Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression.

Cells respond to environmental and developmental stimuli by remodeling their transcriptomes through regulation of both mRNA transcription and mRNA decay. A central goal of biology is identifying the global set of regulatory relationships between factors that control mRNA production and degradation and their target transcripts and construct a predictive model of gene expression. Regulatory relationships are typically identified using transcriptome measurements and causal inference algorithms. RNA kinetic parameters are determined experimentally by employing run-on or metabolic labeling (e.g. 4-thiouracil) methods that allow transcription and decay rates to be separately measured. Here, we develop a deep learning model, trained with single-cell RNA-seq data, that both infers causal regulatory relationships and estimates RNA kinetic parameters. The resulting in silico model predicts future gene expression states and can be perturbed to simulate the effect of transcription factor changes. We acquired model training data by sequencing the transcriptomes of 175,000 individual Saccharomyces cerevisiae cells that were subject to an external perturbation and continuously sampled over a one hour period. The rate of change for each transcript was calculated on a per-cell basis to estimate RNA velocity. We then trained a deep learning model with transcriptome and RNA velocity data to calculate time-dependent estimates of mRNA production and decay rates. By separating RNA velocity into transcription and decay rates, we show that rapamycin treatment causes existing ribosomal protein transcripts to be rapidly destabilized, while production of new transcripts gradually slows over the course of an hour. The neural network framework we present is designed to explicitly model causal regulatory relationships between transcription factors and their genes, and shows superior performance to existing models on the basis of recovery of known regulatory relationships. We validated the predictive power of the model by perturbing transcription factors in silico and comparing transcriptome-wide effects with experimental data. Our study represents the first step in constructing a complete, predictive, biophysical model of gene expression regulation.

yEvo: experimental evolution in high school classrooms selects for novel mutations that impact clotrimazole resistance in Saccharomyces cerevisiae.

Antifungal resistance in pathogenic fungi is a growing global health concern. Nonpathogenic laboratory strains of Saccharomyces cerevisiae are an important model for studying mechanisms of antifungal resistance that are relevant to understanding the same processes in pathogenic fungi. We have developed a series of laboratory modules in which high school students used experimental evolution to study antifungal resistance by isolating azole-resistant S. cerevisiae mutants and examining the genetic basis of resistance. We have sequenced 99 clones from these experiments and found that all possessed mutations previously shown to impact azole resistance, validating our approach. We additionally found recurrent mutations in an mRNA degradation pathway and an uncharacterized mitochondrial protein (Csf1) that have possible mechanistic connections to azole resistance. The scale of replication in this initiative allowed us to identify candidate epistatic interactions, as evidenced by pairs of mutations that occur in the same clone more frequently than expected by chance (positive epistasis) or less frequently (negative epistasis). We validated one of these pairs, a negative epistatic interaction between gain-of-function mutations in the multidrug resistance transcription factors Pdr1 and Pdr3. This high school-university collaboration can serve as a model for involving members of the broader public in the scientific process to make meaningful discoveries in biomedical research.

Transposable Element Mobilization in Interspecific Yeast Hybrids.

Barbara McClintock first hypothesized that interspecific hybridization could provide a "genomic shock" that leads to the mobilization of transposable elements (TEs). This hypothesis is based on the idea that regulation of TE movement is potentially disrupted in hybrids. However, the handful of studies testing this hypothesis have yielded mixed results. Here, we set out to identify if hybridization can increase transposition rate and facilitate colonization of TEs in Saccharomyces cerevisiae × Saccharomyces uvarum interspecific yeast hybrids. Saccharomyces cerevisiae have a small number of active long terminal repeat retrotransposons (Ty elements), whereas their distant relative S. uvarum have lost the Ty elements active in S. cerevisiae. Although the regulation system of Ty elements is known in S. cerevisiae, it is unclear how Ty elements are regulated in other Saccharomyces species, and what mechanisms contributed to the loss of most classes of Ty elements in S. uvarum. Therefore, we first assessed whether TEs could insert in the S. uvarum sub-genome of a S. cerevisiae × S. uvarum hybrid. We induced transposition to occur in these hybrids and developed a sequencing technique to show that Ty elements insert readily and nonrandomly in the S. uvarum genome. We then used an in vivo reporter construct to directly measure transposition rate in hybrids, demonstrating that hybridization itself does not alter rate of mobilization. However, we surprisingly show that species-specific mitochondrial inheritance can change transposition rate by an order of magnitude. Overall, our results provide evidence that hybridization can potentially facilitate the introduction of TEs across species boundaries and alter transposition via mitochondrial transmission, but that this does not lead to unrestrained proliferation of TEs suggested by the genomic shock theory.

Temperature preference can bias parental genome retention during hybrid evolution.

Interspecific hybridization can introduce genetic variation that aids in adaptation to new or changing environments. Here, we investigate how hybrid adaptation to temperature and nutrient limitation may alter parental genome representation over time. We evolved Saccharomyces cerevisiae x Saccharomyces uvarum hybrids in nutrient-limited continuous culture at 15°C for 200 generations. In comparison to previous evolution experiments at 30°C, we identified a number of responses only observed in the colder temperature regime, including the loss of the S. cerevisiae allele in favor of the cryotolerant S. uvarum allele for several portions of the hybrid genome. In particular, we discovered a genotype by environment interaction in the form of a loss of heterozygosity event on chromosome XIII; which species' haplotype is lost or maintained is dependent on the parental species' temperature preference and the temperature at which the hybrid was evolved. We show that a large contribution to this directionality is due to a temperature dependent fitness benefit at a single locus, the high affinity phosphate transporter gene PHO84. This work helps shape our understanding of what forces impact genome evolution after hybridization, and how environmental conditions may promote or disfavor the persistence of hybrids over time.