Genomics


Here are the biogerontological reviews from the last month or so that I’ve found interesting and noteworthy. The field as a whole continues to massively overproduce review papers; by my totally unscientific estimate, these represent less than ten percent of the review abstracts that crossed my desk since Thanksgiving.

The last installment of review roundup can be found here. As always, each Review Roundup is guaranteed to contain at least one link to a review you will find highly educational, or your money back.

Comparative biogerontology:

A while back I attended a NAKFI meeting about aging. Along with a few others, I applied for (and got) a seed grant to use comparative zoology to study aging — in a nutshell, to study the various ways that nature has solved various problems that arise during aging, and see whether we might learn something that could be applied to enhancing human healthspan or lifespan.

The initial small grant funded a series of meetings, culminating in a large-scale gathering of scientist with wide expertise not only in biogerontology but also zoology, evolutionary biology, metabolomics, and other disparate fields. While this conference didn’t end up leading to the creation a single comprehensive Comparative Biogerontology Initiative, as some of my fellow applicants had hoped, it did provoke a great deal of excellent discussion. There are a few smaller-scale efforts currently underway, initiated by people who came together to talk about the original idea.

Two of the attendees of the big meeting have published reviews recently. I haven’t asked them personally but I am assuming that they’re discussing ideas that germinated at the CBI conferences.

Gene regulation:

Inflammation:

Mitochondria:

One of the authors of the first paper is Thomas Nyström, whose lab recently described the role of cell polarity in sorting protein aggregates preferentially into the mother cell during cell division. That story lacked a significant mitochondrial component, so this review is a nice complement to the primary study published earlier this year.

Nuclear organization:

Stem cells:

Leanne Jones, the senior author on this review, is one of the folks writing the proverbial book on the critical interactions between stem cells and the tissue microenvironment. Her lab uses the Drosophila gonad as a model system.

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A couple of worm genomics papers caught my eye this week.

One is about using networks of genes as biomarkers. (The first author is our own turritopsis, and we extend our heartiest congratulations on the publication of this interesting paper.) It’s a neat idea: networks make better biomarkers than single genes; furthermore, thinking about genes as elements of networks allows us to make inferences about the functions of previously unknown genes.

The other paper describes the use of next-generation or “deep” sequencing to characterize the transcriptome of aging in C. elegans. The paper demonstrates that applying the newest sequencing methodologies to gene expression analysis isn’t just faster and more expensive, it’s qualitatively different: it can detect un-annotated genes, antisense products, and isoform switching in a way that array-based technologies can’t.

In a sense, next-gen sequencing it’s extending the idea of “unbiased” gene expression analysis. When arrays first became available, we were all excited about the fact that we no longer had to decide in advance which genes to look at — we could just look at all of them simultaneously, which led to a qualitatively different way of looking at the genome (and, ultimately, of doing science). The new technologies push away another kind of bias: our prior assumptions about what is (or “ought to be”) transcribed within a cell.

ResearchBlogging.orgFortney, K., Kotlyar, M., & Jurisica, I. (2010). Inferring the functions of longevity genes with modular subnetwork biomarkers of Caenorhabditis elegans aging Genome Biology, 11 (2) DOI: 10.1186/gb-2010-11-2-r13

Ruzanov, P., & Riddle, D. (2010). Deep SAGE analysis of the Caenorhabditis elegans transcriptome Nucleic Acids Research DOI: 10.1093/nar/gkq035

Welcome to the tenth edition of Hourglass, our blog carnival about the biology of aging. This month, the carnival has returned home to Ouroboros. In this issue, we have submissions from six bloggers, including a nice mix of veterans and new participants. Several of the posts are united by common themes: we have heavy representation from the neuroscience community, and multiple discussions of the clinical and social payoffs that are likely to result from progress in lifespan extension.

At psique (which hosted Hourglass IX), Laura Kilarski describes an important, evolving online tool for biogerontologists: the Human Aging Genomics Resources:

As I was reading a paper earlier about chromosomal region 11.5p and its putative association with aging (Lescai et al, 2009) I came across an interesting sounding url, namely http://genomics.senescence.info. Turns out that the website is home to HAGR, an interdisciplinary project devoted to the genetic study of aging … GenAge constitutes a major part of the site, and is a manually curated database of genes which could possibly be associated with human aging, largely based on studies done on the usual suspects: Mr. Mouse, Drosophila, C. elegans, and yeast. … The AnAge database on the other hand contains entries for over 4000 animals and some basic life-span-related facts. … And then there’s the ‘Δ Project’, the aim of which is to figure out transcriptional differences between young and old organisms.

