Notes on Open Access Microbiome Definition Re-visited: Old Concepts and New Challenges
Definition and scope of the microbiome
Lays out the lack of a universally agreed definition of "microbiome" due to rapid growth and field diversity.
MicrobiomeSupport workshop (N=~40 leaders) plus an online survey (N>100) informed the definition amendments.
Aim: revive and refine Whipps et al. (1988) definition, incorporating new tech and findings.
Clear distinction between microbiome and microbiota to address scope and components.
Emphasis on a unifying framework to standardize microbiome studies and enable data integration across disciplines.
Relevance to planetary health and anthropogenic change; microbiome standards as a cornerstone for policy and practice.
From the historical development to current concepts
Microbiome research evolved from environmental microbiology and microbial ecology to include health, agriculture, food, biotechnology, and math/informatics.
Major paradigm shifts:
From viewing microbes as just disease agents to recognizing their central role in holobionts (host + microbiota) and ecosystem functioning.
DNA-based methods (16S rRNA, ITS, etc.) enabled cultivation-independent studies and new ecological/functional insights.
High-throughput sequencing and multi-omics (metatranscriptomics, metaproteomics, metabolomics) reveal microbial activity, not just potential.
Key definitions and terms (historical context):
Microbiome: community + theatre of activity (environment, metabolism, signaling molecules, and all molecular structures).
Microbiota: living microorganisms in a defined habitat.
Metagenome: genomes of the microbiota; microbiome includes more than just genomes (functions, metabolites, environment).
The article advocates a pragmatic, revised Whipps-based definition, accommodating viruses, mobile genetic elements, relic DNA, and functional aspects.
Current definitions and gaps in the literature
There are ecological, host-dependent, and genomic/method-driven definitions in the literature.
Ecological: a characteristic microbial community occupying a well-defined habitat with distinct physico-chemical properties.
Host-dependent: microorganisms inhabiting a body space or environment (e.g., human body) and interacting with the host.
Genomic/method-driven: focuses on genes/genomes of microbiota or combined host–microbiome data (metagenomics + meta-omics).
Core issue: macro-ecology concepts do not always fit microbial systems (e.g., dormancy, HGT, microeukaryotes), challenging straightforward applications.
Many definitions focus on microbial genomes alone, ignoring environment and host interactions.
The workshop concluded Whipps et al. (1988) remains the most comprehensive framework, but amendments are needed to include: members, interactions, spatiotemporal dynamics, core microbiota, phenotypes, and host–coevolution.
Amendments proposed by the MicrobiomeSupport discussion
(1) Members of the microbiome: broad inclusion of bacteria, archaea, fungi, algae, and small protists; debate about whether to include phages/viruses, plasmids, and relic DNA.
(2) Interactions within the microbiome and with networks: emphasis on intra- and inter-kingdom interactions and their ecological/evolutionary consequences.
(3) Spatial and temporal characteristics: microbiomes vary in time (circadian, seasonal, developmental) and space (root vs leaf, soil microhabitats, host compartments).
(4) Core microbiota: stable, consistently associated members across habitats; consideration of functional cores beyond mere taxonomic cores.
(5) Moving from functional predictions to phenotypes: shift from gene-centric predictions to observable phenotypes and functions.
(6) Microbiome–host/environment interactions and coevolution: integrate holobiont concept and pathobiome/dysbiosis in disease and health contexts.
The amendments seek a holistic, testable framework that supports cross-disciplinary standardization and interpretation.
Members of the microbiome and the theatre of activity
Microbiota vs microbiome:
Microbiota: all living microorganisms in a defined environment (bacteria, archaea, fungi, algae, protozoa).
Microbiome: microbiota plus their theatre of activity—the molecules they produce (structural components, metabolites, signaling molecules) and the surrounding environment.
Inclusion of mobile genetic elements:
Phages, viruses, plasmids, transposons, relic DNA should be considered part of the microbiome (but not necessarily part of the microbiota, since many are not living organisms).
Relic DNA: extracellular DNA from dead cells; can be a substantial fraction in some habitats (e.g., soil up to ~40%, in some samples up to ~80%), but may have limited impact on diversity estimates.
