Biomarkers


Subhash Katewa (Kapahi lab, Buck Institute) talked about the metabolic adaptations that occur in flies whose lifespan is being extended by dietary restriction (DR). Katewa is studying translational control in DR using a method called translational profiling, which uses the number of ribosomes bound to each mRNA as an index of translational activity (more ribosomes = more translation). He found that DR increases translation of messages that encode a variety of mitochondrial functions; this observation led to some interesting findings about the differential turnover of triglycerides in DR vs ad libitum flies.

Adam Freund (Campisi lab, Buck Institute) spoke about the sources of age-related inflammation, focusing on the senescence-associated secretory phenotype (SASP). Freund has elucidated mechanisms of SASP control that intermediate between the most upstream events in senescence (DNA damage) and its downstream effects (secretion of inflammatory factors). I have it on good authority that he has a completed manuscript on the subject, hopefully to be publshed soon, so I won’t say more about his story here. (Mr. Freund happens to be my baymate.)

Dario Valenzano (Brunet lab, Stanford University) is studying the genetic architecture of longevity in a short-lived fish Nothobranchius furzeri, the shortest-living vertebrate that can be reared in captivity. As a graduate student, Valenzano developed a system of biomarkers for tracking the progress of aging in skin, brain and other tissues – not only physical markers like the senescence-associated beta-galactosidase but also behavioral markers that change over the lifespan. He is now proceeding to map the longevity-associated genes in N. furzeri and testing the sufficiency of the genes he finds. Early results indicate that short-lived and long-lived fish are dying from different causes, as evidenced by a bimodal distribution of death rate vs. age.

Adolfo Sánchez-Blanco (Kim lab, Stanford University Medical School) described the “molecular odometer” for aging in the worm C. elegans. He began with the observation that lifespan is variable, even among clonally identical individuals kept under identical conditions. With genetics and environment taken out of the picture, what makes some individuals live longer than others? In order to address this question, SB had to develop a molecular marker (e.g., promoter activity of some gene) that measures physiological age (as opposed to chronological age), and then determine whether the expression level of that marker in individual worms is predictive of lifespan. SB has identified several such genes whose expression at middle age strongly predicts remaining lifespan. He is now actively looking for interventions that abolish the correlation between marker expression and longevity: if the marker gene’s activity is serving to overcome the life-shortening effect of some stress, then removing that stress will not necessarily abolish the variability in the marker, but will eliminate the correlation between marker levels and lifespan. (This is a subtle but important logical issue; I would have thought that one should look for interventions that drove the population distribution of marker levels toward the favorable side of the distribution. It was clear from questions that a lot of audience members had trouble with this logic, and I’m still not sure I understand it myself.)

(next session)

Two recent computational studies show that expression relationships between genes change with age – for example, some genes have expression levels that are highly correlated in early adulthood but not in old age. Both studies propose new methods for identifying gene groups with this behaviour, and the second also makes a compelling case that many related genes lose coexpression with age. Crucially, the correlation between a pair of genes may change with age even when the average expression levels of both genes do not – so these new coexpression methods are complementary to traditional differential expression analyses of microarray data.

Gillis et al. developed a new framework for identifying pairs of genes differentially coexpressed with age that is based on Haar wavelets, and tested it on a large set of human expression data mined from the handy GEMMA database. Unlike other methods that can interpret data coming from only two groups (e.g. young mice vs. old), the new wavelet method is designed to handle multiple ordered groups – such as animals of many different ages. The authors don’t discuss the biological implications of their results in any detail, instead promising these will be explored in a later paper.

Southworth et al. showed that coexpression patterns of groups of related genes become less coherent as animals age. Using several different methods for grouping genes together (e.g. assigning genes to the same group if they share a function, or if they are targets of the same transcription factor), they calculated intra-group correlation in 16- and 24-month-old mice using data from the AGEMAP study. They identified a surprisingly large number of groups with lower correlation in old mice. One of these is the targets of NF-κB – a transcription factor that, when knocked down, can reverse skin aging. Only a few groups (including one enriched for DNA damage genes) showed higher correlation in old mice. Also, the authors found that genes showing decreases in correlation aren’t randomly located on the chromosome – instead, they form several clusters.

