Genome-wide association analysis on normal hearing function identifies PCDH20 and SLC28A3 as candidates for hearing function and loss

Hearing loss and individual differences in normal hearing both have a substantial genetic basis. Although many new genes contributing to deafness have been identified, very little is known about genes/variants modulating the normal range of hearing ability. To fill this gap, we performed a two-stage meta-analysis on hearing thresholds (tested at 0.25, 0.5, 1, 2, 4, 8 kHz) and on pure-tone averages (low-, medium- and high-frequency thresholds grouped) in several isolated populations from Italy and Central Asia (total N = 2636). Here, we detected two genome-wide significant loci close to PCDH20 and SLC28A3 (top hits: rs78043697, P = 4.71E-10 and rs7032430, P = 2.39E-09, respectively). For both loci, we sought replication in two independent cohorts: B58C from the UK (N = 5892) and FITSA from Finland (N = 270). Both loci were successfully replicated at a nominal level of significance (P < 0.05). In order to confirm our quantitative findings, we carried out RT-PCR and reported RNA-Seq data, which showed that both genes are expressed in mouse inner ear, especially in hair cells, further suggesting them as good candidates for modulatory genes in the auditory system. Sequencing data revealed no functional variants in the coding region of PCDH20 or SLC28A3, suggesting that variation in regulatory sequences may affect expression. Overall, these results contribute to a better understanding of the complex mechanisms underlying human hearing function

A novel INDEL mutation in the EDA gene resulting in a distinct X- linked hypohidrotic ectodermal dysplasia phenotype in an Italian family

A novel INDEL mutation in theEDA gene resulting in a distinctX- linked hypohidroticectoder mal dysplasia phenotypein an Italian familyEditorX-Linked Hypohidrotic Ectodermal Dysplasia (XL-HED; MIM305100) is characterized by hypodontia, misshaped teeth, hypo-hidrosis, sparse hair, peculiar facial features,1,2and occurs in lessthan 1 in every 100.000 individuals.1XL-HED is caused bymutations in the Ectodysplasin-A (EDA) gene located at Xq12-q13 with more than 100 causative mutations reported todate.1,3,4The identification of disease-causing mutations con-firms the diagnosis, however, does not automatically imply agenotype\u2013phenotype correlation

Systematic analysis of factors that improve homologous direct repair (HDR) efficiency in CRISPR/Cas9 technique

The CRISPR/Cas9 bacterial system has proven to be an powerful tool for genetic manipulation in several organisms, but the efficiency of sequence replacement by homologous direct repair (HDR) is substantially lower than random indel creation. Many studies focused on improving HDR efficiency using double sgRNA, cell synchronization cycle, and the delivery of single-stranded oligo DNA nucleotides (ssODN) with a rational design. In this study, we evaluate these three methods' synergistic effects to improve HDR efficiency. For our tests, we have chosen the TNF\u3b1 gene (NM_000594) for its crucial role in various biological processes and diseases. For the first time, our results showed how the use of two sgRNA with asymmetric donor design and triple transfection events dramatically increase the HDR efficiency from an undetectable HDR event to 39% of HDR efficiency and provide a new strategy to facilitate CRISPR/Cas9-mediated human genome editing. Besides, we demonstrated that the TNF\u3b1 locus could be edited with CRISPR/Cas9 methodology, an opportunity to safely correct, in the future, the specific mutations of each patient

A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets

The paper presents an overview of an analytics framework for predictive maintenance service boosted by Machine Learning and asset knowledge, applied to turbomachinery assets. Optimization of the maintenance scenario is performed through a risk model that assesses online health status and probability of failure, by detecting functional anomalies or aging phenomena and evaluating their impact on asset serviceability. Turbomachinery domain knowledge is used to create physics-based models, to configure a severity assessment layer and to properly map maintenance actions to anomaly types. The implemented analytics framework is able also to fore-cast engine behaviour over the future in order to optimize asset operation and maintenance, minimizing downtime and residual risk. Predictive cap-abilities are optimized thanks to the hybrid approach, where physics-based knowledge empowers long term prediction accuracy while data-driven analytics ensure fast-events prognostics. Accuracy of the hybrid approach improves maintenance optimization, allowing activities to be planned properly and in early advance with respect to outage execution