Spinal and bulbar muscular atrophy (SBMA) is a neuromuscular disease caused by the expansion of a polyglutamine tract in the androgen receptor (AR). The mechanism by which expansion of polyglutamine in AR causes muscle atrophy is unknown. Here, we investigated pathological pathways underlying muscle atrophy in SBMA knock-in mice and patients. We show that glycolytic muscles were more severely affected than oxidative muscles in SBMA knock-in mice. Muscle atrophy was associated with early-onset, progressive glycolytic-to-oxidative fiber-type switch. Whole genome microarray and untargeted lipidomic analyses revealed enhanced lipid metabolism and impaired glycolysis selectively in muscle. These metabolic changes occurred before denervation and were associated with a concurrent enhancement of mechanistic target of rapamycin (mTOR) signaling, which induced peroxisome proliferator-activated receptor γ coactivator 1 alpha (PGC1α) expression. At later stages of disease, we detected mitochondrial membrane depolarization, enhanced transcription factor EB (TFEB) expression and autophagy, and mTOR-induced protein synthesis. Several of these abnormalities were detected in the muscle of SBMA patients. Feeding knock-in mice a high-fat diet (HFD) restored mTOR activation, decreased the expression of PGC1α, TFEB, and genes involved in oxidative metabolism, reduced mitochondrial abnormalities, ameliorated muscle pathology, and extended survival. These findings show early-onset and intrinsic metabolic alterations in SBMA muscle and link lipid/glucose metabolism to pathogenesis. Moreover, our results highlight an HFD regime as a promising approach to support SBMA patients.
ABSTRACT Highly conserved hypoxia-inducible factor 1 alpha (HIF1α) and its target 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) play a critical role in the survival of damaged β-cells in type 2 diabetes (T2D) while rendering β-cells non-responsive to glucose stimulation by mitochondrial suppression. HIF1α-PFKFB3 is activated in 30-50% of all β-cells in diabetic islets, leaving an open question of whether targeting this pathway may adjust β-cell mass and function to the specific metabolic demands during diabetogenic stress. Our previous studies of β-cells under amyloidogenic stress by human islet amyloid polypeptide (hIAPP) revealed that PFKFB3 is a metabolic execution arm of the HIF1α pathway with potent implications on Ca 2+ homeostasis, metabolome, and mitochondrial form and function. To discriminate the role of PFKFB3 from HIF1α in vivo , we generated mice with conditional β-cell specific disruption of the Pfkfb3 gene on a hIAPP +/- background and a high-fat diet (HFD) [PFKFB3 βKO + diabetogenic stress (DS)]. PFKFB3 disruption in β-cells under diabetogenic stress led to selective purging of hIAPP-damaged β-cells and the disappearance of bihormonal insulin- and glucagon-positive cells, thus compromised β-cells. At the same time, PFKFB3 disruption led to a three-fold increase in β-cell replication resembling control levels as measured with minichromosome maintenance 2 protein (MCM2). PFKFB3 disruption depleted bihormonal cells while increased β-cell replication that was reflected in the increased β-/α-cell ratio and maintained β-cell mass. Analysis of metabolic performance indicated comparable glucose intolerance and reduced plasma insulin levels in PFKFB3 βKO DS relative to PFKFB3 WT DS mice. In the PFKFB3 βKO DS group, plasma glucagon levels were reduced compared to PFKFB3 WT DS mice and were in line with increased insulin sensitivity. Glucose intolerance in PFKFB3 βKO DS mice could be explained by the compensatory expression of HIF1α after disruption of PFKFB3. Our data strongly suggest that the replication and functional recovery of β-cells under diabetogenic stress depend on selective purification of HIF1α and PFKFB3-positive β-cells. Thus, HIF1α-PFKFB3-dependent activation of cell competition and purging of compromised β-cells may yield functional competent β-cell mass in diabetes.
DNA methylation, specifically the formation of 5-methylcytosine at the C5 position of cytosine, undergoes reproducible changes as organisms age, establishing it as a significant biomarker in aging studies. Epigenetic clocks, which integrate methylation patterns to predict age, often employ linear models based on penalized regression, yet they encounter challenges in handling missing data, count-based bisulfite sequence data, and interpretation.
