Microbiome Study Guide

📖 Core Concepts Microbiome definition 1988: A characteristic microbial community in a defined habitat with distinct physicochemical properties, including microbes and their “theatre of activity.” 2020: A dynamic, interactive micro‑ecosystem occupying a defined habitat, integrating into larger macro‑ecosystems and influencing host health. Microbiota vs. Microbiome vs. Metagenome Microbiota = all living members of a community. Microbiome = microbiota + structural elements, metabolites, mobile genetic elements, relic DNA, and the surrounding environment. Metagenome = the collection of all genes/genomes from the microbiota only. Holobiont – the host plus its associated microbiota considered as a single evolutionary unit. Dysbiosis vs. Eubiosis – Low‑diversity, unstable (disease‑linked) community vs. high‑diversity, stable (health‑linked) community. Phylosymbiosis – More closely related host species tend to harbor more similar microbiomes (strong in mammals, weak in many non‑mammals). Core taxonomic groups – Bacteria, archaea, fungi, algae, small protists; viruses, phages, plasmids are controversial. Adaptive strategies – Oligotrophs: thrive in nutrient‑poor settings, slow growth. Copiotrophs: thrive in nutrient‑rich settings, rapid growth. Multi‑omics – Metagenomics, metatranscriptomics, metaproteomics, metabolomics, and culturomics together reveal composition and activity. --- 📌 Must Remember Cell count: An adult human harbors $10^{13}$ bacterial cells, roughly equal to human cells. Microbiome scope – microbes + genomes + metabolites + environmental context. Dysbiosis = low diversity, variable microbiota → disease; Eubiosis = high diversity, stable → health. Phylosymbiosis is pronounced in mammals; often absent in insects and many non‑mammalian vertebrates. Interaction types – Positive (mutualism, commensalism, synergism) vs. Negative (competition, predation, antagonism, amensalism). TMA → TMAO pathway: Gut microbes convert choline/lecithin/carnitine → trimethylamine (TMA); liver oxidizes TMA → trimethylamine‑N‑oxide (TMAO), a pro‑atherogenic molecule. Data scale – >150 000 metagenome‑assembled genomes (MAGs) recovered from human microbiomes (2019). Dark matter – 40‑70 % of annotated genes have unknown function; >70 % of sequences in poorly studied ecosystems lack cultured representatives. --- 🔄 Key Processes Metagenomic sequencing workflow Sample collection → DNA extraction → library preparation → high‑throughput sequencing → quality filtering → assembly → binning → MAG reconstruction → functional annotation. Co‑occurrence network construction Normalize OTU/ASV table → calculate pairwise correlations (e.g., SparCC) → apply significance threshold → build graph (nodes = taxa, edges = significant associations) → identify hubs/keystones → design validation experiments. Flux Balance Analysis (FBA) for community modeling Reconstruct genome‑scale metabolic model for each taxon → define community objective (e.g., biomass) → set exchange constraints → solve linear programming to predict fluxes across taxa. Culturomics pipeline Inoculate sample on a wide array of media under varied conditions → rapid colony imaging → MALDI‑TOF or 16S sequencing for ID → archive isolates for functional assays. Gnotobiotic mouse experiment Raise germ‑free mice → colonize with defined microbial consortium → control diet → monitor host phenotypes (immune, metabolic) → compare to conventional mice. --- 🔍 Key Comparisons Microbiome vs. Microbiota vs. Metagenome Microbiome: microbes + genes + metabolites + environment. Microbiota: only the living microbes. Metagenome: only the genetic material of the microbiota. Oligotroph vs. Copiotroph Oligotroph: low nutrients → slow growth, low metabolic rate. Copiotroph: nutrient‑rich → rapid growth, high metabolic activity. Dysbiosis vs. Eubiosis Dysbiosis: low diversity, unstable, disease‑associated. Eubiosis: high diversity, stable, health‑associated. Positive vs. Negative microbial interactions Positive: mutualism, commensalism, synergism. Negative: competition, predation, antagonism, amensalism. Marine vs. Human microbiome composition Marine: bacteria, archaea, fungi, protists, viruses; strong host‑specificity (e.g., corals, sponges). Human: bacteria, archaea, fungi, protists, viruses (micro‑animals excluded); distinct body‑site niches (skin, gut, oral, vaginal, etc.). --- ⚠️ Common Misunderstandings “Microbiome = only living microbes.” – It also includes metabolites, DNA remnants, and the physicochemical environment. Equating metagenome with microbiome. – Metagenome lacks the non‑genetic components (metabolites, structural elements). All microbes are pathogens. – The majority are ecologically essential and health‑promoting. Co‑occurrence = direct interaction. – Correlation does not prove causation; experimental validation is needed. Viruses are always part of the microbiome. – Their inclusion is still debated in the literature. --- 🧠 Mental Models / Intuition Ecosystem analogy: Think of the microbiome as a forest—microbes are trees, metabolites are nutrients/water, the host tissue is the soil, and environmental conditions (pH, O₂) are the climate. Holobiont as a single organism: The host’s genome and the collective microbial genome function like a single “super‑genome” guiding evolution. Phylosymbiosis as family resemblance: Just as cousins share more facial features, closely related hosts share more similar microbial communities. --- 🚩 Exceptions & Edge Cases Viruses & plasmids – Their status as microbiome members is still controversial. Phylosymbiosis variability – Strong in mammals; often absent in insects and many non‑mammalian vertebrates. Functional “dark matter” – 40‑70 % of genes lack known function; functional inference may be limited. Uncultured majority – >70 % of sequences in many ecosystems cannot be linked to cultured taxa, even with advanced culturomics. --- 📍 When to Use Which | Question / Goal | Best Method | |-----------------|-------------| | Rapid taxonomic snapshot | Metabarcoding (16S/ITS amplicon) | | Whole‑community functional potential | Metagenomics (shotgun DNA) | | Which genes are actively expressed? | Metatranscriptomics | | Which pathways are actually running? | Metaproteomics | | What small‑molecule metabolites are present? | Metabolomics | | Need isolates for mechanistic work | Culturomics (high‑throughput culturing) | | Build hypothesis about microbial interactions | Co‑occurrence networks (followed by experiments) | | Predict metabolic fluxes in a community | Flux Balance Analysis / Community Metabolic Modeling | | Test causal diet‑microbe effects in mammals | Gnotobiotic mouse models | | Study host‑microbe signaling in a tractable system | Squid‑Vibrio symbiosis or bobtail squid model | --- 👀 Patterns to Recognize From pathogen‑centric to ecological view – Questions will emphasize mutualism, competition, and community resilience. Nutrient level ↔ microbial strategy – Low‑nutrient samples → oligotroph dominance; rich media → copiotroph bloom. Stress‑induced dysbiosis – Elevated temperature/acidification → coral bleaching → shift to opportunistic microbes. Diversity ↔ health – High α‑diversity often signals eubiosis; sudden drops signal dysbiosis. Network hubs = potential keystone taxa – Highly connected nodes in co‑occurrence graphs often drive community function. --- 🗂️ Exam Traps “Microbiome = only living microbes” – Wrong; the definition explicitly includes metabolites, DNA, and environmental context. Choosing metagenomics when only species list is needed – Overkill; metabarcoding is faster and cheaper. Assuming co‑occurrence edges prove causation – They are statistical, not mechanistic, and can be driven by shared habitat preferences. Attributing TMAO risk solely to microbes – Forget the host liver oxidation step; both microbial production and host metabolism are required. Believing all marine animal microbiomes behave like human gut microbiomes – Marine hosts have distinct taxa (e.g., symbiotic algae in corals) and environmental drivers. ---