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Many metabolic liver disorders are refractory to drug therapy and require orthotopic liver transplantation. Here we demonstrate a new strategy, which we call metabolic pathway reprogramming, to treat hereditary tyrosinaemia type I in mice; rather than edit the disease-causing gene, we delete a gene in a disease-associated pathway to render the phenotype benign. Using CRISPR/Cas9 in vivo , we convert hepatocytes from tyrosinaemia type I into the benign tyrosinaemia type III by deleting Hpd (hydroxyphenylpyruvate dioxigenase). Edited hepatocytes ( Fah −/− /Hpd −/− ) display a growth advantage over non-edited hepatocytes ( Fah −/− /Hpd +/+ ) and, in some mice, almost completely replace them within 8 weeks. Hpd excision successfully reroutes tyrosine catabolism, leaving treated mice healthy and asymptomatic. Metabolic pathway reprogramming sidesteps potential difficulties associated with editing a critical disease-causing gene and can be explored as an option for treating other diseases. Hereditary tyrosinaemia type I is caused by a gene defect that leads to a lethal accumulation of toxic metabolites in the liver. Here the authors use CRISPR/Cas9 to 'cure' the disease in mice by inactivating another gene, rather than targeting the disease-causing gene itself, to reroute hepatic tyrosine catabolism.

MBD5-associated neurodevelopmental disorder (MAND) is an umbrella term that describes a group of disorders, 2q23.1 deletion syndrome, 2q23.1 duplication syndrome, and MBD5 variants, that affect the function of methyl-binding domain 5 (MBD5) and share a common set of neurodevelopmental, cognitive, and behavioral impairments. This review provides a comprehensive clinical and molecular synopsis of 2q23.1 deletion syndrome. Approaches to diagnosis, genetic counseling, and up-to-date management are summarized, followed by a discussion of the molecular and functional role of MBD5. Finally, we also include a brief summary of MBD5 variants that affect function of MBD5 and 2q23.1 duplication syndrome.

Untargeted metabolomics is a global molecular profiling technology that can be used to screen for inborn errors of metabolism (IEMs). Metabolite perturbations are evaluated based on current knowledge of specific metabolic pathway deficiencies, a manual diagnostic process that is qualitative, has limited scalability, and is not equipped to learn from accumulating clinical data. Our purpose was to improve upon manual diagnosis of IEMs in the clinic by developing novel computational methods for analyzing untargeted metabolomics data. We employed CTD, an automated computational diagnostic method that “ c onnects t he d ots” between metabolite perturbations observed in individual metabolomics profiling data and modules identified in disease­specific metabolite co-perturbation networks learned from prior profiling data. We also extended CTD to calculate distances between any two individuals (CTDncd) and between an individual and a disease state (CTDdm), to provide additional network-quantified predictors for use in diagnosis. We show that across 539 plasma samples, CTD-based network-quantified measures can reproduce accurate diagnosis of 16 different IEMs, including adenylosuccinase deficiency, argininemia, argininosuccinic aciduria, aromatic l -amino acid decarboxylase deficiency, cerebral creatine deficiency syndrome type 2, citrullinemia, cobalamin biosynthesis defect, GABA-transaminase deficiency, glutaric acidemia type 1, maple syrup urine disease, methylmalonic aciduria, ornithine transcarbamylase deficiency, phenylketonuria, propionic acidemia, rhizomelic chondrodysplasia punctata, and the Zellweger spectrum disorders. Our approach can be used to supplement information from biochemical pathways and has the potential to significantly enhance the interpretation of variants of uncertain significance uncovered by exome sequencing. CTD, CTDdm, and CTDncd can serve as an essential toolset for biological interpretation of untargeted metabolomics data that overcomes limitations associated with manual diagnosis to assist diagnosticians in clinical decision-making. By automating and quantifying the interpretation of perturbation patterns, CTD can improve the speed and confidence by which clinical laboratory directors make diagnostic and treatment decisions, while automatically improving performance with new case data.

We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by devising a scoring function, the Maximum Clique problem being a classic example, where S includes all nodes in G and where the score is defined by the size of the largest subset of S fully connected within G. Major practical obstacles for the plethora of algorithms addressing this type of problem include computational efficiency and, particularly for more complex scores which take edge weights into account, the computational cost of permutation testing, a statistical procedure required to obtain a bound on the p-value for a connectedness score. To address these problems, we developed CTD, “Connect the Dots”, a fast algorithm based on data compression that detects highly connected subsets within S. CTD provides information-theoretic upper bounds on p-values when S contains a small fraction of nodes in G without requiring computationally costly permutation testing. We apply the CTD algorithm to interpret multi-metabolite perturbations due to inborn errors of metabolism and multi-transcript perturbations associated with breast cancer in the context of disease-specific Gaussian Markov Random Field networks learned directly from respective molecular profiling data.

