The human body is a microcosm of diverse microbial communities that includes bacteria, viruses, fungi, and eukaryotes. No two individuals are the same and neither is their microbiota. The role of the microbiome in regulating cellular function and disease has been known and studied for a long time. What has changed, however, is the ability to dissect the intricate structure, function, types, and location of the microbial clusters in the human body, using technologies and assays that are accurate, sensitive, and non-invasive. The microbiota can now be studied, characterized, and analyzed in their natural habitats to reveal exactly how they interact within their clusters and with surrounding human cells. The resulting taxonomic and functional analyses are revealing a far richer connection between the human body and the microbial communities than previously realized.
Microbiome research has been conducted using everything from community surveys to analyzing the microbial genes, RNA, proteins, and metabolites, referred to as metagenomics, metatranscriptomics, metaproteomics, and metabolomics, respectively. Work done by the Human Microbiome Project, MetaHIT, and others have highlighted the diversity and abundance of microbial communities that exist among individuals in different parts of the world, and also across various sites (skin, gut, mouth) on the same individual. The microbial signature of an individual is also shown to change with age, diet, metabolism, and in various states of health.
Microbiome and drug metabolism
It is well known that differences exist in how a person metabolizes a drug, and it is becoming evident that the microbiome may contribute to some of these differences. Bacteria are the most dominant and the most metabolically active of all the microbiota present in humans. Hence, the effect of the gut microbiome on drug metabolism is being closely studied, as most bacteria that metabolize drugs are found in the large intestine. “I have review papers published back in 1970s on the metabolism of drugs by gastrointestinal microorganisms, i.e. gut microbiome, and its impact on drug metabolism, so this is not something new,” says John Erve, Ph.D., President of Jerve Scientific Consulting.
“What is probably new is the fact that drugs can also affect the microbiome. It’s not just the antibiotics, but other classes of drugs that you would not necessarily expect can affect the microbiome too.” Hence, a deeper understanding of the interactions between a drug and the gut microbiome could impact how new drugs are developed and prescribed. “Once a metabolite is detected, scientists in drug development start looking into the mechanism by which it is formed and if it is a cause for concern based on its abundance,” says Erve. “The gut microbiome is becoming increasingly relevant for extended-release formulations and for less soluble drugs that reach the large intestine.”
Some microbiome analysis is being incorporated into early- to late-stage clinical trials to improve patient outcome and the potential for success. Studies are also being done on marketed drugs, and their interactions with the human gut microbiota show that many of the drugs have antibiotic effects, although they are not sold as such. Certain classes of drugs, such as anti-psychotics, anti-cancer, and anti-diabetes drugs, affect the microbiome in different ways. Some cholesterol-lowering statins affect the gut microbiome, which in turn alters the cholesterol metabolism resulting in some patients seeing less or no benefits from the drug. “Some anti-psychotics are associated with metabolic syndrome and adverse side effects like weight gain, which lead to patients stopping the drug. Scientists are now finding that these drugs can have an effect on the gut microbiome, which in turn leads to changes in fat storage,” explains Erve.
Microbiome as biomarkers of disease
Altering the microbiome to increase the effectiveness of a drug could involve strategic use of antibiotics or microbial transplants or dietary changes to eliminate the “bad” microbiota and replace it with “good” ones. Assembling a personalized collection of microbiota from an individual and analyzing the expression of genes, RNA, proteins, and metabolites over time can help track some of these interactions. Any variabilities caused by changes in health, diet, drug response, and other factors could act as prognostic markers and can lead to timely intervention and personalized medicine.
Rebiotix (a Ferring Company) is developing a microbiota-based drug to treat Clostridioides difficile (C. difficile) infections. Heidi Hau, Ph.D., Director of Technology Expansion, manages investigator-initiated, physician-sponsored clinical studies aimed at early data generation for new indications. She is also leading the charge to develop novel microbiome biomarkers. “I am excited to be at the forefront of advancing our next-generation microbiota-based therapeutics and expanding our MRT™ drug technology platform into therapeutic indications beyond recurrent C. difficile infections,” says Hau. “I am particularly passionate about some of the programs we are developing in reproductive medicine, maternal health, and women’s health.”
