Saccharomyces Genome Database: the genomics resource of budding yeast

Transcriptional response of Saccharomyces cerevisiae to lactic acid enantiomers

The model yeast, Saccharomyces cerevisiae, is a popular object for both fundamental and applied research, including the development of biosensors and industrial production of pharmaceutical compounds. However, despite multiple studies exploring S. cerevisiae transcriptional response to various substances, this response is unknown for some substances produced in yeast, such as D-lactic acid (DLA). Here, we explore the transcriptional response of the BY4742 strain to a wide range of DLA concentrations (from 0.05 to 45 mM), and compare it to the response to 45 mM L-lactic acid (LLA). We recorded a response to 5 and 45 mM DLA (125 and 113 differentially expressed genes (DEGs), respectively; > 50% shared) and a less pronounced response to 45 mM LLA (63 DEGs; > 30% shared with at least one DLA treatment). Our data did not reveal natural yeast promoters quantitatively sensing DLA but provide the first description of the transcriptome-wide response to DLA and enrich our understanding of the LLA response. Some DLA-activated genes were indeed related to lactate metabolism, as well as iron uptake and cell wall structure. Additional analyses showed that at least some of these genes were activated only by acidic form of DLA but not its salt, revealing the role of pH. The list of LLA-responsive genes was similar to those published previously and also included iron uptake and cell wall genes, as well as genes responding to other weak acids. These data might be instrumental for optimization of lactate production in yeast and yeast co-cultivation with lactic acid bacteria. KEY POINTS: • We present the first dataset on yeast transcriptional response to DLA. • Differential gene expression was correlated with yeast growth inhibition. • The transcriptome response to DLA was richer in comparison to LLA.

Functional analysis of the zinc finger modules of the Saccharomyces cerevisiae splicing factor Luc7

Identification of splice sites is a critical step in pre-messenger RNA (pre-mRNA) splicing because the definition of the exon/intron boundaries controls what nucleotides are incorporated into mature mRNAs. The intron boundary with the upstream exon is initially identified through interactions with the U1 small nuclear ribonucleoprotein (snRNP). This involves both base-pairing between the U1 snRNA and the pre-mRNA as well as snRNP proteins interacting with the 5' splice site (5'ss)/snRNA duplex. In yeast, this duplex is buttressed by two conserved protein factors, Yhc1 and Luc7. Luc7 has three human paralogs (LUC7L, LUC7L2, and LUC7L3), which play roles in alternative splicing. What domains of these paralogs promote splicing at particular sites is not yet clear. Here, we humanized the zinc finger (ZnF) domains of the yeast Luc7 protein in order to understand their roles in splice site selection using reporter assays, transcriptome analysis, and genetic interactions. Although we were unable to determine a function for the first ZnF domain, humanization of the second ZnF domain to mirror that found in LUC7L or LUC7L2 resulted in altered usage of nonconsensus 5'ss. In contrast, the corresponding ZnF domain of LUC7L3 could not support yeast viability. Further, humanization of Luc7 can suppress mutation of the ATPase Prp28, which is involved in U1 release and exchange for U6 at the 5'ss. Our work reveals a role for the second ZnF of Luc7 in splice site selection and suggests that different ZnF domains may have different ATPase requirements for release by Prp28.

Conversion of polyploid and alloploid Saccharomyces sensu stricto strains to leu2 mutants by genome DNA editing

A large number of recombinant plasmids for the yeast Saccharomyces cerevisiae have been constructed and accumulated over the past four decades. It is desirable to apply the recombinant plasmid resources to Saccharomyces sensu stricto species group, which contains an increasing number of natural isolate and industrial strains. The application to the group encounters a difficulty. Natural isolates and industrial strains are exclusively prototrophic and polyploid, whereas direct application of most conventional plasmid resources imposes a prerequisite in host yeast strains of an auxotrophic mutation (i.e., leu2) that is rescued by a selection gene (e.g., LEU2) on the recombinant plasmids. To solve the difficulty, we aimed to generate leu2 mutants from yeast strains belonging to the yeast Saccharomyces sensu stricto species group by DNA editing. First, we modified an all-in-one type CRISPR-Cas9 plasmid pML104 by adding an antibiotic-resistance gene and designing guide sequences to target the LEU2 gene and to enable wide application in this yeast group. Then, the resulting CRISPR-Cas9 plasmids were exploited to seven strains belonging to five species of the group, including natural isolate, industrial, and allopolyploid strains. Colonies having the designed mutations in the gene appeared successfully by introducing the plasmids and assisting oligonucleotides to the strains. Most of the plasmids and resultant leu2- mutants produced in this study will be deposited in several repository organizations. KEY POINTS: • All-in-one type CRISPR-Cas9 plasmids targeting LEU2 gene were designed for broad application to Saccharomyces sensu stricto group species strains • Application of the plasmids generated leu2 mutants from strains including natural isolates, industrial, and allopolyploid strains • The easy conversion to leu2 mutants permits free access to recombinant plasmids having a LEU2 gene.

The spliceosome impacts morphogenesis in the human fungal pathogen Candida albicans

At human body temperature, the fungal pathogen Candida albicans can transition from yeast to filamentous morphologies in response to host-relevant cues. Additionally, elevated temperatures encountered during febrile episodes can independently induce C. albicans filamentation. However, the underlying genetic pathways governing this developmental transition in response to elevated temperatures remain largely unexplored. Here, we conducted a functional genomic screen to unravel the genetic mechanisms orchestrating C. albicans filamentation specifically in response to elevated temperature, implicating 45% of genes associated with the spliceosome or pre-mRNA splicing in this process. Employing RNA-Seq to elucidate the relationship between mRNA splicing and filamentation, we identified greater levels of intron retention in filaments compared to yeast, which correlated with reduced expression of the affected genes. Intriguingly, homozygous deletion of a gene encoding a spliceosome component important for filamentation (PRP19) caused even greater levels of intron retention compared with wild type and displayed globally dysregulated gene expression. This suggests that intron retention is a mechanism for fine-tuning gene expression during filamentation, with perturbations of the spliceosome exacerbating this process and blocking filamentation. Overall, this study unveils a novel biological process governing C. albicans filamentation, providing new insights into the complex regulation of this key virulence trait.IMPORTANCEFungal pathogens such as Candida albicans can cause serious infections with high mortality rates in immunocompromised individuals. When C. albicans is grown at temperatures encountered during human febrile episodes, yeast cells undergo a transition to filamentous cells, and this process is key to its virulence. Here, we expanded our understanding of how C. albicans undergoes filamentation in response to elevated temperature and identified many genes involved in mRNA splicing that positively regulate filamentation. Through transcriptome analyses, we found that intron retention is a mechanism for fine-tuning gene expression in filaments, and perturbation of the spliceosome exacerbates intron retention and alters gene expression substantially, causing a block in filamentation. This work adds to the growing body of knowledge on the role of introns in fungi and provides new insights into the cellular processes that regulate a key virulence trait in C. albicans.

Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome

BACKGROUND

Recent studies uncovered pervasive transcription and translation of thousands of noncanonical open reading frames (nORFs) outside of annotated genes. The contribution of nORFs to cellular phenotypes is difficult to infer using conventional approaches because nORFs tend to be short, of recent de novo origins, and lowly expressed. Here we develop a dedicated coexpression analysis framework that accounts for low expression to investigate the transcriptional regulation, evolution, and potential cellular roles of nORFs in Saccharomyces cerevisiae.

RESULTS

Our results reveal that nORFs tend to be preferentially coexpressed with genes involved in cellular transport or homeostasis but rarely with genes involved in RNA processing. Mechanistically, we discover that young de novo nORFs located downstream of conserved genes tend to leverage their neighbors' promoters through transcription readthrough, resulting in high coexpression and high expression levels. Transcriptional piggybacking also influences the coexpression profiles of young de novo nORFs located upstream of genes, but to a lesser extent and without detectable impact on expression levels. Transcriptional piggybacking influences, but does not determine, the transcription profiles of de novo nORFs emerging nearby genes. About 40% of nORFs are not strongly coexpressed with any gene but are transcriptionally regulated nonetheless and tend to form entirely new transcription modules. We offer a web browser interface ( https://carvunislab.csb.pitt.edu/shiny/coexpression/ ) to efficiently query, visualize, and download our coexpression inferences.

