New & Noteworthy

Green Monster Evolution to Hulk

February 25, 2022

A clever new study has used a modified yeast strain “ABC₁₆-Green Monster” with fewer export pumps (i.e. more susceptible to drugs) to exert powerful selection pressure on yeast cells. By exposing this strain to multiple libraries of chemical compounds with potential use as antifungal agents, Ottilie et al. in a recent issue of Communications Biology identified an evolved set of 25 genes with frequent mutations.

Resistance-conferring mutations in detail, from Ottilie et al.

In comparing the set of mutations to those frequently found upon extended growth without selection, they noted that intergenic mutations were comparatively rare, presumably due to the heavy selection pressure for functional resistance. For another confirmation of the screen’s effectiveness in isolating useful variants, i.e. not passenger mutations, they introduced 61 of the altered alleles back into the unevolved strain by CRISPR/Cas9 integration and verified that 45 variants across 37 genes restored resistance.

Looking more closely at the mechanisms of action for the observed resistances, they noted mutations clustered in the active sites of target molecules for which the target was previously known. For example, the antifungal drug tavaborole (a type of benzoxaborole) led to identification of four active-site mutations in Cdc60p (the yeast leucyl-tRNA synthetase) that are predicted to interfere with binding to the drug. In a related example, the chemotherapy drug camptothecin inhibits topoisomerase and they isolated two mutations in TOP1 that would be expected to fall in the binding pocket.

Their isolation of mutations in TOR2 and FPR1 (both associated with TOR signaling) in strains with evolved resistance to rapamycin reproduce the findings of a related study looking at rapamycin resistance in yeast. In fact, both studies identified mutations in the same S1975 residue of TOR2. In other interesting findings, they isolated mutations leading to yeast resistance to drugs used typically against soil-transmitted helminths (worms), trypanosomatid parasites, and malarial Plasmodium. Each mutant variant in yeast affords insight into how these pathogens might likewise evolve resistance or could be alternately targeted.

The set of identified mutant genes was highly enriched for transcription factors and among these were two—YRR1 and its paralog YRM1—that together mediated resistance for nearly 25% of the compounds tested. These two zinc transcription factors had previously been shown to activate genes involved in multidrug resistance. In this study, they were mutated 100 times in screens against 19 diverse chemical compounds. Interestingly, deletion of these genes does not confer resistance and the sum of the data suggests the screen identified gain-of-function alleles. In support of this idea, integration of the L611F allele of YRR1 into a susceptible strain by CRISPR/Cas9 reconstructed resistance to a suite of compounds. The researchers hypothesize that modified proteins lead to constitutive activation of genes aiding resistance.

In sum, this study demonstrates the awesome power of yeast genetics (#APOYG) for revealing insight into the molecular underpinnings of medical chemistry. Studies like this one provide not only data but intriguing clues about where to look next—most of which will be easy to test in yeast.

Categories: Research Spotlight

Tags: Drug resistance, Multidrug resistance, Saccharomyces cerevisiae, yeast model for human disease

C-Circles, an ALT Plan for Telomere Restoration

February 18, 2022

The telomerase ribonucleoprotein complex is the primary means by which yeast cells maintain telomeres. However, it turns out that cells lacking functional telomerase have a backup plan to restore telomere length by “alternative lengthening of telomeres” (ALT). ALT employs recombination via extrachromosomal telomere elements called C-circles. In a process for which the reasons remain unclear, C-circles get paired with eroded telomeres at the nuclear pore complex on the nuclear membrane. This pairing requires the SAGA/TREX2 complex and, once paired, the recombination between C-circles and telomeres appears to be effected by Rad59p, the paralog of Rad52p.

Model of type II telomere recombination that relies on telomeric circles, from Aguilera et al.

This interesting model is described in a recent paper in The EMBO Journal, in which Aguilera et al. adapt a method developed in human cancer studies to detect ALT and C-circles in yeast. In humans, ~10% of cancers depend on ALT for unchecked growth. In yeast, cells with ALT were able to be detected as survivors among telomerase mutant (est2∆) cells.

