MT-ATP6 mitochondrial disease identified by newborn screening reveals a distinct biochemical phenotype.

Although decreased citrulline is used as a newborn screening (NBS) marker to identify proximal urea cycle disorders (UCDs), it is also a feature of some mitochondrial diseases, including MT-ATP6 mitochondrial disease. Here we describe biochemical and clinical features of 11 children born to eight mothers from seven separate families who were identified with low citrulline by NBS (range 3-5 µM; screening cutoff >5) and ultimately diagnosed with MT-ATP6 mitochondrial disease. Follow-up testing revealed a pattern of hypocitrullinemia together with elevated propionyl-(C3) and 3-hydroxyisovaleryl-(C5-OH) acylcarnitines, and a homoplasmic pathogenic variant in MT-ATP6 in all cases. Single and multivariate analysis of NBS data from the 11 cases using Collaborative Laboratory Integrated Reports (CLIR; https://clir.mayo.edu) demonstrated citrulline <1st percentile, C3 > 50th percentile, and C5-OH >90th percentile when compared with reference data, as well as unequivocal separation from proximal UCD cases and false-positive low citrulline cases using dual scatter plots. Five of the eight mothers were symptomatic at the time of their child(ren)'s diagnosis, and all mothers and maternal grandmothers evaluated molecularly and biochemically had a homoplasmic pathogenic variant in MT-ATP6, low citrulline, elevated C3, and/or elevated C5-OH. All molecularly confirmed individuals (n = 17) with either no symptoms (n = 12), migraines (n = 1), or a neurogenic muscle weakness, ataxia, and retinitis pigmentosa (NARP) phenotype (n = 3) were found to have an A or U mitochondrial haplogroup, while one child with infantile-lethal Leigh syndrome had a B haplogroup.

Validation of a targeted metabolomics panel for improved second-tier newborn screening.

Improved second-tier assays are needed to reduce the number of false positives in newborn screening (NBS) for inherited metabolic disorders including those on the Recommended Uniform Screening Panel (RUSP). We developed an expanded metabolite panel for second-tier testing of dried blood spot (DBS) samples from screen-positive cases reported by the California NBS program, consisting of true- and false-positives from four disorders: glutaric acidemia type I (GA1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). This panel was assembled from known disease markers and new features discovered by untargeted metabolomics and applied to second-tier analysis of single DBS punches using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a 3-min run. Additionally, we trained a Random Forest (RF) machine learning classifier to improve separation of true- and false positive cases. Targeted metabolomic analysis of 121 analytes from DBS extracts in combination with RF classification at a sensitivity of 100% reduced false positives for GA1 by 83%, MMA by 84%, OTCD by 100%, and VLCADD by 51%. This performance was driven by a combination of known disease markers (3-hydroxyglutaric acid, methylmalonic acid, citrulline, and C14:1), other amino acids and acylcarnitines, and novel metabolites identified to be isobaric to several long-chain acylcarnitine and hydroxy-acylcarnitine species. These findings establish the effectiveness of this second-tier test to improve screening for these four conditions and demonstrate the utility of supervised machine learning in reducing false-positives for conditions lacking clearly discriminating markers, with future studies aimed at optimizing and expanding the panel to additional disease targets.

Host-microbe co-metabolism via MCAD generates circulating metabolites including hippuric acid.

The human gut microbiota produces dozens of small molecules that circulate in blood, accumulate to comparable levels as pharmaceutical drugs, and influence host physiology. Despite the importance of these metabolites to human health and disease, the origin of most microbially-produced molecules and their fate in the host remains largely unknown. Here, we uncover a host-microbe co-metabolic pathway for generation of hippuric acid, one of the most abundant organic acids in mammalian urine. Combining stable isotope tracing with bacterial and host genetics, we demonstrate reduction of phenylalanine to phenylpropionic acid by gut bacteria; the host re-oxidizes phenylpropionic acid involving medium-chain acyl-CoA dehydrogenase (MCAD). Generation of germ-free male and female MCAD-/- mice enabled gnotobiotic colonization combined with untargeted metabolomics to identify additional microbial metabolites processed by MCAD in host circulation. Our findings uncover a host-microbe pathway for the abundant, non-toxic phenylalanine metabolite hippurate and identify ß-oxidation via MCAD as a novel mechanism by which mammals metabolize microbiota-derived metabolites.

