Browsing by Author "Elsea, Sarah H."

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    Biallelic variants in COX4I1 associated with a novel phenotype resembling Leigh syndrome with developmental regression, intellectual disability, and seizures
    (Wiley, 2019-10) Pillai, Nishitha R.; AlDhaheri, Noura S.; Gosh, Rajashri; Lim, Jaehyung; Streff, Haley; Nayak, Anuranjita; Graham, Brett H.; Hanchard, Neil; Elsea, Sarah H.; Scaglia, Fernando; Medical and Molecular Genetics, School of Medicine
    Autosomal recessive COX4I1 deficiency has been previously reported in a single individual with a homozygous pathogenic variant in COX4I1, who presented with short stature, poor weight gain, dysmorphic features, and features of Fanconi anemia. COX4I1 encodes subunit 4, isoform 1 of cytochrome c oxidase. Cytochrome c oxidase is a respiratory chain enzyme that plays an important role in mitochondrial electron transport and reduces molecular oxygen to water leading to the formation of ATP. Defective production of cytochrome c oxidase leads to a variable phenotypic spectrum ranging from isolated myopathy to Leigh syndrome. Here, we describe two siblings, born to consanguineous parents, who presented with encephalopathy, developmental regression, hypotonia, pathognomonic brain imaging findings resembling Leigh‐syndrome, and a novel homozygous variant on COX4I1, expanding the known clinical phenotype associated with pathogenic variants in COX4I1.
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    Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data
    (Springer Nature, 2022-04-21) Thistlethwaite, Lillian R.; Li, Xiqi; Burrage, Lindsay C.; Riehle, Kevin; Hacia, Joseph G.; Braverman, Nancy; Wangler, Michael F.; Miller, Marcus J.; Elsea, Sarah H.; Milosavljevic, Aleksandar; Medical and Molecular Genetics, School of Medicine
    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 "connects the dots" between metabolite perturbations observed in individual metabolomics profiling data and modules identified in disease-specific 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.
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    Correction to: De novo and inherited TCF20 pathogenic variants are associated with intellectual disability, dysmorphic features, hypotonia, and neurological impairments with similarities to Smith-Magenis syndrome
    (Biomed Central, 2019-03-25) Vetrini, Francesco; McKee, Shane; Rosenfeld, Jill A.; Suri, Mohnish; Lewis, Andrea M.; Nugent, Kimberly Margaret; Roeder, Elizabeth; Littlejohn, Rebecca O.; Holder, Sue; Zhu, Wenmiao; Alaimo, Joseph T.; Graham, Brett; Harris, Jill M.; Gibson, James B.; Pastore, Matthew; McBride, Kim L.; Komara, Makanko; Al-Gazali, Lihadh; Al Shamsi, Aisha; Fanning, Elizabeth A.; Wierenga, Klaas J.; Scott, Daryl A.; Ben-Neriah, Ziva; Meiner, Vardiella; Cassuto, Hanoch; Elpeleg, Orly; Lloyd Holder Jr, J.; Burrage, Lindsay C.; Seaver, Laurie H.; Van Maldergem, Lionel; Mahida, Sonal; Soul, Janet S.; Marlatt, Margaret; Matyakhina, Ludmila; Vogt, Julie; Gold, June-Anne; Park, Soo-Mi; Varghese, Vinod; Lampe, Anne K.; Kumar, Ajith; Lees, Melissa; Holder-Espinasse, Muriel; McConnell, Vivienne; Bernhard, Birgitta; Blair, Ed; Harrison, Victoria; Muzny, Donna M.; Gibbs, Richard A.; Elsea, Sarah H.; Posey, Jennifer E.; Bi, Weimin; Lalani, Seema; Xia, Fan; Yang, Yaping; Eng, Christine M.; Lupski, James R.; Liu, Pengfei; Medical and Molecular Genetics, School of Medicine
    It was highlighted that the original article [1] contained a typographical error in the Results section. Subject 17 was incorrectly cited as Subject 1. This Correction article shows the revised statement. The original article has been updated.
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    CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models
    (PLOS, 2021-01) Thistlethwaite, Lillian R.; Petrosyan, Varduhi; Li, Xiqi; Miller, Marcus J.; Elsea, Sarah H.; Milosavljevic, Aleksandar; Medical and Molecular Genetics, School of Medicine
    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.