As other types of extrachromosomal DNA circles were previously reported to associate with the nuclear pore complex, the authors addressed the possibility that C-circles bind the NPC and demonstrated it clearly. They also showed the circles interact with the SAGA/TREX2 complex, which favors telomere recombination.

The novel finding that ALT in yeast so closely mirrors that of some human cancer cells is a boon to study of these cancers. The ability to develop ALT inhibitors in yeast would provide a new set of potential anticancer therapies, making this an ideal model system.

Categories: Research Spotlight

Tags: cancer, cell aging, Saccharomyces cerevisiae, senescence, telomeres, yeast model for human disease

Memories of Deceptive Courtship

February 10, 2022

Yeast courtship provides an excellent model for how a simple organism manages to remember past events. Recent yeast studies reveal how this memory can involve prion-like proteins that build up in a cell and persist as a form of memory. In a recent online issue of Current Biology, Lau et al. show how Whi3p self-templates into large assemblies upon perception of “deceptive courtship,” i.e. when pheromone is perceived but no mating partner appears. The super-assembly state allows escape from the G1 arrest triggered by potential courtship, but also prevents any further response to pheromone. While this pheromone refractory state is stable for the remainder of this mother cell’s life, daughter cells do not inherit this state and are fully pheromone responsive.

The organization of domains in Whi3p and the ability to self-template into large assemblies are features shared with prions. The lack of infectious heritability, though, distinguishes Whi3p as a mnemon rather than a prion. Fascinatingly, Lau et al. show that assembled Whi3p becomes heritable in cells with defective diffusion barriers, where the physical barriers at the bud neck normally restrict the super-assembled protein to the mother cell.

**Whi3 behavior ±pheromone and ±diffusion barrier**, from Lau et al., 2022

In studies of diffusion barrier disruption, the authors observed that the main super-assembly of Whi3p remains in the mother cell, while the daughter cells get “seeds” of Whi3^mnem upon which to assemble further. They note that transmission of seeds is most prevalent in the first few divisions following escape from pheromone arrest, which suggests there is a limiting factor to this diffusion. They propose a model in which the assembled Whi3^mnem “matures” to a form that no longer propagates seeds to daughter cells, even in the absence of a barrier.

Thus, remarkably, prion-like behavior appears to be one of the most rudimentary forms of memory, and may have implication for understanding cell memory in higher organisms.

Categories: Research Spotlight

Tags: mnemons, prion-like proteins, Saccharomyces cerevisiae, yeast cell memory, yeast mating

New ncRNA Represses Zinc Regulator

February 04, 2022

In a twist to an established story, the termination of noncoding RNAs by the NNS complex (NRD1 snoRNA termination complex) appears to be dependent on the phosphorylation of a regulatory component. In the latest issue of Nucleic Acids Research, Haidara et al. show how the NNS-complex component Sen1p acts to repress transcription of the zinc master regular ZAP1 when Sen1p is phosphorylated, which appears to happen in response to excess zinc.

The NNS complex had previously been shown to terminate transcription of PHO84 via antisense RNA. In this current paper, the authors identify the previously unannotated noncoding RNA ZRN1 as lying directly upstream of ZAP1 and, when transcribed without termination, repressing the downstream gene. Termination of ZRN1 transcription by dephosphophorylated Sen1p derepresses ZAP1 mRNA levels via removal of the interfering RNA. As evidence of this relationship, a Sen1p phospho-mimetic mutation (T1623E) results in stable repression of ZAP1 transcription.

*Working model for Sen1p regulation of gene expression, from Haidara et al.*

The model proposed is that zinc excess leads to phosphorylation of Sen1p by an unidentified kinase, which then causes the level of ZRN1 transcript to increase because termination is impaired. This in turn represses ZAP1 mRNA levels by interference, thereby repressing genes responsible for increased zinc uptake and storage.

Interestingly, the same system (i.e. Sen1p as a component of the NNS complex) represses PHO84 expression via interference, and PHO84 encodes a low-affinity Zn transporter that also contributes to zinc homeostasis. Might RNA interference play an expanded regulatory role over what is currently known?