We appreciate the discussion of hopes and difficulties with gene therapy for Amyotrophic lateral sclerosis (ALS) in the excellent review by Scarrott et al. [1]. The authors point out the molecular ...
Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Methods Data availability References Decision letter Author response Article and author information Metrics Abstract Mitochondria play an important role in both normal heart function and disease etiology. We report analysis of common genetic variations contributing to mitochondrial and heart functions using an integrative proteomics approach in a panel of inbred mouse strains called the Hybrid Mouse Diversity Panel (HMDP). We performed a whole heart proteome study in the HMDP (72 strains, n=2-3 mice) and retrieved 848 mitochondrial proteins (quantified in ≥50 strains). High-resolution association mapping on their relative abundance levels revealed three trans-acting genetic loci on chromosomes (chr) 7, 13 and 17 that regulate distinct classes of mitochondrial proteins as well as cardiac hypertrophy. DAVID enrichment analyses of genes regulated by each of the loci revealed that the chr13 locus was highly enriched for complex-I proteins (24 proteins, P=2.2E-61), the chr17 locus for mitochondrial ribonucleoprotein complex (17 proteins, P=3.1E-25) and the chr7 locus for ubiquinone biosynthesis (3 proteins, P=6.9E-05). Follow-up high resolution regional mapping identified NDUFS4, LRPPRC and COQ7 as the candidate genes for chr13, chr17 and chr7 loci, respectively, and both experimental and statistical analyses supported their causal roles. Furthermore, a large cohort of Diversity Outbred mice was used to corroborate Lrpprc gene as a driver of mitochondrial DNA (mtDNA)-encoded gene regulation, and to show that the chr17 locus is specific to heart. Variations in all three loci were associated with heart mass in at least one of two independent heart stress models, namely, isoproterenol-induced heart failure and diet-induced obesity. These findings suggest that common variations in certain mitochondrial proteins can act in trans to influence tissue-specific mitochondrial functions and contribute to heart hypertrophy, elucidating mechanisms that may underlie genetic susceptibility to heart failure in human populations. Editor's evaluation This paper describing the genetic architecture of the heart mitochondrial proteome is important in that it identifies the major role of mitochondria in this process. The data is convincing, and appropriate and validated methodology in line with current state-of-the-art is used. This paper will be of interest to several groups of audiences, including cardiovascular researchers and geneticists. https://doi.org/10.7554/eLife.82619.sa0 Decision letter Reviews on Sciety eLife's review process Introduction 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. Results Genetic architecture of the heart mitochondrial proteome revealed three trans-regulatory hotspots To investigate the effects of genetics on the heart proteome, we first performed a whole heart proteomic analysis in the HMDP (72 strains, n=2–3 mice, female sex; listed in Supplementary file 1a) and surveyed mitochondrial localization using MitoCarta3.0 (Rath et al., 2021). We retrieved abundance data for 848 of these proteins (quantified in ≥50 strains) and performed high-resolution association mapping on their respective abundance levels to the HMDP genotypes. Genetic variants associated to the protein abundance of nearby genes (<1 Mb) were referred to as cis- protein quantitative trait loci (pQTLs) and the remainder were referred to as trans-pQTLs. When a trans-pQTL locus is associated to multiple proteins, we defined it as a trans-pQTL hotspot. Using these criteria, we identified three hotspots, located on chromosome (chr) 7, chr13 and chr17, respectively (Figure 1A). Figure 1 Download asset Open asset Genetic architecture of heart mitochondrial proteome. (A) High-resolution association mapping of 848 heart mitochondrial proteins from 72 HMDP strains to identify pQTL networks. Associations between protein abundance levels and genetic variants located within 1 Mb of the respective gene location were considered as cis-pQTLs (p<1E-05) shown along the diagonal axis and the rest were considered as trans-pQTLs (p<1E-06). Three trans-pQTL hotspots are indicated by arrows. (B) Five WGCNA modules and the