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SGD Newsletter, August 2026

August 20, 2026

About this newsletter:
This is the August 2026 issue of the SGD newsletter. This issue features a celebration of the 30th anniversary of the first eukaryotic genome sequence, a suite of powerful new SGD features including Pathway Pages and Functional Networks, and practical guides to help you get the most out of your yeast research.

Contents

Celebrating 30 Years Since the First Eukaryotic Genome

This year marks the 30th anniversary of a landmark achievement in biology: the publication of the complete Saccharomyces cerevisiae genome sequence in 1996 – the first eukaryotic genome ever sequenced.

Since before the genome was even complete, SGD has served as the authoritative resource for yeast genomics. Over three decades, we’ve evolved from a simple sequence repository to a comprehensive biological knowledge hub, providing expert curation and multi-dimensional annotations that support researchers worldwide.

A special meeting, Life with 6000 Genes, takes place August 31 – September 1, 2026 to commemorate this milestone and the remarkable era of discovery it enabled.

To mark this anniversary, SGD team members have authored a retrospective chronicling our 32-year history, from the founding vision through today’s collaborative database ecosystem. The article will appear in a special issue of FEMS Yeast Research.

Thank you to the yeast research community for three decades of collaboration, contributions, and discoveries!

New Pathway Pages: Comprehensive Views of Yeast Biochemical Pathways

SGD now has Pathway Pages – dedicated pages for each of the 220 manually curated biochemical pathways in yeast.

Pathway pages offer a complete view of specific biochemical pathways in Saccharomyces cerevisiae. Each page integrates information from multiple sources to help you understand what the pathway does, which genes are involved, how gene products interact, and which chemicals are involved.

Interactive Pathway Diagrams

Each pathway page features a pathway diagram showing the chemical reactions and metabolic flow. The diagram displays:

  • Chemical compounds involved (substrates and products)
  • Enzymes catalyzing each reaction
  • Reaction directionality and relationships

A “View interactive diagram at YeastPathways” button takes you to the full interactive version where you can explore the pathway in greater detail.

Pathway Summary

Expert-curated descriptive text explains:

  • What the pathway does and why it’s important
  • Biological context and cellular roles
  • Connections to other metabolic processes
  • Key regulatory mechanisms

For example, the glyoxylate cycle page explains how this essential pathway allows yeast to grow on two-carbon compounds and its role in providing precursors for biosynthesis.

Genes Involved

A comprehensive table of genes participating in the pathway includes:

  • Gene names (standard and systematic)
  • EC numbers for enzymatic activities
  • Functional descriptions
  • Direct links to gene pages for detailed information

This makes it easy to see all the players in a pathway at a glance and access detailed information on any gene of interest.

Functional Networks

Pathway pages now include the same Functional Networks features available on gene pages:

Shared Annotations Network: Visualizes how genes in the pathway share phenotype and GO annotations, helping you identify:

  • Functional relationships between pathway genes
  • Connections to genes in related pathways
  • Potential regulatory relationships

You can filter the network by the number of shared pathway genes to focus on the most relevant connections.

GO-CAMs: When available, Gene Ontology Causal Activity Model pathway models are displayed, showing:

  • Causal relationships between molecular activities
  • How gene products work together mechanistically
  • Regulatory interactions and dependencies

If multiple GO-CAM models involve genes from the pathway, you can switch between them using a dropdown menu.

GO Enrichment Analysis

The GO Enrichment section shows which biological processes are statistically overrepresented among pathway genes. This helps you:

  • Understand the broader biological context
  • See connections to related processes
  • Identify key functional themes

Each enriched term links to the genes involved and shows the statistical significance (p-value).

Browse All Pathways

Explore all 220 curated pathways:

Explore the Redesigned SGD Search Landing Page

SGD’s search landing page has been redesigned to make it easier to find the yeast biological information you need.

Enhanced Search Experience The new landing page features an improved search box with smart autocomplete functionality that suggests genes, chemicals, pathways, and other entities as you type. This makes searching faster and helps you discover relevant results even if you’re not sure of the exact name.

Quick Category Browsing Need to browse rather than search? The new page includes quick access buttons for popular categories including genes, complexes, pathways, and chemicals. Click any category to start exploring without typing a single character.

Advanced Filtering Refine your search results by category or other criteria to quickly zero in on exactly what you’re looking for. The redesigned interface provides clearer visual organization with distinct sections for different data types.

Stay Current with SGD The right side of the page now highlights SGD’s latest activity:

  • New Literature Added shows the most recently curated publications
  • Recent Annotations displays the latest curated data, including new phenotypes, alleles, and GO annotations

This makes it easy to stay up-to-date with the newest information added to SGD.

There are two easy ways to reach the new search landing page:

  1. Click the red “Explore SGD” button on the SGD homepage
  2. Press return/enter in the search box without typing anything (empty search)

Or go directly to: https://www.yeastgenome.org/search

Happy exploring!

New Filtering Options on the Yeast Papers Page

The New Yeast Papers page at SGD (yeastgenome.org/reference/recent) is your rolling monthly snapshot of publications newly added to the database, now with filtering options to help you find the papers most relevant to your research.