CONCLUSIONS

Our results suggest that nORF transcription is highly regulated. Our coexpression dataset serves as an unprecedented resource for unraveling how nORFs integrate into cellular networks, contribute to cellular phenotypes, and evolve.

Translational control of MPS1 links protein synthesis with the initiation of cell division and spindle pole body duplication in Saccharomyces cerevisiae

Protein synthesis underpins cell growth and controls when cells commit to a new round of cell division at a point in late G1 of the cell cycle called Start. Passage through Start also coincides with the duplication of the microtubule-organizing centers, the yeast spindle pole bodies, which will form the 2 poles of the mitotic spindle that segregates the chromosomes in mitosis. The conserved Mps1p kinase governs the duplication of the spindle pole body (SPB) in Saccharomyces cerevisiae. Here, we show that the MPS1 transcript has a short upstream open reading frame (uORF) that represses the synthesis of Mps1p. Mutating the MPS1 uORF makes the cells smaller, accelerates the appearance of Mps1p in late G1, and promotes completion of Start. Monitoring the SPB in the cell cycle using structured illumination microscopy revealed that mutating the MPS1 uORF enabled cells to duplicate their SPB earlier at a smaller cell size. The accelerated Start of MPS1 uORF mutants depends on the G1 cyclin Cln3p and the transcriptional repressor Whi5p but not on the Cln1,2p G1 cyclins. These results identify growth inputs in mechanisms that control duplication of the microtubule-organizing center and implicate these processes in the coupling of cell growth with division.

Trans -eQTL hotspots shape complex traits by modulating cellular states

Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae . We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans -acting regulatory loci. Trans -acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits, but with heterogeneous direction of effect. Our results reveal how trans -acting regulatory hotspots shape complex traits by altering cellular states.

Chemical genomic analysis reveals the interplay between iron chelation, zinc homeostasis, and retromer function in the bioactivity of an ethanol adduct of the feijoa fruit-derived ellagitannin vescalagin

Nature has been a rich source of pharmaceutical compounds, producing 80% of our currently prescribed drugs. The feijoa plant, Acca sellowiana, is classified in the family Myrtaceae, native to South America, and currently grown worldwide to produce feijoa fruit. Feijoa is a rich source of bioactive compounds with anticancer, anti-inflammatory, antibacterial, and antifungal activities; however, the mechanism of action of these compounds is largely not known. Here, we used chemical genetic analyses in the model organism Saccharomyces cerevisiae to investigate the mechanism of action of a feijoa-derived ethanol adduct of vescalagin (EtOH-vescalagin). Genome-wide barcode sequencing analysis revealed yeast strains lacking genes in iron metabolism, zinc metabolism, retromer function, or mitochondrial function were hypersensitive to 0.3 µM EtOH-vescalagin. This treatment increased expression of iron uptake proteins at the plasma membrane, which was a compensatory response to reduced intracellular iron. Likewise, EtOH-vescalagin increased expression of the Cot1 protein in the vacuolar membrane that transports zinc into the vacuole to prevent cytoplasmic accumulation of zinc. Each individual subunit in the retromer complex was required for the iron homeostatic mechanism of EtOH-vescalagin, while only the cargo recognition component in the retromer complex was required for the zinc homeostatic mechanism. Overexpression of either retromer subunits or high-affinity iron transporters suppressed EtOH-vescalagin bioactivity in a zinc-replete condition, while overexpression of only retromer subunits increased EtOH-vescalagin bioactivity in a zinc-deficient condition. Together, these results indicate that EtOH-vescalagin bioactivity begins with extracellular iron chelation and proceeds with intracellular transport of zinc via the retromer complex. More broadly, this is the first report of a bioactive compound to further characterize the poorly understood interaction between zinc metabolism and retromer function.

Schizosaccharomyces pombe as a fundamental model for research on mitochondrial gene expression: Progress, achievements and outlooks

Schizosaccharomyces pombe (fission yeast) is an attractive model for mitochondrial research. The organism resembles human cells in terms of mitochondrial inheritance, mitochondrial transport, sugar metabolism, mitogenome structure and dependence of viability on the mitogenome (the petite-negative phenotype). Transcriptions of these genomes produce only a few polycistronic transcripts, which then undergo processing as per the tRNA punctuation model. In general, the machinery for mitochondrial gene expression is structurally and functionally conserved between fission yeast and humans. Furthermore, molecular research on S. pombe is supported by a considerable number of experimental techniques and database resources. Owing to these advantages, fission yeast has significantly contributed to biomedical and fundamental research. Here, we review the current state of knowledge regarding S. pombe mitochondrial gene expression, and emphasise the pertinence of fission yeast as both a model and tool, especially for studies on mitochondrial translation.

Absence of farnesol salvage in Candida albicans and probably in other fungi

Farnesol salvage, a two-step pathway converting farnesol to farnesyl pyrophosphate (FPP), occurs in bacteria, plants, and animals. This paper investigates the presence of this pathway in fungi. Through bioinformatics, biochemistry, and physiological analyses, we demonstrate its absence in the yeasts Saccharomyces cerevisiae and Candida albicans, suggesting a likely absence across fungi. We screened 1,053 fungal genomes, including 34 from C. albicans, for potential homologs to four genes (Arabidopsis thaliana AtFOLK, AtVTE5, AtVTE6, and Plasmodium falciparum PfPOLK) known to accomplish farnesol/prenol salvage in other organisms. Additionally, we showed that 3H-farnesol was not converted to FPP or any other phosphorylated prenol, and exogenous farnesol was not metabolized within 90 minutes at any phase of growth and did not rescue cells from the toxic effects of atorvastatin, but it did elevate the levels of intracellular farnesol (Fi). All these experiments were conducted with C. albicans. In sum, we found no evidence for farnesol salvage in fungi.

IMPORTANCE

The absence of farnesol salvage constitutes a major difference in the metabolic capabilities of fungi. In terms of fungal physiology, the lack of farnesol salvage pathways relates to how farnesol acts as a quorum-sensing molecule in Candida albicans and why farnesol should be investigated for use in combination with other known antifungal antibiotics. Its absence is essential for a model (K. W. Nickerson et al., Microbiol Mol Biol Rev 88:e00081-22, 2024), wherein protein farnesylation, protein chaperones, and the unfolded protein response are combined under the unifying umbrella of a cell's intracellular farnesol (Fi). In terms of human health, farnesol should have at least two different modes of action depending on whether those cells have farnesol salvage. Because animals have farnesol salvage, we can now see the importance of dietary prenols as well as the potential importance of farnesol in treating neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and multiple sclerosis.

Disordered sequences of transcription factors regulate genomic binding by integrating diverse sequence grammars and interaction types

Intrinsically disordered regions (IDRs) guide transcription factors (TFs) to their genomic binding sites, raising the question of how structure-lacking regions encode for complex binding patterns. We investigated this using the TF Gln3, revealing sets of IDR-embedded determinants that direct Gln3 binding to respective groups of functionally related promoters, and enable tuning binding preferences between environmental conditions, phospho-mimicking mutations, and orthologs. Through targeted mutations, we defined the role of short linear motifs (SLiMs) and co-binding TFs (Hap2) in stabilizing Gln3 at respiration-chain promoters, while providing evidence that Gln3 binding at nitrogen-associated promoters is encoded by the IDR amino-acid composition, independent of SLiMs or co-binding TFs. Therefore, despite their apparent simplicity, TF IDRs can direct and regulate complex genomic binding patterns through a combination of SLiM-mediated and composition-encoded interactions.