Categories: Research Spotlight

Tags: noncoding RNA, Saccharomyces cerevisiae, transcription, zinc homeostasis

New Understanding of ER Membrane Expansion

January 28, 2022

How lipid metabolism gets coordinated with membrane growth in response to environment has remained an unexplained phenomenon. A recent paper explains key components in how cells manage this response.

Organelle biogenesis typically requires synthesis of new membrane and thus depends on lipid metabolism. A genetic screen identifies the ER protein Ice2p as a regulator of lipid metabolism and a major factor for ER membrane biogenesis, from Papagiannidis et al.

Papagiannidis et al. demonstrate a key regulatory step via the transmembrane protein Ice2p. Ice2p affects negative regulation of Pah1p (called lipin in metazoa) by inhibiting the Nem1-Spo7 phosphatase complex, which would normally activate Pah1p by dephosphorylation. The study shows that Ice2p also plays a role in transcriptional regulation of lipid synthesis genes, thereby providing a key pivot in the cellular need to make more membrane in response to ER stress.

Categories: Paper of the Week

Tags: endoplasmic reticulum, lipid regulation, Membrane biogenesis, Saccharomyces cerevisiae

Predicted 3D Structures of Yeast Complexes

January 20, 2022

In an exciting new paper, Humphreys et al. describe the use of deep-learning-based algorithms to predict structures of not only single proteins, but assemblies of proteins. The team used rapid RoseTTAFold combined with the more accurate AlphaFold to build structural models for 106 previously unidentified protein assemblies and 806 complexes that had not been structurally characterized. The complexes have up to five subunits and are involved in numerous critical roles in cell biology.

Examples of predicted complexes from Humphreys et al.

Go look for your own proteins of interest at the ModelArchive and search in the Home page. Also find the link on the resources section of the SGD Interaction and Protein pages.

Categories: Announcements, Data updates, Paper of the Week

Tags: protein complex, Saccharomyces cerevisiae, yeast protein assembly

SGD Newsletter, Fall 2021

December 14, 2021

About this newsletter:
This is the Fall 2021 issue of the SGD newsletter. The goal of this newsletter is to inform our users about new features in SGD and to foster communication within the yeast community. You can view this newsletter as well as previous newsletters on our Community Wiki.

Protein Complex Page Updates

SGD has made recent updates to our protein complex pages to improve clarity and ease of use. The new pages for each complex will have the same format as gene pages, with tabs across the top for each category of information, including a Summary page, a Gene Ontology page, and a Literature page. Just as we do for all of your favorite genes, Gene Ontology and Literature curation for complexes will be ongoing.

If you have any questions or feedback about the updates to our complex pages, please do not hesitate to contact us at any time.

Nomenclature Updates

SGD has long been the keeper of the official Saccharomyces cerevisiae gene nomenclature. Robert Mortimer handed over this responsibility to SGD in 1993 after maintaining the yeast genetic map and gene nomenclature for 30 years.

The accepted format for gene names in S. cerevisiae comprises three uppercase letters followed by a number. The letters typically signify a phrase (referred to as the “Name Description” in SGD) that provides information about a function, mutant phenotype, or process related to that gene, for example “ADE” for “ADEnine biosynthesis” or “CDC” for “Cell Division Cycle”. Gene names for many types of chromosomal features follow this basic format regardless of the type of feature named, whether an ORF, a tRNA, another type of non-coding RNA, an ARS, or a genetic locus. Some S. cerevisiae gene names that pre-date the current nomenclature standards do not conform to this format, such as MRLP38, RPL1A, and OM45.

A few historical gene names predate both the nomenclature standards and the database, and were less computer-friendly than more recent gene names, due to the presence of punctuation. SGD recently updated these gene names to be consistent with current standards and to be more software-friendly by removing punctuation. The old names for these four genes have been retained as aliases.

