Microbial Community Composition and Activity Dynamics

Microbial communities comprise diverse assemblages of bacteria, archaea, fungi and other microscopic life forms that together drive ecosystem processes from nutrient cycling to plant health. Community composition describes the identity and relative abundance of taxa, while activity dynamics refer to the temporal and spatial patterns of metabolic function, viability and dormancy within that assemblage. Advances in molecular and imaging methodologies now allow discrimination between total community membership (DNA-based profiles) and the subset of organisms that are metabolically active or viable (RNA-based profiling, staining techniques or chemical viability markers). Understanding both composition and activity is essential for linking microbial diversity to ecosystem functioning, predicting responses to environmental change and designing microbiome-informed interventions in agriculture, water management and human health. Key challenges include distinguishing living from dead biomass, accounting for relic DNA from lysed cells, resolving dormant seed banks and detecting fine-scale temporal shifts against a background of spatial heterogeneity. Integrative approaches that combine sequencing, chemical labelling and ecological modelling are beginning to reveal the drivers of community assembly and the roles of rare or transient taxa in biogeochemical cycles.

Research from Nature Portfolio

Comparative analyses of DNA-, propidium monoazide- and RNA-based 16S rRNA sequencing in water samples have demonstrated that RNA-based methods more effectively enrich for live bacteria, whereas DNA- and PMA-treated profiles tend to overestimate richness by including dead or dormant cells. This work has clarified methodological biases in aquatic microbiome surveys and underscored the importance of targeting rRNA molecules to infer current metabolic states. In terrestrial ecosystems, parallel DNA and RNA sequencing of oak rhizosphere soils revealed that potentially active bacterial communities can mirror total richness but differ markedly in relative abundance patterns. These findings show that dominant taxa are not always the most active and that low-abundance organisms may exert disproportionate functional influence, informing the selection of microbial inoculants for forest restoration.

Research from all publishers

Systematic benchmarking of RNA-based amplicon sequencing in synthetic and environmental samples has uncovered limitations in using 16S-RNA-seq to quantify activity in complex communities, with minimal compositional distinction between DNA and RNA libraries in real-world settings. A novel high-throughput approach, Revived Amplicon Sequence Variant Monitoring (RAM), has been introduced to track dormant microorganisms over time, detecting far greater diversity of inactive taxa than gene function prediction alone. In soil systems, intensive spatial sampling coupled with relic DNA removal has exposed hidden temporal dynamics; eliminating extracellular DNA enhances detection of short-term shifts in prokaryotic and fungal communities, revealing predictable responses of specific taxa to changing soil conditions and emphasising the masking effect of relic DNA on ecological inference.

Microbial Community Composition and Activity Dynamics publication trend

The graph below shows the total number of articles in microbial community composition and activity dynamics across all publications each year (not limited to Nature Index journals).

Technical terms

16S rRNA amplicon sequencing: A high-throughput method that targets the 16S ribosomal RNA gene to profile bacterial and archaeal community composition.

RNA-based sequencing: Profiling of ribonucleic acid molecules to capture the metabolically active fraction of a microbiome.

Relic DNA: Extracellular or cell-free DNA originating from dead organisms that can bias diversity estimates.

Propidium monoazide (PMA): A DNA-intercalating dye used to selectively remove signals from dead cells in downstream sequencing.

Dormancy: A reversible state of low metabolic activity adopted by microorganisms to survive unfavourable conditions.

References

1. RNA-based amplicon sequencing is ineffective in measuring metabolic activity in environmental microbial communities. Microbiome (2023).
2. Revived Amplicon Sequence Variants Monitoring in Closed Systems Identifies More Dormant Microorganisms. Microorganisms (2023).
3. Comparison of DNA-, PMA-, and RNA-based 16S rRNA Illumina sequencing for detection of live bacteria in water. Scientific Reports (2017).
4. Effects of Spatial Variability and Relic DNA Removal on the Detection of Temporal Dynamics in Soil Microbial Communities. mBio (2020).
5. Metabarcoding reveals that rhizospheric microbiota of Quercus pyrenaica is composed by a relatively small number of bacterial taxa highly abundant. Scientific Reports (2019).