May 12, 2011 § 2 Comments
Many species of bacteria make decisions about which genes to express based, in part, on how dense their populations are. The most famous example of this phenomenon, called “quorum sensing”, is seen in the bioluminescent bacterium Vibrio fischeri, which turns on its light-producing genes only when its population reaches a certain level. This bacterium has evolved a rather unlikely-sounding symbiotic relationship with a squid, in which the light produced by bacteria living in the squid allows the squid to avoid casting a shadow on moonlit nights, which makes it harder for predators to see. More sinisterly, V. fischeri’s relative Vibrio cholerae uses quorum sensing to decide when to produce virulence factors and develop hard-to-treat biofims. Many other pathogenic bacteria do the same, leading to considerable interest in the question of how to block quorum sensing. A recent paper (Chen et al. 2011. A strategy for antagonizing quorum sensing. Molecular Cell PMID: 21504831) discusses one way to prevent bacteria from finding out whether they are present in sufficient numbers to have a desired effect.
There are two main known strategies for quorum sensing: one uses transmembrane receptors that sense secreted molecules in the environment, the other uses cytoplasmic, soluble receptors that act as transcription factors when the quorum signal is present. If the quorum signal isn’t present, the receptors fail to fold properly and get degraded. The transcription factor/sensor in V. fischeri is named LuxR, and the family of proteins that does the same job in other bacteria are therefore dubbed the LuxR-type transcriptional regulators. This paper focuses on a LuxR-type protein in Chromobacterium violaceum, a (rare) human pathogen that uses quorum sensing to control several apparently disconnected processes: biofilm formation, cyanide production, and synthesis of a purple pigment that may also be an antibiotic.
The natural ligand for this LuxR-type protein, called CviR, is an acyl homoserine lactone with a 6-carbon tail (C6-HSL). Any modification that extends the 6-carbon tail — say, to 8 or 10 carbons — reduces the activity of the ligand, and the authors previously showed that adding a bulky chlorophenyl group at the end of the tail produces a signaling antagonist (CL). In this paper, they set out to understand why this happens.
April 25, 2011 § 3 Comments
If you’re interested in looking at how the migration of a microbe into new populations can affect its evolution, the ideal setting for your study is probably a situation where an infected population meets a population that has never been infected before. It helps if the contact between the two populations is limited, so that you can trace the infection more precisely; and it’s even better if the infection happens in neighboring populations at different times. All of these conditions applied in Canada in the early 18th century to mid 19th century, when Mycobacterium tuberculosis was spread from French settlers to indigenous Canadians as a result of contacts made while trading furs. The resulting patterns of M. tuberculosis dispersal have now been described in a recent paper (Pepperell et al. 2011. Dispersal of Mycobacterium tuberculosis via the Canadian fur trade. doi:10.1073/pnas.1016708108).
Much of Canada was completely isolated in the 18th century. The European settlers initially didn’t penetrate very far beyond the Atlantic seaboard. It was the fur trade that created the impetus for developing a vast network of transportation routes, largely based on canoes, that connected the interior with the growing settlements at the edges. The trade also offered career options for fur company employees: guides, translators, navigators and negotiators, and especially the voyageurs who traveled deep into the mysterious interior of Canada to bring back the furs. [The lives they lived look pretty miserable to us now: 14-16 hour days of constant paddling, occasionally interrupted by a portage, in which they would carry at least 180 pounds of furs — repeatedly — across rugged terrain. They often suffered hernias, and they ate mostly pemmican (dried bison meat), but they sang a lot, and so are now considered deeply romantic figures.]
March 1, 2011 § Leave a comment
Blood vessel formation is one of the wonderful adaptive processes in biology. If a tissue is under-oxygenated, it sends out a cry for help and lo and behold, a new blood vessel forms. This is great if the rescued tissue was under-oxygenated because it was cut off from its normal supply by a wound. It’s not so good if the under-oxygenated tissue is a tumor. Tumors that successfully acquire a blood supply of their own can metastasize to different sites by travelling through the circulatory system, and grow much faster than avascular tumors.
