Wilms' Tumor Suppressor Gene WT1

Mrowka, Christian; Schedl, Andreas

doi:10.1681/asn.v11suppl_2s106

Cited by 66 publications

(8 citation statements)

References 59 publications

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“…The choice of canonical transcript may be less clear when alternative isoforms’ expression is more evenly balanced, as in the case of WT1, a key kidney and urogenital transcription factor gene [ 69 ] covered by our benchmarking set. Exon 9 of WT1 has two isoforms, KTS+ and KTS−, named for the additional three amino acids included when the downstream donor is used [ 70 , 71 ]. In the healthy kidney, KTS+ and KTS− are expressed at a 2:1 ratio [ 72 , 73 ].…”

Section: Resultsmentioning

confidence: 99%

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

Smith,

Kitzman

2023

Genome Biol

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Background Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes. Results We benchmark eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compare experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms’ concordance with MPSA measurements, and with each other, is lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieve the best overall performance at distinguishing disruptive and neutral variants, and controlling for overall call rate genome-wide, SpliceAI and Pangolin have superior sensitivity. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues. Conclusion SpliceAI and Pangolin show the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

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Section: Resultsmentioning

confidence: 99%

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

Smith,

Kitzman

2023

Genome Biol

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“…During embryogenesis of the mammalian kidney and eye, they would be both affected by the lack of BMP‐7, exhibiting renal dysplasia and anophthalmia. Wilms’ tumor suppressor ( WT1 ) 65 could also cause aniridia, cataract, corneal clouding, and nephroblastoma (Wilms’ tumor) in children.…”

Section: The Eye/kidney Connection: From Molecular To Clinicalmentioning

confidence: 99%

Retinal image‐based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives

Wen

Liu

et al. 2023

VIEW

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Artificial intelligence (AI) has reformed the healthcare system with its compelling capabilities of processing biomedical data for disease diagnosis, prediction, and individualized management. The eye, as a non-invasive observation window for many systemic diseases, can be used to detect the signs of chronic kidney diseases, and other diseases like hypertension and type 2 diabetes mellitus, based on specific manifestations of retinal images. Recent advances using AI technology have posed a great potential of using retinal images for rapid mass screening and prognosis prediction of kidney diseases. Herein, we outlined the key applications of AI in ophthalmology and the detection of systemic diseases based on retinal imaging, especially the current progress of retinal image-based AI models for the detection and prediction of kidney diseases. We hope to shed light on the current opportunities and future challenges in this field to provide suggestions for further improvement and applications.

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“…Its disruption results in a phenotypic spectrum including isolated NS, syndromic NS with tumors and gonadal dysgenesis, differences of sexual development, Wilms Tumor and leukemia. 1 WT1 undergoes alternative splicing, including in exon 9 at a pair of donors which result in protein isoforms that differ by the three amino acids, KTS. These isoforms have overlapping and district functional roles: both act as transcription factors 2 but with partially different sequence motif specificities and gene targets.…”

Section: Introductionmentioning

confidence: 99%

High-throughput splicing assays identify known and novelWT1exon 9 variants in nephrotic syndrome

Smith

Burugula

Dunn

et al. 2023

Preprint

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Frasier Syndrome (FS) is a rare Mendelian form of nephrotic syndrome caused by variants which disrupt the proper splicing of WT1. This key transcription factor gene is alternatively spliced at exon 9 to produce two isoforms ("KTS+" and "KTS-"), which are normally expressed in the kidney at a ~2:1 (KTS+:KTS-) ratio. FS results from variants that reduce this ratio by disrupting the splice donor of the KTS+ isoform. FS is extremely rare, and it is unclear whether any variants beyond the eight already known could cause FS. To prospectively identify other splicing-disruptive variants, we leveraged a massively parallel splicing assay. We tested every possible single nucleotide variant (n=519) in and around WT1 exon 9 for effects upon exon inclusion and KTS+/- ratio. Splice disruptive variants made up 11% of the tested point variants overall, and were tightly concentrated near the canonical acceptor and the KTS+/- alternate donors. Our map successfully identified all eight known FS or focal segmental glomerulosclerosis variants and 16 additional novel variants which were comparably disruptive to these known pathogenic variants. We also identified 19 variants that, conversely, increased the KTS+/KTS- ratio, of which two are observed in unrelated individuals with 46,XX ovotesticular disorder of sex development (46,XX OTDSD). This splicing effect map can serve as functional evidence to guide the clinical interpretation of newly observed variants in and around WT1 exon 9.

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Wilms' Tumor Suppressor Gene WT1

Cited by 66 publications

References 59 publications

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

Retinal image‐based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives

High-throughput splicing assays identify known and novelWT1exon 9 variants in nephrotic syndrome

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