How many bacteria vs human cells are in the body?

When people ask me what the microbiome is, part of my answer usually includes the fact that there are 10 times as many bacteria in the body as human cells in the body. Unfortunately, I may no longer be able to use that statistic. A recent study out of the Weizmann Institute in Israel states that the number of bacteria may actually be very similar to the number of human cells in the body.

The authors of the study found that the 10:1 ratio of bacterial to human cells goes back to a 1977 study by Dwayne Savage and an earlier 1972 paper estimating the number of bacterial cells in the human body. The Weizmann scientists redid the estimate and found that there were about 39 trillion bacterial cells in the body. They also estimated the number of human cells in the body, about 84% of which are red blood cells, finding there to be about 30 trillion human cells in the body.

While this results in about 1.3 bacterial cell per human cell, the numbers may vary significantly from person to person and could change significantly with each defecation. They estimate that the range of bacterial cells goes from about 30 to 50 trillion in each individual. Women may also have a higher ratio of bacterial cells than human cells because they have fewer human cells, specifically red blood cells.

While this study does not take into account fungi, viruses, and archaea which all make up the human microbiome and would increase the ratio of microbes to human cells, the often stated ratio of 10:1 for bacterial cells to human cells is most likely not accurate. While I will no longer be able to use this fun fact in my description of the microbiome, it does not take away from the importance of bacterial cells in human health. 

Starch rich diets can influence the gut-microbiome and subsequently behavior

The microbiome’s role in modulating the gut-brain axis has been well-supported by a large body of evidence.  Many experiments in the past have demonstrated this in preclinical models by administering probiotics with specific bacterial strains or by fecal microbiome transplant in rodent models, which were then associated with changes in behavior.  Diet has also been implicated in these modulations, as food intake can influence species diversity and composition.  Low-digestible carbohydrates, or resistant starch, have received attention as being beneficial toward health, as these components are not digested but rather fermented by resident microbiota to produce an array of beneficial metabolites.  In a recent study, researchers from Texas Tech University investigated whether a diet rich in resistant starches were also associated with changes in behavior.

48 mice were randomly assigned to 3 different treatment groups, with each group either fed normal corn starch diet, a resistance starch rich diet, or an octenyl-succinate diet for 6 weeks.  The animals were monitored for weight, were subject to robust behavioral tests, and fecal samples were examined for microbiota composition.  The animals on the resistant starch diet exhibited similar weight gains as compared to the normal corn starch diet, and the octenyl-succinate group demonstrated lower weight gain.  Fecal microbiota analysis revealed diet correspondence to specific diet, and that resistant starch diet groups displayed increases in Verrucomicrobia and Actinobacteria as compared to octenyl-succinate and normal corn starch group, respectively.  In all groups, mice displayed significant anxiety-like-behavior in an elevated plus maze, and in open-field tests the mice fed resistance starch rich and octenyl-succinate diet mice exhibited high-anxiety-like behaviors. 

This data again supports that diet manipulation can have marked influence on behavior, and that starch rich diets could perhaps induce undesirable behavioral effect via modulation of the gut-brain axis.  This could be an important drawback to the beneficial components provided for microbial fermentation.  

Drugging the microbiome to decrease atherslcerosis

TMAO is sometimes found in fish.

Trimethylamine N-oxide is a metabolite produced by the microbiome from foods such as red meat and fish.  This metabolite has been independently linked to atherosclerosis, among a host of other diseases.  Researchers at the Cleveland Clinic have been investigating the relationship between the microbiome and this molecule for many years, and we have written about a few of their publications previously.  (Click the TMAO tag below to learn more.)  Most recently, they have researched various compounds that could possibly decrease the production of TMAO by the microbiome.  Last week they published the results of this study in the journal Cell.

The researchers identified a molecule, 3,3-dimethyl-1-butanol (DMB), which inhibited the production of TMAO by gut bacteria.  DMB is a natural product that is commonly found in balsamic vinegar and olive oil.  This molecule was able to shift the microbiome towards bacteria that did not produce TMAO, and importantly, it did not strictly act as an antibiotic and broadly decrease the abundance of microbiome bacteria.  The scientists tested this molecule in mice and showed that it decreased the plasma levels of TMAO in mice that ingested choline.  Moreover, the mice that received DMB had less arterial plaque (i.e. less atherosclerosis).  In addition, the DMB did not appear to have any toxic effects on the mice. 

These researchers hope that the DMB or other agents that lower TMAO levels could possibly be used as therapeutics.   Beyond atherosclerosis, TMAO has been implicated in a number of diseases, ranging from certain cancers to inflammatory diseases.  These diseases are complex though, and their etiologies are not completely understood, so it remains to be seen if this microbiome approach will be successful.  In the mean time, a little less red meat and a little more balsamic vinegar probably won’t hurt.  