So how do the new blood vessels actually form? In the context of a tumor, what happens is roughly this: the tumor sends out protein signals such as VEGF (vascular endothelial growth factor), which diffuses through the tissue until it reaches an existing blood vessel. The endothelial cells that line a blood vessel have receptors for VEGF, and they react to it by producing proteases that chew up the basement membrane that supports the blood vessel. The freed endothelial cells are then able to migrate into the extracellular matrix, again often chewing their way along using proteases such as matrix metalloproteinases. VEGF induces both chemotaxis and proliferation, so the new “sprout” of the blood vessel moves towards the tumor cell (heading up the gradient of VEGF) creating a column of endothelial cells that will later become hollow, grow basement membrane around it, and become able to support blood flow to the tumor. Presto, new blood vessel. In fact, many new blood vessels: the original sprout will generally branch several times, creating a new network to feed the tumor.
Do we completely understand this process? Well, no; for example, we have little understanding of why the sprouts branch. Jeremy Gunawardena pointed out a very nice modeling paper from a couple of years ago (Bauer et al. 2007. A cell-based model exhibiting branching and anastomosis during tumor-induced angiogenesis. Biophys. J. 92 3105-21) that used cell-based modeling to offer some interesting insights about the mechanisms that may be responsible for branching. Alas, as far as I can tell from Google Scholar, this paper has only ever been cited by other modeling papers, although the question of what controls branching (and issues like the role of the cytoskeleton in branching) are active areas of research.
January 4, 2011 § Leave a comment
The interaction between the immune system and cancer is a complicated and puzzling one. On the one hand, there’s evidence that the immune system can help to get rid of tumors. On the other hand, there’s also growing evidence that an inflammatory environment is important for tumor survival and metastasis. A recent paper (Feng et al. 2010. Live imaging of innate immune cell sensing of transformed cells in zebrafish larvae: parallels between tumor initiation and wound inflammation. PLoS Biology, 8 e1000562) aims to model very early events in tumor initiation, and indeed finds that the immune system both attacks and helps early tumors. It appears that transformed cells send out chemoattractant signals in much the same way that wounded tissue does, leading to infiltration by leukocytes that attempt to kill the transformed cells; but this attack is usually insufficient to “heal” the wound, leading to a chronic inflammatory state that seems to help the tumor grow.
Feng et al. used a transgenic zebrafish line in which leukocytes are fluorescently labeled with GFP. They injected a gene construct carrying an oncogenic form of Ras labeled with mCherry, under a couple of different cell-type specific promoters, into the fish embryos. Because zebrafish are transparent, they could then watch the cells that express Ras (which are labeled in red) in real time — in other words, watch as a cell is transforms into an early cancer cell — and see what happens as the GFP-labeled leukocytes (labeled in green) encounter the Ras-expressing cells. They did this at a stage of development when only the cells of the innate immune system (such as neutrophils and macrophages) have developed, allowing them to separate the effects of this part of the immune system from that of later-developing cells such as T and B cells.
The first surprise of this work is how rapidly leukocytes find the cancer cells: both neutrophils and macrophages accumulate near Ras-expressing cells usually before they’ve had a chance to divide. They show interesting behaviors once they’re there: the cancer cell and the leukocyte reach out towards each other with filopodia and lamellipodia, often creating “tethers” that link the cancer cell to the leukocyte. Feng et al. can also see that the immune system cells behave as if attacking the transformed cells: macrophages, true to their name, do this by engulfing whole cells, whereas neutrophils chop off smaller bits. However, it’s not clear that these are real attacks; it’s also possible that the leukocytes were just sampling the cancer cells. The authors didn’t see any apoptosis in the cancer cells, which you might expect to see if a real attack was under way.
