Untargeted Metabolomics Reveals Distinct Metabolic Profiles in MMA Patients with MUT Gene Mutations

Untargeted Metabolomics Reveals Distinct Metabolic Profiles in MMA Patients with MUT Gene Mutations | 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 Research Article Untargeted Metabolomics Reveals Distinct Metabolic Profiles in MMA Patients with MUT Gene Mutations Shuruq Alsuhaymi, Reem H. AlMalki, Maha Al Mogren, Ahamd Alodaib, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6570059/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 Methylmalonic acidemia (MMA), the most prevalent congenital organic acidemia, is inherited in an autosomal recessive pattern due to MUT gene mutations that impair methylmalonyl-CoA mutase (MCM) enzyme activity, leading to the toxic accumulation of methylmalonic acid, which causes mitochondrial dysfunction, metabolic disruptions, and multisystem damage. Newborn screening followed by confirmatory biochemical and genetic tests—such as acylcarnitine analysis and urine organic acid profiling—are widely accepted and routinely used in biochemical genetics labs. However, these conventional methods are limited in their ability to detect novel, clinically relevant biomarkers that may offer deeper insights into MMA pathophysiology. This study highlights the importance of untargeted metabolomics in identifying such biomarkers, with potential applications in predicting long-term prognosis and suggesting novel therapeutic strategies. LC-HRMS was used to analyze serum samples from MUT -confirmed MMA patients (n = 27) and healthy controls (n = 27). A total of 267 dysregulated metabolites were identified in MMA patients, including 185 upregulated and 82 downregulated. These metabolites were associated with key affected pathways, including arachidonic acid, nicotinate and nicotinamide, sphingolipid, glutathione, and purine metabolism. Downregulated metabolites included glutamine, isoleucine, deamido-NAD + , and sphingolipids, while upregulated metabolites included acylcarnitines, succinyladenosine, and leukotriene B4. Notably, biomarkers such as 11,12-epoxyeicosatrienoic acid (AUC = 0.964) and MG (PGF2alpha/0:0/0:0) (AUC = 0.953) are implicated in MMA pathophysiological mechanisms through their association with inflammation, oxidative stress, and altered fatty acid metabolism. These findings may help with improved understanding of disease pathogenesis and ultimately its management. Future research must validate these biomarkers in larger, diverse cohorts and integrate metabolomics with genomics and proteomics to develop comprehensive diagnostic tools and targeted therapies, ultimately improving MMA patient outcomes. Metabolic disorders Inborn errors of metabolism Methylmalonic acidemia Untargeted metabolomics LC-MS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights • Untargeted metabolomics revealed 267 dysregulated metabolites in -confirmed MMA patients, which provides a deeper insight into disease mechanisms compared to traditional diagnostic tools. • Key disrupted pathways included sphingolipid, glutathione, arachidonic acid, and purine metabolism, with significant alterations observed in metabolites like glutamine, deamido-NAD⁺, and leukotriene B4. • Potential biomarkers such as 11,12-epoxyeicosatrienoic acid (AUC = 0.964) and MG (PGF2alpha/0:0/0:0) (AUC = 0.953) demonstrate strong diagnostic accuracy, and their association with inflammation and oxidative stress indicates a key role in prognosis and disease management. 1. Introduction Inborn errors of metabolisms (IEMs) are rare genetic disorders caused by defects in specific enzymes, co-factors, or transport proteins. Over 1,400 IEMs have been identified, with the majority remaining untreatable and their underlying mechanisms yet to be fully elucidated (Ferreira et al., 2019 ; Ismail et al., 2019 ; Stepien et al., 2021 ; Zhou et al., 2022 ). The incidence of IEMs exhibits significant regional variability, often influenced by high consanguinity rates. For example, in Saudi Arabia, where consanguinity rates range from 50–80%, the incidence of IEMs is significantly higher than global averages, with some studies estimating it to be as high as 1 in 569 live births (Al Qurashi et al., 2023 ). Among the many identified IEMs, methylmalonic acidemia (MMA) represents one of the most common organic acidemias (Lee & Kim, 2022 ). This disorder is characterized by the body's impaired ability to metabolize specific proteins and fats, leading to the accumulation of toxic organic acid, primarily methylmalonic acid, in the blood and tissues. This toxic accumulation arises from enzymatic deficiencies, typically inherited in an autosomal recessive (AR) pattern. The genetic basis of MMA is primarily associated with deficiencies in the enzyme methylmalonyl-CoA mutase (MCM) or impairments in the metabolism of its cofactor, adenosyl-cobalamin (a bioactive form of vitamin B12). MCM, encoded by the MUT gene, plays an important role in the catabolism of certain amino acids (valine, isoleucine, methionine, and threonine) and odd-chain fatty acids by catalyzing the conversion of methylmalonyl-CoA to succinyl-CoA, a key intermediate in the tricarboxylic acid (TCA) cycle (Chen et al., 2023 ). Mutations in the MUT gene, such as deletions, insertions, or missense, can result in MCM deficiency or dysfunction, disrupting this conversion and leading to the toxic accumulation of methylmalonic acid. These genetic mutations account for approximately 60% of MMA cases (Garg & Smith, 2017 ). Moreover, MUT gene defects can affect mitochondrial function, reducing ATP generation, and increasing oxidative stress. These mitochondrial dysfunctions contribute to long-term complications, including chronic kidney disease, neurodevelopmental delays, cardiomyopathy, pancreatitis, and bone marrow suppression (Imtiaz et al., 2016 ). The MMA clinical manifestations, mainly in early infancy, range widely from moderate to severe and life-threatening, depending on the type and degree of enzyme deficiency. Common symptoms in affected infants include recurrent vomiting, dehydration, reduced muscle tone (hypotonia), excessive fatigue (lethargy), and poor growth or failure to thrive. Moreover, metabolic acidosis, marked by an excess of acid in the blood, is a defining feature of MMA and, if untreated, can cause serious health complications, including acute metabolic decompensations (AMD) that may lead to coma or death. The underlying pathophysiology of AMD in isolated MMA remains incompletely understood. However, recent studies of MMA patient metabolomes suggest that metabolic acidosis during AMD is driven by the accumulation of acidic intermediates of branched-chain amino acid (BCAA) metabolism rather than methylmalonic acid levels. These findings emphasize the complexity of MMA and the need for further study to elucidate its acute and chronic mechanisms (Keyfi et al., 2016 ; Venditti, 2005 ; Zhang et al., 2023 ). Diagnosing inborn errors of metabolism remains challenging due to the wide range of clinical symptoms that often overlap among different disorders. This complexity requires diagnostic approaches that expand beyond clinical examinations, making the development of a universal diagnostic protocol unfeasible. Newborn screening (NBS) programs play a crucial role in identifying and preventing IEMs early by detecting disorders before symptoms arise, thus enabling timely interventions. However, current NBS procedures that target specific metabolites may provide inaccurate results, particularly in stressed or newly born infants (Millington, 2019 ). Advancements in mass spectrometry techniques, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), have considerably enhanced the accuracy and efficiency of IEM diagnosis. Genetic screening, primarily through next-generation sequencing (NGS), has also emerged as a valuable tool in diagnosing IEMs. Despite these technological advances, significant differences are found in NBS programs across different regions, with varying coverage and screening methods (Wilcken et al., 2003 ). For early diagnosis of MMA, it often applies a combination of newborn screening and biochemical testing. For example, tandem mass spectrometry (MS/MS)-based NBS enables early MMA detection by measuring over 40 metabolites using dry blood spots, including propionylcarnitine (C3), C3/acetylcarnitine (C2), and methionine levels. Confirmatory testing of MMA typically includes analyzing urine for elevated organic acids, including methylmalonic acid, 3-hydroxypropionate, and 2-methylcitrate, using GC-MS. Key biochemical indicators of MMA include hyperlactemia, hyperammonemia, metabolic acidosis, and abnormal urine results. Furthermore, molecular genetic testing confirms the MMA diagnosis by identifying gene mutations, such as MUT , with regional genetic variations affecting clinical results (Chen et al., 2023 ; Forny et al., 2021; Mukherjee & Ray, 2022 ). Emerging technologies like untargeted metabolomics offer a valuable complement to targeted metabolic assays by providing a comprehensive perspective of metabolic pathways. This approach could enhance our understanding of MMA and other IEMs, potentially leading to new therapeutic approaches. Untargeted metabolomics extends the investigation range and provides a deeper insight into disorder mechanisms and disrupted pathways (Millington, 2019 ). However, challenges remain regarding its clinical application, particularly in cost, speed, accuracy, and reproducibility. Studies in specific populations, such as Saudi patients, have determined metabolic biomarkers and pathways in IEM disorders. For instance, a study on medium-chain acyl-CoA dehydrogenase deficiency (MCADD) disorder revealed disruptions in phenylalanine, tyrosine, and tryptophan biosynthesis pathways, with potential biomarkers such as PGP (a21:0/PG/F1alpha) and glutathione, demonstrating the importance of metabolomics in promoting our understanding of IEMs (Sebaa et al., 2023 ). Despite advances in understanding MMA, further research is needed to enhance personalized disease management and patient outcomes. Accordingly, this study aims to conduct metabolic profiling of patients with MUT-confirmed MMA to identify novel, clinically relevant biomarkers and provide deeper insights into the disease's pathophysiology. This study demonstrates the value of untargeted metabolomics in identifying these biomarkers and disrupted metabolic pathways, with potential applications in predicting long-term prognosis and suggesting novel therapeutic strategies. It also examines the impact of genetic and regional variations on disease expression. 2. Materials and Methods 2.1. Ethical Statement The Institutional Review Boards at King Faisal Specialist Hospital and Research Center (KFSHRC) in Riyadh, Saudi Arabia, reviewed and approved the procedures for this study (RAC No. 2160 027). Consent was waived for leftover samples submitted for routine clinical testing. 2.2. Serum Collection Fifty-four serum samples were obtained from the Metabolomics Lab at the Genomic Medicine, Center of Excellence at KFSHRC. This study included twenty-seven samples from patients confirmed to have MUT -deficiency MMA, along with twenty-seven healthy controls. Age- and gender-related metabolic effects were excluded from the raw data during data processing. For age-related effects, we calculated the average age of the healthy control (HC) group and identified metabolites below the average age (BAA) or above the average age (UAA) threshold. We then analyzed these using a Moderated t-test, [BAA] vs. [UAA] Fold Change (FC) cut-off 2.0, P ≤ 0.05). This strategy confirmed removing the dataset's age- and gender-dependent biases, enabling a more precise interpretation of the metabolic changes correlated to MMA pathology. 