Yeast strain development has been essential for improving efficiency, flavour diversity, and quality of beer fermentation. Such efforts often rely on laborious in vitro screening experiments. However, with the increasing availability of large-scale ‘omics’ data sets, it may be possible to replace or complement such experiments with in silico screening. Here, we briefly review the genetics associated with various desirable and undesirable traits in brewing yeast, and demonstrate how recent genomics, transcriptomics, and proteomics data sets derived from the 1,011 yeast genomes project can be exploited for identifying strains with potentially desirable phenotypes. The discussed phenotypes are related to fermentation performance, formation of desirable flavours, and mitigation of off-flavours. Finally, we perform wort fermentations with five strains from diverse backgrounds, with diverse predicted phenotypes, to validate the in silico predictions. Most predicted phenotypes correlated well with the measured phenotypes, including formation of desirable compounds like isoamyl acetate and ethyl octanoate, as well as formation of undesirable compounds like 4-vinyl guaiacol, diacetyl, and ethanethiol. Together, the results indicate that utilizing large ‘omics’ data sets can be a very useful tool for both strain selection and development for beer fermentation, and naturally other food and beverage fermentations as well. Compared to more traditional in vitro screening, this has several benefits, including lower costs, more rapid results and possibility to include more strains. We hope this can inspire and yield improved and more diverse brewing strains to the industry.

Agave spirits have gained global recognition and hold a central position within the cultural heritage of Mexico. Traditional distilleries characterized by open fermentations driven by local microbial communities, persist despite the rise of industrial-scale counterparts. In this review, we explore the environmental conditions and production practices that make the must of cooked agave stems a unique habitat for colonizing microorganisms. Additionally, we review selected studies that have characterized yeast species within these communities, with a focus on their metabolic traits and genomic features. Over fifty fungal species, predominantly Saccharomycetales and few Basidiomycetes, along with a similar number of lactic and acetic acid bacteria, have been identified. Despite variations in the chemical composition of the agave substrate and diversity of cultural practices associated with each traditional fermentation process, yeast species such as Saccharomyces cerevisiae, Kluyveromyces marxianus, Torulaspora delbrueckii, and several Pichia species have been consistently isolated across all agave spirit-producing regions. Importantly, cooked agave must is rich in fermentable sugars, yet it also contains inhibitory compounds that influence the proliferation dynamics of the microbial community. We discuss some of the genetic traits that may enable yeasts to flourish in this challenging environment and how human practices may shape microbial diversity by promoting the selection of microbes that are well-adapted to agave fermentation environments. The increasing demand for agave spirits, combined with concerns about the preservation of natural resources and cultural practices associated with their production, underscores the need to deepen our understanding of all key players, including the yeast communities involved.

The CRISPR-Cas9 system is extremely useful for genome editing in many species, including the model yeast Saccharomyces cerevisiae, and other yeast species. We have previously reported the use of an inducible CRISPR-Cas9 system in Candida glabrata, which allows genome editing but also the study of Double Strand Break (DSB) repair. We report, in this study, a comparable system for C. glabrata, relying on a new plasmid, which is more stable than the previous one. We also report the use of this plasmid to induce DSBs in two additional human pathogens, Candida bracarensis and Candida nivariensis. We examine lethality induced by an in vivo DSB in the three species and describe the different types of Non-Homologous End-Joining (NHEJ) events detected in these three pathogens.

Yeasts in the subphylum Saccharomycotina are found across the globe in disparate ecosystems. A major aim of yeast research is to understand the diversity and evolution of ecological traits, such as carbon metabolic breadth, insect association, and cactophily. This includes studying aspects of ecological traits like genetic architecture or association with other phenotypic traits. Genomic resources in the Saccharomycotina have grown rapidly. Ecological data, however, are still limited for many species, especially those only known from species descriptions where usually only a limited number of strains are studied. Moreover, ecological information is recorded in natural language format limiting high throughput computational analysis. To address these limitations, we developed an ontological framework for the analysis of yeast ecology. A total of 1,088 yeast strains were added to the Ontology of Yeast Environments (OYE) and analyzed in a machine-learning framework to connect genotype to ecology. This framework is flexible and can be extended to additional isolates, species, or environmental sequencing data. Widespread adoption of OYE would greatly aid the study of macroecology in the Saccharomycotina subphylum.

