z

    • PubMed
    • Google Scholar
70. 1. Small KS
    2. Todorčević M
    3. Civelek M
    4. El-Sayed Moustafa JS
    5. Wang X
    6. Simon MM
    7. Fernandez-Tajes J
    8. Mahajan A
    9. Horikoshi M
    10. Hugill A
    11. Glastonbury CA
    12. Quaye L
    13. Neville MJ
    14. Sethi S
    15. Yon M
    16. Pan C
    17. Che N
    18. Viñuela A
    19. Tsai P-C
    20. Nag A
    21. Buil A
    22. Thorleifsson G
    23. Raghavan A
    24. Ding Q
    25. Morris AP
    26. Bell JT
    27. Thorsteinsdottir U
    28. Stefansson K
    29. Laakso M
    30. Dahlman I
    31. Arner P
    32. Gloyn AL
    33. Musunuru K
    34. Lusis AJ
    35. Cox RD
    36. Karpe F
    37. McCarthy MI

    (2018) Regulatory variants at Klf14 influence type 2 diabetes risk via a female-specific effect on Adipocyte size and body composition

    Nature Genetics 50:572–580.

    https://doi.org/10.1038/s41588-018-0088-x

    • Google Scholar
71. 1. Stroud DA
    2. Surgenor EE
    3. Formosa LE
    4. Reljic B
    5. Frazier AE
    6. Dibley MG
    7. Osellame LD
    8. Stait T
    9. Beilharz TH
    10. Thorburn DR
    11. Salim A
    12. Ryan MT

    (2016) Accessory subunits are integral for assembly and function of human mitochondrial complex I

    Nature 538:123–126.

    https://doi.org/10.1038/nature19754

    • PubMed
    • Google Scholar
72. 1. Sun L
    2. Trajkovski M

    (2014) Mir-27 Orchestrates the transcriptional regulation of Brown Adipogenesis

    Metabolism 63:272–282.

    https://doi.org/10.1016/j.metabol.2013.10.004

    • Google Scholar
73. 1. Svenson KL
    2. Gatti DM
    3. Valdar W
    4. Welsh CE
    5. Cheng R
    6. Chesler EJ
    7. Palmer AA
    8. McMillan L
    9. Churchill GA

    (2012) High-resolution genetic mapping using the mouse diversity Outbred population

    Genetics 190:437–447.

    https://doi.org/10.1534/genetics.111.132597

    • PubMed
    • Google Scholar
74. 1. Wallace DC

    (2018) Mitochondrial genetic medicine

    Nature Genetics 50:1642–1649.

    https://doi.org/10.1038/s41588-018-0264-z

    • PubMed
    • Google Scholar
75. 1. Wang J
    2. Song Y
    3. Zhang Y
    4. Xiao H
    5. Sun Q
    6. Hou N
    7. Guo S
    8. Wang Y
    9. Fan K
    10. Zhan D
    11. Zha L
    12. Cao Y
    13. Li Z
    14. Cheng X
    15. Zhang Y
    16. Yang X

    (2012) Cardiomyocyte overexpression of miR-27B induces cardiac hypertrophy and dysfunction in mice

    Cell Research 22:516–527.

    https://doi.org/10.1038/cr.2011.132

    • Google Scholar
76. 1. Wang Y
    2. Oxer D
    3. Hekimi S

    (2015) Mitochondrial function and LifeSpan of mice with controlled Ubiquinone biosynthesis

    Nature Communications 6:6393.

    https://doi.org/10.1038/ncomms7393

    • PubMed
    • Google Scholar
77. 1. Wang JJC
    2. Rau C
    3. Avetisyan R
    4. Ren S
    5. Romay MC
    6. Stolin G
    7. Gong KW
    8. Wang Y
    9. Lusis AJ

    (2016) Genetic dissection of cardiac remodeling in an isoproterenol-induced heart failure mouse model

    PLOS Genetics 12:e1006038.

    https://doi.org/10.1371/journal.pgen.1006038

    • PubMed
    • Google Scholar
78. 1. Wang Y
    2. Cai H
    3. Li H
    4. Gao Z
    5. Song K

    (2018) Atrial overexpression of microRNA-27B attenuates angiotensin II-induced atrial fibrosis and fibrillation by targeting Alk5

    Human Cell 31:251–260.