Resolution considerations:
For eukaryotes, reproductive units are often a reasonable unit; for prokaryotes, species definitions are debated (e.g., >70% DNA-DNA hybridization or ANI-based definitions).
Taxonomic vs functional focus:
A trend toward integrating taxonomic, genomic, and functional information to better capture microbiome dynamics.
Terminology caution:
Terms like bacteriome/archaeome/mycobiome/virome are increasingly used but may misrepresent the holistic concept of the microbiome; prefer genus-level ecological communities when appropriate.
Microbial networks and interactions
Microbes interact within a network that can be positive (mutualism, commensalism), negative (competition, predation, parasitism, antagonism), or neutral.
Life-history strategies (copiotrophs vs oligotrophs; ruderals) influence interaction outcomes and nutrient dynamics.
Secondary metabolites and signaling:
Quorum sensing (e.g., AHLs) regulates cooperative behaviors and biofilm formation.
Direct Interspecies Electron Transfer (DIET) mediates interspecies communication in anaerobic ecosystems.
Volatile compounds enable long-range cross-kingdom signaling.
Fungal highways and transport networks:
Fungi can facilitate bacterial movement and nutrient/water transfer in ecosystems.
Network analysis tools:
Co-occurrence networks help identify hub taxa (highly connected nodes) and modules; hub taxa may be keystone species but require independent functional validation.
Indicator taxa can reflect environmental treatment or conditions, often assessed with qPCR-based approaches.
Testing hypotheses:
Reductionist model systems, isotope labeling (SIP), FISH-CLSM, dual culture assays, and complementary in silico analyses are used to test network-derived hypotheses.
Cautions:
Co-occurrence analyses infer associations but not causal interactions; experimental validation is essential.
Keystone vs hub taxa:
Keystone taxa are hypothesized to have outsized influence on ecosystem function; hub taxa do not automatically equal keystones; in situ functional validation is needed.
Temporal and spatial dynamics of microbiomes
Temporal dynamics span from seconds/minutes (mRNA life cycle) to centuries (coevolution with hosts/environment).
mRNA is a more reliable proxy of metabolic activity than rRNA content for inferring activity.
Spatial structuring is common across ecosystems: leaves vs roots, root surface vs interior, different rhizosphere zones, and micro-aggregates in soils.
Soils harbor immense niche diversity; tillage and plant diversity changes can alter microbiome composition and diversity.
Seed microbiomes and vertical transmission: seeds can harbor core microbiota that may be transmitted to the next plant generation.
Microbial hotspots and hot moments:
Hotspots (rhizosphere, drilosphere, detritusphere) show markedly higher activity; hotspots exhibit dynamic microbiome structure.
Host compartmentalization:
Human body compartments host distinct microbiomes; sampling location (e.g., skin vs gut) matters for interpretation.
Defining and understanding the core microbiota
Core microbiota concept: suite of members consistently found across similar habitats or host genotypes, underpinning stability, plasticity, and function.
Historical definition (Shade & Handelsman): core microbiome = members shared among microbial consortia in similar habitats.
Functional core concept: core vehicles carrying essential functions/genes for holobiont fitness rather than only taxonomic identity (Lemanceau et al.; Toju et al.; Astudillo-García et al.).
Temporal vs spatial stability:
Core microbiota tends to be relatively constant over time and space, while transient microbiota fluctuates with environment and host state.
Practical nuances:
Core definitions can be sensitive to sampling design and resolution; however, core concepts across systems often show robustness to definition variations.
From functional predictions to phenotypes
Omics toolbox and data flow (Fig. 5):
Culturomics (extensive cultivation) → microscopy → metabarcoding (16S/ITS) → metagenomics (functional genes) → metatranscriptomics → metaproteomics → metabolomics.
Metagenome-assembled genomes (MAGs) and genome reconstruction:
Thousands of microbial genomes reconstructed from metagenomes (e.g., 154,723 genomes from 9,428 metagenomes in the global human microbiome project).
Gaps and challenges:
Large gaps between sequence data and cultivable isolates; 85 out of 118 microbial phyla have not been described by a cultured species in many environments.
