Genome-scale resources for Thermoanaerobacterium saccharolyticum

Abstract

Background: Thermoanaerobacterium saccharolyticum is a hemicellulose-degrading thermophilic anaerobe that was previously engineered to produce ethanol at high yield. A major project was undertaken to develop this organism into an industrial biocatalyst, but the lack of genome information and resources were recognized early on as a key limitation.

Results: Here we present a set of genome-scale resources to enable the systems level investigation and development of this potentially important industrial organism. Resources include a complete genome sequence for strain JW/SL-YS485, a genome-scale reconstruction of metabolism, tiled microarray data showing transcription units, mRNA expression data from 71 different growth conditions or timepoints and GC/MS-based metabolite analysis data from 42 different conditions or timepoints. Growth conditions include hemicellulose hydrolysate, the inhibitors HMF, furfural, diamide, and ethanol, as well as high levels of cellulose, xylose, cellobiose or maltodextrin. The genome consists of a 2.7 Mbp chromosome and a 110 Kbp megaplasmid. An active prophage was also detected, and the expression levels of CRISPR genes were observed to increase in association with those of the phage. Hemicellulose hydrolysate elicited a response of carbohydrate transport and catabolism genes, as well as poorly characterized genes suggesting a redox challenge. In some conditions, a time series of combined transcription and metabolite measurements were made to allow careful study of microbial physiology under process conditions. As a demonstration of the potential utility of the metabolic reconstruction, the OptKnock algorithm was used to predict a set of gene knockouts that maximize growth-coupled ethanol production. The predictions validated intuitive strain designs and matched previous experimental results.

Conclusion: These data will be a useful asset for efforts to develop T. saccharolyticum for efficient industrial production of biofuels. The resources presented herein may also be useful on a comparative basis for development of other lignocellulose degrading microbes, such as Clostridium thermocellum.

Figure 1

A comparison between the two versions of the 16 s mRNA found in T. saccharolyticum. A) an alignment and consensus sequence for a heterogeneous segment of the five 16S ribosomal components found in T. saccharolyticum. B) Mfold prediction of the structure of the shorter 16S mRNA [66]. C) Mfold prediction of the structure of the longer 16S mRNA.

Figure 2

Time points between 5 and 60 minutes post-shock with hemicellulose extract. The horizontal axis represents log₂ of the control xylose + acetate expression level (mRNA:gDNA ratio), while the vertical axis represents the hemicellulose extract-treated expression level. All data are the average of duplicate experiments with the exception of the 5 minutes post hemicellulose extract shock which is in triplicate.

Figure 3

Heat map of hierarchical clustering of genes that change in expression level upon the addition of washate with a P value of <0.01 and with a log ₂ ratio >1.0 in at least one time point. The range of log2 mRNA:gDNA ratios is given in the color key.

Figure 4

Example of data from Nimbegen tiled microarrays (bottom) showing transcription units correlated to open reading frames (top).

Figure 5

Inhibitor shock. A) Plot showing the addition of HMF and furfural in culture supernatants and the temporary disruption of growth. B) Plot showing the levels of intracellular citric acid and hydroxymethylfurfurol, as well as the average of all other metabolites. C) A heat map of a hierarchical clustering of the concentration of all monitored intracellular metabolites over the course of the 4 hour experiment.

Figure 6

Phenotypic phase planes for T. saccharolyticum high-ethanol knock out strains. The maximum growth rate is shown as a surface over a range of fluxes for glucose uptake and ethanol production. The wild-type surface (A) shows the maximum growth rate occurring equally across a wide range of ethanol production rates, while the phase planes for the Δldh-pta strain (B) and the Δldh-hfs strain (C) demonstrate that the potential solution space is trimmed in a way that couples maximum growth to high ethanol yield.

Figure 7

Growth envelope for various ethanol strain designs during growth on glucose. ΔLDH-ΔHFS and ΔHFS-ΔLDH-ΔGLUD were both identified by OptKnock as being optimal designs for ethanol production.