    https://doi.org/10.1007/s13577-018-0208-z

    • PubMed
    • Google Scholar
79. 1. Yang C
    2. Farias FHG
    3. Ibanez L
    4. Suhy A
    5. Sadler B
    6. Fernandez MV
    7. Wang F
    8. Bradley JL
    9. Eiffert B
    10. Bahena JA
    11. Budde JP
    12. Li Z
    13. Dube U
    14. Sung YJ
    15. Mihindukulasuriya KA
    16. Morris JC
    17. Fagan AM
    18. Perrin RJ
    19. Benitez BA
    20. Rhinn H
    21. Harari O
    22. Cruchaga C

    (2021) Genomic Atlas of the Proteome from brain, CSF and plasma Prioritizes proteins implicated in neurological disorders

    Nature Neuroscience 24:1302–1312.

    https://doi.org/10.1038/s41593-021-00886-6

    • PubMed
    • Google Scholar
80. 1. Yao C
    2. Joehanes R
    3. Johnson AD
    4. Huan T
    5. Liu C
    6. Freedman JE
    7. Munson PJ
    8. Hill DE
    9. Vidal M
    10. Levy D

    (2017) Dynamic role of Trans regulation of gene expression in relation to complex traits

    The American Journal of Human Genetics 100:571–580.

    https://doi.org/10.1016/j.ajhg.2017.02.003

    • Google Scholar
81. 1. Yao C
    2. Chen G
    3. Song C
    4. Keefe J
    5. Mendelson M
    6. Huan T
    7. Sun BB
    8. Laser A
    9. Maranville JC
    10. Wu H
    11. Ho JE
    12. Courchesne P
    13. Lyass A
    14. Larson MG
    15. Gieger C
    16. Graumann J
    17. Johnson AD
    18. Danesh J
    19. Runz H
    20. Hwang S-J
    21. Liu C
    22. Butterworth AS
    23. Suhre K
    24. Levy D

    (2018) Genome-wide mapping of plasma protein Qtls identifies Putatively causal genes and pathways for cardiovascular disease

    Nature Communications 9:3268.

    https://doi.org/10.1038/s41467-018-05512-x

    • Google Scholar
82. 1. Yin X
    2. Lanza IR
    3. Swain JM
    4. Sarr MG
    5. Nair KS
    6. Jensen MD

    (2014) Adipocyte mitochondrial function is reduced in human obesity independent of fat cell size

    The Journal of Clinical Endocrinology and Metabolism 99:E209–E216.

    https://doi.org/10.1210/jc.2013-3042

    • PubMed
    • Google Scholar
83. 1. Yu J
    2. Lv Y
    3. Di W
    4. Liu J
    5. Kong X
    6. Sheng Y
    7. Huang M
    8. Lv S
    9. Qi H
    10. Gao M
    11. Liang H
    12. Kim S
    13. Fu Z
    14. Zhou H
    15. Ding G

    (2018) Mir-27B-3P regulation in Browning of human visceral Adipose related to central obesity

    Obesity 26:387–396.

    https://doi.org/10.1002/oby.22104

    • PubMed
    • Google Scholar
84. 1. Zhang H
    2. Gong G
    3. Wang P
    4. Zhang Z
    5. Kolwicz SC
    6. Rabinovitch PS
    7. Tian R
    8. Wang W

    (2018) Heart specific knockout of Ndufs4 ameliorates ischemia reperfusion injury

    Journal of Molecular and Cellular Cardiology 123:38–45.

    https://doi.org/10.1016/j.yjmcc.2018.08.022

    • Google Scholar
85. 1. Zhong W
    2. Gummesson A
    3. Tebani A
    4. Karlsson MJ
    5. Hong M-G
    6. Schwenk JM
    7. Edfors F
    8. Bergström G
    9. Fagerberg L
    10. Uhlén M

    (2020) Whole-genome sequence Association analysis of blood proteins in a longitudinal wellness cohort

    Genome Medicine 12:53.

    https://doi.org/10.1186/s13073-020-00755-0

    • PubMed
    • Google Scholar
86. 1. Zhou B
    2. Tian R

    (2018) Mitochondrial dysfunction in pathophysiology of heart failure

    Journal of Clinical Investigation 128:3716–3726.

    https://doi.org/10.1172/JCI120849

    • Google Scholar
87. 1. Zhu J
    2. Vinothkumar KR
    3. Hirst J

    (2016) Structure of mammalian respiratory complex I

    Nature 536:354–358.