The page has always been a great way to stay current with Saccharomyces cerevisiae research, listing newly-added publications over the last 30 days along with the genes, alleles, complexes, and pathways associated with each paper. It’s updated daily, and now you can filter it by:

  • Author: find papers by a specific researcher
  • Journal: browse what’s new in your favorite publications
  • Year: narrow the list by publication year
  • Associated genes: zero in on papers linked to your gene of interest
  • Associated alleles: filter by specific alleles
  • Associated complexes: find papers connected to a complex you’re studying
  • Associated pathways: focus on your pathway of interest

Each filter shows the most frequently occurring values along with reference counts, giving you an at-a-glance view of what’s trending in this month’s yeast literature. Combine multiple filters to get as specific as you need, and use Clear all filters to reset. And as always, you can download the full reference list in .nbib format for your reference manager.

Access the New Yeast Papers page anytime via the Literature menu in the purple SGD navigation bar, or from SGD’s new Search landing page (bookmark this!) or go directly to yeastgenome.org/reference/recent. Happy reading!

Overhauled Complex Pages: Enhanced Data Access and Improved User Experience

SGD’s macromolecular complex pages have been overhauled to make complex information more accessible, comprehensive, and easier to navigate.

Why Protein Complexes Matter

Protein complexes are fundamental functional units in cells that operate as groups of proteins that work together to carry out specific biological processes. Since 2019, SGD has provided detailed information about yeast protein complexes, including subunit composition, functions, interactions, and references.

All Gene Ontology Information on One Page

One of the most significant improvements is the reorganization of Gene Ontology (GO) annotations. Previously, GO information was tucked away on a separate tab, requiring you to navigate away from the main view. Now, all GO annotations are prominently displayed right on the Summary page.

What you can see at a glance:

  • Molecular Function: What the complex does
  • Biological Process: What pathways or processes it participates in
  • Cellular Component: Where in the cell it’s located

All three are now visible directly on the Summary page.

GO-CAM Pathway Models for Complexes

We’ve also integrated GO-CAM (Gene Ontology Causal Activity Models) pathway models directly into complex pages. When available, these models appear below the GO annotations, showing how entire protein complexes fit into larger biological pathways and regulatory networks.

Enhanced Composition Section

Stoichiometry Data: For complexes where the subunit ratios have been experimentally determined, you’ll now see the exact stoichiometry, providing quantitative information about complex architecture.

Structural Information:

  • Expert-curated notes about complex assembly and structural features
  • Direct links to Protein Data Bank (PDB) entries for complexes with experimentally determined 3D structures
  • Protein-protein binding regions and interaction interfaces (when characterized)

Organized Display: Subunits are now grouped by their roles or relationships within the complex, making it easier to understand how the complex is organized.

Easy Navigation: Each subunit links directly to its SGD gene page, so you can access detailed information on individual components.

Shared Biology Networks

Complex pages now include a Shared Biology section that shows GO annotations shared with other complexes, subunits shared between complexes, and ranked lists that prioritize the most functionally related complexes.

Improved User Experience Throughout

  • Clearer section headers make information easy to find
  • Better spacing and organization improve readability
  • Consistent styling with other SGD page types provides a unified experience
  • Logical information hierarchy puts the most commonly accessed data front and center

Functional Networks: Visualizing Gene Relationships at SGD

New Functional Networks sections are now available on gene and complex pages. This addition helps researchers understand how genes and proteins work together in biological systems.

Shared Annotations Networks

Ever wondered which other genes might have similar functions to your gene of interest? The Shared Annotations network visualizes genes that share similar Gene Ontology (GO) annotations with your query gene. These networks are generated based on overlapping GO terms across Molecular Function, Biological Process, and Cellular Component.

Why is this useful?

  • Identify potential interaction partners
  • Find paralogs with related functions
  • Discover genes that may participate in similar biological processes
  • Generate hypotheses for experimental design

Most genes in the S. cerevisiae genome have sufficient GO annotations to generate these networks, providing broad coverage across the yeast proteome.

GO-CAM Pathway Models

GO-CAMs (Gene Ontology Causal Activity Models) represent an exciting advancement in pathway representation. Unlike traditional GO annotations that link individual genes to single terms, GO-CAMs show how multiple gene products work together in integrated pathway models with causal relationships showing how one molecular activity leads to another.

What makes GO-CAMs special?

  • Causal relationships: See how one molecular activity leads to another, including activation, inhibition, and regulatory interactions
  • Integrated view: Combines Molecular Function, Biological Process, and Cellular Component information in one unified model
  • Evidence-based: All models are manually curated by expert biologists and supported by published experimental data

Currently, 470 yeast genes are associated with GO-CAM models, and this number continues to grow as additional pathways are curated.

When multiple GO-CAM models are available for a gene, you can easily switch between them using a pull-down menu. Each model includes a “View GO-CAM at Gene Ontology” link that opens the interactive pathway in AmiGO, where you can explore detailed evidence codes, supporting references, and connections to other pathways.

Functional Networks on Complex Pages

We’ve also added a Shared Biology section to macromolecular complex pages. This section shows:

  • GO annotations shared with other complexes
  • Subunits shared between complexes
  • A ranked list summarizing these relationships

This helps researchers understand how protein complexes relate to each other and identify functionally similar complexes.