respective trans-pQTL hotspots are shown. HMDP, hybrid mouse diversity panel; pQTL, protein quantitative trait locus; WGCNA, weighted gene co-expression network analysis. To identify the aspects of mitochondrial metabolism regulated by these loci, we constructed co-expression protein networks using weighted gene co-expression network analysis (WGCNA; Langfelder and Horvath, 2008) and identified five modules (Figure 1B and Supplementary file 1b). Eigengenes, representing the first principal component of two of these modules (Brown and Green), mapped to the same regions as the chr13 and chr17 loci, respectively. DAVID enrichment analyses (Huang et al., 2009) revealed that the chr13 locus (26 proteins; Figure 2A and Supplementary file 1c) that overlapped with Brown module (67 proteins, 96% overlap) was highly enriched for mitochondrial complex-I proteins (24 proteins, p=2.2E-61), and the chr17 locus (22 proteins; Figure 3A and Supplementary file 1d) that overlapped with Green module (38 proteins, 73% overlap) was highly enriched for mitochondrial ribonucleoprotein complex proteins (17 proteins, p=3.1E-25). The hotspot proteins in the chr7 locus (26 proteins; Figure 4A and Supplementary file 1e) were found primarily in the Turquoise module (402 proteins, 81% overlap) and was highly enriched for ubiquinone biosynthesis (3 proteins, p=6.9E-05). Figure 2 Download asset Open asset Chr13 locus controls mitochondrial complex-I via miR-27b/NDUFS4 axis. (A) Circos plot showing chr13 hotspot. Each line signifies a significant association between the genetic variants and the respective protein levels with candidate genes being highlighted. (B) Manhattan and regional plots of chr13 locus (brown module) eigengene, respectively. Red line signifies genome-wide significance threshold (p<4.1E-06). The peak SNPs and the candidate genes are highlighted. Genotype distribution plots of (C) protein levels and eigengenes, and cardiac phenotypes from (D) ISO-induced HF model and (E) female and (F) male DIO model at peak SNP (rs48592660) associated with chr13 locus. (G) Volcano plot showing differentially expressed protein levels between control and heart-specific Ndufs4-cKO mice (n=5 mice/group). Significantly different proteins (significance cutoff: abs[log2FC]>1 and Padj <0.001) are highlighted in red. (H) Genotype distribution plots of heart miR-27b expression at peak SNP (rs48592660) associated with chr13 locus. (I) Gene-by-trait correlation plot between heart weight phenotype and heart miR-27b expression. (J) Immunoblot analyses of NDUFS4 protein levels in NRVMs transfected with mature miR-27b in the presence or absence of PE treatment. Data are presented as (C–F and H) boxplots showing median and interquartile range with outliers shown as circles (n=68–72 strains for protein levels; n=92–95 strains for ISO-model; n=92–100 strains for DIO-model; n=78 strains for miRNA levels) or (J) mean ± SEM (n=4–6 per group). p Values were calculated using (A and B) FaST-LMM that uses Likelihood-Ratio test; (C–F and H) Unpaired two-tailed Student's t test; (I) BicorAndPvalue function of the WGCNA R-package that uses Unpaired two-tailed Student's t test; (J) two-factor ANOVA corrected by post-hoc "Holm-Sidak's" multiple comparisons test. ISO, isoproterenol; HF, heart failure; DIO, diet-induced obesity; NRVMs, neonatal rat ventricular myocytes; PE, phenylephrine. Figure 2—source data 1 Uncropped blots for Figure 2, panel J. Uncropped immunoblots probed for NDUFS4 (left) and ACTIN (right) protein levels in NRVMs transfected with mature miR-27b in the presence or absence of PE treatment. Corresponding molecular weight markers are labelled on the right side of each blot. https://cdn.elifesciences.org/articles/82619/elife-82619-fig2-data1-v2.pdf Download elife-82619-fig2-data1-v2.pdf Figure 2—source data 2 Raw unedited and uncropped blots for Figure 2, panel J. https://cdn.elifesciences.org/articles/82619/elife-82619-fig2-data2-v2.zip Download elife-82619-fig2-data2-v2.zip Figure 3 Download asset Open asset Chr17 locus controls mitoribosomes via LRPPRC/SLIRP. (A) Circos plot showing chr17 hotspot. Each line signifies a significant association between the genetic variants and the respective protein levels with candidate genes being highlighted. (B) Manhattan and regional plots of chr17 locus (green module) eigengene, respectively. Red line signifies genome-wide significance threshold (P<4.1E-06). The