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On individuality, stochasticity and buffering


ResearchBlogging.org One of the most exciting fields opened in the last years is the new understanding of the existence of something that we could call as “biological heterogeneity”. This new field of study is focused in observing and understanding the differences between reactions of a same kind, between cells of a same kind, and ultimately, between members of the same species. Specially, cellular heterogeneity constitutes a major focus of interest among biologists today, because of its implications in development and diseases such as cancer (Altschuler and Wu, 2010).

Blinded by a deterministic view of the world, born with the discovery of the concept of the gene, biologists forgot, by many years, that not all the processes could be entirely specified by instructions encoded as a biological “bit of information”. And so, to study a specific cellular event, for example, the synthesis of an enzyme to produce a chemical reactions, biologists saw the process as a continuous event, in which all the cells started to synthesize the enzyme, at the same rate, leading to a single “state” which could be measured simply by averaging the measure of the enzyme concentration at the same time (Figure 1A). Hence, the particular amount of an enzyme at a specified time, meant that all the cells had the same amount, with small variations that can be represented as standard deviation.

However, by 1957, Novick and Weiner observed unexpected results from the study of the formation of β-galactosidase in the presence of low concentrations of an inducer. The linear growth of the production of the enzyme was unexpected, because it was known that the concentration of the inducer increases with the production of a permease which allowed the transport of the inducer. Novick and Weiner determined that, at low concentrations of the inducer, the population consisted “essentially of individual bacteria that are either making enzyme at full rate or not making it at all”.  Such a rationale implies reconsider the usual thinking in terms of average in biochemical assays: an average can represent two discrete populations in terms of their behavior (Figure 1B).

Figure 1. Heterogeneity in a cellular response can lead to an “average response”, but the cellular population can either consists in cells showing an incremental and graded response (A) or an ON/OFF response (B). In the latter, drawing conclusions from the average can lead to a wrong interpretation of the biological phenomena.

Nowadays, there is a growing number of publications regarding the concepts of “noise”, “heterogeneity”, “stochasticity” and so on. One key difference between “noise” and “heterogeneity” is that heterogeneity is “a property of a cell population, not of individual cells” (S. Huang, 2009), whereas noise can be defined as a change in the distribution of amount of a measurable trait in a non-expected pattern. Depending on the author, a same word can mean similar (but not equal) things, but the key is to recognize the existence of variations between “biological units” (a cell, for example), even when these units have an equivalent genetic background. For example, individual cells derived from a clone can present different levels of expression for a same gene. These differences can have different origins. One example is heterogeneity between the cells. Two cells can respond differentially to a growth factor, due to a differential spacial localization. In the mouse embryo, at the 8-cell stage, starts the zygotic expression of Cdx2, but is has been reported that the initiation of CDX2 expression is not uniform, and this could be due to the specific locations of the blastomeres in the embryo (Zernicka-Goetz et al, 2009). This class of heterogeneity is referred as “extrinsic heterogeneity”, defined as “cell-to-cell variability in a population caused by non-uniform environmental factors that differentially affect individual cells” (S. Huang, 2009).

Opposed to the extrinsic heterogeneity, there is an “intrinsic heterogeneity”, which cannot be ascribed to environmental differences. In this case, the factors inducing heterogeneity are most probable intracellular. The most attractive source of intrinsic heterogeneity is the “noise” in biological processes. Noise is indeed a property of individual cells, and can be “temporal”, when the changes are observed across a time period, and also at the population levels, when the temporal noise in individual cells triggers different “states”.

Probably, the most interesting hypothesis to explain the presence of noise that transcriptional or translational bursts. Measuring single mRNAs (using FISH), it has been observed a particular mRNA can be transcribed in infrequent but potent bursts leading to cell-to-cell variations in mRNA number. A recent work reviews extensive evidence regarding the variations in mRNA/protein synthesis that can lead to noise (Raj and van Oudenaarden, 2008).

Heterogeneity, as a concept, is relevant when biologists select a scientific question and design experiments to answer that question. For example, studying the expression profile of a gene in time. Simply averaging the amount of the specific gene could lead to the wrong conclusion that the entire population produces a specific time, hiding the fact that one part of the population express high levels of the transcript, whereas the other part of the population express low levels, showing an ON/OFF behavior rather that a OFF-low-mid-high levels.