How to Access

Simply navigate to any gene page at SGD and scroll to the Functional Networks section (located beneath Gene Ontology). For complex pages, look for the Shared Biology section.

Part of a Bigger Picture

The GO-CAM display on SGD gene pages replicates the implementation from the Alliance of Genome Resources, providing a consistent user experience across model organism databases. This integration reflects our commitment to making yeast data accessible and interoperable with other genomic resources.

Redesigned Chemical Pages: Comprehensive Small Molecule Information in One Place

SGD’s chemical pages have been redesigned to provide comprehensive information about small molecules relevant to yeast biology such as metabolites, drugs, and experimental compounds, all in a more accessible, more user-friendly format.

Small molecules play crucial roles in yeast biology, from essential metabolites that keep cells functioning to experimental compounds that reveal how cellular processes work. SGD chemical pages bring together curated information about how these chemicals affect yeast, connecting chemical entities to genes, phenotypes, and biological pathways.

Chemical Structures Front and Center

The redesigned pages now display interactive 2D chemical structures prominently at the top of each page. These structures clearly show molecular connectivity and functional groups, providing immediate visual recognition of the compound.

All Information on One Page

No need to navigate between multiple tabs! Everything you need to know about a chemical is now presented on a single, unified page:

Comprehensive Identifiers and Database Links:

This integration makes it easy to explore the chemical across multiple resources and access broader chemical and metabolic information.

Curated Experimental Data

Each chemical page now includes rich experimental data curated from the yeast literature:

Phenotype Annotations
See the effects of the chemical on yeast:

  • Growth effects
  • Metabolic changes
  • Other cellular processes

This section includes annotation statistics, top genes and phenotypes involving each chemical. Every phenotype annotation is linked to supporting experimental evidence and literature references, so you can trace findings back to the original research.

Gene Ontology Annotations and Enrichment

The GO Annotations section shows how the chemical is annotated within the Gene Ontology framework, connecting it to specific biological processes, molecular functions, and cellular components.

For metabolites, you’ll also find links to relevant metabolic pathways in YeastPathways throughout the GO annotation tables, connecting chemical entities to their biological context in small molecule metabolism.

The GO Enrichment section shows which biological processes and molecular functions are significantly associated with genes affected by the chemical, helping you:

  • Understand the broader biological context of chemical-gene interactions
  • Identify patterns in how chemicals influence cellular systems
  • Generate hypotheses about mechanism of action

Shared Chemicals Network

The Shared Chemicals section displays other chemical entities that share similar properties, annotations, or biological roles. This makes it easy to:

  • Explore chemical families
  • Find compounds with similar effects
  • Identify potential alternative compounds for experiments

Complete Literature Coverage

The References section compiles all publications from which data about the chemical has been curated. This provides direct access to the primary literature and shows you the full scope of research on each compound.

How to: Add a New Gene to the Reference Genome Annotation

SGD maintains the most up-to-date version of the complete genomic sequence of S. cerevisiae strain S288C. If your lab has characterized a gene or genomic feature that isn’t yet annotated, getting it added is a meaningful contribution. Here’s what SGD needs from you to do that:

Publicly Available Data

SGD only adds features based on published data. All coordinates, strand information, and sequence data must already be explicitly reported in a peer-reviewed publication. Depositing the sequence, including the genome sequence version used, in a public repository such as GenBank is also required.

What Should be Included in the Publication:

  • Explicit chromosomal coordinates, including genome sequence version used — stated in the text or a supplemental table, not only inferred from a figure or visualization
  • Strand orientation — sense (+) or antisense (–)
  • Strain background — SGD is built on S288C; note if your data maps to a different strain such as SK1 or W303
  • Feature type — protein-coding gene, ncRNA, pseudogene, regulatory region, etc.
  • GenBank Accession — Accession number from GenBank to identify the feature

When to Reach Out

Genome annotation updates at SGD are released periodically rather than continuously. When a new feature is identified, it is added to the list of new features that will be reviewed for incorporation into the next update. There are three good moments to contact us at sgd-helpdesk@lists.stanford.edu:

Before publication — if your paper is in preparation or under review, reaching out early lets curators know to watch for it. They can review the manuscript details and be ready to act as soon as it is accepted and assigned a PMID.

At or after publication — once your paper is published and indexed in PubMed, contact us with the PMID and point curators to where the relevant data appear in the paper.

If your paper is already in SGD but the feature is missing — SGD has curated thousands of papers and may have captured some findings from a publication while missing others. If you notice that a gene from your own work hasn’t been annotated, let us know and we will revisit the paper.

How to: Find UTR Lengths for Yeast Genes

Have you ever wondered where you can find information about the 5′ and 3′ UTRs (untranslated regions) for a list of yeast genes? If you’re working with Saccharomyces cerevisiae and need UTR information, we have several solutions depending on your needs.

Option 1: Bulk Download Files for Large Datasets

If you’re analyzing multiple genes or need comprehensive UTR data, downloading our complete datasets is the most efficient approach.

Access the SGD Downloads site: http://sgd-archive.yeastgenome.org/sequence/S288C_reference/

Download these two files:

These files contain FASTA-formatted sequences for all annotated ORF UTRs in the yeast genome. Once you download and extract the files, you can easily parse the sequences to determine lengths for your genes of interest. README files with additional details are located in the same folder.