peak SNPs and the candidate genes are highlighted. Genotype distribution plots of (C) protein levels and eigengenes, and cardiac phenotypes from (D) ISO-induced HF model and (E) female and (F) male DIO model at peak SNP (rs46340181) associated with chr17 locus. (G) Protein-by-protein correlation plot between heart LRPPRC and SLIRP abundance levels. (H) Association p values between mtDNA-encoded mRNA expression or protein abundance levels and peak SNP (rs46340181) associated with chr17 locus in both sexes of HMDP. (I) Genotype distribution plots of heart mt-ND1 mRNA expression from female and male DIO model at peak SNPs associated with chr17 (rs46340181) or chr13 (rs48592660) loci, respectively. (J) Association p values between mtDNA-encoded complex-I related mRNA expression or protein abundance levels and peak SNP (rs48592660) associated with chr13 locus in both sexes of HMDP. Data are presented as (C–F and I) boxplots showing median and interquartile range with outliers shown as circles (n=68–72 strains for protein levels; n=92–95 strains for ISO-model; n=92–100 strains for DIO-model). p Values were calculated using (A, B, H and J) FaST-LMM that uses Likelihood-Ratio test; (C–F and I) Unpaired two-tailed Student's t test; (G) BicorAndPvalue function of the WGCNA R-package that uses Unpaired two-tailed Student's t test. Figure 3—source data 1 Raw data for Figure 3, panel H. Association p values between mtDNA-encoded mRNA expression or protein abundance levels and peak SNP (rs46340181) associated with chr17 locus in both sexes of HMDP. p Values were calculated using FaST-LMM that uses Likelihood-Ratio test. https://cdn.elifesciences.org/articles/82619/elife-82619-fig3-data1-v2.xlsx Download elife-82619-fig3-data1-v2.xlsx Figure 3—source data 2 Raw data for Figure 3, panel J. Association p values between mtDNA-encoded complex-I related mRNA expression or protein abundance levels and peak SNP (rs48592660) associated with chr13 locus in both sexes of HMDP. p Values were calculated using FaST-LMM that uses Likelihood-Ratio test. https://cdn.elifesciences.org/articles/82619/elife-82619-fig3-data2-v2.xlsx Download elife-82619-fig3-data2-v2.xlsx Figure 4 with 1 supplement see all Download asset Open asset Lrpprc regulates an mt-encoded eQTL hotspot specifically in heart of DO mice. (A) Heatmap illustrates all eQTL (LOD >6) for mt-encoded transcripts that were identified in Adipose (Wang et al., 2012), Heart (Ruzzenente et al., 2012), Islet (Gu et al., 2016), Liver (Rath et al., 2021), and Muscle (Wang et al., 2016) from Diversity Outbred mice maintained on a Western-style diet. Mt-eQTL are arranged along x-axis according to genomic position from chr1 to chrX; z-axis depicts LOD score, highlighting a hotspot on chr17 at ~85 Mbp. (B) Allele effect patterns for mt-eQTL mapping to chr17 hotspot in heart. Red illustrates alleles associated with increased expression; blue, decreased expression. LOD scores are shown along right margin. (C) SNP association profile for mt-Nd5 eQTL in heart. Lrpprc contains SNPs with strongest association, yellow highlighted region. (D) Mediation of mt-Nd5 eQTL against all transcripts in heart. The LOD score for mt-Nd5 is significantly reduced when conditioned on a Lrpprc cis-eQTL, consistent with genetic regulation of Lrpprc being required for the regulation of mt-Nd5. The mt-Nd5 eQTL (E) and Lrpprc cis-eQTL (F) in heart demonstrate matched and concordant allele effect patterns, suggesting Lrpprc is a positive driver of mt-Nd5. Chr13 locus controls mitochondrial complex-I First, we analyzed the chr13 locus (26 proteins) that was highly enriched for mitochondrial complex-I (Figure 2A). Mapping the eigengene of the chr13 trans-regulated proteins identified the peak SNP (rs48592660). NDUFS4, a protein critical for complex-I assembly and loss of which leads to cardiac hypertrophy (Chouchani et al., 2014) mapped near the locus but outside the region of linkage disequilibrium. However, within the locus was the microRNA(miR)–23b/27b/24–1 cluster, among which miR-27b, a conserved regulator of NDUFS4, was identified via nine miRNA target prediction algorithms and one dataset of experimentally validated miRNA targets (Figure 2B and Supplementary file 1f). Cardiac overexpression of miR-27b has previously been shown to promote cardiac hypertrophy (Wang et al., 2012) but attenuate angiotensin II-induced atrial fibrosis (Wang et al., 2018). We therefore