40–70% of annotated genes in fully sequenced microbial genomes have no known function; many protein families remain uncharacterized.
The number of not-yet-cultured taxa is large; a nomenclature for uncultured taxa (e.g., extended Candidatus concept) is proposed but contentious due to stability and phenotypic concerns.
Cultivation and ecotypes:
The “great plate count anomaly” (Staley & Konopka, 1985) highlights that 90–99.9% of bacterial species fail to grow in standard lab conditions.
Cultivation improvements (culturomics, reference genomes) are essential to link genotype to phenotype and validate metagenomic predictions.
Advanced single-cell and isotope-labeling techniques:
SIP-based methods (DNA/RNA/protein/lipid), FISH-based approaches, BONCAT, and Raman-based single-cell sorting link function to genotype, enabling phenotype-centric microbiome studies.
Technical standards, methodology, and data management
Practical sources of bias in microbiome workflows:
DNA extraction methods, sample handling, primer choice, and PCR biases can skew results; mock communities help benchmark but cannot fully replicate complex environments.
relic DNA can inflate apparent taxonomic diversity; PMA treatment can help differentiate live vs dead DNA but is not universally adopted.
Sequencing and analysis challenges:
Primer biases and variable regions can bias taxonomic identification.
Primer-free sequencing approaches (e.g., full-length SSU rRNA sequencing) reduce primer bias and reveal hidden diversity.
Microbiome data analysis relies on pipelines (QIIIME2, Mothur) and can yield variable results depending on parameters and reference databases.
OTUs have given way to ASVs for higher-resolution, reproducible microbial feature definitions.
Rare taxa (<1% abundance) can be crucial drivers of community dynamics and biogeochemical processes; they are often overlooked with traditional analyses.
Big data and metadata standards:
Large-scale projects (HMP, Earth Microbiome Project) produce massive datasets (e.g., Tara Oceans: 10 TB data and ~23.2 billion predicted protein-coding sequences).
Metadata quality and completeness are essential; FAIR data principles (Findable, Accessible, Interoperable, Reusable) are not always fully realized.
Reproducibility, robustness, and generalizability require standardized metadata and methodological reporting.
Data repositories and standards:
Repositories include SRA (NCBI), ENA (European Nucleotide Archive), CNSA (CNGB Nucleotide Sequence Archive).
“Available upon request” is inadequate; standardized data sharing and metadata curation are needed.
Future directions in standardization:
Development of community standards and platforms to ensure comparability and reusability of microbiome data across labs and technologies.
Future perspectives, applications, and societal relevance
Grand vision: leveraging microbiomes to improve health (humans and animals), agriculture (plants), and ecosystems (environment).
Management strategies:
Directly manipulating microbiomes through transplants, probiotics, or metabolites; or indirectly by altering environmental conditions to shift microbiome structure toward a healthy state.
Human microbiome and precision medicine:
The gut microbiome is a central target for diagnostics and therapeutics; gut–brain, gut–liver, and gut–lung axes illustrate systemic health connections.
Fecal microbiota transplantation is an approved therapy for recurrent C. difficile infection and is explored for obesity, liver disease, and metabolic syndrome.
Plant microbiomes and sustainable agriculture:
Seed and rhizosphere microbiomes are key targets for crop improvement; strategies include seed microbiome preservation, microbial inoculants, and environment-modulating practices.
Phytobiomes Roadmap represents an integrated approach to breeding, farming, and microbiome research.
One Health and planetary health:
The One Health concept emphasizes interconnected health of humans, animals, and the environment; planetary health extends this to broader environmental health and societal decisions.
Urbanization, pollution, and biodiversity loss influence microbiome diversity and resilience; microbial diversity loss correlates with disease risk and antibiotic resistance dynamics.
Planetary boundaries and Anthropocene context:
Four of nine planetary boundaries have been crossed (climate change, loss of biosphere integrity, land-system change, altered biogeochemical cycles), impacting microbiomes and ecosystem services.
Risks and ethical considerations:
Microbiome-based interventions require careful assessment of environmental impact and unintended consequences; balancing benefits with ecological costs is essential.
