Biology subjects

McKnight, D.

Publications and source records attributed to McKnight, D..

4 recordsLinked to original sources

bioRxiv2021

High-throughput Evaluation of Epilepsy-associated KCNQ2 Variants Reveals Functional and Pharmacological Heterogeneity

Hundreds of KCNQ2 variants have been identified by genetic testing of children with early onset epilepsy and/or developmental disability. Voltage-clamp recording from heterologous cells has proved useful for establishing deleterious functional effects of KCNQ2 variants, but procedures adapting these assays for standardized, higher throughput data collection and reporting are lacking. In this study, we employed automated patch clamp recording to assess in parallel the functional and pharmacological properties of 79 missense and 2 in-frame deletion variants of KCNQ2. Among the variants we studied were a training set of 18 pathogenic variants previously studied by voltage-clamp recording, 24 mostly rare population variants, and 39 disease-associated variants with unclear functional effects. Variant KCNQ2 subunits were transiently expressed in a cell line stably expressing KCNQ3 to reconstitute the physiologically relevant channel complex. Variants with severe loss-of-function were also co-expressed 1:1 with WT KCNQ2 in the KCNQ3 cell line to mimic the heterozygous genotype and assess dominant-negative behavior. In total, we analyzed electrophysiological data recorded from 9,480 cells. The functional properties of WT KCNQ2/KCNQ3 channels and pharmacological responses to known blockers and activators determined by automated patch clamp recording were highly concordant with previous findings. Similarly, functional properties of 18 known pathogenic variants largely matched previously published results and the validated automated patch clamp assay. Many of the 39 previously unstudied disease-associated KCNQ2 variants exhibited prominent loss-of-function and dominant-negative effects, providing strong evidence in support of pathogenicity. All variants, exhibit response to retigabine (10 {micro}M), although there were differences in maximal responses. Variants within the ion selectivity filter exhibited the weakest responses whereas retigabine had the strongest effect on gain-of-function variants in the voltage-sensor domain. Our study established a high throughput method to detect deleterious functional consequences of KCNQ2 variants. We demonstrated that dominant-negative loss-of-function is a common mechanism associated with missense KCNQ2 variants but this does not occur with rare population variation in this gene. Importantly, we observed genotype-dependent differences in the response of KCNQ2 variants to retigabine.

neuroscience↗

bioRxiv2019

High throughput Characterization of KCNB1 variants Associated with Developmental and Epileptic Encephalopathy

Pathogenic variants in KCNB1, encoding the voltage-gated potassium channel Kv2.1, are associated with developmental and epileptic encephalopathies (DEE). Previous functional studies on a limited number of KCNB1 variants indicated a range of molecular mechanisms by which variants affect channel function, including loss of voltage sensitivity, loss of ion selectivity, and reduced cell-surface expression. We evaluated a series of 17 KCNB1 variants associated with DEE or neurodevelopmental disorder (NDD) to rapidly ascertain channel dysfunction using high-throughput functional assays. Specifically, we investigated the biophysical properties and cell-surface expression of variant Kv2.1 channels expressed in heterologous cells using high-throughput automated electrophysiology and immunocytochemistry-flow cytometry. Pathogenic variants exhibited diverse functional defects, including altered current density and shifts in the voltage-dependence of activation and/or inactivation, as homotetramers or when co-expressed with wild-type Kv2.1. Quantification of protein expression also identified variants with reduced total Kv2.1 expression or deficient cell-surface expression.\n\nOur study establishes a platform for rapid screening of functional defects of KCNB1 variants associated with DEE and other NDDs, which will aid in establishing KCNB1 variant pathogenicity and may enable discovery of targeted strategies for therapeutic intervention based on molecular phenotype.

genetics↗

bioRxiv2018

Unraveling the effects of spatial variability and relic DNA on the temporal dynamics of soil microbial communities

Few studies have comprehensively investigated the temporal variability in soil microbial communities despite widespread recognition that the belowground environment is dynamic. In part, this stems from the challenges associated with the high degree of spatial heterogeneity in soil microbial communities and because the presence of relic DNA (DNA from non-living cells) may dampen temporal signals. Here we disentangle the relationships among spatial, temporal, and relic DNA effects on bacterial, archaeal, and fungal communities in soils collected from contrasting hillslopes in Colorado, USA. We intensively sampled plots on each hillslope over six months to discriminate between temporal variability, intra-plot spatial heterogeneity, and relic DNA effects on the soil prokaryotic and fungal communities. We show that the intra-plot spatial variability in microbial community composition was strong and independent of relic DNA effects with these spatial patterns persisting throughout the study. When controlling for intra-plot spatial variability, we identified significant temporal variability in both plots over the six-month study. These microbial communities were more dissimilar over time after relic DNA was removed, suggesting that relic DNA hinders the detection of important temporal dynamics in belowground microbial communities. We identified microbial taxa that exhibited shared temporal responses and show these responses were often predictable from temporal changes in soil conditions. Our findings highlight approaches that can be used to better characterize temporal shifts in soil microbial communities, information that is critical for predicting the environmental preferences of individual soil microbial taxa and identifying linkages between soil microbial community composition and belowground processes.\n\nImportanceNearly all microbial communities are dynamic in time. Understanding how temporal dynamics in microbial community structure affect soil biogeochemistry and fertility are key to being able to predict the responses of the soil microbiome to environmental perturbations. Here we explain the effects of soil spatial structure and relic DNA on the determination of microbial community fluctuations over time. We found that intensive spatial sampling is required to identify temporal effects in microbial communities because of the high degree of spatial heterogeneity in soil and that DNA from non-living microbial cells masks important temporal patterns. We identified groups of microbes that display correlated behavior over time and show that these patterns are predictable from soil characteristics. These results provide insight into the environmental preferences and temporal relationships between individual microbial taxa and highlight the importance of considering relic DNA when trying to detect temporal dynamics in belowground communities.

microbiology↗

bioRxiv2018

Genetic variant pathogenicity prediction trained using large-scale disease specific clinical sequencing datasets

Recent advances in DNA sequencing technologies have expanded our understanding of the molecular underpinnings for several genetic disorders, and increased the utilization of genomic tests by clinicians. Given the paucity of evidence to assess each variant, and the difficulty of experimentally evaluating a variants clinical significance, many of the thousand variants that can be generated by clinical tests are reported as variants of unknown clinical significance. However, the creation of population-scale variant databases can significantly improve clinical variant interpretation. Specifically, pathogenicity prediction for novel missense variants can now utilize features describing regional variant constraint. Constrained genomic regions are those that have an unusually low variant count in the general population. Several computational methods have been introduced to capture these regions and incorporate them into pathogenicity classifiers, but these methods have yet to be compared on an independent clinical variant dataset. Here we introduce one variant dataset derived from clinical sequencing panels, and use it to compare the ability of different genomic constraint metrics to determine missense variant pathogenicity. This dataset is compiled from 17,071 patients surveyed with clinical genomic sequencing for cardiomyopathy, epilepsy, or RASopathies. We further utilize this dataset to demonstrate the necessity of disease-specific classifiers, and to train PathoPredictor, a disease-specific ensemble classifier of pathogenicity based on regional constraint and variant level features. PathoPredictor achieves an average precision greater than 90% for variants from all 99 tested disease genes while approaching 100% accuracy for some genes. Accumulation of larger clinical variant datasets and their utilization to train existing pathogenicity metrics can significantly enhance their performance in a disease and gene-specific manner.

genomics↗