Option 2: Query Individual Genes or Gene Lists with AllianceMine

For looking up UTR data on individual genes or specific gene lists, use the Gene -> UTRs template in AllianceMine:

https://www.alliancegenome.org/bluegenes/alliancemine/templates/Gene_UTRs

This tool allows you to input your genes of interest and retrieve UTR information in a structured, easy-to-use format.

Option 3: Browse UTRs Visually with JBrowse

If you prefer to explore UTR features in their genomic context, check out the UTR tracks in SGD’s JBrowse genome browser: https://jbrowse.yeastgenome.org

The visual browser lets you see UTRs alongside other genomic features, making it ideal for examining individual loci or exploring chromosomal regions.

Resource Guide: Where to Order Yeast Strains

One of the most frequent questions we receive at the SGD Helpdesk is: “Where can I order yeast strains for my research?” We’ve compiled a guide to help you locate the strains you need, whether you’re looking for deletion mutants, specific genetic backgrounds, or specialized collections.

Finding Strains Through SGD

SGD makes it easy to locate available strains directly from gene pages. Here’s how:

  1. Start at the Locus Summary page for your gene of interest (example: https://www.yeastgenome.org/locus/S000005000)
  2. Click on the “Phenotype” tab to navigate to the Phenotype Details page (example: https://www.yeastgenome.org/locus/S000005000/phenotype)
  3. Scroll to the “Resources” section at the bottom of the page

The Resources section includes direct links to several strain resources:

European Collections

  • Euroscarf (European Saccharomyces cerevisiae Archive for Functional Analysis) — One of the largest yeast strain collections, providing deletion mutants, overexpression strains, and other specialized collections.
  • National Collection of Yeast Cultures (NCYC) — Maintains over 4,000 yeast strains, including wild-type isolates and reference strains.
  • Industrial Yeasts Collection DBVPG — Houses over 6,000 yeast strains with a focus on industrial and wild yeasts.
  • CABRI (Common Access to Biological Resources and Information) — Provides access to catalogs from multiple European culture collections.

North American Collections

  • ATCC (American Type Culture Collection) — A premier biological resource center offering authenticated yeast strains, including reference strains and mutant collections.

Specialized Collections and Commercial Sources

Alliance of Genome Resources News

Alliance of Genome Resources – Advance Release Notes: 9.1.0

alliance logo.png

The following release notes are being published in advance of an upcoming release of the Alliance of Genome Resources website and dataset, expected August/September 2026, to give ample warning to Alliance end users and developers that data and/or APIs are changing so that they can update their data pipelines accordingly. Until the release goes live, some links and references may not have yet taken effect.

The 9.1.0 release includes data refreshes from each of the model organism source databases as well as various backend improvements.

As of release 9.1.0, there are new Genes and Phenotypes download files on the Alliance Downloads page. Prior releases included Gene Descriptions downloadable files; these files have been removed and replaced by the new Genes download files that contain the respective gene descriptions. All downloadable JSON file formats and some TSV file formats have changed.

Several API endpoints have changed their response payload format to include new, richer data and JSON files formatted according to the Alliance LinkML Data Model, including all endpoints that return download files available on the Alliance Downloads page. These include the following endpoints:

Several TSV files have changed format, including:

  • Variant-Allele TSV download will no longer have the “Variant Id” column (column 6) in favor of the “Variant Symbol” column providing the HGVS name of the variant
  • Disease association TSV download will have new columns — see the full release notes for the complete column specification
  • VCF files now have an additional header line (##reference) to declare the genome assembly version used across the whole file

Note that stable URLs for Alliance download files have changed in many instances and likely need to be updated in downstream consuming applications. For example, the all species disease JSON download stable URL has changed from: https://fms.alliancegenome.org/download/DISEASE-ALLIANCE-JSON_COMBINED.json.gz

to: https://www.alliancegenome.org/download/DISEASE-ALLIANCE_JSON_COMBINED.json.gz

Additional API changes affect disease annotation endpoints and phenotype annotation endpoints. See the full release notes for complete details.

microPublications – Latest Yeast Papers

MicroPub.png

microPublication Biology is part of the emerging genre of rapidly-published research communications. microPublications publishes brief, novel findings, negative and/or reproduced results, and results which may initially lack a broader scientific narrative.

Each article is peer-reviewed, assigned a DOI, and indexed through PubMed and PubMedCentral. Consider microPublications when you have a result that doesn’t necessarily fit into a larger story, but will be of value to others. Latest yeast microPublications:

Upcoming Conferences & Courses

Note: The goal of this newsletter is to inform our users about new features in SGD and to foster communication within the yeast community. If you wish to receive this newsletter via email, please contact the SGD Help Desk at sgd-helpdesk@lists.stanford.edu.

Categories: Uncategorized

Tags: Newsletter, Saccharomyces cerevisiae, yeast

Register now for the 31st International Conference on Yeast Genetics and Molecular Biology

May 16, 2023

Deadlines for #yeast2023 have been extended! Early bird registration is open until June 5, and abstract submission until July 17. For more information, go to yeastflorence2023.com.

