Nutrient State, Aging, and Diet Modulate SAM50-Dependent Mitochondrial Remodeling and Systemic Metabolic Signatures

Nutrient State, Aging, and Diet Modulate SAM50-Dependent Mitochondrial Remodeling and Systemic Metabolic Signatures | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Nutrient State, Aging, and Diet Modulate SAM50-Dependent Mitochondrial Remodeling and Systemic Metabolic Signatures Antentor Othrell Hinton, Jr., Sepiso K Masenga, Victoria Baskerville, and 31 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8704245/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Although Sorting and Assembly Machinery 50 (SAM50) is known to regulate nutritional and metabolic stress related to ageing, its exact role is not well understood. This experimental study combines both human and animal models to understand the role that SAM50 plays in nutrient, age-related metabolic remodeling. We also wanted to define the clinical relevance of SAMM50 genetic variation in human disease. Our study integrated clinical and genetic data from three large and independent human biobanks to assess the clinical implications of genetic variation in SAMM50. We then conducted mechanistic studies in mice using Serial Block-Face Scanning Electron Microscopy and Transmission Electron Microscopy for three-dimension analysis of mitochondrial morphology, immunoblotting, metabolomics/lipidomics, and assessment of metabolic parameters in models of fasting, aging, and a high-fat diet (HFD). Descriptive and inferential statistics were used to describe and test associations in GraphPad prism version 10. Our study demonstrated that common genetic variation within the SAMM50 genetic locus was significantly associated with liver-related metabolic disorders. In mice, nutrient status was associated with expression levels of Sam50 and proteins involved in the respiratory complex. Aging was associated with impaired mitochondria, decreased Sam50 expression, and increased triglyceride and lipid peroxidation, with increased lipid droplet-mitochondria contacts. An HFD was associated with a reduction in Sam50 expression, disruption of mitochondrial structure, and metabolic dysfunction, effects that were only partly reversed by returning to a normal diet. Our results demonstrate that SAM50 expression is associated with nutrient state and age-related signals, thereby orchestrating mitochondrial structure to influence systemic metabolic health. Health sciences/Pathogenesis Biological sciences/Cell biology Sam50 Mitochondrial Dynamics Aging Metabolism Liver Disease High-Fat Diet MICOS Complex Nutrient Sensing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Mitochondria and the proteins regulating its structure and function, are important for cellular energy homeostasis and overall metabolic health 1 . The structure and function of mitochondria are largely determined by ongoing fusion and fission processes, as well as the complex organization of their inner membrane cristae. These activities, are closely linked to cellular nutrient levels, stress responses, and aging 2 , 3 . The mitochondrial sorting and assembly machinery (SAM) is essential for maintaining the structure of the inner membrane and forming important contact sites with other cell organelles 4 . SAM50, a key and highly conserved component of this SAM complex, has two main roles. SAM50 helps assemble beta-barrel proteins into the outer mitochondrial membrane and additionally, it interacts directly with the MICOS complex 5 – 7 . This positions SAM50 as a key regulator of mitochondria structure and function. Prior studies have demonstrated that any stress-related changes in mitochondrial architecture including aging has been linked to metabolic diseases and liver-related disorders such as insulin resistance and hepatic steatosis, respectively 8 – 10 . Although mitochondrial dynamics have been recognized as important in metabolic diseases, the specific role of SAM50 in regulating systemic metabolism in response to environmental and genetic variations remains unclear. We therefore wanted to know if SAM50 expression and function respond to nutritional interventions, including whether diet-induced mitochondrial defects can be reversed. We also sought to elucidate the role of aging on SAM50 expression. This study combines human genetics with mechanistic studies in mice and primary cells to examine how SAM50 functions as a regulator of mitochondrial remodeling responsive to nutrients and aging. Methods Biobank Genetic Association Studies Vanderbilt University Medical Center curates an electronic health record (EHR)-linked biobank, BioVU. This opt-in program collects and stores blood samples from routine clinical visits. Currently, BioVU houses more than 350,000 biological samples linked to de-identified EHRs dating back several decades 11,12 . Genetic data for 90,000 individuals was analyzed and cleaned as previously described 13 . In summary, individuals were genotyped on Illumina’s Multi-Ethnic Genotyping Array (MEGA) and imputed on the Michigan Imputation Server using the Haplotype Reference Consortium (HRC) reference panel. Quality control was performed to filter the genetic data based on previously reported minor allele frequency, individual and variant level missingness, and deviations within Hardy-Weinberg equilibrium. Genetically related individuals were removed from the analysis. Principal component analysis was performed with the 1000 genomes reference populations and downstream analyses were performed within two BioVU populations: individuals of European genetic ancestry (n=70,404) and individuals of African genetic ancestry (n=15,175). Genetically-regulated gene expression (GreX) for SAMM50 was calculated using predictive models P rediXc an, UTMOST, JTI using GTEx version 8 data 14–17 . The model with the highest performance r2 was used for each SAMM50-tissue pair. MultiXcan was then employed to synthesize a cross-tissue SAMM50 GReX model 14,18 . Within genetically-defined ancestry groups, associations between SAMM50 GReX and 1,704 clinical phenotypes (via logistic regression) and 326 laboratory traits (via linear regression) were tested, adjusting for principal components (1-10), sex, age, median age of medical record, and genotyping batch. Clinical laboratory values were extracted from the EHR as previously described (REF). Phenotypes were extracted from the EHR by mapping structured clinical data (ICD9 and ICD10 codes) to phecodes as previously described 13,19 . To validate the clinical relevance of SAMM50 in human disease from additional biobank populations, we explored publicly available summary-level genome-wide association results from two large, independent biobanks (Biobank Japan and UK Biobank). BioBank Japan is a biobank with 260,000 participants from 12 medical institutions across Japan. BioBank Japan is managed by the Institute of Medical Science at the University of Tokyo and the RIKEN Center for Integrative Medical Sciences, the University of Tokyo, and Osaka University performed genotyping on the BioBank Japan samples. The summary results of several BioBank Japan genome-wide association studies (GWAS) are publicly available at the BioBank Japan PheWeb portal (https://pheweb.jp/) 20,21 . Additional information regarding the pheweb repository is available on the github website (https://github.com/statgen/pheweb/). For additional support of our SAMM50 genetic associations, we explored the publicly available GWAS results from UK Biobank data. Similar to the BioBank Japan pheweb portal, the pheweb for UK Biobank includes summary information for genetic analyses performed across 400,000 individuals from the UK. These results include GWAS summary statistics from association studies performed on 1,419 phenotypes extracted from the EHR. We used the UK Biobank data that imputed with the HRC reference panel, as this was the same reference panel used for the BioVU studies. The UK Biobank pheweb was built using PheWeb version 1.3.15 22 . The rationale behind interrogation of these three independent biobanks was to establish robust, convergent and orthogonal evidence supporting associations between SAMM50 and liver-related metabolic disorders and outcomes. Animal care and fasting and refeeding paradigm We conducted all animal experiments at the University of California, Los Angeles (UCLA) and all procedures were performed in accordance with the approved protocols established by the UCLA Institutional Animal Care and Use Committee (IACUC) and in compliance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Ethical approval was obtained prior to the start of the study. We housed the adult mice in a controlled vivarium with a 12-hour light/dark cycle and provided them with free access to food and water unless specified otherwise. Temperature was also controlled and checked daily. For the fasting experiments, fasting was initiated at the beginning of the light cycle to minimize circadian variability and mice were singly housed and subjected to a 24 h fast with continuous access to water. The control mice had unrestricted access to food throughout the study. For refeeding experiments, mice were fasted for 24 h and subsequently refed standard chow for 4 h prior to tissue collection. All animals were euthanized at the same circadian time point to control for diurnal effects on metabolism and mitochondrial protein expression. Tissue collection After the fasting/refeeding period was completed, mice were euthanized for tissue processing. Briefly, tissue was dissected immediately after euthanization, and then briefly rinsed in ice-cold phosphate-buffered saline. This was done in order to remove any residual blood. The tissue was snap frozen in liquid nitrogen and stored at −80°C until further processing. All procedures were conducted according to UCLA IACUC-approved protocols. Protein isolation To initiate protein isolation, the frozen tissues were first homogenized on ice in radioimmunoprecipitation assay buffer which was supplemented with protease and phosphatase inhibitor cocktails. Next, the homogenates were incubated on ice with intermittent mixing followed by clarification through by centrifugation at 14,000 × g for 15 min at 4°C. The resulting supernatants after centrifugation contained total protein lysates which were collected. Protein concentrations were then measured using a bicinchoninic acid assay, following the manufacturer’s instructions. SDS–PAGE and immunoblot analysis We used Sodium Dodecyl Sulfate–Polyacrylamide Gel Electrophoresis to equalize amounts of protein using gradient polyacrylamide gels and then we transferred the protein onto polyvinylidene difluoride membranes. We used 5% nonfat dry milk prepared in Tris-buffered saline containing 0.1% Tween-20 to block the membranes and then incubated them overnight at 4°C. We incubated with primary antibodies against mitochondrial respiratory chain component proteins complexes I, II, III, and V, as well as SAM50 and Tom20. After incubation with horseradish peroxidase-conjugated secondary antibodies specific to the species, the immunoreactive bands were visualized using enhanced chemiluminescence. GAPDH served as the loading control for this procedure. Molecular Biology RNA extraction was performed using TRIzol, followed by reverse transcription and quantitative PCR (qPCR) with SYBR Green and primers specific for Sam50 /SAMM50 and GADPH 23 . For western blot analyses, tissue lysates were prepared in RIPA buffer, resolved by Tris-glycine gels, transferred to nitrocellulose membranes, and probed with antibodies against SAM50 (Proteintech 20824-1-AP), respiratory complex subunits, Tom20, and Tubulin. Detection was carried out using IRDye-conjugated secondary antibodies and visualized on a Li-Cor Odyssey CLx system. Data were presented as fold changes normalization to GAPDH. The qPCR primers utilized were derived from previously published sequences 24 , as detailed in Table 1. Cell culture and serum fasting, and refeeding We used an epithelial hepatoma cell line derived from liver tissue, Hepa-1c1c7 cells, and placed them in a humidified incubator with 5% CO₂ in Dulbecco’s modified Eagle’s medium that was supplemented with 1% L-glutamine, 10% fetal bovine serum and 1% penicillin–streptomycin, at 37°C. The Cells were routinely passaged and used for experiments at 70–80% confluence. For the serum fasting/refeeding experiments, we first washed cells twice with phosphate-buffered saline and incubated them in serum-free DMEM for 6 hours. The control cells were maintained in complete medium having 10% fetal bovine serum. For the refeeding procedure, we first starved the cells for 6 hours and then immediately after refed them with complete medium containing 10% fetal bovine serum for 4 hours. We conducted all experimental procedures and conditions in parallel and collected data at identical time points to minimize variability. Microscopy and Image Analysis Transmission Electron Microscopy (TEM) Tissue fixation, embedding, and sectioning were performed according to standardized protocols 25 to minimize bias. The cells were fixed directly in culture dishes. Fixation reagents included using 2.5% glutaraldehyde in 0.1 M cacodylate buffer, followed by post-fixation in 1% osmium tetroxide. To dehydrate the samples, we used a graded ethanol series followed by embedding them in epoxy resin. Ultrathin sections (90–100 nm) were stained and imaged. Stained was done using uranyl acetate and lead citrate. Images were acquired using a transmission electron microscope at identical magnifications for all conditions. This was done utilizing NIH’s ImageJ software 26,27 . Mitochondrial morphology, area, and number were quantified from randomly selected fields using blinded analysis. Mitochondria were manually outlined to determine both their area per cell and the number of mitochondria relative to cytoplasmic area. These measurements were taken from several cells under each condition, using independent biological replicates. Results are shown as mean ± SEM. Serial Block-Face Scanning Electron Microscopy (SBF-SEM): Three-dimensional ultrastructural analysis was performed as previously described 25,27,28 . Small liver tissue blocks were fixed in glutaraldehyde, and then subsequently fixed with osmium tetroxide. They were then stained, and dehydrated through a graded ethanol series and embedded in epoxy resin, and serially sectioned (50 nm) for imaging using a VolumeScope 2 serial block-face scanning electron microscope equipped with an in-chamber ultramicrotome. Serial images were taken at a voxel resolution suitable for mitochondrial reconstruction, with section thickness kept constant across samples. For 3D reconstruction, 300–400 slices per sample were manually segmented by blinded investigators in Amira software. Morphometric parameters (volume, surface area, sphericity, complexity index) were calculated for approximately 250 mitochondria per experimental condition. Mitochondrial DNA content Real-time polymerase chain reaction was employed to quantify Mitochondrial DNA content from liver tissue. We used the DNeasy Kit according to the manufacturer’s instructions for the extraction and purifying of total DNA from liver samples. In order to amplify mitochondrial and nuclear DNA markers, we used five nanograms of total DNA then employed quantitative PCR from the ABI Prism 7900HT instrument in 384-well plate format with SYBR Green I chemistry and ROX as an internal reference dye. Using the SDS 2.1 software in combination with scripted workflows implemented in Microsoft Access and Microsoft Excel, data acquisition and analysis were automated increasing robustness, accuracy and reproducibility. β-actin served as the reference for nuclear DNA, while cytochrome c oxidase subunit I was used as the marker to measure mitochondrial DNA content. Mitochondrial DNA abundance was expressed relative to the nuclear Rpl13a gene. The primer sequences that were used for amplification in this protocol were as shown below: Cox1 forward, GCC CCC AGA TAT AGC ATT CCC Cox1 reverse, GTT CAT CCT GTT CCT GCT CC Rpl13a forward, GAG GCC CCT ACC ATT TCC GA Rpl13a reverse, GGC TTC AGC CGA ACA ACC TT. Serial block face scanning electron microscopy analysis of hepatic lipid droplets As described above, liver tissue was prepared as per standard protocols for serial block face scanning electron microscopy (SBF SEM). Standard heavy metal staining and resin embedding protocols optimized for ultrastructural preservation of lipid rich organelles were employed. For the acquisition of datasets, we generated volumetric image stacks spanning approximately 10 × 10 × 50 µm of hepatic parenchyma that were acquired at nanometer scale resolution. Following this, individual lipid droplets were then manually segmented across consecutive serial sections using Amira software as earlier described. We calculated lipid droplet volume, surface area, and spatial distribution lipid droplet from reconstructed objects. Quantification of lipid droplet contact site coverage with mitochondria was conducted by assessing the area of direct apposition between lipid droplets and mitochondria relative to the total lipid droplet surface area. We defined Lipid droplet coverage as the proportion of imaged cellular volume occupied by lipid droplets. To minimize bias, all analyses were performed by a trained researcher who was blinded to the identity and knowledge of the age groups. Biochemical Assays Thiobarbituric acid reactive substances analysis Lipid peroxidation was quantified through malondialdehyde determination using the thiobarbituric acid reactive substances (TBARS) assay. For cellular analyses, cells were harvested and prepared according to the manufacturer’s protocol with the TBARS assay kit from Cayman Chemical Company. Tissue measurements involved homogenizing liver samples in an appropriate assay buffer, followed by clarification via centrifugation prior to analysis. TBARS results were normalized to protein content for cellular samples and to tissue weight for liver samples. All assays were conducted in technical duplicates and validated across independent biological replicates. Hepatic and serum triglyceride measurements Hepatic and serum triglycerides were measured using a commercial enzymatic kit (EnzyChrom™, BioAssay Systems) as previously described 24,29 . Briefly, triglyceride levels were measured in liver tissue and serum after a six hour fast. To extract Liver triglycerides, we used a solution of isopropanol and Triton X 100. The resulting extracts clarified by centrifugation were analyzed according to the manufacturer’s protocol. To quantify Serum triglycerides, we measured them directly without extraction. To calculate and quantify Triglyceride concentrations, we used a standard curve generated with known triglyceride standards and normalized to tissue weight for liver samples. Total bile acids in liver tissue were measured using a colorimetric assay (Crystal Chem). The Liver samples first underwent sequential extraction with 95% ethanol overnight, following this, 80% ethanol was then employed for two hours, followed by methanol-chloroform (2:1) for two hours at 50°C. Quantification was performed using a Genzyme Diagnostics bile acid assay kit following the manufacturer instructions, and the final results were then normalized to tissue mass. Metabolomics and Lipidomics For untargeted metabolomics, frozen tissues were extracted using a 40:40:20 acetonitrile:methanol:water mixture containing 0.5% formic acid and 15% ammonium bicarbonate 30 . Extracts were analyzed via HILIC chromatography coupled to an Orbitrap Exploris 240 mass spectrometer in both positive and negative ion modes, with data processing performed using EL-MAVEN (Thermo Scientific) 31 . For lipidomics, tissues were homogenized and lipids extracted with a mixture of Isopropanol/H₂O/Ethyl acetate (30:10:60), spiked with Avanti Lipidomix internal standards. Dried extracts were reconstituted and analyzed using RP-UHPLC on a CSH C18 column interfaced with the same Orbitrap MS in AcquireX mode for MS/MS. Statistical Analysis All statistical analyses for biological assays were performed in GraphPad Prism 10.2.3. For immunoblots, densitometry software with normalization to GAPDH was employed to quantify band intensities. Data are presented as mean ± SEM from independent biological replicates. Statistical comparisons between fasting and refed groups were performed using unpaired two-tailed Student’s t-tests, with statistical significance defined as p < 0.05. Regression models for the PheWAS and LabWAS of BioVU data was performed in R (version 3.6). Bonferroni-corrected p-values were used to determine statistical significance for the PheWAS and LabWAS results and were calculated by dividing 0.05 by the number of phenotypes or lab values tested, respectively. Data from SBF-SEM/TEM studies represent a minimum of three independent biological replicates with analyses executed by blinded investigators. Data are presented as mean ± SEM. Statistical comparisons between age groups were performed using unpaired two-tailed Student’s t-tests, Comparisons among multiple groups utilized one-way ANOVA followed by Fisher’s protected LSD test. Statistical significance was denoted as *p* < 0.05, p < 0.01, *p * < 0.001, and p < 0.0001. RESULTS SAMM50 genetically regulated expression is associated with liver disease susceptibility in humans. To examine the clinical relevance of SAMM50 genetic variation, we interrogated genetic association data from three large and independent biobanks. Within a subset of BioVU, a