Functional consequences of Genetics variant in TMC1 and TMC2 within a United Arab Emirates family with Pre-lingual hearing loss

Hearing loss (HL) is the most prevalent sensory disorder whose etiology comes from environmental and/or genetic factors. Approximately 60 % of HL cases are due to mutations in genes responsible for maintaining a normal hearing function. Despite the monogenic inheritance of hereditary hearing loss (HHL), its diagnosis is challenging as both clinical and genetic heterogeneity characterizes it. Through the development of next-generation sequencing (NGS) techniques, the number of identified mutations responsible for HHL has increased exponentially during the last decade. Mutations in the TMC1 have been reported in several patients with nonsyndromic hereditary hearing loss (NSHHL), more precisely in cases with an autosomal recessive inheritance pattern. In this study, we conducted whole-exome sequencing (WES) analysis of a United Arabs Emirates (UAE) family with autosomal recessive nonsyndromic hearing loss (ARNSHL). This analysis revealed segregation of the TMC1 missense mutation c.596A > T (p.Asn199Ile) with the disease. Bioinformatics analysis supported the pathogenic effect of this mutation and predicted its impact at the proteomics level. Molecular docking analysis of TMC2WT, TMC2R123K, TMC2Q205R, and TMC2R123K + Q205R. Finally, protein docking results suggest a role for TMC2 variants in the phenotypic variability observed within the investigated family.

Modeling incomplete penetrance in arrhythmogenic cardiomyopathy by human induced pluripotent stem cell derived cardiomyocytes

Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) are commonly used to model arrhythmogenic cardiomyopathy (ACM), a heritable cardiac disease characterized by severe ventricular arrhythmias, fibrofatty myocardial replacement and progressive ventricular dysfunction. Although ACM is inherited as an autosomal dominant disease, incomplete penetrance and variable expressivity are extremely common, resulting in different clinical manifestations. Here, we propose hiPSC-CMs as a powerful in vitro model to study incomplete penetrance in ACM. Six hiPSC lines were generated from blood samples of three ACM patients carrying a heterozygous deletion of exon 4 in the PKP2 gene, two asymptomatic (ASY) carriers of the same mutation and one healthy control (CTR), all belonging to the same family. Whole exome sequencing was performed in all family members and hiPSC-CMs were examined by ddPCR, western blot, Wes™ immunoassay system, patch clamp, immunofluorescence and RNASeq. Our results show molecular and functional differences between ACM and ASY hiPSC-CMs, including a higher amount of mutated PKP2 mRNA, a lower expression of the connexin-43 protein, a lower overall density of sodium current, a higher intracellular lipid accumulation and sarcomere disorganization in ACM compared to ASY hiPSC-CMs. Differentially expressed genes were also found, supporting a predisposition for a fatty phenotype in ACM hiPSC-CMs. These data indicate that hiPSC-CMs are a suitable model to study incomplete penetrance in ACM.

Exonic variants in multiple myeloma patients associated with relapsed/ refractory and response to bortezomib regimens

Novel treatment in multiple myeloma represented by proteasome inhibitors, immunomodulatory drugs and monoclonal antibodies have produced a deep response. However, relapses are possible, and all classes of drugs are refractory to patients. Next-generation sequencing has improved our understanding of the multiple myeloma genome related to drug resistance and has discovered many genomic variants. Therefore, this study was conducted to investigate new variants associated with drug resistance in MM patients who relapsed and refractory to bortezomib regimen and daratumumab treatment using next-generation sequencing for whole-exome sequencing. Peripheral blood samples were collected in EDTA tubes from six patients; four were in relapsed and refractory to bortezomib regimens and daratumumab; two patients responded to bortezomib regimens. Whole-exome sequencing was performed by the MGI-DNBSEQ-G400 instrument. We identified 21 variants in multiple myeloma patients. Seventeen variants were found in relapsed and refractory multiple myeloma in 11 genes (GNAQ, PMS1, CREB1, NSUNS2, PIK3CG, ROS1, PMS2, FIT4, KDM5A, STK11 and ZFHX3). And four variants were identified in two patients with response to bortezomib regimens in 4 genes (RAF1, CREB1, ZFHX3 and INSR). We have observed several genetic variants in many genes that may have been associated with the poor prognosis and poor response to treatment in these patients. These values should be further confirmed in large sample studies using the RNA-seq technique to identify genome expression.

Characteristics of miRNA-SNPs in healthy Japanese subjects and non-small cell lung cancer, colorectal cancer, and soft tissue sarcoma patients

Single nucleotide polymorphisms in genes encoding microRNAs (miRNA-SNPs) may affect the maturation steps of miRNAs or target mRNA recognition, leading to changes in the expression of target mRNAs to cause gain- or loss-of-function changes. Several miRNA-SNPs are known to be associated with the risk of diseases such as cancer. The purpose of this study was to comprehensively determine the miRNA-SNPs in Japanese individuals to evaluate the differences in allele frequencies between ethnicities by comparing data from the global population in the 1000 Genomes Project and differences between healthy subjects and cancer patients. We performed next-generation sequencing targeting genes encoding 1809 pre-miRNAs. As a result, 403 miRNA-SNPs (146 miRNA-SNPs per subject on average) were identified in 28 healthy Japanese subjects. We observed significant differences in the allele frequencies between ethnicities in 33 of the 403 miRNA-SNPs. The numbers of miRNA-SNPs per subject in 44 non-small cell lung cancer (NSCLC), 33 colorectal cancer (CRC), and 15 soft tissue sarcoma (STS) patients were almost equal to those in healthy subjects. Significant differences in allele frequencies were observed for 14, 11, and 9 miRNA-SNPs in NSCLC, CRC, and STS patients compared with the frequencies in healthy subjects, suggesting that these SNPs can be biomarkers of risk for each type of cancer assessed. In summary, we comprehensively characterized miRNA-SNPs in Japanese individuals and found differences in allele frequencies of several miRNA-SNPs between ethnicities and between healthy subjects and cancer patients. Studies investigating a larger number of subjects should be performed to confirm the potential of miRNA-SNPs as biomarkers of cancer risk.

Applications and analysis of targeted genomic sequencing in cancer studies

Next Generation Sequencing (NGS) has dramatically improved the flexibility and outcomes of cancer research and clinical trials, providing highly sensitive and accurate high-throughput platforms for large-scale genomic testing. In contrast to whole-genome (WGS) or whole-exome sequencing (WES), targeted genomic sequencing (TS) focuses on a panel of genes or targets known to have strong associations with pathogenesis of disease and/or clinical relevance, offering greater sequencing depth with reduced costs and data burden. This allows targeted sequencing to identify low frequency variants in targeted regions with high confidence, thus suitable for profiling low-quality and fragmented clinical DNA samples. As a result, TS has been widely used in clinical research and trials for patient stratification and the development of targeted therapeutics. However, its transition to routine clinical use has been slow. Many technical and analytical obstacles still remain and need to be discussed and addressed before large-scale and cross-centre implementation. Gold-standard and state-of-the-art procedures and pipelines are urgently needed to accelerate this transition. In this review we first present how TS is conducted in cancer research, including various target enrichment platforms, the construction of target panels, and selected research and clinical studies utilising TS to profile clinical samples. We then present a generalised analytical workflow for TS data discussing important parameters and filters in detail, aiming to provide the best practices of TS usage and analyses.