Can noise or heterogeneity at the cell level translate at the organism level? So far, the vast majority of the work has been focused in studying single cells, or characterizing cell-to-cell variability. But let us to make an exercise: if heterogeneity is a common property of cells, how does the organism to develop in such a patterned structure? For example, imagine a theoretical embryo (Figure 2A). This embryo has 32 cells (a 32-cell stage), and all its cells are performing biochemical and genetic processes influenced by extrinsic heterogeneity (such as the case of Cdx2), and intrinsic noise due to transcriptional bursts, chromatin remodeling and so on. As the embryo develops, three options remains: a) the stochasticity influences the development and the embryo grows in a stochastic pattern (Figure 2C); b) the embryo develops in an ordered pattern because, together with the stochasticity, the embryo harbors a system to buffer the noise (Figure 2A); c) the variability between cells makes a final average noise of zero (Figure 2B), as in a sum of two numbers with different sign (-2 + 2).

Figure 2. Control of noise in a theoretical embryo. Assuming the majority of the cells in the embryo display noise in their biological processes, specially transcription/translation, the embryo can buffer this noise by decreasing the influence of the noise through a biochemical network (A). Another possibility is that the noise becomes stabilized by a vectorial sum (B), where opposing effects of noise (for example, one cell express high levels of a ligand and another cell express low levels, leading to a physiological average”). Finally, if noise predominates, the embryo could display an unpredictable development (C).

Since many years, developmental biologists have been studying biochemical pathways with a special relevance to embryonic development. Any biochemical pathway is candidate to impose a buffer to heterogeneity and noise. Please consider that noise in embryo development is highly relevant, because initially everything start with one cell. It has been proposed that a mechanism to control noise is the negative feedback in circuits (Raj and van Oudenaarden, 2008). Fluctuations above and below the average are pushed back in those feedback loops. One classic example of negative feedback is provided by the Wnt pathway, which have a key role in embryonic development. The Wnt pathway comprises a family of secreted ligands, which can be divided into “canonical” ligands that activates the transcription factor β-catenin, and “non canonical” ligands that activate intracellular effectors which act in a β-catenin independent fashion (in a summarized view, because many data demonstrate that such classification is not always precise). In the canonical pathway, Axin2 is a target of the stabilized β-catenin, and acts as a negative regulator of the pathway, providing negative feedback. Theoretical and experimental evidence from the work of Lea Goentoro and Marc Kirschner (Goentoro and Kirschner, 2009) showed that the canonical Wnt pathway displays an interesting behavior: its activity is dictated by the fold-change in β-catenin levels and not to the absolute level of this transcription factor. The authors propose that, in such a system, noise os buffered, because simple variations in gene expression are not able to activate the pathway: it is required a precise threshold of variation in the components to variate the fold-change of β-catenin and, concomitantly, activate the pathway. Parallel work of Lea Goentoro (Goentoro et al, 2009, in the same issue of the Molecular Cell journal) explain how a circuit can provide fold-change detection, giving to a cell an advantage in a “noisy environment”. It is really interesting that many negative regulators of the Wnt pathway are, in fact, target of β-catenin: the pathway activates its own negative regulators, as evidenced by studying the transcriptional response of antagonists of the pathway in HEK293 cells (Gujral and MacBeath, 2010). It remains an open question whether the Wnt pathway can buffer noise and provide homogeneity to the embryo to allow development. Maybe the developmental patterning is encoded precisely in heterogeneity, but since heterogeneity is unpredictable, and since we can predict the development of the embryo, it seems more likely that the embryo display specific responses, in the form of pathways with negative feedback and response to fold-change of key elements (instead of responding to small changes in the amount of these elements), to buffer noise.