biobank curated by Vanderbilt University Medical Center ( Figure 1, Supplemental Table 1 ), participants had a mean current age of 55.2 years and a median age of medical record of 46.0 years, with an average record length of 8.5 years, providing substantial longitudinal data for phenotype analysis. The cohort consisted of 78% individuals of European genetic ancestry and 17% individuals of African genetic ancestry, Supplemental Table 1. In individuals of European genetic ancestry (n=70,404), the PheWAS for SAMM50 GReX demonstrated 10 significant phenotype associations, most of which were liver-related disease phenotypes ( Figure 2A and Supplemental File 1 ), Significant associations with SAMM50 GReX included chronic liver disease and cirrhosis, cirrhosis of liver without mention of alcohol, other chronic nonalcoholic liver disease, liver abscess and sequelae of chronic liver disease, liver replaced by transplant and alcoholic liver damage. Associated sequelae of advanced liver disease, such as portal hypertension acute gastritis, esophageal bleeding/varices, and disorders of the orbit, were also significantly associated with SAMM50 GReX. The strong, concordant signal across multiple liver-specific phenotypes underscores the hepatic relevance of SAMM50 genetic regulation in human populations. In contrast, the LabWAS for SAMM50 GReX revealed no associations that met the Bonferroni corrected p-value, though nominal signals were seen for hepatic biomarkers like urobilinogen (p=0.03, Figure 2B, Supplemental File 2 ). Analyses in individuals of African genetic ancestry (n=15,175) showed nominal liver associations but did not surpass multiple-testing correction, likely due to reduced statistical power ( Supplemental Files 3-4). Validation of SAMM50 genetic associations with metabolic conditions and liver disease in additional biobank populations. BioBank Japan To validate the BioVU analyses linking SAMM50 GReX to liver disease susceptibility , we next interrogated publicly available summary GWAS data for BioBank Japan. We queried SAMM50 using the BioBank Japan PheWeb platform (https://pheweb.jp/), and found that the SAMM50 genetic locus was significantly associated with liver enzymes (aspartate aminotransferase (AST), and alanine aminotransferase (ALT)), hepatic cancer, liver cirrhosis, type 2 diabetes mellitus (in a multi-ancestry and European metanalysis), and total cholesterol (p < 5.0 x 10 -8 ), highlighting SAMM50’s role in metabolic dysfunction and disease in the specific context of hepatic function ( Table 2) . Furthermore, SAMM50 genetic variation was also significantly associated with abnormalities of platelets and red blood cell indices (p < 5.0 x 10 -8 ). UK Biobank To validate the BioVU and BioBank Japan analyses linking SAMM50 GReX to liver disease susceptibility , we next interrogated the UK Biobank using publicly available summary GWAS data. We queried SAMM50 using the UK Biobank PheWeb platform 22 (https://pheweb.org/UKB-SAIGE/) and found supporting evidence that the SAMM50 genetic locus was significantly associated with liver phenotypes in this large, independent cohort ( Table 2) . Specifically, SAMM50 genetic variation is significantly associated with chronic liver disease and cirrhosis, other chronic nonalcoholic liver disease, alcoholic liver damage, portal hypertension, liver abscess and sequelae of chronic liver disease, and cholelithiasis and cholecystitis (p < 5.0x10 -8 ). We then examined the GWAS data from the UK Biobank for further comparison between SAMM50 genetic variation and Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) (previously referred to as NAFLD, an outdated term), other liver disorders and systemic metabolic traits, Supplementary files 5 . Several SAMM50 variants showed strong association with MASLD and related liver pathologies, including rs2143571-A which was associated with nonalcoholic steatohepatitis-derived hepatocellular carcinoma (Study accession GCST005309; OR = 2.7 (95% CI 1.82–4.0), RAF = 0.43, p = 9 × 10⁻⁷) and MASLD (Study accession GCST005190; OR = 1.437 (95% CI 1.292–1.597) RAF = 0.395, p = 2 × 10⁻¹¹). Other SAMM50 variants, rs2073080-T (GCST001576; OR = 1.47 [95% CI 1.26–1.71], RAF = 0.458, p = 8 × 10⁻⁷) and rs5764430 (GCST90091033; an effect reported as a 0.1360267-unit decrease, p = 7 × 10⁻¹²) were associated with MASLD, Supplementary files 5 . We further found that SAMM50 variants were consistently associated with minimum, median, and maximum hepatic fat levels, as measured by imaging-derived hepatic fat traits. The effect sizes for these associations ranged from approximately +1.0 to +1.5 units across multiple entries. For instance, maximum and median hepatic fat were positively associated with a β of +1.451(95% CI 1.11–1.79; p = 6 × 10⁻¹⁷, GCST90255386) and β = +1.323 (95% CI 0.96–1.68, p = 5 × 10⁻¹³, GCST90255381) We also found strong association of SAMM50 variants with liver enzymes. The variants associated with AST levels included rs7587-T (GCST90019497; β = −0.0394 (95% CI −0.034 to −0.045), p = 2 × 10⁻⁴³), rs12167845-T (GCST90662897; β = −0.0773 (95% CI −0.073 to −0.081), p = 2 × 10⁻³³⁰), Supplementary files 5 . The same and additional variants were associated with ALT levels including rs7587-T (GCST90019492; β = −0.0402 (95% CI −0.035 to −0.046), p = 2 × 10⁻⁴⁵), rs2073080-T (GCST90104161; β = −1.53366 IU/L, p = 3 × 10⁻¹⁰ ). rs3761472-G (GCST007440; β = +0.0362 (95% CI 0.02–0.052), p = 7 × 10⁻⁶). The variant rs7587-T above that was associated with ALT and AST levels was also associated with AST/ALT ratio (GCST90019498; β = +0.0243 [95% CI 0.019–0.030], p = 1 × 10⁻¹⁷). We also found very strong associations between SAMM50 variants and markers of metabolic derangements/disorders such as lipids, uric acid/gout, diabetes and other related conditions. For instance, the variant rs4823182-G shows was associated with type 2 diabetes mellitus (GCST006867; β = +0.0482 [95% CI 0.033–0.063], p = 3 × 10⁻¹⁰ ), hypogonadism (GCST90503324; β = −0.14782159 [95% CI 0.098–0.198], p = 2 × 10⁻⁸ ) while the variant rs2235776-C was associated with total cholesterol (GCST003302; β = +0.026 [95% CI 0.016–0.036], p = 3 × 10⁻⁸). The variant rs7587-T was associated with Triacylglycerol_56:7 (GCST90060551; β = −0.113349 [0.079–0.148], p = 2 × 10⁻¹⁰), Triacylglycerol_58:7 ( GCST90060564; β = −0.113649 [0.078–0.149], p = 3 × 10⁻¹⁰) and Diacylglycerol_38:5 (GCST90060200; β = −0.0767797 [0.057–0.096], p = 1 × 10⁻¹⁴) including cholesterol-to-total lipids ratio in large VLDL for the variant rs2294922-G (GCST90092869; β = −0.0337963 [0.024–0.044], p = 1 × 10⁻¹¹). Gout (PheCode 274.1) at rs4823109 was also associated (GCST90651115; β = −0.0815 [95% CI 0.052–0.111], p = 3 × 10⁻⁸). From the UK biobank, it is evident that SAMM50 is associated with metabolic and hepatic trait enrichment, showing particularly strong signals for liver fat, MASLD/NASH-related phenotypes, liver enzymes, and downstream disease outcomes and severity. In addition, we note that SAMM50 and PNPLA3 jointly show strong associations with MASLD (p = 1 × 10⁻¹⁸), ALT/AST (p = 5 × 10⁻²⁴) and AST (p = 9 × 10⁻¹⁹), highlighting a genomic region densely linked to liver fat and enzyme signals. Multiple entries mapping exclusively to SAMM50 , such as NAFLD and extreme AST/ALT levels, further support its role in hepatic and metabolic traits. Nutrient State is associated with Regulation of SAM50 and Respiratory Complex Expression Next, we aimed to determine how the presence or absence of nutrients relates with the structure and makeup of mitochondria. In this study, we thoroughly examined the expression of mitochondrial respiratory chain protein complexes and the detailed structure of mitochondria in mouse liver following periods of fasting and re-feeding. We also studied cultured cells under serum starvation conditions. We then explored how changes in nutrient levels affect mitochondrial composition. Immunoblot analysis revealed that re-feeding after a 24-hour fast was significantly associated with increased hepatic protein levels of SAM50 (1.00 vs. 1.93, p=0.044) and Complex V (1.00 vs. 0.61, p=0.022) compared to the fasted state, while Complex I (1.00 vs. 0.41, p=0.0006) and Complex III (1.00 vs. 0.43, p=0.008) were decreased. Protein levels of Tom20 and Complex II (SDHA) were unchanged (Figure 3A-G). In cultured cells, 6-hour serum starvation was associated with mitochondrial enlargement and reduced cristae density by transmission electron microscopy (TEM) (Figure 4A, B, D, E). Immunoblotting of these cells showed that serum starvation was significantly associated with decreased protein levels of Complex II (1.00 vs. 0.43, p=0.018), Complex III (1.00 vs. 0.48, p=0.009), and Complex V (1.00 vs. 0.48, p=0.020), but not Tom20 , SAM50, or Complex I (Figure 4C, F-K). Aging is associated with Remodeling of Hepatic Mitochondrial and Lipid Droplet Architecture For our aging experiments, we aimed to investigate the relationship between aging and mitochondrial architecture and composition in the liver. We therefore performed 3D volumetric reconstructions of mitochondria from 3-month and 2-year mouse livers using serial block-face scanning electron microscopy (SBF-SEM), followed by quantitative morphometric and molecular analyses. Using SBF-SEM for 3D reconstruction, we found that aging (2-year vs. 3-month mouse liver) was associated with reduced mitochondrial size, volume, and network complexity and increasing sphericity. Specifically, mitochondrial 3D volume was lower in 2-year (0.66 µm³) compared to 3-month old (0.82 µm³), p<0.0001, and sphericity was higher in older vs young mice (0.80 vs 0.65, p<0.0001). The mitochondrial complexity index was markedly lower in 2-year vs. 3-month old mice liver (1.39 vs. 2.64, p<0.0001) (Figure 5A-G). This structural change was associated with a ~44% difference with Sam50 mRNA expression lower in 2-year vs 3-month mice (0.60 vs. 1.08, p=0.0019) and lower mitochondrial DNA content (0.75 vs. 0.90, p=0.0008) (Figure 5H, I). SBF-SEM further revealed age-dependent expansion of lipid droplets, with the percentage of lipid coverage within the imaged volume about fourfold higher in 2-year vs. 3-month-old (16.38% vs. 3.72%, p<0.0001) (Figure 6, 7A-E). Aged livers exhibited higher lipid droplet-mitochondria contact site coverage (7.39 vs. 4.10, p=0.0004) and a higher mitochondrial surface area engaged in contacts (7.85% vs. 4.14% vs. p=0.0014) (Figure 7F, G). Biochemically, aging was associated with increased lipid peroxidation, as evidenced by higher plasma TBARS (8.05 vs. 96, p=0.003), higher hepatic triglycerides (0.65 mg/g vs. 0.25, p=0.0002), and a doubling of serum triglycerides (0.32 vs. 0.16 mg/dL, p=0.005). Bile acid levels were unchanged (Figure 7H-M). High-Fat Diet is associated with Metabolic Dysfunction and Mitochondrial Remodeling Partially Reversible Upon Dietary Reversal Chronic high-fat diet (HFD) feeding in mice was associated with progressive weight gain and suppressed Sam50 mRNA expression. This suppression was partially restored upon dietary reversal (from HFD to LFD), while the abundance of SAM50 protein remained unchanged across groups (Figure 8A-F). Untargeted metabolomics revealed global hepatic metabolic reprogramming in response to HFD, as evidenced by clear separation in principal component analysis and distinct metabolite signatures (Figure 8G-I). Discussion Our study provides evidence for the role of mitochondrial SAM50 as a critical nutrient-related and age-sensitive regulator of mitochondrial function and structure with direct clinical implications that link it to human hepatic and metabolic disorders. Human Genetic Associations Positioning SAMM50 within Hepatic Disease Pathogenesis We compared the association between SAMM50 genetic variation and liver-related metabolic and non-metabolic disorders across three large biobanks and found a strong relationship exists. Data from BioVU is rich with clinical and genetic data and our findings show that genetically determined lower SAM50 expression is a significant risk factor for a spectrum of liver diseases, including both metabolic and alcohol-associated etiologies such as chronic nonalcoholic liver disease, cirrhosis, portal hypertension, esophageal bleeding related to variceal disease, and liver transplant status. Our findings are clinically meaningful representing disease phenotypes, severity and progression rather than isolated biochemical abnormalities. Our findings from BioVU aligns with and significantly extends recent reviews implicating mitochondrial outer membrane integrity and protein import in the pathogenesis of metabolic liver disease 9,32 . The specificity of the PheWAS signal for hepatic phenotypes underscores the liver's particular vulnerability to perturbations in mitochondrial outer membrane assembly. The dissociation between strong disease associations and weaker LabWAS signals suggests that SAM50 deficiency may predispose to structural pathology and long-term disease progression rather than acutely altering circulating biomarkers, aligning with the observed ultrastructural defects in our mechanistic studies and supports a model where chronic, subcellular architectural decay culminates in clinically overt organ dysfunction 33,34 . The data from BioBank Japan shows the strong association between SAMM50 genetic variation and liver enzymes (ALT and AST) used as biomarkers for liver injury, with ALT being a more liver-specific biomarker. Notably, the strong association between SAMM50 and liver enzymes was very strong, with p-values reaching orders of magnitude beyond conventional GWAS significance thresholds in cohorts exceeding 150,000 individuals. This data suggests that mitochondria SAMM50 plays a critical role in liver-related injury. Additional findings from the BioBank Japan linking SAMM50 with increased odds for type 2 diabetes mellitus, liver cirrhosis, and liver cancer may suggest that the genetic variants of SAMM50 extends beyond subclinical derangements to overt clinical disease. Although beyond the scope of this study, the associations we observed between SAMM50 and several traits including gout, platelet count, and red blood cell indices suggests potential pleiotropic effects of SAMM50 on mitochondrial-dependent metabolic and hematological pathways. Our genetic results are derived from a gene-centered analytical approach, offering validation of SAMM50 as a liver-relevant mitochondrial locus, that is independent of lead variant selection. The UK Biobank data provides complementary information that increases rigor and strengthens generalizability of our BioVU PheWAS and LabWAS findings. From the UK Biobank data that assesses more than 50 associations, we found that multiple independent variants within the SAMM50 locus show robust and reproducible associations with MASLD, nonalcoholic steatohepatitis and nonalcoholic steatohepatitis derived hepatocellular carcinoma with odds ratios ranging from approximately 1.4 to greater than 2.7. Findings related to the strong association between SAMM50 and ALT and AST are similar between the Biobank Japan and UK Biobank highlighting consistency across multiple populations and supporting the role for SAMM50 in liver-related injury. Using UK Biobank data, we also identified associations between SAMM50 genetic variation with quantitative imaging derived measures of hepatic fat including minimum, median and maximum liver fat content thereby, establishing a direct link between this mitochondrial locus and hepatic steatosis. The UK Biobank studies also reveal extensive SAMM50 associations with lipid metabolism including total cholesterol, triglyceride and diacylglycerol species, type 2 diabetes, gout and related metabolic phenotypes. Although several liver associated signals occur in a genomic region shared with PNPLA3, a well-established fatty liver disease gene, multiple associations map directly to SAMM50 alone supporting an independent contribution of this mitochondrial gene to hepatic and metabolic pathology. Taken together, the three datasets establish coherent and highly reproducible disease axis linking SAMM50 in humans. SAMM50 encodes a core component of the mitochondrial outer membrane sorting and assembly machinery and is essential for proper mitochondrial membrane organization and cristae integrity. Thus, the consistent associations we have shown from the three large databases provide a strong human genetics foundation for the experimental findings presented in this study which interrogate mitochondrial structure lipid handling and stress responses. Nutrient effects on Mitochondrial Remodeling We aimed to elucidate the association between nutrient availability or lack on mitochondrial composition and architecture. In this experiment we critically assessed mitochondrial respiratory chain protein complex expression and ultrastructure in mouse liver that was subjected to fasting and re-feeding, as well as in cultured cells that were exposed to serum starvation. Across both in vivo and in vitro systems, we found an association between nutrient deprivation and coordinated but selective changes in mitochondrial protein abundance and morphology, rather than uniform alterations in mitochondrial content. These feeding/refeeding and fasting experiments reveal a layered regulatory mechanism related to SAM50. Fasting/re-feeding and serum starvation dynamically and selectively remodel respiratory protein complex abundance, with SAM50 protein increasing upon nutrient restoration in vivo . These findings therefore suggest that SAM50 expression may potentially be part of a coordinated anabolic response to nutrients, potentially driving mitochondria bioenergetics 7,35 . The contrasting stability of the SAM50 protein during cellular serum starvation, despite the loss of downstream respiratory protein complexes, suggests a potential buffering mechanism or prioritized maintenance of the import machinery during acute stress, which may possibly facilitate rapid recovery 36 . The underlying role is beyond this study. However, a study by Yin et al demonstrated that in nutrient and oxygen deprivation during ischemic stroke and reperfusion injury in rats, Sam50 played a protective role against injury affecting mitochondria and neurons 37 . Although their study was not directly dealing with starvation, it highlights similar deficiency-driven effects. The selective remodeling of complexes I, III, and V underscores that nutrient states do not uniformly regulate all ETC components, meaning that there could be novel regulatory checkpoints involved. This differential regulation has functional consequences, as the shape and density of cristae, governed by complexes like the MICOS/SAM axis and ATP synthase, directly determine the organelle's capacity for energy conversion versus metabolite biosynthesis 3 . Our data suggest SAM50 is a key component in translating nutrient signals into these functional structural adaptations. Aging is associated with Mitochondrial Altered state To determine the relationship between aging and mitochondrial architecture and composition in the liver, we performed 3D volumetric reconstructions of mitochondria from 3-month and 2-year mouse livers using serial block-face scanning electron microscopy (SBF-SEM), followed by quantitative morphometric and molecular analyses. Our studies demonstrates that aged mice were associated with lower mitochondrial size, perimeter, and volume and higher/increased sphericity. We also found that aging is associated with reduced mitochondrial network complexity, decreased SAM50 expression and mtDNA content, expansion of lipid droplet size and abundance, increased lipid droplet–mitochondria contact site coverage indicating that aging enhances physical coupling between mitochondria and lipid droplets in the liver. In addition, we also found that aging is associated with increased lipid peroxidation in the liver and plasma and alters systemic and hepatic lipid metabolism. Taken together, these results reveal that aging drives coordinated remodeling of mitochondrial and lipid droplet architecture in the liver. Our findings that aging is associated with mitochondrial dysfunction, mainly characterized by reduced mitochondria size, volume, complexity, and mtDNA content, coupled with a decline in Sam50 expression, is consistent with its identification as a feature of age-related mitochondrial dysfunction 38–41 . The concurrent expansion of lipid droplets and increase in lipid droplet-mitochondria contact sites in aged liver as found in our study presents a compelling model for age-related metabolic decline 38–41 . Enhanced physical coupling, as extensively reviewed in the context of metabolic health, may reflect an adaptive attempt to facilitate fatty acid flux for β-oxidation in the face of declining mitochondrial oxidative capacity 42,43 . However, this increased interface in a milieu of rising oxidative stress, evidenced by elevated lipid peroxidation, may also create a vicious cycle. Mitochondria lipid-droplet in other systems exhibit unique bioenergetics, and our finding suggests that aging may alter the nature of these specialized contacts, potentially shifting them from a source of metabolic support to a source of lipotoxic stress 43 . This may potentially create a vicious cycle of oxidative damage and further mitochondrial dysfunction. More studies are required to elucidate this in much detail High-Fat Diet and Metabolic Memory: Implications for Intervention The HFD model encapsulates a pathological acceleration of nutrient-stress responses. HFD was associated with a state of