Biallelic Cys141Tyr variant of <i>SEL1L</i> is associated with neurodevelopmental disorders, agammaglobulinemia, and premature death

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Abstract

Suppressor of lin-12-like–HMG-CoA reductase degradation 1 (SEL1L-HRD1) ER-associated degradation (ERAD) plays a critical role in many physiological processes in mice, including immunity, water homeostasis, and energy metabolism; however, its relevance and importance in humans remain unclear, as no disease variant has been identified. Here, we report a biallelic SEL1L variant (p. Cys141Tyr) in 5 patients from a consanguineous Slovakian family. These patients presented with not only ERAD-associated neurodevelopmental disorders with onset in infancy (ENDI) syndromes, but infantile-onset agammaglobulinemia with no mature B cells, resulting in frequent infections and early death. This variant disrupted the formation of a disulfide bond in the luminal fibronectin II domain of SEL1L, largely abolishing the function of the SEL1L-HRD1 ERAD complex in part via proteasomal-mediated self destruction by HRD1. This study reports a disease entity termed ENDI-agammaglobulinemia (ENDI-A) syndrome and establishes an inverse correlation between SEL1L-HRD1 ERAD functionality and disease severity in humans.

Abstract

A biallelic SEL1L C141Y variant causes premature death in 5 patients with early-onset neurodevelopmental disorders and agammaglobulinemia (ENDI-A) due to severe SEL1L-HRD1 ER-associated degradation dysfunction.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

Sue Richards, Nazneen Aziz, Sherri Bale … (2015)

The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.

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Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

S Altschul (1997)

The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.

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Author and article information

Contributors

Denisa Weis:

ORCID: http://orcid.org/0000-0001-8711-3750

Zexin Jason Li

Peter Ciznar:

ORCID: http://orcid.org/0000-0003-0566-1644

Alexander Leiss-Piller

Zhihong Wang:

ORCID: http://orcid.org/0000-0002-5836-8073

Xiaoqiong Wei:

ORCID: http://orcid.org/0000-0001-7576-4820

Serge Weis:

ORCID: http://orcid.org/0000-0001-6750-2599

Katarina Skalicka

Gabriela Hrckova

Lubos Danisovic

Andrea Soltysova

Ling Qi:

ORCID: http://orcid.org/0000-0001-8229-0184

Journal

Journal ID (nlm-ta): J Clin Invest

Journal ID (iso-abbrev): J Clin Invest

Journal ID (publisher-id): J Clin Invest

Title: The Journal of Clinical Investigation

Publisher: American Society for Clinical Investigation

ISSN (Print): 0021-9738

ISSN (Electronic): 1558-8238

Publication date (Electronic, pub): 16 January 2024

Publication date (Electronic, collection): 16 January 2024

Publication date PMC-release: 16 January 2024

Volume: 134

Issue: 2

Electronic Location Identifier: e170882

Affiliations

[1 ]Department of Medical Genetics, Kepler University Hospital, School of Medicine, Johannes Kepler University, Linz, Austria.

[2 ]Department of Pediatrics, Faculty of Medicine, Comenius University Bratislava and National Institute of Children’s Diseases, Bratislava, Slovakia.

[3 ]Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA.

[4 ]Department of Molecular & Integrative Physiology and

[5 ]Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, Michigan, USA.

[6 ]Department of Pediatric Neurology, Faculty of Medicine, Comenius University Bratislava and National Institute of Children’s Diseases, Bratislava, Slovakia.

[7 ]Immunology Outpatient Clinic, Vienna, Austria.

[8 ]Sigmund Freud Private University–Medical School, Vienna, Austria.

[9 ]Division of Neuropathology, Neuromed Campus, Department of Pathology and Molecular Pathology, Kepler University Hospital, Johannes Kepler University, Linz, Austria.

[10 ]Institute of Medical Biology, Genetics and Clinical Genetics, Faculty of Medicine, and

[11 ]Faculty of Natural Sciences, Department of Molecular Biology, Comenius University, Bratislava, Slovakia.

[12 ]Institute for Clinical and Translational Research, Biomedical Research Centre, Slovak Academy of Sciences, Bratislava, Slovakia.

[13 ]University Children’s Hospital, Salzburger Landeskliniken Universitätsklinikum (SALK) and Paracelsus Medical University (PMU), Salzburg, Austria.

Author notes

Address correspondence to: Ling Qi, Department of Molecular Physiology and Biological Physics, University of Virginia, Room 223, Snyder Bldg., 480 Ray C. Hunt Drive, Charlottesville, Virginia 22903, USA. Phone: 1.734.417.5447; Email: xvr2hm@ 123456virginia.edu . Or to: Denisa Weis, Department of Medical Genetics, Med Camp IV, Kepler University Hospital, Krankenhausstraße 26-30, 4020 Linz, Austria. Phone: 0043.5.7680.84.29605; Email: denisa.weis@ 123456kepleruniklinikum.at . Or to: Johannes A. Mayr, University Children’s Hospital, Salzburger Landeskliniken Universitätsklinikum (SALK) and Paracelsus Medical University (PMU), Muellner Hauptstrasse 48, 5020 Salzburg, Austria. Phone: 43.0.5.7255.58546; Email: h.mayr@ 123456salk.at .

Authorship note: DW, LLL, and HHW contributed equally to this work. RGF, JAM, and LQ are co–senior authors.

Author information

Denisa Weis http://orcid.org/0000-0001-8711-3750

Peter Ciznar http://orcid.org/0000-0003-0566-1644

Zhihong Wang http://orcid.org/0000-0002-5836-8073

Xiaoqiong Wei http://orcid.org/0000-0001-7576-4820

Serge Weis http://orcid.org/0000-0001-6750-2599

Ling Qi http://orcid.org/0000-0001-8229-0184

Article

Publisher ID: 170882

DOI: 10.1172/JCI170882

PMC ID: 10786703

PubMed ID: 37943617

SO-VID: 054ac7ac-1db9-4180-ad02-6ab52d58cd66

License:

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 29 March 2023

Date accepted : 8 November 2023

Funding

Funded by: National Institutes of Health, https://doi.org/10.13039/100000002;

Award ID: 1R35GM130292

Funded by: Austrian Science Fund, https://doi.org/10.13039/501100002428;

Award ID: I4695-B

Funded by: National Ataxia Foundation

Award ID: 91803,1036307

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Biallelic Cys141Tyr variant of SEL1L is associated with neurodevelopmental disorders, agammaglobulinemia, and premature death

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Most cited references 50

Highly accurate protein structure prediction with AlphaFold

Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

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