Genome‐scale metabolic model integrated with RNAseq data to identify metabolic states of <i>Clostridium thermocellum</i>

Gowen, Christopher M.; Fong, Stephen S.

doi:10.1002/biot.201000084

Cited by 50 publications

(24 citation statements)

References 20 publications

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“…The concentrations of extracted RNA were determined by spectrophotometric determination (Nanodrop ND-1000; Thermo Fisher Scientific Inc.). The purified RNA (100 ng) was mixed with a random hexamer primer mix, deoxynucleoside triphosphates (dNTPs), RNase inhibitor, and reverse transcriptase (Tetro cDNA synthesis kit; Bioline USA Inc., Taunton, MA) and incubated (25°C for 10 min, 45°C for 30 min, and 85°C for 5 min) to obtain cDNA, according to the manufacturer's instructions (65). cDNA from three cheese-making trials was pooled, dried using a vacuum centrifuge (SpeedVac Concentrator SPD121P; Thermo Scientific), and used as the template for 16S metagenetics.…”

Section: Methodsmentioning

confidence: 99%

Dietary Fibers and Protective Lactobacilli Drive Burrata Cheese Microbiome

Minervini

Conte

Nobile

et al. 2017

Appl Environ Microbiol

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This study was aimed at improving the functional attributes and shelf life of burrata cheese by using protective lactobacilli (Lactobacillus plantarum LPAL and Lactobacillus rhamnosus LRB), fructooligosaccharides, and inulin. Six burrata cheeses were made using (i) the traditional protocol (control), (ii) the addition of 0.5% fructooligosaccharides and inulin (DF cheese), (iii) protective lactobacilli in milk alone (PL cheese), (iv) protective lactobacilli in milk and governing liquid (2PL cheese), (v) protective lactobacilli in milk and dietary fibers (DF_PL cheese), and (vi) protective lactobacilli in milk and governing liquid and dietary fibers (DF_2PL cheese). As expected, DF, DF_PL, and DF_2PL cheeses showed 1.5% of total fibers. Burrata cheeses produced by adding protective lactobacilli only in milk (PL and DF_PL cheeses) showed the lowest acidification during cheese making and storage. Lactic and acetic acids and ethanol were found at the lowest concentrations in these samples. Analyses of cultivable microbiota and the microbiome showed that protective lactobacilli reduced the house microbiota components (e.g., Streptococcus thermophilus, Lactococcus lactis, and Leuconostoc lactis) during cheese making and storage. Protective lactobacilli slowed the growth of staphylococci, coliforms, and Pseudomonas spp., especially in early storage. According to the different microbiome assemblies, burrata samples differed in peptide profiles and the levels of free amino acids. As shown by a sensory analysis, the addition of protective lactobacilli in milk improved the flavor and increased the shelf life of burrata cheese. In comparison to cheeses made using protective cultures only in milk, the shelf lives of those containing cultures also in the governing liquid were not further prolonged and they received lower acceptability scores by the panelists.IMPORTANCE This study provides more in-depth knowledge of the microbiome of burrata cheese and the set-up for a novel biotechnology using prebiotic dietary fibers and protective probiotic Lactobacillus plantarum LPAL and Lactobacillus rhamnosus LRB in milk. The biotechnology proposed in this study should be considered a useful tool to improve the functional value of burrata cheese. The use of protective lactobacilli in milk enhanced the flavor formation and shelf life of burrata cheese.

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Section: Methodsmentioning

confidence: 99%

Dietary Fibers and Protective Lactobacilli Drive Burrata Cheese Microbiome

Minervini

Conte

Nobile

et al. 2017

Appl Environ Microbiol

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“…Clo1313_1483 is annotated as encoding a predicted pyrroloquinoline quinone-associated protein, and previous work suggests that it is expressed in C. thermocellum strain ATCC27405 [32, 33]; however, its function in C. thermocellum strain DSM1313 is unknown.…”

Section: Resultsmentioning

confidence: 99%

Expressing the Thermoanaerobacterium saccharolyticum pforA in engineered Clostridium thermocellum improves ethanol production