mitochondrial stress evidenced by enlarged, rounded mitochondria with disorganized cristae alongside systemic metabolic syndrome. The persistent associated suppression of Sam50 transcription by HFD, and its only partial recovery after dietary reversal, indicates that chronic metabolic stress can impose a lasting "memory" on mitochondrial regulatory pathways, a phenomenon increasingly recognized in metabolic diseases like MASLD 33,44 . The disconnect between recovered transcription and persistent protein levels and ultrastructure suggests that restoring normal mitochondrial architecture lags behind transcriptional normalization. This has critical implications for the timeline and expectations of metabolic recovery after dietary or therapeutic intervention, implying that strategies to actively promote mitochondrial repair may be necessary to reverse this dysfunction. SAM50 acts as a key regulator of mitochondrial outer membrane structure and protein import, and is associated with modulation of inner membrane formation, respiratory complex assembly, and oxidative metabolism. Reduced SAM50 expresison, due to genetics, aging, or nutrient overload, is associated with damaged mitochondrial integrity, decreased respiration efficiency and increased oxidative stress, which can impair cells and organs, especially in the liver. Clinical Implications The findings from this integrated study carry significant translational implications, bridging fundamental mitochondrial biology to human health. The strong genetic association between SAMM50 expression and liver disease risk, coupled with its dynamic regulation by diet and aging, positions SAM50 as a potential biomarker for metabolic liver disease susceptibility and progression. Quantifiable alterations, such as the mitochondrial simplification and increased lipid droplet-mitochondria contacts identified through advanced microscopy, could evolve into novel histopathological or imaging-based biomarkers for staging steatotic liver disease. Therapeutically, the SAM/MICOS axis emerges as a compelling novel target. Strategies designed to bolster SAM50 function or expression could potentially enhance mitochondrial protein import, cristae integrity, and oxidative metabolism, thereby addressing foundational defects in conditions like MASLD. However, the observed "metabolic memory" effect, where HFD-induced suppression of Sam50 transcription and mitochondrial remodeling were only partially reversed, offers a crucial caution. This suggests that therapeutic benefits may require sustained intervention to overcome entrenched mitochondrial dysfunction and that simply removing the metabolic insult through diet alone might be insufficient for full recovery. Consequently, our finding underscores the paramount importance of early dietary and lifestyle intervention, particularly in aging populations where SAM50 expression is naturally in decline, to prevent the initial entrenchment of mitochondrial pathology. Strengths of the Study This study is distinguished by several key methodological strengths that reinforce the validity and impact of its conclusions. Its foremost strength is the translational integration of genetic discovery and mechanism, beginning with data from 3 population-scale genetic studies including a large clinical biobank to establish human disease relevance and then employing controlled in vivo and in vitro models to dissect and assess the underlying cellular pathophysiology. This approach creates a powerful and credible pipeline from population-level genetics to molecular mechanism. Furthermore, our study employs state-of-the-art volumetric imaging, specifically serial block-face scanning electron microscopy, moving beyond the descriptive or conventional 2D snapshots or view and provide three-dimensional quantitative analysis of mitochondrial and lipid droplet morphology which is far more useful. This technique was crucial for rigorously documenting the age-related loss of network complexity and the increase in organelle contact sites. The complementary use of untargeted metabolomics in our study alongside molecular and biochemical assays constitutes a multi-omics framework that connects structural remodeling to global shifts in hepatic metabolic function. Finally, the experimental design thoughtfully models the disease trajectory by examining a spectrum of metabolic states, from acute fasting and natural aging to chronic dietary challenge and attempted reversal, which provides nuanced insights into both the pathogenesis of and potential recovery from metabolic dysfunction. Limitations Our study has several limitations. First, the human genetic associations, while robust in the populations of majority European and Asian genetic ancestry (BioVU, UK Biobank, and BioBank Japan), were underpowered in our BioVU studies with individuals of African genetic ancestry, highlighting the need for more diverse biobanks. Second, the in vivo dietary and aging studies are correlative in that while they show strong associations between SAM50 expression, mitochondrial structure, and metabolism, direct causal manipulations of SAM50 in vivo in these specific contexts (aging, HFD) are needed to definitively establish mechanism. Third, the molecular pathways linking nutrient sensing such as mTOR, and AMPK to the transcriptional and post-transcriptional regulation of Sam50 have not been elucidated. Fourth, the functional consequences of the observed increase in lipid droplet-mitochondria contacts in aging require further investigation to determine whether they are adaptive or maladaptive. Finally, the timeframe for the dietary reversal experiment may have been insufficient to observe full normalization of protein levels and ultrastructure; however longer recovery periods should be examined. In conclusion, our study demonstrates that SAM50 is associated with nutrient state, aging, and diet resulting in changes in mitochondrial structure and systemic metabolism. Firstly, we have demonstrated that SAMM50 genetic variation influences human liver disease risk. Secondly, that SAM50 expression and mitochondrial architecture are dynamically regulated by nutrient availability. Thirdly, that aging is characterized by coordinated loss of SAM50 expression, mitochondrial structural abnormalities, and altered lipid droplet morphology and fourthly, that high-fat diet suppresses Sam50 expression and drives pathological mitochondrial remodeling and metabolic dysfunction, which is only partially reversible upon dietary correction. Our findings position SAM50 as a key regulator and potential target for understanding and potentially intervening in age-related and diet-induced metabolic diseases. Declarations Acknowledgments We would like to acknowledge the Huck Institutes’ Metabolomics Core Facility (RRID:SCR_023864) for use of the OE 240 LCMS and Sergei Koshkin for helpful discussions on sample preparation and analysis. We would also like to acknowledge the Huck Institutes’ Metabolomics Core Facility (RRID:SCR_023864) for use of the OE 240 LCMS and Drs. Imhoi Koo, Ashley Shay, and Sergei Koshkin for helpful discussions on sample preparation and analysis. We would also like to thank UCLA investigators for gifting us old and young human liver samples. We thank the participants of the BioVU biobank at Vanderbilt University Medical Center. We thank the UK Biobank and the Biobank Japan Project for graciously sharing summary level data. The Synthetic Derivative and BioVU projects at VUMC are supported by numerous sources: including the NIH funded Shared Instrumentation Grant S10OD017985 and S10RR025141; CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975 from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711; and additional funding sources listed at https://victr.vumc.org/biovu-funding/. Other funding sources include 2D43TW009744 and R21TW012635 (SKM and AK), T he Howard Hughes Medical Institute Hanna H. Gray Fellows Program Faculty Phase (Grant# GT15655 awarded to MRM) and the Burroughs Welcome Fund PDEP Transition to Faculty (Grant# 1022604 awarded to MRM). This project was funded by the National Institute of Health (NIH) NIDDK T-32, number DK007563 entitled Multidisciplinary Training in Molecular Endocrinology to Z.V.; National Institute of Health (NIH) NIDDK T-32, number DK007563 entitled Multidisciplinary Training in Molecular Endocrinology to A.C.; NSF MCB #2011577I to S.A.M.; The UNCF/Bristol-Myers Squibb E.E. Just Faculty Fund, Career Award at the Scientific Interface (CASI Award) from Burroughs Welcome Fund (BWF) ID # 1021868.01, BWF Ad-hoc Award, NIH Small Research Pilot Subaward to 5R25HL106365-12 from the National Institutes of Health PRIDE Program, DK020593, Vanderbilt Diabetes and Research Training Center for DRTC Alzheimer’s Disease Pilot & Feasibility Program. CZI Science Diversity Leadership grant number 2022- 253529 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation to A.H.J. and O.A.A.; and National Institutes of Health grant HD090061 and the Department of Veterans Affairs Office of Research Award I01 BX005352 to J.G. Howard Hughes Medical Institute Hanna H. Gray Fellows Program Faculty Phase (Grant# GT15655 awarded to M.R.M); and Burroughs Wellcome Fund PDEP Transition to Faculty (Grant# 1022604 awarded to M.R.M). National Institutes of Health Grants: R21DK119879 (to C.R.W.) and R01DK-133698 (to C.R.W.), American Heart Association Grants 16SDG27080009 (to C.R.W.) and 24IVPHA1297559 https://doi.org/10.58275/AHA.24IVPHA1297559.pc.gr.193866 (S.K.M) and by an American Society of Nephrology KidneyCure Transition to Independence Grant (to C.R.W.). NIH Grants R01HL147818, R03HL155041, and R01HL144941 (A. Kirabo). NIH Grant R00DK120876 (D.T.), Harold S. Geneen Charitable Trust Awards Program (D.T.), Alzheimer's Association AARG-NTF-23-1144888 (D.T.). NIH Grant R00AG065445 (P.J.), Alzheimer's Association 24AARG-D-1191292 (P.J.), Wake ADRC REC and Development grant P30AG072947 (P.J.). American Heart Association Grant 23POST1020344 (A.K.). American Heart Association Grant 23CDA1053072 (M. S.). NIH K01AG062757 to (M.T.S.) ANRF (Anusandhan National Research Foundation), ANRF/ECRG/2024/001042/LS, ANRF/IRG/2024/001777/LS. IISER Tirupati, NFSG (P.K). The BioVU project at VUMC is supported by numerous sources: including the NIH funded Shared Instrumentation Grant S10OD017985 and S10RR025141; CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975 from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711; and additional funding sources listed at https://victr.vumc.org/biovu-funding/.23CDA1053072 (M.S.). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NIH. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. Conflict of interest The authors declare that they have no conflict of interest. Authors’ contributions Sepiso K. Masenga, Victoria Baskerville, Mark Petrovic, and Benjamin Rodriguez share co-first authorship. S.K.M., V.B., M.P., and B.R. contributed to investigation, formal analysis, and writing of the original draft. D.L.H., T.W.M-F., Y.D.K., P. Katti, P. Venkhatesh, A. Kirabo, E.G-L., A.C., A. Marshall, C.B., C.V.D., P. Prasad, A. Murphy, J.A., M.A.P., C.E., E.S., J. Schafer, J.B., B.C. Mobley, D.F.D., H. Mobley, N.C.W., M.M.K., D.P., J. Sorrentino, CW, and J.G-G. contributed to investigation, methodology, formal analysis, and/or resources. M.McR. contributed to supervision, resources, and writing – review & editing. A.O.H.J. conceived and supervised the study, acquired funding, administered the project, contributed to investigation and methodology, and wrote, reviewed, and edited the manuscript. All authors reviewed and approved the final version of the manuscript. References Chen W, Zhao H, Li Y. Mitochondrial dynamics in health and disease: mechanisms and potential targets. Sig Transduct Target Ther 2023; 8 : 333. Adebayo M, Singh S, Singh AP, Dasgupta S. Mitochondrial Fusion and Fission: The fine-tune balance for cellular homeostasis. FASEB J 2021; 35 : e21620. Glancy B, Kim Y, Katti P, Willingham TB. The Functional Impact of Mitochondrial Structure Across Subcellular Scales. Front Physiol 2020; 11 . doi:10.3389/fphys.2020.541040. Anand R, Reichert AS, Kondadi AK. Emerging Roles of the MICOS Complex in Cristae Dynamics and Biogenesis. Biology (Basel) 2021; 10 : 600. Ravi R, Routray D, Mahalakshmi R. Mitochondrial Sorting and Assembly Machinery: Chaperoning a Moonlighting Role? Biochemistry 2025; 64 : 312–328. Höhr AIC, Straub SP, Warscheid B, Becker T, Wiedemann N. Assembly of β-barrel proteins in the mitochondrial outer membrane. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research 2015; 1853 : 74–88. Ott C, Ross K, Straub S, Thiede B, Götz M, Goosmann C et al. Sam50 Functions in Mitochondrial Intermembrane Space Bridging and Biogenesis of Respiratory Complexes. Mol Cell Biol 2012; 32 : 1173–1188. Zheng Y, Wang S, Wu J, Wang Y. Mitochondrial metabolic dysfunction and non-alcoholic fatty liver disease: new insights from pathogenic mechanisms to clinically targeted therapy. J Transl Med 2023; 21 : 510. Li X, Chen W, Jia Z, Xiao Y, Shi A, Ma X et al. Mitochondrial Dysfunction as a Pathogenesis and Therapeutic Strategy for Metabolic-Dysfunction-Associated Steatotic Liver Disease. International Journal of Molecular Sciences 2025; 26 . doi:10.3390/ijms26094256. Zong Y, Li H, Liao P, Chen L, Pan Y, Zheng Y et al. Mitochondrial dysfunction: mechanisms and advances in therapy. Sig Transduct Target Ther 2024; 9 : 1–29. Roden DM, Pulley JM, Basford MA, Bernard GR, Clayton EW, Balser JR et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther 2008; 84 : 362–369. Danciu I, Cowan JD, Basford M, Wang X, Saip A, Osgood S et al. Secondary use of clinical data: the Vanderbilt approach. J Biomed Inform 2014; 52 : 28–35. Dennis JK, Sealock JM, Straub P, Lee YH, Hucks D, Actkins K et al. Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. Genome Med 2021; 13 : 6. The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 2020; 369 : 1318–1330. Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet 2015; 47 : 1091–1098. Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM et al. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet 2019; 51 : 568–576. Zhou D, Jiang Y, Zhong X, Cox NJ, Liu C, Gamazon ER. A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis. Nat Genet 2020; 52 : 1239–1246. Barbeira AN, Pividori M, Zheng J, Wheeler HE, Nicolae DL, Im HK. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet 2019; 15 : e1007889. Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 2010; 26 : 1205–1210. Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature 2024; 627 : 347–357. Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 2021; 53 : 1415–1424. Gagliano Taliun SA, VandeHaar P, Boughton AP, Welch RP, Taliun D, Schmidt EM et al. Exploring and visualizing large-scale genetic associations by using PheWeb. Nat Genet 2020; 52 : 550–552. Boudina S, Sena S, Theobald H, Sheng X, Wright JJ, Hu XX et al. Mitochondrial energetics in the heart in obesity-related diabetes: direct evidence for increased uncoupled respiration and activation of uncoupling proteins. Diabetes 2007; 56 : 2457–2466. Pereira RO, Tadinada SM, Zasadny FM, Oliveira KJ, Pires KMP, Olvera A et al. OPA 1 deficiency promotes secretion of FGF 21 from muscle that prevents obesity and insulin resistance. The EMBO journal 2017; 36 : 2126–2145. Hinton A, Katti P, Christensen TA, Mungai M, Shao J, Zhang L et al. A Comprehensive Approach to Sample Preparation for Electron Microscopy and the Assessment of Mitochondrial Morphology in Tissue and Cultured Cells. Adv Biol (Weinh) 2023; : e2200202. Lam J, Katti P, Biete M, Mungai M, AshShareef S, Neikirk K et al. A Universal Approach to Analyzing Transmission Electron Microscopy with ImageJ. Cells 2021; 10 : 2177. Neikirk K, Vue Z, Katti P, Rodriguez BI, Omer S, Shao J et al. Systematic Transmission Electron Microscopy-Based Identification and 3D Reconstruction of Cellular Degradation Machinery. Advanced Biology 2023; 7 : 2200221. Garza-Lopez E, Vue Z, Katti P, Neikirk K, Biete M, Lam J et al. Protocols for Generating Surfaces and Measuring 3D Organelle Morphology Using Amira. Cells 2022; 11 : 65. Tam J, Vemuri VK, Liu J, Bátkai S, Mukhopadhyay B, Godlewski G et al. Peripheral CB1 cannabinoid receptor blockade improves cardiometabolic risk in mouse models of obesity. J Clin Invest 2010; 120 : 2953–2966. Lu W, Wang L, Chen L, Hui S, Rabinowitz JD. Extraction and Quantitation of Nicotinamide Adenine Dinucleotide Redox Cofactors. Antioxidants & Redox Signaling 2018; 28 : 167–179. Wang L, Xing X, Chen L, Yang L, Su X, Rabitz H et al. Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics. Anal Chem 2019; 91 : 1838–1846. Chung GE, Lee Y, Yim JY, Choe EK, Kwak M-S, Yang JI et al. Genetic Polymorphisms of PNPLA3 and SAMM50 Are Associated with Nonalcoholic Fatty Liver Disease in a Korean Population. Gut and Liver 2018; 12 : 316–323. Li Z, Shen W, Wu G, Qin C, Zhang Y, Wang Y et al. The role of SAMM50 in non‐alcoholic fatty liver disease: from genetics to mechanisms. FEBS Open Bio 2021; 11 : 1893–1906. Gopcevic KR, Gkaliagkousi E, Nemcsik J, Acet Ö, Bernal-Lopez MR, Bruno RM et al. Pathophysiology of Circulating Biomarkers and Relationship With Vascular Aging: A Review of the Literature From VascAgeNet Group on Circulating Biomarkers, European Cooperation in Science and Technology Action 18216. Front Physiol 2021; 12 : 789690. Lionello S, Marzaro G, Martinvalet D. SAM50, a side door to the mitochondria: The case of cytotoxic proteases. Pharmacol Res 2020; 160 : 105196. Kozjak V, Wiedemann N, Milenkovic D, Lohaus C, Meyer HE, Guiard B et al. An essential role of Sam50 in the protein sorting and assembly machinery of the mitochondrial outer membrane. J Biol Chem 2003; 278 : 48520–48523. Yin X, Wang J, Yang S, Li H, Shen H, Wang H et al. Sam50 exerts neuroprotection by maintaining the mitochondrial structure during experimental cerebral ischemia/reperfusion injury in rats. CNS Neurosci Ther 2022; 28 : 2230–2244. Jia L, Wei Z, Luoqian J, Wang X, Huang C. Mitochondrial Dysfunction in Aging: Future Therapies and Precision Medicine Approaches. MedComm – Future Medicine 2025; 4 : e70026. Somasundaram I, Jain SM, Blot-Chabaud M, Pathak S, Banerjee A, Rawat S et al. Mitochondrial dysfunction and its association with age-related disorders. Front Physiol 2024; 15 . doi:10.3389/fphys.2024.1384966. Srivastava S. The Mitochondrial Basis of Aging and Age-Related Disorders. Genes (Basel) 2017; 8 : 398. Guo Y, Guan T, Shafiq K, Yu Q, Jiao X, Na D et al. Mitochondrial dysfunction in aging. Ageing Research Reviews 2023; 88 : 101955. Wang G, Sun B, Liu H, Hu M, Xu H, Li H et al. Interactions between lipid droplets and mitochondria in metabolic diseases. Lipids Health Dis 2025; 24 : 357. Fan H, Tan Y, Fan H, Tan Y. Lipid Droplet–Mitochondria Contacts in Health and Disease. International Journal of Molecular Sciences 2024; 25 . doi:10.3390/ijms25136878. Li X, Zhao X, Qin Z, Li J, Sun B, Liu L. Regulation of calcium homeostasis in endoplasmic reticulum–mitochondria crosstalk: implications for skeletal muscle atrophy. Cell Communication and Signaling 2025; 23 : 17. Tables Table 1: qPCR Primers Used Human Gene Primers SAMM50 Forward 5’-AGACGGACAGAGGAATGTCAGC-3’ Reverse 5’-GCAAATGACGCCGTCCTTGAGA-3’ Mouse Gene Sam50 Forward 5’-CACAGCCTGGAAACTTCGAGAG-3’ Reverse 5’-GACAGTGTGACTGGTCTTCCAC-3’ Table 2. SAMM50 associations: BioBank Japan (BBJ) and UK Biobank (UKB) Pheweb Top p-value in gene Phenotype Number of samples Source 3.3e-87 Aspartate transaminase 150,068 BBJ 3.0e-71 Alanine aminotransferase 150,545 BBJ 6.5e-38 Platelet count 148,623 BBJ 4.4e-26 Type 2 diabetes (multi-ancestry meta-analysis) 428,452 / 2,107,149 BBJ 2.8e-24 Chronic liver disease and cirrhosis 2,895/400,055 UKB 3.1e-19 Other chronic nonalcoholic liver disease 1,664/400,055 UKB 4.6e-19 Type 2 diabetes (European meta-analysis) 242,283 / 1,569,734 BBJ 2.8e-14 Cirrhosis 2,551 / 176,175 BBJ 2.7e-13 Mean corpuscular hemoglobin 128,028 BBJ 1.8e-12 Mean corpuscular volume 129,832 BBJ 2.7e-11 Alcoholic liver damage 802/379,355 UKB 1.0e-9 Portal hypertension 529/400,055 UKB 1.8e-9 Uric acids 129,405 BBJ 4.9e-9 Liver abscess and sequelae of chronic liver disease 942/400,055 UKB 1.6e-8 Hepatic cancer 2,122 / 159,201 BBJ 3.2e-8 Total cholesterol 135,808 BBJ 4.2e-8 Cholelithiasis and cholecystitis 16,225/391,307 UKB Additional Declarations There is no conflict of interest Supplementary Files Supplimentalfile1.PHEWASLABWASTABLESSAMM50MANUSCRIPT.xlsx Supplemental file 1. PHEWAS European data Supplimentalfile2.LABWASTABLESSAMM50MANUSCRIPT.xlsx Supplemental file 2. LABWAS European data Supplimentalfile3.PHEWASTABLESSAMM50MANUSCRIPT.xlsx Supplemental file 3. PHEWAS African data Supplimentalfile4.LABWASTABLESSAMM50MANUSCRIPT.xlsx Supplemental file 4. LABWAS African data Supplimentalfile5.UKbiobank.xlsx Supplemental file 5. UK Biobank SupplementalTable1.docx Supplimental Table 1 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8704245","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":581460693,"identity":"95f989c9-3ff5-4a81-af57-007d747a2a17","order_by":0,"name":"Antentor Othrell Hinton, Jr.","email":"data:image/png;base64,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","orcid":"","institution":"Vanderbilt University","correspondingAuthor":true,"prefix":"","firstName":"Antentor","middleName":"Othrell","lastName":"Hinton","suffix":"Jr."