Experimental and Computational Analysis of Newly Identified Pathogenic Mutations in the Creatine Transporter SLC6A8

Creatine is an essential metabolite for the storage and rapid supply of energy in muscle and nerve cells. In humans, impaired metabolism, transport, and distribution of creatine throughout tissues can cause varying forms of mental disability, also known as creatine deficiency syndrome (CDS). So far, 80 mutations in the creatine transporter (SLC6A8) have been associated to CDS. To better understand the effect of human genetic variants on the physiology of SLC6A8 and their possible impact on CDS, we studied 30 missense variants including 15 variants of unknown significance, two of which are reported here for the first time. We expressed these variants in HEK293 cells and explored their subcellular localization and transport activity. We also applied computational methods to predict variant effect and estimate site-specific changes in thermodynamic stability. To explore variants that might have a differential effect on the transporter's conformers along the transport cycle, we constructed homology models of the inward facing, and outward facing conformations. In addition, we used mass-spectrometry to study proteins that interact with wild type SLC6A8 and five selected variants in HEK293 cells. In silico models of the protein complexes revealed how two variants impact the interaction interface of SLC6A8 with other proteins and how pathogenic variants lead to an enrichment of ER protein partners. Overall, our integrated analysis disambiguates the pathogenicity of 15 variants of unknown significance revealing diverse mechanisms of pathogenicity, including two previously unreported variants obtained from patients suffering from the creatine deficiency syndrome.

Automation of high-throughput mRNA-seq library preparation: a robust, hands-free and time efficient methodology

Over the last decade, whole transcriptome profiling, also known as RNA-sequencing (RNA-seq), has quickly gained traction as a reliable method for unbiased assessment of gene expression. Integration of RNA-seq expression data into other omics datasets (e.g., proteomics, metabolomics, or epigenetics) solidifies our understanding of cell-specific regulatory patterns, yielding pathways to investigate the key rules of gene regulation. A limitation to efficient, at-scale utilization of RNA-seq is the time-demanding library preparation workflows, which is a 2-day or longer endeavor per cohort/sample size. To tackle this bottleneck, we designed an automated workflow that increases throughput capacity, while minimizing human error to enhance reproducibility. To this end, we converted the manual protocol of the NEBNext Directional Ultra II RNA Library Prep Kit for Illumina on the Beckman Coulter liquid handler, Biomek i7 Hybrid workstation. A total of 84 RNA samples were isolated from two human cell lines and subjected to comparative manual and automated library preparation methods. Qualitative and quantitative results indicated a high degree of similarity between libraries generated manually or through automation. Yet, there was a significant reduction in both hands-on and assay time from a 2-day manual to a 9-hour automated workflow. Using linear regression analysis, we found the Pearson correlation coefficient between libraries generated manually or by automation to be almost identical to a sample being sequenced twice (R²= 0.985 vs 0.983). This demonstrates that high-throughput automated workflows can be of great benefit to genomic laboratories by enhancing efficiency of library preparation, reducing hands-on time and increasing throughput potential.