Hon

Holwerda

Worthen

et al. 2018

Biotechnol Biofuels

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BackgroundClostridium thermocellum has been the subject of multiple metabolic engineering strategies to improve its ability to ferment cellulose to ethanol, with varying degrees of success. For ethanol production in C. thermocellum, the conversion of pyruvate to acetyl-CoA is catalyzed primarily by the pyruvate ferredoxin oxidoreductase (PFOR) pathway. Thermoanaerobacterium saccharolyticum, which was previously engineered to produce ethanol of high yield (> 80%) and titer (70 g/L), also uses a pyruvate ferredoxin oxidoreductase, pforA, for ethanol production.ResultsHere, we introduced the T. saccharolyticum pforA and ferredoxin into C. thermocellum. The introduction of pforA resulted in significant improvements to ethanol yield and titer in C. thermocellum grown on 50 g/L of cellobiose, but only when four other T. saccharolyticum genes (adhA, nfnA, nfnB, and adhEG544D) were also present. T. saccharolyticum ferredoxin did not have any observable impact on ethanol production. The improvement to ethanol production was sustained even when all annotated native C. thermocellum pfor genes were deleted. On high cellulose concentrations, the maximum ethanol titer achieved by this engineered C. thermocellum strain from 100 g/L Avicel was 25 g/L, compared to 22 g/L for the reference strain, LL1319 (adhA(Tsc)-nfnAB(Tsc)-adhEG544D (Tsc)) under similar conditions. In addition, we also observed that deletion of the C. thermocellum pfor4 results in a significant decrease in isobutanol production.ConclusionsHere, we demonstrate that the pforA gene can improve ethanol production in C. thermocellum as part of the T. saccharolyticum pyruvate-to-ethanol pathway. In our previous strain, high-yield (~ 75% of theoretical) ethanol production could be achieved with at most 20 g/L substrate. In this strain, high-yield ethanol production can be achieved up to 50 g/L substrate. Furthermore, the introduction of pforA increased the maximum titer by 14%.Electronic supplementary materialThe online version of this article (10.1186/s13068-018-1245-2) contains supplementary material, which is available to authorized users.

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“…The previous GEM of C. thermocellum , i SR432 [26, 27], roughly included the cellulosome on top of the dry cell weight (DCW) reaction in a condition-independent manner. However, it is well documented that the cellulosome fraction of total DCW changes from 2 to 20 % when growing on cellobiose versus cellulose, respectively [35], and the protein composition of the cellulosome itself changes when growing on alternative substrates [43, 62, 67, 95, 96].…”

Section: Methodsmentioning

confidence: 99%

“…A repertoire of metabolic pathway analysis tools based on flux balance analysis and elementary mode analysis has recently been developed to analyze these GEMs and have been extensively reviewed [22–25]. A C. thermocellum GEM i SR432 has been constructed previously [26], used as a scaffold for transcriptomic constraints [27], and structurally compared to a number of other Clostridial GEMs [28]. While useful, recent results highlight several limitations of i SR432, e.g., (i) there have been many advancements in the knowledge of C. thermocellum atypical glycolysis [29], pentose phosphate pathway [30], and redox metabolism redundancies [31, 32] which were not included in the original model, (ii) the model was constructed for the strain ATCC 27405, but not DSM 1313 [33], which is the genetically tractable parent strain used in metabolic engineering strategies [34], (iii) the model included a cellulosome term but it was not variable with respect to carbon source, which has been shown to vary substantially [35], and (iv) the model did not accurately predict certain cellular phenotypes like ethanol production [26].…”

Section: Introductionmentioning

confidence: 99%

Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome

Thompson

Dahal

Garcia

et al. 2016

Biotechnol Biofuels

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BackgroundClostridium thermocellum is a gram-positive thermophile that can directly convert lignocellulosic material into biofuels. The metabolism of C. thermocellum contains many branches and redundancies which limit biofuel production, and typical genetic techniques are time-consuming. Further, the genome sequence of a genetically tractable strain C. thermocellum DSM 1313 has been recently sequenced and annotated. Therefore, developing a comprehensive, predictive, genome-scale metabolic model of DSM 1313 is desired for elucidating its complex phenotypes and facilitating model-guided metabolic engineering.ResultsWe constructed a genome-scale metabolic model iAT601 for DSM 1313 using the KEGG database as a scaffold and an extensive literature review and bioinformatic analysis for model refinement. Next, we used several sets of experimental data to train the model, e.g., estimation of the ATP requirement for growth-associated maintenance (13.5 mmol ATP/g DCW/h) and cellulosome synthesis (57 mmol ATP/g cellulosome/h). Using our tuned model, we investigated the effect of cellodextrin lengths on cell yields, and could predict in silico experimentally observed differences in cell yield based on which cellodextrin species is assimilated. We further employed our tuned model to analyze the experimentally observed differences in fermentation profiles (i.e., the ethanol to acetate ratio) between cellobiose- and cellulose-grown cultures and infer regulatory mechanisms to explain the phenotypic differences. Finally, we used the model to design over 250 genetic modification strategies with the potential to optimize ethanol production, 6155 for hydrogen production, and 28 for isobutanol production.ConclusionsOur developed genome-scale model iAT601 is capable of accurately predicting complex cellular phenotypes under a variety of conditions and serves as a high-quality platform for model-guided strain design and metabolic engineering to produce industrial biofuels and chemicals of interest.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-016-0607-x) contains supplementary material, which is available to authorized users.

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Genome‐scale metabolic model integrated with RNAseq data to identify metabolic states of Clostridium thermocellum

Abstract: We would like to add the information that the RNA sequencing data can be assessed at the Sequence Read Archive (SRA) http://www.ncbi.nlm.nih.gov/sra under accession number SRA022779.

Cited by 50 publications

References 20 publications

Dietary Fibers and Protective Lactobacilli Drive Burrata Cheese Microbiome

Dietary Fibers and Protective Lactobacilli Drive Burrata Cheese Microbiome

Expressing the Thermoanaerobacterium saccharolyticum pforA in engineered Clostridium thermocellum improves ethanol production

Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome

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