November 10, 2010 § 3 Comments
Be honest — would you have guessed that red blood cells are mysterious? No, I wouldn’t have either. They’re the simplest cells in our bodies, for goodness sake — they don’t even have DNA. All they do is carry hemoglobin around, picking up oxygen as they pass the lungs and gradually dumping it everywhere else. How hard can that be to understand? And we’ve studied them in various ways for over 450 years.
But indeed it turns out that there are significant holes in our knowledge of how the number, size and hemoglobin concentrations of red blood cells are controlled, and how these control systems go wrong in anemia. We do know where new red blood cells come from — the bone marrow — and we know some of the factors that control the development and release of new red blood cells, such as erythropoietin. The feedback control between “too few red cells” and “more erythropoietin needed” goes mainly through the kidneys; the mechanism the kidney uses to sense oxygen levels (protein hydroxylation) and induce erythropoietin synthesis has been an area of active research. What we know less about is what happens to these new red blood cells once they get out in the circulation.
November 9, 2010 § 3 Comments
Untreated, HIV is normally a death sentence. But not quite always. A small number of people infected with HIV can survive for decades without symptoms. They’re called “elite controllers”, and — although the fact that they’re healthy makes them hard to identify with certainty — they’re thought to comprise less than 1% of the infected population.
Elite controllers, as the name suggests, control the replication of HIV much better than a normal infected person. Although they’re definitely infected, they have very low (to undetectable) amounts of virus circulating in their bloodstream. They are therefore much less likely to pass on the infection, and they maintain perfectly normal levels of CD4 cells. For these few lucky individuals, HIV may be merely an inconvenience.
What makes them special? A genome-wide association study, performed as a result of an impressive collaborative effort (the list of authors is longer than the paper), has come up with a simple and satisfying answer: the genes most clearly associated with being an elite controller are essentially all variants of MHC class I, and identifying the subtypes of MHC class I that are over-represented in the elite controller population makes it possible to pinpoint a handful of amino acids in the peptide-binding groove as important for protection.
October 21, 2010 § Leave a comment
Following up on the papers from the Alber lab I wrote about a few weeks ago, John Higgins pointed out this paper (Panteleev et al. 2010. Task-oriented modular decomposition of biological networks: trigger mechanism in blood coagulation. Biophys. J. 98 1751-1761), which also aims to use modeling to probe the mechanisms of clot formation. There’s an interesting contrast here between the different approaches used by the Alber lab and the authors of this paper. The Alber group embeds their model of the biochemical events of the coagulation cascade in three layers of models of the physics of clot formation: the change in behavior of platelets as they become activated, the shear force of blood flow, and the interactions between the clot and the flowing blood; this allows them to trace the effects of alterations in biochemical events all the way to the predicted behavior of the overall clot. In this paper, Panteleev et al. focus just on the cascade itself, and ask whether it can be broken down into different sub-parts with distinguishable tasks. This is a test of what could be a general divide-and-conquer strategy: identify subtasks, identify the components involved in each subtask, and determine which components are changing rapidly and need to be modeled explicitly, and which are changing slowly and can be approximated as “constant” (a.k.a. “separation of timescales”). If you can do all of this you will end up with a simple(r) model of the key events that drive the behavior you’re interested in, and it might even be simple enough to make you feel that you have an intuitive understanding of what’s going on.
In setting the stage for their approach, Panteleev et al. point out that the mapping between the biological reactions in the coagulation cascade and the task the cascade performs is not straightforward. Only two reactions in the network have obvious functions: binding of factor VIIa to tissue factor (TF) is responsible for recognizing the site of damage, and cleavage of fibrinogen to fibrin causes blood to form a gel, blocking the hole resulting from damage and preventing excessive leakage of precious bodily fluids. Why do we need the dozen or so factors that are involved in the whole cascade? That’s a bit of a straw man, of course; the coagulation cascade is much more than a simple on/off switch. The clotting community, if that’s what they call themselves, have recognized at least 4 subtasks this network needs to accomplish: