Key sub-community dynamics of medium-chain carboxylate production

Abstract

Background: The carboxylate platform is a promising technology for substituting petrochemicals in the provision of specific platform chemicals and liquid fuels. It includes the chain elongation process that exploits reverse β-oxidation to elongate short-chain fatty acids and forms the more valuable medium-chain variants. The pH value influences this process through multiple mechanisms and is central to effective product formation. Its influence on the microbiome dynamics was investigated during anaerobic fermentation of maize silage by combining flow cytometric short interval monitoring, cell sorting and 16S rRNA gene amplicon sequencing.

Results: Caproate and caprylate titres of up to 6.12 g L^-1 and 1.83 g L^-1, respectively, were achieved in a continuous stirred-tank reactor operated for 241 days. Caproate production was optimal at pH 5.5 and connected to lactate-based chain elongation, while caprylate production was optimal at pH 6.25 and linked to ethanol utilisation. Flow cytometry recorded 31 sub-communities with cell abundances varying over 89 time points. It revealed a highly dynamic community, whereas the sequencing analysis displayed a mostly unchanged core community. Eight key sub-communities were linked to caproate or caprylate production (r_S > | ± 0.7|). Amongst other insights, sorting and subsequently sequencing these sub-communities revealed the central role of Bifidobacterium and Olsenella, two genera of lactic acid bacteria that drove chain elongation by providing additional lactate, serving as electron donor.

Conclusions: High-titre medium-chain fatty acid production in a well-established reactor design is possible using complex substrate without the addition of external electron donors. This will greatly ease scaling and profitable implementation of the process. The pH value influenced the substrate utilisation and product spectrum by shaping the microbial community. Flow cytometric single cell analysis enabled fast, short interval analysis of this community and was coupled with 16S rRNA gene amplicon sequencing to reveal the major role of lactate-producing bacteria.

Keywords: 16S rRNA gene sequencing; Anaerobic fermentation; Caproic acid; Caprylic acid; Flow cytometry; MCFA; Microbial chain elongation; Microbial community; Process monitoring; Single cell analytics.

Fig. 1

Compilation of the microbial community dynamics along the eight experimental stages in a and the process parameters in b and c over the course of 241 days. a A flowCyBar representing the frequency distribution and the development of the relative abundances of all 31 sub-communities (gating strategy in Additional file 1: S8). Blue fields mark negative and red ones positive deviations from the mean relative abundance of the respective sub-community. The duration of the experimental stages is colour-coded: 1—start-up , 2—TAN-shortage , 3—consolidation , 4—pH 5.75 , 5—pH 6.0 , 6—pH 6.25 , 7—pH 6.5 and 8—pH 7 . b Shows the pH value and the gas production . c Shows the concentrations of the major compounds involved in the chain elongation process: lactate , ethanol , acetate , butyrate ●, caproate ■ and caprylate ▲

Fig. 2

flowCHIC analysis of the 89 cytometric fingerprints revealed clustering of similar microbial communities over time in an NMDS plot. The sampling points are assigned to their corresponding experimental stages. The dispersion ellipses mark the standard deviation of samples within the specific groups around the weighted average of the group. The NMDS plot is based on a stress value of 0.155

Fig. 3

Spearman’s rank order correlation was applied to test for links between the relative cell abundance values of all 31 sub-communities and caproate (C6) and caprylate (C8) concentrations. The correlation approach comprised the three periods: (2a) the initial caproate and caprylate increase from day 6 to day 45 , (2b) the second caproate increase from day 57 to day 111 and (2c) the second caprylate increase from day 132 to day 185 were analysed. Correlation strength (r_S), significance (p) and corrected significance (p_BH) are provided in Additional file 1: Table 1 S11. Sub-communities chosen for sorting are marked in bold and additionally provided in Additional file 1: Table 3 S11. The strong correlations (r_S > | ± 0.7|) these sub-communities were chosen for are marked with a white dot

Fig. 4

Whole community based on 16S rRNA gene amplicon sequencing at eleven time points along the experiment (day 1, 8, 38, 106, 132, 160, 181, 185, 202, 220, 241). The relative OUT abundances are assigned to time points with the respective caproate ■ and caprylate ▲ concentrations and the eight experimental stages (see Fig. 1). The taxonomic composition is resolved to the genus level applying an abundance threshold of 0.1%. OTUs with abundances below 1% are summarised to “Others”. Details about library preparation, sequencing and sequence data analysis are given in Additional file 1: S13

Fig. 5

Taxonomic composition of eight sub-communities sorted at different time points due to their strong correlations with the target carboxylates (Fig. 3). Every sub-community composition is given with its relative cell abundance ● and the sub-communities mean relative cell abundance . The detailed relative cell abundance development is given in Additional file 1: S12. The relative OTU and cell abundances are assigned to time points with the respective caproate ■ and caprylate ▲ concentrations and eight experimental stages (see Fig. 1). The taxonomic composition is resolved to the genus level applying an abundance threshold of 0.1%. OTUs with abundances below 1% are summarised to “Others”. Details about library preparation, sequencing and sequence data analysis are given in Additional file 1: S13