Thanks hypothesis-generator, now back to the bench (or, why I love microbiome projects).

The collective turkey ileum microbiomeYes, I have fully jumped on the microbiome and metagenomics bandwagon.

In fact, I know of very few labs in my field that have not in some way latched on to studies involving the 16S rRNA surveys in the past few years. We watched as the pioneers developed some fantastic protocols for applying this method to next-gen sequencing. Then, as the Human Microbiome Project emerged and revealed a plethora of associations between the microbiome and health/disease. Next, as even bigger projects were unveiled, such as the Earth Microbiome Project, and sought to sequence the world’s microbes. All of which have been very fruitful and extremely cool.

Sure, there have been modest efforts to characterize the bacterial microbiomes of agricultural animals, but funding for animal ag (mostly through USDA or industry support) has always been a small fraction of what is available through NIH. However, just like with genome sequencing, 16S rRNA surveys are now very much in the hands of the people instead of the large centers and research groups. So, here we are now with the ability to perform these surveys for an extremely low cost.

This brings me to the point of this post. A few years back we got into the 16S rRNA surveying using 454 technology, then it quickly translated into the same using Illumina MiSeq. Sure, we went in with hypotheses (yes, they are necessary even for pilot projects and small industry dollars) – but the hypotheses were more of the “duh, yeah” questions. We hypothesized that differences would be observed between commercial turkeys of differing weights and flocks of different average daily weights. And, we hypothesized that a predictable succession of bacteria would occur in the turkey small intestine. But, we really did not know what those specific changes would be and why they might be important.

So, we collected samples and sequenced. And sequenced. And sequenced some more. We learned how to analyze these data. We looked at the obvious things. And then we looked at the not-so-obvious things. It was a fantastic fishing (or maybe hunting is more appropriate here) expedition. And it totally paid off. We found some really interesting differences in the species of Lactobacilli and Clostridium that change over time in the turkey gut. We found that the successional shifts in the bacteria in the ileum are predictable, and they occur earlier in research flocks where the birds perform better. The work is nearly published in PeerJ. Overall, it was a great success.

As we all have learned, correlation does not equal causation. We have a lot of correlative data to suggest how the turkey microbiome can be manipulated to improve health and performance. Now, we head back to the benchtop to validate these findings and go after the handful of interesting hypotheses that came from this work. I feel like this is the trend of science lately. Technology drives new hypotheses, then the classical approaches are necessary to figure things out (Look to Jeff Gordon’s recent paper for inspiration). There are scientists on both fringes of this, though. Some refuse to use the technology and actually lambast anyone who uses it as pseudoscientists without hypothesis-driven research programs. Then, there are those who do nothing but use the technology and probably deserve to be lambasted. Me? I don’t really care what anyone says. 16S rRNA microbiome surveys have generated or refined more questions for us than I could have dreamed up during the same time. I suspect that most of us fall somewhere in this zone, where we are using the approaches to generate interesting questions from correlative observations, then to go out and get funding to address those questions. 16S rRNA surveys (and probably soon to be better metagenomic approaches) have changed the way that I operate for the better, and I can’t wait for the data from our next survey study.


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