What are the causes and consequences of these changes in gene group correlation? Previous single-cell studies have shown that transcriptional noise, or cell-to-cell variation in the expression levels of individual genes, increases with age. Clearly transcriptional noise is going to affect coexpression to some degree: any increase in a gene’s noise level will automatically reduce its calculated coexpression with other genes. But changes in coexpression can also occur without any corresponding change in noise. These changes may reflect cellular processes that are active or suppressed at different times of life, and many or all such changes (such as a ramped-up DNA damage response in old age) may be adaptive. Further analyses are needed to tease out which age-related coexpression differences result from noise, and which ones are telling us something new.

ResearchBlogging.orgGillis, J., & Pavlidis, P. (2009). A methodology for the analysis of differential coexpression across the human lifespan BMC Bioinformatics, 10 (1) DOI: 10.1186/1471-2105-10-306

Southworth, L., Owen, A., & Kim, S. (2009). Aging Mice Show a Decreasing Correlation of Gene Expression within Genetic Modules PLoS Genetics, 5 (12) DOI: 10.1371/journal.pgen.1000776

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As I was wandering the net today I found a very nice writeup about the 2009 report of an association between the FOXO3A gene and human aging. I found the article at the apparently quite popular but new-to-me blog Singularity Hub.

We mentioned this work in a brief post last year. The overall conclusion is that natural variants in this gene that are associated with extreme longevity. (The FOXO3A gene is a homolog of DAF-16, a longevity determinant in worms.) The 2009 paper describes a study of German centenarians, and is consistent with similar results in Japanese-Americans, published in 2008. Other genetic variants associated with lifespan include the hTERT and hTERC loci, recently described in a study of Ashkenazi Jewish centenarians.

Mostly I’m writing this post to introduce our readers to an interesting site: Singularity Hub contains a lot of excellent biogerontology coverage (in their longevity category). Much of the writing on that topic is by senior editor Aaron Saenz, who does a great job of critically addressing the newest findings in a very reader-friendly and accessible style. I’m going to subscribe to their feed and start reading regularly. Overall it’s a very professional and well-written site, and I’d recommend it to Ouroboros readers.

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SNPedia is a wiki-style index of genetic variants that have interesting phenotypic associations in human beings. The name comes from the acronym for “single nucleotide polymorphisms,” i.e., one-letter variations among different individuals’ genomes.

In honor of the new year, one of the proprietors has posted SNPedia’s Top 10 SNPs of the Year, based on an admittedly “subjective combination of medical importance, statistical believability, and overall general interest.” The variants that made the list are associated with a wide range of phenotypes, but they fall into a few categories:

  • benefits of major drugs (e.g., effect of Plavix on heart disease risk);
  • likelihood of drug side effects (e.g., myopathy in response to statins);
  • risk for specific diseases (CVD, periodontitis, cancers)

The list, especially the items regarding drug efficacy and adverse reactions, got me thinking about anti-aging medicine.

Any hypothetical longevity-enhancing therapies will be more or less effective, and be subject to more or less severe side effects, as a function of individual genetic variation. One consequence of pharmacogenetic variability is that small or insufficiently diverse trial populations (in which specific genetic variants might be underrepresented) can result in misleading results about a therapy’s potential efficacy in the general population. And it’s hard to know, in advance of preliminary results, what the relevant variants might be.

This logic is general to a wide variety of therapies. Drugs are just molecules of varying shapes and sizes, and molecules of all shapes and sizes mediate cell-cell interactions, so it’s likely that pharmacogenetics will influence cellular therapies as well as more conventional pharmaceutical approaches. I suspect that cellular therapies might even be more vulnerable to genetic variation, since cell-cell interactions rely on proteins and other molecules produced by multiple genetic loci – e.g., not just a receptor or a ligand but both the receptor and the ligand acting together – and these pairwise interactions will be even more difficult to tease out than phenotypes that rely on a single locus.

It’s already going to be hard to determine over short intervals whether a given anti-aging therapeutic is effective, since we don’t (yet) have biomarkers that allow us to measure the rate of aging. Most of the best biomarkers are most convincing at the population level, and it’s hard to use them to compare the rate of physiological vs. chronological aging in a single individual. Therefore, proof of efficacy of longevity-enhancing treatments will rely on long studies and sizable populations of subjects – and the existence of unresponsive genotypes in the population will further confound that analysis.

Granted, we already know that building an anti-aging pharmacopeia will be challenging, and I’m not suggesting that this line of reasoning means we should pack up and go home. I mention it mostly because genetic variations will almost certainly play an important role in determining the efficacy of any given therapy, and we had best be prepared for that.