2.3. Metabolites Extraction The chemicals used for LC-MS, including methanol, acetonitrile, deionized water (dH 2 O), and formic acid, were sourced from Fisher Scientific Company (Ottawa, ON, Canada). Serum metabolites were extracted from 50μL of samples collected from MMA patients and healthy controls using 50% acetonitrile and methanol extraction solvent. Samples were prepared randomly and processed simultaneously to reduce variability. Quality control (QC) samples were prepared by aliquoting from each sample and were included to ensure data reliability. The samples were vortexed in a ThermoMixer (Eppendorf, Hamburg, Germany) at 600 rpm and 25 °C for one hour. After vortexing, they were centrifuged at 16,000 rpm and 4 °C for 10 minutes. The resulting supernatants were transferred to new Eppendorf tubes and dried using a SpeedVac concentrator (Thermo Fisher, Christ, Germany) for the LC-MS (Jacob et al., 2019). 2.4. LC-MS Untargeted Metabolomics Analysis For LC-MS sample analysis, the dried extracted samples were reconstituted in a mobile phase containing 50% solution (A: 0.1% formic acid in dH 2 O and (B: 0.1% formic acid in 1:1 ( v/v ) MeOH and ACN). In the beginning, 5µL of the reconstituted sample was injected into the LC column for polar metabolite separation using reversed-phase liquid chromatography on an ACQUITY UPLC XSelect column (100 × 2.1mm × 2.5μm, Waters Ltd., Elstree, UK). The mobile phase flowed at 300μL/min while maintaining the column temperature at 55 °C and the sample temperature at 4 °C. The gradient mode applied for pumping the mobile phases A and B to the column is as follows: 0–16 min 95–5% A, 16–19 min 5% A, 19–20 min 5–95% A, and 20–22 min 5–95% A. The LC eluates were ionized using electrospray ionization (ESI) in both positive and negative modes and separated based on m/z using a Xevo G2-S QTOF mass spectrometer (Waters Ltd., Elstree, UK). The MS source temperature was fixed at 150 ◦C, the desolvation temperature was set at 500 ◦C, and the capillary voltages were kept at 3.20 kV or 3 kV for ESI+ and ESI− modes, respectively. The cone gas flow was 50 L/h, the desolvation gas flow was 800 L/h, and the cone voltage was 40 V. The collision energies for the low and high functions were set to off and 10–50 V, respectively, in the MS E data-independent acquisition (DIA) mode. As recommended by the vendor, the mass spectrometer was calibrated with sodium formate (100–1200 Da) in both ionization modes. The lock spray mass compound, MS leucine-enkephaline (an external reference to the ion m/z 556.2771 in positive mode and 554.2615 in negative mode), was constantly injected, which is responsible for switching between the sample and the reference for every 45 and 60 s in both modes, scan time was 0.5 s, the flow rate was 10 µL/min, and collision energy was 4 V and 30 V for the cone, respectively. The DIA data were gathered in continuum mode with Masslynx™ V4.1 Software (Waters Inc., Milford, MA, USA).QCs were introduced to the instrument randomly to validate the system’s stability. After that, they were analyzed following the routine protocol. The acceptance criteria were to have all the QC samples separated from the other study groups, clustered together, and use their Relative standard deviations (RSD%) < 40% (AlMalki et al., 2023). 2.5. Data Processing and Statistical Analyses The raw MS data were typically processed using Waters' Progenesis QI v.3.0 software (Milford, MA, USA), including aligning based on m/z values and ion retention time, peak filtering, and picking according to peak quality. In addition, features found in at least half of the samples were kept for further analyses. Metaboanalyst version 6.0 (McGill University, Montreal, QB, Canada), (http://www.metaboanalyst.ca) accessed on 3 August 2024, was used for multivariate statistical analyses (Pang et al., 2021). The imported datasets were median-normalization, log- transformation, and Pareto- scaling. It was utilized to perform the partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models. Model fitness (R 2 Y) and predictive ability (Q 2 ) values were used in the evaluation of the created OPLS-DA model (Worley & Powers, 2013). Pathway and biomarker analyses, MMA-linked biomarkers and receiver operating characteristic (ROC) curves were generated utilizing the PLS-DA technique in Metaboanalyst software for broad analysis to identify potential biomarkers. For univariate analysis, mass profiler professional (MPP) software v15.0 (Agilent, Santa Clara, CA, USA) was employed. A volcano plot was applied to determine the significant features with a moderated t-test, a fold-change (FC) cutoff of 2 and FDR p < 0.05. Venn diagram was also generated using MPP, while the heatmap analyses of altered features (in MMA patients compared to the controls) were conducted using Pearson distance measures based on the Pearson similarity test (Gu et al., 2020). 2.6. Metabolite Identification The statistically significant features from each dataset were selected and annotated using Progenesis QI v.3.0 software (Waters Technologies, Milford, MA, USA). For peak annotation, the precursor and product ions were annotated based on accurate mass, fragmentation pattern, and isotopic distributions utilizing databases such as the Human Metabolome Database (HMDB) with a 5 ppm mass error, METLIN MS/MS (www.metlin.scripps.edu), LipidMap, Lipid Blast, KEGG (Wishart et al., 2022). Exogenous compounds, including drugs, food additives, and environmental substances, were excluded from the results. 3. Results 3.1. Sample Demographic Data and Sample Collection We collected serum samples (n=54) from the metabolomics laboratory of King Faisal Specialized Hospital and Research Center (KFSHRC)’s Genome Medicine Center of Excellence (GMCoE). The MMA patient samples (n=27) with MUT gene mutations were confirmed through the standard of care whole genome analysis clinical protocol. The healthy control (HC) group (n=27) was systematically matched for age and gender to minimize demographic influences on metabolic data (Figure S1). To further reduce potential age-related biases, metabolites were categorized based on the HC group's average age and analyzed using a Moderated t-test. This approach effectively removed age- and gender-dependent variations to enhance the accuracy of metabolic interpretations associated with MMA pathology (see Methods for details). The demographic distribution of the sample is summarized in Table 1. The average age of MMA patients was 15.63 ± 1.49 years, while the healthy controls averaged 26.85 ± 0.91 years. MMA patients displayed significantly elevated serum methylmalonic acid levels (744.00 ± 141.02 μM/L), which is the biomarker of the disorder. Table 1 . Sample Demographic Information. Demographic and Clinical Features MMA CTRL p -Value (n = 27) (n = 27) Mean SEM Mean SEM Age (Years) 15.63 1.49 26.85 0.91 4.04E -08 Female (%) 51.9 NA 44.4 NA NA MMA (Cutoff <0.4 uMOL/L) 744.00 141.02 <0.4 uMOL/L NA 2.61E -06 3.2. LC-MS Untargeted Metabolomics Analysis of MMA Patients The detected compound ions were 29,685 in both positive ( n = 16,623) and negative ( n = 13,062) ionization modes, respectively and archived on the Metabolomics Workbench (ST003690) A total of 26,945 ion features remained after excluding age-dependent metabolites ( n =2194 ion features) using a Moderated t.test, P ≤ 0.05, FC 2 (Figure S1). To reduce major variances and verify the normal distribution, the dataset was normalized using median, log-transformation, and Pareto scaling. A partial least squares discriminant analysis (PLS-DA), a supervised tool, demonstrated distinct clustering of samples and clear differentiation between MMA patients (green) and healthy controls (red) (Figure 1A), and quality control QC (blue) (Figure S2). Furthermore, orthogonal partial least squares discriminant analysis (OPLS-DA) confirmed the significant separation between the groups, and the robustness of the created models was evaluated by the fitness of the model (R 2 Y=0.977) and predictive ability (Q 2 =0.814) values in a larger dataset (n=100) (Figure 1B). Then, a filter by frequency was applied on ( n =26,945 ions) with a cut-off percentage of 80% of all samples to exclude missing values. The remaining features were 13,608 features. These features (13,608 ions) were statistically evaluated between two groups, MMA patients and healthy controls (Ctrl), using a volcano plot (Moderated t.test FDRp ≤ 0.05, FC 2) and revealed that 692 significantly dysregulated features, of which 431 were upregulated, and 261 were downregulated in patients with MMA compared to Ctrl (Figure 2). Among these, 536 features were annotated using the human metabolome database (HMDB), Metlin MS/MS, LipidBlast, lipidMap, and KEGG. Only 267 were identified as human endogenous metabolites after excluding exogenous sources such as environmental exposures, foods, and drugs (Table S1). In addition, a heatmap analysis using Pearson distance measures displayed the dysregulation of 267 endogenous metabolites, including 185 upregulated and 82 downregulated in MMA patients, with red and green color intensities representing the relative increase and decrease in metabolite regulation, respectively (Figure 3). 3.3. Metabolic Pathway Analysis We also investigated the most affected metabolic pathways in the MMA patients group using our study's detected dysregulated endogenous metabolites (n=267). Arachidonic acid metabolism, purine metabolism, and nicotinate and nicotinamide metabolism are examples of the most affected pathways in the MMA group (Figure 4). Other pathways were also affected, including glutathione metabolism, sphingolipid metabolism, and steroid hormone biosynthesis (Figure 4 and Table S2). 3.4. Biomarker Analysis Lastly, we performed a receiver operating characteristics (ROC) curve analysis on statistically significant dysregulated metabolites (n=267) to identify and assess the potential biomarkers differentiating between MMA and healthy samples. A PLS-DA method was applied to create a multivariate exploratory ROC analysis to classify and rank features (Figure 5A). The ROC curve features identified using PLS-DA and cross-validation (CV) exhibited area under the curve (AUC) values ranging from 0.95 to 0.996, with confidence intervals (CI) of 0.818-1 and 0.975-1, respectively (Figure 5A). The frequency plots displayed the 15 significant metabolites with the highest VIP scores in the OPLS-DA model, based on their levels in the MMA and healthy samples (Figure 5B). For example, the upregulated endogenous metabolites in MMA compared to the control group included 11,12-epoxyeicosatrienoic acid, 11-Hydroperoxydocosahexaenoic acid and prostaglandin A1, and the downregulated metabolites involved 5-hydroxydec-7-enedioylcarnitine, UDP-N-acetylmuraminate, and N-arachidonoyl tryptophan (Figure 5B). The AUC values of the ROC curves in the MMA patients’ group for upregulated biomarkers; 11, 12-epoxyeicosatrienoic acid and leukotriene B4 were 0.964 and 0.936, respectively (Figure 5C and 5D). For the downregulated biomarker, MG (PGF2alpha/0:0/0:0) was 0.953 (Figure 5E). 