New & Noteworthy

A Simpler Way to Evolve

February 06, 2012

As life evolves there is a tendency for increased complexity.  Up until now, scientists have mostly focused on gain of function mutations as the motor for this change.  This has proven fertile ground for evolution deniers who have claimed that life’s complexity could not have arisen from these rare, gain of function mutations alone.

A new study by Finnigan and coworkers provides an important counterpunch to this argument.  These authors resurrected ancient proteins and showed that an increase in complexity can come from much more common, loss of function mutations.  Time for the deniers to find a new argument…

Making Molecular Machines

Finnigan and coworkers focused on the evolution of a protein called vacuolar H+ -ATPase or V-ATPase for short.  Like other molecular machines, this proton pump consists of many different proteins all working together in a coordinated fashion.  One key part of this machine is a rotary ring called V₀.  (This would be the ring of C proteins in the image to the right.)

In most eukaryotes, V₀ is made up of five identical subunits (called Vma3) and one subunit called Vma16.  In fungi, a third protein, Vma11, has replaced one of the Vma3 subunits.  In other words, the fungal version is a bit more complex than other eukaryotic versions.

Current theories are that these three proteins all arose through gene duplication.  Duplication of the Vma3 gene first led to the Vma16 gene and then later in fungi, Vma3 duplicated again this time becoming Vma11.  Yeast V-ATPase absolutely requires Vma11 to function and other eukaryotic Vma3 family members cannot replace Vma11.

Using the 139 family members of the Vma family available in GenBank, members of the Thornton and Stevens lab recreated the ancestral proteins that existed before and after the Vma11 gene duplication event.  Before the arrival of Vma11, there were only two proteins which the authors have named Anc.3-11 and Anc.16.  Anc.3-11 presumably has functions of both Vma3 and Vma11.  After the gene duplication event, there were three ancient proteins: Anc.3, Anc.11, and Anc.16.

Using these ancient proteins, the authors first showed that Anc.3-11 could substitute for either Vma3 or Vma11 in yeast. It could even partially rescue a yeast strain that lacked both of the other genes.  They then showed Anc.16 could replace Vma16 and most importantly, that the two ancient proteins could replace the three modern ones.  They reconstructed an ancient molecular machine that works.

The next step was to figure out what happened after Anc.3-11 duplicated again and the two genes began to evolve into the separate proteins, Anc.3 and Anc.11.  Again using the GenBank sequences, the authors predicted that two single mutations were an initial step on the way to the separation of Anc.3-11 activities into the Anc3 and the Anc11 proteins. 

The authors engineered each mutation independently into the Anc.3-11 protein and found that one mutation made Anc.3-11 more like Anc.3 and the other made Anc.3-11 more like Anc.11.  The complex now required all three Anc proteins instead of just the two for maximal activity.  The authors had recapitulated the first evolutionary steps that led to the formation of the three subunit V₀ rotary ring.

Finally the authors showed that each of these mutations were loss of function mutations.  The Anc.3-11 protein has two different interfaces that interact with Anc.16.  The first mutation weakened one interface on Anc.3 and the second mutation weakened the other interface on Anc.11 causing both proteins to now be required to reconstitute the ring.  The added complexity arose from a combination of gene duplication and relatively common loss of function mutations.

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

Categories: Research Spotlight

Tags: ATPase, evolution, gain of function, gene duplication, loss of function, mutation, proton pump

SGD Winter 2012 Newsletter

January 30, 2012

SGD sends out its quarterly newsletter to colleagues designated as contacts in SGD. This Winter 2012 newsletter is also available online. If you would like to receive this letter in the future please use the Colleague Submission/Update form to let us know.

Categories: Newsletter

Replicate Late, Mutate More

January 30, 2012

 

Variation in the DNA that results in natural selection does not come about randomly. Where a piece of DNA is in the genome and how it is used affects its chances for being mutated. The end result is that the genomes we see today are the product of these nonrandom mutation rates.

