¹H NMR-based metabolomics approach for understanding the fermentation behaviour ofBacillus licheniformis

Abstract

*Bacillus licheniformis has been found to be one of the persistent dominant microorganisms in Daqu, which is a traditional fermentation starter, and it has been used to intensify certain strains. To understand the impact of B. licheniformis on Daqu, the fermentation behaviour of B. licheniformis was studied using 1 H NMR-based non-targeted analysis and principal component analysis. During the fermentation, 53 compounds were identified. Among them, seven compounds were largely consumed and 17 metabolites were largely accumulated. The macromolecular starch and cellulose were degraded by B. licheniformis, and then utilized to produce acetic acid, lactic acid, propionic acid, succinate acid, etc. Principal component analysis was carried out to study the variations of analytes during the fermentation. Samples collected at each time point could be clearly discriminated and the biomarkers of each time point were defined. A variety of biochemical compounds (such as acetate, ethanol, lactate, pyruvate, malate, maltose and sucrose) were changed during the fermentation of B. licheniformis. The results are useful to reveal how and why B. licheniformis becomes dominant in Daqu, and to reveal its impact on the aroma formation of Daqu and its derived products.

Biochemical characterisation and dominance of different hydrolases in different types of Daqu – a Chinese industrial fermentation starter

“…In our study, protease activities yielded positive correlations with alcohols (ethanol and glycerol in light‐flavour type Daqu ; ethylene glycol in strong‐flavour type Daqu ), amino acids (threonine in light type Daqu ; s‐sulfocysteine in strong‐flavour type Daqu ), organic acids (succinate in light‐flavour type Daqu ), sugars and sugar derivatives (galactitol in in light‐flavour type Daqu ; glucitol, lactose and mannitol in strong‐flavour type Daqu ; glucose in sauce‐flavour‐type Daqu ), secondary metabolites and vitamin (guanidoacetate in strong‐flavour‐type Daqu ; ascorbate in sauce‐flavour‐type Daqu ). Among others, alcohols can polymerise with organic acids to form esters, which are the main flavour compounds in baijiu such as ethyl acetate, ethyl propionate, ethyl lactate and ethyl caproate . The heatmap showed that protease activities could influence the alcohols and organic acids in different flavour type of Daqu significantly and differently.…”

“…1 H NMR spectra were obtained by a Avance III 600‐MHz NMR spectrometer (Bruker, Karlsruhe, Germany) operating at a proton NMR frequency of 600.13 MHz and a temperature of 300 K, using a 5 mm PATXI probe. For each sample, 128 scans were recorded, with the following parameters: pulse sequence, NOESYGPPR1D (RD–90°– t 1 –90°– t m –90°‐acquisition); relaxation delay, 2.00 s; mixing time (for NOESY), 1.00 s; acquisition time, 2.28 s; number of steady states transients (dummy scans), 4; gradient pulse times, 1 ms; solvent suppression, presaturation with spoil gradient; spectral width, 7184 Hz; time domain size, 32 k) …”

Diversity and Function of Microbial Community in Chinese Strong-Flavor Baijiu Ecosystem: A Review

“…et al, 2015 ). B. licheniformis present in daqu were found to secrete amylase ( Li et al, 2014 ), protease, and some flavoring compound precursors ( Yan et al, 2015 ). A recent study showed that inoculation of B. licheniformis in daqu fermentation changed the entire microbial community structure and metabolic profile of daqu ( Wang P. et al, 2017 ).…”

Comparison of volatile and non-volatile metabolites in sufu produced with bacillus licheniformis by rapid fermentation

“…1 H-NMR spectra were obtained using an Avance 600-MHz NMR spectrometer (Bruker, Germany) operating at a proton NMR frequency of 600.13 MHz and a temperature of 300 K, using a 5 mm PATXI probe. For each sample, 128 scans were recorded, with the following parameters: pulse sequence, NOESYGPPR1D (RD-90°-t1-90°-tm-90°-acquisition); relaxation delay, 2.00 s; mixing time (for NOESY), 1.00 s; acquisition time, 2.28 s; number of steady-state transients (dummy scans), 4; gradient pulse times, 1 ms; solvent suppression, presaturation with spoil gradient; spectral width, 7184 Hz; time domain size, 32 k. [14] Metabolites were identified and quantified with Chenomx software (version 7.0; Chenomx Inc., Canada) with the reference of internal standard TSP. Each experiment was repeated three times.…”