A novel PIK3CD C896T mutation detected in bilateral sudden sensorineural hearing loss using next generation sequencing: An indication of primary immunodeficiency

Multiple genetic variants involved in both autoimmunity and autoinflammation detected in Chinese patients with sporadic Meniere's disease: a preliminary study

Background

The mechanisms of Meniere's disease (MD) remain largely unknown. The purpose of this study was to identify possible genetic variants associated with immune regulation in MD.

Methods

The whole immune genome of 16 Chinese patients diagnosed with sporadic MD was sequenced using next-generation sequencing.

Results

Definite pathological variants of MEFV (c.1223G>A, c.1105C>T), COL7A1 (c.5287C>T), and ADA (c.445C>T) contributing to the clinical phenotype were found in three patients. Limited and likely pathological variants of TLR3 (c.2228G>A) and RAB27A (c.560G>A) were detected in one patient each. The following definite pathological variants impairing the structure and function of translated proteins were detected in 10 patients, and multigene variants occurred in five patients: PRF1 (c.710C>A), UNC13D (c.1228A>C), COLEC11 (c.169C>T), RAG2 (c.200G>C), BLM (c.1937G>T), RNF31 (c.2533G>A), FAT4 (c.11498A>G), PEPD (c.788A>G), TNFSF12 (c.470G>A), VPS13B (c.11972A>T), TNFRSF13B (c.226G>A), ERCC6L2 (c.4613A>G), TLR3 (c.2228G>A), ADA (c.445C>T), PEPD (c.151G>A), and MOGS (c.2470G>A). The following limited pathological variants impairing the structure and function of translated proteins were detected in five patients, with double gene variants identified in one patient: EXTL3 (c.1396G>A), MTHFD1 (c.2057G>A), FANCA (c.2039T>C), LPIN2 (c.1814C>T), NBAS (c.4049T>C), and FCN3 (c.734G>A).

Conclusion

Patients with sporadic MD carry multiple genetic variants involved in multiple steps of immune regulation, which might render patients susceptible to developing inflammation via both autoimmune and autoinflammation mechanisms upon internal stress.

Transcriptome-Guided Identification of Drugs for Repurposing to Treat Age-Related Hearing Loss

Age-related hearing loss (ARHL) or presbycusis is a prevalent condition associated with social isolation, cognitive impairment, and dementia. Age-related changes in the cochlea, the auditory portion of the inner ear, are the primary cause of ARHL. Unfortunately, there are currently no pharmaceutical approaches to treat ARHL. To examine the biological processes underlying age-related changes in the cochlea and identify candidate drugs for rapid repurposing to treat ARHL, we utilized bulk RNA sequencing to obtain transcriptomes from the functional substructures of the cochlea—the sensorineural structures, including the organ of Corti and spiral ganglion neurons (OC/SGN) and the stria vascularis and spiral ligament (SV/SL)—in young (6-week-old) and old (2-year-old) C57BL/6 mice. Transcriptomic analyses revealed both overlapping and unique patterns of gene expression and gene enrichment between substructures and with ageing. Based on these age-related transcriptional changes, we queried the protein products of genes differentially expressed with ageing in DrugBank and identified 27 FDA/EMA-approved drugs that are suitable to be repurposed to treat ARHL. These drugs target the protein products of genes that are differentially expressed with ageing uniquely in either the OC/SGN or SV/SL and that interrelate diverse biological processes. Further transcriptomic analyses revealed that most genes differentially expressed with ageing in both substructures encode protein products that are promising drug target candidates but are, nevertheless, not yet linked to approved drugs. Thus, with this study, we apply a novel approach to characterize the druggable genetic landscape for ARHL and propose a list of drugs to test in pre-clinical studies as potential treatment options for ARHL.

Genetic Polymorphisms in Sudden Sensorineural Hearing Loss: An Update

OBJECTIVE

To investigate the association between genetic polymorphisms and sudden sensorineural hearing loss (SSNHL). Most of the SSNHL cases still remain idiopathic, and several etiopathogenetic hypotheses, including a genetic predisposition, have been proposed.