},{"id":581460694,"identity":"a1a75766-55f1-4bfd-8b72-51253f21f4cc","order_by":1,"name":"Sepiso K Masenga","email":"","orcid":"","institution":"Livingstone Center for Prevention and Translational Science","correspondingAuthor":false,"prefix":"","firstName":"Sepiso","middleName":"K","lastName":"Masenga","suffix":""},{"id":581460695,"identity":"caa1a9b1-e2f8-45ba-931d-31fcb3ba1c97","order_by":2,"name":"Victoria Baskerville","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"","lastName":"Baskerville","suffix":""},{"id":581460696,"identity":"aea54a67-9922-42ee-8e79-582104072f48","order_by":3,"name":"Mark Petrovic","email":"","orcid":"","institution":"Pennsylvania State University","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Petrovic","suffix":""},{"id":581460697,"identity":"d514e439-ff67-48d4-bcc2-9da8c4dab25a","order_by":4,"name":"Benjamin Rodriguez","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Rodriguez","suffix":""},{"id":581460698,"identity":"3e20e714-3d26-4850-8182-fc29d2fe6f1e","order_by":5,"name":"David L Hubert","email":"","orcid":"","institution":"Oregon State University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"L","lastName":"Hubert","suffix":""},{"id":581460699,"identity":"b5d7f518-b773-4bb2-bb5f-fb65ef5bade2","order_by":6,"name":"Tyne W Miller-Fleming","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Tyne","middleName":"W","lastName":"Miller-Fleming","suffix":""},{"id":581460700,"identity":"3a12337d-4d99-4d2e-a157-d33b2e23c40e","order_by":7,"name":"Young Do Koo","email":"","orcid":"","institution":"University of California-Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Young","middleName":"Do","lastName":"Koo","suffix":""},{"id":581460701,"identity":"d39e1e77-74c4-49e1-9ed5-bb5957fc6e21","order_by":8,"name":"Prasanna Katti","email":"","orcid":"","institution":"Indian Institute of Science Education and Research (IISER) Tirupati","correspondingAuthor":false,"prefix":"","firstName":"Prasanna","middleName":"","lastName":"Katti","suffix":""},{"id":581460702,"identity":"abfeb812-fdea-4afc-820b-45dc58c971d3","order_by":9,"name":"Prasanna Venkhatesh","email":"","orcid":"","institution":"Indian Institute of Science Education and Research (IISER) Tirupati","correspondingAuthor":false,"prefix":"","firstName":"Prasanna","middleName":"","lastName":"Venkhatesh","suffix":""},{"id":581460703,"identity":"b7096095-b198-4e51-b80a-9f6610c1f9e5","order_by":10,"name":"Annet Kirabo","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Annet","middleName":"","lastName":"Kirabo","suffix":""},{"id":581460704,"identity":"985c6100-12d9-418f-abd1-2de286d8e4f0","order_by":11,"name":"Edgar Garza Lopez","email":"","orcid":"","institution":"Hinton and Garza Lopez Consulting Company","correspondingAuthor":false,"prefix":"","firstName":"Edgar","middleName":"Garza","lastName":"Lopez","suffix":""},{"id":581460705,"identity":"2df30146-0846-4a94-b390-c28432e9d640","order_by":12,"name":"Amber Crabtree","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Amber","middleName":"","lastName":"Crabtree","suffix":""},{"id":581460706,"identity":"49cb0991-7ede-4f72-a120-c7c9d68a59dc","order_by":13,"name":"Andrea Marshall","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Marshall","suffix":""},{"id":581460707,"identity":"add651c4-7eb2-4742-a628-dee548a5c16c","order_by":14,"name":"Campbell Blake","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Campbell","middleName":"","lastName":"Blake","suffix":""},{"id":581460708,"identity":"9dc15b01-11d1-4d0d-9f98-91568cba3f98","order_by":15,"name":"Chandravanu Dash","email":"","orcid":"","institution":"Meharry Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chandravanu","middleName":"","lastName":"Dash","suffix":""},{"id":581460709,"identity":"af87f1bd-4c15-460f-beba-72e92261a29a","order_by":16,"name":"Praveena Prasad","email":"","orcid":"","institution":"Indian Institute of Science Education and Research (IISER) Tirupati","correspondingAuthor":false,"prefix":"","firstName":"Praveena","middleName":"","lastName":"Prasad","suffix":""},{"id":581460710,"identity":"67f45507-450a-4ebe-b9bd-9432faa516ad","order_by":17,"name":"Alexandria Murphy","email":"","orcid":"","institution":"Pennsylvania State University","correspondingAuthor":false,"prefix":"","firstName":"Alexandria","middleName":"","lastName":"Murphy","suffix":""},{"id":581460711,"identity":"2d3fe296-a39c-466e-b084-e60907bc0169","order_by":18,"name":"Jeremiah Afolabi","email":"","orcid":"","institution":"Vanderbilt UniversityMedical Center","correspondingAuthor":false,"prefix":"","firstName":"Jeremiah","middleName":"","lastName":"Afolabi","suffix":""},{"id":581460712,"identity":"01d3f8c0-7158-49fd-9f56-6edc8a6550d4","order_by":19,"name":"Mark A Phillips","email":"","orcid":"","institution":"Oregon State University","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"A","lastName":"Phillips","suffix":""},{"id":581460713,"identity":"4c0faa44-4655-48fc-b3b3-37818ab0e324","order_by":20,"name":"Chantell Evans","email":"","orcid":"","institution":"Duke University","correspondingAuthor":false,"prefix":"","firstName":"Chantell","middleName":"","lastName":"Evans","suffix":""},{"id":581460714,"identity":"228f4a77-cc33-4696-9010-8b561c1b7638","order_by":21,"name":"Estevão Scudese","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Estevão","middleName":"","lastName":"Scudese","suffix":""},{"id":581460715,"identity":"e5b9b483-6593-4bce-8855-02b4a66581fd","order_by":22,"name":"Jenny C. Schafer","email":"","orcid":"","institution":"Vanderbilt University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jenny","middleName":"C.","lastName":"Schafer","suffix":""},{"id":581460716,"identity":"f84b8e45-d749-4fff-8c8b-ee5659ddc843","order_by":23,"name":"Julia Berry","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Berry","suffix":""},{"id":581460717,"identity":"c0ffe6e7-d048-49dc-86ad-cde0fa2b2203","order_by":24,"name":"Bret C Mobley","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Bret","middleName":"C","lastName":"Mobley","suffix":""},{"id":581460718,"identity":"7820b431-f46f-459b-ab83-5f8df5bfe5a2","order_by":25,"name":"Dao Fu Dai","email":"","orcid":"","institution":"Johns Hopkins University","correspondingAuthor":false,"prefix":"","firstName":"Dao","middleName":"Fu","lastName":"Dai","suffix":""},{"id":581460719,"identity":"e8f3c4a9-83cf-4e98-8e4f-f1448dd9b475","order_by":26,"name":"Harrison Mobley","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Harrison","middleName":"","lastName":"Mobley","suffix":""},{"id":581460720,"identity":"3e5cc73b-fba3-4700-a18f-a2ca50b5c447","order_by":27,"name":"Nathan C Winn","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Nathan","middleName":"C","lastName":"Winn","suffix":""},{"id":581460721,"identity":"593af4b7-f360-4a81-8f93-1e7edf45deb1","order_by":28,"name":"Mohd M Khan","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"M","lastName":"Khan","suffix":""},{"id":581460722,"identity":"b4481fb7-c9dc-4c1d-a177-125981d8a14d","order_by":29,"name":"Dea Pulatani","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Dea","middleName":"","lastName":"Pulatani","suffix":""},{"id":581460723,"identity":"9992b17d-ddf0-4346-a266-d73f72f7fb02","order_by":30,"name":"Joseph Sorrentino","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Sorrentino","suffix":""},{"id":581460724,"identity":"e6940d91-a0e6-497d-8734-47c64aa7678a","order_by":31,"name":"Joyonna Gamble-George","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Joyonna","middleName":"","lastName":"Gamble-George","suffix":""},{"id":581460725,"identity":"4ccb7626-ee24-471e-a958-973659bd05f4","order_by":32,"name":"Melanie McReynolds","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"McReynolds","suffix":""},{"id":581460726,"identity":"f2957c5a-251b-485d-9024-65ce5c52739e","order_by":33,"name":"Celestine Wanjalla","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Celestine","middleName":"","lastName":"Wanjalla","suffix":""}],"badges":[],"createdAt":"2026-01-26 23:25:23","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8704245/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8704245/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102736452,"identity":"7e9ff02c-9545-4acb-b8c2-300e5beac138","added_by":"auto","created_at":"2026-02-16 06:25:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":248086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenome-wide association study (PheWAS) and clinical lab-wide association scan (LabWAS) for SAMM50 genetically-regulated gene expression (GReX) in BioVU.\u003c/strong\u003eClinical phenotypes and laboratory values from BioVU participants were extracted from Vanderbilt’s de-identified electronic health record database (n= 85,615, top left panel). Genetically-regulated gene expression for SAMM50 was calculated in BioVU participants using models built from the GTEx version 8 data (top right panel), which contains genotype data matched to RNA-Seq data from 838 donors across 49 tissues. Imputed gene expression was calculated and tested for association across up to 1,704 phenotypes and 329 clinical lab tests using regression models in MultiXcan (bottom panel), accounting for genetic ancestry (principal components/PC 1-10), sex, age, median age of medical record and genotyping batch.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/f5b160b0e1edbf5a6359d45c.png"},{"id":102749500,"identity":"035bc27f-a522-4e31-ac54-631d91b2c27d","added_by":"auto","created_at":"2026-02-16 09:12:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57931,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenome-wide association study (PheWAS) and clinical laboratory-wide association scan (LabWAS) for SAMM50 genetically-regulated gene expression (GReX) in BioVU individuals of European genetic ancestry.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Phenotypes from BioVU participants were extracted from Vanderbilt’s de-identified electronic health record database and genetically-regulated gene expression for SAMM50 was calculated in BioVU participants of European ancestry (n=70,404). The GREX for SAMM50 was tested for association across 1,704 phenotypes using logistic regression models, accounting for genetic ancestry (principal components/PC 1-10), sex, age, median age of medical record, and genotyping batch. Associations that met the Bonferroni-corrected threshold (red line, p \u0026lt; 2.934272 x 10\u003csup\u003e-5\u003c/sup\u003e) are labeled with phenotype name. Blue line represents p = 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eB. Clinical lab values from BioVU participants were extracted from Vanderbilt’s de-identified electronic health record database and genetically-regulated gene expression for SAMM50 was calculated in BioVU participants of European ancestry (n=70,404). The GREX for SAMM50 was tested for association across 329 lab values using linear regression models, accounting for genetic ancestry (principal components/PC 1-10), sex, age, median age of medical record, and genotyping batch. No associations met the Bonferroni-corrected threshold (red line, p \u0026lt; 1.519757 X 10\u003csup\u003e-4\u003c/sup\u003e). Blue line represents p = 0.05.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/76c4c164161ead583f552904.png"},{"id":102736474,"identity":"6ca504f0-f93a-4a6b-97e3-dd111bda6233","added_by":"auto","created_at":"2026-02-16 06:25:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":449885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFasting and re-feeding alter mitochondrial protein and the expression of respiratory complex in mouse liver.\u003c/strong\u003e A) Representative immunoblots of mitochondrial respiratory chain complexes and mitochondrial markers in mouse liver after 24 h fasting versus re-feeding in two independent cohorts. Immunoblots show expression of Complex III (Cytochrome B), Complex V (ATP5A), Complex I (Cytochrome C Oxidase), and Complex II (SDHA), along with mitochondrial markers SAM50 and \u003cem\u003eTom20\u003c/em\u003e, with GAPDH used as a loading control. Quantification of protein expression levels normalized to loading control (B-G) show that re-feeding significantly increased expression of (C) SAM50 (1.00 vs. 1.93, p=0.0.044), decreased the expression of (D) Complex I (1.00 vs. 0.41, p=0.0006), (F) complex III (1.00 vs. 0.43, p=0.008) and (G) Complex V (1.00 vs. 0.61, p=0.022), while (B) \u003cem\u003eTom20\u003c/em\u003e(1.00 vs. 1.68 p=0.327), and (E) Complex II levels (1.00 vs. 1.50, p=0.299, respectively), were unchanged. Data are presented as mean ± SEM. ns = not significant; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/66b258a5a8cf6d4db6211a34.png"},{"id":102736450,"identity":"8e111d50-21f0-4a9a-8906-d133ca00a499","added_by":"auto","created_at":"2026-02-16 06:25:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1203682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum starvation alters mitochondrial ultrastructure and respiratory complex expression in cultured cells.\u003c/strong\u003e Representative transmission electron microscopy (TEM) images of cells under (A) fed and (B) serum-fasted (SF, 6 h) conditions. Compared with (A) fed cells, (B) serum-fasted cells displayed larger (D) mitochondria with fewer cristae, reduced (E) mitochondrial number, and increased autophagosomes, indicating remodeling of mitochondrial structure under nutrient deprivation. Scale bar = 10 μm. (C) Immunoblots of mitochondrial respiratory chain complexes and markers, including Complex III (Cytochrome B), Complex I (Cytochrome C Oxidase), Complex II (SDHA), and Complex V (ATP5A), along with mitochondrial outer membrane proteins SAM50 and \u003cem\u003eTom20\u003c/em\u003e, with GAPDH as a loading control. Quantification of protein expression shows no significant changes in (F) \u003cem\u003eTom20 \u003c/em\u003e(1.00 VS. 1.02, P=0.963), (5) SAM50 (1.00 vs. 1.05, p=0.744), or (H) Complex I between fed and serum-fasted cells (1.00 vs. 0.61, p=0.131), whereas (I) Complex II (1.00 vs. 0.43, p=0.0183), (J) Complex III (1.00 vs. 0.48, p=0.0087), and (K) Complex V (1.00 vs. 0.48, p=0.0199) levels were significantly increased upon re-feeding. Data are presented as mean ± SEM. ns = not significant; *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/563d65cb6eafa51dbf6d00ae.png"},{"id":102736479,"identity":"c49d6c78-4f8c-4005-817d-ceed187c2ba8","added_by":"auto","created_at":"2026-02-16 06:25:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":793578,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAging reduces hepatic mitochondrial network size and complexity and is associated with decreased Sam50 expression and mtDNA content.\u003c/strong\u003eRepresentative SBF SEM–derived 3D reconstructions of liver mitochondria from (A, A’) 3 month and (B, B’) 2-year mice are shown (each color denotes an individually segmented mitochondrial object). Top panels (A and B) show transverse views and bottom panels (A’ and B’) show orthogonal (side or transverse) views of the reconstructed mitochondrial population. Scale bars, 2 µm. Quantification of 3D mitochondrial morphology demonstrates significant age associated reductions in (C) 3D area (µm²) (6.3 vs. 4.5, p\u0026lt;0.0001), (D) perimeter (8982 vs. 7568, p\u0026lt;0.0001), and (E) 3D volume (µm³) (0.82 vs. 0.66, p\u0026lt;0.0001), accompanied by increased (F) sphericity (0.65 vs. 0.80, p\u0026lt;0.0001), consistent with a shift toward smaller, more rounded mitochondrial structures in aged liver. Aged samples also exhibit a marked reduction in (G) complexity index (2.64 vs. 1.39, p\u0026lt;0.0001), indicating diminished network complexity. Molecular analyses reveal decreased (H) Sam50 mRNA expression (qPCR) (1.08 vs. 0.60, p=0.0019) and reduced (I) relative mtDNA content (mtDNA normalized to nuclear DNA) (0.90 vs. 0.75, p=0.0008) in 2-year liver compared with 3-month. Bars represent mean with individual data points overlaid where shown. Significance is indicated as \u003cstrong\u003e**\u003c/strong\u003ep \u0026lt; 0.01, \u003cstrong\u003e***\u003c/strong\u003e p \u0026lt; 0.001, \u003cstrong\u003e****\u003c/strong\u003e p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/6f4160cc408bd5f852c768a0.png"},{"id":102736483,"identity":"11753bff-d697-432a-bc72-34a7501c3112","added_by":"auto","created_at":"2026-02-16 06:25:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2228315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerial block-face scanning electron microscopy (SBF-SEM) workflow for 3D reconstruction of a single hepatic lipid droplet.\u003c/strong\u003e\u003cbr\u003e\nSchematic illustrates the SBF-SEM acquisition volume used for 3D imaging of (A) mouse (B) liver tissue, showing a representative stack spanning approximately 10 µm × 10 µm in the x–y plane and ~50 µm in the z-dimension. Serial ultrathin sections were collected sequentially and aligned to generate a volumetric dataset. (C) Middle panels (1–16) show consecutive serial sections through an individual lipid droplet (highlighted in yellow) across the z-axis, demonstrating changes in cross-sectional area as the droplet is entered, traversed, and exited. (D) Bottom panels show the resulting 3D surface reconstruction of the same lipid droplet generated from the segmented serial sections, displayed from two orthogonal viewing angles (rotated 90°) to illustrate overall shape and volume. Scale bars: 3 µm (top SBF-SEM volume), 0.5 µm (serial sections and 3D renderings).\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/4954736d5bd286da0840f23b.png"},{"id":102736453,"identity":"258ed449-bd91-40b7-96f6-07b989bc6d83","added_by":"auto","created_at":"2026-02-16 06:25:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":592528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAging promotes hepatic lipid droplet expansion, enhanced mitochondria–lipid droplet interactions, and systemic lipid dysregulation in mouse liver.\u003c/strong\u003e\u003cbr\u003e\nTop panels show 3D SBF-SEM reconstructions of lipid droplets in livers from (A) 3-month and (B) 2-year mice. (A’-B’) Individual lipid droplets are rendered in yellow, illustrating a marked increase in lipid droplet size, abundance, and clustering with age. Middle panels show representative 3D reconstructions of mitochondria–lipid droplet interactions, with lipid droplets (yellow) in direct contact with mitochondria (colored surfaces), highlighting increased physical association and expanded contact interfaces in 2-year liver compared with 3-month. Quantitative analyses demonstrate significant age-associated increases in lipid droplet 3D (C) area (1.63 vs. 2.42, p\u0026lt;0.0001) and (D) 3D volume (0.12 vs. 0.15, p=0.034), along with elevated (E) percentage of lipid coverage within the imaged volume (3.72 vs. 16.38, p\u0026lt;0.0001). Aged liver also exhibits increased (F) lipid droplet–mitochondria contact site coverage (4.10 vs. 7.39, p=0.0004) and a higher (G) percentage of mitochondrial surface coverage by lipid droplets (4.14 vs. 7.85, p=0.0014), indicating enhanced organelle coupling. Biochemical analyses reveal elevated (H and I) thiobarbituric acid reactive substances (TBARS) in both (H) plasma (3.96 vs. 8.05, p=0.003) and (I) liver tissue (0.14 vs. 0.29, p=0.0016) from 2-year mice, consistent with increased lipid peroxidation and oxidative stress. Systemic metabolic profiling shows increased (J) hepatic triglyceride content (0.25 vs. 0.65, p=0.0002), altered (K) liver weight as a percentage of total body weight (4.02 vs. 3.37, p=0.0039), and elevated ( L) serum triglycerides (0.16 vs. 0.32, p=0.0047) in aged mice, while (M) bile acid levels (0.83 vs. o.83, p=0.728) remain unchanged. Bars represent mean with individual data points overlaid where indicated. Statistical significance is shown as * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001, **** p \u0026lt; 0.0001; ns, not significant.\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/97c135c989bac73dc54f08a8.png"},{"id":102736477,"identity":"5ab41b4a-cd34-4c8a-bdaa-8b92de039404","added_by":"auto","created_at":"2026-02-16 06:25:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":294102,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh-fat diet suppresses \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSam50 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eexpression and rewires hepatic metabolism, with partial transcriptional recovery following diet reversal.\u003c/strong\u003e\u003cbr\u003e\n(A) Immunoblots of \u003cem\u003eSam50\u003c/em\u003e expression in liver tissue from mice fed a low-fat diet (LFD) or high-fat diet (HFD), with β-actin as a loading control. (B) Sam50 protein abundance assessed by densitometry shows significant differences between LFD and HFD groups (0.03 vs. 0.01, p=0.012) but no differences between 12-week LDF and 8-week HFD/4-week LFD (0.03 VS. 0.04, p=0.224) at the analyzed time point, indicating changes in transcriptional regulation and steady-state protein levels. (C) Body weight trajectories of mice maintained on LFD, HFD, or switched from HFD to LFD (HFD→LFD) over 12 weeks, with arrows indicating tissue harvest time points. HFD feeding results in a marked increase in body weight compared with LFD, while diet reversal attenuates further weight gain. Top right panels show representative (D) Western blots for Sam50 and ACTIN from liver tissue across diet groups. Quantification of (E) Sam50 mRNA expression reveals a significant reduction in HFD compared with LFD, with partial restoration following HFD→LFD at the transcript level. In contrast, (F) Sam50 protein abundance assessed by densitometry shows no significant differences between groups at the analyzed time point, indicating a dissociation between transcriptional regulation and steady-state protein levels. (G) untargeted hepatic metabolomics comparing LFD and HFD groups. (G) Principal component analysis (PCA) demonstrates clear separation between diets, indicating global metabolic remodeling with HFD feeding. (H) Differential metabolite analysis highlights diet-dependent changes in multiple metabolites (FDR \u0026lt; 0.05), and (I) hierarchical clustering heatmap reveals distinct metabolic signatures associated with LFD and HFD conditions. Together, these data indicate that HFD robustly alters hepatic metabolism and suppresses Sam50 gene expression, while short-term dietary reversal is sufficient to normalize transcription but not protein abundance, consistent with delayed mitochondrial outer membrane remodeling. Data are presented as mean ± SEM with individual data points overlaid where shown. Statistical significance is indicated as *p \u0026lt; 0.05, ***p \u0026lt; 0.001; ns, not significant.