Metabolic diversity in human populations and correlation with genetic and ancestral geographic distances.

DNA polymorphic markers and self-defined ethnicity groupings are used to group individuals with shared ancient geographic ancestry. Here we studied whether ancestral relationships between individuals could be identified from metabolic screening data reported by the California newborn screening (NBS) program. NBS data includes 41 blood metabolites measured by tandem mass spectrometry from singleton babies in 17 parent-reported ethnicity groupings. Ethnicity-associated differences identified for 71% of NBS metabolites (29 of 41, Cohen's d > 0.5) showed larger differences in blood levels of acylcarnitines than of amino acids (P < 1e-4). A metabolic distance measure, developed to compare ethnic groupings based on metabolic differences, showed low positive correlation with genetic and ancient geographic distances between the groups' ancestral world populations. Several outlier group pairs were identified with larger genetic and smaller metabolic distances (Black versus White) or with smaller genetic and larger metabolic distances (Chinese versus Japanese) indicating the influence of genetic and of environmental factors on metabolism. Using machine learning, comparison of metabolic profiles between all pairs of ethnic groupings distinguished individuals with larger genetic distance (Black versus Chinese, AUC = 0.96), while genetically more similar individuals could not be separated metabolically (Hispanic versus Native American, AUC = 0.51). Additionally, we identified metabolites informative for inferring metabolic ancestry in individuals from genetically similar populations, which included biomarkers for inborn metabolic disorders (C10:1, C12:1, C3, C5OH, Leucine-Isoleucine). This work sheds new light on metabolic differences in healthy newborns in diverse populations, which could have implications for improving genetic disease screening.

Targeted plasma metabolomics combined with machine learning for the diagnosis of severe acute respiratory syndrome virus type 2.

Introduction: The routine clinical diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is largely restricted to real-time reverse transcription quantitative PCR (RT-qPCR), and tests that detect SARS-CoV-2 nucleocapsid antigen. Given the diagnostic delay and suboptimal sensitivity associated with these respective methods, alternative diagnostic strategies are needed for acute infection. Methods: We studied the use of a clinically validated liquid chromatography triple quadrupole method (LC/MS-MS) for detection of amino acids from plasma specimens. We applied machine learning models to distinguish between SARS-CoV-2-positive and negative samples and analyzed amino acid feature importance. Results: A total of 200 samples were tested, including 70 from individuals with COVID-19, and 130 from negative controls. The top performing model overall allowed discrimination between SARS-CoV-2-positive and negative control samples with an area under the receiver operating characteristic curve (AUC) of 0.96 (95%CI 0.91, 1.00), overall sensitivity of 0.99 (95%CI 0.92, 1.00), and specificity of 0.92 (95%CI 0.85, 0.95). Discussion: This approach holds potential as an alternative to existing methods for the rapid and accurate diagnosis of acute SARS-CoV-2 infection.

Detecting lysosomal storage disorders by glycomic profiling using liquid chromatography mass spectrometry.