Finally, we must consider that not all the noise is a bad thing during development. Two examples show us that the heterogeneity is necessary during development. In the Drosophila eye, the optical units that compose the eye contains photoreceptors, which specification is provided by a stochastic process, where in a specific cell type (R7), the stochastic expression of the spineless gene dictates the Rh4 gene expression, which in turn is permissive to the expression of the Rh6 gene in the R8 cells. Failing to express spineless above a specific threshold, is permissive to Rh3 expression in R7 cells, which in turn instruct the expression of the Rh5 gene in R8 cells (reviewed in Samoilov et al, 2006). The second example is presented by the work of Raj and coworkers in a recent paper in Nature (Raj et al, 2010), in which they study the effect of modify a genetic network that controls intestinal differentiation in C. elegans. Mutant conditions increase transcriptional noise, which leads to an ON/OFF state of a master regulatory gene. One direct implication is that the incomplete penetrance of some mutations can be explained by stochasticity. Another implication of this work (and underestimated by the authors) is that there are natural buffering systems in organisms that control noise in gene expression, and altering these buffers triggers developmental responses.

Buffering noise is an unexplored field with great implications in development and disease. For example, disruption of buffers in adult humans may lead to cancer development due to stochastic expression of oncogenes. This field is in its infancy, despite the concept of variability relies in the foundations of modern biology, since Darwin studied variations and similarities between species and recognizing that variations are relevant properties of living systems.

References

Altschuler, S., & Wu, L. (2010). Cellular Heterogeneity: Do Differences Make a Difference? Cell, 141 (4), 559-563 DOI: 10.1016/j.cell.2010.04.033

Huang, S. (2009). Non-genetic heterogeneity of cells in development: more than just noise Development, 136 (23), 3853-3862 DOI: 10.1242/dev.035139

Raj, A., & Vanoudenaarden, A. (2008). Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences Cell, 135 (2), 216-226 DOI: 10.1016/j.cell.2008.09.050

Samoilov, M., Price, G., & Arkin, A. (2006). From Fluctuations to Phenotypes: The Physiology of Noise Science’s STKE, 2006 (366) DOI: 10.1126/stke.3662006re17

Novick, A. (1957). Enzyme Induction as an All-or-None Phenomenon Proceedings of the National Academy of Sciences, 43 (7), 553-566 DOI: 10.1073/pnas.43.7.553

Zernicka-Goetz, M., Morris, S., & Bruce, A. (2009). Making a firm decision: multifaceted regulation of cell fate in the early mouse embryo Nature Reviews Genetics, 10 (7), 467-477 DOI: 10.1038/nrg2564

Goentoro, L., & Kirschner, M. (2009). Evidence that Fold-Change, and Not Absolute Level, of β-Catenin Dictates Wnt Signaling Molecular Cell, 36 (5), 872-884 DOI: 10.1016/j.molcel.2009.11.017

Goentoro, L., Shoval, O., Kirschner, M., & Alon, U. (2009). The Incoherent Feedforward Loop Can Provide Fold-Change Detection in Gene Regulation Molecular Cell, 36 (5), 894-899 DOI: 10.1016/j.molcel.2009.11.018

Gujral TS, & MacBeath G (2010). A system-wide investigation of the dynamics of Wnt signaling reveals novel phases of transcriptional regulation. PloS one, 5 (4) PMID: 20383323

Johnston Jr., R., & Desplan, C. (2010). A Penetrating Look at Stochasticity in Development Cell, 140 (5), 610-612 DOI: 10.1016/j.cell.2010.02.018

Raj, A., Rifkin, S., Andersen, E., & van Oudenaarden, A. (2010). Variability in gene expression underlies incomplete penetrance Nature, 463 (7283), 913-918 DOI: 10.1038/nature08781

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Global Biodiversity Indicators at-a-glance

ResearchBlogging.orgI really wanted to talk about this article before. A few weeks ago, it was published in Science a work by an extensive work by a high number of researchers, focused in the review and discussion of indicators of global indicators of biodiversity. Citing the article, in 2002 world leaders committed, through the Convention on Biological Diversity, “to achieve by 2010 a significant reduction of the current rate of biodiversity loss”.

Now, 2010 has been named the International Year of Biodiversity. And a team reported in this article, numbers and trends showing the current state of several indicators of biodiversity, grouped into four main categories: indicators of state, pressure, response and benefits.