hypothesized that the chr13 locus regulated complex-I proteins by influencing miR-27b and thus NDUFS4 protein levels. Indeed, we observed higher levels of both the chr13 locus eigengene and NDUFS4 with the GG allele of the peak locus SNP (Figure 2C). To identify the functional relevance of this peak SNP, we analyzed its association in two independent heart stress models, namely, isoproterenol (ISO)-induced heart failure (Wang et al., 2016) and diet-induced obesity (DIO) (Parks et al., 2013) models. We observed significantly lower left ventricular mass under ISO stress (Figure 2D) and lower heart weight under DIO stress in both sexes (Figure 2E) of strains harboring the GG allele, thus confirming the directionality of genetic impacts on NDUFS4 protein levels and hypertrophic response. NDUFS4 heart-specific knockout mice had reduced mitochondrial complex-I proteins NDUFS4 is an 18 kDa accessory subunit that is essential for the mitochondrial complex-I assembly (Zhu et al., 2016; Stroud et al., 2016; Gu et al., 2016; Kahlhöfer et al., 2017; Scacco et al., 2003). Loss-of-function mutations in NDUFS4 leads to complex-I deficiency causing a neuromuscular disease, Leigh syndrome (Leshinsky-Silver et al., 2009) and is also involved in cardiomyopathies (Chouchani et al., 2014; Karamanlidis et al., 2013; Zhang et al., 2018). Taken together, we wanted to experimentally test our hypothesis that NDUFS4 protein independently controls the complex-I protein abundance levels in heart. For this, we performed whole heart proteomic analyses in both control and heart-specific Ndufs4-cKO mice (n=5 mice/group). Among the abundance data for 3575 proteins, we observed 31 proteins to be significantly different between the control and cKO groups (significance cutoff: abs[log2FC]>1 and Padj <0.001; listed in Supplementary file 1g). Strikingly, 21 of these proteins were found in the chr13 locus and these were highly enriched for complex-I proteins (30 proteins, p=3.8E-80). Notably, only one protein, NDUFAF2, was up regulated in Ndufs4-cKO mice (Figure 2G). This is intriguing because NDUFAF2 has been reported to stabilize complex-I in the absence of NDUFS4 and to sustain complex-I activity (Adjobo-Hermans et al., 2020; Leong et al., 2012) indicating this may be a compensatory mechanism. MiR-27b controls NDUFS4 protein levels and heart weights As an independent corroboration and to understand the consequence of the chr13 locus on miR-27b expression, we independently sequenced miRNAs from DIO-stressed female HMDP strains (n=78 strains). We found that strains harboring the GG allele had higher miR-27b expression (Figure 2H), and there was a significant inverse correlation between heart weights and miR-27b expression in these mice (Figure 2I). Based on these observations, we hypothesize that miR-27b increases NDUFS4 protein levels thereby reducing heart weights. To validate this observation, we transfected neonatal rat ventricular myocytes (NRVMs) with mature miR-27b in the presence or absence of phenylephrine (PE) treatment. The precursor miR-27b has two mature arms: miR-27b-5p and miR-27b-3p that originates from the 5' or 3' strand of the precursor miR-27b, respectively and are almost complementary to each other. The stability and functionality of each arm depends on the tissue/cell type and according to miRBase Sequence database (Kozomara and Griffiths-Jones, 2011) (database of published miRNA sequences and annotation), miR-27b-3p (98.8%) is more abundant than miR-27b-5p (1.2%) and therefore is assumed to be the stable and functional version. Immunoblot analyses revealed that NDUFS4 protein levels were reduced with PE treatment in the control cells but miR-27b-3p consistently increased NDUFS4 protein levels in both control and PE-treated conditions while miR-27b-5p increased NDUFS4 protein levels only in the PE-treated conditions (Figure 2J). Taken together, we conclude that the chr13 locus affects mitochondrial complex-I proteins through the miR-27b/NDUFS4 axis, thereby controlling heart weights. Chr17 locus controls mitoribosomes Next, we analyzed the chr17 locus (22 proteins) that was highly enriched for mitochondrial ribosomal proteins (Figure 3A). We mapped the eigengene of the significantly associated mitochondrial proteins to identify the peak SNP (rs46340181). We identified LRPPRC as a candidate as it was the only protein controlled in cis by the peak SNP (Figure 3B). Importantly, LRPPRC