Ordered and disordered regions of the Origin Recognition Complex direct differential in vivo binding at distinct motif sequences

The Origin Recognition Complex (ORC) seeds replication-fork formation by binding to DNA replication origins, which in budding yeast contain a 17bp DNA motif. High resolution structure of the ORC-DNA complex revealed two base-interacting elements: a disordered basic patch (Orc1-BP4) and an insertion helix (Orc4-IH). To define the ORC elements guiding its DNA binding in vivo, we mapped genomic locations of 38 designed ORC mutants, revealing that different ORC elements guide binding at different sites. At silencing-associated sites lacking the motif, ORC binding and activity were fully explained by a BAH domain. Within replication origins, we reveal two dominating motif variants showing differential binding modes and symmetry: a non-repetitive motif whose binding requires Orc1-BP4 and Orc4-IH, and a repetitive one where another basic patch, Orc1-BP3, can replace Orc4-IH. Disordered basic patches are therefore key for ORC-motif binding in vivo, and we discuss how these conserved, minor-groove interacting elements can guide specific ORC-DNA recognition.

Suppressing Anaphase-Promoting Complex/Cyclosome-Cell Division Cycle 20 Activity to Enhance the Effectiveness of Anti-Cancer Drugs That Induce Multipolar Mitotic Spindles

Paclitaxel induces multipolar spindles at clinically relevant doses but does not substantially increase mitotic indices. Paclitaxel's anti-cancer effects are hypothesized to occur by promoting chromosome mis-segregation on multipolar spindles leading to apoptosis, necrosis and cyclic-GMP-AMP Synthase-Stimulator of Interferon Genes (cGAS-STING) pathway activation in daughter cells, leading to secretion of type I interferon (IFN) and immunogenic cell death. Eribulin and vinorelbine have also been reported to cause increases in multipolar spindles in cancer cells. Recently, suppression of Anaphase-Promoting Complex/Cyclosome-Cell Division Cycle 20 (APC/C-CDC20) activity using CRISPR/Cas9 mutagenesis has been reported to increase sensitivity to Kinesin Family 18a (KIF18a) inhibition, which functions to suppress multipolar mitotic spindles in cancer cells. We propose that a way to enhance the effectiveness of anti-cancer agents that increase multipolar spindles is by suppressing the APC/C-CDC20 to delay, but not block, anaphase entry. Delaying anaphase entry in genomically unstable cells may enhance multipolar spindle-induced cell death. In genomically stable healthy human cells, delayed anaphase entry may suppress the level of multipolar spindles induced by anti-cancer drugs and lower mitotic cytotoxicity. We outline specific combinations of molecules to investigate that may achieve the goal of enhancing the effectiveness of anti-cancer agents.

Single-cell heterogeneity in ribosome content and the consequences for the growth laws

Previous work has suggested that the ribosome content of a cell is optimized to maximize growth given the nutrient availability. The resulting correlation between ribosome number and growth rate appears to be independent of the rate limiting nutrient and has been reported in many organisms. The robustness and universality of this observation has given it the classification of a "growth law." These laws have had powerful impacts on many biological disciplines. They have fueled predictions about how organisms evolve to maximize reproduction, and informed models about how cells regulate growth. Due to methodological limitations, this growth law has rarely been studied at the level of individual cells. While populations of fast-growing cells tend to have more ribosomes than populations of slow-growing cells, it is unclear if individual cells tightly regulate their ribosome content to match their environment. Here, we use recent ground-breaking single-cell RNA sequencing techniques to study this growth law at the single-cell level in two different microbes, S. cerevisiae (a single-celled yeast and eukaryote) and B. subtilis (a bacterium and prokaryote). In both species, we find enormous variation in the ribosomal content of single cells that is not predictive of growth rate. Fast-growing populations include cells showing transcriptional signatures of slow growth and stress, as do cells with the highest ribosome content we survey. Broadening our focus to the levels of non-ribosomal transcripts reveals subpopulations of cells in unique transcriptional states suggestive of divergent growth strategies. These results suggest that single-cell ribosome levels are not finely tuned to match population growth rates or nutrient availability, at least not in a way that can be captured by a unifying law that applies to all cell types. Overall, this work encourages the expansion of these "laws" and other models that predict how growth rates are regulated or how they evolve to consider single-cell heterogeneity.

Structure-guided Evolutionary Analysis of Interactome Network Rewiring at Single Residue Resolution in Yeasts

Protein-protein interactions (PPIs) are known to rewire extensively during evolution leading to lineage-specific and species-specific changes in molecular processes. However, the detailed molecular evolutionary mechanisms underlying interactome network rewiring are not well-understood. Here, we combine high-confidence PPI data, high-resolution three-dimensional structures of protein complexes, and homology-based structural annotation transfer to construct structurally-resolved interactome networks for the two yeasts S. cerevisiae and S. pombe. We then classify PPIs according to whether they are preserved or different between the two yeast species and compare site-specific evolutionary rates of interfacial versus non-interfacial residues for these different categories of PPIs. We find that residues in PPI interfaces evolve significantly more slowly than non-interfacial residues when using lineage-specific measures of evolutionary rate, but not when using non-lineage-specific measures. Furthermore, both lineage-specific and non-lineage-specific evolutionary rate measures can distinguish interfacial residues from non-interfacial residues for preserved PPIs between the two yeasts, but only the lineage-specific measure is appropriate for rewired PPIs. Finally, both lineage-specific and non-lineage-specific evolutionary rate measures are appropriate for elucidating structural determinants of protein evolution for residues outside of PPI interfaces. Overall, our results demonstrate that unlike tertiary structures of single proteins, PPIs and PPI interfaces can be highly volatile in their evolution, thus requiring the use of lineage-specific measures when studying their evolution. These results yield insight into the evolutionary design principles of PPIs and the mechanisms by which interactions are preserved or rewired between species, improving our understanding of the molecular evolution of PPIs and PPI interfaces at the residue level.

An epitope-tagged Swd2 reveals the different requirements of Swd2 concentration in H3K4 methylation and viability

Swd2/Cps35 is a common component of the COMPASS H3K4 methyltransferase and CPF transcription termination complex in Saccharomyces cerevisiae. The deletion of SWD2 is lethal, which results from transcription termination defects in snoRNA genes. This study isolated a yeast strain that showed significantly reduced protein level of Swd2 following epitope tagging at its N-terminus (9MYC-SWD2). The reduced level of Swd2 in the 9MYC-SWD2 strain was insufficient for the stability of the Set1 H3K4 methyltransferase, H3K4me3 and snoRNA termination, but the level was enough for viability and growth similar to the wildtype strain. In addition, we presented the genes differentially regulated by the essential protein Swd2 under optimal culture conditions for the first time. The expression of genes known to be decreased in the absence of Set1 and H3K4me3, including NAD biosynthetic process genes and histone genes, was decreased in the 9MYC-SWD2 strain, as expected. However, the effects of Swd2 on the ribosome biogenesis (RiBi) genes were opposite to those of Set1, suggesting that the expression of RiBi genes is regulated by more complex relationship between COMPASS and other Swd2-containing complexes. These data suggest that different concentrations of Swd2 are required for its roles in H3K4me3 and viability and that it may be either contributory or contrary to the transcriptional regulation of Set1/H3K4me3, depending on the gene group.

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer's disease dementia

BACKGROUND

Transcriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia.

METHODS

We first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene.

RESULTS

We identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD.

CONCLUSION

We applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.

Fungal microtubule organizing centers are evolutionarily unstable structures

For most Eukaryotic species the requirements of cilia formation dictate the structure of microtubule organizing centers (MTOCs). In this study we find that loss of cilia corresponds to loss of evolutionary stability for fungal MTOCs. We used iterative search algorithms to identify proteins homologous to those found in Saccharomyces cerevisiae, and Schizosaccharomyces pombe MTOCs, and calculated site-specific rates of change for those proteins that were broadly phylogenetically distributed. Our results indicate that both the protein composition of MTOCs as well as the sequence of MTOC proteins are poorly conserved throughout the fungal kingdom. To begin to reconcile this rapid evolutionary change with the rigid structure and essential function of the S. cerevisiae MTOC we further analyzed how structural interfaces among proteins influence the rates of change for specific residues within a protein. We find that a more stable protein may stabilize portions of an interacting partner where the two proteins are in contact. In summary, while the protein composition and sequences of the MTOC may be rapidly changing the proteins within the structure have a stabilizing effect on one another. Further exploration of fungal MTOCs will expand our understanding of how changes in the functional needs of a cell have affected physical structures, proteomes, and protein sequences throughout fungal evolution.

