Looking at a scary future

Roy Kishony pointed me to this fascinating but worrying paper (D’Costa et al.  2006.  Sampling the antibiotic resistome Science. 311 374-7), which has apparently influenced the work of the Kishony lab quite a bit.

The authors wanted to know what kinds of antibiotic resistance genes exist in the wild.  Often it turns out that the antibiotic-resistant strains that arise in the clinic didn’t evolve their resistance from scratch.  A good deal of drug resistance comes from genes that originally evolved to protect an antibiotic-producing bacterium from killing itself with its own poison, which are acquired by the pathogenic bacterium via horizontal gene transfer.  So, the authors argue, finding out what pre-formed resistance strategies are available might well help us to predict — and perhaps prepare to defend against — the problems of the future. They selected 21 commonly used antibiotics, with origins that ranged from completely natural, through semi-synthetic, to entirely synthetic, and looked to see whether they could find strains that are resistant to them in bacteria from natural soil samples.

Friday Feature: Variable death

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It’s Friday, and what better way to get into the mood for beer hour than to talk about death?  To make sure that the mood doesn’t get too lugubrious, let’s stick to the death of entities that are unlikely to remind you of anyone you know, such as single cells.  This video shows HeLa cells undergoing apoptosis, from work described in Spencer SL, Gaudet S, Albeck JG, Burke JM, Sorger PK. 2009.  Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature 459 428-32.  PMC2858974.  You’ll see that the greenest cells round up and bleb and die very fast, whereas the less green cells die much more slowly.  And thereby hangs a tale.

Apoptosis — programmed cell death — is one of those processes that you would think would be entirely predictable.  If you’ve triggered a cell to die, you’d think that would be the end of it.  But this turns out not to be true.  If a clonal population of cells is exposed to TRAIL (TNF-related apoptosis-inducing ligand), typically some survive; and the time to death is also very variable.   On the face of it, this variability in genetically identical cells is quite mystifying.  What makes it more than a mere curiosity is that this same variable response might be important in clinical settings, where it’s typical for cancer therapeutics to kill some cancer cells and spare others (a phenomenon called “fractional kill”).

Sabrina Spencer (who has now left the Sorger lab for post-doctoral training in the Meyer lab), set out to ask what kind of mechanism might explain this variable death.  The most traditional and obvious candidate, perhaps, is cell cycle phase; another obvious (though less traditional) hypothesis is that some yet-to-be-identified key factor is present at very small numbers per cell, so that chance fluctuations in its level create significant differences in the functioning of the death pathway.

But there is a third possible explanation.

Good luck, Julio!

This month, we say a tearful goodbye to Julio Saez-Rodriguez.  Julio is setting up his own group at the European Bioinformatics Institute (EBI) in Hinxton, near Cambridge UK, starting on July 1.  EBI is a great fit for him, with its focus on developing computational tools to serve the whole biological community.  There’s an added bonus in that he will be about 3,000 miles closer to his home town in Spain.

Julio came to the Department when the Sorger lab joined us from MIT.  He’s been a major driver of the Sorger lab’s efforts to develop logical models that represent real cellular pathways.  Much of the time, our view of a biological pathway is a kind of average, created by combining the lists of interactions identified in dozens or hundreds of cell lines.  But the pathway doesn’t have to be the same in every cell type — the differences in behavior between cells have to come from somewhere, and by now we know that there are not enough genes in the human genome to allow each cell type to have whole pathways that are different from other cell types.  When Julio looked at liver cells, for example (with Leo Alexopoulos), he started with a consensus model assembled from the literature of the pathways controlling the response to seven different cytokines.  Using an extensive dataset of signal/response measurements taken exclusively from HepG2 cells, he asked how well the literature model could predict actual cell behavior.  What he found was that in order to make the consensus pathway work, he had to drop several “proven” interactions in the network — and add several that had indeed been observed in one cell type or another, but were not sufficiently consistently observed to get into the consensus model.

One day, the signaling community will realize that Julio has done them a huge favor.  Instead of fighting over who’s right and who’s wrong, everyone can be right — as long as you’re studying different cell types, or (if you get desperate) different variants of the same cell types.  Studying one cell type instead of another is like entering an alternate universe: anything can happen somewhere.

Julio plans to continue to work on developing models to help us understand the logic, and the cell specificity, of signaling pathways.   He’s hiring post-docs, and he’s also interested in collaborators and short-term visitors; you still have a week or so to chat to him about the possibilities.  (The UK is warmer than Boston in the winter, although it has to be said that it is also grey and drizzly.)

Good luck, Julio, and keep in touch!