METHODS

A literature review was conducted using different databases: Medline/PubMed, EMBASE, and CINAHL, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. All databases have been searched from May 2016 to April 2020.

RESULTS

Genetic susceptibility could represent a key element in the pathogenesis of SSNHL. A number of genetic polymorphisms related to (1) inner ear microvascular disease and endothelial dysfunction and (2) to inner ear oxidative stress and inflammation have been addressed in the current literature.

CONCLUSIONS

The potential identification of a genetic profile related to SSNHL could provide a more accurate prognostic evidence of idiopathic SSNHL (ISSNHL), offering to the patients not only early-prevention strategies but eventually information on various inheritance modalities.

Autoinflammatory characteristics and short-term effects of delivering high-dose steroids to the surface of the intact endolymphatic sac and incus in refractory Ménière's disease

Objective

To investigate immune-related genetic background in intractable Meniere’s disease (MD) and the immediate results of a novel therapy by delivering steroids to the surface of the intact endolymphatic sac (ES) and incus in a sustainable manner.

Case report and methods

Candidate genes involved in immune regulation were sequenced using a next-generation sequencing method in a patient with intractable MD. Mutations were confirmed using the Sanger sequencing method. The ES was exposed, and gelatin sponge particles were immersed in high-dose methylprednisolone solution and placed onto the surface of ES. “L”-shaped gelatin sponge strips were immersed in dexamethasone solution and served as a guiding device for the steroids by touching the incus and gelatin sponge particles on the surface of the ES. Gelatin sponge particles immersed in dexamethasone solution were placed around the gelatin sponge strips and sealed using fibrin glue.

Results

Autoinflammation in the refractory MD case was indicated by genotype, including novel heterozygous mutations of PRF1, UNC13D, SLC29A3, ITCH, and JAK3, as well as phenotype. The vertigo was fully relieved immediately after operation. Tinnitus and aural fullness were resolved 3 weeks after operation, whereas hearing improved in 2 mon postoperation. No recurrence was noted during the 5-monfollow-up, and the final MRI supported the novel therapeutic hypothesis.

Conclusion

Autoinflammation was involved in a refractory MD. This novel therapy, which involves the delivery of steroids to the surface of the intact ES and incus, is effective in relieving vertigo and tinnitus and improves hearing function of refractory MD.

Enhancing the prediction of disease–gene associations with multimodal deep learning

Motivation

Computationally predicting disease genes helps scientists optimize the in-depth experimental validation and accelerates the identification of real disease-associated genes. Modern high-throughput technologies have generated a vast amount of omics data, and integrating them is expected to improve the accuracy of computational prediction. As an integrative model, multimodal deep belief net (DBN) can capture cross-modality features from heterogeneous datasets to model a complex system. Studies have shown its power in image classification and tumor subtype prediction. However, multimodal DBN has not been used in predicting disease–gene associations.

Results

In this study, we propose a method to predict disease–gene associations by multimodal DBN (dgMDL). Specifically, latent representations of protein-protein interaction networks and gene ontology terms are first learned by two DBNs independently. Then, a joint DBN is used to learn cross-modality representations from the two sub-models by taking the concatenation of their obtained latent representations as the multimodal input. Finally, disease–gene associations are predicted with the learned cross-modality representations. The proposed method is compared with two state-of-the-art algorithms in terms of 5-fold cross-validation on a set of curated disease–gene associations. dgMDL achieves an AUC of 0.969 which is superior to the competing algorithms. Further analysis of the top-10 unknown disease–gene pairs also demonstrates the ability of dgMDL in predicting new disease–gene associations.

Availability and implementation

Prediction results and a reference implementation of dgMDL in Python is available on https://github.com/luoping1004/dgMDL.

Supplementary information

Supplementary data are available at Bioinformatics online.