Using EMBL-EBI Services via Web Interface and Programmatically via Web Services

The European Bioinformatics Institute (EMBL-EBI)'s Job Dispatcher framework provides access to a wide range of core databases and analysis tools that are of key importance in bioinformatics. As well as providing web interfaces to these resources, web services are available using REST and SOAP protocols that enable programmatic access and allow their integration into other applications and analytical workflows and pipelines. This article describes the various options available to researchers and bioinformaticians who would like to use our resources via the web interface employing RESTful web services clients provided in Perl, Python, and Java or who would like to use Docker containers to integrate the resources into analysis pipelines and workflows. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Retrieving data from EMBL-EBI using Dbfetch via the web interface Alternate Protocol 1: Retrieving data from EMBL-EBI using WSDbfetch via the REST interface Alternate Protocol 2: Retrieving data from EMBL-EBI using Dbfetch via RESTful web services with Python client Support Protocol 1: Installing Python REST web services clients Basic Protocol 2: Sequence similarity search using FASTA search via the web interface Alternate Protocol 3: Sequence similarity search using FASTA via RESTful web services with Perl client Support Protocol 2: Installing Perl REST web services clients Basic Protocol 3: Sequence similarity search using NCBI BLAST+ RESTful web services with Python client Basic Protocol 4: Sequence similarity search using HMMER3 phmmer REST web services with Perl client and Docker Support Protocol 3: Installing Docker and running the EMBL-EBI client container Basic Protocol 5: Protein functional analysis using InterProScan 5 RESTful web services with the Python client and Docker Alternate Protocol 4: Protein functional analysis using InterProScan 5 RESTful web services with the Java client Support Protocol 4: Installing Java web services clients Basic Protocol 6: Multiple sequence alignment using Clustal Omega via web interface Alternate Protocol 5: Multiple sequence alignment using Clustal Omega with Perl client and Docker Support Protocol 5: Exploring the RESTful API with OpenAPI User Inferface.

Long-Read Sequencing Genome Assembly of Ceratocystis fimbriata Enables Development of Molecular Diagnostics for Sweetpotato Black Rot

Ceratocystis fimbriata is a destructive fungal pathogen of sweetpotato (Ipomoea batatas) that leads to losses at all stages of sweetpotato production. Accurate detection of C. fimbriata would allow for more efficient deployment of management tactics in sweetpotato production. To develop a diagnostic assay, a hybrid genome assembly of C. fimbriata isolate AS236 was generated. The resulting 31.7-MB assembly was near-chromosome level, with 18 contigs, 6,481 predicted genes, and a BUSCO completion score of 98.4% when compared with the fungus-specific lineage database. Additional Illumina DNA reads from C. manginecans, C. platani, and a second C. fimbriata isolate (C1421) were then mapped to the assembled genome using BOWTIE2 and counted using HTSeq, which identified 148 genes present only within C. fimbriata as molecular diagnostic candidates; 6 single-copy and 35 highly multi-copy (>40 BLAST hits), as determined through a self-BLAST-P alignment. Primers for PCR were designed in the 200-bp flanking region of the first exon for each candidate, and the candidates were validated against a diverse DNA panel containing Ceratocystis species, sweetpotato pathogens, and plants. After validation, two diagnostic candidates amplified only C. fimbriata DNA and were considered to be highly specific to the species. These genetic markers will serve as valuable diagnostic tools with multiple applications including the detection of C. fimbriata in seed, soil, and wash water in sweetpotato production.

Natural proteome diversity links aneuploidy tolerance to protein turnover

Accessing the natural genetic diversity of species unveils hidden genetic traits, clarifies gene functions and allows the generalizability of laboratory findings to be assessed. One notable discovery made in natural isolates of Saccharomyces cerevisiae is that aneuploidy-an imbalance in chromosome copy numbers-is frequent1,2 (around 20%), which seems to contradict the substantial fitness costs and transient nature of aneuploidy when it is engineered in the laboratory3-5. Here we generate a proteomic resource and merge it with genomic1 and transcriptomic6 data for 796 euploid and aneuploid natural isolates. We find that natural and lab-generated aneuploids differ specifically at the proteome. In lab-generated aneuploids, some proteins-especially subunits of protein complexes-show reduced expression, but the overall protein levels correspond to the aneuploid gene dosage. By contrast, in natural isolates, more than 70% of proteins encoded on aneuploid chromosomes are dosage compensated, and average protein levels are shifted towards the euploid state chromosome-wide. At the molecular level, we detect an induction of structural components of the proteasome, increased levels of ubiquitination, and reveal an interdependency of protein turnover rates and attenuation. Our study thus highlights the role of protein turnover in mediating aneuploidy tolerance, and shows the utility of exploiting the natural diversity of species to attain generalizable molecular insights into complex biological processes.

Nucleosomal DNA has topological memory

One elusive aspect of the chromosome architecture is how it constrains the DNA topology. Nucleosomes stabilise negative DNA supercoils by restraining a DNA linking number difference (∆Lk) of about -1.26. However, whether this capacity is uniform across the genome is unknown. Here, we calculate the ∆Lk restrained by over 4000 nucleosomes in yeast cells. To achieve this, we insert each nucleosome in a circular minichromosome and perform Topo-seq, a high-throughput procedure to inspect the topology of circular DNA libraries in one gel electrophoresis. We show that nucleosomes inherently restrain distinct ∆Lk values depending on their genomic origin. Nucleosome DNA topologies differ at gene bodies (∆Lk = -1.29), intergenic regions (∆Lk = -1.23), rDNA genes (∆Lk = -1.24) and telomeric regions (∆Lk = -1.07). Nucleosomes near the transcription start and termination sites also exhibit singular DNA topologies. Our findings demonstrate that nucleosome DNA topology is imprinted by its native chromatin context and persists when the nucleosome is relocated.

Genome-wide nucleosome and transcription factor responses to genetic perturbations reveal chromatin-mediated mechanisms of transcriptional regulation

Epigenetic mechanisms contribute to gene regulation by altering chromatin accessibility through changes in transcription factor (TF) and nucleosome occupancy throughout the genome. Despite numerous studies focusing on changes in gene expression, the intricate chromatin-mediated regulatory code remains largely unexplored on a comprehensive scale. We address this by employing a factor-agnostic, reverse-genetics approach that uses MNase-seq to capture genome-wide TF and nucleosome occupancies in response to the individual deletion of 201 transcriptional regulators in Saccharomyces cerevisiae, thereby assaying nearly one million mutant-gene interactions. We develop a principled approach to identify and quantify chromatin changes genome-wide, observing differences in TF and nucleosome occupancy that recapitulate well-established pathways identified by gene expression data. We also discover distinct chromatin signatures associated with the up- and downregulation of genes, and use these signatures to reveal regulatory mechanisms previously unexplored in expression-based studies. Finally, we demonstrate that chromatin features are predictive of transcriptional activity and leverage these features to reconstruct chromatin-based transcriptional regulatory networks. Overall, these results illustrate the power of an approach combining genetic perturbation with high-resolution epigenomic profiling; the latter enables a close examination of the interplay between TFs and nucleosomes genome-wide, providing a deeper, more mechanistic understanding of the complex relationship between chromatin organization and transcription.

