Abstract Number: PB1884
Meeting: ISTH 2020 Congress
Background: Upshaw-Schulman syndrome (USS) is a disorder due to deficiency of ADAMTS13 activity and accounts for approximately 2-4% of cases. Its mode of inheritance is autosomal recessive.
Aims: To show relationship between phenotype-genotype versus in-silico prediction tools in 16 patients with clinical and laboratory phenotype of USS and with 18 disease-causing variants (DCV) (11 novel and 7 previously reported).
Methods: The 29 exons and intron-exon boundaries of the ADAMTS13 were amplified by PCR and sequenced (Sanger method). Eighteen DCV were found; 9 were novel (N). A total of 14 in-silico prediction tools were used to evaluate to deleterious effect (PolyPhen-2; SIFT; Panter; Mutation-taster; Meta-SNP; CADD; SNP&GO; FATHMM; PhD-SNP; Provean; BDGP; HSF; ASSP and I-Mutant to evaluate the stability of the protein).11 available for missense DCV and only 3 for nonsense, 4 for frameshift and 4 for splicing site. Informed consent and institutional ethical approval was obtained from the patient.
Results: Following DCV were described:
Missense DCV: p.Asn146Tyr; p.Arg193Trp; p.Arg692Cys; p.Arg409Trp; p.Ala893Pro; p.Arg1060Trp; p.His1077Pro; p.Asp1240Tyr; p.Asp1362Val.
Frameshift DCV: c.774_775insccgcgcc (p.Gly259Profs*133); c.794_803del (p.Arg267Cysfs*2); c.2321del (p.Gly774Alafs*4); c.4050del (p.Glu1351Argfs*9).
Splice site DCV: c.539+1G>A; c.987+5G>A; c.2610+5G>C.
Nonsense DCV: p.Tyr617*; p.Arg910*.
The analysis of in-silico prediction results according to the DCV is shown in table 1 and table 2.
Conclusions: Using variants of known pathogenicity, we assessed a variety of predictive algorithms in ADAMTS13. Optimum predictions were found by different tools in all nonsense and splicing sites DCV, and in most of missense and frameshift DCV. A good relationship between phenotype-genotype versus in-silico prediction was achieved.
In the context of individual DCV, the alignment quality and score are important in achieving more accurate predictions of pathogenicity.
These methods demonstrated themselves to be useful; however, in a punctual case they were not comparable with each possibility due to the difference in information and algorithms they used for predicting deleteriousness.
| DCV | Domain affected | Percentage of pathogenicity | |
| p.Asn146Tyr | novel | Metalloprotease | Deleterious:11; (100%) |
| p.Arg193Trp | known | Metalloprotease | Deletereius:11; (100% |
| p.Arg409Trp | known | TSP1-1 | Deleterious:10; (90.9%) |
| p.Arg692Cys | known | TSP1-2 | Deletereius:8; (72.7%) |
| p.Ala893Pro | novel | TSP1-3 | Deleterious:3; (27.3%) |
| p.Arg1060Trp | known | TSP1-7 | Deleterious:8; (72.7%) |
| p.His1077Pro | novel | TSP1-7 | Deleterious:7; (63.6%) |
| p.Asp1240Tyr | novel | CUB-2 | Deleterious:7; (63.6%) |
| p.Asp1362Val | known | CUB-2 | Deleterious:8; (81.8%) |
[Table 1. In-silico prediction study: domain affected and percentage of pathogenicity of missense DCV in USS.]
| DCV | Domain affected | Percentage of pathogenicity | |
| p.Gly259Profs*133 | known | Metalloprotease | Deleterious:3; (75%) |
| p.Arg267Cysfs*2 | novel | Metalloprotease | Deleterious:4; (100%) |
| p.Gly774Alafs*4 | novel | TSP1-3 | Deleterious:3; (75%) |
| p.Glu1351Argfs*9 | novel | CUB-2 | Deleterious:4; (100%) |
| c.539+1G>A | novel | Metalloprotease | Deleterious:4; (100%) |
| c.987+5G>A | novel | Disintegrin | Deleterious:4; (100%) |
| c.2610+5G>C | novel | TSP1-4 | Deleterious:4; (100%) |
| p.Tyr617* | novel | spacer | Deleterious:3; (100%) |
| p.Arg910* | known | TPS1-5 | Deleterious:3; (100%) |
[Table 2. In-silico prediction study: domain affected and percentage of pathogenicity of frameshift, splicing site, non-sense DCV in USS]
To cite this abstract in AMA style:
Paiva J, Woods AI, Kinen A, Casinelli MM, Sanchez-Luceros A. Correlation between Phenotype-genotype versus in-silico Prediction of Novel and Known Disease-causing Variants in Upshaw-Schulman Syndrome Patients in a Single Institution of Argentina [abstract]. Res Pract Thromb Haemost. 2020; 4 (Suppl 1). https://abstracts.isth.org/abstract/correlation-between-phenotype-genotype-versus-in-silico-prediction-of-novel-and-known-disease-causing-variants-in-upshaw-schulman-syndrome-patients-in-a-single-institution-of-argentina/. Accessed January 27, 2022.« Back to ISTH 2020 Congress
ISTH Congress Abstracts - https://abstracts.isth.org/abstract/correlation-between-phenotype-genotype-versus-in-silico-prediction-of-novel-and-known-disease-causing-variants-in-upshaw-schulman-syndrome-patients-in-a-single-institution-of-argentina/