Legacy gene names

ORF	Old gene name	New gene name
YGL234W	ADE5,7	ADE57
YER069W	ARG5,6	ARG56
YBR208C	DUR1,2	DUR12
YIL154C	IMP2′	IMP21

New systematic nomenclature for yeast genes not in the reference genome

For many years, a widely adopted systematic nomenclature has existed for yeast protein-coding genes, or ORFs, as many yeast researchers call them. Readers of the last SGD newsletter will recall that, earlier this year, SGD adopted a new systematic nomenclature for the entire annotated complement of ncRNAs.

We have just put into place a new systematic nomenclature for S. cerevisiae genes that are not found in the reference genome of strain S288C (“non-reference” genes). This new systematic nomenclature is similar to, but distinct from, that used for ORFs and that used for ncRNAs. Non-reference genes are designated by a symbol consisting of three uppercase letters and a four-digit number, as follows: Y for “Yeast”, SC for “Saccharomyces cerevisiae”, and a four-digit number corresponding to the sequential order in which the gene was added to SGD. We currently have 55 of these genes in SGD, some of which are old favorites like MAL21/YSC0004 and MATA/YSC0046, while others are more recent additions like XDH1/YSC0051. Going forward, as evidence is published pointing to other S. cerevisiae genes not present in the S288C reference genome, they will be added to the annotation using the next sequential number available. We already have 15 more of these YSC0000 names reserved by researchers and awaiting publication.

If you have some non-reference genes for which these names would be appropriate, please let us know!

New links to AlphaFold 3D Predicted Protein Structure Database

Would you like to see the shape of your protein?

SGD now contains links to AlphaFold in the Resources sections of the Summary, Protein, and Homology pages for every gene.

The links through SGD give quick access to EMBL’s European Bioinformatics Institute (EMBL-EBI), which offers a new, highly accurate tool for predicting protein structure with speed and clarity.
Given a peptide sequence for an uncharacterized protein, AlphaFold will model predicted domains and provide relative confidence levels for each portion of the prediction.
The predicted domains can then be compared to known protein structures (using a tool such as PDBeFold) to seek matches to characterized protein families.
Whether or not a family is identified, the comparison will yield clues to protein function to help design the next experiments.

DIOPT Orthologs and New Queries in YeastMine

We recently replaced HomoloGene, Ensembl, TreeFam and PANTHER homology datasets in YeastMine with homology data from DIOPT (DRSC integrative ortholog prediction tool). DIOPT integrates orthology predictions from multiple sources, including HomoloGene, Ensembl, TreeFam, and PANTHER. Using the Gene->Non-fungal and S. cerevisiae Homologs pre-generated query, you can look for DIOPT homologs for a single or multiple yeast genes. The results table provides identifiers and standard names for the yeast and homologous genes, as well as organism and predictive score information. As with other YeastMine templates, results can be saved as lists and analyzed further.

Pre-generated queries for human homolog(s) of your favorite yeast gene and their corresponding disease associations remain largely unchanged. You can begin with your favorite human gene or disease keyword and retrieve the yeast counterparts of the relevant gene(s). As an example, you can search for the S. cerevisiae homologs of all human genes associated with disorders that contain the keyword “diabetes” (view search). The results table provides identifiers and standard names for the yeast and human genes, OMIM gene and disease identifiers and name, as well as predictive algorithm sources and scores.

Alliance of Genome Resources – Recent Release

The Alliance of Genome Resources, a collaborative effort from SGD and other model organism databases (MOD), released version 4.1 this past August. Notable improvements and new features include:

Human and model organism high throughput (HTP) variant data
- Human variants are imported from Ensembl
- Model organism HTP variants are submitted by Alliance members (FlyBase, RGD, SGD, Wormbase) or imported from EVA (MGI and ZFIN).
- Added HTP variants to the Alleles and Variants table on gene pages (e.g. rat Lepr Gene page) and to the table on the Alleles and Variants Details page (e.g. rat Lepr Alleles and Variants Details.
- Created a report page for Human and model organism HTP variants (e.g. human variant rs1041354454).
- Expanded Allele Category in search to “Allele/Variant” and added a search for HTP variants.
On Gene Pages, a new Pathways widget displays via tabs:
- Reactome models of pathways for human gene products as well as inferred pathways for model organism genes based on orthology to human genes.
- Reactome reactions for gene products (e.g. human TP53 Gene page)
- Gene Ontology Causal Activity Models (GO-CAMs). These provide a framework to represent a biological system by linking together multiple GO annotations. PMID:31548717 (e.g. worm nsy-1 Gene page).
Experimental conditions are include for Disease and Phenotype data in tables on Gene, Allele, and Disease pages (e.g. zebrafish scn1lab Gene page).
AllianceMine added Orthologs, and Allele and Variants (low throughput) data types to this release. You can now query for these data types via pre-made template queries.
The Alliance Community Forum is released. The Forum permits discussions across six model organism communities—flies, mice, yeast, rats, worms, and zebrafish. More details will follow.

Upcoming Conferences and Courses

Fungal Genetics – the premier meeting for the international community of fungal geneticists
- Asilomar Conference Grounds, Pacific Grove, California (and Online)
- March 15 – 20, 2022
36th International Specialised Symposium on Yeasts (ISSY36) – Yeast Sea to Sky – Yeast in the Genomics Era
- University of British Columbia, Vancouver
- July 12 – 16, 2022
CSHL Yeast Genetics & Genomics – modern, intensive laboratory course that teaches students full repertoire of genetic and genomic approaches
- Cold Spring Harbor Laboratory, NY
- July 26 – August 15, 2022
Yeast Genetics Meeting – the premier meeting for students, postdoctoral scholars, research staff, and principal investigators studying various aspects of eukaryotic biology in yeast
- University of California, Los Angeles
- August 17 – 21, 2022

Gene Ontology Consortium Fall 2021 Meeting

From October 12-14, SGD biocurators attended the Gene Ontology Consortium’s Fall Meeting with participants from around the world. The goal of these meetings is to bring together data scientists with diverse backgrounds (curators, programmers, etc.) for lively discussions regarding how to better capture, curate, analyze, and serve data to researchers, educators, students, and other life science professionals. Our goal in participating in these meetings each year is to find ways to make SGD even better for you!

Discussion topics included, but were not limited to:

LitSuggest – web-based system for biomedical literature recommendation and curation
ECO, Evidence and Conclusions Ontology – terms used to describe types of evidence and assertion methods
PAINT, Phylogenetic Annotation and INference Tool from PANTHER – orthology between reference genome genes and human disease genes

Happy Holidays from SGD!

We know that 2021 has been another challenging year for everyone. Our thoughts go out to all those who have been impacted by recent events. We wish you and your family, friends, and lab mates the best during the upcoming holidays.

Stanford University will be closed for two weeks starting December 20, and will reopen on January 3rd, 2022. Although SGD staff members will be taking time off, the website will be up and running throughout the winter break, and we will resume responding to user requests and questions in the new year.

Categories: Newsletter

Tags: Newsletter, Saccharomyces cerevisiae

Protein Complex Page Updates

December 01, 2021

SGD has updated our protein complex pages to have the same format as gene pages, with tabs across the top for each category of information, including a Summary page, a new Gene Ontology page, and a new Literature page for each complex. Just as we do for all of your favorite genes, Gene Ontology and Literature curation for complexes will be ongoing.

If you have any questions or feedback about the updates to our complex pages, please do not hesitate to contact us at any time.

Categories: Announcements, Data updates, Website changes

Tags: protein complex, Saccharomyces cerevisiae

New links to AlphaFold 3D Predicted Protein Structure Database

November 09, 2021

Would you like to see the shape of your protein?

SGD now contains links to AlphaFold in the Resources section of the Summary, Protein and Homology pages for every gene.

The links through SGD give quick access to EMBL–European Bioinformatics Institute‘s new, highly accurate tool for predicting protein structure.
Given a peptide sequence for an uncharacterized protein, AlphaFold will model predicted domains and provide relative confidence levels for each portion of the prediction.
The predicted domains can then be compared to known protein structures (using a tool such as PDBeFold to seek matches to characterized protein families).
Whether or not a family is identified, the comparison will yield clues to protein function to help design the next experiments.