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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

Individuals of the same species age at different rates, and these differences should be reflected in their gene expression profiles. However, most microarray studies of aging are designed only to capture the gene changes that occur with age in a “typical” individual and (with rare exceptions) ignore individual variability – all animals of a given age are lumped together into a group, and different age groups are compared.

To study how gene changes are related to individual longevity, we need another type of data in addition to gene expression profiles: the survival time of individual animals after their gene expression is measured. With this information, we could determine which transcriptional responses are associated with a longer lifespan, and in principle even develop a personalized medicine approach to aging: we could train a machine learning algorithm to peek at the expression levels of a handful of crucial genes and predict your physiological age – and the number of healthy years you have left.

Previous microarray studies of aging animals didn’t include survival times because the animals were sacrificed at the time of sample collection (in order to get enough RNA), and studies of aging humans haven’t included survival times because we live too long.

Recently, some human survival data – together with matching gene expression data from lymphoblastoid cell lines – have become available from a long-range study that began in the early 1980s. In the first aging study to take advantage of this resource, Kerber et al. mine the data to identify gene changes associated with longevity:

Gene Expression Profiles Associated with Aging and Mortality in Humans
We investigated the hypothesis that gene expression profiles in cultured cell lines from adults, aged 57-97 years, contain information about the biological age and potential longevity of the donors. We studied 104 unrelated grandparents from 31 Utah CEU (Centre d’Etude du Polymorphisme Humain – Utah) families, for whom lymphoblastoid cell lines were established in the 1980s. Combining publicly available gene expression data from these cell lines, and survival data from the Utah Population Database, we tested the relationship between expression of 2,151 always-expressed genes, age, and survival of the donors. Approximately 16% of 2,151 expression levels were associated with donor age: 10% decreased in expression with age, and 6% increased with age. CDC42 and CORO1A exhibited strong associations both with age at draw and survival after draw, (multiple comparisons-adjusted Monte Carlo p-value < 0.05). In general, gene expressions that increased with age were associated with increased mortality. Gene expressions that decreased with age were generally associated with reduced mortality. A multivariate estimate of biological age modeled from expression data was dominated by CDC42 expression, and was a significant predictor of survival after blood draw. A multivariate model of survival as a function of gene expression was dominated by CORO1A expression. This model accounted for approximately 23% of the variation in survival among the CEU grandparents. Some expression levels were negligibly associated with age in this cross-sectional dataset, but strongly associated with inter-individual differences in survival. These observations may lead to new insights regarding the genetic contribution to exceptional longevity.

The novel aspect of this study was the integration of gene expression and survival data to identify genes associated with longevity; the authors also identified genes associated with chronological age using both univariate and multivariate models.

A brief summary of some of their major findings:

  • A six-gene model accounts for 23% of the variation in survival time
    The authors trained a penalized regression model to predict survival time on the basis of the expression levels of roughly 2000 genes. After training, only six genes had non-zero model coefficients: CORO1A, FXR2, CBX5, PIK3CA, AKAP2, and CUL3. The model was dominated by the expression levels of CORO1A (which is negatively associated with mortality) and FXR2 (which is positively associated with mortality). CORO1A has been implicated in mitochondrial apoptosis, and FXR2 is involved in Fragile X syndrome; the exact role of these two genes in aging has yet to be determined.
  • Genes associated with age are not necessarily associated with survival (and vice versa)
    The authors used linear regression to identify individual gene changes that were associated with chronological age, and a proportional hazards model to identify changes associated with survival. Among the top 10 genes identified by each test, only one gene appears on both lists (CORO1A) – i.e., genes that are strongly associated with longevity are not necessarily strongly associated with survival. This is an important point – it means that in order to identify gene expression biomarkers of physiological age and longevity, we need more microarray studies that report survival data.

Looking at expression data alone, it is difficult to tell which of the very many age-related gene changes are good and which are bad, i.e., whether a given gene change causes a problem associated with aging or is part of some beneficial damage-control response – an issue which we previously discussed in the context of gender differences in brain aging. With survival data, we can now ask a specific question of each gene: is its age-related response associated with increased or with reduced mortality? For nine of the ten genes most strongly related to survival in this study, relative overexpression was associated with reduced mortality. This strongly suggests that those genes (including CORO1A) are doing something good, i.e. that they are involved in some sort of defense or repair mechanisms.

The expression dataset used by the authors of this study is publically available through GEO: GSE1485, GSE2552.

