Archive for the 'Cell biology' Category

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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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.

Milk, Prions and Evolution

ResearchBlogging.orgPrion protein (PrP) is the focus of some neurodegenerative diseases. It is believed that misfolded prion protein (PrPsc, or “scrapie”) is the infectious agent responsible for bovine spongiform encephalopathy (BSE), and Creutzfeldt-Jacob disease (CJD), among others. PrPsc propagates by conversion of normal (healthy) prion protein (PrPc).

Several questions arise in the research community. Two of them are: a) can prions be transmitted from domestic animals into humans, and b) how the prion protein propagates inside a healthy subject. Food safety is a major concern. Milk and their derivatives are a subject of study, because of their wide consumption in most countries. A study [1] from Didier and coworkers shows that the normal prion protein is detected in mammary gland and milk fractions of cow, goat and sheep. PrPc protein is detected in all milk fractions (skimmed milk, acid whey, cream) in goat and sheep. Although the authors failed to detect PrPc in bovine milk, they refer to other study that successful detected the protein in bovine milk using a different approach. Some conclusions of this study are that analytical methods to detect the prion protein should be improved (hopefully, at an industrial scale), to avoid the variability between studies, specially considering the high levels of prion protein detected in cream fractions and the widespread use of cream in cooking.

Scrapie in milk?

Another conclusion is that the prion protein exist at low levels in mammary gland and milk. Even detecting PrPc in milk requires hard work (enrichment of protein concentration, for example). But someone expect that PrPsc should exist at even lower levels, specially in asymptomatic animals. Then, PrPsc can be undetected in an industrial quality control. What happens if the common assumption that infectious prion protein is not present in milk is wrong? There is evidence of infectious prion transmission via milk; Timm Konold and colleagues published evidence in lambs fed with infected and healthy milk, but using lambs from a genotype with high susceptibility to scrapie [2], observing high levels of infection. Thinking in human population, if  populations with genetic susceptibility to scrapie are identified, then health measures should be implemented in those populations to avoid the exposure to potentially contaminated milk and cream.

Once inside the cell, then what?

A few days ago, a comment in Science raised the question  “What makes a prion infectious?” [3]. The article referred to two papers published recently. One of them raises interesting questions regarding the possibility of disease development after drinking contaminated milk, for example. A research team from the Department of Infectology in the Scripps Institute, showed that prions in cell culture are able to “evolve” [4]. Prions, viewed as infectious agents, exists as strains, which are a specific conformer of the protein, and they are able to multimerize forming seeds. One hypothesis, called “protein-only”, assumes that each strain is associated with a different conformer of PrPsc, and the infectious agent is composed of a misfolded conformer exclusively (without cofactors). Many strains can exist, since many conformers are able to arise from PrPc misfolding. Experiments from the work of Li and colleagues shows that, indeed, these strains exist, and they were able to identify prions strains sensitive to swainsonine (inhibitor of the formation of N-linked glycans). In a series of experiments they showed that the strains identified in a time-lapse isolated from a cell line infected with a brain homogenate changed over time: in the first days, they identified swa-resistant strains (and competent for R33, a neuroblastoma-derived cell line), but then they identified swa-insensitive and R33-incompetent, after they transferring to PK1 cells. These and remaining experiments suggests that the prion population changed over time, and there are different strains that can “compete” if the growth conditions are advantageous to a specific strain.The authors, based in their results, conclude that prions show the hallmarks of Darwinian evolution: they are subject to mutation and to selective amplification. Obviously, these findings are relevant to Medicine, since drug discovery should consider the fact that, in disease conditions, the raise of a infectious prion can lead to mutation (more likely by binding of a prion to some cellular cofactor leading to a small variations in the misfolded structure) of some monomers, causing strain evolution, some of which can growth in the presence of some drug, replace the remaining strains and lead to resistance.

It is evident that we are far from understand the biochemical and molecular foundations of scrapie disease and mechanism, and the new evidence suggest a complex scenario, specially regarding to the deveolpment of new drugs to fight the clinical symptoms and the CJD and BSE diseases.