Proton pump inhibitors affect the microbiome

Proton pump inhibitors (PPI) are used to reduce gastric acid production in individuals’ guts and are prescribed to treat ulcers, gastroesophogeal reflux disease (GERD), and other conditions associated with acid production. It is one of the most commonly used drugs in the world. We know (and have written about) that PPIs are associated with increased intestinal infections, specifically Clostridium difficile, and the gut microbiome plays an important role in infections of the intestine. A recent study looked at the influence that PPIs had on the gut microbiome.

The team of researchers studied the gut microbiome of 1815 individuals. They looked at PPI users vs non-users. Of those sampled, 215 of them were taking a PPI at the time that a sample was taken. It was found that those taking the PPIs had lower microbial diversity compared to those not taking PPIs. They also found that bacteria usually found in the mouth was over-represented in the fecal samples of those taking PPIs, including those in the Rothia genus. They also observed an increase in Enterococcus, Streptococcus, Staphylococcus, and Escherichia coli, a potentially pathogenic bacterium.

PPI usage effects are more prominent than those of most other drugs, including antibiotics. The results of this study are consistent with a less healthy microbiome and allow us to better understand why PPIs may lead to an increase of susceptibility to intestinal infections like C. diff.

Microbiome affects blood glucose levels after eating, can help predict glycemic response to foods

Postprandial (post-meal) glycemic response (PPGR) is the effect that food has on blood glucose levels.   Eating a sugary candy, for example, will raise blood glucose levels, whereas drinking water will not.  PPGR remains an important predictor for metabolic syndrome and type II diabetes, so it has an important role the obesity epidemic.  Unfortunately, PPGR is difficult to predict, and efforts that are based on individual foods themselves have failed.  New research shows that there are many factors, including the microbiome, that are important to predicting blood glucose after a meal.  The research out of Israel and published in the journal Cell presents a new model that can more accurately predict PPGR that is based on personalized factors.

The researchers catalogued 800 peoples’ meals over 7 days while continuously measuring their blood glucose levels.  In addition they monitored their gut microbiota, weight, sleep, and various other lifestyle factors.  After evaluating the data, the scientists realized that identical foods had vastly different PPGRs.  For example, bread could have a 8 fold variation in glycemic response depending on the individual.  In order to explain these differences, the scientists identified several significant associations between the microbiome and the PPGR from specific foods.  For example, on the phyla level high abundances of Proteobacteria and Enterobacteriaceae were associated with poor glycemic controls.  On the species level Eubacterium rectale, which is known to ferment fiber, was correlated with low glycemic response, and Parabacteroides distasonis, which had previously been associated with obesity, was correlated with hight glycemic response.  The scientists then aggregated all of their data, including microbiome data, and created a predictive algorithm for the PPGR from foods for individuals.  This algorithm accurately predicted the glycemic response from foods on a personalized level, and was more informative than general food based predictions.

This study speaks to the power of personalized medicine that is based on the microbiome.  Knowledge of our own microbiome could be used to advise our dietary choices in order to choose foods that will lead to low PPGR, and decrease our risk for metabolic syndrome.  Overall, the scientists determined that of all foods, eating fiber was most beneficial because it lowers glycemic response over the long term.

Antibiotics affect the mouth and gut differently

When we discuss antibiotic resistance, it’s not always clear where the resistance is developing or how exactly the resistance develops. A study out of the UK and Sweden looked at two niches, the gut and the mouth, to understand the difference between how the different parts of the body react to antibiotics.

The scientists discovered that these two parts of the body reacted and recovered very differently after a one-week course of antibiotics. They took fecal and saliva samples prior to the antibiotic regime and then gave the study participants a weeklong course of clindamycin, ciprofloxacin, minocycline, amoxicillin, or a placebo and continued taking fecal and saliva samples for a year.

They found that the oral microbiome recovered much faster than the gut microbiome back to its normal state. It took much longer for the gut microbiome to recover and for participants taking ciprofloxacin, diversity was changed even after 12 months. They also found that while participants largely had genes associated with antibiotic resistance in their gut prior to the trial, the amount of antibiotic resistant genes increased after taking the antibiotic. Antibiotic resistant genes in the mouth remained largely stable before and after treatment.  It was also observed that butyrate production, a health associated short-chain fatty acid, was severely affected by ciprofloxacin and clindamycin.

This raises a number of questions like why does the oral microbiome recover so much faster than the gut microbiome? And why isn’t there a similar increase in antibiotic resistant genes in the mouth like we see in the gut? While this study raises many questions, it provides an opportunity to look at the mouth and better understand what is unique about that environment in comparison to the gut.