Global metabolomic profiling offers novel opportunities for the discovery of biomarkers and for the elucidation of pathogenic mechanisms that might lead to the development of novel therapies. GLUT1 deficiency syndrome (GLUT1-DS) is an inborn error of metabolism due to reduced function of glucose transporter type 1. Clinical presentation of GLUT1-DS is heterogeneous and the disorder mirrors patients with epilepsy, movement disorders, or any paroxysmal events or unexplained neurological manifestation triggered by exercise or fasting. The diagnostic biochemical hallmark of the disease is a reduced cerebrospinal fluid (CSF)/blood glucose ratio and the only available treatment is ketogenic diet. This study aimed at advancing our understanding of the biochemical perturbations in GLUT1-DS pathogenesis through biochemical phenotyping and the treatment of GLUT1-DS with a ketogenic diet. Metabolomic analysis of three CSF samples from GLUT1-DS patients not on ketogenic diet was feasible inasmuch as CSF sampling was used for diagnosis before to start with ketogenic diet. The analysis of plasma and urine samples obtained from GLUT1-DS patients treated with a ketogenic diet showed alterations in lipid and amino acid profiles. While subtle, these were consistent findings across the patients with GLUT1-DS on ketogenic diet, suggesting impacts on mitochondrial physiology. Moreover, low levels of free carnitine were present suggesting its consumption in GLUT1-DS on ketogenic diet. 3-hydroxybutyrate, 3-hydroxybutyrylcarnitine, 3-methyladipate, and N-acetylglycine were identified as potential biomarkers of GLUT1-DS on ketogenic diet. This is the first study to identify CSF, plasma, and urine metabolites associated with GLUT1-DS, as well as biochemical changes impacted by a ketogenic diet. Potential biomarkers and metabolic insights deserve further investigation.

[This corrects the article DOI: 10.1371/journal.pcbi.1008550.].

Background Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive. Utilizing systems approaches and focusing on intermediate molecular phenotypes might facilitate biologic understanding. Results The availability of exome sequencing of two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study allowed us to investigate the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites. To assess the findings, we built metabolomic causal networks for each population separately and utilized structural equation modeling. We then validated our findings with a set of independent samples. By use of methods based on concepts of Mendelian randomization of genetic variants, we showed that some of the affected metabolites are risk predictors in the causal pathway of disease. For example, LoF mutations in the gene KIAA1755 were identified to elevate the levels of eicosapentaenoate ( p -value = 5E-14), an essential fatty acid clinically identified to increase essential hypertension. We showed that this gene is in the pathway to triglycerides, where both triglycerides and essential hypertension are risk factors of metabolomic disorder and heart attack. We also identified that the gene CLDN17, harboring loss-of-function mutations, had pleiotropic actions on metabolites from amino acid and lipid pathways. Conclusion Using systems biology approaches for the analysis of metabolomics and genetic data, we integrated several biological processes, which lead to findings that may functionally connect genetic variants with complex diseases.

Background RARS2 -related mitochondrial disorder is an autosomal recessive mitochondrial encephalopathy caused by biallelic pathogenic variants in the gene encoding the mitochondrial arginyl-transfer RNA synthetase 2 ( RARS2 , MIM *611524, NM_020320.5). RARS2 catalyzes the transfer of L-arginine to its cognate tRNA during the translation of mitochondrially-encoded proteins. The classical presentation of RARS2 -related mitochondrial disorder includes pontocerebellar hypoplasia (PCH), progressive microcephaly, profound developmental delay, feeding difficulties, and hypotonia. Most patients also develop severe epilepsy by three months of age, which consists of focal or generalized seizures that frequently become pharmacoresistant and lead to developmental and epileptic encephalopathy (DEE). Case presentation Here, we describe a six-year-old boy with developmental delay, hypotonia, and failure to thrive who developed an early-onset DEE consistent with Lennox-Gastaut Syndrome (LGS), which has not previously been observed in this disorder. He had dysmorphic features including bilateral macrotia, overriding second toes, a depressed nasal bridge, retrognathia, and downslanting palpebral fissures, and he did not demonstrate progressive microcephaly. Whole genome sequencing identified two variants in RARS2 , c.36 + 1G > T, a previously unpublished variant that is predicted to affect splicing and is, therefore, likely pathogenic and c.419 T > G (p.Phe140Cys), a known pathogenic variant. He exhibited significant, progressive generalized brain atrophy and ex vacuo dilation of the supratentorial ventricular system on brain MRI and did not demonstrate PCH. Treatment with a ketogenic diet (KD) reduced seizure frequency and enabled him to make developmental progress. Plasma untargeted metabolomics analysis showed increased levels of lysophospholipid and sphingomyelin-related metabolites. Conclusions Our work expands the clinical spectrum of RARS2 -related mitochondrial disorder, demonstrating that patients can present with dysmorphic features and an absence of progressive microcephaly, which can help guide the diagnosis of this condition. Our case highlights the importance of appropriate seizure phenotyping in this condition and indicates that patients can develop LGS, for which a KD may be a viable therapeutic option. Our work further suggests that analytes of phospholipid metabolism may serve as biomarkers of mitochondrial dysfunction.