    https://doi.org/10.1038/nature19095

    • Google Scholar

Author details

1. Karthickeyan Chella Krishnan

   Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing

   For correspondence

   chellakn@ucmail.uc.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4895-2495

2. Elie-Julien El Hachem

   Department of Integrative Biology and Physiology, Field Systems Biology, Sciences Sorbonne Université, Paris, France

   Contribution

   Formal analysis, Investigation, Visualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5033-6117

3. Mark P Keller

   Biochemistry Department, University of Wisconsin-Madison, Madison, United States

   Contribution

   Formal analysis, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

4. Sanjeet G Patel

   Department of Surgery/Division of Cardiac Surgery, University of Southern California Keck School of Medicine, Los Angeles, United States

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Luke Carroll

   Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, Australia

   Contribution

   Formal analysis, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

6. Alexis Diaz Vegas

   Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, Australia

   Contribution

   Formal analysis, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

7. Isabela Gerdes Gyuricza

   Jackson Laboratory, Bar Harbor, United States

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

8. Christine Light

   Cardiovascular Biology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, United States

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

9. Yang Cao

   Department of Medicine/Division of Cardiology, University of California, Los Angeles, Los Angeles, United States

   Contribution

   Formal analysis, Investigation, Visualization

   Competing interests

   No competing interests declared

10. Calvin Pan

    Department of Medicine/Division of Cardiology, University of California, Los Angeles, Los Angeles, United States

    Contribution

    Formal analysis, Investigation, Visualization

    Competing interests

    No competing interests declared

11. Karolina Elżbieta Kaczor-Urbanowicz

    1. Division of Oral Biology and Medicine, UCLA School of Dentistry, Los Angeles, United States
    2. UCLA Institute for Quantitative and Computational Biosciences, Los Angeles, United States

    Contribution

    Formal analysis, Investigation, Visualization, Methodology

    Competing interests

    No competing interests declared

12. Varun Shravah

    Department of Chemistry, University of California, Los Angeles, United States

    Contribution

    Formal analysis, Investigation

    Competing interests

    No competing interests declared

13. Diana Anum

    Department of Integrative Biology and Physiology, University of California, Los Angeles, United States

    Contribution

    Formal analysis, Investigation

    Competing interests

    No competing interests declared

14. Matteo Pellegrini

    UCLA Institute for Quantitative and Computational Biosciences, Los Angeles, United States

    Contribution

    Resources, Supervision, Funding acquisition

    Competing interests

    No competing interests declared

15. Chi Fung Lee

    1. Cardiovascular Biology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, United States
    2. Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, United States

    Contribution

    Resources, Supervision, Funding acquisition

    Competing interests

    No competing interests declared

16. Marcus M Seldin

    1. Center for Epigenetics and Metabolism, Irvine, United States
    2. Department of Biological Chemistry, University of California, Irvine, United States

    Contribution

    Formal analysis, Investigation, Visualization

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8026-4759

17. Nadia A Rosenthal

    Jackson Laboratory, Bar Harbor, United States

    Contribution

    Resources, Supervision, Funding acquisition

    Competing interests

    No competing interests declared

18. Gary A Churchill

    Jackson Laboratory, Bar Harbor, United States

    Contribution

    Resources, Supervision, Funding acquisition

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9190-9284

19. Alan D Attie

    Biochemistry Department, University of Wisconsin-Madison, Madison, United States

    Contribution

    Resources, Supervision, Funding acquisition

    Competing interests

    No competing interests declared

20. Benjamin Parker

    Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia

    Contribution

    Formal analysis, Investigation, Visualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

21. David E James

    Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, Australia

    Contribution

    Resources, Supervision, Funding acquisition, Writing – review and editing

    Competing interests

    Senior Editor, eLife; Senior Editor for the Systems Genetics Special Issue (DEJ had no involvement in the decision making for this submission)

    "This ORCID iD identifies the author of this article:" 0000-0001-5946-5257

22. Aldons J Lusis

    1. Department of Medicine/Division of Cardiology, University of California, Los Angeles, Los Angeles, United States
    2. Department of Human Genetics, University of California, Los Angeles, United States
    3. Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing – review and editing

    For correspondence

    JLusis@mednet.ucla.edu

    Competing interests

    No competing interests declared

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