The indicators of state are related with the state of biodiversity in terms of species and ecosystems. Eight indicators show a decline. For example, the Living Planet Index (mean population trends of vertebrates) show a continuous decline since 1970. The Red List Index, indicating the risk of extinction of mammals, birds, amphibians and corals, also show a decline. Other indicators are Forest Extent, Coral Reef Condition and Water Quality Index.

Indicators of pressure on biodiversity are more related with difficulties upon the improvement of biodiversity. One example is the Exploitation of Fish Stocks, which exhibits a great increase. All the indicators of pressure (Ecological Footprint, Nitrogen Deposition Rate, Alien Species in Europe, and Climate Impact Indicator) show an increase.

Although the numbers are discouraging, some hope arises when we look at the positive numbers. Indicators of response are increasing, and the authors show some data related with improvements in biodiversity. For example, at least 16 bird species extinctions were prevented by conservation actions during 1994-2004. Also, deforestation in Amazonia decreased, protected areas increased and several countries have policies and agreements related with the prevention of spread of alien species.

This post is not intended to be a review of the article. Instead, it offers a view at-a-glance, a general commentary of the article. I advice to all the readers of this post to read the article and to visit the webpage of the Convention on Biological Diversity, where you can download more information about the mission and goals for this year and for the future.

In words of the authors of the article, “our results show that, despite a few encouraging achievements, efforts to address the loss of biodiversity need to be substantially strengthened”.

References

Butchart, S., Walpole, M., Collen, B., van Strien, A., Scharlemann, J., Almond, R., Baillie, J., Bomhard, B., Brown, C., Bruno, J., Carpenter, K., Carr, G., Chanson, J., Chenery, A., Csirke, J., Davidson, N., Dentener, F., Foster, M., Galli, A., Galloway, J., Genovesi, P., Gregory, R., Hockings, M., Kapos, V., Lamarque, J., Leverington, F., Loh, J., McGeoch, M., McRae, L., Minasyan, A., Morcillo, M., Oldfield, T., Pauly, D., Quader, S., Revenga, C., Sauer, J., Skolnik, B., Spear, D., Stanwell-Smith, D., Stuart, S., Symes, A., Tierney, M., Tyrrell, T., Vie, J., & Watson, R. (2010). Global Biodiversity: Indicators of Recent Declines Science, 328 (5982), 1164-1168 DOI: 10.1126/science.1187512

Secretariat of the Convention on Biological Diversity (2010) Global Biodiversity Outlook 3., & Montréal, 94 pages. (2010). Global Biodiversity Outlook 3 Convention on Biological Diversity

References Management in Mac: new guide on using Zotero

As I mentioned before, the most successful posts on this blog (in terms of visits and comments) are those related with reference management in Mac. A long time ago, I published a post in which I wrote about the advantages of Papers, or more exactly, the disadvantages of Mendeley in Mac, and I recommended using Papers+Zotero in Mac. But I had a problem with the new versions of Zotero.

Now, I solved the problem with Zotero, and I will explain the steps to have a fully functional Zotero+Papers combination for making bibliographies in Mac.

NOTE: The following steps are valid to use the latest version of Zotero with Word 2004. Since the Office for Mac is very bad, I have no intentions to purchase the Word 2008. But plugins and instructions are available for using Zotero with Word 2008 in the official site.

Step 1. Updating.

In order to have a fully functional set up, you have to install the newest version of Firefox (3.6.6.). You also have to install the most recent version of Zotero, and the Phyton extension and the latest word plugin. The last two (PhytonExt+Word Plugin) are available in this page of Zotero, under the “Mac OSX” section. Follow all the instructions; it’s easy.

Step 2: Updating the library in Zotero.

I recommend deleting all your libraries and start from scratch. If you have Papers, take a time in updating and matching all your PDFs. Once you are ready, go to File>Export>BibTeX Library* (it’s the one that works best for me). *Caution, I have both Papers and Zotero in Spanish, so some names and menus could be different.

Once in Zotero, in the Actions menu, I go to Import. Select the *.bib library that you exported from Papers, and then wait. Depending on the size of the library, it will take a little time to have an updated library. But, when it’s finished, you will be ready to work.

Step 3. Working in Word.

You are now ready to use Zotero in Word. In Tools>Customize, select the Zotero Bar, and place it where you feel it’s more comfortable.

