Limits...
Genome-scale resources for Thermoanaerobacterium saccharolyticum.

Currie DH, Raman B, Gowen CM, Tschaplinski TJ, Land ML, Brown SD, Covalla SF, Klingeman DM, Yang ZK, Engle NL, Johnson CM, Rodriguez M, Shaw AJ, Kenealy WR, Lynd LR, Fong SS, Mielenz JR, Davison BH, Hogsett DA, Herring CD - BMC Syst Biol (2015)

Bottom Line: The genome consists of a 2.7 Mbp chromosome and a 110 Kbp megaplasmid.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.

View Article: PubMed Central - PubMed

Affiliation: Mascoma Corporation, 67 Etna Rd, 03766, Lebanon, NH, USA. Devin.H.Currie.GR@dartmouth.edu.

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.

No MeSH data available.


Related in: MedlinePlus

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4518999&req=5

Fig7: 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.

Mentions: We attempted to determine if any other knockout strain designs would maximize ethanol production at an optimal growth rate. The bilevel optimization algorithm OptKnock [46] was used to search for knockout strain designs that would improve production of ethanol by coupling it to improved growth rate. When OptKnock searches a maximum of 2 reaction knockouts, optimal ethanol production is predicted when knocking out LDH and HFS. When allowing three reactions knockouts, OptKnock finds a marginal improvement by deleting LDH, HFS, and glutamate dehydrogenase (GLUD). Removal of GLUD forces the cell to use the reactions glutamate synthase (GLUS) and glutamine synthetase (GLNS) in order to incorporate ammonium, spending an additional mole of ATP per mole of ammonium (Table 3). This inefficiency predicts only a marginal improvement in ethanol production of 0.3% over the ∆LDH-∆HFS strain (Figure 7).Table 3


Genome-scale resources for Thermoanaerobacterium saccharolyticum.

Currie DH, Raman B, Gowen CM, Tschaplinski TJ, Land ML, Brown SD, Covalla SF, Klingeman DM, Yang ZK, Engle NL, Johnson CM, Rodriguez M, Shaw AJ, Kenealy WR, Lynd LR, Fong SS, Mielenz JR, Davison BH, Hogsett DA, Herring CD - BMC Syst Biol (2015)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4518999&req=5

Fig7: 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.
Mentions: We attempted to determine if any other knockout strain designs would maximize ethanol production at an optimal growth rate. The bilevel optimization algorithm OptKnock [46] was used to search for knockout strain designs that would improve production of ethanol by coupling it to improved growth rate. When OptKnock searches a maximum of 2 reaction knockouts, optimal ethanol production is predicted when knocking out LDH and HFS. When allowing three reactions knockouts, OptKnock finds a marginal improvement by deleting LDH, HFS, and glutamate dehydrogenase (GLUD). Removal of GLUD forces the cell to use the reactions glutamate synthase (GLUS) and glutamine synthetase (GLNS) in order to incorporate ammonium, spending an additional mole of ATP per mole of ammonium (Table 3). This inefficiency predicts only a marginal improvement in ethanol production of 0.3% over the ∆LDH-∆HFS strain (Figure 7).Table 3

Bottom Line: The genome consists of a 2.7 Mbp chromosome and a 110 Kbp megaplasmid.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.

View Article: PubMed Central - PubMed

Affiliation: Mascoma Corporation, 67 Etna Rd, 03766, Lebanon, NH, USA. Devin.H.Currie.GR@dartmouth.edu.

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.

No MeSH data available.


Related in: MedlinePlus