🧩 1. Why This Paper?

Microbiome research exploded across medicine, agriculture, and ecology—but no one agreed on what “microbiome” actually means. So, an international panel of 40 experts (the MicrobiomeSupport project) met in Austria (2019) to fix that. Their goal: ✅ Create a clear, unified definition of “microbiome” ✅ Differentiate it from “microbiota” ✅ Address research challenges like inconsistent methods and missing standards

🧬 2. A Quick History: From Microbes to Microbiomes

Let’s travel back in time! ⏳

Result? A new worldview: all eukaryotes (plants, animals, humans) are meta-organisms, inseparable from their microbes.

📖 3. What Exactly Is a Microbiome?

Many definitions existed — some focused on genes, others on microbes, or hosts. The most famous old one came from Whipps et al. (1988):

“A characteristic microbial community in a well-defined habitat with distinct physicochemical properties—its theatre of activity.” 🎭

Berg et al. agree this is still the best, but they update it for modern science:

🧠 Microbiome = all microorganisms (the microbiota) + their “theatre of activity” — that means:

• microbial structures (DNA, proteins, lipids…)
• metabolites and signaling molecules
• the environmental conditions they live in 💡 In other words, microbiome = living microbes + what they do + where they do it.

🧫 4. Microbiome ≠ Microbiota

Even relic DNA, phages, and viruses belong to the microbiome, but not to the microbiota.

🌐 5. Microbial Networks & Interactions

Microbes live in complex networks — think of it like social media for bacteria! They form positive (mutualism), neutral, or negative (competition/predation) relationships. They “talk” via:

• Quorum sensing molecules (like bacterial text messages 📱)
• Direct electron transfer (DIET) in anaerobes ⚡
• Volatile compounds (long-distance signals)
• Fungal highways that transport water, nutrients, and even bacteria 🍄➡️🦠

🔑 Some microbes act as keystone species, shaping whole communities—though not every “hub” species in a network is a keystone!

🕰️ 6. Temporal & Spatial Dynamics

Microbiomes are dynamic — they change over time and space:

• Temporal: from seconds (mRNA turnover) to years (evolution with hosts).
• Spatial: even within one leaf, root, or gut, microbes differ by microhabitat.

🧭 Microbial hotspots = areas of intense activity (like rhizosphere soil zones). Understanding these scales helps predict when and where microbiomes change — essential for disease prevention or biotechnological control.

🌱 7. Core Microbiota

Because microbiomes are so complex, scientists look for a core microbiota — the consistent, stable members always present. Two ways to define it:

• Taxonomic core → species consistently found
• Functional core → essential genes or traits for the ecosystem or host

The “core” stays stable; the “transient” microbiota shifts with seasons, diet, or environment.

🔬 8. From Prediction to Phenotype

We’ve got mountains of DNA data—but that doesn’t always reveal what microbes do. To fix that, researchers use:

• Multi-omics (genomics, transcriptomics, proteomics, metabolomics)
• Culturomics (high-throughput culturing)
• Isotope tracing & Raman spectroscopy (to link activity to species)
• Single-cell tools like RACS and BONCAT to connect genotype ↔ phenotype

Still, many microbial genes have unknown functions (40–70% in many genomes!), so functional validation is key.

🤝 9. Host–Microbe Coevolution

Microbes and hosts evolve together — forming holobionts (host + microbiota).

• Mutualistic coevolution → beneficial symbioses (e.g., plant roots & fungi)
• Antagonistic coevolution → host–pathogen arms races
• Pathobiome concept → disease is linked to community imbalance (dysbiosis), not just a single pathogen.

💡 Anna Karenina principle: healthy microbiomes look similar; sick ones are all disturbed in their own unique way.

⚙️ 10. Technical Standards & Pitfalls

Microbiome research is booming—but messy:

• Different DNA extraction methods bias results.
• Relic DNA from dead cells can distort data (up to 40% of sequences!).
• Tools like PMA can filter out dead-cell DNA.
• Bioinformatics pipelines (QIIME2, Mothur) vary — ASVs now preferred over OTUs for precision.
• Need for FAIR data principles (Findable, Accessible, Interoperable, Reusable).

📊 Lack of standardization = poor reproducibility. Establishing shared pipelines and metadata rules is crucial.

🌎 11. Future Perspectives

Microbiomes could transform health, food, and the planet 🌿🧍‍♀️🐄

Human microbiome → key in personalized medicine (probiotics, fecal transplants, gut-brain/liver/lung axes). Plant microbiome → basis for the next “green revolution” (sustainable agriculture, biofertilizers, seed microbiomes). One Health & Planetary Health concepts → humans, animals, plants, and the environment are connected through microbial diversity.

⚠️ But: Anthropocene threats (climate change, pollution, biodiversity loss) are also microbiome threats. Understanding microbial resilience is vital for global sustainability.

🧠 12. Final Definition (2020 Updated)

Microbiome: A characteristic microbial community in a well-defined habitat with distinct physicochemical properties, including the microorganisms and their theatre of activity. It forms a dynamic, interactive micro-ecosystem integrated within larger ecosystems (like hosts), crucial for their functioning and health.

Microbiota: the living microorganisms. Theatre of activity: microbial structures, metabolites, mobile genetic elements, relic DNA, and the environmental conditions they inhabit.

🪄 13. Why It Matters

By standardizing the term “microbiome,” we can:

• Compare studies across disciplines
• Build predictive models
• Design precise, ecosystem-friendly applications in medicine, farming, and climate solutions 🌍