Gene expression

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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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:



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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We are all descendents of an unbroken line of cell divisions, dating back to the last common ancestor of all life on Earth. At some point, long after our lineage had acquired features like nuclei and mitochondria, a less distant ancestor stumbled on a major innovation: it grew a body, bringing with it the advantages of cell and tissue specialization.

For many multicellular organisms, this specialization included a distinction between the mortal cells (the “soma”) and the potentially immortal cells (the “germ line”) that are capable of participating in the creation of new organisms. When you look at us, most of what you see is soma — the germ line is safely tucked away in the gonad, which is (usually) itself tucked away someplace safe.

But both the germ line and soma are made of cells. How is it that the soma is mortal while the germ line is, for practical purposes, immortal?

The disposable soma theory of aging begins from the premise that an organism has access to a finite amount of resources (broadly, energy and matter), and that it must distribute these resources in a way that maximizes reproductive fitness. First dibs goes to the germ line (without which it doesn’t matter, in a fitness sense, what becomes of the rest of the organism) and the rest gets divided among the cells of the soma.

For the moment, all we really need to take away from this model is that the germ line and soma are maintained in different ways, either in quality or extent. The germ line is doing something differently than the soma, the upshot of which is that the germ line is immortal. (A strict interpreter of the theory would presume that this “something” is resource-intensive, so that it wouldn’t be possible to apply the strategy to the soma. It’s also possible, however, that it’s simply inconsistent with optimal somatic functions — e.g., that making a muscle the best muscle it can be requires that myocytes not partake of the germ line strategy for immortality, for some structural reason that has nothing to do with resource allocation per se.)

One oh-wow corollary of this model is that if somatic cells could be made more like germ line cells, they would live longer. This prediction has a deliciously outrageous quality — yet is so simple that upon first hearing it, I reached for the nearest journal with the intention of rolling it up and smacking myself repeatedly on the forehead. Fortunately, there was a copy of Nature handy.

To be honest, it didn’t really happen that way. That copy of Nature contained the very article that introduced me to this concept: Curran et al. have shown that in long-lived mutants of the worm C. elegans, somatic tissues start acting like germ line cells:

A soma-to-germline transformation in long-lived Caenorhabditis elegans mutants

Unlike the soma, which ages during the lifespan of multicellular organisms, the germ line traces an essentially immortal lineage. Genomic instability in somatic cells increases with age, and this decline in somatic maintenance might be regulated to facilitate resource reallocation towards reproduction at the expense of cellular senescence. Here we show that Caenorhabditis elegans mutants with increased longevity exhibit a soma-to-germline transformation of gene expression programs normally limited to the germ line. Decreased insulin-like signalling causes the somatic misexpression of the germline-limited pie-1 and pgl family of genes in intestinal and ectodermal tissues. The forkhead boxO1A (FOXO) transcription factor DAF-16, the major transcriptional effector of insulin-like signalling, regulates pie-1 expression by directly binding to the pie-1 promoter. The somatic tissues of insulin-like mutants are more germline-like and protected from genotoxic stress. Gene inactivation of components of the cytosolic chaperonin complex that induce increased longevity also causes somatic misexpression of PGL-1. These results indicate that the acquisition of germline characteristics by the somatic cells of C. elegans mutants with increased longevity contributes to their increased health and survival.

Just to be clear: the somatic tissues of the long-lived mutants had not actually transformed into germ line cells as such, nor were the mutant worms festooned with extra gonads (though admittedly, that would be totally awesome). Rather, the somatic tissues exhibited gene expression patterns ordinarily found only in the germ line.

On the correlation vs. causation issue: The authors showed, using RNAi knockdowns, that the germ line-restricted genes were required for the longevity enhancement due to the mutation in daf-2 (worm insulin/IGF). There’s a bit of a wrinkle: in wildtype animals, blocking these same genes actually resulted in an increase in lifespan. How to explain that? The proffered rationale is that in the wildtype, germ line-restricted genes are only present in the germ line. Knocking them down has no effect on somatic tissue, but might reduce the activity of germ line cells; it’s been known for some time that ablating part of the gonad has life-extending consequences in wildtype animals.

The critical observation, in any case, is that the germ line genes are turned on in daf-2 mutants, and this activation is necessary in order for daf-2 mutation to extend lifespan.

Next questions, in rough order of difficulty:

  1. Does the soma-to-germ line transition occur in other long-lived mutants, or in calorie restricted animals?
  2. By what mechanisms are the germ line-restricted genes extending the somatic lifespan?
  3. Will this finding generalize to other metazoans?
  4. Do the germ line genes expressed in daf-2 soma contribute to germ line immortality?