Laura describes HAGR in depth and also provides some of her own analysis of the available resources.

On another age-related subject, neurodegeneration, Laura discusses the potential value of regular brain scans for early ascertainment of diseases such as Parkinson’s. Free brain scans for all! It’s a moving piece, which underscores the human cost of neurodegenerative illness and describes the author’s personal reactions on the subject, while also addressing important clinical and scientific issues.

As we age, we all suffer from some level of neurodegeneration, though in most cases this falls below the threshold of a clinical pathology. Slow chronic change isn’t the only form of age-related brain damage: let’s not forget about strokes, which can wipe out otherwise healthy neurons in macroscopic regions of the brain. While the risk factors for stroke and neurodegeneration are distinct, therapies might ultimately be quite similar — since in both cases, the goal is to regrow neurons to replace those that have been lost. At Brain Stimulant, Mike tell us about a clinical trial that will use stem cells to treat stroke:

The company Reneuron has just recently gotten the go ahead to commence a new trial that will use stem cells to treat patients with stroke damage. The trial will use stem cells to replace missing brain matter in those who have had stroke brain trauma. They are injecting doses of approximately 20 million stem cells into the stroke patients brain. Interestingly these ReN001 stem cells will not require a patient to have immunosuppression therapy.

He goes on to discuss the future challenges posed by the prospect for brain engineering: precise cell delivery, control of axon sprouting and pathfinding, and the possibility of using non-invasive methods to encourage the growth of new cells.

Also coming from a neuroscience perspective, Christopher Harris of Best Before Yesterday writes about What we need to accelerate biomedical research and fight aging.

A few hundred years ago I could not have been born. I was massive – 5.5kg – and the birth eventually turned caesarean and took many long hours. I owe my life to medical science. One day, 11 years later, I was out biking and realized for the first time that the annihilation following my death would be infinite. Now, 25 years after my complicated birth, I think a lot about whether medical science, rejuvenation research of the SENS variety in particular, will save me a second time.

What do we need? According to Harris: (1) Safe and inexpensive brain surgery (to install devices that can manipulate the reward circuitry of the brain); (2) Widespread use of enhanced motivation through deep brain stimulation (specifically to encourage exercise and healthy living); and (3) Rewarding brain stimulation for research centers (to accelerate scientific progress).

One of my favorite new sites, the Science of Aging Timeline, has a new entry about the Sinclair lab’s discovery of sirtuin-activating compounds:

Working off a model of calorie restriction via sirtuins David Sinclair et al. worked to find molecules which could modulate sitruins activity, and thus longevity.

They accomplished this by screening a number of small molecule libraries, which included analogues of epsilon-acetyl lysine, NAD+, NAD+ precursors, nucleotides and purinergic ligands. Results from the screening where assayed against human SIRT1 to identify potential inhibitors, and the following molecules where found: Resveratrol, Butein, Piceatannol, Isoliquiritigenin, Fisetin, and Quercetin. Of all of these, resveratrol proved to be the most potent …

In the copious spare time left when he’s not working on the comprehensive history of biogerontology, timeline curator Paul House has started another ambitious project: a catalog of all the labs working on aging. It’s early days yet, and only a few labs are listed, but I’ve already seen Paul take one great idea (the timeline) from seed to oak, so I have every confidence that this page will grow substantially in the weeks and months to come. Those who are interested in having their labs listed on the page can send Paul an email.

Over at Fight Aging!, Reason continues excellent coverage of recent papers in biogerontology; I daresay that the detail of coverage on primary scientific literature has improved even further in the past month or so, concomitant with the site’s participation in the ResearchBlogging tracking system for blog posts about journal articles. For this edition of Hourglass, Reason has submitted two excellent analyses of recent papers, and a third piece of a more philosophical bent:

It is from the last piece that I’ve chosen an excerpt:

Wouldn’t it be nice to wake up and find that we were all immortal? That would save a whole lot of work, uncertainty, and existential angst – and we humans are nothing if not motivated to do less work. The best of us toil endlessly in search of saving a few minutes here and a few minutes there. So it happens that there exist a range of metaphysical lines of thought – outside the bounds of theology – that suggest we humans are immortal. We should cast a suspicious eye upon any line of philosophy that would be extraordinarily convenient if true, human nature being what it is.