Loss of the yeast transporter Agp2 upregulates the pleiotropic drug-resistant pump Pdr5 and confers resistance to the protein synthesis inhibitor cycloheximide

The transmembrane protein Agp2, initially shown as a transporter of L-carnitine, mediates the high-affinity transport of polyamines and the anticancer drug bleomycin-A5. Cells lacking Agp2 are hyper-resistant to polyamine and bleomycin-A5. In these earlier studies, we showed that the protein synthesis inhibitor cycloheximide blocked the uptake of bleomycin-A5 into the cells suggesting that the drug uptake system may require de novo synthesis. However, our recent findings demonstrated that cycloheximide, instead, induced rapid degradation of Agp2, and in the absence of Agp2 cells are resistant to cycloheximide. These observations raised the possibility that the degradation of Agp2 may allow the cell to alter its drug resistance network to combat the toxic effects of cycloheximide. In this study, we show that membrane extracts from agp2Δ mutants accentuated several proteins that were differentially expressed in comparison to the parent. Mass spectrometry analysis of the membrane extracts uncovered the pleiotropic drug efflux pump, Pdr5, involved in the efflux of cycloheximide, as a key protein upregulated in the agp2Δ mutant. Moreover, a global gene expression analysis revealed that 322 genes were differentially affected in the agp2Δ mutant versus the parent, including the prominent PDR5 gene and genes required for mitochondrial function. We further show that Agp2 is associated with the upstream region of the PDR5 gene, leading to the hypothesis that cycloheximide resistance displayed by the agp2Δ mutant is due to the derepression of the PDR5 gene.

Transcriptome-wide mRNA condensation precedes stress granule formation and excludes stress-induced transcripts

Stress-induced condensation of mRNA and proteins into stress granules is conserved across eukaryotes, yet the function, formation mechanisms, and relation to well-studied conserved transcriptional responses remain largely unresolved. Stress-induced exposure of ribosome-free mRNA following translational shutoff is thought to cause condensation by allowing new multivalent RNA-dependent interactions, with RNA length and associated interaction capacity driving increased condensation. Here we show that, in striking contrast, virtually all mRNA species condense in response to multiple unrelated stresses in budding yeast, length plays a minor role, and instead, stress-induced transcripts are preferentially excluded from condensates, enabling their selective translation. Using both endogenous genes and reporter constructs, we show that translation initiation blockade, rather than resulting ribosome-free RNA, causes condensation. These translation initiation-inhibited condensates (TIICs) are biochemically detectable even when stress granules, defined as microscopically visible foci, are absent or blocked. TIICs occur in unstressed yeast cells, and, during stress, grow before the appearance of visible stress granules. Stress-induced transcripts are excluded from TIICs primarily due to the timing of their expression, rather than their sequence features. Together, our results reveal a simple system by which cells redirect translational activity to newly synthesized transcripts during stress, with broad implications for cellular regulation in changing conditions.

SNP-SVant: A Computational Workflow to Predict and Annotate Genomic Variants in Organisms Lacking Benchmarked Variants

Whole-genome sequencing is widely used to investigate population genomic variation in organisms of interest. Assorted tools have been independently developed to call variants from short-read sequencing data aligned to a reference genome, including single nucleotide polymorphisms (SNPs) and structural variations (SVs). We developed SNP-SVant, an integrated, flexible, and computationally efficient bioinformatic workflow that predicts high-confidence SNPs and SVs in organisms without benchmarked variants, which are traditionally used for distinguishing sequencing errors from real variants. In the absence of these benchmarked datasets, we leverage multiple rounds of statistical recalibration to increase the precision of variant prediction. The SNP-SVant workflow is flexible, with user options to tradeoff accuracy for sensitivity. The workflow predicts SNPs and small insertions and deletions using the Genome Analysis ToolKit (GATK) and predicts SVs using the Genome Rearrangement IDentification Software Suite (GRIDSS), and it culminates in variant annotation using custom scripts. A key utility of SNP-SVant is its scalability. Variant calling is a computationally expensive procedure, and thus, SNP-SVant uses a workflow management system with intermediary checkpoint steps to ensure efficient use of resources by minimizing redundant computations and omitting steps where dependent files are available. SNP-SVant also provides metrics to assess the quality of called variants and converts between VCF and aligned FASTA format outputs to ensure compatibility with downstream tools to calculate selection statistics, which are commonplace in population genomics studies. By accounting for both small and large structural variants, users of this workflow can obtain a wide-ranging view of genomic alterations in an organism of interest. Overall, this workflow advances our capabilities in assessing the functional consequences of different types of genomic alterations, ultimately improving our ability to associate genotypes with phenotypes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Predicting single nucleotide polymorphisms and structural variations Support Protocol 1: Downloading publicly available sequencing data Support Protocol 2: Visualizing variant loci using Integrated Genome Viewer Support Protocol 3: Converting between VCF and aligned FASTA formats.

CURE on yeast genes of unknown function increases students' bioinformatics proficiency and research confidence

Course-based undergraduate research experiences (CUREs) can reduce barriers to research opportunities while increasing student knowledge and confidence. However, the number of widely adopted, easily transferable CUREs is relatively small. Here, we describe a CURE aimed at determining the function of poorly characterized Saccharomyces cerevisiae genes. More than 20 years after sequencing of the yeast genome, nearly 10% of open reading frames (ORFs) still have at least one uncharacterized Gene Ontology (GO) term. We refer to these genes as "ORFans" and formed a consortium aimed at assigning functions to them. Specifically, over 70 faculty members attended summer workshops to learn the bioinformatics workflow and basic laboratory techniques described herein. Ultimately, this CURE was adapted for implementation at 34 institutions, resulting in over 1,300 students conducting course-based research on ORFans. Pre-/post-tests confirmed that students gained both (i) an understanding of gene ontology and (ii) knowledge regarding the use of bioinformatics to assign gene function. After using these data to craft their own hypotheses, then testing their predictions by constructing and phenotyping deletion strains, students self-reported significant gains in several areas, including computer modeling and exposure to a project where no one knows the outcome. Interestingly, most net gains self-reported by ORFan Gene Project participants were greater than published findings for CUREs assessed with the same survey instrument. The surprisingly strong impact of this CURE may be due to the incoming lack of experience of ORFan Project participants and/or the independent thought required to develop testable hypotheses from complex data sets.

Distinct types of intramitochondrial protein aggregates protect mitochondria against proteotoxic stress

Mitochondria consist of hundreds of proteins, most of which are inaccessible to the proteasomal quality control system of the cytosol. How cells stabilize the mitochondrial proteome during challenging conditions remains poorly understood. Here, we show that mitochondria form spatially defined protein aggregates as a stress-protecting mechanism. Two different types of intramitochondrial protein aggregates can be distinguished. The mitoribosomal protein Var1 (uS3m) undergoes a stress-induced transition from a soluble, chaperone-stabilized protein that is prevalent under benign conditions to an insoluble, aggregated form upon acute stress. The formation of Var1 bodies stabilizes mitochondrial proteostasis, presumably by sequestration of aggregation-prone proteins. The AAA chaperone Hsp78 is part of a second type of intramitochondrial aggregate that transiently sequesters proteins and promotes their folding or Pim1-mediated degradation. Thus, mitochondrial proteins actively control the formation of distinct types of intramitochondrial protein aggregates, which cooperate to stabilize the mitochondrial proteome during proteotoxic stress conditions.

Synchronized assembly of the oxidative phosphorylation system controls mitochondrial respiration in yeast

Control of protein stoichiometry is essential for cell function. Mitochondrial oxidative phosphorylation (OXPHOS) presents a complex stoichiometric challenge as the ratio of the electron transport chain (ETC) and ATP synthase must be tightly controlled, and assembly requires coordinated integration of proteins encoded in the nuclear and mitochondrial genome. How correct OXPHOS stoichiometry is achieved is unknown. We identify the Mitochondrial Regulatory hub for respiratory Assembly (MiRA) platform, which synchronizes ETC and ATP synthase biogenesis in yeast. Molecularly, this is achieved by a stop-and-go mechanism: the uncharacterized protein Mra1 stalls complex IV assembly. Two "Go" signals are required for assembly progression: binding of the complex IV assembly factor Rcf2 and Mra1 interaction with an Atp9-translating mitoribosome induce Mra1 degradation, allowing synchronized maturation of complex IV and the ATP synthase. Failure of the stop-and-go mechanism results in cell death. MiRA controls OXPHOS assembly, ensuring correct stoichiometry of protein machineries encoded by two different genomes.

