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).

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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 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 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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