Dual AAV-based PCDH15 gene therapy achieves sustained rescue of visual function in a mouse model of Usher syndrome 1F.

Mutations in the PCDH15 gene, encoding protocadherin-15, are among the leading causes of Usher syndrome type 1 (USH1F), and account for up to 12% USH1 cases worldwide. A founder truncating variant of PCDH15 has a ∼2% carrier frequency in Ashkenazi Jews accounting for nearly 60% of their USH1 cases. Although cochlear implants can restore hearing perception in USH1 patients, presently there are no effective treatments for the vision loss due to retinitis pigmentosa. We established a founder allele-specific Pcdh15 knockin mouse model as a platform to ascertain therapeutic strategies. Using a dual-vector approach to circumvent the size limitation of adeno-associated virus, we observed robust expression of exogenous PCDH15 in the retinae of Pcdh15KI mice, sustained recovery of electroretinogram amplitudes and key retinoid oxime, substantially improved light-dependent translocation of phototransduction proteins, and enhanced levels of retinal pigment epithelium-derived enzymes. Thus, our data raise hope and pave the way for future gene therapy trials in USH1F subjects.

Genetic association analysis of 77,539 genomes reveals rare disease etiologies.

The genetic etiologies of more than half of rare diseases remain unknown. Standardized genome sequencing and phenotyping of large patient cohorts provide an opportunity for discovering the unknown etiologies, but this depends on efficient and powerful analytical methods. We built a compact database, the 'Rareservoir', containing the rare variant genotypes and phenotypes of 77,539 participants sequenced by the 100,000 Genomes Project. We then used the Bayesian genetic association method BeviMed to infer associations between genes and each of 269 rare disease classes assigned by clinicians to the participants. We identified 241 known and 19 previously unidentified associations. We validated associations with ERG, PMEPA1 and GPR156 by searching for pedigrees in other cohorts and using bioinformatic and experimental approaches. We provide evidence that (1) loss-of-function variants in the Erythroblast Transformation Specific (ETS)-family transcription factor encoding gene ERG lead to primary lymphoedema, (2) truncating variants in the last exon of transforming growth factor-ß regulator PMEPA1 result in Loeys-Dietz syndrome and (3) loss-of-function variants in GPR156 give rise to recessive congenital hearing impairment. The Rareservoir provides a lightweight, flexible and portable system for synthesizing the genetic and phenotypic data required to study rare disease cohorts with tens of thousands of participants.

Proposed therapy, developed in a Pcdh15-deficient mouse, for progressive loss of vision in human Usher syndrome.

Usher syndrome type I (USH1) is characterized by deafness, vestibular areflexia, and progressive retinal degeneration. The protein-truncating p.Arg245* founder variant of PCDH15 (USH1F) has an ~2% carrier frequency amongst Ashkenazi Jews accounts for ~60% of their USH1 cases. Here, longitudinal phenotyping in 13 USH1F individuals revealed progressive retinal degeneration, leading to severe vision loss with macular atrophy by the sixth decade. Half of the affected individuals were legally blind by their mid-50s. The mouse Pcdh15R250X variant is equivalent to human p.Arg245*. Homozygous Pcdh15R250X mice also have visual deficits and aberrant light-dependent translocation of the phototransduction cascade proteins, arrestin, and transducin. Retinal pigment epithelium (RPE)-specific retinoid cycle proteins, RPE65 and CRALBP, were also reduced in Pcdh15R250X mice, indicating a dual role for protocadherin-15 in photoreceptors and RPE. Exogenous 9-cis retinal improved ERG amplitudes in Pcdh15R250X mice, suggesting a basis for a clinical trial of FDA-approved retinoids to preserve vision in USH1F patients.

Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach.

Pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) technique was analyzed to determine the significance of clinical and phenotypic variables as well as environmental conditions that can identify the underlying causes of child ALL. Fifty pediatric patients (n = 50) included who were diagnosed with acute lymphoblastic leukemia (ALL) according to the inclusion and exclusion criteria. Clinical variables comprised of the blood biochemistry (CBC, LFTs, RFTs) results, and distribution of type of ALL, i.e., T ALL or B ALL. Phenotypic data included the age, sex of the child, and consanguinity, while environmental factors included the habitat, socioeconomic status, and access to filtered drinking water. Fifteen different features/attributes were collected for each case individually. To retrieve most useful discriminating attributes, four different supervised ML algorithms were used including classification and regression trees (CART), random forest (RM), gradient boosted machine (GM), and C5.0 decision tree algorithm. To determine the accuracy of the derived CART algorithm on future data, a ten-fold cross validation was performed on the present data set. The ALL was common in children of age below 5 years in male patients whole belonged to middle class family of rural areas. (B-ALL) was most frequent as compared with T-ALL. The consanguinity was present in 54% of cases. Low levels of platelets and hemoglobin and high levels of white blood cells were reported in child ALL patients. CART provided the best and complete fit for the entire data set yielding a 99.83% model fit accuracy, and a misclassification of 0.17% on the entire sample space, while C5.0 reported 98.6%, random forest 94.44%, and gradient boosted machine resulted in 95.61% fitting. The variable importance of each primary discriminating attribute is platelet 43%, hemoglobin 24%, white blood cells 4%, and sex of the child 4%. An overall accuracy of 87.4% was recorded for the classifier. Platelet count abnormality can be considered as a major factor in predicting pediatric ALL. The machine learning algorithms can be applied efficiently to provide details for the prognosis for better treatment outcome. Graphical Abstract Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach.

Polymorphism of Interleukin-10 (IL-10, -1082 G/A) and Interleukin-28B (IL-28B, C/T) In Pediatric Acute Lymphoblastic Leukemia (ALL).

We conducted genotypic analyses of interleukin-10 (IL-10) (-1082 G/A; GG, GA, AA) and interleukin-28B (CC, CT, TT) genes polymorphisms in acute lymphoblastic leukemia (ALL) pediatric patients in descriptive study to evaluate the prevalence of these mutations. In amplification refractory mutation system-PCR (ARMS-PCR), one reaction was carried out for each patient's DNA sample. For IL-28B gene, two forward and two reverse primers specific for C-allele and T-allele were used separately. For human IL-10 gene, two different forward primers specific for A and G alleles were used in combination with common reverse primer. IL-10 gene promoter showed highest frequency (n=29, 58%) of heterozygous (GA) allele, while genotypic analysis of IL-28B gene showed highest frequency (n=28, 56%) of homozygous (CC) allele. The IL-10 (AA) genotype related to its protein's less production in body which may be associated with the least survival of ALL's patients, while IL-28b (CT and TT) genotypes may be associated with less IFNλ3 levels and less life expectancy.