4. Discussion Methylmalonic acidemia (MMA) is the most prevalent congenital organic acidemia caused by mutations in the MUT gene that impair methylmalonyl-CoA mutase (MCM) enzyme activity. Most laboratories report a ≤ 0.40 nmol/mL reference interval for MMA or µmol/L (Samara et al.). Clinically, individuals with classical isolated MMA often experience failure to thrive, vomiting, neurological problems, metabolic acidosis, and hyperammonemia. Although extensive research has been conducted on MUT dysfunction, the metabolic disruptions and underlying pathological mechanisms of MMA remain poorly understood, and no curative treatments are available (Forny et al., 2023; Forny et al., 2014). High levels of the NBS marker are not exclusive to MMA but can also occur in other metabolic disorders, such as propionic acidemia and cobalamin-related deficiencies (e.g., cblA, cblB, cblC), which share interconnected metabolic pathways (Held et al., 2022). Genetic mutations in MMUT , MMAA , MMAB , and cobalamin-related genes ( LMBRD1 , ABCD4 , MTRR ) further expand the complexity of the disease spectrum, which leads to MMA accumulation (Beyzaei et al., 2024; List et al., 2021). This accumulation has also been linked to non-metabolic conditions, including chronic kidney disease and neurological impairments such as developmental delay and hypotonia (Manoli et al., 2016). Therefore, this study examines the metabolic profiles of MMA patients with MUT mutations to identify potential biomarkers and altered pathways that could enhance our understanding of MMA pathophysiology specifically and its broader clinical implications. Our untargeted metabolomics investigations of MMA sera revealed distinctively different metabolic profiles in MMA patients compared to their corresponding healthy group, reflecting the extensive metabolic disruptions inherent to this disorder. A wide array of dysregulated metabolites distinguished the patient group from the unaffected ones. In general, several potential biomarkers, including amino acids, lipid species, acylcarnitine compounds, and oxidative stress markers, were identified significantly; these are associated with various biochemical and physiological changes indicative of MMA pathology, such as inflammation, oxidative stress, mitochondrial dysfunction, and alterations in amino and lipid metabolism. In NBS for MMA diagnosis, the level of propionylcarnitine (C3) serves as the primary biomarker. However, elevated C3 levels can also indicate propionic acidemia, requiring measurement of MMA levels in body for confirmation (Held et al., 2022). Although, methylmalonic acid interferents can result in false diagnoses (Monostori et al., 2023), our untargeted metabolomics analysis identified this biomarker exclusively in MMA patients, validating this approach and demonstrating the practical application of metabolomics in clinical biochemistry. As mentioned above, previous studies suggest that metabolic acidosis in MMA, particularly during AMD, is primarily driven by the accumulation of acidic intermediates from BCAA metabolism rather than methylmalonic acid itself (Keyfi et al., 2016; Venditti, 2005; Zhang et al., 2023). However, our findings reveal a significant reduction in amino acids such as L-glutamine and L-isoleucine, along with their conjugates, including N-arachidonoyl phenylalanine and N-arachidonoyl tryptophan, in MMA patients. This depletion is likely due to mitochondrial dysfunction, a hallmark of MMA, which disrupts amino acid metabolism and leads to the accumulation of toxic intermediates (Chandler et al., 2009; Li & Hoppe, 2023). In contrast, healthy individuals maintain balanced amino acid levels, ensuring normal metabolic homeostasis (Horton et al., 2006; Ling et al., 2023). Notably, the depletion of BCAAs, particularly isoleucine, may exacerbate muscle wasting and metabolic stress, similar to the reductions observed in chronic renal failure. Given that BCAA supplementation has been shown to help protein balance and reduce metabolic toxicity in other conditions (Alvestrand et al., 1982; Holeček, 2018), our findings suggest that amino acid depletion is a key contributor to metabolic imbalances in MMA. These results underscore the complexity of MMA pathophysiology and highlight the need for further research to explore targeted metabolic interventions to mitigate amino acid deficiencies. Moreover, nicotinic acid adenine dinucleotide (NAAD) was found to be downregulated in the MMA patients compared to healthy control. NAAD is also referred to as deamido-NAD + ([Internet]. 2023 [cited 2024-07-29].). NAD + is a vital element in various biological and metabolic processes, such as DNA repair and energy production. It serves as a coenzyme in catalyzing fundamental cellular redox reactions, including glycolysis, fatty acid beta-oxidation, and the tricarboxylic acid cycle TCA, where it is reduced to NADH. NAD + also serves as a substrate for NAD + -consuming enzymes such as sirtuins and poly-ADP-ribose polymerases (PARPs), which regulate several important cellular processes (Xie et al., 2020). The conversion of L-glutamine and NAAD to L-glutamic acid and NAD + , facilitated by glutamine-dependent NAD + synthetase, is another vital metabolic pathway ([Internet]. 2023 [cited 2024-07-29].; Johnson & Imai, 2018). Studies have linked decreased NAD + levels to several disorders, including metabolic and neurodegenerative diseases (Oyama et al., 2024). For example, research into mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome has shown that mitochondrial dysfunction associated with reduced NAD + levels exacerbates the disease’s symptoms, including stroke-like episodes and muscle weakness (Seo et al., 2018). Plasma MMA, a marker of cobalamin deficiency, may also indicate lysosomal iron trapping associated with oxidative stress and mitochondrial dysfunction in metabolic disturbances like MMA, contributing to clinical manifestations such as neurodevelopmental abnormalities and organ dysfunction (Vermorken et al., 2021; Zhao et al., 2014). Correspondingly, our findings indicate that elevated MMA levels are linked to diminished NAD + levels, potentially leading to mitochondrial and lysosomal dysfunction. As NAD + is important in maintaining mitochondrial efficiency and lysosomal homeostasis, its depletion may exacerbate disease pathology. Additionally, high levels of MMA have been found to reduce S-formylglutathione levels in the MMA group, highlighting an impairment of the formaldehyde detoxification pathway. Formaldehyde, a toxic aldehyde, is detoxified through its combination with glutathione to form S-hydroxymethylglutathione, which is subsequently oxidized to produce S-formylglutathione by formaldehyde dehydrogenase. This pathway is crucial in converting formaldehyde into a less hazardous form that can be metabolized and eliminated. Glutathione is vital as an antioxidant to neutralize reactive oxygen species (ROS) and repair damage caused by oxidative stress, thereby maintaining cellular redox homeostasis (Monostori et al., 2009; Wu et al., 2004). Reduced S-formylglutathione levels may indicate impaired formaldehyde detoxification due to decreased formaldehyde dehydrogenase activity or glutathione depletion. Given the role of glutathione-dependent pathways in maintaining redox balance, the observed reduction in S-formylglutathione could promote mitochondrial dysfunction and cellular damage by the accumulation of toxic aldehydes (Uotila & Koivusalo, 1996). In our MMA cohort, this imbalance may increase toxic aldehydes and promote mitochondrial dysfunction and oxidative stress, key factors in MMA pathophysiology, emphasizing its relevance to the metabolic abnormalities observed in the disease phenotypes (Chandler et al., 2009). Furthermore, increased pyroglutamic acid levels have been identified in MMA patients as a byproduct of glutathione metabolism. Similarly, elevated pyroglutamic acid levels have been reported in systemic lupus erythematosus (SLE), linked to oxidative stress and glutathione dysregulation. While MMA and SLE are distinct—MMA being a metabolic disorder and SLE an autoimmune disease—both share biochemical pathways such as oxidative stress and distributed glutathione homeostasis (Zhang et al., 2021). In MMA, mitochondrial dysfunction and impaired glutathione homeostasis contribute to pyroglutamic acid accumulation, suggesting that pyroglutamic acid could be a potential biomarker for MMA detection and managing. On the other hand, some acylcarnitine species have been detected, including undec-8-enoylcarnitine, tridec-6-enoylcarnitine, octenoyl-L-carnitine, octadecenoylcarnitine, and hexanoylcarnitine, which were upregulated in MMA patients compared to healthy control. MMA patients commonly receive L-carnitine to promote mitochondrial activity by eliminating excess toxic acylcarnitine species and maintaining the acyl-CoA/CoA ratio (Penn et al., 1986; Virmani & Cirulli, 2022). Elevated acylcarnitines could indicate different disorder pathologies, primarily related to disturbances in fatty acid metabolism and mitochondrial function. These abnormalities often imply underlying metabolic disorders, including medium-chain acyl-CoA dehydrogenase (MCAD) deficiency or other fatty acid oxidation disorders, in which the body cannot efficiently break down fatty acids for energy. It also reflects mitochondrial dysfunction, as these compounds accumulate when mitochondrial enzymes are affected in fatty acid metabolism, like in the case of IEM disorders (McCann et al., 2021). This result indicates that high serum acylcarnitines in our MMA patient group is evidence for altered mitochondrial function and homeostasis and impaired fatty acid oxidation, especially with the MUT gene patients (Schumann et al., 2023). In addition, succinyladenosine, a byproduct of adenylosuccinate (S-AMP) conversion to adenylate (AMP) in the purine nucleotide cycle, was significantly upregulated in the serum of MMA patients compared to unaffected individuals. Elevation of succinyladenosine has been previously reported to be associated with some IEM disorders, for example, adenylosuccinate lyase (ADSL) deficiency and fumarase deficiency, which cause disruptions in purine metabolism and mitochondrial processes (Donti et al., 2016; Tregoning et al., 2013). Similarly, our study's finding of elevated succinyladenosine in MMA patients suggests potential impairments in these metabolic and mitochondrial functions. Furthermore, our analysis revealed that 11, 12-epoxyeicosatrienoic acid (11, 12-EET), which is cytochrome P450-derived eicosanoids, was upregulated significantly in the sera of MMA patients with mutations in the MUT gene. Eicosanoids are bioactive lipid intermediates produced by the enzymatic and/or non-enzymatic oxidation of arachidonic acid. They serve as significant indicators of physiological and pathological processes like cancer, atherosclerosis, and neurodegenerative diseases, particularly the cytochrome P450-derived eicosanoids, which have been linked to inflammation regulations (Gomez et al., 2019; Kim et al., 2021; Panigrahy et al., 2010). MMA patients often have chronic inflammation and oxidative stress. Thus, this may shift arachidonic acid metabolism towards cytochrome P450 pathways, increasing the production of 11, 12-EET. Therefore, this detected increased levels of 11, 12-EET could indicate several aspects related to MMA pathophysiology, which included inflammation, oxidative stress, mitochondrial dysfunction, and altered lipid metabolism. MG (PGF2alpha/0:0/0:0) is an oxidized monoacylglycerol also plays a key signaling mediator and regulator for different cellular processes (e.g., apoptosis and inflammation) with involvement in lipid metabolism and membrane dynamics. It is a significant metabolite detected downregulated in the MMA group, indicating any alterations of its level could disrupt the metabolic dynamics and lipid pathways. Additionally, elevated levels of neuromodulatory peptides (e.g., neuromedin N), glycolysis intermediates (e.g., fructose 6-phosphate), and steroid metabolites (e.g., estrone glucuronide) in the MMA sera patients could imply a broad impact on the overall metabolic homeostasis of patients’ profiles. In this study, various altered metabolic pathways were identified; for example, the most significantly affected pathway being arachidonic acid metabolism, which included some detected metabolites like prostaglandins and leukotrienes. These metabolites are potent eicosanoid lipid mediators derived from arachidonic acid that play key functions in homeostasis and inflammation (Funk, 2001). They were upregulated in the MMA group, suggesting their involvement in the inflammatory responses related to MMA pathophysiological processes. Another pathway that affected the MMA patients’ profiles is glutathione metabolism, which is not surprisingly detected this pathway as its involvement in the oxidative stress events associated with the MMA pathology. The highest levels of pyroglutamic acid and different glutamyl-amino acids in MMA patients, which were found in the study, may indicate an increase in the redox regulation process. Indeed, it has been reported that elevated pyroglutamic acid excretion suggests a defect in the metabolic pathway associated with the synthesis of the intracellular reducing agent glutathione and the response to oxidative stress (Brooker et al., 2007). Furthermore, the MMA patient group reduced the level of deamino-NAD + , which has been found to be involved in the pathway of nicotinate and nicotinamide metabolism (NAD + metabolism). Several related MMA pathophysiologic mechanisms can explain reduced levels of deamino-NAD + in patients' metabolic profiles. For instance, disturbances in NAD + metabolism, which may be caused by nutrient disturbance, genetic mutations (like, in our case, the MUT gene mutation) or deficiencies in certain enzymes (e.g., MCM), can influence the levels of NAD + and its metabolites (deamino-NAD + ) (Xie et al., 2020; Zapata-Perez et al., 2021). Oxidative stress, and inflammation can profoundly affect NAD + metabolism by reducing the levels of NAD + and thus decreasing its derivatives (Xie et al., 2020). 