31° International Conference on Yeast Genetics and Molecular Biology ICYGMB31 20-25August2023 Florence, Italy

Categories: Conferences

Tags: ICYGMB31, yeast

GENETICS Knowledgebase and Database Resources

May 08, 2023

The May 2023 issue of GENETICS features the second annual collection of Model Organism Database articles. Scientists from Alliance of Genome Resources member groups SGD, RGD, ZFIN, Gene Ontology, and Xenbase have provided updates on recent activities and innovations. Be sure to browse the issue and get acquainted with these excellent Knowledgebase and Database Resource papers at GENETICS. Cover art by Vivid Biology.

SGDhttps://doi.org/10.1093/genetics/iyac191
RGDhttps://doi.org/10.1093/genetics/iyad042
ZFINhttps://doi.org/10.1093/genetics/iyad032
Gene Ontology (GO)https://doi.org/10.1093/genetics/iyad031
Xenbasehttps://doi.org/10.1093/genetics/iyad018

Categories: Announcements

Tags: Saccharomyces cerevisiae, yeast

GWAS Shows Potential in Yeast

August 09, 2012

Maybe GWAS will prove to be more useful in model organisms like yeast.

The idea behind a genome wide association study (GWAS) makes perfect sense.  Compare the DNA of one group of people with a disease to another group that doesn’t have the disease, identify the DNA region specific to the disease group, and then find the specific gene and mutations that lead to the disease.

In theory, this sort of study should have become routine once we had the human genome sequenced.  In practice, it has turned out to be less useful than everyone hoped.

Now, this doesn’t appear to be any fault with the technique itself.  Instead, it has more to do with the fact that many human diseases are simply too complex for GWAS to handle.

Most common human diseases appear to result from multiple genetic pathways and/or multiple genes.  Throw in environmental effects and GWAS quickly becomes overwhelmed.  At least for now, too many patients and controls would be needed for this powerful technique to have a real chance at deciphering most common human diseases.

But that doesn’t mean the technique isn’t useful.  It is very good at finding single genes involved in strongly expressed traits.  And this might be ideal for certain model organisms.

In a study just out in the latest issue of GENETICS, Connelly and Akey set out to investigate how well GWAS would work in the yeast, Saccharomyces cerevisiae.  In many respects, this yeast appears to be made for GWAS.

It has a small, easily sequenced genome, there is on average a polymorphism every 168 base pairs or so, and its linkage disequilibrium is low.  There are genome sequences from 36 wild and laboratory strains publicly available, all as diverse as can be. 

But this yeast isn’t perfect.  The chromosomal structure between strains tends to be much more varied than between two humans.  This is predicted to introduce a high error rate.  And this is just what Connelly and Akey saw when they ran some simulations.   

They found that the error rate was too high in the simulations to draw any meaningful conclusions.  But they also found that by using a more sophisticated analytical technique called EMMA, they were able to partly correct for some of these errors. 

Simulations are one thing, but how about real life?  Connelly and Akey next tested the method by applying it to a practical problem: identifying the genetic reasons for differences in mitochondrial DNA (mtDNA) copy number in yeast.  What they found mimicked the simulation data. 

Using more traditional analytical approaches on the data obtained from GWAS, they found 73 potential causative SNPs.  But when they switched to analyzing the data with EMMA, they found a single SNP that was significant.  It took a bit of hand waving, but the gene associated with this SNP could possibly be implicated in mtDNA copy number.  And then again, it might not.

This “significant” SNP was found amidst lots of errors and in a background of high p values.  In other words, this finding may not be a real one after all.  This experiment does not give confidence that GWAS can be used when all known strains of yeast are compared.

But if the strains to be included are selected more carefully, it may still prove to be a useful tool.  When Connelly and Akey focused on strains that were structurally similar, they found that the error rate was much lower.  Low enough that in the near term, scientists may be using GWAS to figure out how things work in model organisms.  

Hopefully the findings from GWAS applied to model organisms will illuminate disease mechanisms in humans. Then maybe GWAS can realize its full potential, although not in the way it was originally envisioned.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: genome wide association study, GWAS, yeast

Ethanol from Waste

June 16, 2012

Scientists are coming up with ways for yeast to use waste like this to generate ethanol.

Biofuels hold the promise to significantly slow down global warming.  But this will only be the case if they come from something besides corn.

We don’t want them to come from the parts of other plants we eat, either.  Shunting food towards fuel will only jack up food prices and put the lives of the poorest at risk.  Policy makers should not have to decide between feeding the poor and running their cars.

No, to make biofuels worth our time, we need to be able to turn agricultural waste, grass, saplings, etc. into ethanol.  Unfortunately this stuff is mostly cellulose and lignin and we don’t have anything that can efficiently ferment this “lignocellulosic biomass.” 

Many groups are working towards creating strains of Saccharomyces yeast, the predominant fungal organism used for large-scale industrial processes, to do this job.  None have yet been created that can do the job well enough to be industrially viable. They are either poor fermentors or are genetically modified so that they include non-yeast genes.  Ideally any strain would include only Saccharomyces genes, to avoid the public’s fear and loathing of genetically modified organisms.  

This is where a new study in GENETICS by Schwartz and coworkers comes in.  This group is working towards engineering a yeast that can ferment the pentoses like xylose that make up a good chunk of this otherwise inedible biomass, using genes that are naturally occurring in Saccharomyces.  They haven’t yet created such a yeast, but they have at least identified a couple of key genes involved in utilizing xylose.

