Jungsoo Gim

Diagnostic Application of Targeted Resequencing for Familial Nonsyndromic Hearing Loss

Identification of causative genes for hereditary nonsyndromic hearing loss (NSHL) is important to decide treatment modalities and to counsel the patients. Due to the genetic heterogeneity in sensorineural genetic disorders, the high-throughput method can be adapted for the efficient diagnosis. To this end, we designed a new diagnostic pipeline to screen all the reported candidate genes for NSHL. For validation of the diagnostic pipeline, we focused upon familial NSHL cases that are most likely to be genetic, rather than to be infectious or environmental. Among the 32 familial NSHL cases, we were able to make a molecular genetic diagnosis from 12 probands (37.5%) in the first stage by their clinical features, characteristic inheritance pattern and further candidate gene sequencing of GJB2, SLC26A4, POU3F4 or mitochondrial DNA. Next we applied targeted resequencing on 80 NSHL genes in the remaining 20 probands. Each proband carried 4.8 variants that were not synonymous and had the occurring frequency of less than three among the 20 probands. These variants were then filtered out with the inheritance pattern of the family, allele frequency in normal hearing 80 control subjects, clinical features. Finally NSHL-causing candidate mutations were identified in 13(65%) of the 20 probands of multiplex families, bringing the total solve rate (or detection rate) in our familial cases to be 78.1% (25/32) Damaging mutations discovered by the targeted resequencing were distributed in nine genes such as WFS1, COCH, EYA4, MYO6, GJB3, COL11A2, OTOF, STRC and MYO3A, most of which were private. Despite the advent of whole genome and whole exome sequencing, we propose targeted resequencing and filtering strategy as a screening and diagnostic tool at least for familial NSHL to find mutations based upon its efficacy and cost-effectiveness.

Multiphasic analysis of whole exome sequencing data identifies a novel mutation of ACTG1 in a nonsyndromic hearing loss family

BackgroundThe genetic heterogeneity of sensorineural hearing loss is a major hurdle to the efficient discovery of disease-causing genes. We designed a multiphasic analysis of copy number variation (CNV), linkage, and single nucleotide variation (SNV) of whole exome sequencing (WES) data for the efficient discovery of mutations causing nonsyndromic hearing loss (NSHL).ResultsFrom WES data, we identified five distinct CNV loci from a NSHL family, but they were not co-segregated among patients. Linkage analysis based on SNVs identified six candidate loci (logarithm of odds [LOD] >1.5). We selected 15 SNVs that co-segregated with NSHL in the family, which were located in six linkage candidate loci. Finally, the novel variant p.M305T in ACTG1 (DFNA20/26) was selected as a disease-causing variant.ConclusionsHere, we present a multiphasic CNV, linkage, and SNV analysis of WES data for the identification of a candidate mutation causing NSHL. Our stepwise, multiphasic approach enabled us to expedite the discovery of disease-causing variants from a large number of patient variants.

Enhancing inhibitory synaptic function reverses spatial memory deficits in Shank2 mutant mice

ABSTRACT Autism spectrum disorders (ASDs) are a group of developmental disorders that cause variable and heterogeneous phenotypes across three behavioral domains such as atypical social behavior, disrupted communications, and highly restricted and repetitive behaviors. In addition to these core symptoms, other neurological abnormalities are associated with ASD, including intellectual disability (ID). However, the molecular etiology underlying these behavioral heterogeneities in ASD is unclear. Mutations in SHANK2 genes are associated with ASD and ID. Interestingly, two lines of Shank2 knockout mice (e6–7 KO and e7 KO) showed shared and distinct phenotypes. Here, we found that the expression levels of Gabra2, as well as of GABA receptor‐mediated inhibitory neurotransmission, are reduced in Shank2 e6–7, but not in e7 KO mice compared with their own wild type littermates. Furthermore, treatment of Shank2 e6–7 KO mice with an allosteric modulator for the GABAA receptor reverses spatial memory deficits, indicating that reduced inhibitory neurotransmission may cause memory deficits in Shank2 e6–7 KO mice. This article is part of the Special Issue entitled ‘Ionotropic glutamate receptors’. HIGHLIGHTSExpression of Gabra2 gene coding GABAA receptor &agr;2 subunit was reduced in Shank2 e6–7 KO mice, but not in Shank2 e7 KO mice.GABAA receptor‐mediated synaptic transmission was impaired in Shank2 e6–7 KO mice, resulting in reduced I/E ratio.A GABAA receptor &agr;2 agonist L838,417 restored the impaired I/E ratio in Shank2 e6–7 KO mice.L838,417 treatment reversed spatial memory deficits in Shank2 e6–7 KO mice without affecting the social deficit.

