Next-generation sequencing to genetically diagnose a diverse range of inherited eye disorders in 15 consanguineous families from Pakistan.

Inherited retinal dystrophies (IRDs) are characterized by photoreceptor dysfunction or degeneration. Clinical and phenotypic overlap between IRDs makes the genetic diagnosis very challenging and comprehensive genomic approaches for accurate diagnosis are frequently required. While there are previous studies on IRDs in Pakistan, causative genes and variants are still unknown for a significant portion of patients. Therefore, there is a need to expand the knowledge of the genetic spectrum of IRDs in Pakistan. Here, we recruited 52 affected and 53 normal individuals from 15 consanguineous Pakistani families presenting non-syndromic and syndromic forms of IRDs. We employed single molecule Molecular Inversion Probes (smMIPs) based panel sequencing and whole genome sequencing to identify the probable disease-causing variants in these families. Using this approach, we obtained a 93% genetic solve rate and identified 16 (likely) causative variants in 14 families, of which seven novel variants were identified in ATOH7, COL18A1, MERTK, NDP, PROM1, PRPF8 and USH2A while nine recurrent variants were identified in CNGA3, CNGB1, HGSNAT, NMNAT1, SIX6 and TULP1. The novel MERTK variant and one recurrent TULP1 variant explained the intra-familial locus heterogeneity in one of the screened families while two recurrent CNGA3 variants explained compound heterozygosity in another family. The identification of variants in known disease-associated genes emphasizes the utilization of time and cost-effective screening approaches for rapid diagnosis. The timely genetic diagnosis will not only identify any associated systemic issues in case of syndromic IRDs, but will also aid in the acceleration of personalized medicine for patients affected with IRDs.

Numerical Approximation of a Nonequilibrium Model of Gradient Elution Chromatography Considering Different Functional Relationships between Model Parameters and Solvent Composition.

In this paper, a rigorous theoretical study is conducted to analyze the influence of varying solvent compositions on the retention characteristics of elution profiles within a fixed-bed liquid chromatographic column. In gradient chromatography, the propagation speed of elution profiles is manipulated through a progressive variation in the mobile-phase composition. Consequently, enhanced separation of the mixture components can be achieved together with a reduction in the requisite recycling times for subsequent injections. In other words, both the efficiency and the selectivity of the column can be enhanced. The lumped kinetic model coupled with the convection-diffusion equation for the volume fraction of the solvent is applied to simulate the process. The resulting nonlinear model equations are numerically solved by applying a semidiscrete second-order finite-volume method. The numerical solutions are utilized to quantify the effects of gradient starting and ending times, solvent composition, solvent strength parameters, and gradient slope on the concentration profiles. Additionally, temporal numerical moments are plotted versus the starting and ending times of the gradient, and standard performance criteria are presented for evaluating the process performance. The outcomes of this investigation will contribute to further enhancements in gradient elution chromatography.

Unsupervised mutual transformer learning for multi-gigapixel Whole Slide Image classification.

The classification of gigapixel Whole Slide Images (WSIs) is an important task in the emerging area of computational pathology. There has been a surge of interest in deep learning models for WSI classification with clinical applications such as cancer detection or prediction of cellular mutations. Most supervised methods require expensive and labor-intensive manual annotations by expert pathologists. Weakly supervised Multiple Instance Learning (MIL) methods have recently demonstrated excellent performance; however, they still require large-scale slide-level labeled training datasets that require a careful inspection of each slide by an expert pathologist. In this work, we propose a fully unsupervised WSI classification algorithm based on mutual transformer learning. The instances (i.e., patches) from gigapixel WSIs are transformed into a latent space and then inverse-transformed to the original space. Using the transformation loss, pseudo labels are generated and cleaned using a transformer label cleaner. The proposed transformer-based pseudo-label generator and cleaner modules mutually train each other iteratively in an unsupervised manner. A discriminative learning mechanism is introduced to improve normal versus cancerous instance labeling. In addition to the unsupervised learning, we demonstrate the effectiveness of the proposed framework for weakly supervised learning and cancer subtype classification as downstream analysis. Extensive experiments on four publicly available datasets show better performance of the proposed algorithm compared to the existing state-of-the-art methods.

Identification of novel NLRP3 inhibitors as therapeutic options for epilepsy by machine learning-based virtual screening, molecular docking and biomolecular simulation studies.

The NOD-Like Receptor Protein-3 (NLRP3) inflammasome is a key therapeutic target for the treatment of epilepsy and has been reported to regulate inflammation in several neurological diseases. In this study, a machine learning-based virtual screening strategy has investigated candidate active compounds that inhibit the NLRP3 inflammasome. As machine learning-based virtual screening has the potential to accurately predict protein-ligand binding and reduce false positives outcomes compared to traditional virtual screening. Briefly, classification models were created using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) machine learning methods. To determine the most crucial features of a molecule's activity, feature selection was carried out. By utilizing 10-fold cross-validation, the created models were analyzed. Among the generated models, the RF model obtained the best results as compared to others. Therefore, the RF model was used as a screening tool against the large chemical databases. Molecular operating environment (MOE) and PyRx software's were applied for molecular docking. Also, using the Amber Tools program, molecular dynamics (MD) simulation of potent inhibitors was carried out. The results showed that the KNN, SVM, and RF accuracy was 0.911 %, 0.906 %, and 0.946 %, respectively. Moreover, the model has shown sensitivity of 0.82 %, 0.78 %, and 0.86 % and specificity of 0.95 %, 0.96 %, and 0.98 % respectively. By applying the model to the ZINC and South African databases, we identified 98 and 39 compounds, respectively, potentially possessing anti-NLRP3 activity. Also, a molecular docking analysis produced ten ZINC and seven South African compounds that has comparable binding affinities to the reference drug. Moreover, MD analysis of the two complexes revealed that the two compounds (ZINC000009601348 and SANC00225) form stable complexes with varying amounts of binding energy. The in-silico studies indicate that both compounds most likely display their inhibitory effect by inhibiting the NLRP3 protein.

Loss-of-function variant in the LRR domain of SLITRK2 implicated in a neurodevelopmental disorder.

Background: Neurodevelopmental disorders are characterized by different combinations of intellectual disability (ID), communication and social skills deficits, and delays in achieving motor or language milestones. SLITRK2 is a postsynaptic cell-adhesion molecule that promotes neurite outgrowth and excitatory synapse development. Methods and Results: In the present study, we investigated a single patient segregating Neurodevelopmental disorder. SLITRK2 associated significant neuropsychological issues inherited in a rare X-linked fashion have recently been reported. Whole-exome sequencing and data analysis revealed a novel nonsense variant [c.789T>A; p.(Cys263*); NM_032539.5; NP_115928.1] in exon 5 of the SLITRK2 gene (MIM# 300561). Three-dimensional protein modeling revealed substantial changes in the mutated SLITRK2 protein, which might lead to nonsense-medicated decay. Conclusion: This study confirms the role of SLITRK2 in neuronal development and highlights the importance of including the SLITRK2 gene in the screening of individuals presenting neurodevelopmental disorders.