In a recent perspective article published in the magazine Metabolism of Nature, Researchers reveal groundbreaking advances in the metabolism of the human gut microbial community, highlighting current challenges facing the field. They provide recommendations for current computational tools and methodologies that can streamline and standardize such studies, emphasizing the benefits of linking individual microbial assemblages and their metabolic pathways and extrapolating these findings to the ecosystem level. Finally, they outline best practices for gut microbiome research with the goal of revolutionizing microbiome manipulation and therapeutic approaches in the not-too-distant future.
Perspective: Emerging tools and best practices for studying gut microbial community metabolism. Image credit: Anatomy Image / Shutterstock
Record
The “gut microbiome”, also known as “gut microbiota” or “gut flora”, is the sum of bacteria, fungi, viruses and archaea that inhabit the digestive tract of animals (called their “hosts”) in a predominantly symbiotic association. The research investigated this symbiotic relationship in humans and model organisms and revealed that the community composition, relative diversity and abundance of these microbes profoundly affect the body chemistry of the hosts, strongly influencing the health of the latter.
While conventionally believed to affect host health through regulation of digestive tract function, a growing body of evidence highlights the role of the gut microbiome and its metabolism in promoting or altering the function, risks, and outcomes of infections and immunity, digestion. or more recently, even cancer treatment. These findings necessitate a holistic understanding of the interplay between host metabolism and microbial communities. Extracting and elucidating the mechanisms that underpin these biotransformations could revolutionize future disease management and treatment at the individual level.
Why does this perspective exist and what does it aim to contribute?
No two people, and by extension, their microbiome complexes, are identical. Substantial diversity in strain-level genetics and their associated phenotypic outcomes has hindered scientific progress in tailoring manipulations of the gut microbiome for medicinal purposes. Additional challenges in establishing an environmental context and consolidating the vast knowledge base of microbial metabolism have presented numerous challenges to the development of patient or even population microbiome interventions.
Encouragingly, recent advances in gut microbial metabolism have sought to address these challenges by developing computational and methodological tools, including annotation and curation tools for 1. metabolic modeling, 2. community metabolic network analyses, and 3. centralized and publicly available knowledge repositories. Unfortunately, given the broad scope and interdisciplinary nature of the field, many of these developments remain invisible to researchers and clinicians. Furthermore, the relative newness of the field and the lack of standard sampling methodologies and result reporting conventions further intensifies the learning curve for prospective gut microbiome studies.
This perspective article aims to streamline this process by summarizing historical and ongoing challenges in gut microbiome metabolomics research, highlighting the best data resources and analytical tools currently available for studies in the field, and recommending practices and methodologies for standardizing and streamlining future studies.
Challenges in understanding microbial community metabolism
Historically, microbial metabolism research has depended on textbook model systems such as Escherichia coli (E. coli) and mammalian cells. Unfortunately, these single-cell models differ substantially from the very diverse gut microbial communities on multiple fronts – 1. the gastrointestinal tract and, in turn, the gut microbial communities are predominantly anaerobic. While a substantial literature describes carbohydrate metabolism in anaerobic environments in detail, gut flora often use poorly understood alternative metabolites as sources of nutrition (e.g., nucleotides and amino acids). Changes in human diet are increasingly associated with transitions (both short- and long-term) in gut flora composition, yet the mechanism underlying these interactions remains unknown.
“E. coli substrain K-12 MG1655 is the best-studied microorganism on the planet, yet 6% of its genes have no predicted or known function, and ~83% of the characteristic metabolites produced by this organism are unknown.”
2. Unlike uniform E. coli populations or mammalian cell lines, the overall health of the gut microbiome depends on the interactions between all of its dynamically changing microbes. We still do not understand these interconnected metabolic interactions in individual human subjects, let alone have an annotated database of all possible ecosystem-scale interactions.
How can we meet these challenges?
Before attempting to elucidate large-scale metabolic interactions, we must first deepen our understanding of the metabolism of individual microbes. State-of-the-art tools such as GutSMASH, SIMMER and MAGI can help annotate metabolic gene functions using physical organization, genomic and chemical structures, respectively.
Once this is achieved, or at least advanced for a subset of microbes, COBRA (Constraint-Based Reconstruction and Analysis), BiGG and DEMETER software can be used to construct genome-wide metabolic maps to infer individual microbiome-wide metabolic capacities and interactions them with host environments. Artificial intelligence-based approaches such as “deep phenotyping” (BacterAI) tools can be used to design and optimize the workflow, substantially accelerating data acquisition, curation and analysis for these single-microbe metabolomics studies.
When moving from the individual to the community/ecosystem level, metabolomics approaches can provide key insights to elucidate collective behaviors and responses of the gut flora. MASST is one such tool capable of rapidly searching publicly available databases for hypothetical or desired mass spectral information. When combined with metagenomic data, these mass spectral data can further elucidate the ecology of microbial assemblages. The latter can be achieved using the MICOM framework.
“With regard to metabolic models of an organism, the quality of predictions from community models depends on their underlying data. In particular, community models are prone to overemphasizing the metabolic roles of better-studied model taxa such as E. coli. To address these biases, community-level uncertainty estimation and experimental validation are also important areas for future method development.’
Finally, few things are more tedious and require more time/resources than reinventing the wheel. Unfortunately, research is often repeated due to the multidisciplinary nature and rapid progress of research in gut metabolism. Standardizing study methodologies and outcome reporting schemes alongside creating an infrastructure for data sharing and knowledge synthesis can help overcome this limitation. The National Microbiome Data Collaborative recently created the “FAIR” (Findable, Accessible, Interoperable, Reusable) standard to address this need. The Chemical Translation Service (for metabolomics and chemoinformatics) and SeqCode (for microbes) can deal with discrepancies in nomenclature schemes.
“Given the size and scope of microbial community metabolism research, literature informatics tools and artificial intelligence language models can also be valuable resources. Tools such as Babel and scite.ai can identify and evaluate relevant references to questions in various fields, such as studies of a particular enzyme family or associations of a particular microorganism with a particular nutrient.
conclusions
Despite its substantial recent development, research on gut metabolism remains in its infancy. Standardizing methodologies and popularizing state-of-the-art tools would allow for maximum incremental development using minimal waste of time and resources. The not-too-distant future may provide clinicians and patients with the knowledge base needed to personalize interventions based on the latter’s unique gut ecosystems. Current computational predictions, experimental validations, and the interconnection of these two lines of evidence may prove the next-generation leap in personalized healthcare in tomorrow’s world.