5. Conclusion Our untargeted metabolomics analysis of serum samples from MMA patients revealed unique metabolic profiles compared to healthy controls, highlighting the significant metabolic disruptions characteristic of this disorder. Several potential biomarkers and disrupted pathways were identified, which provide valuable insights into the biochemical and physiological changes underlying MMA pathology, including inflammation, oxidative stress, and mitochondrial dysfunction. For example, significant downregulated metabolites in MMA patients include glutamine, isoleucine, deamido-NAD + , S-formylglutathione, sphingolipids, and MG (PGF2alpha/0:0/0:0). Conversely, upregulated metabolites included acylcarnitines, succinyladenosine, 11,12-epoxyeicosatrienoic acid, and leukotriene B4. These metabolites are strongly linked to MMA-related pathophysiological mechanisms. In addition, the pathways most significantly affected by MMA pathogenesis were identified, including arachidonic acid metabolism, glutathione metabolism, and nicotinate and nicotinamide metabolism. Validating these markers in bigger or longitudinal cohorts will improve the utilization of this discovery for better diagnosis, most probably, the screening and Metabotyping of the patients for better intervention. However, this study improves the understanding of MMA pathophysiology and identifies significant potential biomarkers and altered pathways. Several limitations should be addressed in future research. These include the need for larger patient cohort studies to validate the identified biomarkers and the altered metabolic pathways and assess the clinical applicability of these findings across diverse populations. Future investigations integrating metabolomics data with genomics and proteomics are required to develop comprehensive multi-omics diagnostic tools and targeted therapeutic strategies for MMA patients. Declarations Ethical Approval Statement The Institutional Review Boards at King Faisal Specialist Hospital and Research Center (KFSHRC) in Riyadh, Saudi Arabia, reviewed and approved the procedures for this study (RAC No. 2160 027). Consent was waived for leftover samples submitted for routine clinical testing. CRediT Authorship Contribution Statement Shuruq Alsuhaymi, Reem AlMalki, and Anas Abdel Rahman contributed to data curation and performed the formal analysis. Mariusz Jaremko was responsible for funding acquisition. Methodology was developed by Shuruq Alsuhaymi, Reem AlMalki, and Maha Al Mogren . Project administration was carried out by Mariusz Jaremko and Anas Abdel Rahman. Resources were provided by Ahamd Alodaib and Ahamd Alfares. Shuruq Alsuhaymi prepared the original draft of the manuscript, and Reem AlMalki, Anas Abdel Rahman, Majed Dasouki , and Abdul-Hamid Emwas contributed to writing—review and editing. Declaration of Interest The authors declare no competing financial or personal interests that could have influenced this work. Funding Sources The authors would like to thank King Abdullah University of Science and Technology (KAUST) for providing financial support. The Smart Health Initiative (SHI) is also acknowledged by Mariusz Jaremko for funding received through the Baseline grant (BAS/1/1085-01-01). Data Availability Statement The data supporting the findings of this study have been deposited in the Metabolomics Workbench repository (Study ID: ST003690, Project ID: PR002289) and will be made publicly available upon publication at https://www.metabolomicsworkbench.org. References [Internet]., H. M. D. (2023). [cited 2024-07-29].). Nicotinic acid adenine dinucleotide. 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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-6570059","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452622070,"identity":"10fc7c77-ecff-4b86-a378-480fdb97e3ff","order_by":0,"name":"Shuruq Alsuhaymi","email":"","orcid":"","institution":"King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Shuruq","middleName":"","lastName":"Alsuhaymi","suffix":""},{"id":452622071,"identity":"7cfd4405-6e18-4550-b8ba-184eeb890093","order_by":1,"name":"Reem H. AlMalki","email":"","orcid":"","institution":"King Faisal Specialist Hospital and Research Centre (KFSHRC)","correspondingAuthor":false,"prefix":"","firstName":"Reem","middleName":"H.","lastName":"AlMalki","suffix":""},{"id":452622072,"identity":"5c4c1d39-e105-49cb-8012-9e61f5213010","order_by":2,"name":"Maha Al Mogren","email":"","orcid":"","institution":"King Faisal Specialist Hospital and Research Centre (KFSHRC)","correspondingAuthor":false,"prefix":"","firstName":"Maha","middleName":"Al","lastName":"Mogren","suffix":""},{"id":452622073,"identity":"0fc93b97-7d04-494d-ba7a-fc991cb7b4db","order_by":3,"name":"Ahamd Alodaib","email":"","orcid":"","institution":"King Faisal Specialist Hospital and Research Centre (KFSHRC)","correspondingAuthor":false,"prefix":"","firstName":"Ahamd","middleName":"","lastName":"Alodaib","suffix":""},{"id":452622076,"identity":"6c806675-f952-42a4-b562-8fd8a34925b1","order_by":4,"name":"Abdul-Hamid Emwas","email":"","orcid":"","institution":"King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Abdul-Hamid","middleName":"","lastName":"Emwas","suffix":""},{"id":452622077,"identity":"529749b5-b5c7-4cf0-86bc-641aa783d765","order_by":5,"name":"Majed Dasouki","email":"","orcid":"","institution":"AdventHealth Genomics \u0026 Personalized Health at Orlando","correspondingAuthor":false,"prefix":"","firstName":"Majed","middleName":"","lastName":"Dasouki","suffix":""},{"id":452622078,"identity":"52329d50-5018-4056-9654-5bd79008714a","order_by":6,"name":"Ahmad Alfares","email":"","orcid":"","institution":"King Faisal Specialist Hospital and Research Centre (KFSHRC)","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Alfares","suffix":""},{"id":452622080,"identity":"3f2ca030-9e34-4f8f-8837-db4b0f979cc3","order_by":7,"name":"Mariusz Jaremko","email":"","orcid":"","institution":"King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Mariusz","middleName":"","lastName":"Jaremko","suffix":""},{"id":452622081,"identity":"de61eba7-781e-42cc-85fc-e6d0e38e40c0","order_by":8,"name":"Anas M. Abdel Rahman","email":"data:image/png;base64,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","orcid":"","institution":"King Faisal Specialist Hospital and Research Centre (KFSHRC)","correspondingAuthor":true,"prefix":"","firstName":"Anas","middleName":"M. Abdel","lastName":"Rahman","suffix":""}],"badges":[],"createdAt":"2025-05-01 07:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6570059/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6570059/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82558926,"identity":"430e8d36-35ca-4a14-9904-a68663eeae48","added_by":"auto","created_at":"2025-05-13 01:24:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64480,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Partial least squares discriminant analysis (PLS-DA) based on (26,945 ions) displaying separation between MMA and healthy controls, with excluded outliers of MMA group. (B) OPLS-DA model based on (26,945 ions) shows evident separation between MMA and Ctrl. The robustness of the created models was evaluated by the fitness of the model (R\u003csup\u003e2\u003c/sup\u003eY=0.977) and predictive ability (Q\u003csup\u003e2\u003c/sup\u003e=0.814) values in a larger dataset (n=100).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6570059/v1/6bddacab26257c2a958c6a8a.jpg"},{"id":82558927,"identity":"178c2f7b-64e6-4021-b3f3-233f172a0270","added_by":"auto","created_at":"2025-05-13 01:24:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26216,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot shows significantly dysregulated metabolites in MMA patients compared to healthy controls (Ctrl). A total of 692 metabolites were significantly dysregulated, with 431 upregulated (red) and 261 downregulated (blue). The plot displays the metabolic disturbances in MMA patients through fold-change and statistical significance, showing metabolites with a fold-change ≥ 2 and p-value ≤ 0.05 considered significant.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6570059/v1/4b3299a4963f94965531625c.jpg"},{"id":82558930,"identity":"70780709-d844-4a0e-b9f2-eb0c5a4840d2","added_by":"auto","created_at":"2025-05-13 01:24:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":207611,"visible":true,"origin":"","legend":"\u003cp\u003eA hierarchical cluster analysis (HCA) \u0026nbsp;\u0026nbsp;illustrates (A) upregulated and (B) downregulated metabolites in MMA patients \u0026nbsp;\u0026nbsp;compared to healthy controls (Ctrl). The HCA was performed using Ward’s \u0026nbsp;\u0026nbsp;linkage method and Euclidean distance to cluster metabolites and samples \u0026nbsp;\u0026nbsp;based on similarity patterns. As shown on the color-scaled bar, green \u0026nbsp;\u0026nbsp;represents upregulated metabolites, while red indicates downregulated \u0026nbsp;\u0026nbsp;metabolites. Distinct clustering patterns highlight the metabolic differences \u0026nbsp;\u0026nbsp;between MMA patients and healthy controls.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6570059/v1/27c371b127f87dc60d09e345.jpg"},{"id":82558931,"identity":"dfd32f4b-36a7-4ee8-9238-94f1b09fe2f2","added_by":"auto","created_at":"2025-05-13 01:24:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePathway analysis of 267 significantly dysregulated endogenous metabolites in MMA patients compared to healthy controls.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6570059/v1/a6b55e3f382ea19be905c351.jpg"},{"id":82558953,"identity":"cca2f8ba-0f9e-4dea-8693-611cb2d07a93","added_by":"auto","created_at":"2025-05-13 01:24:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82706,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Receiver operating characteristics (ROC) curve between MMA vs Ctrl. (B) The frequency plot shows the significantly dysregulated endogenous metabolites between MMA vs Ctrl. (C) ROC curve of individual biomarkers, 11, 12-epoxyeicosatrienoic acid (AUC= 0.964) and (D) leukotriene B4 (AUC= 0.963) were upregulated, and (E) MG (PGF2alpha/0:0/0:0) (AUC= 0.953) was downregulated in patients MMA compared to Ctrl.