BACKGROUND: Urine and plasma biomarker testing for lysosomal storage disorders by liquid chromatography mass spectrometry (LC-MS) currently requires multiple analytical methods to detect the abnormal accumulation of oligosaccharides, mucopolysaccharides, and glycolipids. To improve clinical testing efficiency, we developed a single LC-MS method to simultaneously identify disorders of oligosaccharide, mucopolysaccharide, and glycolipid metabolism with minimal sample preparation. METHODS: We created a single chromatographic method for separating free glycans and glycolipids in their native form, using an amide column and high pH conditions. We used this glycomic profiling method both in untargeted analyses of patient and control urines using LC ion-mobility high-resolution MS (biomarker discovery), and targeted analyses of urine, serum, and dried blood spot samples by LC-MS/MS (clinical validation). RESULTS: Untargeted glycomic profiling revealed twenty biomarkers that could identify and subtype mucopolysaccharidoses. We incorporated these with known oligosaccharide and glycolipid biomarkers into a rapid test that identifies at least 27 lysosomal storage disorders, including oligosaccharidoses, mucopolysaccharidoses, sphingolipidoses, glycogen storage disorders, and congenital disorders of glycosylation and de-glycosylation. In a validation set containing 115 urine samples from patients with lysosomal storage disorders, all were unambiguously distinguished from normal controls, with correct disease subtyping for 88% (101/115) of cases. Glucosylsphingosine was reliably elevated in dried blood spots from Gaucher disease patients with baseline resolution from galactosylsphingosine. CONCLUSION: Glycomic profiling by liquid chromatography mass spectrometry identifies a range of lysosomal storage disorders. This test can be used in clinical evaluations to rapidly focus a diagnosis, as well as to clarify or support additional gene sequencing and enzyme studies.

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza.

BACKGROUND: Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flight mass spectrometry (LC/Q-TOF) combined with machine learning for the diagnosis of influenza infection. METHODS: We analyzed nasopharyngeal swab samples by LC/Q-TOF to identify distinct metabolic signatures for diagnosis of acute illness. Machine learning models were performed for classification, followed by Shapley additive explanation (SHAP) analysis to analyze feature importance and for biomarker discovery. FINDINGS: A total of 236 samples were tested in the discovery phase by LC/Q-TOF, including 118 positive samples (40 influenza A 2009 H1N1, 39 influenza H3 and 39 influenza B) as well as 118 age and sex-matched negative controls with acute respiratory illness. Analysis showed an area under the receiver operating characteristic curve (AUC) of 1.00 (95% confidence interval [95% CI] 0.99, 1.00), sensitivity of 1.00 (95% CI 0.86, 1.00) and specificity of 0.96 (95% CI 0.81, 0.99). The metabolite most strongly associated with differential classification was pyroglutamic acid. Independent validation of a biomarker signature based on the top 20 differentiating ion features was performed in a prospective cohort of 96 symptomatic individuals including 48 positive samples (24 influenza A 2009 H1N1, 5 influenza H3 and 19 influenza B) and 48 negative samples. Testing performed using a clinically-applicable targeted approach, liquid chromatography triple quadrupole mass spectrometry, showed an AUC of 1.00 (95% CI 0.998, 1.00), sensitivity of 0.94 (95% CI 0.83, 0.98), and specificity of 1.00 (95% CI 0.93, 1.00). Limitations include lack of sample suitability assessment, and need to validate these findings in additional patient populations. INTERPRETATION: This metabolomic approach has potential for diagnostic applications in infectious diseases testing, including other respiratory viruses, and may eventually be adapted for point-of-care testing. FUNDING: None.

A conserved role for AMP-activated protein kinase in NGLY1 deficiency.

Mutations in human N-glycanase 1 (NGLY1) cause the first known congenital disorder of deglycosylation (CDDG). Patients with this rare disease, which is also known as NGLY1 deficiency, exhibit global developmental delay and other phenotypes including neuropathy, movement disorder, and constipation. NGLY1 is known to regulate proteasomal and mitophagy gene expression through activation of a transcription factor called "nuclear factor erythroid 2-like 1" (NFE2L1). Loss of NGLY1 has also been shown to impair energy metabolism, but the molecular basis for this phenotype and its in vivo consequences are not well understood. Using a combination of genetic studies, imaging, and biochemical assays, here we report that loss of NGLY1 in the visceral muscle of the Drosophila larval intestine results in a severe reduction in the level of AMP-activated protein kinase α (AMPKα), leading to energy metabolism defects, impaired gut peristalsis, failure to empty the gut, and animal lethality. Ngly1-/- mouse embryonic fibroblasts and NGLY1 deficiency patient fibroblasts also show reduced AMPKα levels. Moreover, pharmacological activation of AMPK signaling significantly suppressed the energy metabolism defects in these cells. Importantly, the reduced AMPKα level and impaired energy metabolism observed in NGLY1 deficiency models are not caused by the loss of NFE2L1 activity. Taken together, these observations identify reduced AMPK signaling as a conserved mediator of energy metabolism defects in NGLY1 deficiency and suggest AMPK signaling as a therapeutic target in this disease.