Definition of Smad3 Phosphorylation Events That Affect Malignant and Metastatic Behaviors in Breast Cancer Cells

Smad3, a major intracellular mediator of TGFβ signaling, functions as both a positive and negative regulator in carcinogenesis. In response to TGFβ, the TGFβ receptor phosphorylates serine residues at the Smad3 C-tail. Cancer cells often contain high levels of the MAPK and CDK activities, which can lead to the Smad3 linker region becoming highly phosphorylated. Here, we report, for the first time, that mutation of the Smad3 linker phosphorylation sites markedly inhibited primary tumor growth, but significantly increased lung metastasis of breast cancer cell lines. In contrast, mutation of the Smad3 C-tail phosphorylation sites had the opposite effect. We show that mutation of the Smad3 linker phosphorylation sites greatly intensifies all TGFβ-induced responses, including growth arrest, apoptosis, reduction in the size of putative cancer stem cell population, epithelial-mesenchymal transition, and invasive activity. Moreover, all TGFβ responses were completely lost on mutation of the Smad3 C-tail phosphorylation sites. Our results demonstrate a critical role of the counterbalance between the Smad3 C-tail and linker phosphorylation in tumorigenesis and metastasis. Our findings have important implications for therapeutic intervention of breast cancer.

DRAK2 Participates in a Negative Feedback Loop to Control TGF-β/Smads Signaling by Binding to Type I TGF-β Receptor

TGF-β1 is a multifunctional cytokine that mediates diverse biological processes. However, the mechanisms by which the intracellular signals of TGF-β1 are terminated are not well understood. Here, we demonstrate that DRAK2 serves as a TGF-β1-inducible antagonist of TGF-β signaling. TGF-β1 stimulation rapidly induces DRAK2 expression and enhances endogenous interaction of the type I TGF-β receptor with DRAK2, thereby blocking R-Smads recruitment. Depletion of DRAK2 expression markedly augmented the intensity and the extent of TGF-β1 responses. Furthermore, a high level of DRAK2 expression was observed in basal-like and HER2-enriched breast tumors and cell lines, and depletion of DRAK2 expression suppressed the tumorigenic ability of breast cancer cells. Thus, these studies define a function for DRAK2 as an intrinsic intracellular antagonist participating in the negative feedback loop to control TGF-β1 responses, and aberrant expression of DRAK2 increases tumorigenic potential, in part, through the inhibition of TGF-β1 tumor suppressor activity.

Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.

Family‐based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family‐based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family‐based designs. In this report, we describe one such implementation: the multivariate family‐based rare variant association tool (mFARVAT). mFARVAT is a quasi‐likelihood‐based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows.

Predicting multi-class responses to preoperative chemoradiotherapy in rectal cancer patients

BackgroundPreoperative chemoradiotherapy (CRT) has become a widely used treatment for improving local control of disease and increasing survival rates of rectal cancer patients. We aimed to identify a set of genes that can be used to predict responses to CRT in patients with rectal cancer.MethodsGene expression profiles of pre-therapeutic biopsy specimens obtained from 77 rectal cancer patients were analyzed using DNA microarrays. The response to CRT was determined using the Dworak tumor regression grade: grade 1 (minimal, MI), grade 2 (moderate, MO), grade 3 (near total, NT), or grade 4 (total, TO).ResultsTop ranked genes for three different feature scores such as a p-value (pval), a rank product (rank), and a normalized product (norm) were selected to distinguish pre-defined groups such as complete responders (TO) from the MI, MO, and NT groups. Among five different classification algorithms, supporting vector machine (SVM) with the top 65 norm features performed at the highest accuracy for predicting MI using a 5-fold cross validation strategy. On the other hand, 98 pval features were selected for predicting TO by elastic net (EN). Finally we combined TO- and MI-finder models to build a three-class classification model and validated it using an independent dataset of rectal cancer mRNA expression.ConclusionsWe identified MI- and TO-finders for predicting preoperative CRT responses, and validated these data using an independent public dataset. This stepwise prediction model requires further evaluation in clinical studies in order to develop personalized preoperative CRT in patients with rectal cancer.