Recent advances in newborn screening (NBS) have improved the diagnosis of inborn errors of metabolism (IEMs); however, many potentially treatable IEMs are not included on NBS panels, nor are they covered in standard, first-line biochemical testing. To examine the utility of untargeted metabolomics as a primary screening tool for IEMs by comparing the diagnostic rate of clinical metabolomics with the recommended traditional metabolic screening approach. This cross-sectional study compares data from 4464 clinical samples received from 1483 unrelated families referred for trio testing of plasma amino acids, plasma acylcarnitine profiling, and urine organic acids (June 2014 to October 2018) and 2000 consecutive plasma samples from 1807 unrelated families (July 2014 to February 2019) received for clinical metabolomic screening at a College of American Pathologists and Clinical Laboratory Improvement Amendments-certified biochemical genetics laboratory. Data analysis was performed from September 2019 to August 2020. Metabolic and molecular tests performed at a genetic testing reference laboratory in the US and available clinical information for each patient were assessed to determine diagnostic rate. The diagnostic rate of traditional metabolic screening compared with clinical metabolomic profiling was assessed in the context of expanded NBS. Of 1483 cases screened by the traditional approach, 912 patients (61.5%) were male and 1465 (98.8%) were pediatric (mean [SD] age, 4.1 [6.0] years; range, 0-65 years). A total of 19 families were identified with IEMs, resulting in a 1.3% diagnostic rate. A total of 14 IEMs were detected, including 3 conditions not included in the Recommended Uniform Screening Panel for NBS. Of the 1807 unrelated families undergoing plasma metabolomic profiling, 1059 patients (58.6%) were male, and 1665 (92.1%) were pediatric (mean [SD] age, 8.1 [10.4] years; range, 0-80 years). Screening identified 128 unique cases with IEMs, giving an overall diagnostic rate of 7.1%. In total, 70 different metabolic conditions were identified, including 49 conditions not presently included on the Recommended Uniform Screening Panel for NBS. These findings suggest that untargeted metabolomics provided a 6-fold higher diagnostic yield compared with the conventional screening approach and identified a broader spectrum of IEMs. Notably, with the expansion of NBS programs, traditional metabolic testing approaches identify few disorders beyond those covered on the NBS. These data support the capability of clinical untargeted metabolomics in screening for IEMs and suggest that broader screening approaches should be considered in the initial evaluation for metabolic disorders.

MBD5 -associated neurodevelopmental disorder (MAND) is an autism spectrum disorder (ASD) characterized by intellectual disability, motor delay, speech impairment and behavioral problems; however, the biological role of methyl-CpG-binding domain 5, MBD5, in neurodevelopment and ASD remains largely undefined. Hence, we created neural progenitor cells (NPC) derived from individuals with chromosome 2q23.1 deletion and conducted RNA-seq to identify differentially expressed genes (DEGs) and the biological processes and pathways altered in MAND. Primary skin fibroblasts from three unrelated individuals with MAND and four unrelated controls were converted into induced pluripotent stem cell (iPSC) lines, followed by directed differentiation of iPSC to NPC. Transcriptome analysis of MAND NPC revealed 468 DEGs (q < 0.05), including 20 ASD-associated genes. Comparison of DEGs in MAND with SFARI syndromic autism genes revealed a striking significant overlap in biological processes commonly altered in neurodevelopmental phenotypes, with TGFβ, Hippo signaling, DNA replication, and cell cycle among the top enriched pathways. Overall, these transcriptome deviations provide potential connections to the overlapping neurocognitive and neuropsychiatric phenotypes associated with key high-risk ASD genes, including chromatin modifiers and epigenetic modulators, that play significant roles in these disease states.