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6570059/v1/03b9c4155a1bf6132dff8cb5.jpg"},{"id":96919359,"identity":"cf4499b5-96c6-4192-ad55-0456c8fefe94","added_by":"auto","created_at":"2025-11-27 14:13:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1313714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6570059/v1/85725e3f-a916-4db9-b755-6b819b80e4ac.pdf"},{"id":82561716,"identity":"a50ccc8d-6fef-491d-8d65-3d849a3af6af","added_by":"auto","created_at":"2025-05-13 01:40:05","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":44421,"visible":true,"origin":"","legend":"","description":"","filename":"MMASupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6570059/v1/0073d6b10cc19d2f368ad43e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Untargeted Metabolomics Reveals Distinct Metabolic Profiles in MMA Patients with MUT Gene Mutations","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Untargeted metabolomics revealed 267 dysregulated metabolites in -confirmed MMA patients, which provides a deeper insight into disease mechanisms compared to traditional diagnostic tools.\u003c/p\u003e\u003cp\u003e\u0026bull; Key disrupted pathways included sphingolipid, glutathione, arachidonic acid, and purine metabolism, with significant alterations observed in metabolites like glutamine, deamido-NAD⁺, and leukotriene B4.\u003c/p\u003e\u003cp\u003e\u0026bull; Potential biomarkers such as 11,12-epoxyeicosatrienoic acid (AUC\u0026thinsp;=\u0026thinsp;0.964) and MG (PGF2alpha/0:0/0:0) (AUC\u0026thinsp;=\u0026thinsp;0.953) demonstrate strong diagnostic accuracy, and their association with inflammation and oxidative stress indicates a key role in prognosis and disease management.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eInborn errors of metabolisms (IEMs) are rare genetic disorders caused by defects in specific enzymes, co-factors, or transport proteins. Over 1,400 IEMs have been identified, with the majority remaining untreatable and their underlying mechanisms yet to be fully elucidated (Ferreira et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ismail et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Stepien et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The incidence of IEMs exhibits significant regional variability, often influenced by high consanguinity rates. For example, in Saudi Arabia, where consanguinity rates range from 50\u0026ndash;80%, the incidence of IEMs is significantly higher than global averages, with some studies estimating it to be as high as 1 in 569 live births (Al Qurashi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the many identified IEMs, methylmalonic acidemia (MMA) represents one of the most common organic acidemias (Lee \u0026amp; Kim, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This disorder is characterized by the body's impaired ability to metabolize specific proteins and fats, leading to the accumulation of toxic organic acid, primarily methylmalonic acid, in the blood and tissues. This toxic accumulation arises from enzymatic deficiencies, typically inherited in an autosomal recessive (AR) pattern. The genetic basis of MMA is primarily associated with deficiencies in the enzyme methylmalonyl-CoA mutase (MCM) or impairments in the metabolism of its cofactor, adenosyl-cobalamin (a bioactive form of vitamin B12). MCM, encoded by the \u003cem\u003eMUT\u003c/em\u003e gene, plays an important role in the catabolism of certain amino acids (valine, isoleucine, methionine, and threonine) and odd-chain fatty acids by catalyzing the conversion of methylmalonyl-CoA to succinyl-CoA, a key intermediate in the tricarboxylic acid (TCA) cycle (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Mutations in the \u003cem\u003eMUT\u003c/em\u003e gene, such as deletions, insertions, or missense, can result in MCM deficiency or dysfunction, disrupting this conversion and leading to the toxic accumulation of methylmalonic acid. These genetic mutations account for approximately 60% of MMA cases (Garg \u0026amp; Smith, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, \u003cem\u003eMUT\u003c/em\u003e gene defects can affect mitochondrial function, reducing ATP generation, and increasing oxidative stress. These mitochondrial dysfunctions contribute to long-term complications, including chronic kidney disease, neurodevelopmental delays, cardiomyopathy, pancreatitis, and bone marrow suppression (Imtiaz et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe MMA clinical manifestations, mainly in early infancy, range widely from moderate to severe and life-threatening, depending on the type and degree of enzyme deficiency. Common symptoms in affected infants include recurrent vomiting, dehydration, reduced muscle tone (hypotonia), excessive fatigue (lethargy), and poor growth or failure to thrive. Moreover, metabolic acidosis, marked by an excess of acid in the blood, is a defining feature of MMA and, if untreated, can cause serious health complications, including acute metabolic decompensations (AMD) that may lead to coma or death. The underlying pathophysiology of AMD in isolated MMA remains incompletely understood. However, recent studies of MMA patient metabolomes suggest that metabolic acidosis during AMD is driven by the accumulation of acidic intermediates of branched-chain amino acid (BCAA) metabolism rather than methylmalonic acid levels. These findings emphasize the complexity of MMA and the need for further study to elucidate its acute and chronic mechanisms (Keyfi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Venditti, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiagnosing inborn errors of metabolism remains challenging due to the wide range of clinical symptoms that often overlap among different disorders. This complexity requires diagnostic approaches that expand beyond clinical examinations, making the development of a universal diagnostic protocol unfeasible. Newborn screening (NBS) programs play a crucial role in identifying and preventing IEMs early by detecting disorders before symptoms arise, thus enabling timely interventions. However, current NBS procedures that target specific metabolites may provide inaccurate results, particularly in stressed or newly born infants (Millington, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Advancements in mass spectrometry techniques, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), have considerably enhanced the accuracy and efficiency of IEM diagnosis. Genetic screening, primarily through next-generation sequencing (NGS), has also emerged as a valuable tool in diagnosing IEMs. Despite these technological advances, significant differences are found in NBS programs across different regions, with varying coverage and screening methods (Wilcken et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). For early diagnosis of MMA, it often applies a combination of newborn screening and biochemical testing. For example, tandem mass spectrometry (MS/MS)-based NBS enables early MMA detection by measuring over 40 metabolites using dry blood spots, including propionylcarnitine (C3), C3/acetylcarnitine (C2), and methionine levels. Confirmatory testing of MMA typically includes analyzing urine for elevated organic acids, including methylmalonic acid, 3-hydroxypropionate, and 2-methylcitrate, using GC-MS. Key biochemical indicators of MMA include hyperlactemia, hyperammonemia, metabolic acidosis, and abnormal urine results. Furthermore, molecular genetic testing confirms the MMA diagnosis by identifying gene mutations, such as \u003cem\u003eMUT\u003c/em\u003e, with regional genetic variations affecting clinical results (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Forny et al., 2021; Mukherjee \u0026amp; Ray, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmerging technologies like untargeted metabolomics offer a valuable complement to targeted metabolic assays by providing a comprehensive perspective of metabolic pathways. This approach could enhance our understanding of MMA and other IEMs, potentially leading to new therapeutic approaches. Untargeted metabolomics extends the investigation range and provides a deeper insight into disorder mechanisms and disrupted pathways (Millington, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, challenges remain regarding its clinical application, particularly in cost, speed, accuracy, and reproducibility. Studies in specific populations, such as Saudi patients, have determined metabolic biomarkers and pathways in IEM disorders. For instance, a study on medium-chain acyl-CoA dehydrogenase deficiency (MCADD) disorder revealed disruptions in phenylalanine, tyrosine, and tryptophan biosynthesis pathways, with potential biomarkers such as PGP (a21:0/PG/F1alpha) and glutathione, demonstrating the importance of metabolomics in promoting our understanding of IEMs (Sebaa et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite advances in understanding MMA, further research is needed to enhance personalized disease management and patient outcomes. Accordingly, this study aims to conduct metabolic profiling of patients with MUT-confirmed MMA to identify novel, clinically relevant biomarkers and provide deeper insights into the disease's pathophysiology. This study demonstrates the value of untargeted metabolomics in identifying these biomarkers and disrupted metabolic pathways, with potential applications in predicting long-term prognosis and suggesting novel therapeutic strategies. It also examines the impact of genetic and regional variations on disease expression.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ethical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Boards at King Faisal Specialist Hospital and Research Center (KFSHRC) in Riyadh, Saudi Arabia, reviewed and approved the procedures for this study (RAC No. 2160 027). Consent was waived for leftover samples submitted for routine clinical testing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eSerum Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFifty-four serum samples were obtained from the Metabolomics Lab at the Genomic Medicine, Center of Excellence at KFSHRC. This study included twenty-seven samples from patients confirmed to have \u003cem\u003eMUT\u003c/em\u003e-deficiency MMA, along with twenty-seven healthy controls. Age- and gender-related metabolic effects were excluded from the raw data during data processing. For age-related effects, we calculated the average age of the healthy control (HC) group and identified metabolites below the average age (BAA) or above the average age (UAA) threshold. We then analyzed these using a Moderated t-test, [BAA] vs. [UAA] Fold Change (FC) cut-off 2.0, P\u0026nbsp;\u0026le;\u0026nbsp;0.05). This strategy confirmed removing the dataset\u0026apos;s age- and gender-dependent biases, enabling a more precise interpretation of the metabolic changes correlated to MMA pathology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eMetabolites Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe chemicals used for LC-MS, including methanol, acetonitrile, deionized water (dH\u003csub\u003e2\u003c/sub\u003eO), and formic acid, were sourced from Fisher Scientific Company (Ottawa, ON, Canada). Serum metabolites were extracted from 50\u0026mu;L of samples collected from MMA patients and healthy controls using 50% acetonitrile and methanol extraction solvent. Samples were prepared randomly and processed simultaneously to reduce variability. Quality control (QC) samples were prepared by aliquoting from each sample and were included to ensure data reliability. The samples were vortexed in a ThermoMixer (Eppendorf, Hamburg, Germany) at 600 rpm and 25 \u0026deg;C for one hour. After vortexing, they were centrifuged at 16,000 rpm and 4 \u0026deg;C for 10 minutes. The resulting supernatants were transferred to new Eppendorf tubes and dried using a SpeedVac concentrator (Thermo Fisher, Christ, Germany) for the LC-MS (Jacob et al., 2019).\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e2.4.