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Record W7161960130 · doi:10.82308/32288

The role of claudin sequence variants in the formation of kidney stones

2021· dissertation· en· W7161960130 on OpenAlexaboutno aff
Yuan Zhuang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNeuroscience
TopicBarrier Structure and Function Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClaudinParacellular transportKidneyTight junctionNephronIn silicoGeneKidney stonesAllele

Abstract

fetched live from OpenAlex

It is known that genetic risk factors contribute to the formation of calcium-based kidney stones. The majority of calcium is reabsorbed via paracellular transport through tight junctions along the epithelium of the nephron. Claudins are a family of integral membrane proteins that are expressed in tight junctions. Claudins determine the selectivity and permeability of different nephron segments. Variants of several CLDN genes have been found to be associated with kidney stones. Therefore, I hypothesize that sequence variants in claudin genes that regulate paracellular transport of calcium in the nephron will be associated with the formation of kidney stones.Ninety adult patients (45 male; 45 female) with recurrent calcium-based kidney stones were recruited from one urologist’s kidney stone clinic. The majority of the patients self-identified as Canadian-European for their ethnicity. Most of the patients presented with their first kidney stone at less than 40 years of age. Sixteen non-synonymous heterozygous variants were identified by next generation sequencing, and 13 were confirmed by Sanger sequencing. Of those variants, 4 are novel and 9 are rare (Minor Allele Frequency (MAF)<1%). In silico prediction software was used to predict the impact of the amino acid change on the protein structure and function. The novel variants were: CLDN11 S157F, CLDN16 K29E, CLDN17 A94V, and CLDN18 H212D. Based on the in silico results, CLDN11 S157F and CLDN17 A94V are predicted to be pathogenic. The rare variants are CLDN4 A82T, CLDN4 A113T, CLDN6 P211T, CLDN7 V55I, CLDN8 A94V, CLDN8 M97T, CLDN12 M98V, CLDN23 A90T, and CLDN24 V97I. Among these, CLDN4 A82T and CLDN8 A94V are predicted to be deleterious. The majority of the claudin variants are located in the second transmembrane domain of the claudin protein. The rest of the variants are located in different domains. CLDN7 V55I is located in the first extracellular loop, and CLDN11 S157F is located in the second extracellular loop. Finally, CLDN6 P211T and CLDN18 H212D are both located in the C-terminal tail.To evaluate the functional consequences of the claudin sequence variants, the claudin variants were generated by site-directed mutagenesis and cloned into a mammalian expression vector, pEGFP. Claudin variants and the corresponding wildtype (WT) sequences were transiently and/or stably transfected into HEK293 and MDCK II cells to determine if the protein encoded by the variant was able to co-localize to tight junctions. CLDN4 A82T, CLDN4 A113T, and CLDN8 A94V and their corresponding WT sequences were transiently transfected into HEK293 cells. Immunofluorescence (IF) showed that CLDN4 A82T and A113T proteins co-localize with ZO-1, a tight junction marker, like the CLDN4 WT protein. In contrast, the mutant protein CLDN8 A94V was unable to localize to tight junctions and exhibited submembranous expression in transiently transfected HEK293 cells. IF results of stably transfected MDCK cells that express CLDN4 WT and A82T showed that CLDN4 WT and A82T were able to localize to the tight junction. However, there was more cytoplasmic expression noted in MDCK II cells transfected with CLDN4 A82T. Measurement of transepithelial electrical resistance (TEER) of stably transfected MDCK II stable cells showed that CLDN4 A82T had no impact on the integrity and permeability of tight junctions. Similar future approaches including IF, TEER measurements, use of dextrans and assessment of relative paracellular calcium flux will be conducted to understand how claudin variants contribute to kidney stone formation

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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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.026
GPT teacher head0.286
Teacher spread0.259 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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