Publications by authors named "S H Elsea"

156 Publications

Acute Strokelike Presentation and Long-term Evolution of Diffusion Restriction Pattern in Ethylmalonic Encephalopathy.

J Child Neurol 2021 Apr 26:8830738211006507. Epub 2021 Apr 26.

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.

Ethylmalonic encephalopathy is a rare autosomal recessive mitochondrial disorder caused by pathogenic biallelic variants in the gene. The phenotype of this disease has been attributed to deficiency in the mitochondrial sulfur dioxygenase leading to many downstream effects. Ethylmalonic encephalopathy classically presents with developmental regression, petechiae, acrocyanosis, and chronic diarrhea. The neurologic phenotype includes hypotonia, spastic diplegia, ataxia, and developmental delay. As more patients with this condition are described, the neurologic phenotype continues to expand. Although strokelike episodes or metabolic strokes have been studied in other mitochondrial disorders, they have not been thoroughly reported in this disorder. Herein, we describe 3 patients with ethylmalonic encephalopathy who presented clinically with strokelike episodes and strokelike abnormalities on brain magnetic resonance imaging in the setting of acute illness, and the long-term sequelae with evolution into cystic changes in one of these subjects.
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http://dx.doi.org/10.1177/08830738211006507DOI Listing
April 2021

Missense substitutions at a conserved 14-3-3 binding site in HDAC4 cause a novel intellectual disability syndrome.

HGG Adv 2021 Jan 14;2(1):100015. Epub 2021 Jan 14.

Exeter Genomics Laboratory, Royal Devon and Exeter NHS Foundation Trust, Exeter EX2 5DW, UK.

Histone deacetylases play crucial roles in the regulation of chromatin structure and gene expression in the eukaryotic cell, and disruption of their activity causes a wide range of developmental disorders in humans. Loss-of-function alleles of , a founding member of the class IIa deacetylases, have been reported in brachydactyly-mental retardation syndrome (BDMR). However, while disruption of HDAC4 activity and deregulation of its downstream targets may contribute to the BDMR phenotype, loss of HDAC4 function usually occurs as part of larger deletions of chromosome 2q37; BDMR is also known as chromosome 2q37 deletion syndrome, and the precise role of HDAC4 within the phenotype remains uncertain. Thus, identification of missense variants should shed new light on the role of HDAC4 in normal development. Here, we report seven unrelated individuals with a phenotype distinct from that of BDMR, all of whom have heterozygous missense variants that affect a major regulatory site of HDAC4, required for signal-dependent 14-3-3 binding and nucleocytoplasmic shuttling. Two individuals possess variants altering Thr244 or Glu247, whereas the remaining five all carry variants altering Pro248, a key residue for 14-3-3 binding. We propose that the variants in all seven individuals impair 14-3-3 binding (as confirmed for the first two variants by immunoprecipitation assays), thereby identifying deregulation of HDAC4 as a pathological mechanism in a previously uncharacterized developmental disorder.
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http://dx.doi.org/10.1016/j.xhgg.2020.100015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841527PMC
January 2021

CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.

PLoS Comput Biol 2021 01 29;17(1):e1008550. Epub 2021 Jan 29.

Quantitative and Computational Biosciences Program, Baylor College of Medicine, Houston, Texas, United States of America.

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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http://dx.doi.org/10.1371/journal.pcbi.1008550DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875364PMC
January 2021

A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data.

Bioinformatics 2020 12;36(Suppl_2):i787-i794

Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.

Motivation: Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Computational approaches, such as metabolic network simulations and machine learning, could significantly help to exploit metabolomic data to aid the diagnostic process. While the former suffers from limited predictive accuracy, the latter is normally able to generalize only to IEMs for which sufficient data are available. Here, we propose a hybrid approach that exploits the best of both worlds by building a mapping between simulated and real metabolic data through a novel method based on Siamese neural networks (SNN).

Results: The proposed SNN model is able to perform disease prioritization for the metabolic profiles of IEM patients even for diseases that it was not trained to identify. To the best of our knowledge, this has not been attempted before. The developed model is able to significantly outperform a baseline model that relies on metabolic simulations only. The prioritization performances demonstrate the feasibility of the method, suggesting that the integration of metabolic models and data could significantly aid the IEM diagnosis process in the near future.

Availability And Implementation: Metabolic datasets used in this study are publicly available from the cited sources. The original data produced in this study, including the trained models and the simulated metabolic profiles, are also publicly available (Messa et al., 2020).
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http://dx.doi.org/10.1093/bioinformatics/btaa841DOI Listing
December 2020

Sickle cell disease in Grenada: Quality of life and barriers to care.

Mol Genet Genomic Med 2021 01 17;9(1):e1567. Epub 2020 Dec 17.

School of Allied Health, Baylor College of Medicine, Houston, TX, USA.

Background: Grenada is a small, resource-limited Caribbean country with a high incidence of sickle cell disease (SCD). Since little is known about the challenges facing individuals living with SCD in the West Indies, we sought to assess barriers to healthcare and the impact of SCD on quality of life in Grenada.

Methods: Both adults aged 18+ (n = 19) and caregivers of children aged 2-17 (n = 26) completed validated survey measures regarding barriers to care and quality of life, along with a genetics knowledge questionnaire. Caregivers also completed a caregiver burden scale. Survey scores were calculated, and responses were analyzed for an association between demographic variables.

Results: The Barriers to Care Questionnaire, in which lower scores indicate more barriers, revealed that both adults (mean = 69.9) and children (mean = 75.5) with SCD experienced reduced access to care. The Adult Sickle Cell Quality of Life Measurement Information System indicated increased depression and loneliness in adults, with the lowest scores in the Emotional subscale. However, the Pediatric Quality of Life Inventory answered by caregivers of children with SCD showed the lowest scores in the Physical Functioning subscale. Further analysis using the Caregiver Burden Scale-Zarit Burden Interview revealed that 53.8% of caregivers of children with SCD indicated "little to no burden," which may reflect a difference in cultural expectations of a caregiver between high-income countries and Grenada. Finally, ~80% of respondents knew that SCD was a genetic condition; however, 61%-84% could not correctly indicate recurrence risks, demonstrating a need for additional education.

Conclusion: These data provide new insights regarding the experience of living with SCD in Grenada and support the need for further investigations into specific barriers to healthcare delivery, which could also improve education and well-being for those affected by SCD in Grenada and in the broader Caribbean community.
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http://dx.doi.org/10.1002/mgg3.1567DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7963427PMC
January 2021