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003eLC-MS Untargeted Metabolomics Analysis\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eFor LC-MS sample analysis, the dried extracted samples were reconstituted in a mobile phase containing 50% solution (A: 0.1% formic acid in dH\u003csub\u003e2\u003c/sub\u003eO and (B: 0.1% formic acid in 1:1 (\u003cem\u003ev/v\u003c/em\u003e) MeOH and ACN). In the beginning, 5\u0026micro;L of the reconstituted sample was injected into the LC column for polar metabolite separation using reversed-phase liquid chromatography on an ACQUITY UPLC XSelect column (100 \u0026times; 2.1mm \u0026times; 2.5\u0026mu;m, Waters Ltd., Elstree, UK). The mobile phase flowed at 300\u0026mu;L/min while maintaining the column temperature at 55 \u0026deg;C and the sample temperature at 4 \u0026deg;C. The gradient mode applied for pumping the mobile phases A and B to the column is as follows: 0\u0026ndash;16 min 95\u0026ndash;5% A, 16\u0026ndash;19 min 5% A, 19\u0026ndash;20 min 5\u0026ndash;95% A, and 20\u0026ndash;22 min 5\u0026ndash;95% A. The LC eluates were ionized using electrospray ionization (ESI) in both positive and negative modes and separated based on m/z using a Xevo G2-S QTOF mass spectrometer (Waters Ltd., Elstree, UK). The MS source temperature was fixed at 150 ◦C, the desolvation temperature was set at 500 ◦C, and the capillary voltages were kept at 3.20 kV or 3 kV for ESI+ and ESI\u0026minus; modes, respectively. The cone gas flow was 50 L/h, the desolvation gas flow was 800 L/h, and the cone voltage was 40 V. The collision energies for the low and high functions were set to off and 10\u0026ndash;50 V, respectively, in the MS\u003csup\u003eE\u003c/sup\u003e data-independent acquisition (DIA) mode. As recommended by the vendor, the mass spectrometer was calibrated with sodium formate (100\u0026ndash;1200 Da) in both ionization modes. The lock spray mass compound, MS leucine-enkephaline (an external reference to the ion m/z 556.2771 in positive mode and 554.2615 in negative mode), was constantly injected, which is responsible for switching between the sample and the reference for every 45 and 60 s in both modes, scan time was 0.5 s, the flow rate was 10 \u0026micro;L/min, and collision energy was 4 V and 30 V for the cone, respectively. The DIA data were gathered in continuum mode with Masslynx\u0026trade; V4.1 Software (Waters Inc., Milford, MA, USA).QCs were introduced to the instrument randomly to validate the system\u0026rsquo;s stability. After that, they were analyzed following the routine protocol. The acceptance criteria were to have all the QC samples separated from the other study groups, clustered together, and use their Relative standard deviations (RSD%) \u0026lt; 40% (AlMalki et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.5.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eData Processing and Statistical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw MS data were typically processed using Waters\u0026apos; Progenesis QI v.3.0 software (Milford, MA, USA), including aligning based on m/z values and ion retention time, peak filtering, and picking according to peak quality. In addition, features found in at least half of the samples were kept for further analyses. Metaboanalyst version 6.0 (McGill University, Montreal, QB, Canada), (http://www.metaboanalyst.ca) accessed on 3 August 2024, was used for multivariate statistical analyses (Pang et al., 2021). The imported datasets were median-normalization, log- transformation, and Pareto- scaling. It was utilized to perform the partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models. Model fitness (R\u003csup\u003e2\u003c/sup\u003eY) and predictive ability (Q\u003csup\u003e2\u003c/sup\u003e) values were used in the evaluation of the created OPLS-DA model (Worley \u0026amp; Powers, 2013). Pathway and biomarker analyses, MMA-linked biomarkers and receiver operating characteristic (ROC) curves were generated utilizing the PLS-DA technique in Metaboanalyst software for broad analysis to identify potential biomarkers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor univariate analysis, mass profiler professional (MPP) software v15.0 (Agilent, Santa Clara, CA, USA) was employed. A volcano plot was applied to determine the significant features with a moderated t-test, a fold-change (FC) cutoff of 2 and FDR p \u0026lt; 0.05. Venn diagram was also generated using MPP, while the heatmap analyses of altered features (in MMA patients compared to the controls) were conducted using Pearson distance measures based on the Pearson similarity test (Gu et al., 2020).\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e2.6.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003eMetabolite Identification\u003c/h4\u003e\n\u003cp\u003eThe statistically significant features from each dataset were selected and annotated using Progenesis QI v.3.0 software (Waters Technologies, Milford, MA, USA). For peak annotation, the precursor and product ions were annotated based on accurate mass, fragmentation pattern, and isotopic distributions utilizing databases such as the Human Metabolome Database (HMDB) with a 5 ppm mass error, METLIN MS/MS (www.metlin.scripps.edu), LipidMap, Lipid Blast, KEGG (Wishart et al., 2022). Exogenous compounds, including drugs, food additives, and environmental substances, were excluded from the results.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sample Demographic Data and Sample Collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected serum samples (n=54) from the metabolomics laboratory of King Faisal Specialized Hospital and Research Center (KFSHRC)’s Genome Medicine Center of Excellence (GMCoE). The MMA patient samples (n=27) with \u003cem\u003eMUT\u003c/em\u003e gene mutations were confirmed through the standard of care whole genome analysis clinical protocol.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;healthy control (HC) group\u0026nbsp;(n=27) was systematically matched for\u0026nbsp;age and gender\u0026nbsp;to minimize demographic influences on metabolic data (Figure S1). To further reduce potential age-related biases, metabolites were categorized based on the\u0026nbsp;HC group's average age\u0026nbsp;and analyzed using a\u0026nbsp;Moderated t-test. This approach effectively removed age- and gender-dependent variations to enhance the accuracy of metabolic interpretations associated with\u0026nbsp;MMA pathology\u0026nbsp;(see Methods for details). The demographic distribution of the sample is summarized in Table 1. The average age of MMA patients was 15.63 ± 1.49 years, while the healthy controls averaged 26.85 ± 0.91 years. MMA patients displayed significantly elevated serum methylmalonic acid levels (744.00 ± 141.02 μM/L), which is the biomarker of the disorder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Sample Demographic Information.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic and Clinical Features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTRL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(n\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(n\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (Years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.04E\u003csup\u003e-08\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMMA (Cutoff \u0026lt;0.4 uMOL/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e744.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e141.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.4 uMOL/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.61E\u003csup\u003e-06\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;LC-MS Untargeted Metabolomics Analysis of MMA Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe detected compound ions were 29,685 in both positive (\u003cem\u003en\u003c/em\u003e= 16,623) and negative (\u003cem\u003en\u003c/em\u003e= 13,062) ionization modes, respectively and archived on the Metabolomics Workbench (ST003690)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 26,945 ion features remained after excluding age-dependent metabolites (\u003cem\u003en\u003c/em\u003e=2194 ion features) using a Moderated t.test, P ≤ 0.05, FC 2 (Figure S1). To reduce major variances and verify the normal\u0026nbsp;distribution, the dataset\u0026nbsp;was normalized using median, log-transformation, and Pareto scaling. A partial least squares discriminant analysis (PLS-DA), a supervised tool, demonstrated distinct clustering of samples and clear differentiation between MMA patients (green) and healthy controls (red) (Figure 1A), and quality control QC (blue) (Figure S2). Furthermore, orthogonal partial least squares discriminant analysis (OPLS-DA) confirmed the significant separation between the groups, and the robustness of the created models was evaluated by the fitness of the model (R\u003csup\u003e2\u003c/sup\u003eY=0.977) and predictive ability (Q\u003csup\u003e2\u003c/sup\u003e=0.814) values in a larger dataset (n=100) (Figure 1B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThen, a filter by frequency was applied on (\u003cem\u003en\u003c/em\u003e=26,945 ions) with a cut-off percentage of 80% of all samples to exclude missing values. The remaining features were 13,608 features. These features (13,608 ions) were statistically evaluated between two groups, MMA patients and healthy controls (Ctrl), using a volcano plot (Moderated t.test FDRp ≤ 0.05, FC 2) and revealed that 692 significantly dysregulated features, of which 431 were upregulated, and 261 were downregulated in patients with MMA compared to Ctrl (Figure 2). Among these, 536 features were annotated using the human metabolome database (HMDB), Metlin MS/MS, LipidBlast, lipidMap, and KEGG. Only 267 were identified as human endogenous metabolites after excluding exogenous sources such as environmental exposures, foods, and drugs (Table S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, a heatmap analysis\u0026nbsp;using Pearson distance measures\u0026nbsp;displayed the dysregulation of 267 endogenous metabolites, including 185 upregulated and 82 downregulated in MMA patients, with red and green color intensities representing the relative increase and decrease in metabolite regulation, respectively (Figure 3). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Metabolic Pathway Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe also investigated the most affected metabolic pathways in the MMA patients group using our study's detected dysregulated endogenous metabolites (n=267). Arachidonic acid metabolism, purine metabolism, and nicotinate and nicotinamide metabolism are examples of the most affected pathways in the MMA group (Figure 4). Other pathways were also affected, including glutathione metabolism, sphingolipid metabolism, and steroid hormone biosynthesis (Figure 4 and Table S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Biomarker Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLastly, we performed a receiver operating characteristics (ROC) curve analysis on statistically significant dysregulated metabolites (n=267) to identify and assess the potential biomarkers differentiating between MMA and healthy samples. A PLS-DA method was applied to create a multivariate exploratory ROC analysis to classify and rank features (Figure 5A). The ROC curve features identified using PLS-DA and cross-validation (CV) exhibited area under the curve (AUC) values ranging from 0.95 to 0.996, with confidence intervals (CI) of 0.818-1 and 0.975-1, respectively (Figure 5A). The frequency plots displayed the 15 significant metabolites with the highest VIP scores in the OPLS-DA model, based on their levels in the MMA and healthy samples (Figure 5B). For example, the upregulated endogenous metabolites in MMA compared to the control group included 11,12-epoxyeicosatrienoic acid, 11-Hydroperoxydocosahexaenoic acid and prostaglandin A1, and the downregulated metabolites involved 5-hydroxydec-7-enedioylcarnitine, UDP-N-acetylmuraminate, and N-arachidonoyl tryptophan (Figure 5B). The AUC values of the ROC curves in the MMA patients’ group for upregulated biomarkers; 11, 12-epoxyeicosatrienoic acid and leukotriene B4 were 0.964 and 0.936, respectively (Figure 5C and 5D). For the downregulated biomarker, MG (PGF2alpha/0:0/0:0) was 0.953 (Figure 5E).