together with SLIRP controls mitochondrial mRNA stability, enabling polyadenylation and translation (Siira et al., 2017; Chujo et al., 2012; Ruzzenente et al., 2012). Loss-of-function mutations in LRPPRC cause a congenital mitochondrial disease called Leigh syndrome, French-Canadian type that is often characterized by mitochondrial complex IV deficiency and impaired mitochondrial respiration and in some cases, neonatal cardiomyopathy and congenital cardiac abnormalities have been reported (Mootha et al., 2003; Oláhová et al., 2015). It is noteworthy that SLIRP is also under the control of the chr17 locus (Figure 3A). Further, our phenotypic associations revealed that the eigengene and LRPPRC were inversely associated with the TT allele (Figure 3C). This was functionally translated into lower heart weight in strains harboring the TT allele in both sexes under DIO stress only (Figure 3D–F). We also observed that abundance levels of both LRPPRC and SLIRP proteins were strongly correlated with each other (Figure 3G) and controlled by the chr17 locus (Figure 3A), thus demonstrating that they are co-regulated. LRPPRC/SLIRP protein complex controls mitochondrial transcript levels Based on our current observations and published data, we hypothesized that high LRPPRC/SLIRP protein complex stabilizes mitochondrial transcripts, thus reducing the need to upregulate mitochondrial translation. To test this, we independently sequenced the heart transcripts from our HMDP mice that underwent DIO stress. We observed that the chr17 locus peak SNP (rs46340181), which strongly controls both LRPPRC and SLIRP proteins, is only associated with the mitochondrial mRNA expression and not their respective protein levels (Figure 3H). Moreover, this phenomenon was observed in both sexes, explaining the lack of sex bias in phenotypic associations (Figure 3E and F). In contrast, the chr13 locus that controlled complex-I proteins did not show strong associations with transcript levels in either sex (Figure 3I and J). As a specific example, Figure 3I shows that the mRNA expression of mt-ND1 was higher in strains harboring the TT allele in both sexes under DIO stress, illustrating that upregulated LRPPRC/SLIRP is stabilizing the mitochondrial transcript (Figure 3C), resulting in reduced heart weights (Figure 3D–F). Corroboration with Diversity Outbred mice also identifies a heart-specific chr17 mt-eQTL hotspot controlled by LRPPRC As independent corroboration, we utilized the Diversity Outbred (DO) mice (Svenson et al., 2012) to further explore our chr17 locus. To extend our observations about the genetics of mitochondrially encoded (mt-encoded) gene regulation, we asked if mt-encoded genes are subjected to genetic regulation in multiple tissues from a large cohort of DO mice. We used RNA-sequencing to survey gene expression that included transcripts encoded by the mitochondrial genome, in five tissues (adipose, heart, islet, liver and skeletal muscle) from ~500 DO mice that were maintained on a Western-style diet high in fat and sucrose. All DO mice were genotyped at >140 K SNPs with the GigaMUGA microarray (Morgan et al., 2015), enabling eQTL analysis of the mt-encoded transcripts. Among the five tissues surveyed, we identified 157 mt-eQTL (LOD >6) for 15 mt-encoded genes, including all 13 protein-coding genes, and the two genes that encode for ribosomal RNA proteins (Supplementary file 1h). Given that the mitochondrial and nuclear genomes are distinct, mt-encoded eQTL at nuclear loci reflect trans-acting mechanisms that bridge the two genomes (Ali et al., 2019). While all tissues yielded multiple mt-eQTL, heart demonstrated a striking hotspot where 14 mt-encoded genes uniquely mapped to chr17 at ~85 Mbp with LOD scores ranging from 52 (mt-Nd5) to 6 (mt-Co2) (Figure 4A; listed in Supplementary file 1h). The founder allele-effect signatures for the 14 mt-eQTL at the hotspot were the same and partitioned the eight haplotypes into 'high' (A/J, B6, 129, WSB) versus 'low' (NOD, NZO, CAST, PWK) subgroups (Figure 4B), suggesting a common causal variant behind the co-mapping of the mt-eQTL. The SNP association profile for the mt-Nd5 eQTL identified a block of SNPs directly over the Lrpprc gene locus (Figure 4C), including one missense variant (rs33393440, Lys466Glu), which is present in the high allele subgroup (Supplementary file 1i). Taken together, these results suggest that SNPs within