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Record W4282546381 · doi:10.1186/s13073-022-01064-4

Gain and loss of TASK3 channel function and its regulation by novel variation cause KCNK9 imprinting syndrome

2022· article· en· W4282546381 on OpenAlexaff
Margot A. Cousin, Emma L. Veale, Nikita R. Dsouza, Swarnendu Tripathi, Robyn G. Holden, Maria Arélin, Geoffrey Beek, Mir Reza Bekheirnia, Jasmin Beygo, Vikas Bhambhani, Martin G. Bialer, Stefania Bigoni, Cyrus Boelman, Jenny Carmichael, Thomas Courtin, Benjamin Cogné, Ivana Dabaj, Diane Doummar, Laura Fazilleau, Alessandra Ferlini, Ralitza H. Gavrilova, John M. Graham, Tobias B. Haack, Jane Juusola, Sarina G. Kant, Saima Kayani, Boris Keren, Petra Ketteler, Chiara Klöckner, Tamara T. Koopmann, Teresa Kruisselbrink, Alma Kuechler, Laëtitia Lambert, Xénia Latypova, Robert Roger Lebel, Magalie S. Leduc, Emanuela Leonardi, Andrea M. Lewis, Wendy Liew, Keren Machol, Samir Mardini, Kirsty McWalter, Cyril Mignot, Julie C. McLaughlin, Alessandra Murgia, Vinodh Narayanan, Caroline Nava, Sonja Neuser, Mathilde Nizon, Davide Ognibene, Joohyun Park, Konrad Platzer, Céline Poirsier, Maximilian Radtke, Keri Ramsey, Cassandra Runke, María J. Guillen Sacoto, Fernando Scaglia, Marwan Shinawi, Stephanie Spranger, Ee Shien Tan, John Taylor, Anne-Sophie Trentesaux, Filippo Pinto e Vairo, Rebecca Willaert, Neda Zadeh, Raúl Urrutia, Dusica Babovic‐Vuksanovic, Michael T. Zimmermann, Alistair Mathie, Eric W. Klee

Bibliographic record

VenueGenome Medicine · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Syndromes and Imprinting
Canadian institutionsBC Children's Hospital
FundersCenter for Individualized Medicine, Mayo ClinicDeutsche Forschungsgemeinschaft
KeywordsLoss functionPhenotypeImprinting (psychology)BiologyGeneticsGenomic imprintingIntellectual disabilityBioinformaticsNeuroscienceGeneComputational biologyDNA methylationGene expression

Abstract

fetched live from OpenAlex

BACKGROUND: Genomics enables individualized diagnosis and treatment, but large challenges remain to functionally interpret rare variants. To date, only one causative variant has been described for KCNK9 imprinting syndrome (KIS). The genotypic and phenotypic spectrum of KIS has yet to be described and the precise mechanism of disease fully understood. METHODS: This study discovers mechanisms underlying KCNK9 imprinting syndrome (KIS) by describing 15 novel KCNK9 alterations from 47 KIS-affected individuals. We use clinical genetics and computer-assisted facial phenotyping to describe the phenotypic spectrum of KIS. We then interrogate the functional effects of the variants in the encoded TASK3 channel using sequence-based analysis, 3D molecular mechanic and dynamic protein modeling, and in vitro electrophysiological and functional methodologies. RESULTS: We describe the broader genetic and phenotypic variability for KIS in a cohort of individuals identifying an additional mutational hotspot at p.Arg131 and demonstrating the common features of this neurodevelopmental disorder to include motor and speech delay, intellectual disability, early feeding difficulties, muscular hypotonia, behavioral abnormalities, and dysmorphic features. The computational protein modeling and in vitro electrophysiological studies discover variability of the impact of KCNK9 variants on TASK3 channel function identifying variants causing gain and others causing loss of conductance. The most consistent functional impact of KCNK9 genetic variants, however, was altered channel regulation. CONCLUSIONS: This study extends our understanding of KIS mechanisms demonstrating its complex etiology including gain and loss of channel function and consistent loss of channel regulation. These data are rapidly applicable to diagnostic strategies, as KIS is not identifiable from clinical features alone and thus should be molecularly diagnosed. Furthermore, our data suggests unique therapeutic strategies may be needed to address the specific functional consequences of KCNK9 variation on channel function and regulation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.216
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2022
Admission routes1
Has abstractyes

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