top of page

Pretty Light Cooking

The intestinal microbiome is a vital component of a human body that plays a variety of roles in our health. There’s a lot we still don’t know about this complex microbial community that lives inside of us. For example, what influences the kinds of microbes we have in our intestines? Is it genetics or diet (or both)? Are my microbes significantly different from your microbes (probably), but why?


Like in most scientific fields there are more questions here than answers. The study we are discussing today tries to answer one of these questions: Does cooking shape the intestinal microbiome?


" Are my microbes significantly different from your microbes (probably), but why?"
 

Gut microbes - the ultimate food critics?

At the molecular level, cooked food is different from raw food. Food that has been cooked is more easily digested and thus decreases the amount of energy that the microbes in the colon receive - more of the food can be absorbed in the stomach so less is available for downstream digestion processes. Previous studies have found that humans who follow plant-based diets vs animal-based diets have different microbes living in their intestines (David et al 2014), but until this work we didn’t really understand how cooked vs raw food could impact the type of microbes we have living in our intestine. The researchers had four test groups for their study: 1) mice fed raw organic lean beef, 2) mice fed cooked organic lean beef, 3) mice fed raw sweet potatoes, 4) mice fed cooked sweet potatoes. Their decision to use these food groups was deliberate because these are things we know humans eat now and ate in the past, and likely influenced the type of microbes we have in our intestines today.


The researchers performed this complex study by collecting both human and mice samples from the distal gut, extracted the genetic material (DNA) from those samples and started their investigation. They focused on the bacterial component of the microbial community. For this purpose they sequenced specific bacterial genes called 16S rRNA genes which allowed them to determine which kinds of bacteria were present in the samples. Then they used a variety of molecular approaches to count how many of the bacteria were present, as well as how active those bacteria were in each sample. All together the results generated by these experiments can tell us a lot about the intestinal microbiome and how it responds to change.


The authors found changes in the type of microbes between the mice fed beef and fed sweet potatoes, which matches what was found in humans (David et al 2014). The group of mice that received beef (whether cooked or uncooked) had fundamentally similar microbes. On the other hand, mice that were fed raw vs cooked sweet potatoes ( tubers) had very different intestinal gut microbes from each other. The diet of raw tubers resulted in lower alpha diversity (the number of different species of microbes), less bacteria, and an increase in one specific type of bacteria particularly suited for breaking down raw veggies.

Statical tests can tell us how similar microbial communities are to each other, and when these analyses are plotted on a graph, similar communities cluster together. The graph above shows that mice fed meat have microbial communities that are more similar to each other than to the mice fed vegetables.



Why did cooking the sweet potato change the composition of the intestinal gut microbiome of mice?

The authors hypothesized that since sweet potatoes are mainly made of starch and water, cooking changed the availability and structure of the starches in the potatoes. This resulted in more starch being absorbed by the mice before it could reach the microbes in the intestine, thus there was less starch as well as structurally different starch for the microbes to consume.


To test this idea further the researchers decided to feed the mice other types of vegetables - both starch-rich (sweet potatoes, white potatoes, corn, and peas) and starch-poor (carrots and beets) as well as veggies with hard-to-digest starch (potatoes) and easy-to-digest starch (corn and peas). They only found significant differences in the structure of the microbial community between cooked and raw potatoes. Cooking the low-starch or easy-to-digest-starch-rich foods did not significantly change the intestinal microbial community. Meaning that cooking hard-to-digest veggies results in less nutrients for intestinal microbes, causing the microbes living in the intestine to change. The mice eating cooked potatoes (either sweet or white), the diversity of microbes was greater than in the mice receiving raw potatoes.

Another goal of this research was to see if cooking changed the ‘energy balance’ between the host and microbes. This was done by taking the microbes from mice fed raw or cooked tubers and putting them inside germ-free mice (mice without a gut microbiome), and looking at how the germ-free mice changed after the microbe transplantation. The germ-free mice actually gained weight after receiving the raw-tuber mice microbes. The researchers suggested that the mice eating raw-tubers actually ate more because they were receiving less energy from their diet, and as a result their microbes weren’t helping their body signal that they were full.


Beta diversity = (alpha diversity mouse 1 - # of shared species) + (alpha diversity mouse 2 - # of shared species). Describes the differences between the microbial communities in two mice. Here the beta diversity (differences between two mice) is the lowest between the green mice, meaning their microbes or more similar to each other than to the red mouse.

Beta diversity = (alpha diversity of one mouse - # of shared species) + (alpha diversity of another mouse - # of shared species). This measure aims to describe the differences between the microbial communities in two mice. Here the beta diversity is the lowest between the green mice, meaning their microbes or more similar to each other than to the red mouse. The microbes wearing chefs hats denote that that mouse received cooked food.


The next question is, does all of this mice data apply to humans? This was also tested in this research project. Human volunteers were fed raw and cooked plant-based diets in two, three day periods of time. Over this short period of time the beta diversity of the microbial communities was significantly different between humans that ate raw vs cooked vegetables. This result indicates that the mice model can be applied to humans (to a certain extent) and that cooking food causes distinct and significant changes to the gut microbiome. The authors conclude the paper by suggesting that the adoption of cooking by ancient humans probably played a role in the development of the intestinal microbes we have today.

 

Glossary


Alpha diversity - is the average number of different types of species (diversity) in sites or habitats at a local scale.

Beta diversity - measures the difference between microbial communities between different mice.


Distal gut or descending colon - is the part of the colon from the splenic flexure to the beginning of the sigmoid colon; characterized by very dense gut flora. Energy balance - the balance of calories consumed compared to calories burned.


Significant difference - in science the word significant is reserved for differences or changes that were determined statistically to be a ‘true’ difference.

 

Works Cited

1. Carmody, R.N., Bisanz, J.E., Bowen, B.P., Maurice, C.F., Lyalina, S., Louie, K.B., Treen, D., Chadaideh, K.S., Rekdal, V.M., Bess, E.N. and Spanogiannopoulos, P., 2019. Cooking shapes the structure and function of the gut microbiome. Nature Microbiology, 4(12), pp.2052-2063.

2. David, L.A., Maurice, C.F., Carmody, R.N., Gootenberg, D.B., Button, J.E., Wolfe, B.E., Ling, A.V., Devlin, A.S., Varma, Y., Fischbach, M.A. and Biddinger, S.B., 2014. Diet rapidly and reproducibly alters the human gut microbiome. Nature, 505(7484), pp.559-563.

3. Hugenholtz, F. and de Vos, W.M., 2018. Mouse models for human intestinal microbiota research: a critical evaluation. Cellular and Molecular Life Sciences, 75(1), pp.149-160.



Comments


bottom of page