ResearchBlogging.orgKerber, R., O’Brien, E., & Cawthon, R. (2009). Gene expression profiles associated with aging and mortality in humans Aging Cell, 8 (3), 239-250 DOI: 10.1111/j.1474-9726.2009.00467.x

In recent years, dozens of large-scale gene expression studies (many of them available through the Gene Aging Nexus) have tracked the transcriptional changes that occur with aging. However, these studies usually identify few genes showing statistically significant changes; worse, there is poor overlap across studies – i.e. genes found to be very significant in one study are often not significant in others.

It’s true that these problems are common to microarray studies of other phenotypes – experimental noise and biological variability make this type of data hard to interpret – but for aging the difficulties seem especially pronounced. Aging is complex and global: it happens in every tissue (and possibly differently in every tissue), at both the cellular and organismal levels, and involves many independent biochemical pathways. On top of that, rates of aging can vary substantially for different individuals in the same species, while within the same individual, transcriptional noise increases with age.

So how can we identify a set of genes that are consistently age-associated? In the latest issue of Bioinformatics, Magalhães et al. (the developers of HAGR) develop a statistical methodology for identifying trends of age-regulation across studies and apply it to a collection of 27 different mammalian microarray studies of aging:

Meta-analysis of age-related gene expression profiles identifies common signatures of aging

Motivation: Numerous microarray studies of aging have been conducted, yet given the noisy nature of gene expression changes with age, elucidating the transcriptional features of aging and how these relate to physiological, biochemical and pathological changes remains a critical problem.
Results: We performed a meta-analysis of age-related gene expression profiles using 27 datasets from mice, rats and humans. Our results reveal several common signatures of aging, including 56 genes consistently overexpressed with age, the most significant of which was APOD, and 17 genes underexpressed with age. We characterized the biological processes associated with these signatures and found that age-related gene expression changes most notably involve an overexpression of inflammation and immune response genes and of genes associated with the lysosome. An underexpression of collagen genes and of genes associated with energy metabolism, particularly mitochondrial genes, as well as alterations in the expression of genes related to apoptosis, cell cycle and cellular senescence biomarkers, were also observed. By employing a new method that emphasizes sensitivity, our work further reveals previously unknown transcriptional changes with age in many genes, processes and functions. We suggest these molecular signatures reflect a combination of degenerative processes but also transcriptional responses to the process of aging. Overall, our results help to understand how transcriptional changes relate to the process of aging and could serve as targets for future studies.
Availability: http://genomics.senescence.info/uarrays/signatures.html

To summarize their basic method: the authors reanalyzed data in each of the 27 microarray studies separately to produce a list of differentially expressed genes for each one. Then, they counted up the number of times a gene was differentially expressed with age in the group of studies, and determined whether that number was significantly larger than what would be expected by chance.

Of the 73 genes they found to be consistently age-regulated, 13 have been previously validated (e.g. by qRT-PCR) – a corroboration that strongly supports the new method. The other 60 genes have yet to be investigated.

A couple of points worth noting:

  • This is the first rigorous, large-scale integration of mammalian aging microarray data
    Mining collections of dozens or even hundreds of gene expression datasets to identify global trends is becoming increasingly popular, especially in cancer research (cancer seems to be the research area that sees the most sophisticated applications of bioinformatics). But for aging – an area where the data are noisier, and there is perhaps an even stronger need for integrative computational approaches – few studies have compared more than a handful of expression datasets at once, and none in mammals. Several studies have compared multiple mammalian microarrays on a smaller scale (e.g. Goertzel et al. investigated the effect of calorie restriction on mouse aging; as part of larger studies, Zahn et al. and Adler et al. compared aging in humans and mice).
  • Their analysis is designed to pick out genes that participate in a general aging program
    The microarray studies used in this meta-analysis span a diverse range of tissues, and even multiple species (human, mouse, and rat), so genes emerge as significant here only if they demonstrate a strong age-associated profile across a range of very different conditions. While this approach will likely fail to identify those genes that are age-regulated only in a single tissue, the advantage is that those genes that do come out of this analysis are likely to be the really interesting ones – components of a common aging program that operates in multiple tissues.

ResearchBlogging.orgde Magalhaes, J., Curado, J., & Church, G. (2009). Meta-analysis of age-related gene expression profiles identifies common signatures of aging Bioinformatics, 25 (7), 875-881 DOI: 10.1093/bioinformatics/btp073

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