End of the year with chemogenomics

Taken from jurvetson at:
www.flickr.com/photos/jurvetson/3156246099/

Around this time of the year it is customary to make an assessment of the year that is ending and to make a mental list of things we wish for in the year ahead. Here is my personal (but work related :) take on this tradition.

My academic year ended with the publication of two works related to chemogenomics. Chemogenomics or chemical genomics tries to study the genome-wide response to a compound. Usually, collections of knock-outs or over-expression of large number of genes are grown in the presence or absence of a small molecule to assess the fitness cost (or advantage) of that perturbation to the drug response. This is what was done in these two works.

In the first one, Laura Kapitzky (a former postdoc colleague in the lab) used a collection of KO strains both in S. cerevisiae and S. pombe to essay for the growth in the presence of different compounds. The objective was to study the evolution of the drug response in these distantly related fungi. In line with what was previously observed in the lab for genetic-interactions and kinase-substrate interactions we found that drug-gene functional interactions were poorly correlated across these two species. Perhaps one interesting highlight from this project was that we could combine data from both fungi to improve the prediction of the mode-of-action of the compounds.

The second project, in which I was only minimally involved in, was a similar chemogenomic screen but at a much larger scale. As the tittle implies "Phenotypic Landscape of a Bacterial Cell" (behind paywall), is a very comprehensive study of the response of the E.coli whole knock-out library against an array of compounds and conditions. Robert, Athanasios and other members of the Carol Gross lab did an amazing job of creating this resource and picking some of the first gems from it.

Something that I wanted to highlight here was not so much what was discovered but what I was left wanting. These sort of growth measurements tell us a lot about drug-gene relationships. We also have a growing knowledge of how genes genetically interact either by similar growth measurements in double-mutants or by predictions (as in STRING). These should allow us then to make prediction about how drugs interact. If two drugs can act in synergy to decrease the growth of a bug we should be able to rationalize that in terms of drug-gene and gene-gene interactions. I find this is a very interesting area of research. Naively these sort of data should allow us to predict drug combinations that target a specific species (i.e. pathogen) or diseased tissue but not the host or the healthy tissue. Here is a scientific wish for 2011, that these and other related datasets will give us a handle on this interesting problem.

As for the future, I am entering the final year of my current funding source (thank you HFSP) so my attention is turning into finding either some more funds or another job. I will continue working on the evolution of signalling systems, in particular trying to find the function of post-translational modifications (aka P1). Unfortunately the project failed as an open science initiative, something that I have mostly given up for now. I think the main reason it didn't work was because of lack of collaborators of similar (open) interests and non-overlapping skill sets as Greg and Neil were discussing in the Nodalpoint podcast a while ago.

See you all in 2011 !

Predicting and explaining drug-drug interactions

I am generally interested in chemogenomic studies and drug interaction studies as a complement to what we work on in the Krogan lab (genetic interactions). Much like in genetic interaction screening, where the fitness of double mutant strains is compared with that of the individual single mutants, chemogenomics tries to identify drug-gene interactions while drug-drug interaction screening attempts to find cases where the combined effect of two compounds on fitness is different from the expected from the combination of the single independent effects.

I read two recent papers that I found interesting regarding drug-drug interactions. One was by Bollenbach and colleagues from the Kishony lab (published in Cell) and the other was by Jansen and colleagues (published in MSB). In the first, the authors present an explanation for a previously observed drug-drug interaction. It had been previously shown that the combination of DNA and protein synthesis inhibitors results in lower reduction of fitness than expected by a neutral combination model (termed antagonist interaction). The authors show in this paper that, in the presence of DNA synthesis inhibitors, ribosomal genes are not optimally expressed. This imbalance between ribosomal production and cell growth is detrimental to the cell and can be, at least in part, corrected by protein synthesis inhibitors, explaining why these can suppress the effects of the DNA synthesis inhibitors.

Although it is a relatively simple idea (once described), I think it shows how complex these drug-drug interactions can be and to some extent also how these can provide information about a cell.

In the second paper I mentioned, Jansen and colleagues try to develop an approach to predict drug-drug interactions based on chemogenomic data. There are many obvious reasons why this would be very useful and I find this line of research extremely interesting. What I was surprised with was the simplicity of the approach and the disappointing benchmarks.