\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/fc111feef6480cc31e5ce17f.png"},{"id":103152979,"identity":"9cb61cef-68c3-4cd1-a9b2-ab140e193568","added_by":"auto","created_at":"2026-02-22 05:17:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8403756,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/f5907ac8-e539-47ff-8a97-cb629f41fef3.pdf"},{"id":102736459,"identity":"d557f604-2a10-4ca9-b1cf-e32bd447922b","added_by":"auto","created_at":"2026-02-16 06:25:44","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":135460,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental file 1. PHEWAS European data\u003c/p\u003e","description":"","filename":"Supplimentalfile1.PHEWASLABWASTABLESSAMM50MANUSCRIPT.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/8a1eac7e56227e56d91fe9c7.xlsx"},{"id":102736480,"identity":"d460e090-8194-40e0-99c5-e160d90e23fb","added_by":"auto","created_at":"2026-02-16 06:25:47","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":31354,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental file 2. LABWAS European data\u003c/p\u003e","description":"","filename":"Supplimentalfile2.LABWASTABLESSAMM50MANUSCRIPT.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/4329f722da19cced34abe9d8.xlsx"},{"id":102736482,"identity":"88b3dcfa-e524-4d88-9bd8-3f73395df61d","added_by":"auto","created_at":"2026-02-16 06:25:48","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":110467,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental file 3. PHEWAS African data\u003c/p\u003e","description":"","filename":"Supplimentalfile3.PHEWASTABLESSAMM50MANUSCRIPT.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/c19da0a7fe97a24ccd633b0b.xlsx"},{"id":102736487,"identity":"9df61bbf-7035-47d3-9815-76fcab1ee87d","added_by":"auto","created_at":"2026-02-16 06:25:49","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":28425,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental file 4. LABWAS African data\u003c/p\u003e","description":"","filename":"Supplimentalfile4.LABWASTABLESSAMM50MANUSCRIPT.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/fb88d85edc0118dc1512bf80.xlsx"},{"id":102736481,"identity":"c31f4b30-3048-44b9-a811-3eda540b6202","added_by":"auto","created_at":"2026-02-16 06:25:48","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":20430,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental file 5. UK Biobank\u003c/p\u003e","description":"","filename":"Supplimentalfile5.UKbiobank.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/edac2b8442f34484ab4ccdc0.xlsx"},{"id":102736484,"identity":"dc941c8f-846e-4f75-a462-3d8539e8b062","added_by":"auto","created_at":"2026-02-16 06:25:48","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":18186,"visible":true,"origin":"","legend":"Supplimental Table 1","description":"","filename":"SupplementalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8704245/v1/8bad1945a3709500d47c12fd.docx"}],"financialInterests":"There is no conflict of interest","formattedTitle":"Nutrient State, Aging, and Diet Modulate SAM50-Dependent Mitochondrial Remodeling and Systemic Metabolic Signatures","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMitochondria and the proteins regulating its structure and function, are important for cellular energy homeostasis and overall metabolic health \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The structure and function of mitochondria are largely determined by ongoing fusion and fission processes, as well as the complex organization of their inner membrane cristae. These activities, are closely linked to cellular nutrient levels, stress responses, and aging \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The mitochondrial sorting and assembly machinery (SAM) is essential for maintaining the structure of the inner membrane and forming important contact sites with other cell organelles \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. SAM50, a key and highly conserved component of this SAM complex, has two main roles. SAM50 helps assemble beta-barrel proteins into the outer mitochondrial membrane and additionally, it interacts directly with the MICOS complex \u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This positions SAM50 as a key regulator of mitochondria structure and function.\u003c/p\u003e \u003cp\u003ePrior studies have demonstrated that any stress-related changes in mitochondrial architecture including aging has been linked to metabolic diseases and liver-related disorders such as insulin resistance and hepatic steatosis, respectively \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Although mitochondrial dynamics have been recognized as important in metabolic diseases, the specific role of SAM50 in regulating systemic metabolism in response to environmental and genetic variations remains unclear. We therefore wanted to know if SAM50 expression and function respond to nutritional interventions, including whether diet-induced mitochondrial defects can be reversed. We also sought to elucidate the role of aging on SAM50 expression. This study combines human genetics with mechanistic studies in mice and primary cells to examine how SAM50 functions as a regulator of mitochondrial remodeling responsive to nutrients and aging.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eBiobank Genetic Association Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVanderbilt University Medical Center curates an electronic health record (EHR)-linked biobank, BioVU. This opt-in program collects and stores blood samples from routine clinical visits. Currently, BioVU houses more than 350,000 biological samples linked to de-identified EHRs dating back several decades \u003csup\u003e11,12\u003c/sup\u003e. Genetic data for 90,000 individuals was analyzed and cleaned as previously described \u003csup\u003e13\u003c/sup\u003e. In summary, individuals were genotyped on Illumina\u0026rsquo;s Multi-Ethnic Genotyping Array (MEGA) and imputed on the Michigan Imputation Server using the Haplotype Reference Consortium (HRC) reference panel. Quality control was performed to filter the genetic data based on previously reported minor allele frequency, individual and variant level missingness, and deviations within Hardy-Weinberg equilibrium. Genetically related individuals were removed from the analysis. Principal component analysis was performed with the 1000 genomes reference populations and downstream analyses were performed within two BioVU populations: individuals of European genetic ancestry (n=70,404) and individuals of African genetic ancestry (n=15,175). Genetically-regulated gene expression (GreX) for SAMM50 was calculated using predictive models P\u003cem\u003erediXc\u003c/em\u003ean, UTMOST, JTI using GTEx version 8 data \u003csup\u003e14\u0026ndash;17\u003c/sup\u003e. The model with the highest performance r2 was used for each SAMM50-tissue pair. MultiXcan was then employed to synthesize a cross-tissue SAMM50 GReX model \u003csup\u003e14,18\u003c/sup\u003e. Within genetically-defined ancestry groups, associations between SAMM50 GReX and 1,704 clinical phenotypes (via logistic regression) and 326 laboratory traits (via linear regression) were tested, adjusting for principal components (1-10), sex, age, median age of medical record, and genotyping batch. Clinical laboratory values were extracted from the EHR as previously described (REF). Phenotypes were extracted from the EHR by mapping structured clinical data (ICD9 and ICD10 codes) to phecodes as previously described \u003csup\u003e13,19\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo validate the clinical relevance of \u003cem\u003eSAMM50\u003c/em\u003e in human disease from additional biobank populations, we explored publicly available summary-level genome-wide association results from two large, independent biobanks (Biobank Japan and UK Biobank). BioBank Japan is a biobank with 260,000 participants from 12 medical institutions across Japan. BioBank Japan is managed by the Institute of Medical Science at the University of Tokyo and the RIKEN Center for Integrative Medical Sciences, the University of Tokyo, and Osaka University performed genotyping on the BioBank Japan samples. The summary results of several BioBank Japan genome-wide association studies (GWAS) are publicly available at the BioBank Japan PheWeb portal (https://pheweb.jp/)\u003csup\u003e20,21\u003c/sup\u003e. Additional information regarding the pheweb repository is available on the github website (https://github.com/statgen/pheweb/). For additional support of our SAMM50 genetic associations, we explored the publicly available GWAS results from UK Biobank data. Similar to the BioBank Japan pheweb portal, the pheweb for UK Biobank includes summary information for genetic analyses performed across 400,000 individuals from the UK. These results include GWAS summary statistics from association studies performed on 1,419 phenotypes extracted from the EHR. We used the UK Biobank data that imputed with the HRC reference panel, as this was the same reference panel used for the BioVU studies. The UK Biobank pheweb was built using PheWeb version 1.3.15 \u003csup\u003e22\u003c/sup\u003e. The rationale behind interrogation of these three independent biobanks was to establish robust, convergent and orthogonal evidence supporting associations between SAMM50 and liver-related metabolic disorders and outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal care and fasting and refeeding paradigm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted all animal experiments at the University of California, Los Angeles (UCLA) and all procedures were performed in accordance with the approved protocols established by the UCLA Institutional Animal Care and Use Committee (IACUC) and in compliance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Ethical approval was obtained prior to the start of the study. We housed the adult mice in a controlled vivarium with a 12-hour light/dark cycle and provided them with free access to food and water unless specified otherwise. Temperature was also controlled and checked daily. For the fasting experiments, fasting was initiated at the beginning of the light cycle to minimize circadian variability and mice were singly housed and subjected to a 24 h fast with continuous access to water. The control mice had unrestricted access to food throughout the study. For refeeding experiments, mice were fasted for 24 h and subsequently refed standard chow for 4 h prior to tissue collection. All animals were euthanized at the same circadian time point to control for diurnal effects on metabolism and mitochondrial protein expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter the fasting/refeeding period was completed, mice were euthanized for tissue processing. Briefly, tissue was dissected immediately after euthanization, and then briefly rinsed in ice-cold phosphate-buffered saline. This was done in order to remove any residual blood. The tissue was snap frozen in liquid nitrogen and stored at \u0026minus;80\u0026deg;C until further processing. All procedures were conducted according to UCLA IACUC-approved protocols.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo initiate protein isolation, the frozen tissues were first homogenized on ice in radioimmunoprecipitation assay buffer which was supplemented with protease and phosphatase inhibitor cocktails. Next, the homogenates were incubated on ice with intermittent mixing followed by clarification through by centrifugation at 14,000 \u0026times; g for 15 min at 4\u0026deg;C. The resulting supernatants after centrifugation contained total protein lysates which were collected. Protein concentrations were then measured using a bicinchoninic acid assay, following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSDS\u0026ndash;PAGE and immunoblot analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used Sodium Dodecyl Sulfate\u0026ndash;Polyacrylamide Gel Electrophoresis to equalize amounts of protein using gradient polyacrylamide gels and then we transferred the protein onto polyvinylidene difluoride membranes. We used 5% nonfat dry milk prepared in Tris-buffered saline containing 0.1% Tween-20 to block the membranes and then incubated them overnight at 4\u0026deg;C. We incubated with primary antibodies against mitochondrial respiratory chain component proteins complexes I, II, III, and V, as well as SAM50 and Tom20. After incubation with horseradish peroxidase-conjugated secondary antibodies specific to the species, the immunoreactive bands were visualized using enhanced chemiluminescence. GAPDH served as the loading control for this procedure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Biology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA extraction was performed using TRIzol, followed by reverse transcription and quantitative PCR (qPCR) with SYBR Green and primers specific for \u003cem\u003eSam50\u003c/em\u003e/SAMM50 \u003cem\u003eand GADPH\u0026nbsp;\u003c/em\u003e\u003csup\u003e23\u003c/sup\u003e. For western blot analyses, tissue lysates were prepared in RIPA buffer, resolved by Tris-glycine gels, transferred to nitrocellulose membranes, and probed with antibodies against SAM50 (Proteintech 20824-1-AP), respiratory complex subunits, Tom20, and Tubulin. Detection was carried out using IRDye-conjugated secondary antibodies and visualized on a Li-Cor Odyssey CLx system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData were presented as fold changes normalization to GAPDH. The qPCR primers utilized were derived from previously published sequences \u003csup\u003e24\u003c/sup\u003e, as detailed in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture and serum fasting, and refeeding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used an epithelial hepatoma cell line derived from liver tissue, Hepa-1c1c7 cells, and placed them in a humidified incubator with 5% CO₂ in Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium that was supplemented with 1% L-glutamine, 10% fetal bovine serum and 1% penicillin\u0026ndash;streptomycin, at 37\u0026deg;C. The Cells were routinely passaged and used for experiments at 70\u0026ndash;80% confluence.\u003c/p\u003e\n\u003cp\u003eFor the serum fasting/refeeding experiments, we first washed cells twice with phosphate-buffered saline and incubated them in serum-free DMEM for 6 hours. The control cells were maintained in complete medium having 10% fetal bovine serum. For the refeeding procedure, we first starved the cells for 6 hours and then immediately after refed them with complete medium containing 10% fetal bovine serum for 4 hours. We conducted all experimental procedures and conditions in parallel and collected data at identical time points to minimize variability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicroscopy and Image Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTransmission Electron Microscopy (TEM)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTissue fixation, embedding, and sectioning were performed according to standardized protocols \u003csup\u003e25\u003c/sup\u003e to minimize bias. The cells were fixed directly in culture dishes. Fixation reagents included using 2.5% glutaraldehyde in 0.1 M cacodylate buffer, followed by post-fixation in 1% osmium tetroxide. To dehydrate the samples, we used a graded ethanol series followed by embedding them in epoxy resin. Ultrathin sections (90\u0026ndash;100 nm) were stained and imaged. Stained was done using uranyl acetate and lead citrate. Images were acquired using a transmission electron microscope at identical magnifications for all conditions. This was done utilizing NIH\u0026rsquo;s ImageJ software \u003csup\u003e26,27\u003c/sup\u003e. Mitochondrial morphology, area, and number were quantified from randomly selected fields using blinded analysis. Mitochondria were manually outlined to determine both their area per cell and the number of mitochondria relative to cytoplasmic area. These measurements were taken from several cells under each condition, using independent biological replicates. Results are shown as mean \u0026plusmn; SEM.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSerial Block-Face Scanning Electron Microscopy (SBF-SEM):\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThree-dimensional ultrastructural analysis was performed as previously described \u003csup\u003e25,27,28\u003c/sup\u003e. Small liver tissue blocks were fixed in glutaraldehyde, and then subsequently fixed with osmium tetroxide. They were then stained, and dehydrated through a graded ethanol series and embedded in epoxy resin, and serially sectioned (50 nm) for imaging using a VolumeScope 2 serial block-face scanning electron microscope equipped with an in-chamber ultramicrotome. Serial images were taken at a voxel resolution suitable for mitochondrial reconstruction, with section thickness kept constant across samples.\u003c/p\u003e\n\u003cp\u003eFor 3D reconstruction, 300\u0026ndash;400 slices per sample were manually segmented by blinded investigators in Amira software. Morphometric parameters (volume, surface area, sphericity, complexity index) were calculated for approximately 250 mitochondria per experimental condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMitochondrial DNA content\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReal-time polymerase chain reaction was employed to quantify Mitochondrial DNA content from liver tissue. We used the DNeasy Kit according to the manufacturer\u0026rsquo;s instructions for the extraction and purifying of total DNA from liver samples. In order to amplify mitochondrial and nuclear DNA markers, we used five nanograms of total DNA then employed quantitative PCR from the ABI Prism 7900HT instrument in 384-well plate format with SYBR Green I chemistry and ROX as an internal reference dye. Using the SDS 2.1 software in combination with scripted workflows implemented in Microsoft Access and Microsoft Excel, data acquisition and analysis were automated increasing robustness, accuracy and reproducibility. \u0026beta;-actin served as the reference for nuclear DNA, while cytochrome c oxidase subunit I was used as the marker to measure mitochondrial DNA content. Mitochondrial DNA abundance was expressed relative to the nuclear Rpl13a gene. The primer sequences that were used for amplification in this protocol were as shown below:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCox1 forward, GCC CCC AGA TAT AGC ATT CCC\u003c/p\u003e\n\u003cp\u003eCox1 reverse, GTT CAT CCT GTT CCT GCT CC\u003c/p\u003e\n\u003cp\u003eRpl13a forward, GAG GCC CCT ACC ATT TCC GA\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRpl13a reverse, GGC TTC AGC CGA ACA ACC TT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSerial block face scanning electron microscopy analysis of hepatic lipid droplets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs described above, liver tissue was prepared as per standard protocols for serial block face scanning electron microscopy (SBF SEM). Standard heavy metal staining and resin embedding protocols optimized for ultrastructural preservation of lipid rich organelles were employed. For the acquisition of datasets, we generated volumetric image stacks spanning approximately 10 \u0026times; 10 \u0026times; 50 \u0026micro;m of hepatic parenchyma that were acquired at nanometer scale resolution. Following this, individual lipid droplets were then manually segmented across consecutive serial sections using Amira software as earlier described. We calculated lipid droplet volume, surface area, and spatial distribution lipid droplet from reconstructed objects. Quantification of lipid droplet contact site coverage with mitochondria was conducted by assessing the area of direct apposition between lipid droplets and mitochondria relative to the total lipid droplet surface area. We defined Lipid droplet coverage as the proportion of imaged cellular volume occupied by lipid droplets. To minimize bias, all analyses were performed by a trained researcher who was blinded to the identity and knowledge of the age groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiochemical Assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThiobarbituric acid reactive substances analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLipid peroxidation was quantified through malondialdehyde determination using the thiobarbituric acid reactive substances (TBARS) assay. For cellular analyses, cells were harvested and prepared according to the manufacturer\u0026rsquo;s protocol with the TBARS assay kit from Cayman Chemical Company. Tissue measurements involved homogenizing liver samples in an appropriate assay buffer, followed by clarification via centrifugation prior to analysis. TBARS results were normalized to protein content for cellular samples and to tissue weight for liver samples. All assays were conducted in technical duplicates and validated across independent biological replicates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHepatic and serum triglyceride measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHepatic and serum triglycerides were measured using a commercial enzymatic kit (EnzyChrom\u0026trade;, BioAssay Systems) as previously described \u003csup\u003e24,29\u003c/sup\u003e.