Cross-disciplinary integration:
Stegen et al.’s framework calls for greater cross-talk between environmental microbiome science and medical microbiome applications to guide interventions.
Practical roadmap for researchers:
Use a holistic, hologenome-based framework; consider core vs transient microbiota; incorporate temporal and spatial design into sampling; study microbe–host and microbe–microbe interactions; combine multi-omics with cultivation-based validation; ensure rigorous data standards and metadata; and align with One Health goals.
Conclusions and actionable recommendations
The paper advocates reviving the Whipps et al. definition with clarifications: a characteristic microbial community occupying a well-defined habitat with distinct physio-chemical properties, including the theatre of activity (molecules and environment) that shapes ecological niches.
Key distinctions:
Microbiome includes all community members plus their functional milieu and environment.
Microbiota comprises the living organisms themselves; microbiome extends beyond to include interactions and environment.
Nine central points for microbiome studies:
(1) Core microbiota as a suite of shared, stable community members across habitats.
(2) Caution in applying macro-ecology concepts to microbes; test applicability in microbes with unique traits (HGT, dormancy).
(3) Experimental design should integrate spatial, temporal, and developmental scales to capture core and transient microbiota.
(4) Microbiome research is highly technology-driven; maintain a toolbox approach to minimize bias from any single method.
(5) Include microbial functions and multilevel approaches to gain deeper mechanistic understanding.
(6) Promote cultivation-based approaches to link sequence data to phenotypes and ecotypes.
(7) Recognize microbial interactions as foundational to functioning and evolution; study interactions explicitly.
(8) Embrace host–microbe coevolution within a holobiont framework; consider pathobiome and dysbiosis concepts for health outcomes.
(9) Exercise caution with anthropocentric classifications (beneficial/pathogenic) and account for host and microbiome context.
Practical implications for research practice:
Develop standardized experimental designs with explicit sampling strategies to capture core and transient microbiotas.
Use multi-omics in concert with cultivation to link function to ecology.
Employ rigorous data standards, including comprehensive metadata and FAIR data practices.
Validate network-derived hypotheses with model systems and targeted experiments.
Consider One Health planetary health perspectives in research planning and policy.
Quantitative references and notable numbers from the article
Economics and industry context:
Human microbiome research spending exceeded in the past decade.
The agri-food microbiome sector shows a Compound Annual Growth Rate (CAGR) of with projected value over by 2025.
Workshop and survey scale:
Approximately leading researchers in the workshop; online survey responses exceeded experts globally.
Relic DNA and sequencing biases:
Relic DNA can comprise up to of soil sequences, and in some samples up to .
Genomic data and cultivation gaps:
Reconstructed genomes: microbial genomes from metagenomes (global human microbiome data).
Cultivability gap in many habitats: of bacterial species cannot be grown under standard lab conditions (great plate count anomaly).
Phyla lacking cultured representatives: of phyla, have not had a cultured species described.
Functional unknowns: of annotated genes in microbial genomes have no known function.
Data size and analysis:
Tara Oceans project example: about ; ~ of data; ~.
Ecological and health contexts:
Four of nine planetary boundaries have been crossed (climate change, biosphere integrity, land-system change, altered biogeochemical cycles); cross-disciplinary research is needed to understand microbiome responses to these pressures.
Summary of core concepts to remember for the exam
The microbiome is not just the sum of microbes; it includes the environment, molecules, and interactions that define ecological niches (the theatre of activity).
The microbiota are the living members; the microbiome encompasses both those members and their functional milieu, including mobile genetic elements and relic DNA.
Core microbiota are stable, consistently associated members across habitats, while transient microbiota vary with environmental state.
Microbial networks and interactions shape community structure and function; hub/keystone taxa can influence ecosystem dynamics but require functional validation.
Temporal and spatial dynamics are essential to understanding microbiome function, requiring sampling designs that capture core and transient components across time and space.
Technological advances drive microbiome science, but standardization, cultivation, and metadata quality are critical for reproducibility and cross-study comparisons.
A holobiont framework and the One Health/Planetary Health context connect microbiomes to human, animal, and environmental health and policy.
The field must balance exciting applications (therapies, sustainable agriculture) with careful assessment of ecological and societal impacts.