\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u0026nbsp; \u0026nbsp;Methylmalonic acidemia (MMA) is the most prevalent congenital organic acidemia caused by mutations in the \u003cem\u003eMUT\u003c/em\u003e gene that impair methylmalonyl-CoA mutase (MCM) enzyme activity. Most laboratories report a ≤ 0.40 nmol/mL reference interval for MMA or µmol/L (Samara et al.). Clinically, individuals with classical isolated MMA often experience failure to thrive, vomiting, neurological problems, metabolic acidosis, and hyperammonemia. Although extensive research has been conducted on \u003cem\u003eMUT\u003c/em\u003e dysfunction, the metabolic disruptions and underlying pathological mechanisms of MMA remain poorly understood,\u0026nbsp;and no curative treatments are available\u0026nbsp;(Forny et al., 2023; Forny et al., 2014). High levels of the NBS marker are not exclusive to MMA but can also occur in other metabolic disorders, such as propionic acidemia and cobalamin-related deficiencies (e.g., cblA, cblB, cblC), which share interconnected metabolic pathways\u0026nbsp;(Held et al., 2022). Genetic mutations in \u003cem\u003eMMUT\u003c/em\u003e, \u003cem\u003eMMAA\u003c/em\u003e, \u003cem\u003eMMAB\u003c/em\u003e, and cobalamin-related genes (\u003cem\u003eLMBRD1\u003c/em\u003e, \u003cem\u003eABCD4\u003c/em\u003e, \u003cem\u003eMTRR\u003c/em\u003e) further expand the complexity of the disease spectrum, which leads to MMA accumulation\u0026nbsp;(Beyzaei et al., 2024; List et al., 2021). This accumulation has also been linked to non-metabolic conditions, including chronic kidney disease and neurological impairments such as developmental delay and hypotonia\u0026nbsp;(Manoli et al., 2016). Therefore, this study examines the metabolic profiles of MMA patients with \u003cem\u003eMUT\u003c/em\u003e mutations to identify potential biomarkers and altered pathways that could enhance our understanding of MMA pathophysiology specifically and its broader clinical implications.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Our untargeted metabolomics investigations of MMA sera\u0026nbsp;revealed distinctively\u0026nbsp;different metabolic profiles in MMA\u0026nbsp;patients compared to their corresponding healthy group, reflecting the extensive metabolic disruptions inherent to this disorder. A wide\u0026nbsp;array\u0026nbsp;of dysregulated\u0026nbsp;metabolites distinguished the patient group from the unaffected ones. In general, several potential biomarkers, including amino acids, lipid species, acylcarnitine compounds, and oxidative stress markers, were identified significantly; these are associated with various biochemical and physiological changes indicative of MMA pathology, such as inflammation, oxidative stress, mitochondrial dysfunction, and alterations in amino and lipid metabolism.\u003c/p\u003e\n\u003cp\u003eIn NBS for MMA diagnosis, the level of propionylcarnitine (C3) serves as the primary biomarker. However, elevated C3 levels can also indicate propionic acidemia, requiring measurement of MMA levels in body for confirmation (Held et al., 2022). Although, methylmalonic acid interferents can result in false diagnoses (Monostori et al., 2023), our untargeted metabolomics analysis\u0026nbsp;identified this biomarker exclusively in MMA patients, validating this approach and demonstrating the practical application of metabolomics in clinical biochemistry.\u003c/p\u003e\n\u003cp\u003eAs mentioned above, previous studies suggest that metabolic acidosis in MMA, particularly during AMD, is primarily driven by the accumulation of acidic intermediates from BCAA metabolism rather than methylmalonic acid itself\u0026nbsp;(Keyfi et al., 2016; Venditti, 2005; Zhang et al., 2023).\u0026nbsp;However, our findings reveal a significant reduction in amino acids such as L-glutamine and L-isoleucine, along with their conjugates, including N-arachidonoyl phenylalanine and N-arachidonoyl tryptophan, in MMA patients. This depletion is likely due to mitochondrial dysfunction, a hallmark of MMA, which disrupts amino acid metabolism and leads to the accumulation of toxic intermediates\u0026nbsp;(Chandler et al., 2009; Li \u0026amp; Hoppe, 2023). In contrast, healthy individuals maintain balanced amino acid levels, ensuring normal metabolic homeostasis\u0026nbsp;(Horton et al., 2006; Ling et al., 2023). Notably, the depletion of BCAAs, particularly isoleucine, may exacerbate muscle wasting and metabolic stress, similar to the reductions observed in chronic renal failure. Given that BCAA supplementation has been shown to help protein balance and reduce metabolic toxicity in other conditions\u0026nbsp;(Alvestrand et al., 1982; Holeček, 2018), our findings suggest that amino acid depletion is a key contributor to metabolic imbalances in MMA. These results underscore the complexity of MMA pathophysiology and highlight the need for further research to explore targeted metabolic interventions to mitigate amino acid deficiencies.\u003c/p\u003e\n\u003cp\u003eMoreover, nicotinic acid adenine dinucleotide (NAAD) was found to be downregulated in the MMA patients compared to healthy control. NAAD is also referred to as deamido-NAD\u003csup\u003e+\u003c/sup\u003e([Internet]. 2023 [cited 2024-07-29].). NAD\u003csup\u003e+\u003c/sup\u003e is a vital element in various biological and metabolic processes, such as DNA repair and energy production. It serves as a coenzyme in catalyzing fundamental cellular redox reactions, including glycolysis, fatty acid beta-oxidation, and the tricarboxylic acid cycle TCA, where it is reduced to NADH. NAD\u003csup\u003e+\u003c/sup\u003e also serves as a substrate for NAD\u003csup\u003e+\u003c/sup\u003e-consuming enzymes such as sirtuins and poly-ADP-ribose polymerases (PARPs), which regulate several important cellular processes\u0026nbsp;(Xie et al., 2020). The conversion of L-glutamine and NAAD to L-glutamic acid and NAD\u003csup\u003e+\u003c/sup\u003e, facilitated by glutamine-dependent NAD\u003csup\u003e+\u003c/sup\u003e synthetase, is another vital metabolic pathway\u0026nbsp;([Internet]. 2023 [cited 2024-07-29].; Johnson \u0026amp; Imai, 2018). Studies have linked decreased NAD\u003csup\u003e+\u003c/sup\u003e levels to several disorders, including metabolic and neurodegenerative diseases\u0026nbsp;(Oyama et al., 2024).\u0026nbsp;For example, research into mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome has shown that mitochondrial dysfunction associated with reduced NAD\u003csup\u003e+\u003c/sup\u003e levels exacerbates the disease’s symptoms, including stroke-like episodes and muscle weakness\u0026nbsp;(Seo et al., 2018).\u0026nbsp;Plasma MMA, a marker of cobalamin deficiency, may also indicate lysosomal iron trapping associated with oxidative stress and mitochondrial dysfunction in metabolic disturbances like MMA, contributing to clinical manifestations such as neurodevelopmental abnormalities and organ dysfunction\u0026nbsp;(Vermorken et al., 2021; Zhao et al., 2014). Correspondingly, our findings indicate that elevated MMA levels are linked to diminished NAD\u003csup\u003e+\u003c/sup\u003e levels, potentially leading to mitochondrial and lysosomal dysfunction. As NAD\u003csup\u003e+\u003c/sup\u003e is important in maintaining mitochondrial efficiency and lysosomal homeostasis, its depletion may exacerbate disease pathology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, high levels of MMA have been found to reduce S-formylglutathione levels in the MMA group, highlighting an impairment of the formaldehyde detoxification pathway. Formaldehyde, a toxic aldehyde, is detoxified through its combination with glutathione to form S-hydroxymethylglutathione, which is subsequently oxidized to produce S-formylglutathione by formaldehyde dehydrogenase. This pathway is crucial in converting formaldehyde into a less hazardous form that can be metabolized and eliminated. Glutathione is vital as an antioxidant to neutralize reactive oxygen species (ROS) and repair damage caused by oxidative stress, thereby maintaining cellular redox homeostasis (Monostori et al., 2009; Wu et al., 2004). Reduced S-formylglutathione levels may indicate impaired formaldehyde detoxification due to decreased formaldehyde dehydrogenase activity or glutathione depletion. Given the role of glutathione-dependent pathways in maintaining redox balance, the observed reduction in S-formylglutathione could promote mitochondrial dysfunction and cellular damage by the accumulation of toxic aldehydes (Uotila \u0026amp; Koivusalo, 1996). In our MMA cohort, this imbalance may increase toxic aldehydes and promote mitochondrial dysfunction and oxidative stress, key factors in MMA pathophysiology, emphasizing its relevance to the metabolic abnormalities observed in the disease phenotypes (Chandler et al., 2009).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, increased pyroglutamic acid levels have been identified in MMA patients as a byproduct of glutathione metabolism. Similarly, elevated pyroglutamic acid levels have been reported in systemic lupus erythematosus (SLE), linked to oxidative stress and glutathione dysregulation. While MMA and SLE are distinct—MMA being a metabolic disorder and SLE an autoimmune disease—both share biochemical pathways such as oxidative stress and distributed glutathione homeostasis (Zhang et al., 2021). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn MMA, mitochondrial dysfunction and impaired glutathione homeostasis contribute to pyroglutamic acid accumulation, suggesting that pyroglutamic acid could be a potential biomarker for MMA detection and managing.\u003c/p\u003e\n\u003cp\u003eOn the other hand, some acylcarnitine species have been detected, including undec-8-enoylcarnitine, tridec-6-enoylcarnitine, octenoyl-L-carnitine, octadecenoylcarnitine, and hexanoylcarnitine, which were upregulated in MMA patients compared to healthy control. MMA patients commonly receive L-carnitine to promote mitochondrial activity by eliminating excess toxic acylcarnitine species and maintaining the acyl-CoA/CoA ratio (Penn et al., 1986; Virmani \u0026amp; Cirulli, 2022).\u0026nbsp;Elevated acylcarnitines could indicate different disorder pathologies, primarily related to disturbances in fatty acid metabolism and mitochondrial function. These abnormalities often imply underlying metabolic disorders, including medium-chain acyl-CoA dehydrogenase (MCAD) deficiency or other fatty acid oxidation disorders, in which the body cannot efficiently break down fatty acids for energy. It also reflects mitochondrial dysfunction, as these compounds accumulate when mitochondrial enzymes are affected in fatty acid metabolism, like in the case of IEM disorders\u0026nbsp;(McCann et al., 2021). This result indicates that high serum acylcarnitines in our MMA patient group is evidence for altered mitochondrial function and homeostasis and impaired fatty acid oxidation, especially with the \u003cem\u003eMUT\u003c/em\u003e gene patients\u0026nbsp;(Schumann et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, succinyladenosine, a byproduct of adenylosuccinate (S-AMP) conversion to adenylate (AMP) in the purine nucleotide cycle, was significantly upregulated in the serum of MMA patients compared to unaffected individuals. Elevation of succinyladenosine has been previously reported to be associated with some IEM disorders, for example, adenylosuccinate lyase (ADSL) deficiency and fumarase deficiency, which cause disruptions in purine metabolism and mitochondrial processes (Donti et al., 2016; Tregoning et al., 2013). Similarly, our study's finding of elevated succinyladenosine in MMA patients suggests potential impairments in these metabolic and mitochondrial functions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, our analysis revealed that 11, 12-epoxyeicosatrienoic acid (11, 12-EET), which is cytochrome P450-derived eicosanoids, was upregulated significantly in the sera of MMA patients with mutations in the \u003cem\u003eMUT\u003c/em\u003e