AMP-independent activator of AMPK for treatment of mitochondrial disorders.

Mitochondrial diseases are a clinically heterogenous group of disorders caused by respiratory chain dysfunction and associated with progressive, multi-systemic phenotype. There is no effective treatment or cure, and no FDA-approved drug for treating mitochondrial disease. To identify and characterize potential therapeutic compounds, we developed an in vitro screening assay and identified a group of direct AMP-activated protein kinase (AMPK) activators originally developed for the treatment of diabetes and metabolic syndrome. Unlike previously investigated AMPK agonists such as AICAR, these compounds allosterically activate AMPK in an AMP-independent manner, thereby increasing specificity and decreasing pleiotropic effects. The direct AMPK activator PT1 significantly improved mitochondrial function in assays of cellular respiration, energy status, and cellular redox. PT1 also protected against retinal degeneration in a mouse model of photoreceptor degeneration associated with mitochondrial dysfunction and oxidative stress, further supporting the therapeutic potential of AMP-independent AMPK agonists in the treatment of mitochondrial disease.

Whole genome sequencing for mutation discovery in a single case of lysosomal storage disease (MPS type 1) in the dog.

Mucopolysaccharidosis (MPS) is a metabolic storage disorder caused by the deficiency of any lysosomal enzyme required for the breakdown of glycosaminoglycans. A 15-month-old Boston Terrier presented with clinical signs consistent with lysosomal storage disease including corneal opacities, multifocal central nervous system disease and progressively worsening clinical course. Diagnosis was confirmed at necropsy based on histopathologic evaluation of multiple organs demonstrating accumulation of mucopolysaccharides. Whole genome sequencing was used to uncover a frame-shift insertion affecting the alpha-L-iduronidase (IDUA) gene (c.19_20insCGGCCCCC), a mutation confirmed in another Boston Terrier presented 2 years later with a similar clinical picture. Both dogs were homozygous for the IDUA mutation and shared coat colors not recognized as normal for the breed by the American Kennel Club. In contrast, the mutation was not detected in 120 unrelated Boston Terriers as well as 202 dogs from other breeds. Recent inbreeding to select for recessive and unusual coat colors may have concentrated this relatively rare allele in the breed. The identification of the variant enables ante-mortem diagnosis of similar cases and selective breeding to avoid the spread of this disease in the breed. Boston Terriers carrying this variant represent a promising model for MPS I with neurological abnormalities in humans.

Reducing False-Positive Results in Newborn Screening Using Machine Learning.

Newborn screening (NBS) for inborn metabolic disorders is a highly successful public health program that by design is accompanied by false-positive results. Here we trained a Random Forest machine learning classifier on screening data to improve prediction of true and false positives. Data included 39 metabolic analytes detected by tandem mass spectrometry and clinical variables such as gestational age and birth weight. Analytical performance was evaluated for a cohort of 2777 screen positives reported by the California NBS program, which consisted of 235 confirmed cases and 2542 false positives for one of four disorders: glutaric acidemia type 1 (GA-1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). Without changing the sensitivity to detect these disorders in screening, Random Forest-based analysis of all metabolites reduced the number of false positives for GA-1 by 89%, for MMA by 45%, for OTCD by 98%, and for VLCADD by 2%. All primary disease markers and previously reported analytes such as methionine for MMA and OTCD were among the top-ranked analytes. Random Forest's ability to classify GA-1 false positives was found similar to results obtained using Clinical Laboratory Integrated Reports (CLIR). We developed an online Random Forest tool for interpretive analysis of increasingly complex data from newborn screening.

Metabolic profiling by reversed-phase/ion-exchange mass spectrometry.

