Functional characterization of the RAD51D E233G genetic variant

Nadkarni, Aditi; Furda, Amy M.; Rajesh, Changanamkandath; McInnes, Campbell; Ruch, Randall J.; Pittman, Douglas L.

doi:10.1097/fpc.0b013e32831db2fd

Cited by 6 publications

(8 citation statements)

References 36 publications

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“…We note that determining the disruptive effects of nsSNPs using SNP-IN tool may not be sensitive to the cases when these mutations trigger such mechanisms indirectly. For instance, recent functional analysis of E233G mutation in RAD51L3 found a two-fold decrease in the interaction of the protein with RAD51C, compared to the wild-type [84]. The authors suggested that the mutation residue might disrupt the inter-domain interactions RAD51L3, altering protein structure and folding of the protein, which in turn affected its interaction with RAD51C.…”

Section: Resultsmentioning

confidence: 99%

Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

et al. 2014

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Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of large-scale protein-protein interaction networks, and can be useful for functional annotation of disease-associated SNPs. SNIP-IN tool is freely accessible as a web-server at http://korkinlab.org/snpintool/.

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Section: Resultsmentioning

confidence: 99%

Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

et al. 2014

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“…The frequency of the Thr/Met-E/G combined genotype was higher in cases than in controls, but the difference was borderline significant. Nadkarni et al [44] suggested that the E233G variant affects RAD51D functions and protein interactions. A larger case-control study would likely be required to determine whether this composite genotype is associated with increased familial BC risk.…”

Section: Discussionmentioning

confidence: 99%

Variants in DNA double-strand break repair genes and risk of familial breast cancer in a South American population

Jara

Dubois

Gaete

et al. 2010

Breast Cancer Res Treat

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The double-strand break (DSB) DNA repair pathway has been implicated in breast cancer (BC). RAD51 and its paralogs XRCC3 and RAD51D play an important role in the repair of DSB through homologous recombination (HR). Some polymorphisms including XRCC3-Thr241Met, RAD51-135G>C, and RAD51D-E233G have been found to confer increased BC susceptibility. In order to detect novel mutations that may contribute to BC susceptibility, 150 patients belonging to 150 Chilean BRCA1/2-negative families were screened for mutations in XRCC3. No mutations were detected in the XRCC3 gene. In addition, using a case-control design we studied the XRCC3-Thr241Met, and RAD51D-E233G polymorphisms in 267 BC cases and 500 controls to evaluate their possible association with BC susceptibility. The XRCC3 Met/Met genotype was associated with an increased BC risk (P = 0.003, OR = 2.44 [95%CI 1.34-4.43]). We did not find an association between E233G polymorphism and BC risk. We also analyzed the effect of combined genotypes among RAD51-135G>C, Thr241Met, and E233G polymorphisms on BC risk. No interaction was observed between Thr241Met and 135G>C. The combined genotype Thr/Met-E/G was associated with an increased BC risk among women who (a) have a family history of BC, (b) are BRCA1/2-negative, and (c) were <50 years at onset (n = 195) (P = 0.037, OR = 10.5 [95%CI 1.16-94.5]). Our results suggested that the variability of the DNA HR repair genes XRCC3 and RAD51D may play a role in BC risk, but this role may be underlined by a mutual interaction between these genes. These findings should be confirmed in other populations.

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“…In addition to the direct interactions, there were another 10 proteins/genes in network which indirectly interacted with HNF1B; among those, 6 proteins/genes including REL (55,56), CRCC2 (57), PMS2 (58), ZC3H11A (10), FOXA1 (59) and RAD51D (60) have been proven to be associated with drug resistance in ovarian and other cancers. For example, REL contributes directly to elevated uPA gene expression in human ovarian cancer cells (55), thereby promoting the multiple functions of uPA during tumor growth and metastasis, including drug resistance (56).…”

Section: Resultsmentioning

confidence: 99%

Downregulation of HNF1 homeobox B is associated with drug resistance in ovarian cancer

Zhang²,

Gao

et al. 2014

Oncology Reports

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Abstract. The expression of HNF1 homeobox B (HNF1B) is associated with cancer risk in several tumors, including ovarian cancer, and its decreased expression play roles in cancer development. However, the study of HNF1B and cancer is limited, and its association with drug resistance in cancer has never been reported. On the basis of array data retrieved from Oncomine and Gene Expression Omnibus (GEO) online database, we found that the mRNA expression of HNF1B in 586 ovarian serous cystadenocarcinomas and in platinum-resistant A2780 epithelial ovarian cancer cells was significantly decreased, indicating a potential role of HNF1B in drug resistance in ovarian cancer. Based on this finding, comprehensive bioinformatics analyses, including protein/ gene interaction, protein-small molecule/chemical interaction, biological process annotation, gene co-occurrence and pathway enrichment analysis and microRNA-mRNA interaction, were performed to illustrate the association of HNF1B with drug resistance in ovarian cancer. We found that among the proteins/ genes, small molecules/chemicals and microRNAs which directly interacted with HNF1B, the majority was associated with drug resistance in cancer, particularly in ovarian cancer. Biological process annotation revealed that HNF1B closely related to 24 biological processes which were all notably associated with ovarian cancer and drug resistance. These results indicated that the downregulation of HNF1B may contribute to drug resistance in ovarian cancer, via its direct interactions with these drug resistance-related proteins/genes, small molecules/chemicals and microRNAs, and via its regulations on the drug resistance-related biological processes. Pathway enrichment analysis of 36 genes which co-occurred with HNF1B, ovarian cancer and drug resistance indicated that the HNF1B may perform its drug resistance-related functions through 4 pathways including ErbB signaling, focal adhesion, apoptosis and p53 signaling. Collectively, in this study, we illustrated for the first time that HNF1B may contribute to drug resistance in ovarian cancer, potentially through the 4 pathways. The present study may pave the way for further investigation of the drug resistance-related functions of HNF1B in ovarian cancer.

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Functional characterization of the RAD51D E233G genetic variant

Cited by 6 publications

References 36 publications

Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

Variants in DNA double-strand break repair genes and risk of familial breast cancer in a South American population

Downregulation of HNF1 homeobox B is associated with drug resistance in ovarian cancer

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