Disease-associated nonsense and frame-shift variants resulting in the truncation of the GluN2A or GluN2B C-terminal domain decrease NMDAR surface expression and reduce potentiating effects of neurosteroids.

N-methyl-D-aspartate receptors (NMDARs) play a critical role in normal brain function, and variants in genes encoding NMDAR subunits have been described in individuals with various neuropsychiatric disorders. We have used whole-cell patch-clamp electrophysiology, fluorescence microscopy and in-silico modeling to explore the functional consequences of disease-associated nonsense and frame-shift variants resulting in the truncation of GluN2A or GluN2B C-terminal domain (CTD). This study characterizes variant NMDARs and shows their reduced surface expression and synaptic localization, altered agonist affinity, increased desensitization, and reduced probability of channel opening. We also show that naturally occurring and synthetic steroids pregnenolone sulfate and epipregnanolone butanoic acid, respectively, enhance NMDAR function in a way that is dependent on the length of the truncated CTD and, further, is steroid-specific, GluN2A/B subunit-specific, and GluN1 splice variant-specific. Adding to the previously described effects of disease-associated NMDAR variants on the receptor biogenesis and function, our results improve the understanding of the molecular consequences of NMDAR CTD truncations and provide an opportunity for the development of new therapeutic neurosteroid-based ligands.

SCRAMBLER: A Tool for De Novo CRISPR Array Reconstruction and Its Application for Analysis of the Structure of Prokaryotic Populations.

CRISPR arrays are prokaryotic genomic loci consisting of repeat sequences alternating with unique spacers acquired from foreign nucleic acids. As one of the fastest-evolving parts of the genome, CRISPR arrays can be used to differentiate closely related prokaryotic lineages and track individual strains in prokaryotic communities. However, the assembly of full-length CRISPR arrays sequences remains a problem. Here, we developed SCRAMBLER, a tool that includes several pipelines for assembling CRISPR arrays from high-throughput short-read sequencing data. We assessed its performance with model data sets (Escherichia coli strains containing different CRISPR arrays and imitating prokaryotic communities of different complexities) and intestinal microbiomes of extant and extinct pachyderms. Evaluation of SCRAMBLER's performance using model data sets demonstrated its ability to assemble CRISPR arrays correctly from reads containing pairs of spacers, yielding a precision rate of >80% and a recall rate of 60-85% when checked against ground-truth data. Likewise, SCRAMBLER successfully assembled CRISPR arrays from the environmental samples, as attested by their matching with database entries. SCRAMBLER, an open-source software (github.com/biolab-tools/SCRAMBLER), can facilitate analysis of the composition and dynamics of CRISPR arrays in complex communities.