\u003cem\u003e\u0026nbsp;Briefly,\u0026nbsp;\u003c/em\u003etriglyceride levels were measured in liver tissue and serum after a six hour fast. To extract Liver triglycerides, we used a solution of isopropanol and Triton X 100. The resulting extracts clarified by centrifugation were analyzed according to the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e\n\u003cp\u003eTo quantify Serum triglycerides, we measured them directly without extraction. To calculate and quantify Triglyceride concentrations, we used a standard curve generated with known triglyceride standards and normalized to tissue weight for liver samples.\u003c/p\u003e\n\u003cp\u003eTotal bile acids in liver tissue were measured using a colorimetric assay (Crystal Chem). The Liver samples first underwent sequential extraction with 95% ethanol overnight, following this, 80% ethanol was then employed for two hours, followed by methanol-chloroform (2:1) for two hours at 50\u0026deg;C. Quantification was performed using a Genzyme Diagnostics bile acid assay kit following the manufacturer instructions, and the final results were then normalized to tissue mass.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolomics and Lipidomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor untargeted metabolomics, frozen tissues were extracted using a 40:40:20 acetonitrile:methanol:water mixture containing 0.5% formic acid and 15% ammonium bicarbonate \u003csup\u003e30\u003c/sup\u003e. Extracts were analyzed via HILIC chromatography coupled to an Orbitrap Exploris 240 mass spectrometer in both positive and negative ion modes, with data processing performed using EL-MAVEN (Thermo Scientific) \u003csup\u003e31\u003c/sup\u003e. For lipidomics, tissues were homogenized and lipids extracted with a mixture of Isopropanol/H₂O/Ethyl acetate (30:10:60), spiked with Avanti Lipidomix internal standards. Dried extracts were reconstituted and analyzed using RP-UHPLC on a CSH C18 column interfaced with the same Orbitrap MS in AcquireX mode for MS/MS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses for biological assays were performed in GraphPad Prism 10.2.3. For immunoblots, densitometry software with normalization to GAPDH was employed to quantify band intensities. Data are presented as mean \u0026plusmn; SEM from independent biological replicates. Statistical comparisons between fasting and refed groups were performed using unpaired two-tailed Student\u0026rsquo;s t-tests, with statistical significance defined as p \u0026lt; 0.05. Regression models for the PheWAS and LabWAS of BioVU data was performed in R (version 3.6). Bonferroni-corrected p-values were used to determine statistical significance for the PheWAS and LabWAS results and were calculated by dividing 0.05 by the number of phenotypes or lab values tested, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData from SBF-SEM/TEM studies represent a minimum of three independent biological replicates with analyses executed by blinded investigators. Data are presented as mean \u0026plusmn; SEM. Statistical comparisons between age groups were performed using unpaired two-tailed Student\u0026rsquo;s t-tests, Comparisons among multiple groups utilized one-way ANOVA followed by Fisher\u0026rsquo;s protected LSD test. Statistical significance was denoted as *p* \u0026lt; 0.05, \u003cstrong\u003ep\u003c/strong\u003e \u0026lt; 0.01, \u003cstrong\u003e*p\u003c/strong\u003e* \u0026lt; 0.001, and p \u0026lt; 0.0001.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eSAMM50 genetically regulated expression is associated with liver disease susceptibility in humans.\u003c/strong\u003e\u003cbr\u003e To examine the clinical relevance of SAMM50 genetic variation, we interrogated genetic association data from three large and independent biobanks. Within a subset of BioVU, a biobank curated by Vanderbilt University Medical Center (\u003cstrong\u003eFigure 1, Supplemental Table 1\u003c/strong\u003e), participants had a mean current age of 55.2 years and a median age of medical record of 46.0 years, with an average record length of 8.5 years, providing substantial longitudinal data for phenotype analysis. The cohort consisted of 78% individuals of European genetic ancestry and 17% individuals of African genetic ancestry, \u003cstrong\u003eSupplemental Table 1.\u003c/strong\u003e In individuals of European genetic ancestry (n=70,404), the PheWAS for SAMM50 GReX demonstrated 10 significant phenotype associations, most of which were liver-related disease phenotypes (\u003cstrong\u003eFigure 2A and Supplemental File 1\u003c/strong\u003e), Significant associations with SAMM50 GReX included chronic liver disease and cirrhosis, cirrhosis of liver without mention of alcohol, other chronic nonalcoholic liver disease, liver abscess and sequelae of chronic liver disease, liver replaced by transplant and alcoholic liver damage. Associated sequelae of advanced liver disease, such as portal hypertension acute gastritis, esophageal bleeding/varices, and disorders of the orbit, were also significantly associated with SAMM50 GReX. The strong, concordant signal across multiple liver-specific phenotypes underscores the hepatic relevance of SAMM50 genetic regulation in human populations. In contrast, the LabWAS for SAMM50 GReX revealed no associations that met the Bonferroni corrected p-value, though nominal signals were seen for hepatic biomarkers like urobilinogen (p=0.03, \u003cstrong\u003eFigure 2B, Supplemental File 2\u003c/strong\u003e). Analyses in individuals of African genetic ancestry (n=15,175) showed nominal liver associations but did not surpass multiple-testing correction, likely due to reduced statistical power (\u003cstrong\u003eSupplemental Files 3-4).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of SAMM50 genetic associations with metabolic conditions and liver disease in additional biobank populations.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBioBank Japan\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the BioVU analyses linking SAMM50 GReX to \u003cstrong\u003eliver disease susceptibility\u003c/strong\u003e, we next interrogated publicly available summary GWAS data for BioBank Japan. \u0026nbsp;We queried SAMM50 using the BioBank Japan PheWeb platform (https://pheweb.jp/), and found that the SAMM50 genetic locus was significantly associated with liver enzymes (aspartate aminotransferase (AST), and alanine aminotransferase (ALT)), \u0026nbsp;hepatic cancer, liver cirrhosis, type 2 diabetes mellitus (in a multi-ancestry and European metanalysis), and total cholesterol (p \u0026lt; 5.0 x 10\u003csup\u003e-8\u003c/sup\u003e),\u0026nbsp;highlighting SAMM50\u0026rsquo;s role in metabolic dysfunction and disease in the specific context of hepatic function (\u003cstrong\u003eTable 2)\u003c/strong\u003e. Furthermore, SAMM50 genetic variation was also significantly associated with abnormalities of platelets and red blood cell indices\u0026nbsp;(p \u0026lt; 5.0 x 10\u003csup\u003e-8\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUK Biobank\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the BioVU and BioBank Japan analyses linking SAMM50 GReX to\u0026nbsp;\u003cstrong\u003eliver disease susceptibility\u003c/strong\u003e, we next interrogated the UK Biobank using publicly available summary GWAS data. \u0026nbsp;We queried\u0026nbsp;SAMM50 using the UK Biobank PheWeb platform \u0026nbsp;\u003csup\u003e22\u003c/sup\u003e(https://pheweb.org/UKB-SAIGE/) and found supporting evidence that the SAMM50 genetic locus was significantly associated with liver phenotypes in this large, independent cohort (\u003cstrong\u003eTable 2)\u003c/strong\u003e. Specifically, SAMM50 genetic variation is significantly associated with chronic liver disease and cirrhosis, other chronic nonalcoholic liver disease, alcoholic liver damage, portal hypertension, liver abscess and sequelae of chronic liver disease, and cholelithiasis and cholecystitis (p \u0026lt; 5.0x10\u003csup\u003e-8\u003c/sup\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe then examined the GWAS data from the UK Biobank for further comparison between \u003cem\u003eSAMM50\u0026nbsp;\u003c/em\u003egenetic\u003cem\u003e\u0026nbsp;\u003c/em\u003evariation and Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) (previously referred to as NAFLD, an outdated term), other liver disorders and systemic metabolic traits, \u003cstrong\u003eSupplementary files 5\u003c/strong\u003e. Several \u003cem\u003eSAMM50\u003c/em\u003e variants showed strong association with MASLD and related liver pathologies, including\u0026nbsp;rs2143571-A which was associated with nonalcoholic steatohepatitis-derived hepatocellular carcinoma \u0026nbsp;(Study accession GCST005309; OR = 2.7 (95% CI 1.82\u0026ndash;4.0), RAF = 0.43, p = 9 \u0026times; 10⁻⁷) and MASLD (Study accession GCST005190; OR = 1.437 (95% CI 1.292\u0026ndash;1.597) RAF = 0.395, p = 2 \u0026times; 10⁻\u0026sup1;\u0026sup1;). Other\u003cem\u003e\u0026nbsp;SAMM50\u003c/em\u003e variants, rs2073080-T (GCST001576; OR = 1.47 [95% CI 1.26\u0026ndash;1.71], RAF = 0.458, p = 8 \u0026times; 10⁻⁷) and rs5764430 (GCST90091033; an effect reported as a 0.1360267-unit decrease,\u0026nbsp;p = 7 \u0026times; 10⁻\u0026sup1;\u0026sup2;) were associated with MASLD,\u0026nbsp;\u003cstrong\u003eSupplementary files 5\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe further found that \u003cem\u003eSAMM50\u003c/em\u003e variants were consistently associated with\u0026nbsp;minimum, median, and maximum hepatic fat levels, as measured by imaging-derived hepatic fat traits. The effect sizes for these associations ranged from approximately +1.0 to +1.5 units\u0026nbsp;across multiple entries. For instance, maximum and median hepatic fat were positively associated with a \u0026beta; of +1.451(95% CI 1.11\u0026ndash;1.79; p = 6 \u0026times; 10⁻\u0026sup1;⁷, GCST90255386) and \u0026beta; = +1.323 (95% CI 0.96\u0026ndash;1.68, p = 5 \u0026times; 10⁻\u0026sup1;\u0026sup3;, GCST90255381)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also found strong association of \u003cem\u003eSAMM50\u0026nbsp;\u003c/em\u003evariants with liver enzymes. The variants associated with AST levels included\u0026nbsp;rs7587-T (GCST90019497; \u0026beta; = \u0026minus;0.0394 (95% CI \u0026minus;0.034 to \u0026minus;0.045), p = 2 \u0026times; 10⁻⁴\u0026sup3;), rs12167845-T\u0026nbsp;(GCST90662897; \u0026beta; = \u0026minus;0.0773 (95% CI \u0026minus;0.073 to \u0026minus;0.081), p = 2 \u0026times; 10⁻\u0026sup3;\u0026sup3;⁰),\u0026nbsp;\u003cstrong\u003eSupplementary files 5\u003c/strong\u003e. The same and additional variants were associated with ALT levels including rs7587-T (GCST90019492; \u0026beta; = \u0026minus;0.0402 (95% CI \u0026minus;0.035 to \u0026minus;0.046), p = 2 \u0026times; 10⁻⁴⁵), rs2073080-T (GCST90104161; \u0026beta; = \u0026minus;1.53366 IU/L, p = 3 \u0026times; 10⁻\u0026sup1;⁰\u0026nbsp;). rs3761472-G\u0026nbsp;(GCST007440; \u0026beta; = +0.0362 (95% CI 0.02\u0026ndash;0.052), p = 7 \u0026times; 10⁻⁶). The variant rs7587-T\u0026nbsp;above that was associated with ALT and AST levels was also associated with AST/ALT ratio\u0026nbsp;(GCST90019498; \u0026beta; = +0.0243 [95% CI 0.019\u0026ndash;0.030], p = 1 \u0026times; 10⁻\u0026sup1;⁷).\u003c/p\u003e\n\u003cp\u003eWe also found very strong associations between \u003cem\u003eSAMM50\u003c/em\u003e variants and markers of metabolic derangements/disorders such as lipids, uric acid/gout, diabetes and other related conditions. For instance, the variant rs4823182-G shows was associated with type 2 diabetes mellitus (GCST006867; \u0026beta; = +0.0482 [95% CI 0.033\u0026ndash;0.063], p = 3 \u0026times; 10⁻\u0026sup1;⁰ ), hypogonadism (GCST90503324; \u0026beta; = \u0026minus;0.14782159 [95% CI 0.098\u0026ndash;0.198], p = 2 \u0026times; 10⁻⁸ ) while the variant rs2235776-C was associated with total cholesterol (GCST003302; \u0026beta; = +0.026 [95% CI 0.016\u0026ndash;0.036], p = 3 \u0026times; 10⁻⁸). The variant rs7587-T was associated with Triacylglycerol_56:7 (GCST90060551; \u0026beta; = \u0026minus;0.113349 [0.079\u0026ndash;0.148], p = 2 \u0026times; 10⁻\u0026sup1;⁰), Triacylglycerol_58:7 ( GCST90060564; \u0026beta; = \u0026minus;0.113649 [0.078\u0026ndash;0.149], p = 3 \u0026times; 10⁻\u0026sup1;⁰) and Diacylglycerol_38:5 (GCST90060200; \u0026beta; = \u0026minus;0.0767797 [0.057\u0026ndash;0.096], p = 1 \u0026times; 10⁻\u0026sup1;⁴) including cholesterol-to-total lipids ratio in large VLDL for the variant rs2294922-G (GCST90092869; \u0026beta; = \u0026minus;0.0337963 [0.024\u0026ndash;0.044], p = 1 \u0026times; 10⁻\u0026sup1;\u0026sup1;). \u0026nbsp;Gout (PheCode 274.1) at rs4823109 was also associated (GCST90651115; \u0026beta; = \u0026minus;0.0815 [95% CI 0.052\u0026ndash;0.111], p = 3 \u0026times; 10⁻⁸). From the UK biobank, it is evident that \u003cem\u003eSAMM50\u003c/em\u003e is associated with metabolic and hepatic trait enrichment, showing particularly strong signals for liver fat, MASLD/NASH-related phenotypes, liver enzymes, and downstream disease outcomes and severity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, we note that SAMM50 and PNPLA3 jointly show strong associations with MASLD (p = 1 \u0026times; 10⁻\u0026sup1;⁸), ALT/AST (p = 5 \u0026times; 10⁻\u0026sup2;⁴) and AST (p = 9 \u0026times; 10⁻\u0026sup1;⁹), highlighting a genomic region densely linked to liver fat and enzyme signals. Multiple entries mapping exclusively to \u003cem\u003eSAMM50\u003c/em\u003e, such as NAFLD and extreme AST/ALT levels, further support its role in hepatic and metabolic traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNutrient State is associated with Regulation of SAM50 and Respiratory Complex Expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we aimed to determine how the presence or absence of nutrients relates with the structure and makeup of mitochondria. In this study, we thoroughly examined the expression of mitochondrial respiratory chain protein complexes and the detailed structure of mitochondria in mouse liver following periods of fasting and re-feeding. We also studied cultured cells under serum starvation conditions. We then explored how changes in nutrient levels affect mitochondrial composition.\u003c/p\u003e\n\u003cp\u003eImmunoblot analysis revealed that re-feeding after a 24-hour fast was significantly associated with increased hepatic protein levels of SAM50 (1.00 vs. 1.93, p=0.044) and Complex V (1.00 vs. 0.61, p=0.022) compared to the fasted state, while Complex I (1.00 vs. 0.41, p=0.0006) and Complex III (1.00 vs. 0.43, p=0.008) were decreased. Protein levels of Tom20 and Complex II (SDHA) were unchanged \u003cstrong\u003e(Figure 3A-G).\u003c/strong\u003e In cultured cells, 6-hour serum starvation was associated with mitochondrial enlargement and reduced cristae density by transmission electron microscopy (TEM) \u003cstrong\u003e(Figure 4A, B, D, E).\u003c/strong\u003e Immunoblotting of these cells showed that serum starvation was significantly associated with decreased protein levels of Complex II (1.00 vs. 0.43, p=0.018), Complex III (1.00 vs. 0.48, p=0.009), and Complex V (1.00 vs. 0.48, p=0.020), but not \u003cem\u003eTom20\u003c/em\u003e, SAM50, or Complex I \u003cstrong\u003e(Figure 4C, F-K).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAging is associated with Remodeling of Hepatic Mitochondrial and Lipid Droplet Architecture\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;For our aging experiments, we aimed to investigate the relationship between aging and mitochondrial architecture and composition in the liver. We therefore performed 3D volumetric reconstructions of mitochondria from 3-month and 2-year mouse livers using serial block-face scanning electron microscopy (SBF-SEM), followed by quantitative morphometric and molecular analyses.\u003c/p\u003e\n\u003cp\u003eUsing SBF-SEM for 3D reconstruction, we found that aging (2-year vs. 3-month mouse liver) was associated with reduced mitochondrial size, volume, and network complexity and increasing sphericity. Specifically, mitochondrial 3D volume was lower in 2-year (0.66 \u0026micro;m\u0026sup3;) compared to 3-month old (0.82 \u0026micro;m\u0026sup3;), p\u0026lt;0.0001, and sphericity was higher in older vs young mice (0.80 vs 0.65, p\u0026lt;0.0001). The mitochondrial complexity index was markedly lower in 2-year vs. 3-month old mice liver (1.39 vs. 2.64, p\u0026lt;0.0001) \u003cstrong\u003e(Figure 5A-G).\u003c/strong\u003e This structural change was associated with a ~44% difference with Sam50 mRNA expression lower in 2-year vs 3-month mice (0.60 vs. 1.08, p=0.0019) and lower mitochondrial DNA content (0.75 vs. 0.90, p=0.0008) \u003cstrong\u003e(Figure 5H, I).\u003c/strong\u003e SBF-SEM further revealed age-dependent expansion of lipid droplets, with the percentage of lipid coverage within the imaged volume about fourfold higher in 2-year vs. 3-month-old (16.38% vs. 3.72%, p\u0026lt;0.0001) \u003cstrong\u003e(Figure 6, 7A-E).\u003c/strong\u003e Aged livers exhibited higher lipid droplet-mitochondria contact site coverage (7.39 vs. 4.10, p=0.0004) and a higher mitochondrial surface area engaged in contacts (7.85% vs. 4.14% vs. p=0.0014) \u003cstrong\u003e(Figure 7F, G).\u003c/strong\u003e Biochemically, aging was associated with increased lipid peroxidation, as evidenced by higher plasma TBARS (8.05 vs. 96, p=0.003), higher hepatic triglycerides (0.65 mg/g vs. 0.25, p=0.0002), and a doubling of serum triglycerides (0.32 vs. 0.16 mg/dL, p=0.005). Bile acid levels were unchanged \u003cstrong\u003e(Figure 7H-M).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-Fat Diet is associated with Metabolic Dysfunction and Mitochondrial Remodeling Partially Reversible Upon Dietary Reversal\u003c/strong\u003e\u003cbr\u003eChronic high-fat diet (HFD) feeding in mice was associated with progressive weight gain and suppressed Sam50 mRNA expression. This suppression was partially restored upon dietary reversal (from HFD to LFD), while the abundance of SAM50 protein remained unchanged across groups \u003cstrong\u003e(Figure 8A-F).\u003c/strong\u003e Untargeted metabolomics revealed global hepatic metabolic reprogramming in response to HFD, as evidenced by clear separation in principal component analysis and distinct metabolite signatures \u003cstrong\u003e(Figure 8G-I).\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study provides evidence for the role of mitochondrial SAM50 as a critical nutrient-related and age-sensitive regulator of mitochondrial function and structure with direct clinical implications that link it to human hepatic and metabolic disorders.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHuman Genetic Associations Positioning SAMM50 within Hepatic Disease Pathogenesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe compared the association between \u003cem\u003eSAMM50\u003c/em\u003e genetic variation and liver-related metabolic and non-metabolic disorders across three large biobanks and found a strong relationship exists. Data from BioVU is rich with clinical and genetic data and our findings show that genetically determined lower SAM50 expression is a significant risk factor for a spectrum of liver diseases, including both metabolic and alcohol-associated etiologies such as chronic nonalcoholic liver disease, cirrhosis, portal hypertension, esophageal bleeding related to variceal disease, and liver transplant status. Our findings are clinically meaningful representing disease phenotypes, severity and progression rather than isolated biochemical abnormalities. Our findings from BioVU aligns with and significantly extends recent reviews implicating mitochondrial outer membrane integrity and protein import in the pathogenesis of metabolic liver disease\u0026nbsp;\u003csup\u003e9,32\u003c/sup\u003e. The specificity of the PheWAS signal for hepatic phenotypes underscores the liver\u0026apos;s particular vulnerability to perturbations in mitochondrial outer membrane assembly. The dissociation between strong disease associations and weaker LabWAS signals suggests that SAM50 deficiency may predispose to structural pathology and long-term disease progression rather than acutely altering circulating biomarkers, aligning with the observed ultrastructural defects in our mechanistic studies and supports a model where chronic, subcellular architectural decay culminates in clinically overt organ dysfunction\u0026nbsp;\u003csup\u003e33,34\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe data from BioBank Japan shows the strong association between \u003cem\u003eSAMM50\u003c/em\u003e genetic variation and liver enzymes (ALT and AST) used as biomarkers for liver injury, with ALT being a more liver-specific biomarker. Notably, the strong association between \u003cem\u003eSAMM50\u003c/em\u003e and liver enzymes was very strong, with p-values reaching orders of magnitude beyond conventional GWAS significance thresholds in cohorts exceeding 150,000 individuals. This data suggests that mitochondria \u003cem\u003eSAMM50\u003c/em\u003e plays a critical role in liver-related injury. Additional findings from the BioBank Japan \u0026nbsp;linking \u003cem\u003eSAMM50\u003c/em\u003e with increased odds for type 2 diabetes mellitus, liver cirrhosis, and liver cancer may suggest that the genetic variants of \u003cem\u003eSAMM50\u003c/em\u003e extends beyond subclinical derangements to overt clinical disease. Although beyond the scope of this study, the associations we observed between \u003cem\u003eSAMM50\u003c/em\u003e and several traits including gout, platelet count, and red blood cell indices suggests potential pleiotropic effects of \u003cem\u003eSAMM50\u0026nbsp;\u003c/em\u003eon mitochondrial-dependent metabolic and hematological pathways. Our genetic results are derived from a gene-centered analytical approach, offering validation of \u003cem\u003eSAMM50\u003c/em\u003e as a liver-relevant mitochondrial locus, that is independent of lead variant selection.