The microbiota and metabolome dynamics and their interactions modulate solid-state fermentation process and enhance clean recycling of brewers' spent grain

Abstract

The massive yield of brewers' spent grain (BSG) waste inevitably threaten environmental health. Here, solid-state fermentation (SSF) technology featuring multi-strain (MS) inoculation and high-throughput sequencing technology were employed to facilitate the sustainable and clean recycling of BSG waste while revealing the associated underlying microbiological and metabolic mechanisms. MS inoculation displayed a lower pH value (3.91 vs. 4.12) and neutral detergent fiber content (446.24 vs. 476.23 g/kg DM), a higher levels of lactic acid (86.64 vs. 33.07 g/kg DM), acetic acid (6.13 vs. 4.87 g/kg DM), propionic acid (2.78 vs. 2.18 g/kg DM) and crude protein (307.5 vs. 289.15 g/kg DM) than those in the control group. Moreover, MS inoculation inhibited the formation of non-protein-N and ammonia-N, and spoilage microorganism resuscitation, while enhanced substrate preservation. Microbiologically, during the SSF, the group treated with MS inoculation exhibited an increase in the relative abundance of Leuconostoc (0.58%∼6.60%), Weissella (6.22%∼15.42%), Enterococcus (3.15%∼9.08%), Bacillus (17.63%∼31.29%), Lactobacillus (12.89%∼8.29%), Pseudoalteromonas (12.87%∼16.29%), and a decrease in the relative abundance of Acinetobacter (0.79%∼0.02%) and Enterobacteriaceae (0.78%∼0.24%). Metabolically, starch and sucrose metabolism, arginine and proline metabolism, and phenylalanine metabolism significantly influenced the quality of extruded BSG fermented by MS during SSF. The examination of the correlation between the microbiota, metabolites, and fermentation parameters revealed that complex interactions between microbes and the environment factors impact metabolite production. Collectively, inoculating with MS improved fermentation quality and stability, facilitated the clean recycling of BSG, which is linked to complex interactions among microbes, the environment factors and metabolite production.

Keywords: Brewer’s spent grain waste; environmental factor analysis; metabolome-microbiome interactions; multi-strain inoculation; solidstate fermentation technology.

FIGURE 1

(A) The community dissimilarities in different treatments and fermentation time, calculated by unweighted UniFrac distances, with coordinates calculated by principal coordinates analysis (PCoA). (B) The community dissimilarities in different treatments and fermentation time, calculated by weighted UniFrac distances, with coordinates calculated by principal coordinates analysis (PCoA). (C) The variations of community alpha-diversities (Chao1 richness and Shannon index). Con, control; MS, multi-strain.

FIGURE 2

(A) Relative abundance of bacteria at the genus level. (B) Relative abundance of bacteria at the top 25 species level. (C) Linear discriminant analysis effect sizes (LEfSe) analysis of bacterial communities at different fermentation time points in Con group. (D) Linear discriminant analysis effect sizes (LEfSe) analysis of bacterial communities at different fermentation time points in MS group. Con, control; MS, multi-strain.

FIGURE 3

(A) Dynamics of bacterial functional profiles during SSF processes analyzed by PICRUSt in level 1 metabolic pathways; (B) Dynamics of bacterial functional profiles during SSF processes analyzed by PICRUSt in level 3 KEGG ortholog functional predictions of the relative abundances of the top 20 metabolic functions. Con, control; MS, multi-strain.

FIGURE 4

(A) Relative compositions of the main metabolites for different fermentation time. (B) Top 20 metabolites at different fermentation times. Con, control; MS, multi-strain.

FIGURE 5

(A) A scatter plot of the top 20 distinct metabolites was identified by applying variable importance projection (VIP). (B) Enrichment analysis of pathways. Number (dot), number of metabolites annotated to pathways. Con, control; MS, multi-strain.

FIGURE 6

Relationships among the microbiota, metabolites and fermentation quality. (A) Relationships among the microbiota, metabolites, and fermentation parameters in Con group during SSF. (B) Relationships among the microbiota, metabolites, and fermentation parameters in MS group during SSF. **0.001 < P < 0.01, *0.01 < P < 0.05, respectively. Con, control; MS, multi-strain.

FIGURE 7

Functional pathway changes of integrated microbiome and metabolomics. KEGG level 2 was selected based on significantly different metabolic data. Using 16S data to predict the abundance of KEGG levels 2 and 3. CP, crude protein; Non-protein-N, non-protein nitrogen; Ammonia-N, ammonia nitrogen; WSC, water-soluble carbohydrates; NDF, neutral detergent fiber; LA, lactic acid; AA, acetic acid; PA, propionic acid.