The researchers took what seemed to be a straightforward approach.  Collect and screen various yeast strains for their ability to grow on xylose and isolate the relevant gene(s) from the best of them.  Sounds easy enough except that most of the strains they’ve found are terrible sporulators.  This means that they couldn’t use conventional methods to isolate the genes they were interested in and so had to come up with new methods.

First they needed to find some way to get the strain to sporulate.  They were able to force sporulation by creating a tetraploid intermediate between the xylose fermenting strain, CBS1502, and the reference strain, CBS7001, by adding an inducible HO gene.  During this process, they noticed that the ability to utilize xylose segregated in a 3:1 pattern.  This usually means that two genes are involved.

They next needed a way to identify these two genes.  What they did was to pool 21 spores that could ferment xylose and 21 that could not.  They then purified the DNA from each pool and compared them using high throughput sequencing.  They eventually found two genes that were key to getting this yeast to use xylose as its carbon source.  (They also found at least two other “bonus” genes that seemed to boost its ability to use xylose).

One of the genes, GRE3, was a known member of a xylose utilization pathway.  But the other gene, the molecular chaperone APJ1, was not known to be involved in metabolizing xylose.  The authors hypothesize that APJ1 might stabilize the GRE3 mRNA.

These two genes may not be enough to create an industrially viable, xylose fermenting Saccharomyces just yet.  But the novel methods of gene isolation presented in this study may allow researchers to find additional genes that might one day get them there.  Then we will have a way to get ethanol without the large carbon footprint and without the human cost.

 

A genetic engineering approach to getting yeast to ferment agricultural waste

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: biofuel, biomass, ethanol, fermentation, lignocellulosic biomass, Saccharomyces, Saccharomyces cerevisiae, yeast

Cancer’s Chromosomal Chaos Explained (Partly)

June 01, 2012

Because they have the wrong number of chromosomes, cancers can sample many different genetic combinations.

One reason cancer is so tricky to treat has to do with its adaptability.  It can quickly try out new genetic combinations until it hits upon one that can survive whatever treatment a doctor is currently throwing at it.  The result is return of the cancer after remission.

One way cancer is able to change its genetics so rapidly has to do with chromosome instability.  The number of chromosomes in a cancer cell is much less stable than in a normal cell.  This allows the cancer cell to constantly explore a wide range of chromosomal combinations.

It is still an open question how this dynamic instability happens.  The gene-centric theory suggests that mutations in key genes are the main driving force.  The chromosome-centric model says that having the wrong number of chromosomes is the critical component.

Distinguishing between these two models using cancer cells has proven difficult because these cells always have mutated genes.  There is simply no way to look at just chromosome numbers in this system.  This is where yeast can help.

In a recent paper published in PLoS Genetics, Zhu and coworkers used yeast to explore whether altered chromosome number was sufficient to explain chromosome instability.  They found that chromosome numbers alone can explain some but not all of chromosomal instability.

The authors created various chromosomal combinations in yeast by sporulating isogenic triploid yeast cells.  These cells had different numbers of genetically identical chromosomes.  They then explored the stability of each chromosome number combination using both FACS and qPCR.

What they found was that chromosome number certainly impacted chromosomal stability.  Chromosome number became less and less stable as the chromosome number veered further and further from the haploid state.  Of course, once the cells became diploid, stability returned. 

The authors explain this with the idea that there is only so much cellular machinery to move chromosomes to the proper place during mitosis.  As more and more chromosomes are added to the cell, the machinery becomes increasingly taxed, resulting in more and more errors. 

But once the diploid state is reached, all the genes are present to make twice as much mitotic machinery.  Now stable chromosome segregation can happen.

This was the broad pattern Zhu and coworkers observed but it certainly wasn’t the whole story.  The authors found islands of stability in the chromosomal chaos. 

For example, very often when there were equal numbers of chromosome VII (ChrVII) and chromosome X (ChrX), the chromosome number was more stable than predicted.   They explored this further and found evidence that suggested that at least part of this was due to the MAD1 gene on ChrVII and the MAD2 gene on ChrX. 

Stable chromosome numbers required that these genes be present in a 1:1 ratio.  Once the ratio strayed from one, chromosomal instability increased.  But these genes don’t explain everything.  There were unstable combinations where the MAD1/MAD2 ratio was correct.  As might be expected, there are other gene combinations that can lead to instability as well.

So incorrect chromosome number alone can explain the chromosomal instability seen in cancer cells.  But genes clearly play a role too, as evidenced by the islands of stability and the MAD1 gene and MAD2 genes.  As usual, reality is probably a combination of the two models. 

So it looks like chromosome number does play an important role in chromosomal instability.  Too many chromosomes may overtax the mitotic machinery so that chromosomes end up mis-segregated.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight, Yeast and Human Disease

Tags: cancer, chromosomal instability, chromosome, genetics, Saccharomyces cerevisiae, yeast

Yeast, Place your Bets

May 25, 2012

Life is a balancing act.  An organism needs to grow and divide as fast as possible in its current environment.  But it also needs to be able to survive when the environment changes.

One way nature has come up with to deal with this balancing act is called bet hedging.  Basically some members in a population grow well in one set of circumstances and another set grows well in another.

Now this makes obvious sense when looking at members of a species that vary genetically.  Where it gets interesting is when bet hedging happens in a clonal population.

The idea is that even though they share the same DNA, there are epigenetic differences that cause subtle variations in gene expression levels between individuals.  These differences in gene expression patterns result in altered survival rates under different circumstances.

This phenomenon has been difficult to study because researchers need to focus on individuals and not populations.  Growth curves in liter flasks are of little use.

But now Levy and coworkers have come up with a new high throughput assay that allows them to look at how a few individuals are growing.  This has allowed them to quantitate how different individuals grow in a population and why the slower growers and/or the elderly are better able to survive stress.

The assay uses time-lapse bright-field microscopy to look at tens of thousands of microcolonies all at once.  What they find is a wide range of growth rates.  Somewhere between 1.3-10% of microcolonies grow at less than half the rate of the population as a whole (the number depends on the strain). 

The researchers identified multiple genes that impacted the range of growth rates within a population without necessarily affecting the overall growth rate.  In other words, this phenomenon isn’t simply due to chance–there are key genetic factors that help determine the amount of individual to individual variation in a population.

Levy and coworkers focused on Tsl1p, a component of the trimeric complex that synthesizes trehalose.  What they found was that those cells that made more Tsl1p divided less often and so grew more slowly.  Remember again, this is in a clonal population.

Trehalose is thought to help preserve proper protein folding under stress.  So the idea is that some subset of individuals is primed for stress but that in turn, this preparation makes them grow more slowly.  And this is just what the researchers found.

When they subjected colonies to heat stress, those that made lots of trehalose were more likely to survive.  But the survivors didn’t stay slow growing for long.  After multiple generations, the population returned to the original growth rate with the original individual to individual variation.  The phenotype was reversible.

Finally the researchers discovered that older yeast cells tended to make more trehalose and so survived stress better.  It may be that as a yeast cell gets older, it makes more Tsl1p which helps to set up the range of growth rates among individuals.  This may be one way individual to individual variation has evolved in yeast.

Bet hedging is obviously a great way to ensure the survival of a clonal population. Under ideal conditions, the fast growers can grow like mad, spreading themselves far and wide.  But when conditions become more hostile, a few slower, tougher individuals can survive to keep the population alive.

Video showing that slow growers survive heat shock and then revert to fast growers.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: bet hedging, epigenetics, Saccharomyces cerevisiae, Tsl1, yeast

When Heat is Not the Burning Issue

May 04, 2012

Just because a mutation looks temperature sensitive, that doesn't mean it is.

When is a temperature sensitive mutation not a temperature sensitive mutation? When the process being studied is affected by temperature too.

Mutations conferring temperature sensitivity – that is, a phenotype that only appears at higher- or lower-than-normal growth temperature due to the loss or alteration of function of a gene product at that temperature – have for decades been an invaluable tool in dissecting many biological processes in yeast and other model organisms. Now there is reason to question whether some temperature sensitive phenotypes might actually be the result of a more complex interaction between multiple genes.

This synthetic genetic interaction is easy to mistake for the effect of a single mutation. A scientist starts out with a mutation that weakens a gene in a certain process. Unfortunately for the scientist, the process being studied is itself weakened by higher temperatures. The effect of the higher temperature combines with the effect of the mutation to shut down the process. The mutation looks temperature sensitive even when it isn’t.

And this does not appear to be merely a theoretical concern. As Paschini and coworkers show in a new study out in GENETICS, something similar may have happened with key mutations used to study telomere function in yeast.

These researchers looked at several mutations but we’ll focus on the work they did with cdc13-1. A key experiment that had been previously done with this mutation dealt with the effects of the loss of cdc13 function in the absence of RAD9.

Basically, researchers had found that prolonged incubation of a cdc13-1/Δrad9 strain at 36° severely compromised viability. These results were used to infer CDC13 function based on its loss at 36°. However, Paschini and coworkers provide compelling data that cdc13-1 behaves equally poorly at 23° and 36°.

First off, they showed that prolonged incubation of wild type yeast at the temperatures used in these studies (36°) resulted in shorter telomeres. They found very little effect on telomere length at 32°.

Next they showed that biochemically, cdc13-1 didn’t behave like a temperature sensitive mutation. Strains with cdc13-1 produced around 4-fold less protein at both 23° and 36° and the protein that was made bound telomeres equally well at both temperatures.

They argue from these two pieces of data that the loss of viability comes from a combination of the compromised cdc13-1 mutation and the effects of higher temperature on telomere function. Something is going on in the rad9 experiment but it is not due to an increased loss of CDC13 function at higher temperatures. There is some other factor involved that is being inhibited.

Of course it could be that cdc13-1 still confers temperature sensitivity, but that they didn’t have the right biochemical assay to see it. To address this issue, they generated five new mutations in cdc13 that behaved more like traditional temperature sensitive mutations.

They focused on one, cdc13-S611L, that was compromised for protein production at temperatures of 32° and above. They then repeated the rad9 double mutant experiment at 32° and 36°. They found that viability was compromised only at 36° even though Cdc13p was equally absent at both 32° and 36°. These results suggest that the loss of viability at 36° is not only the result of cdc13-1.