However, it’s easier said than done as only 5–10% of the human population share the same type of bacteria, and bacterial genomes within the same genus have only 25% similarity. Hence, the bacterial dataset has high dimensionality and a large degree of variability. “Taxonomy is often used for data reduction, which leads to different species of bacteria being grouped together,” says Liping Zhao, Ph.D., Eveleigh-Fenton Chair of Applied Microbiology in the Department of Biochemistry and Microbiology at Rutgers University who studies the effect of nutrition on gut microbiota and metabolic health.
“There are no reliable microbiome-based biomarkers for disease diagnosis in the clinic yet, as they are mostly taxonomy-based and not robust,” says Zhao. “The higher up you move in the hierarchy of the taxonomy, the more bias you get.”
Rebiotix has taken a more creative, community-based approach to classical biomarker development. They have developed the Microbiome Health Index™, or MHI™ to understand changes in a C. difficile patient’s microbiome before and after receiving treatment. “Additionally, we are exploring multi-omics approaches that include a combination of untargeted community metagenomics, transcriptomics, metabolomics, proteomics, and immune signatures (human microbiomes) to mine data for biomarkers that may help drive new therapeutics for new indications,” adds Hau.
The study of the microbiome demands a core expertise in microbiology, but it also requires an understanding of metabolism, chemistry, nutrition, clinical science, and more. “We need great minds adept at life sciences, computational biology, mathematics, bioinformatics, and statistics, and these scientists will fuel a bright future of innovation and discovery in biomarkers,” says Hau. “The next scientific revolution will be fueled in part by what we learn today in microbiome sciences.”
Microbial challenges
Everyone is curious to find out about the microbiota that exists in their body, but the key lies in finding the causality. “It’s easy to compare microbiota from diseased versus healthy patients and jot the differences, but the challenge is understanding whether the microbiota caused the disease or is it a result of the disease?” says Zhao. “Hence, in every microbiome study we try to design the experiment to collect evidence for causality.” Zhao is looking at changes in the microbiota of obese and hyper-diabetic patients before, during, and after a clinical trial where patients are on a specially designed nutrition regimen. Microbiota from these patients are transplanted in germ-free mice that are given the same diet and these mice are used as animal models to study molecular mechanisms of the disease. Using DNA sequencing, metabolomics, and other technologies, the molecular level variations are profiled and correlated with clinical parameters within the same time window.
“We use a discovery approach to identify candidate bacteria that can be correlated to the disease,” says Zhao. The bacteria correlated to the disease are then isolated and inoculated in the germ-free mouse models to replicate the disease phenotype. “These bacteria can be either detrimental or beneficial and can be used as biomarker or drug target for the disease.”
However, these studies can be extremely challenging. Sample handling, collection, and storage for microbial studies is critical in determining the quality and accuracy of the data. As diversity of the microbial composition changes by location and with time, taking as many samples as possible from various sites is important. Necessary care and precautions must be taken during sample collection to minimize contamination with other “foreign organisms” and reagents. People collecting the samples have to ensure that their microbiota does not contaminate the sample. “You need to maintain a high level of quality control standards right from sample collection, to DNA extraction and sequencing, to data analysis,” says Zhao. “Every step has to be controlled carefully or you could introduce random errors and bias. We also have to develop ways to detect contamination and errors, when they do occur.”
DNA-based microbiome studies are done using either shotgun metagenomics or looking at targeted marker genes. Targeted sequencing of specific genetic markers can quickly identify the types of microbial communities existing in a given region or sample, by comparing the results to existing reference databases. However, according to Zhao, this approach is flawed, as sequences that don’t find a match in the database get discarded or set aside. “The more valuable your sample, the more novel it is, and the more likely it is for the data to be thrown away as there may be no close enough reference to give it a taxonomic name,” says Zhao. “If we throw away the novel part of our data, we will always be limited by what’s already known and can never move forward.”
Instead, Zhao recommends that microbiome researchers take a discovery approach and look at the reference databases only at the end of the study, and not at the beginning. “Each sequence represents a unique bacterium or a unique part of the genome. It’s important to look at how each sequence behaves and responds to intervention and use this behavior to track down potentially important bacteria. Only by doing this we will find things that have not been studied before.”