Metabolic profiling is commonly achieved by mass spectrometry (MS) following reversed-phase (RP) and hydrophilic interaction chromatography (HILIC) either performed independently, leading to overlapping datasets, or in a coupled configuration, requiring multiple liquid chromatography (LC) systems. To overcome these limitations, we developed a single, 20-minute chromatographic method using an in-line RP-ion-exchange (IEX) column arrangement and a single LC system. This configuration separates clinically significant polar and non-polar compounds without derivatization or ion-pairing reagents, allowing ionization in both polarities. An in-house library was created with 397 authentic standards, including acylcarnitines, amino acids, bile acids, nucleosides, organic acids, steroid hormones, and vitamins. Analysis of pooled plasma and urine samples revealed 5445 and 4111 ion features, leading to 88 and 82 confirmed metabolite identifications, respectively. Metabolites were detected at clinically relevant concentrations with good precision, and good chromatographic separation was demonstrated for clinically significant isomers including methylmalonic acid and succinic acid, as well as alloisoleucine and isoleucine/leucine. Evaluation of the samples by unsupervised principal component analysis showed excellent analytical quality.

Ethnic variability in newborn metabolic screening markers associated with false-positive outcomes.

Newborn screening (NBS) programmes utilise information on a variety of clinical variables such as gestational age, sex, and birth weight to reduce false-positive screens for inborn metabolic disorders. Here we study the influence of ethnicity on metabolic marker levels in a diverse newborn population. NBS data from screen-negative singleton babies (n = 100 000) were analysed, which included blood metabolic markers measured by tandem mass spectrometry and ethnicity status reported by the parents. Metabolic marker levels were compared between major ethnic groups (Asian, Black, Hispanic, White) using effect size analysis, which controlled for group size differences and influence from clinical variables. Marker level differences found between ethnic groups were correlated to NBS data from 2532 false-positive cases for four metabolic diseases: glutaric acidemia type 1 (GA-1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). In the result, 79% of the metabolic markers (34 of 43) had ethnicity-related differences. Compared to the other groups, Black infants had elevated GA-1 markers (C5DC, Cohen's d = .37, P < .001), Hispanics had elevated MMA markers (C3, Cohen's d = .13, P < .001, and C3/C2, Cohen's d = .27, P < .001); and Whites had elevated VLCADD markers (C14, Cohen's d = .28, P < .001, and C14:1, Cohen's d = .22, P < .001) and decreased OTCD markers (citrulline, Cohen's d = -.26, P < .001). These findings correlated with the higher false-positive rates in Black infants for GA-1, in Hispanics for MMA, and in Whites for OTCD and for VLCADD. Web-based tools are available to analyse ethnicity-related changes in newborn metabolism and to support developing methods to identify false-positives in metabolic screening.

Timing of Newborn Blood Collection Alters Metabolic Disease Screening Performance.

Blood collection for newborn genetic disease screening is preferably performed within 24-48 h after birth. We used population-level newborn screening (NBS) data to study early postnatal metabolic changes and whether timing of blood collection could impact screening performance. Newborns were grouped based on their reported age at blood collection (AaBC) into early (12-23 h), standard (24-48 h), and late (49-168 h) collection groups. Metabolic marker levels were compared between the groups using effect size analysis, which controlled for group size differences and influence from the clinical variables of birth weight and gestational age. Metabolite level differences identified between groups were correlated to NBS data from false-positive cases for inborn metabolic disorders including carnitine transport defect (CTD), isovaleric acidemia (IVA), methylmalonic acidemia (MMA), and phenylketonuria (PKU). Our results showed that 56% of the metabolites had AaBC-related differences, which included metabolites with either decreasing or increasing levels after birth. Compared to the standard group, the early-collection group had elevated marker levels for PKU (phenylalanine, Cohen's d = 0.55), IVA (C5, Cohen's d = 0.24), MMA (C3, Cohen's d = 0.23), and CTD (C0, Cohen's d = 0.23). These findings correlated with higher false-positive rates for PKU (P < 0.05), IVA (P < 0.05), and MMA (P < 0.001), and lower false-positive rate for CTD (P < 0.001) in the early-collection group. Blood collection before 24 h could affect screening performance for some metabolic disorders. We have developed web-based tools integrating AaBC and other variables for interpretive analysis of screening data.