\u003c/p\u003e\n\u003cp\u003eThe UK Biobank data provides complementary information that increases rigor and strengthens generalizability of our BioVU PheWAS and LabWAS \u0026nbsp; findings. From the UK Biobank data that assesses more than 50 associations, we found that multiple independent variants within the SAMM50 locus show robust and reproducible associations with MASLD, nonalcoholic steatohepatitis and nonalcoholic steatohepatitis derived hepatocellular carcinoma with odds ratios ranging from approximately 1.4 to greater than 2.7. Findings related to the strong association between SAMM50 and ALT and AST are similar between the Biobank Japan and UK Biobank highlighting consistency across multiple populations and supporting the role for SAMM50 in liver-related injury. Using UK Biobank data, we also identified associations between SAMM50 genetic variation with quantitative imaging derived measures of hepatic fat including minimum, median and maximum liver fat content thereby, establishing a direct link between this mitochondrial locus and hepatic steatosis. The UK Biobank studies also reveal extensive SAMM50 associations with lipid metabolism including total cholesterol, triglyceride and diacylglycerol species, type 2 diabetes, gout and related metabolic phenotypes. Although several liver associated signals occur in a genomic region shared with PNPLA3, a well-established fatty liver disease gene, multiple associations map directly to SAMM50 alone supporting an independent contribution of this mitochondrial gene to hepatic and metabolic pathology.\u003c/p\u003e\n\u003cp\u003eTaken together, the three datasets establish coherent and highly reproducible disease axis linking \u003cem\u003eSAMM50\u0026nbsp;\u003c/em\u003ein humans. \u003cem\u003eSAMM50\u0026nbsp;\u003c/em\u003eencodes a core component of the mitochondrial outer membrane sorting and assembly machinery and is essential for proper mitochondrial membrane organization and cristae integrity. Thus, the consistent associations we have shown from the three large databases provide a strong human genetics foundation for the experimental findings presented in this study which interrogate mitochondrial structure lipid handling and stress responses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNutrient effects on Mitochondrial Remodeling\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe aimed to elucidate the association between nutrient availability or lack on mitochondrial composition and architecture. In this experiment we critically assessed mitochondrial respiratory chain protein complex expression and ultrastructure in mouse liver that was subjected to fasting and re-feeding, as well as in cultured cells that were exposed to serum starvation. Across both in vivo and in vitro systems, we found an association between nutrient deprivation and coordinated but selective changes in mitochondrial protein abundance and morphology, rather than uniform alterations in mitochondrial content. These feeding/refeeding and fasting experiments reveal a layered regulatory mechanism related to SAM50. Fasting/re-feeding and serum starvation dynamically and selectively remodel respiratory protein complex abundance, with SAM50 protein increasing upon nutrient restoration\u0026nbsp;\u003cem\u003ein vivo\u003c/em\u003e. These findings therefore suggest that SAM50 expression may potentially be part of a coordinated anabolic response to nutrients, potentially driving mitochondria bioenergetics\u0026nbsp;\u003csup\u003e7,35\u003c/sup\u003e. The contrasting stability of the SAM50 protein during cellular serum starvation, despite the loss of downstream respiratory protein complexes, suggests a potential buffering mechanism or prioritized maintenance of the import machinery during acute stress, which may possibly facilitate rapid recovery\u0026nbsp;\u003csup\u003e36\u003c/sup\u003e. The underlying role is beyond this study. However, a study by Yin et al demonstrated that in nutrient and oxygen deprivation during ischemic stroke and reperfusion injury in rats, Sam50 played a protective role against injury affecting mitochondria and neurons\u0026nbsp;\u003csup\u003e37\u003c/sup\u003e. Although their study was not directly dealing with starvation, it highlights similar deficiency-driven effects. The selective remodeling of complexes I, III, and V underscores that nutrient states do not uniformly regulate all ETC components, meaning that there could be novel regulatory checkpoints involved. This differential regulation has functional consequences, as the shape and density of cristae, governed by complexes like the MICOS/SAM axis and ATP synthase, directly determine the organelle\u0026apos;s capacity for energy conversion versus metabolite biosynthesis\u0026nbsp;\u003csup\u003e3\u003c/sup\u003e. Our data suggest SAM50 is a key component in translating nutrient signals into these functional structural adaptations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAging is associated with Mitochondrial Altered state\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the relationship between aging and mitochondrial architecture and composition in the liver, we performed 3D volumetric reconstructions of mitochondria from 3-month and 2-year mouse livers using serial block-face scanning electron microscopy (SBF-SEM), followed by quantitative morphometric and molecular analyses. Our studies demonstrates that aged mice were associated with lower mitochondrial size, perimeter, and volume and higher/increased sphericity. We also found that aging is associated with reduced mitochondrial network complexity, decreased SAM50 expression and mtDNA content, expansion of lipid droplet size and abundance, increased lipid droplet\u0026ndash;mitochondria contact site coverage indicating that aging enhances physical coupling between mitochondria and lipid droplets in the liver. In addition, we also found that aging is associated with increased lipid peroxidation in the liver and plasma and alters systemic and hepatic lipid metabolism. Taken together, these results reveal that aging drives coordinated remodeling of mitochondrial and lipid droplet architecture in the liver.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings that aging is associated with mitochondrial dysfunction, mainly characterized by reduced mitochondria size, volume, complexity, and mtDNA content, coupled with a decline in \u003cem\u003eSam50\u003c/em\u003e expression, is consistent with its identification as a feature of age-related mitochondrial dysfunction\u0026nbsp;\u003csup\u003e38\u0026ndash;41\u003c/sup\u003e.\u0026nbsp;The concurrent expansion of lipid droplets and increase in lipid droplet-mitochondria contact sites in aged liver as found in our study presents a compelling model for age-related metabolic decline\u0026nbsp;\u003csup\u003e38\u0026ndash;41\u003c/sup\u003e.\u0026nbsp;Enhanced physical coupling, as extensively reviewed in the context of metabolic health, may reflect an adaptive attempt to facilitate fatty acid flux for \u0026beta;-oxidation in the face of declining mitochondrial oxidative capacity\u0026nbsp;\u003csup\u003e42,43\u003c/sup\u003e. However, this increased interface in a milieu of rising oxidative stress, evidenced by elevated lipid peroxidation, may also create a vicious cycle. Mitochondria lipid-droplet in other systems exhibit unique bioenergetics, and our finding suggests that aging may alter the nature of these specialized contacts, potentially shifting them from a source of metabolic support to a source of lipotoxic stress\u0026nbsp;\u003csup\u003e43\u003c/sup\u003e. This may potentially create a vicious cycle of oxidative damage and further mitochondrial dysfunction. More studies are required to elucidate this in much detail\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHigh-Fat Diet and Metabolic Memory: Implications for Intervention\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe HFD model encapsulates a pathological acceleration of nutrient-stress responses. HFD was associated with a state of mitochondrial stress evidenced by enlarged, rounded mitochondria with disorganized cristae alongside systemic metabolic syndrome. The persistent associated suppression of\u0026nbsp;\u003cem\u003eSam50\u003c/em\u003e transcription by HFD, and its only partial recovery after dietary reversal, indicates that chronic metabolic stress can impose a lasting \u0026quot;memory\u0026quot; on mitochondrial regulatory pathways, a phenomenon increasingly recognized in metabolic diseases like MASLD\u0026nbsp;\u003csup\u003e33,44\u003c/sup\u003e. The disconnect between recovered transcription and persistent protein levels and ultrastructure suggests that restoring normal mitochondrial architecture lags behind transcriptional normalization. This has critical implications for the timeline and expectations of metabolic recovery after dietary or therapeutic intervention, implying that strategies to actively promote mitochondrial repair may be necessary to reverse this dysfunction.\u003c/p\u003e\n\u003cp\u003eSAM50 acts as a key regulator of mitochondrial outer membrane structure and protein import, and is associated with modulation of inner membrane formation, respiratory complex assembly, and oxidative metabolism. Reduced SAM50 expresison, due to genetics, aging, or nutrient overload, is associated with damaged mitochondrial integrity, decreased respiration efficiency and increased oxidative stress, which can impair cells and organs, especially in the liver.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical Implications\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe findings from this integrated study carry significant translational implications, bridging fundamental mitochondrial biology to human health. The strong genetic association between \u003cem\u003eSAMM50\u003c/em\u003e expression and liver disease risk, coupled with its dynamic regulation by diet and aging, positions SAM50 as a potential biomarker for metabolic liver disease susceptibility and progression. Quantifiable alterations, such as the mitochondrial simplification and increased lipid droplet-mitochondria contacts identified through advanced microscopy, could evolve into novel histopathological or imaging-based biomarkers for staging steatotic liver disease. Therapeutically, the SAM/MICOS axis emerges as a compelling novel target. Strategies designed to bolster SAM50 function or expression could potentially enhance mitochondrial protein import, cristae integrity, and oxidative metabolism, thereby addressing foundational defects in conditions like MASLD. However, the observed \u0026quot;metabolic memory\u0026quot; effect, where HFD-induced suppression of Sam50 transcription and mitochondrial remodeling were only partially reversed, offers a crucial caution. This suggests that therapeutic benefits may require sustained intervention to overcome entrenched mitochondrial dysfunction and that simply removing the metabolic insult through diet alone might be insufficient for full recovery. Consequently, our finding underscores the paramount importance of early dietary and lifestyle intervention, particularly in aging populations where SAM50 expression is naturally in decline, to prevent the initial entrenchment of mitochondrial pathology.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStrengths of the Study\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study is distinguished by several key methodological strengths that reinforce the validity and impact of its conclusions. Its foremost strength is the translational integration of genetic discovery and mechanism, beginning with data from 3 population-scale genetic studies including a large clinical biobank to establish human disease relevance and then employing controlled \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e models to dissect and assess the underlying cellular pathophysiology. This approach creates a powerful and credible pipeline from population-level genetics to molecular mechanism. Furthermore, our study employs state-of-the-art volumetric imaging, specifically serial block-face scanning electron microscopy, moving beyond the descriptive or conventional 2D snapshots or view and provide three-dimensional quantitative analysis of mitochondrial and lipid droplet morphology which is far more useful. This technique was crucial for rigorously documenting the age-related loss of network complexity and the increase in organelle contact sites. The complementary use of untargeted metabolomics in our study alongside molecular and biochemical assays constitutes a multi-omics framework that connects structural remodeling to global shifts in hepatic metabolic function. Finally, the experimental design thoughtfully models the disease trajectory by examining a spectrum of metabolic states, from acute fasting and natural aging to chronic dietary challenge and attempted reversal, which provides nuanced insights into both the pathogenesis of and potential recovery from metabolic dysfunction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLimitations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study has several limitations. First, the human genetic associations, while robust in the populations of majority European and Asian genetic ancestry (BioVU, UK Biobank, and BioBank Japan), were underpowered in our BioVU studies with individuals of African genetic ancestry, highlighting the need for more diverse biobanks. Second, the\u0026nbsp;\u003cem\u003ein vivo\u003c/em\u003e dietary and aging studies are correlative in that while they show strong associations between SAM50 expression, mitochondrial structure, and metabolism, direct causal manipulations of SAM50\u0026nbsp;\u003cem\u003ein vivo\u003c/em\u003e in these specific contexts (aging, HFD) are needed to definitively establish mechanism. Third, the molecular pathways linking nutrient sensing such as mTOR, and AMPK to the transcriptional and post-transcriptional regulation of\u0026nbsp;\u003cem\u003eSam50\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ehave not been elucidated. Fourth, the functional consequences of the observed increase in lipid droplet-mitochondria contacts in aging require further investigation to determine whether they are adaptive or maladaptive. Finally, the timeframe for the dietary reversal experiment may have been insufficient to observe full normalization of protein levels and ultrastructure; however longer recovery periods should be examined.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study demonstrates that SAM50 is associated with nutrient state, aging, and diet resulting in changes in mitochondrial structure and systemic metabolism. Firstly, we have demonstrated that \u003cem\u003eSAMM50\u003c/em\u003e genetic variation influences human liver disease risk. Secondly, that SAM50 expression and mitochondrial architecture are dynamically regulated by nutrient availability. Thirdly, that aging is characterized by coordinated loss of SAM50 expression, mitochondrial structural abnormalities, and altered lipid droplet morphology and fourthly, that high-fat diet suppresses\u0026nbsp;\u003cem\u003eSam50\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eexpression and drives pathological mitochondrial remodeling and metabolic dysfunction, which is only partially reversible upon dietary correction. Our findings position SAM50 as a key regulator and potential target for understanding and potentially intervening in age-related and diet-induced metabolic diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the Huck Institutes\u0026rsquo; Metabolomics\u0026nbsp;Core Facility (RRID:SCR_023864) for use of the OE 240 LCMS and Sergei Koshkin for helpful discussions on sample preparation and analysis.\u0026nbsp;We would also like to acknowledge the Huck Institutes\u0026rsquo; Metabolomics Core Facility (RRID:SCR_023864) for use of the OE 240 LCMS and Drs. Imhoi Koo, Ashley Shay, and Sergei Koshkin for helpful discussions on sample preparation and analysis. We would also like to thank UCLA investigators for gifting us old and young human liver samples. We thank the participants of the BioVU biobank at Vanderbilt University Medical Center. We thank the UK Biobank and the Biobank Japan Project for graciously sharing summary level data.\u003c/p\u003e\n\u003cp\u003eThe Synthetic Derivative and BioVU projects at VUMC are supported by numerous sources: including the NIH funded Shared Instrumentation Grant S10OD017985 and S10RR025141; CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975 from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711; and additional funding sources listed at https://victr.vumc.org/biovu-funding/. Other funding sources include 2D43TW009744 and R21TW012635 (SKM and AK), \u003cstrong\u003eT\u003c/strong\u003ehe Howard Hughes Medical Institute Hanna H. Gray Fellows Program Faculty Phase (Grant# GT15655 awarded to MRM) and the Burroughs Welcome Fund PDEP Transition to Faculty (Grant# 1022604 awarded to MRM).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis project was funded by the National Institute of Health (NIH) NIDDK T-32, number DK007563 entitled Multidisciplinary Training in Molecular Endocrinology to Z.V.; National Institute of Health (NIH) NIDDK T-32, number DK007563 entitled Multidisciplinary Training in Molecular Endocrinology to A.C.; NSF MCB #2011577I to S.A.M.; The UNCF/Bristol-Myers Squibb E.E. Just Faculty Fund, Career Award at the Scientific Interface (CASI Award) from Burroughs Welcome Fund (BWF) ID # 1021868.01, BWF Ad-hoc Award, NIH Small Research Pilot Subaward to 5R25HL106365-12 from the National Institutes of Health PRIDE Program, DK020593, Vanderbilt Diabetes and Research Training Center for DRTC Alzheimer\u0026rsquo;s Disease Pilot \u0026amp; Feasibility Program. CZI Science Diversity Leadership grant number 2022- 253529 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation to A.H.J. and O.A.A.; and National Institutes of Health grant HD090061 and the Department of Veterans Affairs Office of Research Award I01 BX005352 to J.G. Howard Hughes Medical Institute Hanna H. Gray Fellows Program Faculty Phase (Grant# GT15655 awarded to M.R.M); and Burroughs Wellcome Fund PDEP Transition to Faculty (Grant# 1022604 awarded to M.R.M). National Institutes of Health Grants: R21DK119879 (to C.R.W.) and R01DK-133698 (to C.R.W.), American Heart Association Grants 16SDG27080009 (to C.R.W.) and 24IVPHA1297559 https://doi.org/10.58275/AHA.24IVPHA1297559.pc.gr.193866 (S.K.M) and by an American Society of Nephrology KidneyCure Transition to Independence Grant (to C.R.W.). NIH Grants R01HL147818, R03HL155041, and R01HL144941 (A. Kirabo). NIH Grant R00DK120876 (D.T.), Harold S. Geneen Charitable Trust Awards Program (D.T.), Alzheimer\u0026apos;s Association AARG-NTF-23-1144888 (D.T.). NIH Grant R00AG065445 (P.J.), Alzheimer\u0026apos;s Association 24AARG-D-1191292 (P.J.), Wake ADRC REC and Development grant P30AG072947 (P.J.). American Heart Association Grant 23POST1020344 (A.K.). American Heart Association Grant 23CDA1053072 (M. S.). NIH K01AG062757 to (M.T.S.) ANRF (Anusandhan National Research Foundation), ANRF/ECRG/2024/001042/LS, ANRF/IRG/2024/001777/LS. IISER Tirupati, NFSG (P.K). The BioVU project at VUMC is supported by numerous sources: including the NIH funded Shared Instrumentation Grant S10OD017985 and S10RR025141; CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975 from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711; and additional funding sources listed at https://victr.vumc.org/biovu-funding/.23CDA1053072 (M.S.). \u0026nbsp;Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NIH. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSepiso K. Masenga, Victoria Baskerville, Mark Petrovic, and Benjamin Rodriguez share co-first authorship. S.K.M., V.B., M.P., and B.R. contributed to investigation, formal analysis, and writing of the original draft. D.L.H., T.W.M-F., Y.D.K., P. Katti, P. Venkhatesh, A. Kirabo, E.G-L., A.C., A. Marshall, C.B., C.V.D., P. Prasad, A. Murphy, J.A., M.A.P., C.E., E.S., J. Schafer, J.B., B.C. Mobley, D.F.D., H. Mobley, N.C.W., M.M.K., D.P., J. Sorrentino, CW, and J.G-G. contributed to investigation, methodology, formal analysis, and/or resources. M.McR. contributed to supervision, resources, and writing \u0026ndash; review \u0026amp; editing. A.O.H.J. conceived and supervised the study, acquired funding, administered the project, contributed to investigation and methodology, and wrote, reviewed, and edited the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen W, Zhao H, Li Y. Mitochondrial dynamics in health and disease: mechanisms and potential targets. \u003cem\u003eSig Transduct Target Ther\u003c/em\u003e 2023; \u003cstrong\u003e8\u003c/strong\u003e: 333.