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    De novo and inherited TCF20 pathogenic variants are associated with intellectual disability, dysmorphic features, hypotonia, and neurological impairments with similarities to Smith-Magenis syndrome
    (BMC, 2019-02-28) Vetrini, Francesco; McKee, Shane; Rosenfeld, Jill A.; Suri, Mohnish; Lewis, Andrea M.; Nugent, Kimberly Margaret; Roeder, Elizabeth; Littlejohn, Rebecca O.; Holder, Sue; Zhu, Wenmiao; Alaimo, Joseph T.; Graham, Brett; Harris, Jill M.; Gibson, James B.; Pastore, Matthew; McBride, Kim L.; Komara, Makanko; Al-Gazali, Lihadh; Al Shamsi, Aisha; Fanning, Elizabeth A.; Wierenga, Klaas J.; Scott, Daryl A.; Ben-Neriah, Ziva; Meiner, Vardiella; Cassuto, Hanoch; Elpeleg, Orly; Holder, J. Lloyd, Jr.; Burrage, Lindsay C.; Seaver, Laurie H.; Van Maldergem, Lionel; Mahida, Sonal; Soul, Janet S.; Marlatt, Margaret; Matyakhina, Ludmila; Vogt, Julie; Gold, June-Anne; Park, Soo-Mi; Varghese, Vinod; Lampe, Anne K.; Kumar, Ajith; Lees, Melissa; Holder-Espinasse, Muriel; McConnell, Vivienne; Bernhard, Birgitta; Blair, Ed; Harrison, Victoria; The DDD study; Muzny, Donna M.; Gibbs, Richard A.; Elsea, Sarah H.; Posey, Jennifer E.; Bi, Weimin; Lalani, Seema; Xia, Fan; Yang, Yaping; Eng, Christine M.; Lupski, James R.; Liu, Pengfei; Medical and Molecular Genetics, School of Medicine
    BACKGROUND: Neurodevelopmental disorders are genetically and phenotypically heterogeneous encompassing developmental delay (DD), intellectual disability (ID), autism spectrum disorders (ASDs), structural brain abnormalities, and neurological manifestations with variants in a large number of genes (hundreds) associated. To date, a few de novo mutations potentially disrupting TCF20 function in patients with ID, ASD, and hypotonia have been reported. TCF20 encodes a transcriptional co-regulator structurally related to RAI1, the dosage-sensitive gene responsible for Smith-Magenis syndrome (deletion/haploinsufficiency) and Potocki-Lupski syndrome (duplication/triplosensitivity). METHODS: Genome-wide analyses by exome sequencing (ES) and chromosomal microarray analysis (CMA) identified individuals with heterozygous, likely damaging, loss-of-function alleles in TCF20. We implemented further molecular and clinical analyses to determine the inheritance of the pathogenic variant alleles and studied the spectrum of phenotypes. RESULTS: We report 25 unique inactivating single nucleotide variants/indels (1 missense, 1 canonical splice-site variant, 18 frameshift, and 5 nonsense) and 4 deletions of TCF20. The pathogenic variants were detected in 32 patients and 4 affected parents from 31 unrelated families. Among cases with available parental samples, the variants were de novo in 20 instances and inherited from 4 symptomatic parents in 5, including in one set of monozygotic twins. Two pathogenic loss-of-function variants were recurrent in unrelated families. Patients presented with a phenotype characterized by developmental delay, intellectual disability, hypotonia, variable dysmorphic features, movement disorders, and sleep disturbances. CONCLUSIONS: TCF20 pathogenic variants are associated with a novel syndrome manifesting clinical characteristics similar to those observed in Smith-Magenis syndrome. Together with previously described cases, the clinical entity of TCF20-associated neurodevelopmental disorders (TAND) emerges from a genotype-driven perspective.
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    Untargeted Metabolomics of Slc13a5 Deficiency Reveal Critical Liver–Brain Axis for Lipid Homeostasis
    (MDPI, 2022-04-14) Milosavljevic, Sofia; Glinton, Kevin E.; Li, Xiqi; Medeiros, Cláudia; Gillespie, Patrick; Seavitt, John R.; Graham, Brett H.; Elsea, Sarah H.; Medical and Molecular Genetics, School of Medicine
    Though biallelic variants in SLC13A5 are known to cause severe encephalopathy, the mechanism of this disease is poorly understood. SLC13A5 protein deficiency reduces citrate transport into the cell. Downstream abnormalities in fatty acid synthesis and energy generation have been described, though biochemical signs of these perturbations are inconsistent across SLC13A5 deficiency patients. To investigate SLC13A5-related disorders, we performed untargeted metabolic analyses on the liver, brain, and serum from a Slc13a5-deficient mouse model. Metabolomic data were analyzed using the connect-the-dots (CTD) methodology and were compared to plasma and CSF metabolomics from SLC13A5-deficient patients. Mice homozygous for the Slc13a5tm1b/tm1b null allele had perturbations in fatty acids, bile acids, and energy metabolites in all tissues examined. Further analyses demonstrated that for several of these molecules, the ratio of their relative tissue concentrations differed widely in the knockout mouse, suggesting that deficiency of Slc13a5 impacts the biosynthesis and flux of metabolites between tissues. Similar findings were observed in patient biofluids, indicating altered transport and/or flux of molecules involved in energy, fatty acid, nucleotide, and bile acid metabolism. Deficiency of SLC13A5 likely causes a broader state of metabolic dysregulation than previously recognized, particularly regarding lipid synthesis, storage, and metabolism, supporting SLC13A5 deficiency as a lipid disorder.