Quantitative Analysis of Underivatized Amino Acids by Liquid Chromatography-Tandem Mass Spectrometry.

Quantitative amino acid analysis has diverse applications in clinical diagnostics, biomedical research, and agriculture. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) enables more rapid and specific detection of amino acids in comparison to traditional, gold-standard ninhydrin-based methods. However, triple quadrupole mass spectrometers are unable to definitively differentiate isomers and are susceptible to ion suppression, both of which prevent accurate quantitation. Therefore, appropriate chromatography must be applied before ionization.We have shown that two-dimensional LC enables rapid and specific amino acid quantitation without derivatization by resolving isomers, such as alloisoleucine, isoleucine, and leucine, and reducing matrix effects (Le et al., J Chromatogr B Analyt Technol Biomed Life Sci 944:166-174, 2014). In this clinically validated protocol, we provide an updated description of the chromatographic setup and selected reaction monitoring (SRM) transitions. Then, we describe sample processing for serum, plasma, urine, cerebral spinal fluid, and dried blood spots. Most importantly, we outline a singular quantitative design for efficient data analysis of the listed sample types and quality assurance strategies to ensure test fidelity. Lastly, we share extensive knowledge critical to the success of this method. A liquid sample can be processed and be ready for injection within 5 min, and each sample is analyzed by the MS in 14.5 min.

Biomarkers of oxidative stress, inflammation, and vascular dysfunction in inherited cystathionine ß-synthase deficient homocystinuria and the impact of taurine treatment in a phase 1/2 human clinical trial.

STUDY OBJECTIVE: A phase 1/2 clinical trial was performed in individuals with cystathionine ß synthase (CBS) deficient homocystinuria with aims to: (a) assess pharmacokinetics and safety of taurine therapy, (b) evaluate oxidative stress, inflammation, and vascular function in CBS deficiency, and (c) evaluate the impact of short-term taurine treatment. METHODS: Individuals with pyridoxine-nonresponsive CBS deficiency with homocysteine >50 µM, without inflammatory disorder or on antioxidant therapy were enrolled. Biomarkers of oxidative stress and inflammation, endothelial function (brachial artery flow-mediated dilation [FMD]), and disease-related metabolites obtained at baseline were compared to normal values. While maintaining current treatment, patients were treated with 75 mg/kg taurine twice daily, and treatment response assessed after 4 hours and 4 days. RESULTS: Fourteen patients (8-35 years; 8 males, 6 females) were enrolled with baseline homocysteine levels 161 ± 67 µM. The study found high-dose taurine to be safe when excluding preexisting hypertriglyceridemia. Taurine pharmacokinetics showed a rapid peak level returning to near normal levels at 12 hours, but had slow accumulation and elevated predosing levels after 4 days of treatment. Only a single parameter of oxidative stress, 2,3-dinor-8-isoprostaglandin-F2α, was elevated at baseline, with no elevated inflammatory parameters, and no change in FMD values overall. Taurine had no effect on any of these parameters. However, the effect of taurine was strongly related to pretreatment FMD values; and taurine significantly improved FMD in the subset of individuals with pretreatment FMD values <10% and in individuals with homocysteine levels >125 µM, pertinent to endothelial function. CONCLUSION: Taurine improves endothelial function in CBS-deficient homocystinuria in patients with preexisting reduced function.

Combining newborn metabolic and DNA analysis for second-tier testing of methylmalonic acidemia.

PURPOSE: Improved second-tier tools are needed to reduce false-positive outcomes in newborn screening (NBS) for inborn metabolic disorders on the Recommended Universal Screening Panel (RUSP). METHODS: We designed an assay for multiplex sequencing of 72 metabolic genes (RUSPseq) from newborn dried blood spots. Analytical and clinical performance was evaluated in 60 screen-positive newborns for methylmalonic acidemia (MMA) reported by the California Department of Public Health NBS program. Additionally, we trained a Random Forest machine learning classifier on NBS data to improve prediction of true and false-positive MMA cases. RESULTS: Of 28 MMA patients sequenced, we found two pathogenic or likely pathogenic (P/LP) variants in a MMA-related gene in 24 patients, and one pathogenic variant and a variant of unknown significance (VUS) in 1 patient. No such variant combinations were detected in MMA false positives and healthy controls. Random Forest-based analysis of the entire NBS metabolic profile correctly identified the MMA patients and reduced MMA false-positive cases by 51%. MMA screen-positive newborns were more likely of Hispanic ethnicity. CONCLUSION: Our two-pronged approach reduced false positives by half and provided a reportable molecular finding for 89% of MMA patients. Challenges remain in newborn metabolic screening and DNA variant interpretation in diverse multiethnic populations.