The end-result from a chemogenomic screen is a vector of drug-gene interaction scores that tell us how the combination of a drug with each mutant (normally KO strains) affect growth when compared to neutral expectation from the combined effect of the individual perturbations. It had been previously shown that drugs that have a similar drug-gene vectors tend to have similar mechanisms of action (Parsons et al. 2006 Cell). What Jansen and colleagues now claim is that the similarity of drug-gene vectors are predictive not only of similar mode of action but also of drug-drug interactions. Specifically, they try to show that drugs with similar profiles are more likely to be synergistic, such that the combined effect of both drugs is expected to be more detrimental to the cell  that the expected neutral combination.

Although the authors show experimental validation of their predictions with an accuracy of 56% they also benchmark their predictions using drug pairs  previously known to be synergistic. This benchmark is somewhat disappointing since they only see a significant enrichment of these true-positive pairs for a narrow range of cut-offs and with 2 out of 3 ways of calculating drug-profile similarity. I wish the authors had comment on this difference between the relatively poor performance based on benchmark and the very high accuracy observed in their experimental tests. They also show that these predicted synergistic pairs are well conserved from S. cerevisiae to C. albicans which is contradictory to a previous Nature Biotech paper that I mentioned in previous post.

Are drug-synergies this easy to predict and so well conserved across species? I am personally not convinced based on the data from this paper alone so I am holding off for further validation by other groups or additional larger datasets/benchmarks.

Drug synergies tend to be context specific

A little over a year ago I mentioned a paper published in MSB on how drug-combinations could be used to study pathways. Recently, some of the same authors have now published a study in Nature Biotech analyzing drug combinations under different contexts (i.e. different tissues, different species, different outputs, etc).

The underlying methodology of the study is essentially the same as in above mentioned paper. The authors try to study the effect of combining drugs on specific phenotypes. One example of a phenotype could be the inhibition of growth of a pathogenic strain. Different concentrations of two drugs are combined in a matrix form as described in figure 1a (reproduced below) and the phenotype is measured for each case. Two drugs are said to be synergistic if the measured impact on the phenotype of the combined drugs is greater than expected by a neutral model.

The authors ask themselves if drug synergy is or not context dependent. This is an important question for combinatorial therapeutics since we would like to have treatments that are context dependent (i.e. specific). The most straightforward example would be drug treatments against pathogens. Ideally, combinations of drugs would act synergistically against the pathogens but not against the host. Another example would be drug combinations targeting the expression of a particular gene (ex. TNF-alpha) without showing synergy at targeting general cell viability.

In order to test this the authors performed simulations of E.coli metabolism growing under different conditions and a astonishing  panel of ~94000 experimental dose matrices covering several different types of therapeutic conditions. In each experiment, two drugs are tested against a control and a test phenotype and the synergy is measured and compared. The results are summarized as the synergy of the two drugs in the test case and the selectivity of this synergy towards the test phenotype. In other words, for each experiment the authors tested if the synergistic drug pairs in the test phenotype (ex inhibition of growth of the pathogen) are also acting in synergy on the control phenotype (ex. inhibition of growth of host cells).

I reproduce above fig 2b with the results from the flux balance simulations of E.coli metabolism. In these simulations "drugs" were implemented as ideal enzyme inhibitors that reduced flux of their targets. Each cross on this figure represents a "drug" pair targeting two enzymes of the E.coli metabolism.  The test and control phenotypes are, in this case, fermentation versus aerobic conditions. In this plot the authors show that synergistic drug pairs under fermentation tend to have a high selectivity for that condition when compared to aerobic conditions.

The authors then went on to show that this was also the case for most of the experimental cases studied. Some of the experimental cases included cell lines derived from different tissues, highlighting the complexity of drug-interactions in multicellular organisms. These results are consistent with the observation that negative genetic interactions are poorly conserved across species (Tischler et al. Nat Genet. 2008, Roguev et al. Science 2008). Although these results are promising, in respect to the usefulness of combinatorial therapeutic strategies, they emphasize the degree of divergence of cellular interaction networks across species and perhaps even tissues. I am obviously biased but I think that fundamental studies of chemogenomics across species will help us to better understand the potential of combinatorial therapeutics.

There are several examples in this paper of specific interesting cases of drug synergies but most of the results are in supplementary materials. Given that most of the authors are affiliated with a company I expect that there will be little real therapeutic value in the data. Still, it looks like an interesting set for anyone interested in studying drug-gene networks.

Lehár, J., Krueger, A., Avery, W., Heilbut, A., Johansen, L., Price, E., Rickles, R., Short III, G., Staunton, J., Jin, X., Lee, M., Zimmermann, G., & Borisy, A. (2009). Synergistic drug combinations tend to improve therapeutically relevant selectivity Nature Biotechnology, 27 (7), 659-666 DOI: 10.1038/nbt.1549