The response to single-gene duplication implicates translation as a key vulnerability in aneuploid yeast

Aneuploidy produces myriad consequences in health and disease, yet models of the deleterious effects of chromosome amplification are still widely debated. To distinguish the molecular determinants of aneuploidy stress, we measured the effects of duplicating individual genes in cells with varying chromosome duplications, in wild-type cells and cells sensitized to aneuploidy by deletion of RNA-binding protein Ssd1. We identified gene duplications that are nearly neutral in wild-type euploid cells but significantly deleterious in euploids lacking SSD1 or SSD1+ aneuploid cells with different chromosome duplications. Several of the most deleterious genes are linked to translation; in contrast, duplication of other translational regulators, including eI5Fa Hyp2, benefit ssd1Δ aneuploids over controls. Using modeling of aneuploid growth defects, we propose that the deleterious effects of aneuploidy emerge from an interaction between the cumulative burden of many amplified genes on a chromosome and a subset of duplicated genes that become toxic in that context. Our results suggest that the mechanism behind their toxicity is linked to a key vulnerability in translation in aneuploid cells. These findings provide a perspective on the dual impact of individual genes and overall genomic burden, offering new avenues for understanding aneuploidy and its cellular consequences.

Whole-genome duplication in the Multicellularity Long Term Evolution Experiment

Whole-genome duplication (WGD) is widespread across eukaryotes and can promote adaptive evolution1-4. However, given the instability of newly-formed polyploid genomes5-7, understanding how WGDs arise in a population, persist, and underpin adaptations remains a challenge. Using our ongoing Multicellularity Long Term Evolution Experiment (MuLTEE)8, we show that diploid snowflake yeast (Saccharomyces cerevisiae) under selection for larger multicellular size rapidly undergo spontaneous WGD. From its origin within the first 50 days of the experiment, tetraploids persist for the next 950 days (nearly 5,000 generations, the current leading edge of our experiment) in ten replicate populations, despite being genomically unstable. Using synthetic reconstruction, biophysical modeling, and counter-selection experiments, we found that tetraploidy evolved because it confers immediate fitness benefits in this environment, by producing larger, longer cells that yield larger clusters. The same selective benefit also maintained tetraploidy over long evolutionary timescales, inhibiting the reversion to diploidy that is typically seen in laboratory evolution experiments. Once established, tetraploidy facilitated novel genetic routes for adaptation, playing a key role in the evolution of macroscopic multicellular size via the origin of evolutionarily conserved aneuploidy. These results provide unique empirical insights into the evolutionary dynamics and impacts of WGD, showing how it can initially arise due to its immediate adaptive benefits, be maintained by selection, and fuel long-term innovations by creating additional dimensions of heritable genetic variation.

Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model

Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will only be maintained if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74-94% of the variance in aneuploid strains' growth rates is explained by the additive cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of snoRNAs and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.

An adaptive biomolecular condensation response is conserved across environmentally divergent species

Cells must sense and respond to sudden maladaptive environmental changes-stresses-to survive and thrive. Across eukaryotes, stresses such as heat shock trigger conserved responses: growth arrest, a specific transcriptional response, and biomolecular condensation of protein and mRNA into structures known as stress granules under severe stress. The composition, formation mechanism, adaptive significance, and even evolutionary conservation of these condensed structures remain enigmatic. Here we provide a remarkable view into stress-triggered condensation, its evolutionary conservation and tuning, and its integration into other well-studied aspects of the stress response. Using three morphologically near-identical budding yeast species adapted to different thermal environments and diverged by up to 100 million years, we show that proteome-scale biomolecular condensation is tuned to species-specific thermal niches, closely tracking corresponding growth and transcriptional responses. In each species, poly(A)-binding protein-a core marker of stress granules-condenses in isolation at species-specific temperatures, with conserved molecular features and conformational changes modulating condensation. From the ecological to the molecular scale, our results reveal previously unappreciated levels of evolutionary selection in the eukaryotic stress response, while establishing a rich, tractable system for further inquiry.

Profiling the compendium of changes in Saccharomyces cerevisiae due to mutations that alter availability of the main methyl donor S-Adenosylmethionine

The SAM1 and SAM2 genes encode for S-Adenosylmethionine (AdoMet) synthetase enzymes, with AdoMet serving as the main cellular methyl donor. We have previously shown that independent deletion of these genes alters chromosome stability and AdoMet concentrations in opposite ways in Saccharomyces cerevisiae. To characterize other changes occurring in these mutants, we grew wildtype, sam1Δ/sam1Δ, and sam2Δ/sam2Δ strains in 15 different Phenotypic Microarray plates with different components and measured growth variations. RNA-Sequencing was also carried out on these strains and differential gene expression determined for each mutant. We explored how the phenotypic growth differences are linked to the altered gene expression, and hypothesize mechanisms by which loss of the SAM genes and subsequent AdoMet level changes, impact pathways and processes. We present 6 stories, discussing changes in sensitivity or resistance to azoles, cisplatin, oxidative stress, arginine biosynthesis perturbations, DNA synthesis inhibitors, and tamoxifen, to demonstrate the power of this novel methodology to broadly profile changes due to gene mutations. The large number of conditions that result in altered growth, as well as the large number of differentially expressed genes with wide-ranging functionality, speaks to the broad array of impacts that altering methyl donor abundance can impart. Our findings demonstrate that some cellular changes are directly related to AdoMet-dependent methyltransferases and AdoMet availability, some are directly linked to the methyl cycle and its role in production of several important cellular components, and others reveal impacts of SAM gene mutations on previously unconnected pathways.

Long-read direct RNA sequencing of the mitochondrial transcriptome of Saccharomyces cerevisiae reveals condition-dependent intron abundance

Mitochondria fulfil many essential roles and have their own genome, which is expressed as polycistronic transcripts that undergo co- or posttranscriptional processing and splicing. Due to the inherent complexity and limited technical accessibility of the mitochondrial transcriptome, fundamental questions regarding mitochondrial gene expression and splicing remain unresolved, even in the model eukaryote Saccharomyces cerevisiae. Long-read sequencing could address these fundamental questions. Therefore, a method for the enrichment of mitochondrial RNA and sequencing using Nanopore technology was developed, enabling the resolution of splicing of polycistronic genes and the quantification of spliced RNA. This method successfully captured the full mitochondrial transcriptome and resolved RNA splicing patterns with single-base resolution and was applied to explore the transcriptome of S. cerevisiae grown with glucose or ethanol as the sole carbon source, revealing the impact of growth conditions on mitochondrial RNA expression and splicing. This study uncovered a remarkable difference in the turnover of Group II introns between yeast grown in either mostly fermentative or fully respiratory conditions. Whether this accumulation of introns in glucose medium has an impact on mitochondrial functions remains to be explored. Combined with the high tractability of the model yeast S. cerevisiae, the developed method enables to monitor mitochondrial transcriptome responses in a broad range of relevant contexts, including oxidative stress, apoptosis and mitochondrial diseases.

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single-cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle

Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.

