Scalable detection of technically challenging variants through modified next-generation sequencing.

BACKGROUND: Some clinically important genetic variants are not easily evaluated with next-generation sequencing (NGS) methods due to technical challenges arising from high- similarity copies (e.g., PMS2, SMN1/SMN2, GBA1, HBA1/HBA2, CYP21A2), repetitive short sequences (e.g., ARX polyalanine repeats, FMR1 AGG interruptions in CGG repeats, CFTR poly-T/TG repeats), and other complexities (e.g., MSH2 Boland inversions). METHODS: We customized our NGS processes to detect the technically challenging variants mentioned above with adaptations including target enrichment and bioinformatic masking of similar sequences. Adaptations were validated with samples of known genotypes. RESULTS: Our adaptations provided high-sensitivity and high-specificity detection for most of the variants and provided a high-sensitivity primary assay to be followed with orthogonal disambiguation for the others. The sensitivity of the NGS adaptations was 100% for all of the technically challenging variants. Specificity was 100% for those in PMS2, GBA1, SMN1/SMN2, and HBA1/HBA2, and for the MSH2 Boland inversion; 97.8%-100% for CYP21A2 variants; and 85.7% for ARX polyalanine repeats. CONCLUSIONS: NGS assays can detect technically challenging variants when chemistries and bioinformatics are jointly refined. The adaptations described support a scalable, cost-effective path to identifying all clinically relevant variants within a single sample.

Zebrafish behavioral profiling identifies multitarget antipsychotic-like compounds.

Many psychiatric drugs act on multiple targets and therefore require screening assays that encompass a wide target space. With sufficiently rich phenotyping and a large sampling of compounds, it should be possible to identify compounds with desired mechanisms of action on the basis of behavioral profiles alone. Although zebrafish (Danio rerio) behavior has been used to rapidly identify neuroactive compounds, it is not clear what types of behavioral assays would be necessary to identify multitarget compounds such as antipsychotics. Here we developed a battery of behavioral assays in larval zebrafish to determine whether behavioral profiles can provide sufficient phenotypic resolution to identify and classify psychiatric drugs. Using the antipsychotic drug haloperidol as a test case, we found that behavioral profiles of haloperidol-treated zebrafish could be used to identify previously uncharacterized compounds with desired antipsychotic-like activities and multitarget mechanisms of action.

The strength of genetic interactions scales weakly with mutational effects.

BACKGROUND: Genetic interactions pervade every aspect of biology, from evolutionary theory, where they determine the accessibility of evolutionary paths, to medicine, where they can contribute to complex genetic diseases. Until very recently, studies on epistatic interactions have been based on a handful of mutations, providing at best anecdotal evidence about the frequency and the typical strength of genetic interactions. In this study, we analyze a publicly available dataset that contains the growth rates of over five million double knockout mutants of the yeast Saccharomyces cerevisiae. RESULTS: We discuss a geometric definition of epistasis that reveals a simple and surprisingly weak scaling law for the characteristic strength of genetic interactions as a function of the effects of the mutations being combined. We then utilized this scaling to quantify the roughness of naturally occurring fitness landscapes. Finally, we show how the observed roughness differs from what is predicted by Fisher's geometric model of epistasis, and discuss the consequences for evolutionary dynamics. CONCLUSIONS: Although epistatic interactions between specific genes remain largely unpredictable, the statistical properties of an ensemble of interactions can display conspicuous regularities and be described by simple mathematical laws. By exploiting the amount of data produced by modern high-throughput techniques, it is now possible to thoroughly test the predictions of theoretical models of genetic interactions and to build informed computational models of evolution on realistic fitness landscapes.

Synthetic approaches to understanding biological constraints.

Microbes can be readily cultured and their genomes can be easily manipulated. For these reasons, laboratory systems of unicellular organisms are increasingly used to develop and test theories about biological constraints, which manifest themselves at different levels of biological organization, from optimal gene-expression levels to complex individual and social behaviors. The quantitative description of biological constraints has recently advanced in several areas, such as the formulation of global laws governing the entire economy of a cell, the direct experimental measurement of the trade-offs leading to optimal gene expression, the description of naturally occurring fitness landscapes, and the appreciation of the requirements for a stable bacterial ecosystem.