Public interest in the significance of the microbiome has been increasing but until now testing has not always been sufficiently rigorous, says Jeremy Wilkinson.
Decoding the microbiome: strain-level analysis to separate good from bad
The gut microbiome has captured widespread attention, engaging consumers, clinicians and researchers alike. Trends such as #GutTok, the rise of “gut shots” and the increasing use of home testing kits reflect growing popularity in understanding how the microbiome interacts with health [1] [2]. However, this enthusiasm has often outpaced the robust science needed to fully decode and apply microbiome insights to health research programmes – until now.
For years, studies have shown that gut bacteria play a significant role in gastrointestinal diseases, metabolic disorders, and even cancer [3]. But distinguishing between a healthy microbiome and an unhealthy one remains a challenge because of the limitations of microbiome tests available today. Advances in the accuracy and accessibility of genomic sequencing technology and bioinformatics are making up this shortfall by enabling scientists to examine gut bacteria with far greater precision to identify what defines a healthy microbiome. This clearer distinction is key to enabling researchers to bridge the gap between microbiome buzz and real-world applications.
Why has defining a ‘good’ vs ‘bad’ microbiome been so difficult?
Until recently, microbiome tests have been limited to identifying bacteria at the genus or species level. However, this high-level view poses a challenge for researchers since not all strains within a species behave the same way. For example, while some strains of Escherichia coli (E. coli) aid digestion, others can cause serious illness. Therefore, accurate strain-level identification is essential for distinguishing between beneficial, harmful or neutral bacteria.
Fortunately, microbiome testing is evolving, with new tests now offering deeper strain-level analysis. These tests use advanced, highly accurate long-read sequencing and bioinformatics tools for detailed analysis to understand the subtle differences between strains, which previous tests couldn’t detect. These insights enable researchers to identify bacterial strains with unprecedented accuracy and better determine whether they support gut health or contribute to disease.
For instance, the latest long-read sequencing technologies have enabled researchers to map the complete microbial 16S rRNA gene, a key marker used for bacterial identification. In comparison, previous methods only partially capture this gene, lacking the resolution needed to distinguish between bacteria at the strain level or understand their functional roles [4].
Enthusiasm has often outpaced the robust science needed to fully decode and apply microbiome insights to health research programmes – until now
Meanwhile, advances in bioinformatics tools, underpinned by machine learning and artificial intelligence, are making genome assembly, mapping and analysis easier and more accurate. Microbiome sequencing generates massive datasets, with many of the unique sequences corresponding to independent microorganisms, which are difficult to analyse. Recent innovations – such as algorithms that enhance the speed and accuracy of data processing, AI-driven tools for pattern recognition and ML models that predict microbial behaviours and functions – are revolutionising analysis.
What defines a ‘good’ and ‘bad’ microbiome?
A good and bad microbiome can be differentiated by several key factors – including bacteria diversity, the presence of antibiotic-resistant genes and the levels of pathogenic versus beneficial bacteria.
Distinguishing characteristics of a strong vs weaker gut microbiome include:
Diversity of bacteria: Healthier microbiomes have a high diversity of gut bacteria, enhancing the microbiome’s resilience and adaptability. In contrast, low diversity is linked to an increased risk of infections, inflammation and gut disorders.
Proteobacteria and fusobacteria levels: Elevated levels of proteobacteria and fusobacteria contribute to inflammation, IBS and an increased risk of cancer. A healthier microbiome typically has lower levels of these pathogenic bacteria.
Antibiotic-resistant genes: Fewer antibioticresistant bacteria support a healthier microbial composition and improve the effectiveness of antibiotic treatments. Elevated numbers, on the other hand, indicate past antibiotic use or overexposure to antimicrobial substances. as many as the bacteria inhabiting the gut.
Levels of beneficial bacteria: High levels of probiotic species and short-chain fatty acid (SCFA) producers aid digestion, immunity and gut barrier protection. Low levels can negatively affect digestion, immune health and gut stability.
Case study: microbiome research in action
We are starting to see the benefits of deeper microbial research in action, with scientists profiling gut microbiomes and seeking to understand how altering a person’s microbiome may have beneficial health outcomes. One example being explored by researchers at GutID is the gut-brain axis [5].
The gut is responsible for producing 95% of the body’s serotonin and there is growing interest in how specific bacteria influence mental health conditions such as anxiety and depression [6]. GutID researchers tracked a patient experiencing an overgrowth of Alistipes, a bacterial strain associated with anxiety disorders.
Through targeted dietary changes, they successfully restored balance in their microbiome and reduced anxiety symptoms.
Bridging the gap: from research to real-world applications
The ability to generate and analyse highresolution microbiome data is providing valuable insights for both patients and researchers. Innovations in sequencing technology are already shedding light on gutrelated health conditions that were once elusive. With ongoing development of microbiome tests and continued investment, strain-level data will become more accessible, bridging the gap between microbiome research and real-world applications. This progress could unlock a wealth of new opportunities in gut health, disease prevention, and personalised medicine – almost as many as the bacteria inhabiting the gut.
Dr Jeremy Wilkinson is microbial genomics lead at PacBio
References:
1 Joshi T. The rise of Guttok: How gut health went from cringe to cool. Metro. 2023 Aug 26; Available from: https://metro.co.uk/2023/08/26/the-rise-ofguttok-how-gut-health-went-fromcringe-to-cool-19381308/
2 M&S and Waitrose partner Zoe eyes US expansion after multimillion pound investment. Grocery Gazette. 2024 Jul 30; Available from: https://www.grocerygazette.co.uk/2024/07/30/zoe-us-millioninvestment/
3 Sun H, Zhao Y, Wang K, Bi Z, Huang J, Li Z, et al. Gut microbiota mediates the therapeutic effect of rifaximin in hepatic encephalopathy by inhibiting inflammation and modulating bile acid metabolism. Cell Death Dis. 2021 Oct 14. Available from: https://www.nature.com/articles/s41419-021-03829-y
4 Johnson JS, Spakowicz DJ, Hong B-Y, Petersen LM, Demkowicz P, Chen L, et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun. 2019 Nov 6. Available from: https://www.nature. com/articles/s41467-019-13036-1
5 GutID by Intus Bio. The Science Behind GutID: Precision, Innovation, and Impact. Available from: https://www.gutid.com/pages/science
6 Appleton J. The gut-brain axis: Influence of microbiota on mood and mental health. Integr Med (Encinitas). 2018 Aug. Available from: https://pmc.ncbi.nlm.nih.gov/ articles/PMC6469458/