Submillisecond Atomistic Molecular Dynamics Simulations Reveal Hydrogen Bond-Driven Diffusion of a Guest Peptide in Protein-RNA Condensate

Liquid-liquid phase separation mediated by proteins and/or nucleic acids is believed to underlie the formation of many distinct condensed phases, or membraneless organelles, within living cells. These condensates have been proposed to orchestrate a variety of important processes. Despite recent advances, the interactions that regulate the dynamics of molecules within a condensate remain poorly understood. We performed accumulated 564.7 μs all-atom molecular dynamics (MD) simulations (system size ∼200k atoms) of model condensates formed by a scaffold RNA oligomer and a scaffold peptide rich in arginine (Arg). These model condensates contained one of three possible guest peptides: the scaffold peptide itself or a variant in which six Arg residues were replaced by lysine (Lys) or asymmetric dimethyl arginine (ADMA). We found that the Arg-rich peptide can form the largest number of hydrogen bonds and bind the strongest to the scaffold RNA in the condensate, relative to the Lys- and ADMA-rich peptides. Our MD simulations also showed that the Arg-rich peptide diffused more slowly in the condensate relative to the other two guest peptides, which is consistent with a recent fluorescence microscopy study. There was no significant increase in the number of cation-π interactions between the Arg-rich peptide and the scaffold RNA compared to the Lys-rich and ADMA-rich peptides. Our results indicate that hydrogen bonds between the peptides and the RNA backbone, rather than cation-π interactions, play a major role in regulating peptide diffusion in the condensate.

Resistance mechanisms of Saccharomyces cerevisiae against silver nanoparticles with different sizes and coatings

To investigate the underlying resistance mechanisms of Saccharomyces cerevisiae against Ag-NPs with different particle sizes and coatings, transcriptome sequencing (RNA-seq) technology was used to characterize the transcriptomes from S. cerevisiae exposed to 20-PVP-Ag, 100-PVP-Ag, 20-CIT-Ag and 100-CIT-Ag, respectively. The steroid biosynthesis was found as a general pathway for Ag-NPs stress responding, in which ERG6 and ERG3 were inhibited and ERG11, ERG25 and ERG5 were significantly up-regulated to resist the stress by supporting the later mutation and resistance and modulate drug efflux indirectly. The resistance mechanism of S. cerevisiae to 20-PVP-Ag seems different from that of 100-PVP-Ag, 20-CIT-Ag and 100-CIT-Ag. Under the 20-PVP-Ag, transmembrane transporter activity, transition metal ion homeostasis and oxidative phosphorylation pathway were main resistance pathways to enhance cell transport processes. While 100-PVP-Ag, 20-CIT-Ag and 100-CIT-Ag mainly impacted RNA binding, structural constituent of ribosome and ribosome pathway which can provide more energy to maintain the number and function of protein in cells. This study reveals the differences in resistance mechanisms of S. cerevisiae to Ag-NPs with different particle sizes and coatings, and explains several main regulatory mechanisms used to respond to silver stress. It will provide theoretical basis for the study of chemical risk assessment.

Systematic profiling of ale yeast protein dynamics across fermentation and repitching

Studying the genetic and molecular characteristics of brewing yeast strains is crucial for understanding their domestication history and adaptations accumulated over time in fermentation environments, and for guiding optimizations to the brewing process itself. Saccharomyces cerevisiae (brewing yeast) is among the most profiled organisms on the planet, yet the temporal molecular changes that underlie industrial fermentation and beer brewing remain understudied. Here, we characterized the genomic makeup of a Saccharomyces cerevisiae ale yeast widely used in the production of Hefeweizen beers, and applied shotgun mass spectrometry to systematically measure the proteomic changes throughout 2 fermentation cycles which were separated by 14 rounds of serial repitching. The resulting brewing yeast proteomics resource includes 64,740 protein abundance measurements. We found that this strain possesses typical genetic characteristics of Saccharomyces cerevisiae ale strains and displayed progressive shifts in molecular processes during fermentation based on protein abundance changes. We observed protein abundance differences between early fermentation batches compared to those separated by 14 rounds of serial repitching. The observed abundance differences occurred mainly in proteins involved in the metabolism of ergosterol and isobutyraldehyde. Our systematic profiling serves as a starting point for deeper characterization of how the yeast proteome changes during commercial fermentations and additionally serves as a resource to guide fermentation protocols, strain handling, and engineering practices in commercial brewing and fermentation environments. Finally, we created a web interface (https://brewing-yeast-proteomics.ccbb.utexas.edu/) to serve as a valuable resource for yeast geneticists, brewers, and biochemists to provide insights into the global trends underlying commercial beer production.

Invertebrate genetic models of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a common adult-onset neurodegenerative disease characterized by the progressive death of motor neurons in the cerebral cortex, brain stem, and spinal cord. The exact mechanisms underlying the pathogenesis of ALS remain unclear. The current consensus regarding the pathogenesis of ALS suggests that the interaction between genetic susceptibility and harmful environmental factors is a promising cause of ALS onset. The investigation of putative harmful environmental factors has been the subject of several ongoing studies, but the use of transgenic animal models to study ALS has provided valuable information on the onset of ALS. Here, we review the current common invertebrate genetic models used to study the pathology, pathophysiology, and pathogenesis of ALS. The considerations of the usage, advantages, disadvantages, costs, and availability of each invertebrate model will also be discussed.

Shared and more specific genetic determinants and pathways underlying yeast tolerance to acetic, butyric, and octanoic acids

BACKGROUND

The improvement of yeast tolerance to acetic, butyric, and octanoic acids is an important step for the implementation of economically and technologically sustainable bioprocesses for the bioconversion of renewable biomass resources and wastes. To guide genome engineering of promising yeast cell factories toward highly robust superior strains, it is instrumental to identify molecular targets and understand the mechanisms underlying tolerance to those monocarboxylic fatty acids. A chemogenomic analysis was performed, complemented with physiological studies, to unveil genetic tolerance determinants in the model yeast and cell factory Saccharomyces cerevisiae exposed to equivalent moderate inhibitory concentrations of acetic, butyric, or octanoic acids.

RESULTS

Results indicate the existence of multiple shared genetic determinants and pathways underlying tolerance to these short- and medium-chain fatty acids, such as vacuolar acidification, intracellular trafficking, autophagy, and protein synthesis. The number of tolerance genes identified increased with the linear chain length and the datasets for butyric and octanoic acids include the highest number of genes in common suggesting the existence of more similar toxicity and tolerance mechanisms. Results of this analysis, at the systems level, point to a more marked deleterious effect of an equivalent inhibitory concentration of the more lipophilic octanoic acid, followed by butyric acid, on the cell envelope and on cellular membranes function and lipid remodeling. The importance of mitochondrial genome maintenance and functional mitochondria to obtain ATP for energy-dependent detoxification processes also emerged from this chemogenomic analysis, especially for octanoic acid.

CONCLUSIONS

This study provides new biological knowledge of interest to gain further mechanistic insights into toxicity and tolerance to linear-chain monocarboxylic acids of increasing liposolubility and reports the first lists of tolerance genes, at the genome scale, for butyric and octanoic acids. These genes and biological functions are potential targets for synthetic biology approaches applied to promising yeast cell factories, toward more robust superior strains, a highly desirable phenotype to increase the economic viability of bioprocesses based on mixtures of volatiles/medium-chain fatty acids derived from low-cost biodegradable substrates or lignocellulose hydrolysates.

Evolutionary rate covariation is a reliable predictor of co-functional interactions but not necessarily physical interactions

Co-functional proteins tend to have rates of evolution that covary over time. This correlation between evolutionary rates can be measured over the branches of a phylogenetic tree through methods such as evolutionary rate covariation (ERC), and then used to construct gene networks by the identification of proteins with functional interactions. The cause of this correlation has been hypothesized to result from both compensatory coevolution at physical interfaces and nonphysical forces such as shared changes in selective pressure. This study explores whether coevolution due to compensatory mutations has a measurable effect on the ERC signal. We examined the difference in ERC signal between physically interacting protein domains within complexes compared to domains of the same proteins that do not physically interact. We found no generalizable relationship between physical interaction and high ERC, although a few complexes ranked physical interactions higher than nonphysical interactions. Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC, and we hypothesize that the stronger signal instead comes from selective pressures on the protein as a whole and maintenance of the general function.