gene. Eicosanoids are bioactive lipid intermediates produced by the enzymatic and/or non-enzymatic oxidation of arachidonic acid. They serve as significant indicators of physiological and pathological processes like cancer, atherosclerosis, and neurodegenerative diseases, particularly the cytochrome P450-derived eicosanoids, which have been linked to inflammation regulations (Gomez et al., 2019; Kim et al., 2021; Panigrahy et al., 2010). MMA patients often have chronic inflammation and oxidative stress. Thus, this may shift arachidonic acid metabolism towards cytochrome P450 pathways, increasing the production of 11, 12-EET. Therefore, this detected increased levels of 11, 12-EET could indicate several aspects related to MMA pathophysiology, which included inflammation, oxidative stress, mitochondrial dysfunction, and altered lipid metabolism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMG (PGF2alpha/0:0/0:0) is an oxidized monoacylglycerol also plays a key signaling mediator and regulator for different cellular processes (e.g., apoptosis and inflammation) with involvement in lipid metabolism and membrane dynamics. It is a significant metabolite detected downregulated in the MMA group, indicating any alterations of its level could disrupt the metabolic dynamics and lipid pathways. Additionally, elevated levels of neuromodulatory peptides (e.g., neuromedin N), glycolysis intermediates (e.g., fructose 6-phosphate), and steroid metabolites (e.g., estrone glucuronide) in the MMA sera patients could imply a broad impact on the overall metabolic homeostasis of patients’ profiles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;In this study, various altered metabolic pathways were identified; for example, the most significantly affected pathway being arachidonic acid metabolism, which included some detected metabolites like prostaglandins and leukotrienes. These metabolites are potent eicosanoid lipid mediators derived from arachidonic acid that play key functions in homeostasis and inflammation (Funk, 2001). They were upregulated in the MMA group, suggesting their involvement in the inflammatory responses related to MMA pathophysiological processes. Another pathway that affected the MMA patients’ profiles is glutathione metabolism, which is not surprisingly detected this pathway as its involvement in the oxidative stress events associated with the MMA pathology. The highest levels of pyroglutamic acid and different glutamyl-amino acids in MMA patients, which were found in the study, may indicate an increase in the redox regulation process. Indeed, it has been reported that elevated pyroglutamic acid excretion suggests a defect in the metabolic pathway associated with the synthesis of the intracellular reducing agent glutathione and the response to oxidative stress (Brooker et al., 2007). Furthermore, the MMA patient group reduced the level of deamino-NAD\u003csup\u003e+\u003c/sup\u003e, which has been found to be involved in the pathway of nicotinate and nicotinamide metabolism (NAD\u003csup\u003e+\u0026nbsp;\u003c/sup\u003emetabolism). Several related MMA pathophysiologic mechanisms can explain reduced levels of deamino-NAD\u003csup\u003e+\u003c/sup\u003e in patients' metabolic profiles. For instance, disturbances in NAD\u003csup\u003e+\u003c/sup\u003e metabolism, which may be caused by nutrient disturbance, genetic mutations (like, in our case, the \u003cem\u003eMUT\u003c/em\u003e gene mutation) or deficiencies in certain enzymes (e.g., MCM), can influence the levels of NAD\u003csup\u003e+\u003c/sup\u003e and its metabolites (deamino-NAD\u003csup\u003e+\u003c/sup\u003e)\u0026nbsp;(Xie et al., 2020; Zapata-Perez et al., 2021). Oxidative stress, and inflammation can profoundly affect NAD\u003csup\u003e+\u003c/sup\u003e metabolism by reducing the levels of NAD\u003csup\u003e+\u003c/sup\u003e and thus decreasing its derivatives\u0026nbsp;(Xie et al., 2020).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur untargeted metabolomics analysis of serum samples from MMA patients revealed unique metabolic profiles compared to healthy controls, highlighting the significant metabolic disruptions characteristic of this disorder. Several potential biomarkers and disrupted pathways were identified, which provide valuable insights into the biochemical and physiological changes underlying MMA pathology, including inflammation, oxidative stress, and mitochondrial dysfunction. \u0026nbsp;For example, significant downregulated metabolites in MMA patients include glutamine, isoleucine, deamido-NAD\u003csup\u003e+\u003c/sup\u003e, S-formylglutathione, sphingolipids, and MG (PGF2alpha/0:0/0:0). Conversely, upregulated metabolites included acylcarnitines, succinyladenosine, 11,12-epoxyeicosatrienoic acid, and leukotriene B4. These metabolites are strongly linked to MMA-related pathophysiological mechanisms. In addition, the pathways most significantly affected by MMA pathogenesis were identified, including arachidonic acid metabolism, glutathione metabolism, and nicotinate and nicotinamide metabolism. Validating these markers in bigger or longitudinal cohorts will improve the utilization of this discovery for better diagnosis, most probably, the screening and Metabotyping of the patients for better intervention. However, this study improves the understanding of MMA pathophysiology and identifies significant potential biomarkers and altered pathways. Several limitations should be addressed in future research. These include the need for larger patient cohort studies to validate the identified biomarkers and the altered metabolic pathways and assess the clinical applicability of these findings across diverse populations. Future investigations integrating metabolomics data with genomics and proteomics are required to develop comprehensive multi-omics diagnostic tools and targeted therapeutic strategies for MMA patients.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Boards at King Faisal Specialist Hospital and Research Center (KFSHRC) in Riyadh, Saudi Arabia, reviewed and approved the procedures for this study (RAC No. 2160 027). Consent was waived for leftover samples submitted for routine clinical testing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT Authorship Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShuruq Alsuhaymi, Reem AlMalki, and Anas Abdel Rahman\u0026nbsp;\u003c/strong\u003econtributed to data curation and performed the formal analysis. \u003cstrong\u003eMariusz\u003c/strong\u003e \u003cstrong\u003eJaremko\u003c/strong\u003ewas responsible for funding acquisition. Methodology was developed by \u003cstrong\u003eShuruq Alsuhaymi, Reem AlMalki, and Maha Al Mogren\u003c/strong\u003e. Project administration was carried out by \u003cstrong\u003eMariusz\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eJaremko\u003c/strong\u003e\u003cstrong\u003eand Anas Abdel Rahman.\u003c/strong\u003e Resources were provided by \u003cstrong\u003eAhamd\u003c/strong\u003e \u003cstrong\u003eAlodaib and Ahamd\u003c/strong\u003e \u003cstrong\u003eAlfares.\u003c/strong\u003e \u003cstrong\u003eShuruq Alsuhaymi\u003c/strong\u003e prepared the original draft of the\u0026nbsp;manuscript, and \u003cstrong\u003eReem AlMalki, Anas Abdel Rahman, Majed\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDasouki\u003c/strong\u003e\u003cstrong\u003e, and Abdul-Hamid Emwas\u003c/strong\u003e contributed to writing\u0026mdash;review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial or personal interests that could have influenced this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank King Abdullah University of Science and Technology (KAUST) for providing financial support. The Smart Health Initiative (SHI) is also acknowledged by Mariusz Jaremko for funding received through the Baseline grant (BAS/1/1085-01-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study have been deposited in the Metabolomics Workbench repository (Study ID: ST003690, Project ID: PR002289) and will be made publicly available upon publication at https://www.metabolomicsworkbench.org.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e[Internet]., H. M. D. (2023). [cited 2024-07-29].). Nicotinic acid adenine dinucleotide. 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Application of the Artificial Intelligence Algorithm Model for Screening of Inborn Errors of Metabolism. \u003cem\u003eFront Pediatr\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 855943. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fped.2022.855943\u003c/span\u003e\u003cspan address=\"10.3389/fped.2022.855943\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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":"Metabolic disorders, Inborn errors of metabolism, Methylmalonic acidemia, Untargeted metabolomics, LC-MS","lastPublishedDoi":"10.21203/rs.3.rs-6570059/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6570059/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMethylmalonic acidemia (MMA), the most prevalent congenital organic acidemia, is inherited in an autosomal recessive pattern due to \u003cem\u003eMUT\u003c/em\u003e gene mutations that impair methylmalonyl-CoA mutase (MCM) enzyme activity, leading to the toxic accumulation of methylmalonic acid, which causes mitochondrial dysfunction, metabolic disruptions, and multisystem damage. Newborn screening followed by confirmatory biochemical and genetic tests\u0026mdash;such as acylcarnitine analysis and urine organic acid profiling\u0026mdash;are widely accepted and routinely used in biochemical genetics labs. However, these conventional methods are limited in their ability to detect novel, clinically relevant biomarkers that may offer deeper insights into MMA pathophysiology. This study highlights the importance of untargeted metabolomics in identifying such biomarkers, with potential applications in predicting long-term prognosis and suggesting novel therapeutic strategies. LC-HRMS was used to analyze serum samples from \u003cem\u003eMUT\u003c/em\u003e-confirmed MMA patients (n\u0026thinsp;=\u0026thinsp;27) and healthy controls (n\u0026thinsp;=\u0026thinsp;27). A total of 267 dysregulated metabolites were identified in MMA patients, including 185 upregulated and 82 downregulated. These metabolites were associated with key affected pathways, including arachidonic acid, nicotinate and nicotinamide, sphingolipid, glutathione, and purine metabolism. Downregulated metabolites included glutamine, isoleucine, deamido-NAD\u003csup\u003e+\u003c/sup\u003e, and sphingolipids, while upregulated metabolites included acylcarnitines, succinyladenosine, and leukotriene B4. Notably, biomarkers such as 11,12-epoxyeicosatrienoic acid (AUC\u0026thinsp;=\u0026thinsp;0.964) and MG (PGF2alpha/0:0/0:0) (AUC\u0026thinsp;=\u0026thinsp;0.953) are implicated in MMA pathophysiological mechanisms through their association with inflammation, oxidative stress, and altered fatty acid metabolism. These findings may help with improved understanding of disease pathogenesis and ultimately its management. Future research must validate these biomarkers in larger, diverse cohorts and integrate metabolomics with genomics and proteomics to develop comprehensive diagnostic tools and targeted therapies, ultimately improving MMA patient outcomes.\u003c/p\u003e","manuscriptTitle":"Untargeted Metabolomics Reveals Distinct Metabolic Profiles in MMA Patients with MUT Gene Mutations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 01:23:59","doi":"10.21203/rs.3.rs-6570059/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":"25621faa-0fbb-4528-99fa-06f32379e499","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-27T06:38:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 01:23:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6570059","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6570059","identity":"rs-6570059","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}