Genomes from yeast to human are subject to pervasive transcription. A single round of pervasive transcription is sufficient to alter local chromatin conformation, nucleosome dynamics and gene expression, but experimentally is can be hard to distinguish from background signals. Size selected native elongating transcript sequencing (ssNET-Seq) was developed to precisely map transcription units (TU) independent of expression levels. RNAPII-associated nascent transcripts are fractionation into different size ranges before library construction. When anchored to the transcription start sites (TSS) of annotated genes, the combined pattern of the output metagenes define the expected reference pattern for a TU. Bioinformatic pattern matching to the reference identified 9542 TU in Saccharomyces cerevisiae, of which 47% are coding and 53% are non-coding. 3113 (33%) are newly identified unannotated non-coding TU. Anchoring all TU to the TSS or polyadenylation site (PAS) of annotated coding regions reveals distinctive architectures of linked pairs of divergent TU approximately 200nt apart. The Reb1 transcription factor is enriched 30nt downstream of the PAS only when an upstream (TSS-60nt) non-coding TU co-occurs with a downstream (TSS+150nt) coding TU and supports nucleosome depletion in the generation of the pervasive nascent transcriptome. The potential for extensive transcriptional interference is evident from low abundance unannotated TUs with variable TSS (median-240nt) initiating within a 500nt window upstream of, and transcribing over, the promoters of protein coding genes. This study confirms a highly interleaved yeast genome with different types of transcription units altering the chromatin landscape in distinctive ways, with the potential to exert extensive regulatory control.

Polyadenylation occurs at numerous sites within 3’ untranslated regions (3’ UTRs) but rarely within coding regions. How does Pol II travel through long coding regions without generating poly(A) sites, yet then permits promiscuous polyadenylation once it reaches the 3’ UTR? The cleavage/polyadenylation (CpA) machinery preferentially associates with 3’ UTRs, but it is unknown how its recruitment is restricted to 3’ UTRs during Pol II elongation. Unlike coding regions, 3’ UTRs have long AT-rich stretches of DNA that may be important for restricting polyadenylation to 3’ UTRs. Recognition of the 3’ UTR could occur at the DNA (AT-rich), RNA (AU-rich), or RNA:DNA hybrid rU:dA- and/or rA:dT-rich) level. Based on the nucleic acid critical for 3’ UTR recognition, there are three classes of models, not mutually exclusive, for how the CpA machinery is selectively recruited to 3’ UTRs, thereby restricting where polyadenylation occurs: 1) RNA-based models suggest that the CpA complex directly (or indirectly through one or more intermediary proteins) binds long AU-rich stretches that are exposed after Pol II passes through these regions. 2) DNA-based models suggest that the AT-rich sequence affects nucleosome depletion or the elongating Pol II machinery, resulting in dissociation of some elongation factors and subsequent recruitment of the CpA machinery. 3) RNA:DNA hybrid models suggest that preferential destabilization of the Pol II elongation complex at rU:dA- and/or rA:dT-rich duplexes bridging the nucleotide addition and RNA exit sites permits preferential association of the CpA machinery with 3’ UTRs. Experiments to provide evidence for one or more of these models are suggested.

Yeast research is entering into a new period of scholarship, with new scientific tools, new questions to ask, and new issues to consider. The politics of emerging and critical technology can no longer be separated from the pursuit of basic science in fields, such as synthetic biology and engineering biology. Given the intensifying race for technological leadership, yeast research is likely to attract significant investment from government, and that it offers huge opportunities to the curious minded from a basic research standpoint. This article provides an overview of new directions in yeast research, and places these trends in their geopolitical context. At the highest level, yeast research is situated within the ongoing convergence of the life sciences with the information sciences. This convergent effect is most strongly pronounced in areas of AI-enabled tools for the life sciences, and the creation of synthetic genomes, minimal genomes, pan-genomes, neochromosomes and metagenomes using computer assisted design tools and methodologies. Synthetic yeast futures encompass basic and applied science questions that will be of intense interest to government and non-government funding sources. It is essential for the yeast research community to map and understand the context of their research in order to ensure their collaborations turn global challenges into research opportunities.