\u003c/li\u003e\n\u003cli\u003eAdebayo M, Singh S, Singh AP, Dasgupta S. Mitochondrial Fusion and Fission: The fine-tune balance for cellular homeostasis. \u003cem\u003eFASEB J\u003c/em\u003e 2021; \u003cstrong\u003e35\u003c/strong\u003e: e21620.\u003c/li\u003e\n\u003cli\u003eGlancy B, Kim Y, Katti P, Willingham TB. The Functional Impact of Mitochondrial Structure Across Subcellular Scales. \u003cem\u003eFront Physiol\u003c/em\u003e 2020; \u003cstrong\u003e11\u003c/strong\u003e. doi:10.3389/fphys.2020.541040.\u003c/li\u003e\n\u003cli\u003eAnand R, Reichert AS, Kondadi AK. Emerging Roles of the MICOS Complex in Cristae Dynamics and Biogenesis. \u003cem\u003eBiology (Basel)\u003c/em\u003e 2021; \u003cstrong\u003e10\u003c/strong\u003e: 600.\u003c/li\u003e\n\u003cli\u003eRavi R, Routray D, Mahalakshmi R. Mitochondrial Sorting and Assembly Machinery: Chaperoning a Moonlighting Role? \u003cem\u003eBiochemistry\u003c/em\u003e 2025; \u003cstrong\u003e64\u003c/strong\u003e: 312\u0026ndash;328.\u003c/li\u003e\n\u003cli\u003eH\u0026ouml;hr AIC, Straub SP, Warscheid B, Becker T, Wiedemann N. Assembly of \u0026beta;-barrel proteins in the mitochondrial outer membrane. \u003cem\u003eBiochimica et Biophysica Acta (BBA) - Molecular Cell Research\u003c/em\u003e 2015; \u003cstrong\u003e1853\u003c/strong\u003e: 74\u0026ndash;88.\u003c/li\u003e\n\u003cli\u003eOtt C, Ross K, Straub S, Thiede B, G\u0026ouml;tz M, Goosmann C \u003cem\u003eet al.\u003c/em\u003e Sam50 Functions in Mitochondrial Intermembrane Space Bridging and Biogenesis of Respiratory Complexes. \u003cem\u003eMol Cell Biol\u003c/em\u003e 2012; \u003cstrong\u003e32\u003c/strong\u003e: 1173\u0026ndash;1188.\u003c/li\u003e\n\u003cli\u003eZheng Y, Wang S, Wu J, Wang Y. Mitochondrial metabolic dysfunction and non-alcoholic fatty liver disease: new insights from pathogenic mechanisms to clinically targeted therapy. \u003cem\u003eJ Transl Med\u003c/em\u003e 2023; \u003cstrong\u003e21\u003c/strong\u003e: 510.\u003c/li\u003e\n\u003cli\u003eLi X, Chen W, Jia Z, Xiao Y, Shi A, Ma X \u003cem\u003eet al.\u003c/em\u003e Mitochondrial Dysfunction as a Pathogenesis and Therapeutic Strategy for Metabolic-Dysfunction-Associated Steatotic Liver Disease. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e 2025; \u003cstrong\u003e26\u003c/strong\u003e. doi:10.3390/ijms26094256.\u003c/li\u003e\n\u003cli\u003eZong Y, Li H, Liao P, Chen L, Pan Y, Zheng Y \u003cem\u003eet al.\u003c/em\u003e Mitochondrial dysfunction: mechanisms and advances in therapy. \u003cem\u003eSig Transduct Target Ther\u003c/em\u003e 2024; \u003cstrong\u003e9\u003c/strong\u003e: 1\u0026ndash;29.\u003c/li\u003e\n\u003cli\u003eRoden DM, Pulley JM, Basford MA, Bernard GR, Clayton EW, Balser JR \u003cem\u003eet al.\u003c/em\u003e Development of a large-scale de-identified DNA biobank to enable personalized medicine. \u003cem\u003eClin Pharmacol Ther\u003c/em\u003e 2008; \u003cstrong\u003e84\u003c/strong\u003e: 362\u0026ndash;369.\u003c/li\u003e\n\u003cli\u003eDanciu I, Cowan JD, Basford M, Wang X, Saip A, Osgood S \u003cem\u003eet al.\u003c/em\u003e Secondary use of clinical data: the Vanderbilt approach. \u003cem\u003eJ Biomed Inform\u003c/em\u003e 2014; \u003cstrong\u003e52\u003c/strong\u003e: 28\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eDennis JK, Sealock JM, Straub P, Lee YH, Hucks D, Actkins K \u003cem\u003eet al.\u003c/em\u003e Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. \u003cem\u003eGenome Med\u003c/em\u003e 2021; \u003cstrong\u003e13\u003c/strong\u003e: 6.\u003c/li\u003e\n\u003cli\u003eThe GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. \u003cem\u003eScience\u003c/em\u003e 2020; \u003cstrong\u003e369\u003c/strong\u003e: 1318\u0026ndash;1330.\u003c/li\u003e\n\u003cli\u003eGamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ \u003cem\u003eet al.\u003c/em\u003e A gene-based association method for mapping traits using reference transcriptome data. \u003cem\u003eNat Genet\u003c/em\u003e 2015; \u003cstrong\u003e47\u003c/strong\u003e: 1091\u0026ndash;1098.\u003c/li\u003e\n\u003cli\u003eHu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM \u003cem\u003eet al.\u003c/em\u003e A statistical framework for cross-tissue transcriptome-wide association analysis. \u003cem\u003eNat Genet\u003c/em\u003e 2019; \u003cstrong\u003e51\u003c/strong\u003e: 568\u0026ndash;576.\u003c/li\u003e\n\u003cli\u003eZhou D, Jiang Y, Zhong X, Cox NJ, Liu C, Gamazon ER. A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis. \u003cem\u003eNat Genet\u003c/em\u003e 2020; \u003cstrong\u003e52\u003c/strong\u003e: 1239\u0026ndash;1246.\u003c/li\u003e\n\u003cli\u003eBarbeira AN, Pividori M, Zheng J, Wheeler HE, Nicolae DL, Im HK. Integrating predicted transcriptome from multiple tissues improves association detection. \u003cem\u003ePLoS Genet\u003c/em\u003e 2019; \u003cstrong\u003e15\u003c/strong\u003e: e1007889.\u003c/li\u003e\n\u003cli\u003eDenny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K \u003cem\u003eet al.\u003c/em\u003e PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. \u003cem\u003eBioinformatics\u003c/em\u003e 2010; \u003cstrong\u003e26\u003c/strong\u003e: 1205\u0026ndash;1210.\u003c/li\u003e\n\u003cli\u003eSuzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM \u003cem\u003eet al.\u003c/em\u003e Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. \u003cem\u003eNature\u003c/em\u003e 2024; \u003cstrong\u003e627\u003c/strong\u003e: 347\u0026ndash;357.\u003c/li\u003e\n\u003cli\u003eSakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S \u003cem\u003eet al.\u003c/em\u003e A cross-population atlas of genetic associations for 220 human phenotypes. \u003cem\u003eNat Genet\u003c/em\u003e 2021; \u003cstrong\u003e53\u003c/strong\u003e: 1415\u0026ndash;1424.\u003c/li\u003e\n\u003cli\u003eGagliano Taliun SA, VandeHaar P, Boughton AP, Welch RP, Taliun D, Schmidt EM \u003cem\u003eet al.\u003c/em\u003e Exploring and visualizing large-scale genetic associations by using PheWeb. \u003cem\u003eNat Genet\u003c/em\u003e 2020; \u003cstrong\u003e52\u003c/strong\u003e: 550\u0026ndash;552.\u003c/li\u003e\n\u003cli\u003eBoudina S, Sena S, Theobald H, Sheng X, Wright JJ, Hu XX \u003cem\u003eet al.\u003c/em\u003e Mitochondrial energetics in the heart in obesity-related diabetes: direct evidence for increased uncoupled respiration and activation of uncoupling proteins. \u003cem\u003eDiabetes\u003c/em\u003e 2007; \u003cstrong\u003e56\u003c/strong\u003e: 2457\u0026ndash;2466.\u003c/li\u003e\n\u003cli\u003ePereira RO, Tadinada SM, Zasadny FM, Oliveira KJ, Pires KMP, Olvera A \u003cem\u003eet al.\u003c/em\u003e OPA 1 deficiency promotes secretion of FGF 21 from muscle that prevents obesity and insulin resistance. \u003cem\u003eThe EMBO journal\u003c/em\u003e 2017; \u003cstrong\u003e36\u003c/strong\u003e: 2126\u0026ndash;2145.\u003c/li\u003e\n\u003cli\u003eHinton A, Katti P, Christensen TA, Mungai M, Shao J, Zhang L \u003cem\u003eet al.\u003c/em\u003e A Comprehensive Approach to Sample Preparation for Electron Microscopy and the Assessment of Mitochondrial Morphology in Tissue and Cultured Cells. \u003cem\u003eAdv Biol (Weinh)\u003c/em\u003e 2023; : e2200202.\u003c/li\u003e\n\u003cli\u003eLam J, Katti P, Biete M, Mungai M, AshShareef S, Neikirk K \u003cem\u003eet al.\u003c/em\u003e A Universal Approach to Analyzing Transmission Electron Microscopy with ImageJ. \u003cem\u003eCells\u003c/em\u003e 2021; \u003cstrong\u003e10\u003c/strong\u003e: 2177.\u003c/li\u003e\n\u003cli\u003eNeikirk K, Vue Z, Katti P, Rodriguez BI, Omer S, Shao J \u003cem\u003eet al.\u003c/em\u003e Systematic Transmission Electron Microscopy-Based Identification and 3D Reconstruction of Cellular Degradation Machinery. \u003cem\u003eAdvanced Biology\u003c/em\u003e 2023; \u003cstrong\u003e7\u003c/strong\u003e: 2200221.\u003c/li\u003e\n\u003cli\u003eGarza-Lopez E, Vue Z, Katti P, Neikirk K, Biete M, Lam J \u003cem\u003eet al.\u003c/em\u003e Protocols for Generating Surfaces and Measuring 3D Organelle Morphology Using Amira. \u003cem\u003eCells\u003c/em\u003e 2022; \u003cstrong\u003e11\u003c/strong\u003e: 65.\u003c/li\u003e\n\u003cli\u003eTam J, Vemuri VK, Liu J, B\u0026aacute;tkai S, Mukhopadhyay B, Godlewski G \u003cem\u003eet al.\u003c/em\u003e Peripheral CB1 cannabinoid receptor blockade improves cardiometabolic risk in mouse models of obesity. \u003cem\u003eJ Clin Invest\u003c/em\u003e 2010; \u003cstrong\u003e120\u003c/strong\u003e: 2953\u0026ndash;2966.\u003c/li\u003e\n\u003cli\u003eLu W, Wang L, Chen L, Hui S, Rabinowitz JD. Extraction and Quantitation of Nicotinamide Adenine Dinucleotide Redox Cofactors. \u003cem\u003eAntioxidants \u0026amp; Redox Signaling\u003c/em\u003e 2018; \u003cstrong\u003e28\u003c/strong\u003e: 167\u0026ndash;179.\u003c/li\u003e\n\u003cli\u003eWang L, Xing X, Chen L, Yang L, Su X, Rabitz H \u003cem\u003eet al.\u003c/em\u003e Peak Annotation and Verification Engine for Untargeted LC\u0026ndash;MS Metabolomics. \u003cem\u003eAnal Chem\u003c/em\u003e 2019; \u003cstrong\u003e91\u003c/strong\u003e: 1838\u0026ndash;1846.\u003c/li\u003e\n\u003cli\u003eChung GE, Lee Y, Yim JY, Choe EK, Kwak M-S, Yang JI \u003cem\u003eet al.\u003c/em\u003e Genetic Polymorphisms of PNPLA3 and SAMM50 Are Associated with Nonalcoholic Fatty Liver Disease in a Korean Population. \u003cem\u003eGut and Liver\u003c/em\u003e 2018; \u003cstrong\u003e12\u003c/strong\u003e: 316\u0026ndash;323.\u003c/li\u003e\n\u003cli\u003eLi Z, Shen W, Wu G, Qin C, Zhang Y, Wang Y \u003cem\u003eet al.\u003c/em\u003e The role of SAMM50 in non‐alcoholic fatty liver disease: from genetics to mechanisms. \u003cem\u003eFEBS Open Bio\u003c/em\u003e 2021; \u003cstrong\u003e11\u003c/strong\u003e: 1893\u0026ndash;1906.\u003c/li\u003e\n\u003cli\u003eGopcevic KR, Gkaliagkousi E, Nemcsik J, Acet \u0026Ouml;, Bernal-Lopez MR, Bruno RM \u003cem\u003eet al.\u003c/em\u003e Pathophysiology of Circulating Biomarkers and Relationship With Vascular Aging: A Review of the Literature From VascAgeNet Group on Circulating Biomarkers, European Cooperation in Science and Technology Action 18216. \u003cem\u003eFront Physiol\u003c/em\u003e 2021; \u003cstrong\u003e12\u003c/strong\u003e: 789690.\u003c/li\u003e\n\u003cli\u003eLionello S, Marzaro G, Martinvalet D. SAM50, a side door to the mitochondria: The case of cytotoxic proteases. \u003cem\u003ePharmacol Res\u003c/em\u003e 2020; \u003cstrong\u003e160\u003c/strong\u003e: 105196.\u003c/li\u003e\n\u003cli\u003eKozjak V, Wiedemann N, Milenkovic D, Lohaus C, Meyer HE, Guiard B \u003cem\u003eet al.\u003c/em\u003e An essential role of Sam50 in the protein sorting and assembly machinery of the mitochondrial outer membrane. \u003cem\u003eJ Biol Chem\u003c/em\u003e 2003; \u003cstrong\u003e278\u003c/strong\u003e: 48520\u0026ndash;48523.\u003c/li\u003e\n\u003cli\u003eYin X, Wang J, Yang S, Li H, Shen H, Wang H \u003cem\u003eet al.\u003c/em\u003e Sam50 exerts neuroprotection by maintaining the mitochondrial structure during experimental cerebral ischemia/reperfusion injury in rats. \u003cem\u003eCNS Neurosci Ther\u003c/em\u003e 2022; \u003cstrong\u003e28\u003c/strong\u003e: 2230\u0026ndash;2244.\u003c/li\u003e\n\u003cli\u003eJia L, Wei Z, Luoqian J, Wang X, Huang C. Mitochondrial Dysfunction in Aging: Future Therapies and Precision Medicine Approaches. \u003cem\u003eMedComm \u0026ndash; Future Medicine\u003c/em\u003e 2025; \u003cstrong\u003e4\u003c/strong\u003e: e70026.\u003c/li\u003e\n\u003cli\u003eSomasundaram I, Jain SM, Blot-Chabaud M, Pathak S, Banerjee A, Rawat S \u003cem\u003eet al.\u003c/em\u003e Mitochondrial dysfunction and its association with age-related disorders. \u003cem\u003eFront Physiol\u003c/em\u003e 2024; \u003cstrong\u003e15\u003c/strong\u003e. doi:10.3389/fphys.2024.1384966.\u003c/li\u003e\n\u003cli\u003eSrivastava S. The Mitochondrial Basis of Aging and Age-Related Disorders. \u003cem\u003eGenes (Basel)\u003c/em\u003e 2017; \u003cstrong\u003e8\u003c/strong\u003e: 398.\u003c/li\u003e\n\u003cli\u003eGuo Y, Guan T, Shafiq K, Yu Q, Jiao X, Na D \u003cem\u003eet al.\u003c/em\u003e Mitochondrial dysfunction in aging. \u003cem\u003eAgeing Research Reviews\u003c/em\u003e 2023; \u003cstrong\u003e88\u003c/strong\u003e: 101955.\u003c/li\u003e\n\u003cli\u003eWang G, Sun B, Liu H, Hu M, Xu H, Li H \u003cem\u003eet al.\u003c/em\u003e Interactions between lipid droplets and mitochondria in metabolic diseases. \u003cem\u003eLipids Health Dis\u003c/em\u003e 2025; \u003cstrong\u003e24\u003c/strong\u003e: 357.\u003c/li\u003e\n\u003cli\u003eFan H, Tan Y, Fan H, Tan Y. Lipid Droplet\u0026ndash;Mitochondria Contacts in Health and Disease. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e 2024; \u003cstrong\u003e25\u003c/strong\u003e. doi:10.3390/ijms25136878.\u003c/li\u003e\n\u003cli\u003eLi X, Zhao X, Qin Z, Li J, Sun B, Liu L. Regulation of calcium homeostasis in endoplasmic reticulum\u0026ndash;mitochondria crosstalk: implications for skeletal muscle atrophy. \u003cem\u003eCell Communication and Signaling\u003c/em\u003e 2025; \u003cstrong\u003e23\u003c/strong\u003e: 17.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"696\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 696px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1: qPCR Primers Used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eHuman Gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003ePrimers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eSAMM50\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-AGACGGACAGAGGAATGTCAGC-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-GCAAATGACGCCGTCCTTGAGA-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMouse Gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003eSam50\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-CACAGCCTGGAAACTTCGAGAG-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-GACAGTGTGACTGGTCTTCCAC-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. SAMM50 associations: BioBank Japan (BBJ) and UK Biobank (UKB) Pheweb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTop p-value in gene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.3e-87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eAspartate transaminase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e150,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBBJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.0e-71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eAlanine aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e150,545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBBJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e6.5e-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003ePlatelet count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e148,623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBBJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.4e-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eType 2 diabetes (multi-ancestry meta-analysis)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e428,452 / 2,107,149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBBJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.8e-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eChronic liver disease and cirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e2,895/400,055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eUKB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1e-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eOther chronic nonalcoholic liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e1,664/400,055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eUKB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.6e-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eType 2 diabetes (European meta-analysis)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e242,283 / 1,569,734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBBJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.8e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eCirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e2,551 / 176,175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBBJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.7e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eMean corpuscular hemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e128,028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBBJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.8e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eMean corpuscular volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e129,832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBBJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.7e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eAlcoholic liver damage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e802/379,355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eUKB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.0e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003ePortal hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e529/400,055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eUKB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.8e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eUric acids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e129,405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBBJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.9e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eLiver abscess and sequelae of chronic liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e942/400,055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eUKB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.6e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eHepatic cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e2,122 / 159,201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBBJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.2e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e135,808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBBJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.2e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eCholelithiasis and cholecystitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e16,225/391,307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eUKB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sam50, Mitochondrial Dynamics, Aging, Metabolism, Liver Disease, High-Fat Diet, MICOS Complex, Nutrient Sensing","lastPublishedDoi":"10.21203/rs.3.rs-8704245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8704245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough Sorting and Assembly Machinery 50 (SAM50) is known to regulate nutritional and metabolic stress related to ageing, its exact role is not well understood. This experimental study combines both human and animal models to understand the role that SAM50 plays in nutrient, age-related metabolic remodeling. We also wanted to define the clinical relevance of SAMM50 genetic variation in human disease. Our study integrated clinical and genetic data from three large and independent human biobanks to assess the clinical implications of genetic variation in SAMM50. We then conducted mechanistic studies in mice using Serial Block-Face Scanning Electron Microscopy and Transmission Electron Microscopy for three-dimension analysis of mitochondrial morphology, immunoblotting, metabolomics/lipidomics, and assessment of metabolic parameters in models of fasting, aging, and a high-fat diet (HFD). Descriptive and inferential statistics were used to describe and test associations in GraphPad prism version 10.\u003c/p\u003e \u003cp\u003eOur study demonstrated that common genetic variation within the SAMM50 genetic locus was significantly associated with liver-related metabolic disorders. In mice, nutrient status was associated with expression levels of Sam50 and proteins involved in the respiratory complex. Aging was associated with impaired mitochondria, decreased Sam50 expression, and increased triglyceride and lipid peroxidation, with increased lipid droplet-mitochondria contacts. An HFD was associated with a reduction in Sam50 expression, disruption of mitochondrial structure, and metabolic dysfunction, effects that were only partly reversed by returning to a normal diet. Our results demonstrate that SAM50 expression is associated with nutrient state and age-related signals, thereby orchestrating mitochondrial structure to influence systemic metabolic health.\u003c/p\u003e","manuscriptTitle":"Nutrient State, Aging, and Diet Modulate SAM50-Dependent Mitochondrial Remodeling and Systemic Metabolic Signatures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 06:25:21","doi":"10.21203/rs.3.rs-8704245/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e1eb4196-629d-473c-bd33-e3784f385603","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61858747,"name":"Health sciences/Pathogenesis"},{"id":61858748,"name":"Biological sciences/Cell biology"}],"tags":[],"updatedAt":"2026-02-22T05:16:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 06:25:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8704245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8704245","identity":"rs-8704245","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}