Moving on from a philosophical post written by a scientifically minded life-extension advocate, our next posts are scientific posts written about life extension from a political philosopher. Colin Farrelly of In Search of Enlightenment has submitted two long, thoughtful articles, the first about the clinical and social importance of tackling aging, the second about the cognitive biases that affect the way we think about risk and the significance of aging as a cause of mortality:

The “availability heuristic” was a new one on me. Here’s an operational definition as it applies to our thinking about aging:

In a rational world, aging research would be at the forefront of a global collaborative initiative to improve the health and economic prospects of today’s aging populations (and all future generations).

But humans are not rational. We suffer many cognitive biases. One prominent bias is the availability heuristic. Risks that are easily brought to mind are given a higher probability; and conversely, the less vivid a risk, the more likely we are to underestimate the probability of their occurring.

The two tests above reveal how prominent this heuristic is in your own comprehension of the risks facing yourself, your loved ones and humanity. Because death by aging is not something that is vivid is most people’s minds (though it is in the minds of the scientists who study the biology of aging and thus know all too well how it affects a species functional capacities), odds are you probably underestimated it as a risk of mortality.

The benefits of lifespan extension, both with regard to human health and society as a whole is sometimes called the Longevity Dividend. Alvaro Fernandez from SharpBrains sent in a long piece about the Longevity Dividend (written by a contributor from the Kronos Longevity Research Institute). Ever heard of the Longevity Dividend? Perhaps Gray is the New Gold:

The Longevity Dividend is a theory that says we hope to intervene scientifically to slow the aging process, which will also delay the onset of age-related diseases. Delaying aging just seven years would slash rates of conditions like cancer, diabetes, Alzheimer’s disease and heart disease in half. That’s the longevity part. … The dividend comes from the social, economic, and health bonuses that would then be available to spend on schools, energy, jobs, infrastructure—trillions of dollars that today we spend on healthcare services. In fact, at the rate we’re going, by the year 2020 one out of every $5 spent in this country will be spent on healthcare. Obviously, something has to change.

Alvaro, the editor of SharpBrains and founder of the parent website, has recently published a book, The SharpBrains Guide to Brain Fitness, which is the subject of this recent (and quite favoriable) review. If you’re interested in learning more, here’s list of cognitive fitness references, based on the authors’ research for the book.

That’s all for now. If you’d like to host a future installation of Hourglass, please email me.

In the post-genomic age, annotation serves an essential function: it takes us from a point where we’re struggling with the allegorical phone books of raw sequence to a point where we’re productively accessing comprehensively compiled knowledge about the function of specific genes. Typically, gene annotation is accomplished in part by machines (e.g., computers running hidden Markov models to identify open reading frames, BLASTing to identify known homologs in other organisms, and occasionally bootstrapping their way to predicted functions) and in part by expert human curators, who (in ways that machines still can’t) collate and summarize published studies that have addressed the functions and interactions of specific genes.

The process used to work pretty well, back in the day when the only complete eukaryotic genome was the genetically tractable and well-studied budding yeast S. cerevisiae — but now it’s starting to come apart. The number of genes, while still imprecisely known, is unquestionably finite, but as the number of studies and interactions grows, the problem of annotation is growing exponentially — still finite, certainly (?) but far beyond the ability of the top-down expert-only annotation systems to keep up.

Because the process is bottlenecked by curators (delightful human beings, to be sure, but who have finite energy and time at their disposal), it takes times for new data to be incorporated into existing systems. Therefore, the most recent findings regarding a given gene — often reflecting the most exciting new direction for research — are often missing from curated functional annotations.

Another problem with expert curation is that the types of information included may implicitly reflect the priorities and agendas of the experts doing the curating — again, not to bag on curators, but it’s simply impossible for a small group of people to identify all the different ways that everyone in the scientific world might want to grope each of >30,000 different elephants. (For those of you who missed it: This is the biogerontology “hook” for this post. Have you ever been frustrated when a gene annotation fails to include a reference to a piece of aging-related data on your favorite gene, even when you know it’s out there — and then realized what that means about how many other aging-related annotations are also missing?)

Enter the power of the mobilized mob: In July, Huss et al. announced an initiative to democratize the annotation of gene function, using an established web site that happens to be the most widely used open editing system in the world: Wikipedia. (Perhaps you’ve heard of it.) The idea is to kick-start the (arguably inevitable) process of making Wikipedia a central hub for community-generated annotation of gene function.