ResearchBlogging.orgCurran, S., Wu, X., Riedel, C., & Ruvkun, G. (2009). A soma-to-germline transformation in long-lived Caenorhabditis elegans mutants Nature DOI: 10.1038/nature08106

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

Things happen faster when it’s warmer. This is true all the way down to the molecular level: many chemical reactions are accelerated at increased temperature. This leads to a fairly straightforward potential explanation for the longstanding observation that among organisms that are unable to regulate their own body temperature, higher temperature means a shorter lifespan — namely, the biochemical changes that underlie aging are simply happening faster.

This is probably true to some extent, but it’s not the whole story. A study by Lee and Kenyon reveals that there is active bioregulation of lifespan in response to temperature:

Many ectotherms, including C. elegans, have shorter life spans at high temperature than at low temperature. High temperature is generally thought to increase the “rate of living” simply by increasing chemical reaction rates. In this study, we questioned this view and asked whether the temperature dependence of life span is subject to active regulation.

We show that thermosensory neurons play a regulatory role in the temperature dependence of life span. Surprisingly, inhibiting the function of thermosensory neurons by mutation or laser ablation causes animals to have even shorter life spans at warm temperature. Thermosensory mutations shorten life span by decreasing expression of daf-9, a gene required for the synthesis of ligands that inhibit the DAF-12, a nuclear hormone receptor. The short life span of thermosensory mutants at warm temperature is completely suppressed by a daf-12(-) mutation.

Our data suggest that thermosensory neurons affect life span at warm temperature by changing the activity of a steroid-signaling pathway that affects longevity. We propose that this thermosensory system allows C. elegans to reduce the effect that warm temperature would otherwise have on processes that affect aging, something that warm-blooded animals do by controlling temperature itself.

In other words, higher temperatures do indeed shorten lifespan, but they shorten them even more if the animal is unaware of the higher temperatures. Thermosensory neurons sense the adverse conditions and presumably activate a program that counteracts the life-shortening effects of a warmer environment.

Despite the rhetoric in the abstract, this doesn’t put an end to the “rate of living” hypothesis. There’s clearly a shortening of lifespan in response to elevated temperature; the existence of a pathway that limits that shortening doesn’t argue either way about the role (if any) played by the acceleration of biochemical reactions or cellular/systemic events at under warmer conditions.

So, then: what’s the mechanism of the relative lifespan extension conferred by thermosensory neurons? Still unknown, but given the well-funded lab group in question, I’d be surprised if an expression profiling experiment were far behind. My money is on heat shock proteins, regulated not in response to heat, but in response to neuroendocrine factors secreted by temperature-sensitive nerve cells.

ResearchBlogging.orgLee, S., & Kenyon, C. (2009). Regulation of the Longevity Response to Temperature by Thermosensory Neurons in Caenorhabditis elegans Current Biology DOI: 10.1016/j.cub.2009.03.041

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.

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

The genome era and the advent of high-throughput technologies have brought about a huge increase in the amount of data available to biologists: each genome contains tens of thousands of genes, whose products can potentially interact with each other in an astronomical number of ways. This quantitative change has created a need for a qualitative change in the way we perform analyses: the human brain is not very good at understanding thousands of things at once, let alone millions or billions, so we must find new ways to extract comprehensible patterns from torrents of data.

Many of the techniques being developed to analyze large biological networks fall under the umbrella of systems biology. Some of the newest tools have been used guide genetic perturbation studies in yeast, resulting in the discovery of novel lifespan control genes. What can such network analysis tell us about human aging?

To address this question, Bell et al. compiled a list of gerontogenes (i.e., genes whose wildtype function is associated with accelerated aging, and whose loss-of-function mutants are associated with longer life) from model systems, and studied the connectivity of these genes within the context of interaction data obtained from a large-scale (though not comprehensive) two-hybrid screen of human proteins.

A Human Protein Interaction Network Shows Conservation of Aging Processes between Human and Invertebrate Species
We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.

The authors’ focus on genes studied in model organisms is well motivated; genes that control aging in one species are more likely than one would expect from chance to affect aging in another species, even if those species are as diverged as yeast and worms.

The findings: compared to the genome as a whole, longevity genes tend to be more highly connected network, often acting as “hubs” within the network; furthermore, these genes are more connected to one another than the average gene, forming a “longevity network” that stands out against the web of all interactions.

In conjunction with expression data, this network has predictive power: genes that interact with components of the longevity network and exhibit increased expression in aging muscle are very likely to function as gerontogenes in C. elegans. This finding demonstrates once again the significant conservation of lifespan control systems across large evolutionary distances. Perhaps more importantly, it also shows that applying network analyses to large data sets can do more than merely catalog information. With the right combination of high-throughput data, a good network model and the right kinds of statistics, the tools of systems biology can reveal new biology that otherwise would have taken us a very long time to discover.

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