Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics

Abstract

Background Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. Methods We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. Results We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. Conclusions The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems.

Soil Prokaryotic and Fungal Biome Structures Associated with Crop Disease Status across the Japan Archipelago

“…Profiling of soil microbial biodiversity was performed by targeting archaea, bacteria, and fungi. For the amplification of the 16S rRNA V4 region of archaea and bacteria (prokaryotes), the set of the forward primer 515f (5'-GTG YCA GCM GCC GCG GTA A -3') and the reverse primer 806rB (5'-GGA CTA CNV GGG TWT CTA AT -3') were used as described elsewhere (54). The primers were fused with 3-6-mer Ns for improved Illumina sequencing quality and Illumina sequencing primers.…”

“…The ramp rate through the thermal cycles was set to 1 °C/sec to prevent the generation of chimeric sequences. In the PCR, we added five artificial DNA sequence variants with different concentrations (i.e., standard DNA gradients; 1.0 ×10 -4 , 5.0 ×10 -5 , 2.0 ×10 -5 , 1.0 ×10 -5 , and 5.0 ×10 -6 nM; Table S1) to the PCR master mix solution as detailed elsewhere (54). By comparing the number of sequencing reads between the artificial standard DNA and real prokaryotic DNA, the concentration of prokaryotic 16S rRNA genes in template DNA samples were calibrated (54).…”

“…For fungal OTUs, putative functional groups (e.g., "plant pathogen") were inferred using the program FungalTraits (80). The estimation of DNA concentrations of the prokaryotic 16S rRNA and fungal ITS regions was performed, respectively, based on the calibration with the standard DNA gradients (artificial DNA variants introduced to the PCR master mix solutions) using the bioinformatic pipeline detailed elsewhere (54).…”

Order among chaos: high throughput MYCroplanters can distinguish interacting drivers of host infection in a highly stochastic system

“…Inferring cause-effect relationships from microbiome data requires contending with the high degree of dimensionality and stochasticity in microbiome structure and function [11][12][13] .…”

Basins of attraction of microbiome structure and soil ecosystem functions

“…While classic studies targeting freshwater and terrestrial biomes have explored basins of attraction based on simple characterization of community states (e.g., tree cover percentages), recent technical advances in microbial community (microbiome) research have come to provide opportunities for deepening our knowledge of biological community stability (Amor et al, 2020; Costea et al, 2017; Faust et al, 2015; Shaw et al, 2019; Toju et al, 2018; Zaneveld et al, 2017). Based on amplicon and shotgun sequencing technologies, large datasets of microbial species/taxonomic compositions have been made available, providing a basis for exploring reproducible states in microbiome community structure (Amor et al, 2020; H Fujita et al, 2023; Hayashi et al, 2024). Such high-throughput DNA sequencing studies in medicine, for example, have shown that human individuals can be classified into three or four semi-discrete clusters in terms of their intestinal microbiome compositions (Arumugam et al, 2011; Wu et al, 2011) [see also (Jeffery et al, 2012; Knights et al, 2014)].…”