In any open science initiative, the first (skeptical) question anyone raises is: Who’s going to do the hard part? For instance: open notebooks might be a great concept in the abstract, but it’s beyond the realm of reasonability to expect every lab in the world to take a month of three to whip up a software solution to enable that idea. (Fortunately, in the case of that example, there’s an answer to the question: OpenWetWare.) The same question applies to annotation: Given that the theoretical tools for the open annotation of gene function technically exist (just as they do for the open annotation of every episode of Battlestar Galactica), what barrier must be overcome in order to get people to actually use those tools?

Sometimes an enabling technology can be as simple as a thoughtful template for future efforts of the same kind. Here, the initiators of the project simply created a “stub” — an standard format for new gene entries, automatically populated from existing annotations, which other Wikipedia editors are then free to expand and modify:

In principle, a comprehensive gene wiki could have naturally evolved out of the existing Wikipedia framework, and as described above, the beginnings of this process were already underway. However, we hypothesized that growth could be greatly accelerated by systematic creation of gene page stubs, each of which would contain a basal level of gene annotation harvested from authoritative sources. Here we describe an effort to automatically create such a foundation for a comprehensive gene wiki. Moreover, we demonstrate that this effort has begun the positive-feedback loop between readers, contributors, and page utility, which will promote its long-term success. …

Each gene stub consists of a sidebar detailing the symbols and aliases, external identifiers, gene function (as represented in Gene Ontology), and genomic location. Although gene stubs are primarily focused on human genes, links to their mouse orthologs are also provided. When available, links to the Protein Data Bank are displayed under a thumbnail ribbon diagram, and gene expression patterns across diverse human tissues are shown as thumbnail bar charts. Links to the primary databases are included when available. In addition, the central area of the gene stub shows a gene summary and a list of relevant references in the literature, both of which were provided by Entrez Gene.

As with most good ideas, the rest is obvious: More and more scientists will find themselves turning to Wikipedia as a first-line source of information about genes that catch their eye. When they notice that an entry is incomplete (say, because it’s missing an important biogerontological implication) or unclear, they’ll make a little change. The little changes will add up. And a self-correcting, bottom-up system for gene annotation will be born.

Hey, I just noticed that there’s no reference to Huss et al.‘s paper in the “Genome annotation” subsection of the Wikipedia entry for “Genome project” (link is version-specific, for posterity’s sake). Anyone care to fix that?

Related articles elsewhere:

  • iTNews Australia: a nice, smart, lay-level description of the concept
  • Slashdot: the discussion is notable for containing a lot of commentary by people who didn’t bother to read the relevant article

Our understanding of aging in animals owes a great debt to a large body of careful work in a single-celled organism, the brewer’s yeast Saccharomyces cerevisiae. Indeed, as I’ve argued before, yeast is one of the two organisms with the strongest credible claim to have started modern biogerontology. An unusually large crop of yeast aging papers have appeared over the last few months, and I thought it would be appropriate to spend a few paragraphs describing them — in honor of this humble organism that rises our bread, ferments our beer, and has done so much to open our eyes to the fundamental mechanisms of aging.

For those unfamiliar with the yeast field or simply wishing a clearly written and nearly comprehensive summary, Steinkraus et al. provide the historical perspective. The piece thoroughly reviews the development of yeast as a model system in aging, as well as the arguments in favor of a connection between results in yeast and well-established (but sometimes hard-to-test) hypotheses in animals.

Based on the influence that yeast has already had on biogerontology as a whole, it seems fair to claim that it will continue to reveal fundamentals of aging that are conserved across evolution. Now, however, there is quantitative evidence to back up that claim: Smith et al. have used bioinformatic and genomic approaches to study the conservation between known longevity genes in yeast and worm, and they show that yeast mutants in worm longevity genes are significantly more likely to be long-lived than randomly chosen mutants — suggesting that

genes that modulate aging have been conserved not only in sequence, but also in function, over a billion years of evolution.

Given this functional conservation, it is reasonable to use yeast to help answer questions about aging in general, so long as these questions are cell-biological in scope.

For instance: NAD+/NADH ratios are thought to be an important metric of the cellular energy balance, and appear to have effects both within the mitochondria and the cytosol. The mitochondrial inner membrane, however, is impermeable to both NAD+ and NADH. How, then, is information about energy balance communicated between the two cellular compartments? Easlon et al. report that two components of the malate-aspartate NADH shuttle (which transports metabolites across the mitochondrial membrane, resulting in equilibration of the cytosolic and mitochondrial NAD+/NADH pools) are involved in controlling longevity. The two proteins, Mdh1 and Aat1, are required for longevity enhancement by calorie restriction (CR), and overexpression of both proteins can increase lifespan independent of caloric conditions (but in a Sir2-dependent manner, about which see more below).

