Journal Club: Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine

The biggest reason that I was struck by this paper was not the specific finding that they made (as important as that may be), but rather the general theme that it supports: that microbes impact human health via the specific genes that are encoded by individual strains. 

The (very) short summary I would give for this paper is that the presence of specific bacterial strains decreases the effectiveness of an anticancer drug (gemcitabine) because those bacterial strains encode an enzyme that chemically modifies and deactivates that drug. The authors did a lot of careful and intricate work demonstrating that these bacteria are present within tumors, and that the effect can be linked to one particular enzyme. The figure below presents some microscopy used to detect bacteria in human tumor samples.

Fig. 4. Characterization of bacteria in human pancreatic ductal adenocarcinomas.

After acknowledging the hard work done by the authors, and the significant contribution that this paper will undoubtedly have on the field, I'd like to point out a larger theme. The authors found that a particular enzyme was responsible for this phenotype, which was the decreased efficacy of an anticancer drug. However they absolutely did not find that this enzyme was encoded by a particular genus or species of bacteria. Instead, the enzyme was found to be present and active sporadically across the entire range of Proteobacteria, a phylum found commonly across the human microbiome. In other words, the name that we give to bacteria (their taxonomic identity) is not as important as the particular set of genes in their genome. 

The world of microbiome research is more exciting than ever because of findings just like this one. We know that microbes are important for our health, and studies like this are starting to show us exactly why that is.

Microbiome Research at the Fred Hutch Cancer Research Center

This week I'm joining the Fred Hutchinson Cancer Research Center as a scientist studying the impact of the microbiome on human health and disease. I will continue to use next-generation sequencing data to characterize the composition of complex microbial communities (with an eye towards functional metagenomics), and I'm excited to start collaborating with Hutch scientists and contributing to this vibrant research community. 

Please don't hesitate to reach out if you have any questions or would like to collaborate on interesting projects. I'll be using this website to post periodic updates on research that catches my eye, and you can follow me on Twitter (@sminot) to stay updated. 

Journal Club: Long-term taxonomic and functional divergence from donor bacterial strains following fecal microbiota transplantation in immunocompromised patients

I was lucky enough to see Dr. Ami Bhatt present the data from this paper at ASM 2017, and I thought that it made a strong case for an underappreciated point in discussions of the microbiome. When I walked away from the talk (and put down the paper), I thought to myself, "Wow, the diversity of bacterial strains may even direct interventions like FMT!" PLoS One, 2017 doi: 10.1371/journal.pone.0182585.

The core point I'd like to emphasize for background is that different bacteria from the same species (such as E. coli) generally contain a different set of genes in their genome. When I say "generally," I mean that if you isolate the E. coli from two people, they will almost certainly contain a different set of genes. The number of genes that differ between isolates can vary, but something like 5-10% would be unsurprising for E. coli (see Gordienko, 2013, J Bacteriol for details). In other words, I like to keep in mind that your E. coli is special and unique.

Now, getting back to the paper, they were studying patients who underwent "fecal microbiota transplantation" (FMT), in which a formulation of microbes from a healthy person's microbiome (stool) are delivered directly to the lower intestine. One interesting observation the made is that even though a specific set of bacterial strains are being physically placed in the gut, the genetic makeup of those strains change quickly in the days, weeks, and months following FMT. 

Fig 3. The total gene complement of E. coli in Subject A experiences large-scale remodeling in association with FMT, resulting in broad reductions in genes significantly enriched for virulence factors.

The figure above follows the detection of individual genes (rows) over the days following FMT (columns). When you look at this figure, you can see that the set of E. coli genes in this person's microbiome changes almost immediately after FMT. In other words, just because you put a specific strain in someone's gut, it doesn't mean it will stay there. 

To wrap up, I'd like to make sure I don't come off as dismissive or discouraging of FMT. I am a big believer in FMT and have read a number of studies describing its positive benefits for patients with life threatening illnesses. However, I am of the opinion that "healthy" microbiomes are beneficial because of the specific genes within them, and I'd like to encourage everyone to keep looking for those genes so that we can figure out when and how FMT provides the most possible benefit to human health.

Journal Club: Revised computational metagenomic processing uncovers hidden and biologically meaningful functional variation in the human microbiome

I've been thinking a lot about functional metagenomics recently, and this recent paper from Ohad Manor and Elhanan Borenstein gave me a lot to think about. Microbiome 2017 5:19
DOI: 10.1186/s40168-017-0231-4

When you describe a group of microbes in the human microbiome (let's say the gut), you can (a) write down a list of all of the bacterial and viral species that are present or you can (b) write down a list of all of the genes that are present. Of course, you can do some combination of (a) and (b), or (c) something completely different, but (a) and (b) are good places to start. 

The reason I bring this up is that depending on whether you read the literature on (a) species-oriented analysis or (b) gene-oriented analysis, you might get radically different ideas about the microbiome. Recent work on (a) species-level analysis suggests that people generally have distinct, unique sets of microbes in their gut that persist over time. However, work on (b) gene-level (or "functional") analysis suggests that everybody's microbiome is basically the same. Of course, that's an oversimplification, but I think it makes the point. 

Getting to Manor & Borenstein 2017, the big point I got from their paper is that one possibility for why we haven't been seeing the same degree of individual uniqueness in functional (or gene-based) profiles of the human microbiome may be because we're not doing the statistical analysis quite right. The figure below shows an example. On the right side in (b) and (c) you see the typical abundance metric in blue, and their improved abundance metric in red. While glycolysis shows a greater range of variation (b), the RNA polymerase pathway shows a much smaller range of variation (c). This makes the important point that the field hasn't yet come to a consensus on how to analyze this data, and improvements in statistical analysis may give us a dramatically different idea for what matters in the microbiome. 

There's a lot more detail to the paper, and it's worth a close read if you are working in this field. My personal impression is that everything is still up in the air when it comes to data analysis for functional metagenomics. People come at it from many different angles, all the way from de novo assembly to reference alignment. I'm looking forward to seeing what approaches end up showing us what's interesting and important about the human microbiome. 

Journal Club: Two dynamic regimes in the human gut microbiome

One of the most interesting papers I've read in that past year was recently published by Sean Gibbons in Eric Alm's group at MIT, titled Two dynamic regimes in the human gut microbiome. 

In this paper they analyzed time-series data from the human microbiome, tracking the abundance of different bacteria over time (using a commonly used method called "16S"). What they were really interested in was figuring out how bacteria tend to behave over time. Specifically, do bacteria tend to stay at the same abundance over time, or change in abundance over time (and if so, change in what way?). The figure below is from their paper, and it gives you an idea of how a single bacteria might change over time. The top-left image shows a bacteria (also called an "OTU" in this case) which keeps the same abundance over time. The bottom-left image shows a bacteria that is "conditionally rare" – it bursts onto the scene out of nowhere and then disappears immediately (like The Buggles).

I'm not going to pretend to understand all of the math that they used, but the big take-home I got from this paper is that bacteria tend to stay at the same abundance over time, perturbed periodically by the "conditionally rare" newcomers, but ultimately reverting to their prior state. 

This finding is a nice demonstration of a phenomenon that has been hinted in a few different experiments: that each person's microbiome tends to be stable and persistent over time. As researchers and companies try to develop techniques for manipulating the human microbiome, it's worth keeping in mind that the microbiome seems to be fairly stable, and we don't really know why.