Many bacteria-related diseases, such as inflammatory bowel disease or colorectal cancer, are associated with an overgrowth of gut bacteria considered “bad.” However, a study published in the journal ‘Cell‘ shows that changes in microbial load, rather than disease, may be the factor driving the presence of these disease-associated harmful species.
Traditionally it was believed that some bacteria were directly harmful and responsible for these pathologies, but new analyzes show that the microbial load, influenced by factors such as age, diet, sex, country of origin and use of antibiotics, is what largely explains the appearance of typical bacterial fingerprints in patient samples, without necessarily being related to the disease itself.
This discovery redefines the role of microbes in health and disease. Peer Bork, del Heidelberg EMBL and one of the study’s authors, says the team was surprised to see that many bacteria that appeared linked to disease were better explained by changes in microbial load, suggesting that these microbes are more associated with symptoms such as constipation or diarrhea than with disease. itself.
Bioburden is an important concept in the microbiome, but experimental studies that measure it are often expensive and complex. Using predictive machine learning models, the team was able to overcome this limitation, developing a method that estimates fecal microbial load based on the composition of the microbiome. This has allowed us to explore differences in microbial load across a wide range of health and disease studies using large metagenomic databases.
For the study, the researchers analyzed thousands of metagenomes and experimental bioburden data from the European GALAXY and MicrobLiver projects, as well as MetaCardis.
This technique allows other microbiome studies to predict microbial load without having to measure it experimentally.
While the study does not establish causality or reveal precise mechanisms of action, it opens the door for future research focused on identifying bacteria directly related to disease, as well as adapting this predictive model to other environments such as marine and terrestrial microbiomes.
The model, says Baltasar Mayo, professor of CSIC research at the Dairy Institute of Asturias (IPLA-CSIC) from Science Media Center does not establish causal relationships between total microbial load and disease, but the authors believe that this total microbial load may be a major confounder for the association between disease and gut microbes. “Taking into account this total microbial load could allow us to specifically focus on a few key species in each disease.” That is, it has no immediate practical use, but it may be of great importance for subsequent studies addressing these associations between microbes and diseases,” he adds.
Interview Between Time.news Editor and Peer Bork
Time.news Editor: Welcome, Peer Bork, and thank you for joining us today. Your recent research published in Cell has been quite groundbreaking. Can you start by explaining the main finding of your study regarding gut bacteria and their role in diseases like inflammatory bowel disease and colorectal cancer?
Peer Bork: Thank you for having me. Our study sheds new light on the relationship between gut bacteria and certain diseases. Traditionally, we believed specific “bad” bacteria were directly responsible for conditions like inflammatory bowel disease and colorectal cancer. However, our research indicates that it’s actually changes in microbial load—how many and what types of bacteria are present—that may trigger the presence of these harmful species, rather than the bacteria themselves being directly linked to the disease.
Editor: That’s intriguing! So, you’re suggesting that the microbial load, influenced by age, diet, sex, country of origin, and antibiotic use, plays a more significant role than previously thought?
Bork: Exactly. Our analyses show that what we regarded as harmful bacterial profiles in patients could instead be understood as a response to shifts in overall microbial load. These shifts can result in symptoms like constipation or diarrhea without necessarily causing the disease itself.
Editor: This finding challenges the long-standing view of the direct harm caused by certain bacteria. What motivated you and your team to explore this aspect of gut microbiomes?
Bork: It stems from a broader objective to unravel the complex interactions within the microbiome. We knew that the microbiome is influenced by a multitude of factors, but we wanted to use a more holistic approach to see if changing the angle from direct causation to microbial load would yield different insights.
Editor: Fascinating. You mentioned at the onset the concept of bioburden in your study. Can you elaborate on that for our readers?
Bork: Bioburden essentially refers to the number of microbes present in a given environment—in this case, the gut. In microbiomics, measuring bioburden can be challenging due to the complexity and cost of experimental studies. However, using innovative predictive machine learning models has enabled us to estimate fecal microbial load purely based on the composition of the microbiome, thus making our research more efficient and comprehensive.
Editor: That’s quite revolutionary! How do you believe this new understanding of microbial load will impact future research or clinical practices?
Bork: This study opens new avenues for both research and clinical practice. It suggests that we should focus more on monitoring changes in microbial load rather than solely associating specific bacteria with diseases. This shift could inform preventative strategies and therapies that promote a healthier microbial balance, ultimately lowering the risk for these diseases.
Editor: It sounds like this could lead to personalized approaches in treatment based on an individual’s microbiome profile. What are the next steps for your research team?
Bork: Absolutely. Our next steps involve further validating our findings across diverse populations and exploring interventions that might help regulate microbial load more effectively. We’re also keen on expanding the use of machine learning to predict health outcomes based on the microbiome’s composition and load.
Editor: Thank you, Peer, for sharing your insights with us today. It’s clear that understanding our gut bacteria is a complex but crucial aspect of health. We look forward to seeing how your work unfolds in the future.
Bork: Thank you. It’s been a pleasure discussing this important topic with you.