Post-transcriptional regulation shapes the transcriptome of quiescent budding yeast

To facilitate long-term survival, cells must exit the cell cycle and enter quiescence, a reversible non-replicative state. Budding yeast cells reprogram their gene expression during quiescence entry to silence transcription, but how the nascent transcriptome changes in quiescence is unknown. By investigating the nascent transcriptome, we identified over a thousand noncoding RNAs in quiescent and G1 yeast cells, and found noncoding transcription represented a larger portion of the quiescent transcriptome than in G1. Additionally, both mRNA and ncRNA are subject to increased post-transcriptional regulation in quiescence compared to G1. We found that, in quiescence, the nuclear exosome-NNS pathway suppresses over one thousand mRNAs, in addition to canonical noncoding RNAs. RNA sequencing through quiescent entry revealed two distinct time points at which the nuclear exosome controls the abundance of mRNAs involved in protein production, cellular organization, and metabolism, thereby facilitating efficient quiescence entry. Our work identified a previously unknown key biological role for the nuclear exosome-NNS pathway in mRNA regulation and uncovered a novel layer of gene-expression control in quiescence.

Functional Analysis of the Zinc Finger Modules of the S. cerevisiae Splicing Factor Luc7

Identification of splice sites is a critical step in pre-mRNA splicing since definition of the exon/intron boundaries controls what nucleotides are incorporated into mature mRNAs. The intron boundary with the upstream exon is initially identified through interactions with the U1 snRNP. This involves both base pairing between the U1 snRNA and the pre-mRNA as well as snRNP proteins interacting with the 5' splice site/snRNA duplex. In yeast, this duplex is buttressed by two conserved protein factors, Yhc1 and Luc7. Luc7 has three human paralogs (LUC7L, LUC7L2, and LUC7L3) which play roles in alternative splicing. What domains of these paralogs promote splicing at particular sites is not yet clear. Here, we humanized the zinc finger domains of the yeast Luc7 protein in order to understand their roles in splice site selection using reporter assays, transcriptome analysis, and genetic interactions. While we were unable to determine a function for the first zinc finger domain, humanization of the second zinc finger domain to mirror that found in LUC7L or LUC7L2 resulted in altered usage of nonconsensus 5' splice sites. In contrast, the corresponding zinc finger domain of LUC7L3 could not support yeast viability. Further, humanization of Luc7 can suppress mutation of the ATPase Prp28, which is involved in U1 release and exchange for U6 at the 5' splice site. Our work reveals a role for the second zinc finger of Luc7 in splice site selection and suggests that different zinc finger domains may have different ATPase requirements for release by Prp28.

Reprogramming nucleolar size by genetic perturbation of the extranuclear Rab GTPases Ypt6 and Ypt32

Reprogramming organelle size has been proposed as a potential therapeutic approach. However, there have been few reports of nucleolar size reprogramming. We addressed this question in Saccharomyces cerevisiae by studying mutants having opposite effects on the nucleolar size. Mutations in genes involved in nuclear functions (KAR3, CIN8, and PRP45) led to enlarged nuclei/nucleoli, whereas mutations in secretory pathway family genes, namely the Rab-GTPases YPT6 and YPT32, reduced nucleolar size. When combined with mutations leading to enlarged nuclei/nucleoli, the YPT6 or YPT32 mutants can effectively reprogram the nuclear/nucleolar size almost back to normal. Our results further indicate that null mutation of YPT6 causes secretory stress that indirectly influences nuclear localization of Maf1, the negative regulator of RNA Polymerase III, which might reduce the nucleolar size by inhibiting nucleolar transcript enrichment.

Interpreting protein abundance in Saccharomyces cerevisiae through relational learning

MOTIVATION

Proteomic profiles reflect the functional readout of the physiological state of an organism. An increased understanding of what controls and defines protein abundances is of high scientific interest. Saccharomyces cerevisiae is a well-studied model organism, and there is a large amount of structured knowledge on yeast systems biology in databases such as the Saccharomyces Genome Database, and highly curated genome-scale metabolic models like Yeast8. These datasets, the result of decades of experiments, are abundant in information, and adhere to semantically meaningful ontologies.

RESULTS

By representing this knowledge in an expressive Datalog database we generated data descriptors using relational learning that, when combined with supervised machine learning, enables us to predict protein abundances in an explainable manner. We learnt predictive relationships between protein abundances, function and phenotype; such as α-amino acid accumulations and deviations in chronological lifespan. We further demonstrate the power of this methodology on the proteins His4 and Ilv2, connecting qualitative biological concepts to quantified abundances.

AVAILABILITY AND IMPLEMENTATION

All data and processing scripts are available at the following Github repository: https://github.com/DanielBrunnsaker/ProtPredict.

CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.

Calcineurin-dependent contributions to fitness in the opportunistic pathogen Candida glabrata

The protein phosphatase calcineurin is vital for the virulence of the opportunistic fungal pathogen Candida glabrata. The host-induced stresses that activate calcineurin signaling are unknown, as are the targets of calcineurin relevant to virulence. To potentially shed light on these processes, millions of transposon insertion mutants throughout the genome of C. glabrata were profiled en masse for fitness defects in the presence of FK506, a specific inhibitor of calcineurin. Eighty-seven specific gene deficiencies depended on calcineurin signaling for full viability in vitro both in wild-type and pdr1∆ null strains lacking pleiotropic drug resistance. Three genes involved in cell wall biosynthesis (FKS1, DCW1, FLC1) possess co-essential paralogs whose expression depended on calcineurin and Crz1 in response to micafungin, a clinical antifungal that interferes with cell wall biogenesis. Interestingly, 80% of the FK506-sensitive mutants were deficient in different aspects of vesicular trafficking, such as endocytosis, exocytosis, sorting, and biogenesis of secretory proteins in the endoplasmic reticulum (ER). In response to the experimental antifungal manogepix that blocks GPI-anchor biosynthesis in the ER, calcineurin signaling increased and strongly prevented cell death independent of Crz1, one of its major targets. Comparisons between manogepix, micafungin, and the ER-stressing tunicamycin reveal a correlation between the degree of calcineurin signaling and the degree of cell survival. These findings suggest that calcineurin plays major roles in mitigating stresses of vesicular trafficking. Such stresses may arise during host infection and in response to antifungal therapies.IMPORTANCECalcineurin plays critical roles in the virulence of most pathogenic fungi. This study sheds light on those roles in the opportunistic pathogen Candida glabrata using a genome-wide analysis in vitro. The findings could lead to antifungal developments that also avoid immunosuppression.

Selected cachaça yeast strains share a genomic profile related to traits relevant to industrial fermentation processes

The isolation and selection of yeast strains to improve the quality of the cachaça-Brazilian Spirit-have been studied in our research group. Our strategy considers Saccharomyces cerevisiae as the predominant species involved in sugarcane juice fermentation and the presence of different stressors (osmolarity, temperature, ethanol content, and competition with other microorganisms). It also considers producing balanced concentrations of volatile compounds (higher alcohols and acetate and/or ethyl esters), flocculation capacity, and ethanol production. Since the genetic bases behind these traits of interest are not fully established, the whole genome sequencing of 11 different Saccharomyces cerevisiae strains isolated and selected from different places was analyzed to identify the presence of a specific genetic variation common to cachaça yeast strains. We have identified 20,128 single-nucleotide variants shared by all genomes. Of these shared variants, 37 were new variants (being six missenses), and 4,451 were identified as missenses. We performed a detailed functional annotation (using enrichment analysis, protein-protein interaction network analysis, and database and in-depth literature searches) of these new and missense variants. Many genes carrying these variations were involved in the phenotypes of flocculation, tolerance to fermentative stresses, and production of volatile compounds and ethanol. These results demonstrate the existence of a genetic profile shared by the 11 strains under study that could be associated with the applied selective strategy. Thus, this study points out genes and variants that may be used as molecular markers for selecting strains well suited to the fermentation process, including genetic improvement by genome editing, ultimately producing high-quality beverages and adding value.IMPORTANCEThis work demonstrates the existence of new genetic markers related to different phenotypes used to select yeast strains and mutations in genes directly involved in producing flavoring compounds and ethanol, and others related to flocculation and stress resistance.