https://m.whitehouse.gov/blog/2015/05/21/whats-next-microbiome



What's Next for the Microbiome?

Posted by Jo Handelsman and Elizabeth R. Stulberg on May 21, 2015 at 02:15 PM EDT

Advances in gene-sequencing technology have expanded our knowledge of microorganisms, revealing new species in every environment from the upper atmosphere — an extreme environment not before known to harbor life — to the human lung, formerly thought to be sterile. Vast networks of microbial communities are found frequently in places as familiar as the human gut and the soil beneath our feet. These communities contribute to essential life processes, sometimes in unanticipated ways. For example, bacteria have been shown to clean up waterways after oil spills, help people digest foods such as seaweed, and contribute to global nitrogen and carbon cycling.

Knowing these communities exist is just the first step — now researchers are applying this knowledge to benefit human health and the planet. Addressing fundamental questions common across the study of communities of microorganisms, or “microbiomes,” can help propel the field forward toward practical applications in areas as diverse as environmental remediation, food production and nutrition, and medical research.

Analysis and modification of the microbiome promises to provide transformative treatments for human health. To take one example, the gut microbiome appears to play a role in several diseases, including obesity. With more research, scientists may be able to treat obesity by using a specific probiotic, prebiotic, or changes in diet that influence the composition of the microbiome. There is precedent that an approach like this could work. To treat the chronic diarrhea caused by an intestinal infection of the dangerous pathogen C. difficile, researchers are investigating how microbiome therapies can help treat this disease that affects half a million people in the United States each year. In a remarkable clinical trial, patients who were extremely ill due to C. difficile infection returned to full health when they received transplants of a donor’s healthy microbiome.

Advances in microbiome science also have enormous agricultural and environmental potential. The plant microbiome, for instance, has been shown to affect drought tolerance, and communities of microorganisms living in the bovine rumen affects the cows’ nutritional status and their release of methane, an important greenhouse gas. Further research could lead to strategies to enrich soil and prevent erosion, produce more nutritious crops, and develop tools for restoring damaged ecosystems. Already, scientists are investigating how collections of bacteria could be used to promote growth and prevent diseases in crops and livestock instead of relying on the diminishing supply of useful antibiotics. Meanwhile, the remarkable bacteria recently found to inhabit the upper troposphere may have important impacts on the water cycle, genesis of clouds and storms, and climate.

Given the demonstrated and potential value of microbiome research in such diverse applications, the White House Office of Science and Technology Policy (OSTP) is issuing a Request for Information to provide a broad community of stakeholders, including experts and members of the public an opportunity to comment on the current status and needs of microbiome research. The Request for Information can be found in the Federal Register here. OSTP encourages experts and interested individuals from across sectors and scientific disciplines to share your feedback on this critically important topic.

Jo Handelsman is Associate Director for Science at the White House Office of Science and Technology Policy.

Elizabeth R. Stulberg is a Policy Analyst at the White House Office of Science and Technology Policy.

http://www.sgutranscripts.org/wiki/SGU_Episode_462

Correlation and Causation (30:56)

RECOMMEND: http://theness.com/neurologicablog/index.php/correlation-and-causation/

S: Rebecca, far and away, the most common item we've had emailed to us over the last week was this new website that sheds some light, perhaps on correlation and causation.

R: So, yeah. This, I really love this. It's a site called, "Spurious Correlations," by Tyler Vigan – Vygan? Maybe? T-Y-L-E-R V-I-G-E-N dot com. He wrote an algorithm, basically, that searches through statistics from the U.S. Census, and the CDC. And it takes that data, and finds things that have correlations, and then charts them. So, a random one would be, the number of people who drowned by falling into a swimming pool, which correlates with the number of films Nicolas Cage has appeared in.

Or, the per capita consumption of cheese in the U.S., which correlates to the number of people who died by becoming tangled in their bed sheets. And it's brilliant. He's got the stats for each year beneath each graph, and he lists how close the correlation is. It's brilliant.

And it's a nice way, a nice, easy way to point people in that direction if they have trouble understanding that correlation does not equal causation, which is kind of a basic sort of logical fallacy that we talk about all the time on here, but can be really convincing. For instance, it's one of the reasons why we think that people suspect that vaccines cause autism. Because children tend to be diagnosed with autism around the same age that they get a bunch of vaccines; just after they get a bunch of vaccines.

So, people put the two together; there's a correlation there; they assume that there must be a causation, that vaccines must have caused the autism. But, of course, correlation does not mean causation. So, yeah. This is a really good site to point people to, to say, you know, just because the divorce rate in Maine has been decreasing as has been the per capita consumption of margarine in the U.S., it does not mean that more pe – less, fewer people are getting divorced in Maine because they're not eating margarine any more.

E: But it might.

R: But it might.

(Rogues laugh)

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S: But what it demonstrates is the power of data mining.

R: Yeah.

S: If you have large data sets, and you are looking for any possible correlation, you'll find them. And you'll find them that they seem stunning. Which is why whenever we use correlational, observational data, I mean, obviously sometimes correlation is causation. It's just not necessarily so. But the first question you always have to ask yourself, "Is the correlation real?"

Before I try to explain the correlation, etcetera, let's just first find out if it's real. And when you find a correlation that involved any kind of data mining, then you have to verify it by looking at an independent set of data to see if it continues to be true with fresh data. Otherwise, don't even waste your time speculating about what's causing it, because it's, you haven't even established yet that the correlation's real. Yeah, so I think it's, it's a great site for driving home this one lesson

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