Elevated methylmalonic acidemia (MMA) screening markers in Hispanic and preterm newborns.

Analysis of California newborn screening (NBS) data revealed a high prevalence of Hispanic infants testing positive for methylmalonic acidemia (MMA), a trend seen for both true- and false-positive cases. Here we show that Hispanic infants have significantly higher levels of MMA screening markers than non-Hispanics. Preterm birth and increased birth weight were found to be associated with elevated MMA marker levels but could not entirely explain these differences. While the preterm birth rate was higher in Blacks than Hispanics, Black infants had on average the lowest MMA marker levels. Preterm birth was associated with lower birth weight and increased MMA marker levels suggesting that gestational age is the stronger predictive covariate compared to birth weight. These findings could help explain why MMA false-positive results are more likely in Hispanic than in Black infants, which could inform screening and diagnostic procedures for MMA and potentially other disorders in newborns.

Medical Biochemistry Without Rote Memorization: Multi-Institution Implementation and Student Perceptions of a Nationally Standardized Metabolic Map for Learning and Assessment.

Despite the growing number of patients worldwide with metabolism-related chronic diseases, medical biochemistry education is commonly perceived as focusing on recall of facts irrelevant for patient care. The authors suggest that this focus on rote memorization of pathways creates excessive cognitive load that may interfere with learners' development of an integrated understanding of metabolic regulation and dysregulation. This cognitive load can be minimized by providing appropriate references during learning and assessment. Biochemistry educators collaborated to develop a medically relevant pathways of human metabolism map (MetMap) that is now being used at many medical schools as a nationally standardized resource during learning and assessments. To assess impact, students from three medical schools were surveyed about its benefits and disadvantages. Responses were obtained from 481 students (84%) and were examined using thematic analysis. Five main themes emerged as perceived benefits of using the MetMap: (1) aids visual and mental organization, (2) promotes deep learning and applied understanding, (3) decreases emphasis on memorization, (4) reduces anxiety on exams, and (5) aids recall. Perceived disadvantages were (1) fear of underpreparation for licensing exams, (2) overwhelming nature of the map, and (3) reduced motivation for and time spent studying. Results affirm that students' perceive use of the MetMap promotes focus on broader metabolic concepts and deep versus surface learning, supporting a shift in cognitive load toward desired goals. Although the long-term impact on learning needs to be further studied, the use of the MetMap represents a step toward open-reference exams that reflect "real-world" practice.

Phosphorylation of MCAD selectively rescues PINK1 deficiencies in behavior and metabolism.

PTEN-induced putative kinase 1 (PINK1) is a mitochondria-targeted kinase whose mutations are a cause of Parkinson's disease. We set out to better understand PINK1's effects on mitochondrial proteins in vivo. Using an unbiased phosphoproteomic screen in Drosophila, we found that PINK1 mediates the phosphorylation of MCAD, a mitochondrial matrix protein critical to fatty acid metabolism. By mimicking phosphorylation of this protein in a PINK1 null background, we restored PINK1 null's climbing, flight, thorax, and wing deficiencies. Owing to MCAD's role in fatty acid metabolism, we examined the metabolic profile of PINK1 null flies, where we uncovered significant disruptions in both acylcarnitines and amino acids. Some of these disruptions were rescued by phosphorylation of MCAD, consistent with MCAD's rescue of PINK1 null's organismal phenotypes. Our work validates and extends the current knowledge of PINK1, identifies a novel function of MCAD, and illuminates the need for and effectiveness of metabolic profiling in models of neurodegenerative disease.