Background: Primary ciliary dyskinesia (PCD) is a rare recessive hereditary disorder characterized by dysmotility to immotility of ciliated and flagellated structures. Its main symptoms are respiratory, caused by defective ciliary beating in the epithelium of the upper airways (nose, bronchi and paranasal sinuses). Impairing the drainage of inhaled microorganisms and particles leads to recurrent infections and pulmonary complications. To date, 5 genes encoding 3 dynein protein arm subunits (DNAI1, DNAH5 and DNAH11), the kinase TXNDC3 and the X-linked RPGR have been found to be mutated in PCD. Objectives: We proposed to determine the impact of the DNAI1 gene on a cohort of unrelated PCD patients (n = 104) recruited without any phenotypic preselection. Methods: We used denaturing high-performance liquid chromatography and sequencing to screen for mutations in the coding and splicing site sequences of the gene DNAI1. Results: Three mutations were identified: a novel missense variant (p.Glu174Lys) was found in 1 patient and 2 previously reported variants were identified (p.Trp568Ser in 1 patient and IVS1+2_3insT in 3 patients). Overall, mutations on both alleles of gene DNAI1 were identified in only 2% of our clinically heterogeneous cohort of patients. Conclusion: We conclude that DNAI1 gene mutation is not a common cause of PCD, and that major or several additional disease gene(s) still remain to be identified before a sensitive molecular diagnostic test can be developed for PCD.

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Background: SARS-CoV-2 genotyping has been instrumental to monitor virus evolution and transmission during the pandemic. The reliability of the information extracted from the genotyping efforts depends on a number of aspects, including the quality of the input material, applied technology and potential laboratory-specific biases. These variables must be monitored to ensure genotype reliability. The current lack of guidelines for SARS-CoV-2 genotyping leads to inclusion of error-containing genome sequences in studies of viral spread and evolution. Results: We used clinical samples and synthetic viral genomes to evaluate the impact of experimental factors, including viral load and sequencing depth, on correct sequence determination using an amplicon-based approach. We found that at least 1000 viral genomes are necessary to confidently detect variants in the genome at frequencies of 10% or higher. The broad applicability of our recommendations was validated in >200 clinical samples from six independent laboratories. The genotypes of clinical isolates with viral load above the recommended threshold cluster by sampling location and period. Our analysis also supports the rise in frequency of 20A.EU1 and 20A.EU2, two recently reported European strains whose dissemination was favoured by travelling during the summer 2020. Conclusions: We present much-needed recommendations for reliable determination of SARS-CoV-2 genome sequence and demonstrate their broad applicability in a large cohort of clinical samples.

Homologous Recombination Deficiency (HRD) is a predictive biomarker of poly-ADP ribose polymerase 1 inhibitors (PARPi) response. Most HRD detection methods are based on genome wide enumeration of scarring events and require deep genome sequence profiles (> 30x). The cost and workflow-specific biases introduced by these genome profiling methods currently limits clinical adoption of HRD testing. We introduce the Genomic Integrity Index (GII), a Convolutional Neuronal Network, that leverages features from low pass (1x) Whole Genome Sequencing data to distinguish HRD positive and negative samples. In a cohort of 230 ovarian and breast cancer, we found GII supports accurate stratification of samples yielding results that are highly concordant with state-of-the-art HRD detection methods (0.865<AUC<0.996) which require 50x deeper coverage. We conclude that the deep learning framework supporting GII allows accurate detection of HRD from shallow genome profiles, reducing biases and data generation costs making it uniquely suited for clinical applications.