One of the first places this became apparent was in transcribed genes. Scientists found that the transcribed strand of active genes has fewer mutations than the nontranscribed strand. They found the major reason for this was transcription-coupled repair.

Now in a new study in yeast, Agier and Fischer have shown that when a piece of DNA is replicated affects its chance of being mutated too. They compared the genomes of 39 different strains of Saccharomyces cerevisiae and found that late replicating DNA is 1.3 times more likely to be mutated compared to early replicating DNA. This is consistent with a recent study by Chen and coworkers that showed a similar result in the human genome.

This means that if a piece of DNA happens to be further away from an origin of replication, it will build up more mutations over time. And while a 1.3 fold increase in mutation rate might seem small, it is predicted to have a significant impact on genomic variation and natural selection on an evolutionary time scale.

There are a number of potential models for why late replicating DNA is more likely to be mutated. One hypothesis is that cells use different repair mechanisms at different times during S phase: cells in early S-phase repair replication errors with relatively error-free repair mechanisms like template switching with newly formed sister chromatids, while cells in late S-phase tend to rely on more error-prone translesion repair pathways.

Other possible models rely on potential differences between the cellular environment in early and late S-phase. They include altered metabolism, increased presence of single stranded DNA, or even a slow decrease in DNA repair as S-phase progresses. The researchers do not know which, if any, of these mechanisms is responsible for the change in mutation rate.

It may even be that different mechanisms are responsible in yeast and humans. Agier and Fischer found that in yeast, the leading strand had higher rates of substitution towards C and A than did the lagging strand. Chen et. al. found the opposite to be true in human cells. Either they use different mechanisms or similar mechanisms can end up with opposite results.

These findings suggest that the genomes observed today are at least partly the result of the nonrandom nature of neutral mutations. Highly expressed genes near an origin of replication are much less likely to be mutated than are genes with low expression more distant from an origin of replication.

And there are other known and yet to be discovered ways that certain DNA ends up more mutated than other DNAs. Just like in real estate, the key to mutation rate is location, location, location.

Categories: Research Spotlight

Tags: DNA replication, mutation, S phase, translesion, yeast

SGD: New look, new features!

January 26, 2012

SGD has added more than just a new look, we’ve added some great new features!

View the short video “We’ve added more than just a new look…” on Vimeo to learn about our enhanced Search Box and our new navigational menu bar.

Categories: Tutorial, Website changes

New data tracks added to GBrowse

January 26, 2012

SGD has added a mélange of data tracks to our GBrowse genome viewer from six publications covering various applications of high-throughput sequencing, including genome-wide distributions of DNase I-protected genomic footprints (Hesselberth et al. 2009), recombination-associated double strand breakpoints (Pan et al. 2011), polyadenylation sites (Ozsolak et al. 2010), antisense ncRNAs (Yassour et al. 2010), cryptic unstable transcripts (CUTs) (Neil et al. 2009) and Xrn1-sensitive unstable transcripts (XUTs) (van Dijk et al. 2011). You can now also easily download data tracks, metadata and supplementary data by clicking on the ‘?’ icon on each data track within GBrowse. Please watch our video tutorial for more information on how to download data from GBrowse. We welcome new data submissions pre- or post-publication and invite authors to work with us to integrate their data into our GBrowse and PBrowse viewers. Please contact us if you are interested in participating or have questions and comments. Happy browsing!

View Downloading GBrowse Data at SGD on Vimeo.

Categories: New Data, Tutorial

Updated Resource: YPL+

January 25, 2012

Links to YPL+ (the Yeast Protein Localization^Plus Database) have been added to the “Protein Information” section of SGD Locus Summary pages. YPL+ is a recently upgraded version of the YPL image database, and has been expanded to include GFP-localization data for more than 3500 genes. Data in YPL+ are derived from a collection of GFP fusion constructs generated by C-terminal chromosomal tagging (Huh et al., 2003, Nature 425, 686-691) as well as a collection of proteins involved in lipid-metabolism, constructed by in vivo recombination (Natter et al., 2005, Mol. Cell. Proteomics 4(5), 662-672). Thanks to Sepp Kohlwein for help in setting up these links.

Categories: New Data, Website changes

More Going on in the Ribosome Than Expected

January 20, 2012

As scientists peer ever more deeply into a cell, the picture of how things work becomes more and more complicated.  This was true when scientists took a hard look at transcription and gene regulation and found lots of little RNAs scurrying around the cell, regulating genes.  And it now appears to be true for what is being translated and how translation is regulated.

In a new study, Brar and coworkers used ribosome profiling to explore what happens in yeast cells during meiosis at the level of translation.  What they found was that a whole lot more was being translated (or at the very least gumming up the translation machinery) than anyone expected. They also found that translation is as finely regulated as is transcription. 

And this doesn’t just happen in yeast.  The same group has also generated similar findings in mice embryos as well.  Results with human cells should be right around the corner…

Ribosome Profiling

In ribosome profiling, scientists determine what RNAs are contained in a ribosome at a given time point.  The basic idea is that they isolate ribosomes, treat them with nucleases and then harvest the associated 30-35 nucleotide long mRNAs.  They then sequence all of the isolated RNAs and identify where they came from.

Like lots of biology these days, this technique has only become possible with the advent of cheap, robust sequencing.  In fact, the size of these sequences is ideal for modern sequencing techniques.

Researchers in the Weissman lab are finding all sorts of interesting things using this new tool.  For example, in meiosis they were better able to determine which proteins are involved at various stages of meiosis, to see how involved “untranslated” mRNA leaders are in translation, and to identify smaller, previously ignored transcripts associated with ribosomes.  In this post we’ll just focus on the last point but encourage the reader to learn about the study’s other findings here.

Of Shorter ORFs

Ribosome profiling has revealed that a lot more is being translated in yeast than the standard set of genes identified in the Saccharomyces Genome Database (SGD).  For example, Brar and coworkers found that the mRNA of many open reading frames (ORFs) shorter than the usual 250 or so base pairs were associated with the ribosomes.  Shorter ORFs like these aren’t routinely thought of as genes and so have not been extensively studied.

However, given how many of these ORFs were associated with ribosomes, scientists probably should start paying more attention now.  Even before meiosis, 5% of the ribosomes tested in yeast contained RNAs from these shorter ORFs.  Once meiosis kicked in, the number went up to an astonishing 30%.

Since scientists have only just started to focus on them, it isn’t surprising they don’t know how many of these smaller ORFs are translated into smaller peptides.  Or what any of these peptides that do get translated might be doing in a cell.

In a recent study, Kondo and coworkers have shown that one of these ORFs is translated into a peptide and proposed it affects how the transcription regulator Shavenbaby works in Drosophila.  Work similar to this will need to get underway before we have a good handle on what exactly is going on with these shorter ORFs.

Whatever they turn out to do, these small ORFs will probably change what we consider to be a gene.  Again.  The cell just keeps getting more and more complicated! 

Lengthy but informative lecture on ribosome profiling.

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

Categories: Research Spotlight

Tags: regulation, ribosome, ribosome profiling, translation, untranslated leader, UTR

Proud to be an SGDer

January 14, 2012

Thank you to those who have shared your thoughts and comments about the new site.  I am very proud of our new look and all the features it incorporates.  For the past year we worked with web design professionals and conducted studies to determine an optimal design for the SGD pages. Since the inception of SGD, the standards for computer-human interfaces and website usability have advanced and we realize that we must embrace these changes in order to reach out to all communities that depend on SGD.  The new pages address many previously identified issues and the new design allows the 21,000 weekly users of SGD to more effectively find the information they require.  In addition to providing a modern look, the new design greatly decreases the learning curve for new users.  

I am delighted that our Search has been enhanced to provide auto-suggest and auto-complete features.  The new Search interface gives access to more types of information and facilitates the discovery of huge amounts of information integrated at SGD.  Easier access to all the data is also facilitated by recent tool enhancements and data additions.  Over the past year several hundred new datasets have been added to the Genomic Browser and we will continue to add new data at about the same rate.  This year will also see the addition of new types of data, in particular strain genomic sequence.

I appreciate that change can be difficult. I hope that adjusting to the new site will not be too onerous and, in addition to the data which you are accustomed to getting from the site, you will discover new datatypes useful to you that you may not have realized were contained within SGD. I thank you again for providing me feedback on the new and more powerful SGD and ask that you please continue to send questions and comments to the SGD HelpDesk.

Wishing you all the best in 2012,
Mike

J. Michael Cherry, Ph.D.         
Associate Professor (Research)
Department of Genetics
Stanford University
Stanford, CA  94305-5120

Categories: Website changes

SGD Website Updated

January 11, 2012

Welcome to SGD’s new look! All of the information and functionality you are familiar with at SGD is still available, but has been repackaged in order to provide better access to data and to provide additional tools and services. One exciting new feature is the SGD blog where we will highlight and discuss research articles and topics. To fully access the updated SGD site, you may need to clear the cache on your browser. We encourage you to explore the new site and send us feedback.

Categories: Website changes

Yeast with Dementia

January 04, 2012

Even though it doesn’t have a brain, yeast is teaching us a lot about Alzheimer’s.  Researchers are using this simple eukaryote to figure out what previously identified Alzheimer’s-related genes may be doing in humans as well as to identify new genes that might be involved in this terrible disease.  Studies like this may even one day help scientists find better treatments.

Alzheimer’s is a form of dementia that hits about 50% of people over 85.  The video below has a great summary of the how the disease progresses:

As the video states, plaques and tangles are linked to the memory loss that is associated with Alzheimer’s.  Scientists know that the plaques are  amyloids of misfolded AΒ peptides and that AΒ peptides that come from the amyloid precursor protein (APP).  What they don’t know is how AΒ peptides cause their damage and if it can be stopped.  And so far, genome wide association studies (GWAS) in humans have not shed much light on this problem either.

That isn’t to say that GWAS have been a waste of time.  They haven’t.  These studies have identified a number of alleles of a few genes that impact a person’s risk for ending up with Alzheimer’s.  They just haven’t been able to link the build up of plaques with the identified genes.  This is where yeast comes in.

Treusch and coworkers created a strain of yeast in which the AΒ peptide was sent to the endoplasmic reticulum.  This mimics what happens to the peptide in the cells of Alzheimer’s patients.  These yeast grew more slowly and developed protein complexes reminiscent of plaques.

They then added each of 5532 yeast open reading frames to this strain to identify genes that specifically affected its growth rate.  Of the 40 different yeast genes they found, two (YAP1802 and INP52) were yeast homologs of human genes (PICALM and SYNJ1) that had already been identified to be important in Alzheimer’s risks.  These results validated the screen and gave the researchers the confidence to dive deeper into their results.

The researchers decided to focus on the 12 genes that had very close human homologs.  Of these 12 genes, 10 dealt with endocytosis and the cytoskeleton and at least three had been implicated in previous genome wide association studies in humans.  Further work by these authors validated four of these genes by showing that they had similar effects on AΒ cell toxicity in the worm model C. elegans.

In one of the most interesting parts of the study, the researchers used the yeast strain to show why the GWAS-identified gene PICALM affects Alzheimer’s patients.  Rather than modifying APP trafficking as had been previously proposed, their results support a model where PICALM lessens the impact of misfolded AΒ plaques on the cell. 

This study is another example of the awesome power of yeast genetics.  Who would have thought that a brainless yeast could teach us so much about Alzheimer’s?

Simple explanation of the genetics of Alzheimer’s

More information about Alzheimer’s

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

Categories: Research Spotlight, Yeast and Human Disease

Tags: alzheimer's, amyloid, APP, model organism, PICALM, plaque, yeast

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