When you need to insert a citation, just click in the “Zotero Insert Citation” icon (the first one, from left to right) in the Zotero Bar. A window will show up. In this window, you can select the citation style. When you are ready, a new window will open, showing your library, and you can now select the reference you want to cite.  When you are finished, and need to insert the final bibliography, just click in the “Zotero Insert Bibliography” icon (the third icon), and then you are ready. You have a muanuscrpit with references and a bibliography.

Please note that in the official site in Zotero, there are full instructions in the usage of Zotero and the plugin. Instructions here.

The Node: an interesting virtual coffee break

I have been far from the blog and all the stuff unrelated with Pubmed and journals. But a few days ago I found a new feature in the Development’s journal homepage. It is called “The Node“, and its description is as follows: “We’d like you to think of the Node as a way to spend your coffee breaks“. According to The Node, more exactly, according to Earl Wilson, who (and I’m not sure because there are so many ‘Earl Wilson’s) was a famous columnist, the best place to share information and ideas is, actually, the coffee break.

The concept itself is very precise and interesting. I found myself many times, in meetings, talking with colleagues about data, experiments, ideas. Personally, I love the poster sessions, because of that sense of lack of formality, sometimes drinking beer and talking for hours about science. In an oral presentation, it’s just too short. You show up in front of the scientist, you try to explain in only 10 minutes the data gathered in months, often years of research. And, most of the time, the people in front of you are desperate to make a “smart” question (which means, 99% of the time, to try to ask something impossible to answer so they seem smart, kind of “Oh my, he must publish in Cell”). Oral presentations, specially in 10 minutes, are against the whole idea of meeting someone: to talk, to discuss, to share.

Returning to the original idea… usually, in meetings, the coffee breaks are the ideal place to share and connect. Often, coffee breaks are conducted in open spaces, but the coffee and cookies are placed in just two or three tables, and you have a little chance to came across with the keynote speaker of the day, or with that guy that you saw in the last session talking about that topic very related with your PhD research thesis. You say “Hi!” to that guy, and well, you have to leave the table because fifty other people are trying to get cookies. And then, voilá! you are talking with people about science. “How did you make the experiment with the zebrafish embryos?” “Oh, well, we discovered that adding 0.2M of ….“. I found that in these situations, colleagues are more open to share technical tips and advice. I have very good experiences in coffee breaks and poster sessions, at least here in Chile. That’s the spirit of The Node, according to the creators: to rescue that sense of sharing and talking.

Ideas like The Node and some others around the internet (like Benchfly) are very valuable. Most of the time, you can assist to one or two meetings per year, and sometimes you just can’t go, either because of funding, or time, or because you are trying to get that paper published once and for all. But virtual coffee breaks allow us to connect with people working in similar fields, and to share experiences about science and scientist’s life.

The most successful post: Paper versus Mendeley, Zotero and stuff.

I am really surprised. When I started this blog, I wanted to share my thoughts about science, about being scientific, about research… and also about Mac in research. When I began to use Mac, it was difficult to me because I didn’t knew so much about software, tools, and so. And then, once, I wrote a post, almost like a review, about software to manage papers and references.

To date, it is the most visited post. I can imagine that many people are looking for information about which software is best for their needs. I never intended to make an explicit publicity on a specific software. I just wanted to express my experience about using those softwares.

Now, I want to make some updates to that post, and about managing references in Mac.

1. About Mendeley: I consider myself a reasonable person, specially being a scientist. Therefore, when a new version of Mendeley is released, I install it and try to use it. But, a few minutes later, I send the program to the Trash. Even more, when a fellow ask me about a software to manage papers and references, I ask: “Mac or Windows?” If the response is “Windows”, then I answer: “Give a look to Mendeley. Give it a try”. Almost every time, my friend returns, days later, and say to me “I uninstalled Mendeley. It ‘s just… complicated”.

