In a recent perspectives article published in the journal Nature Metabolism, researchers unravel cutting-edge advances in human gut microbial community metabolism, highlighting current challenges faced in the field. They provide recommendations for current computational tools and methodologies that may streamline and standardize such studies, emphasizing the benefits of linking individual-specific microbial assemblages and their metabolic pathway and extrapolating these findings to the ecosystem level. Finally, they list the best practices for gut microbiome research aimed at revolutionizing microbiome manipulation and therapeutic approaches in the not-so-distant future.
Perspective: Emerging tools and best practices for studying gut microbial community metabolism. Image Credit: Anatomy Image / Shutterstock
Background
The ‘gut microbiome,’ also known as ‘gut microbiota’ or ‘gut flora,’ is the summation of bacteria, fungi, viruses, and archaea that inhabit the digestive tracts of animals (called their ‘hosts’) in a predominantly symbiotic association. Research has investigated this symbiotic relationship in humans and model organisms and revealed that the community composition, relative diversity, and abundance of these microbes profoundly impact the chemical composition of the hosts’ bodies, strongly influencing the latter’s health.
While conventionally believed to influence hosts’ health via the modulation of the digestive system functioning, a growing body of evidence highlights the role of the gut microbiome and its metabolism in promoting or altering the functioning, risks, and outcomes of infections and immunity, digestion, or most recently, even cancer treatment. These findings make a holistic understanding of the interplay between host metabolism and microbial communities essential. Deriving and elucidating the mechanisms underpinning these biotransformations could revolutionize future disease management and treatment on an individual level.
Why does this perspective exist, and what does it aim to contribute?
No two humans, and by extension, their microbiome assemblages, are identical. Substantial diversity in strain-level genetics and their associated phenotypic outcomes has hindered scientific advances in personalizing gut microbiome manipulations for medical purposes. Additional challenges in establishing an environmental context and consolidating the vast knowledge base of microbial metabolism have presented numerous challenges in developing patient—or even population-specific microbiome interventions.
Encouragingly, recent advances in gut microbial metabolism have attempted to address these challenges by developing computational and methodological tools, including annotation and curation tools for 1. metabolism modeling, 2. community metabolic network analyses, and 3. centralized and publically available knowledge repositories. Unfortunately, given the field’s broad scope and interdisciplinary nature, many of these advances remain invisible to researchers and clinicians. Furthermore, the relative novelty of the field and lack of standard sampling methodologies and outcome reporting conventions further steepens the learning curve for prospective gut microbiome studies.
The present perspectives article aims to streamline this process by summarising historic and ongoing challenges in gut microbiome metabolism research, highlighting the best data resources and analytical tools currently available for studies in the field, and recommending practices and methodologies to standardize and streamline 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 type models differ substantially from highly diverse gut microbial communities on multiple fronts – 1. the gastrointestinal tract, and in turn, gut microbial communities are mainly anaerobic. While substantial literature details carbohydrate metabolism in anaerobic environments, gut flora frequently use poorly understood alternative metabolites as nutrition sources (e.g., nucleotides and amino acids). Alterations in an individuals’ diets are increasingly being linked to transitions (both short- and long-term) in their gut floral composition, yet the mechanism underpinning these interactions remains elusive.
“E. coli K-12 substrain MG1655 is the best-studied microorganism on the planet, yet 6% of its genes have no predicted or known function, and ~83% of metabolite features produced by this organism are unidentified.”
2. Unlike uniform E. coli populations or mammalian cell lines, the overall health of the gut microbiome depends on the interactions between all its dynamically changing constituent microbes. We still do not understand these interlinked metabolic interactions in individual human subjects, let alone have an annotated database of all possible interactions at the ecosystem scale.
How can we address these challenges?
Before attempting to elucidate the broad-scale metabolic interactions, we must first deepen our understanding of the metabolism of individual microbes. Cutting-edge 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 progressed for a subset of microbes, COBRA( COnstraint-Based Reconstruction and Analysis), BiGG, and DEMETER software can be utilized to construct genome-level metabolic maps to hypothesize individual microbiome-level metabolic capabilities and their interactions with host environments. Artificial intelligence-based approaches such as ‘deep phenotyping’ tools (BacterAI) can be used to design and optimize workflow, substantially accelerating data acquisition, curation, and analysis for these single-microbe metabolism studies.
When moving from the individual- to the community/ecosystem level, metabolomics approaches can provide key insights for elucidating collective behaviors and responses of gut flora. MASST is one such tool capable of rapidly searching publicly available databases for hypothesized or desired mass spectra information. When combined with metagenomic data, this mass spectra data can further elucidate the ecology of microbial assemblages. The latter can be achieved using the MICOM framework.
“As for single-organism metabolic models, the quality of predictions from community models depends on their underlying data. In particular, community models are susceptible to overemphasizing the metabolic roles of better-studied model taxa like E. coli. To counteract these biases, community-level uncertainty estimation and experimental validation are also important areas for future methods development.”
Finally, few things are more tedious and time/resource-consuming than reinventing the wheel. Unfortunately, research is often repeated due to gut metabolic research’s interdisciplinary nature and rapid progress. Standardizing study methodologies and outcome reporting schemes in parallel with establishing infrastructure for data sharing and knowledge synthesis may help overcome this limitation. The National Microbiome Data Collaborative has recently established the ‘FAIR’ (Findable, Accessible, Interoperable, Reusable) standard to address this need. The Chemical Translation Service (for metabolomics and cheminformatics) and SeqCode (for microbes) can address discrepancies in nomenclatural schemes.
“Given the size and scope of research on microbial community metabolism, literature informatics tools and AI language models can also be valuable resources. Tools like Babel and scite.ai can identify and assess relevant references for queries across fields, such as studies of a particular enzyme family, or associations of a particular microorganism with a particular nutrient. Another useful example is PaperBLAST, which identifies publications that mention genes with high sequence homology to a gene of interest and has been used to hypothesize novel gene functions.”
Conclusions
Despite its substantial recent growth, gut metabolic research remains in its infancy. Standardizing methodologies and popularizing cutting-edge tools would allow for maximum additional growth using minimum time and resource wastage. The not-too-distant future may provide clinicians and patients with the knowledge base required for personal interventions based on the latter’s unique gut ecosystems. Current computational predictions, experimental validations, and the interlinking of these two lines of evidence may prove the next generational jump in personalized healthcare in tomorrow’s world.