Low-abundance populations distinguish microbiome performance in plant cell wall deconstruction

Abstract

Background: Plant cell walls are interwoven structures recalcitrant to degradation. Native and adapted microbiomes can be particularly effective at plant cell wall deconstruction. Although most understanding of biological cell wall deconstruction has been obtained from isolates, cultivated microbiomes that break down cell walls have emerged as new sources for biotechnologically relevant microbes and enzymes. These microbiomes provide a unique resource to identify key interacting functional microbial groups and to guide the design of specialized synthetic microbial communities.

Results: To establish a system assessing comparative microbiome performance, parallel microbiomes were cultivated on sorghum (Sorghum bicolor L. Moench) from compost inocula. Biomass loss and biochemical assays indicated that these microbiomes diverged in their ability to deconstruct biomass. Network reconstructions from gene expression dynamics identified key groups and potential interactions within the adapted sorghum-degrading communities, including Actinotalea, Filomicrobium, and Gemmatimonadetes populations. Functional analysis demonstrated that the microbiomes proceeded through successive stages that are linked to enzymes that deconstruct plant cell wall polymers. The combination of network and functional analysis highlighted the importance of cellulose-degrading Actinobacteria in differentiating the performance of these microbiomes.

Conclusions: The two-tier cultivation of compost-derived microbiomes on sorghum led to the establishment of microbiomes for which community structure and performance could be assessed. The work reinforces the observation that subtle differences in community composition and the genomic content of strains may lead to significant differences in community performance. Video Abstract.

Keywords: Biomass deconstruction; Lignocellulose degradation; Microbiome; Transcriptomic network.

Fig. 1

Experimental overview. Green waste compost samples were collected and used to inoculate M9TE media mixed with sorghum biomass and samples were incubated for 8 weeks (Tier 1). After 56 days of incubation, each of the three communities from Tier 1 (comm1, comm2, comm3) were used to inoculate a second series of flasks (Tier 2) with either wild-type (WT) or stacked bmr6 × bmr12 mutant (SM) sorghum. Tier 2 samples were incubated for 2 weeks. Changes in biomass content, enzymatic activities, lignin content, and bacterial community dynamics and activity were assessed using the suit of tools depicted in the image. MAGs metagenome-assembled genomes, NIMS nanostructure-initiator mass spectrometry, DNS dinitrosalicylic acid, ABSL acetyl bromide soluble lignin

Fig. 2

A Ordination plot for microbial communities growing on sorghum and analyzed using amplicon sequencing. B Dry mass. C NIMS results. Both correspond to end-point analyses after a 14-day incubation. D–E DNS analysis for CMCase and xylanase activity of adapted communities inoculated with SM and WT sorghum. F Lignin content from small-scale biomass analysis. The icons within the barplots indicate the Tier1 community used for inoculation of the Tier 2 experiment. Circle – comm1, triangle – comm2, and square – comm3. Error bars indicate standard deviation (n = 3). Bars labeled with the same letter are not significantly different (ANOVA and Tukey test; p > 0.05)

Fig. 3

A Community composition for Tier 2 adapted communities and corresponding Tier 1 source inoculum. Dendrograms were calculated based on a Jaccard distance matrix. B Relative proportion of dominant communities calculated from TPM-normalized coverage data. Only populations with a relative proportion above 0.08 are shown in the figure. LCBD is the local contribution to community dispersion calculated with the R package adespatial. C Ordination plot depicting metagenome composition of Tier 2 adapted communities and corresponding Tier 1 source inoculum. The ellipses were calculated around barycenters with a confidence level of 0.99 using the stat_conf_ellipse function in ggpubr v.0.2.4. D Gene proportion per MAG for selected GHs

Fig. 4

A RMT-based network reconstructed based on the 14-day metatranscriptome profiles of SDM1 and SDM3 samples. Only significant links with a correlation above 0.9 were retained in the network. B Illustration of putative population interactions derived from the RMT network. MAGs connected to the central four MAGs were retained only if connecting by 50 or more links (arbitrary value). Numbers on top of the lines connecting nodes indicate the number of detected links between MAGs. C Differential expression patterns for genes with a log₂-fold change higher than 1 and lower than –1 with a p value < 0.01

Fig. 5

Top panel shows the average patterns of expression for each of the categories, A pectin, B hemicellulose, and C cellulose. The assignments are based on enzymatic activities identified for carbohydrate-active enzymes that are involved in lignocellulose deconstruction (www.cazy.org). The bottom panels depict the different groups of lignocellulose degrading bacterial populations and corresponding gene expression patterns, D pectin, E hemicellulose, and F cellulose. Stars indicate the time points at which gene expression was significantly higher than in the opposite treatment (p < 0.01, log₂fold > 1). GH43 was classified as pectin-degrading enzymes, though this family also cleaves arabinoxylan bonds in hemicellulose [54]

Fig. 6

Schematic representation of the expression patterns for aromatic-degrading genes. The heatmaps are colored based on normalized counts for the targeted genes. Stars indicate the time points at which gene expression was significantly higher than in the opposite treatment (p < 0.01, log₂fold > 1)