Although microorganisms are traditionally used to investigate unicellular processes, the yeast Saccharomyces cerevisiae has the ability to form colonies with highly complex, multicellular structures. Colonies with the "fluffy" morphology have properties reminiscent of bacterial biofilms and are easily distinguished from the "smooth" colonies typically formed by laboratory strains. We have identified strains that are able to reversibly toggle between the fluffy and smooth colony-forming states. Using a combination of flow cytometry and high-throughput restriction-site associated DNA tag sequencing, we show that this switch is correlated with a change in chromosomal copy number. Furthermore, the gain of a single chromosome is sufficient to switch a strain from the fluffy to the smooth state, and its subsequent loss to revert the strain back to the fluffy state. Because copy number imbalance of six of the 16 S. cerevisiae chromosomes and even a single gene can modulate the switch, our results support the hypothesis that the state switch is produced by dosage-sensitive genes, rather than a general response to altered DNA content. These findings add a complex, multicellular phenotype to the list of molecular and cellular traits known to be altered by aneuploidy and suggest that chromosome missegregation can provide a quick, heritable, and reversible mechanism by which organisms can toggle between phenotypes. colony morphology | copy number variation | phenotypic switching | bet-hedging

Defects in serine biosynthesis resulting from loss of function mutations in PHGDH, PSAT1, and PSPH cause a set of rare, autosomal recessive diseases known as Neu‐Laxova syndrome (NLS) or serine‐deficiency disorders. The diseases present with a broad range of phenotypes including lethality, severe neurological manifestations, seizures, and intellectual disability. However, because L‐serine supplementation, especially if started prenatally, can ameliorate and in some cases even prevent symptoms, knowledge of pathogenic variants is medically actionable. Here, we describe a functional assay that leverages the evolutionary conservation of an enzyme in the serine biosynthesis pathway, phosphoserine aminotransferase, and the ability of the human protein‐coding sequence (PSAT1) to functionally replace its yeast ortholog (SER1). Results from our quantitative, yeast‐based assay agree well with clinical annotations and expectations based on the disease literature. Using this assay, we have measured the functional impact of the 199 PSAT1 variants currently listed in ClinVar, gnomAD, and the literature. We anticipate that the assay could be used to comprehensively assess the functional impact of all SNP‐accessible amino acid substitution mutations in PSAT1, a resource that could aid variant interpretation and identify potential NLS carriers.

Tetrad analysis has been a gold standard genetic technique for several decades. Unfortunately, the manual nature of the process has relegated its application to small-scale studies and limited its integration with rapidly evolving DNA sequencing technologies. We have developed a rapid, high-throughput method, called Barcode Enabled Sequencing of Tetrads (BEST), that replaces the manual processes of isolating, disrupting and spacing tetrads. BEST uses a meiosis-specific GFP fusion protein to isolate tetrads by fluorescence-activated cell sorting and molecular barcodes that are read during genotyping to identify spores derived from the same tetrad. Maintaining tetrad information allows accurate inference of missing genetic markers and full genotypes of missing (and presumably nonviable) individuals. By removing the bottleneck of manual dissection, hundreds or even thousands of tetrads can be isolated in minutes. We demonstrate the approach in Saccharomyces cerevisiae, but BEST is readily transferable to microorganisms in which meiotic mapping is significantly more laborious.

Microorganisms often form multicellular structures such as biofilms and structured colonies that can influence the organism’s virulence, drug resistance, and adherence to medical devices. Phenotypic classification of these structures has traditionally relied on qualitative scoring systems that limit detailed phenotypic comparisons between strains. Automated imaging and quantitative analysis have the potential to improve the speed and accuracy of experiments designed to study the genetic and molecular networks underlying different morphological traits. For this reason, we have developed a platform that uses automated image analysis and pattern recognition to quantify phenotypic signatures of yeast colonies. Our strategy enables quantitative analysis of individual colonies, measured at a single time point or over a series of time-lapse images, as well as the classification of distinct colony shapes based on image-derived features. Phenotypic changes in colony morphology can be expressed as changes in feature space trajectories over time, thereby enabling the visualization and quantitative analysis of morphological development. To facilitate data exploration, results are plotted dynamically through an interactive Yeast Image Analysis web application (YIMAA; http://yimaa.cs.tut.fi) that integrates the raw and processed images across all time points, allowing exploration of the image-based features and principal components associated with morphological development.