Validation of a Modified Child-Turcotte-Pugh Classification System Utilizing Insulin-Like Growth Factor-1 for Patients with Hepatocellular Carcinoma in an HBV Endemic Area.

Background Recently, a modified insulin-like growth factor-1 (IGF)–Child-Turcotte-Pugh (CTP) classification was proposed to improve the original CTP classification. This study aimed to validate the new IGF-CTP classification system as a prognostic maker for patients with hepatocellular carcinoma (HCC) in a hepatitis B virus endemic area. Methods We conducted a post-hoc analysis of a prospective cohort study. We used Harrell’s C-index and U-statistics to compare the prognostic performance of both IGF-CTP and CTP classifications for overall survival. We evaluated the relationship between HCC stage and the four components of the IGF-CTP classification (serum levels of IGF-1, albumin, and total bilirubin and prothrombin time [PT]) using nonparametric trend analysis. Results We included a total of 393 patients in this study. In all, 55 patients died during the median follow-up of 59.1 months. There was a difference between IGF-CTP class and CTP class in 14% of patients. Overall, the IGF-CTP classification system had a higher prognostic value (C-index = 0.604, 95% confidence interval [CI] = 0.539–0.668) than the CTP system (C-index = 0.558, 95% CI = 0.501–0.614), but the difference was not statistically significant (P = .07 by U-statistics). A lower serum level of IGF-1 was related to a more advanced cancer stage (P < .01). The remaining components of the IGF-CTP classification were not significantly related to tumor stage (P = .11 for total bilirubin; P = .33 for albumin; and P = .39 for PT). Conclusions The IGF-CTP classification was slightly better than the original CTP classification for predicting survival of patients with HCC in a chronic hepatitis B endemic area. This is most likely due to the fact that serum IGF-1 levels reflect underlying HCC status.

Targeted Proteomics Predicts a Sustained Complete-Response after Transarterial Chemoembolization and Clinical Outcomes in Patients with Hepatocellular Carcinoma: A Prospective Cohort Study

This study was aimed to identify blood-based biomarkers to predict a sustained complete response (CR) after transarterial chemoembolization (TACE) using targeted proteomics. Consecutive patients with HCC who had undergone TACE were prospectively enrolled (training (n = 100) and validation set (n = 80)). Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). In the training set, the MRM-MS assay identified five marker candidate proteins (LRG1, APCS, BCHE, C7, and FCN3). When this five-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Furthermore, the ensemble model was an independent predictor of rapid progression (hazard ratio (HR), 2.889; 95% confidence interval (CI), 1.612-5.178; P value < 0.001) and overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024-3.848; P value = 0.042) in the entire population by multivariate analysis. Targeted proteomics-based ensemble model can predict clinical outcomes after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.

LPEseq: Local-Pooled-Error Test for RNA Sequencing Experiments with a Small Number of Replicates.

RNA-Sequencing (RNA-Seq) provides valuable information for characterizing the molecular nature of the cells, in particular, identification of differentially expressed transcripts on a genome-wide scale. Unfortunately, cost and limited specimen availability often lead to studies with small sample sizes, and hypothesis testing on differential expression between classes with a small number of samples is generally limited. The problem is especially challenging when only one sample per each class exists. In this case, only a few methods among many that have been developed are applicable for identifying differentially expressed transcripts. Thus, the aim of this study was to develop a method able to accurately test differential expression with a limited number of samples, in particular non-replicated samples. We propose a local-pooled-error method for RNA-Seq data (LPEseq) to account for non-replicated samples in the analysis of differential expression. Our LPEseq method extends the existing LPE method, which was proposed for microarray data, to allow examination of non-replicated RNA-Seq experiments. We demonstrated the validity of the LPEseq method using both real and simulated datasets. By comparing the results obtained using the LPEseq method with those obtained from other methods, we found that the LPEseq method outperformed the others for non-replicated datasets, and showed a similar performance with replicated samples; LPEseq consistently showed high true discovery rate while not increasing the rate of false positives regardless of the number of samples. Our proposed LPEseq method can be effectively used to conduct differential expression analysis as a preliminary design step or for investigation of a rare specimen, for which a limited number of samples is available.