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Record W2803716386 · doi:10.1038/s41467-018-04193-w

Mutations in six nephrosis genes delineate a pathogenic pathway amenable to treatment

2018· article· en· W2803716386 on OpenAlexaff
Shazia Ashraf, Hiroki Kudo, Jia Rao, Atsuo Kikuchi, Eugen Widmeier, Jennifer A. Lawson, Weizhen Tan, Tobias Hermle, Jillian K. Warejko, Shirlee Shril, Merlin Airik, Tilman Jobst‐Schwan, Svjetlana Lovric, Daniela A. Braun, Heon Yung Gee, David Schapiro, Amar J. Majmundar, Carolin E. Sadowski, Werner L. Pabst, Ankana Daga, Amelie T. van der Ven, Johanna Magdalena Schmidt, Boon Chuan Low, Anjali Bansal Gupta, Brajendra K. Tripathi, Jenny Wong, Kirk N. Campbell, Kay Metcalfe, Denny Schanze, Tetsuya Niihori, Hiroshi Kaito, Kandai Nozu, Hiroyasu Tsukaguchi, Ryojiro Tanaka, Kiyoshi Hamahira, Yasuko Kobayashi, Takumi Takizawa, Ryo Funayama, Keiko Nakayama, Yoko Aoki, Naonori Kumagai, Kazumoto Iijima, Henry Fehrenbach, Jameela A. Kari, Sherif El Desoky, Sawsan M. Jalalah, Radovan Bogdanović, Nataša Stajić, H. Zappel, Assel Rakhmetova, Sharon-Rose Wassmer, Therese Jungraithmayr, J. Strehlau, Aravind Selvin Kumar, Arvind Bagga, Neveen A. Soliman, Shrikant Mane, Lewis Kaufman, Douglas R. Lowy, Mohamad Aman Jairajpuri, Richard P. Lifton, York Pei, Martin Zenker, Shigeo Kure, Friedhelm Hildebrandt

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesScience and Engineering Research BoardHoward Hughes Medical InstituteDeutsche Akademie der Naturforscher Leopoldina - Nationale Akademie der WissenschaftenYale UniversityNational Research Foundation of KoreaYonsei University College of MedicineDepartment of Science and Technology, Ministry of Science and Technology, IndiaDeutsche ForschungsgemeinschaftNational Research FoundationJapan Agency for Medical Research and DevelopmentASN Foundation for Kidney ResearchNational Institutes of HealthYonsei University
KeywordsRHOAGene knockdownGuanine nucleotide exchange factorCDC42GTPaseMutantGenePhenotypeNephrosisNephrotic syndromeBiologyGeneticsCell biologySignal transductionEndocrinology

Abstract

fetched live from OpenAlex

No efficient treatment exists for nephrotic syndrome (NS), a frequent cause of chronic kidney disease. Here we show mutations in six different genes (MAGI2, TNS2, DLC1, CDK20, ITSN1, ITSN2) as causing NS in 17 families with partially treatment-sensitive NS (pTSNS). These proteins interact and we delineate their roles in Rho-like small GTPase (RLSG) activity, and demonstrate deficiency for mutants of pTSNS patients. We find that CDK20 regulates DLC1. Knockdown of MAGI2, DLC1, or CDK20 in cultured podocytes reduces migration rate. Treatment with dexamethasone abolishes RhoA activation by knockdown of DLC1 or CDK20 indicating that steroid treatment in patients with pTSNS and mutations in these genes is mediated by this RLSG module. Furthermore, we discover ITSN1 and ITSN2 as podocytic guanine nucleotide exchange factors for Cdc42. We generate Itsn2-L knockout mice that recapitulate the mild NS phenotype. We, thus, define a functional network of RhoA regulation, thereby revealing potential therapeutic targets.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.028
GPT teacher head0.329
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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

Citations126
Published2018
Admission routes1
Has abstractyes

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