It seems that, for many people, Mendeley is slow, complicated, and inefficient. Besides, it’s a huge program, considering the lack of remarkable features inside it. I really want Mendeley being a good software, but the opinion of my friends is the same as mine.

b) About Papers: I love Papers. It’s my software to manage my articles. But I feel that, since a long time, the team behind Papers just relaxes. There is no real improvement in every new version of Papers; only the typical “a bug is fixed when you make that-thing-that-you-do once every two years”, and no real improvement in metadata retrieval. I paid for Papers, and if a new version with real improvements in metadata retrieval from the journals, a good system for managing a bibliography with integration woth Word and Pages, and with new tools for making annotations in the articles, I will be glad to pay for a new release. But, in summary, I feel that Papers just got delayed in time.

c) About Zotero: One day, I received that message: “A new version of Zotero….” Of course, as an obedient fan of Zotero, I installed the new version… And I never could use Zotero again. I needed a new version of the Word toolbar. It didn’t work. I tried to go back to the old version of Zotero. Nothing. Also, the Word for Mac is awful. Then I got a huge amount of work, and I never looked back to Zotero. I need more time to solve the problem, but my feelings about Zotero are not optimistic.

That’s all I have to say about this topic at this moment. If you want to know more about bibliographic management, you should read this post.

Pros and cons to be a scientist in Chile

Usually, I write post about a specific topic. A paper, news from somewhere. But I have been a little busy and distracted to write something that specific. Today I have the need to talk about scientist’s life here, in Chile. Why? I am living a vocational crisis, or so. See, to be a scientist here has its disadvantages. For example, I really hate the buildings. As you may know, with so many earthquakes, and also because of a bad culture regarding constructions, the buildings are pretty ugly. I went once to US and I became crazy with the beauty of the universities. Here I have some reasons to why you shouldn’t be interested in making science in Chile.

Reasons to why I am not happy being a Scientist in Chile

a) Infrastructure: it is evident that a first disadvantage of making science, at least in the lab, is the lack of a proper and extensive infrastructure. Buildings are small, and I have witnessed some deadly fights for a small lab between colleagues. This problem is a real concern, because the scientific population in Chile is growing and the Facilities and Buildings are not growing at all. And I’m not talking about the lack of beauty and comfort of our facilities.

b) Equipment: also, the lack of cutting-edge equipment is discouraging. Even if you have a good Research grant, buying an equipment here costs several times more than in US. Hey, we are at the end of the world: we are exactly at the opposite point compared to China, and we are located at the other end compared to US and Europe. That really makes everything more expensive, including Taq Polymerase.

c) Distance: the location of Chile has another difficulty: it makes expensive to attend meetings, courses and events in US or Europe. Also, making an internship has the same obstacle.

d) Government Grants: the money that Chile expend in R&D is small, compared even with other countries of South America. Accordingly, there is a lack of good grants, and they are not enough for the scientific community in Chile.

In summary: there is few money to make science; the existing money is not enough to buy good equipment and reagents, because we are far from the producers of those reagents and equipments, and also we are not allowed to attend meetings and courses because travel expenses are huge. And you have to deal with it in your ugly building, with only one coffee shop (if you’re lucky; don’t even dream about having a Starbucks nearby).

Of course, it can’t be that bad. So when I am crying about all this stuff, I remember that being a scientist in Chile can be also very exciting and funny.

Reasons why, after all, I am very happy to be a Scientist in Chile

a) The scientists: Chile has a great number of good scientists. The PhD programs are competitive, and the Chilean meetings have a  very good level. The creativity allow us, usually, to have important guests attending the meetings in Chile, facilitating great talks and in a very nice environment (see the photo for an example). The classes are good, and in general, the formation on the PhD programs is very good.

A place called “Ojos del Caburgua” (Eyes of Caburgua), close to the town where is held the Annual Meeting of the Chilean Society of Cell Biology.

b) Publications: closely linked to the previous point. The number and level of the publications is good, specially when you compare the productivity in Chile with other countries in South America or even with other countries of similar characteristics. I am making my PhD Research Project in a lab that has publications in Nature Cell Biology, Development, Developmental Biology and Genome Biology in the last years, which is a very competitive job considering our reality.

c) The challenges: being in a country like Chile makes things harder. You have no cutting-edge equipment to make that great experiment. So you have to go back to the basics. One of our professors always ask: how would you do this experiments, if you were back in the sixties? And it really helps sometimes.

d) Chile itself: You will be working in one of the most beautiful countries in the world. Once, I worked in a side-project in an Evolutionary Biology Lab. The people went to the Atacama Desert one week, to study the reproduction of some species, and two weeks later, they went to the rain forest in the south, and so on. If you are a geologist or an astronomist, for example, you will be delighted with the beauty of our land. Also the variety of landscapes and species opens a lot of opportunities in research.