References

[1] Didier A, Gebert R, Dietrich R, Schweiger M, Gareis M, Märtlbauer E, & Amselgruber WM (2008). Cellular prion protein in mammary gland and milk fractions of domestic ruminants. Biochemical and biophysical research communications, 369 (3), 841-4 PMID: 18325321

[2] Konold T, Moore SJ, Bellworthy SJ, & Simmons HA (2008). Evidence of scrapie transmission via milk. BMC veterinary research, 4 PMID: 18397513

[3] Supattapone S (2010). Biochemistry. What makes a prion infectious? Science (New York, N.Y.), 327 (5969), 1091-2 PMID: 20185716

[4] Li J, Browning S, Mahal SP, Oelschlegel AM, & Weissmann C (2010). Darwinian evolution of prions in cell culture. Science (New York, N.Y.), 327 (5967), 869-72 PMID: 20044542

About Stem Cells and the Holy Grail

A recent news article in Nature caught me into deep thoughts. The article reviewed some of the main developments in the field of induced Pluripotent Stem (iPS) cells. The formula seems simple: I have my “whatever” cell, and by introduction of a cocktail of genes, eventually I will find out that three or four genes are able to reprogramming the cell.

However, it seems that not every tale about iPS cells is so simple. Several issues are of the concern of scientists and medical doctors. For example, the efficiency of the production of the iPS cells, and the purity. Also, whether this cells will induce the formation of teratomas or tumours. I strongly recommend the News Article by Monya Baker in Nature [1] to read about the subject.

I share some of the enthusiasm about iPS cells. Working with primary cultures of stem cells is hard, slow and sometimes disappointing. For example, working with bone marrow derived stem cells is a slow process; from obtaining the sample until reaching a fourth passage, can take even four months, when cells are isolated from older donors (I worked with this model four years; I know what I’m talking about). If we can use these cells for the treatment of a disease, months can be lethal for the patient. Even so, cells are progressively loosing their “stemness”. iPS cells seems to circumvent some issues regarding efficiency. However, the artificial induction of a stemness state is a subject of relatively little study; by now, the focus of the scientists has been the improving of the methods for the development of the iPS cells, without worry about the mechanisms [1]. Then, the next step should be the further knowledge of mechanisms. In this scenario, Systems Biology should take an important place. We need to gain insight about what genes, what metabolic pathways, what proteins, what non-coding RNAs, what micro-RNAs, are being induced, are working, are being repressed.

From Bruneau Lab

From Bruneau Lab

Maybe a fresh approach is provided by the work by Takeuchi and Bruneau published online in Nature [2]. The authors showed that mouse mesoderm cells can be transdifferentiated into cardiac myocytes by the introduction of three genes: Gata4, Tbx5 (two cardiac transcription factors) and Baf60c (a cardiac-specific subunit of the BAF chromatin-remodelling complex). The novelty resides in that the authors further handle their work providing data about the mechanisms (something which is lacking in several other works): they show that Gata4 binds Tnnt2 and Nppa (cardiac genes) only in the presence of Baf60c, using chromatin immunoprecipitation. They even provide a model and a “minimal” regulatory gene network. This work can be considered a step forward in the way researchers are studying reprogramming. It is not a matter of just “we will insert these genes and quantify how fast the cells are induced to pluripotency”, but also the “how”. And it seems very reasonable thinking about the possible effect of the genes being introduced.

References

[1] News Feature Article:

Fast and Furious. M. Baker. Nature, 2009, Vol 458, pp. 962-965

[2] Takeuchi, J.K. and Bruneau B.G.

Directed transdifferentiation of mouse mesoderm to heart tissue by defined factors.

Nature, 2009, doi:10.1038/nature08039

[3] Bruneau’s Lab Page (including videos):

http://web.mac.com/bruneaulab/Bruneau_lab/Welcome.html


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