If this and other results hold up, scientists will need to rethink what previous experiments meant and they may need to modify their models. This should also get other researchers thinking about their temperature sensitive mutations.  It is important to confirm biochemically that a mutation indeed makes a specific gene product temperature sensitive. Because sometimes even if it quacks, it isn’t a duck…

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: cdc13, synthetic lethal, telomere, temperature sensitive, yeast

Idling Transcription Factors

April 24, 2012

Transcription factors may lie in wait on the DNA, waiting for a signal to pounce. Image courtesy of DesktopNexus http://www.desktopnexus.com/.

A new study by Lickwar and coworkers suggests that many transcription factors fidget on and off the DNA, waiting for some signal to get to work.  Once they get that signal, they clamp down and start affecting the activity of nearby genes.

If true this would help explain some perplexing results researchers have been getting with chromosomal immunoprecipitation (ChIP) assays.  Transcription factors appear to be bound at many places where they are not affecting any nearby genes.  Now we might have an idea why.

These researchers came up with this model through the use of an elegant, in vivo competition study.  What they did was to set up a yeast strain that contained two different versions of the transcription factor Rap1p.  One version was tagged with a FLAG epitope and was under the control of RAP1’s endogenous, constitutive promoter.   The other version was tagged with a Myc epitope and was under the control of an inducible promoter.

They started out seeing where Rap1p was bound in the absence of the inducer by using an antibody against FLAG.  This is the equivalent of a typical ChIP experiment.  They found Rap1p was bound in many places throughout the genome including sites where it did not appear to affect any nearby genes.

Then they added the inducer galactose and at various time points repeated the ChIP experiment with antibodies against either FLAG or Myc.  They were basically looking for how quickly the Myc-tagged Rap1p replaced the FLAG-tagged Rap1p with the idea that less stably bound transcription factors would be replaced more quickly.

They indeed found that some sites were better able to withstand the onslaught of Myc-tagged Rap1p.  And more importantly, that these sites were near genes most influenced by Rap1p.  In other words it appears that the more stably bound the Rap1p, the bigger the effect it has on nearby genes.

They then went on to show that more stable binding correlated with lower nucleosome occupancy and stronger in vitro binding.  From this data they propose a model where the level of the effect on transcription is the result of a competition between nucleosome and transcription factor binding.  Stronger transcription factor binding keeps nucleosomes away so transcription can proceed.

They took the model one step further and proposed that transcription factors are idling on the DNA, waiting for a signal to bind more tightly and influence the activity of nearby genes.  In other words, transcription factors are ready to have an effect at a moment’s notice.

This part of the model still has to be proven though.  All that has been shown so far is that a slow off rate is required for effective transcription activation by Rap1p.  What we don’t know is whether this translates to other transcription factors or if idling Rap1p is ever more stably bound.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: ChIP, chromosomal immunoprecipitation, competition, off rate, stable binding, transcription factor, yeast

Yeast to the Rescue (of Personalized Medicine)

April 09, 2012

Yeast may help genomic researchers with their torrent of data.

Genomic scientists are quickly being overwhelmed by all of the data they are generating.  As trillions of A’s, T’s, C’s and G’s come pouring out of sequencers all over the world, how is anyone going to make sense of it all?

One idea is to use yeast to quickly figure out what effect certain differences have on a gene’s function.  Now this won’t be that useful for differences outside of genes or in genes that aren’t shared by yeast and humans.  But that still leaves an awful lot of SNPs that we might be able to better understand using the awesome power of yeast genetics.

In the most recent issue of GENETICS, Mayfield and coworkers use yeast to study a large number of variants in the human cystathione-beta synthase (CBS) gene.  They chose this gene because it is involved in the metabolic disease homocystinuria, different variants respond to treatment in unpredictable ways, and it can substitute for the yeast homolog, CYS4.

The hope was that they would be able to group CBS variants based on their phenotype in yeast and that this would let them predict which treatments would work for novel variants.  They were definitely able to group variants based on phenotype.  Time will only tell whether they can use this to better treat patients who come into the clinic with novel variants of the gene.

They looked at 84 known alleles of CBS that affected an amino acid with a single base pair change (81 were from homocystinuria patients).  They grouped these alleles based on growth phenotypes in yeast under varying conditions.  For example, they determined how well each grew in the absence of glutathione.  Only those alleles that were still functional would support growth.  They also varied the amount of glutathione, looked at the effect of heme and vitamin B6, studied metabolite profiles with mass spectroscopy and so on. 

From this they were able to group many of the alleles in clinically meaningful ways.  This means that when a novel allele comes up in a patient, they can screen it in this yeast assay to see if it falls within a known group.  At least 38 never before seen missense mutations have been found in the CBS gene since 2010 and undoubtedly new ones will keep appearing as more DNA is sequenced.

The study also revealed alleles that were more difficult to interpret in this assay.  For example, some alleles known to cause disease did not affect yeast growth.  This might mean that their particular mutation needs something human and/or patient specific to manifest itself or that the enzyme function is fine but something else is wrong. 

This study provided a powerful proof of principle.  The next step will be to see how well it works in practice and if any patients can benefit.

Benjamin deals with his homocystinuria

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight, Yeast and Human Disease

Tags: CBS gene, CYS4, high throughput screen, homocystinuria, personalized medicine, yeast

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