Lrpprc may be responsible for the heart-specific mt-eQTL hotspot. We used mediation analysis to identify potential causal gene underlying physiological or molecular QTL as described (Keller et al., 2016; Keller et al., 2018; Keller et al., 2019). In mediation analysis of gene expression, trans-eQTL are conditioned on the expression of all other genes, including those at the locus to which the trans-eQTL map. If the genetic signal of the trans-eQTL decreases upon conditioning of a specific gene, that gene becomes a strong candidate as the driver of the trans-eQTL. We focused on the trans-eQTL for mt-Nd5 at the chr17 locus in heart, as this demonstrated the strongest genetic signal (Figure 4B). Mediation against Lrpprc resulted in a large drop in the LOD score for mt-Nd5 eQTL (Figure 4D). Similar results were observed for mediation of the other mt-eQTL to the heart hotspot (Figure 4—figure supplement 1). The allele effect patterns of the mt-Nd5 eQTL (Figure 4E) and that for the Lrpprc eQTL (Figure 4F) demonstrate the same, and concordant high and low haplotype subgroups. Taken together, our results in DO mice are consistent with that in the HMDP mouse cohort, and strongly suggest that LRPPRC functions as a positive driver of the mt-eQTL hotspot in heart. Chr7 locus affects CoQ metabolism Finally, we analyzed the chr7 locus (26 proteins), which unlike the chr13 or chr17 loci, had no major representation of a single mitochondrial protein complex but was moderately enriched for ubiquinone biosynthesis (Figure 5A). Mapping the eigengene of the significantly associated mitochondrial proteins identified the peak SNP (rs32451909). We identified COQ7 as a strong candidate as it was the only protein exhibiting a cis-regulation at the locus in the region of linkage disequilibrium (Figure 5B). COQ7 catalyzes a critical step in the biosynthesis of coenzyme Q (CoQ). Among several functions, CoQ participates in electron transport facilitating ATP synthesis. CoQ also has a clear role in heart failure (Sharma et al., 2016). Phenotypically, we observed lower levels of both the eigengene and COQ7 with the GG allele (Figure 5C). This was functionally translated into higher heart weight in strains containing GG allele in both sexes under DIO stress only (Figure 5D–F). We also observed that abundance levels of other COQ proteins were strongly correlated with COQ7 protein, thus demonstrating that they are co-regulated (Figure 5G–I). At least two of these proteins, COQ3 and COQ6, are associated with the chr7 locus (Figure 5A). When we measured the CoQ levels in both the mitochondrial fractions and total heart lysates from DIO-stressed female HMDP strains (n=15 strains), we observed a significant upregulation in the levels of both CoQ9 and CoQ10 in the lysates from HMDP mice harboring the GG allele (Figure 5J). Taken together, we conclude that the chr7 locus controls CoQ metabolism via regulation of the COQ7 protein. Figure 5 Download asset Open asset Chr7 locus affects CoQ metabolism via COQ7. (A) Circos plot showing chr7 hotspot. Each line s
DNA methylation stands out as one of the most extensively investigated epigenetic modifications 14 within the realm of eukaryotic biology. DNA methylation-based predictive models for chronological 15 age, referred to as epigenetic clocks, serve as widely used tools for investigating age-related pathologies 16 and physiological alterations. Discrepancies between predicted and actual chronological ages are 17 frequently interpreted as manifestations of biological age acceleration, a phenomenon linked to the 18 onset of various disorders. A plethora of epigenetic clocks have been developed in the literature (Di 19 Lena et al., 2021), and several studies have demonstrated associations between epigenetic age 20 acceleration and pathological conditions (Horvath, Raj, 2018). This active area of research is currently 21 engaged in endeavors to enhance the predictive capabilities of epigenetic clocks and facilitate the 22 translation of their applications into the realm of predictive medicine. The most significant conclusion drawn from the first and second volumes of this research topic is that 73 although DNA methylation analysis shows great potential, there is a pressing need for further 74 investigation and refinement of methodologies to fully harness its predictive power and translate its 75 findings into actionable insights for clinical practice.
