Between Illness and Health: The Hidden Role of Microbial Load

New Study Shows How Microbial Load Affects Gut Health and Disease

The billions of microorganisms living in our intestines remain with us throughout our lives. This ecosystem, called the microbiome, has attracted considerable attention in recent years as a way to understand human health and disease. However, microbiome research has so far focused on microorganisms' species diversity , while microbial load —the total number of microorganisms in the intestines—has often been overlooked. A new study by the Bork group at the European Molecular Biology Laboratory (EMBL = European Molecular Biology Laboratory) in Heidelberg, published in Cell on November 14, 2024, aims to address this gap by revealing the effect of microbial load on gut health.

Graphical Summary of the Study:

This graphic shows how fecal microbial load affects gut microbiome variation and how it relates to different factors. The machine learning model developed in the study estimates microbial load using microbial composition data. The model was applied to a large worldwide dataset and showed that microbial load is an important determinant of the gut microbiome. It also highlights that microbiome changes observed in many diseases arise from changes in microbial load and may not be directly related to the disease itself.

Sections of the Graphic:

1. Upper Section: Estimating Microbial Load

   • The machine learning model uses the relative microbiome profile to estimate microbial load (the number of microbial cells) per gram.
   • The prediction model is based on data from the GALAXY/MicrobLiver (n=1,894) and MetaCardis (n=1,812) consortia.

2. Lower Section: Relationship Between Microbial Load and Host Factors

   • Low Microbial Load: This has been associated with diseases such as Crohn's disease, ulcerative colitis, and liver cirrhosis. In these conditions, microbiome diversity decreased, while certain microorganism species were depleted in a shared pattern.
   • High Microbial Load: This has been associated with conditions such as colorectal cancer, multiple sclerosis, and constipation. In these conditions, microbiome diversity increased, with Akkermansia muciniphila and Alistipes species particularly prominent.

3. Demographic and Environmental Influences:

   • Microbial load varies with factors such as age, sex, diet, antibiotic use, and country.

Moving from Composition to Load Analysis in the Microbiome

Microbiome research generally examines the proportions of different microorganism species in the gut, that is, microbial composition. For example, a particular bacterial species may account for 2 percent in healthy individuals, while that proportion may rise to 4 percent in disease. This could lead to the conclusion that this bacterium is associated with the disease. However, this analysis overlooks changes in the total number of bacteria.

This is where microbial load comes into play. Microbial load refers to the density of microorganisms in the intestines and is usually measured as the number of microorganisms per gram of feces. For example, a healthy individual may have a total of 1,000 bacteria, while this number may fall to 500 during disease. In this case, even if the proportion of a particular bacterial species appears to increase, its actual number may not have changed. Microbial load therefore also needs to be assessed to avoid such misinterpretations.

Artificial Intelligence and Estimating Microbial Load

Measuring microbial load generally requires time-consuming and costly experimental methods. In this new study, however, the Bork group developed an innovative approach to estimating microbial load using machine learning technology. The study's first author, Dr. Suguru Nishijima, explains the main aim of their work: “We wanted to develop a model that does not require additional experimental methods.”

Using datasets from more than 3,700 individuals obtained through the GALAXY/MicrobLiver and Metacardis consortia, the group developed a model that could estimate microbial load from microbial composition data. The model also performed successfully on a new dataset it had not encountered before. Following this success, the researchers applied the model to data from more than 27,000 individuals, analyzing 159 studies collected from 45 countries.

Factors Affecting Microbial Load

The analyses revealed that many factors influence microbial load. For example, diarrhea reduces microbial load, while constipation may increase it. Women generally have a higher microbial load than men. Younger individuals also have a lower microbial load than older people. In addition, many diseases and the medicines used to treat them can cause significant changes in microbial load.

Reassessing the Relationship Between Disease and the Microbiome

The study found that many microbial species previously associated with diseases were actually better explained by changes in microbial load. This suggests that disease–microbiome relationships need to be reassessed. However, some disease–microbe associations remained stable, confirming that these associations were indeed related to the disease.

Future Outlook: New Horizons in Microbiome Research

This study emphasizes that microbial load is a measure that should not be overlooked in microbiome research. The machine learning model developed has been made freely available to researchers worldwide. The method has considerable potential for studying not only the gut microbiome but also other microbial ecosystems, such as oceans, soil, and rivers.

Conclusion

This study by the Bork group shows that accounting for microbial load is vital to avoiding false-positive and false-negative results. This innovative approach could usher in a new era in understanding human health and studying environmental microbiomes.