These years as a scientist have been very exciting. Sometimes I get sad about some specific issue (usually, a scientific discussion with my advisor, or the lack of a reagent to make a new experiment, or the high price of a reagent to buy it), but at the end of the day, I go happy to home.

The Red Queen hypothesis in a glass

ResearchBlogging.org
What factors predominate in evolution? In daily life, the constant evolution of our lives is influenced by our conditions and by external factors. If I want to build a house with my own hands, I have to consider my abilities, some of which are genetic (I am small, thin and I am not strong, so I can’t carry heavy materials), and also I have to check how many money I can spend; hence, the “evolution” of the house, sort of, depends on both factors.

Two hypothesis, the “Red Queen” and the “Court Jester”, view evolution in these terms [1]. The Red Queen hypothesis view evolution as a balance of biotic (intrinsic) pressures, whereas the Court Jester model propose that evolution, speciation and extinction rarely happen except in response to unpredictable changes in the physical environment. Finally, it seems reasonable that evolution proceeds as a mixture of both models, where the Court Jester model operates in a time scale far longer than the Red Queen. Locally, in an ecological niche, the competition (biotic factor) between events en species shapes the local evolution, but in a larger scale, such as earthquakes, rise of mountains, and separation of physical spaces, provide a definitive barrier shaping long-term evolution, although events such as migration in birds and migration between continents should be taken into account.

The Court Jester model seems more logical to be imagined. Darwin viewed evolution in terms of biotic factors, but in his journeys, he observed marked differences in similar species in long distances, being islands a hallmark of evolutionary observation. But the Red Queen hypothesis, in biological terms, remained a challenge to be resolved in a laboratory. In a recent paper published in Nature [2], Paterson and coworkers provided a genetic evidence for the Red Queen hypothesis, using a smart experimental design. They used co-cultures of the bacterium Pseudomonas fluorescens and its viral phage Φ2. The molecular evolution rate in the phage was higher when both bacterium and phage coevolved with each other that when phage evolved against a constant host genotype. Remarkably, the genes that most rapidly evolved were involved in host infection, after 12 serial transfers (being each transfer every 48 hours). Consequently, coevolved phage populations varied in the range and identity of host genotypes that they were able to infect, but phage from evolved populations failed to infect any coevolved hosts.

How fast a bacterium can coevolve inside a human organism? For example, in a hospital, there is a spreading of infectious bacteria. Could be possible a coevolution of the microorganism with its host, allowing the mutation and adaptation of the bacteria in order to be able to infect more hosts? It will be interesting the extrapolation of the findings from Paterson and coworkers, at the clinical level. An example is provided by a brief review in PLoS Genetics [3]. Neisseria meningitidis, a major cause of morbidity and mortality in childhood, in a lapse of three decades of observation showed little variation, but a few loci showed variation, including the gene coding for a transferrin binding protein (tbpB). However, it seems that the genetic variation occurs at expenses of the “transmission fitness”.  It will be interesting to see if this technique can be improved to study more complex and bigger “cosmos” and specially for disease-causing bacterium, or viruses.

References

[1] Benton MJ (2009). The Red Queen and the Court Jester: species diversity and the role of biotic and abiotic factors through time. Science (New York, N.Y.), 323 (5915), 728-32 PMID: 19197051

[2] Paterson S, Vogwill T, Buckling A, Benmayor R, Spiers AJ, Thomson NR, Quail M, Smith F, Walker D, Libberton B, Fenton A, Hall N, & Brockhurst MA (2010). Antagonistic coevolution accelerates molecular evolution. Nature, 464 (7286), 275-8 PMID: 20182425

[3] Falush D (2009). Toward the use of genomics to study microevolutionary change in bacteria. PLoS genetics, 5 (10) PMID: 19855823


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