Another outstanding question involves how cellular energy balance is coordinated with the rates of catabolic and anabolic processes, and how this coordination impinges on regulation of longevity. We know that in yeast, the effects of CR are mediated by pathways involving the nutrient sensor TOR and the kinase Sch9. (Brief aside: longevity-enhancing mutations of Sch9 can also suppress genomic instability; new results from Qin et al. show that genomic instability is also associated with lifespan variation in yeast). Sch9 regulates, among other things, ribosome biogenesis; both CR and Sch9 mutation cause ribosome synthesis to decrease — but are the ribosome and longevity phenotypes related? Very likely yes: Steffen et al. report that multiple means of downregulating ribosome synthesis all extend lifespan, implying that reducing production of ribosomes is essential in order to reap the benefits of CR.

As the tools of biology have adapted, so has the yeast field (sometimes leading the charge, as in the case of the earliest microarray-based expression profiling experiments). Murakami et al. have developed a high-throughput method for measuring yeast lifespan. In this first report, the authors primarily demonstrate the use of their method on known mutants, arguing that their results are similar but with lower variance. (Brief aside: they also demonstrate that CR-induced lifespan extension does not require SIR2 or any other yeast sirtuin, adding fuel to the controversy about whether sirtuins play any role in CR in yeast; for more, see here and here.) The increased precision of their technique will allow detection of subtler aging-related phenotypes than were previously detectable, very likely allowing us to add to the list of genes known to regulate lifespan. The high-throughput aspects of the method, of course, open the door to testing small-molecule drugs that could delay aging in yeast — historically a fruitful approach though not without its potential pitfalls.

If you’ve made it this far, feel free to toast S. cerevisiae, perhaps with a beer.

(Before I depart, I just want to mention — since it’s not necessarily clear from the first authors’ names — that four of the papers mentioned above, as well as many of the papers described in earlier Ouroboros posts linked above, are the result of the combined work of the Kaeberlein and Kennedy labs at U-Wash Seattle. Both of them worked together in the Guarente lab back in the day, and they’ve been in the yeast aging field from its very beginning. Clearly, their combined work is continuing to advance the field.)

For all you genomics and systems-level junkies out there, here are two very juicy genome-scale (in one case, proteome-scale) studies of two very different aging-related phenomena: From Miller et al., we have a systems-level analysis of gene expression in both Alzheimer’s disease (AD) and normal aging. The authors use the transcriptional data in conjunction with genome annotation to identify pathways that are coherently regulated, either by neurodegenerative changes or age-related decline. Since the authors focus on brain tissue rather than the more easily accessible (or biopsy-able) parts of the body, these findings will be more relevant to understanding the pathology of AD than to its diagnosis. Hence, this approach is complementary to recent studies aimed at identifying proteins that are differentially expressed in the blood of AD patients and can therefore be used as diagnostic biomarkers for the disease.

Meanwhile, Krüger et al. have used mass spectrometry to characterize the tyrosine phosphoproteome of the insulin signaling pathway — in other words, they looked for proteins that are differentially tyrosine-phosphorylated as a result of insulin action. In addition to rounding up proteins already known to be involved, they also report the identification of several novel effectors of insulin signaling. The technique appears quite robust, and I look forward to seeing this methodology extended to other aging-related signaling networks (such as the closely related IGF-1 pathway).

Continuing with his recent favorite theme of sending people your body fluids in the mail, Attila Csordás at Partial Immortalization has a very thorough treatment of Silicon Valley “personal genomics” startup 23andMe.

Attila’s treatment is as detailed as any in the popular press, bringing to bear his own scholarly/scientific viewpoint and approaching the issues from multiple perspectives (including that of hobbits). If you haven’t been following the big launch of a company that’s sure to drive discussion on the personal impact of the genomics revolution (at least, among those with $1000 to spend on a profile), rush on over and check it out.

Oh — almost forgot — the aging connection: While the company is initially devoted to assessment of disease risk based on known associations, they’re also going to attempt to use a novel application of social networking to bring private citizens into studies that will seek to define heretofore unknown genetic risk factors for other conditions. With clever study design (and possibly simply with shrewd data-mining techniques), one can imagine any number of ways for longevity researchers to capitalize on the sudden influx of people willing to volunteer their genetic information for analysis and follow-up.

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