Analyzing the Differences and Correlations Between Key Metabolites and Dominant Microorganisms in Different Regions of Daqu Based on Off-target Metabolomics and High-throughput Sequencing

Overview

Journal Heliyon

Specialty Social Sciences

Date 2024 Sep 17

PMID 39286152

Authors

Yijie Dai

Lei Yu

Jintao Ao

Rui Wang

Affiliations

Soon will be listed here.

Abstract

Daqu is usually produced in an open environment, which makes its quality unstable. The microbial community of Daqu largely determines its quality. Therefore, in order to improve the fermentation characteristics of Daqu, samples were collected from Jinsha County (MT1), Xishui County (MT2), and Maotai Town (MT3) in Guizhou Province to explore the microbial diversity of Daqu and its impact on Daqu's metabolites.Off-target metabolomics was used to detect metabolites, and high-throughput sequencing was used to detect microorganisms. Metabolomics results revealed that MT1 and MT2 had the highest relative fatty acid content, whereas MT3 had the highest organooxygen compound content. Principal component analysis and partial least squares discriminant analysis revealed significant differences in the metabolites among the three groups, followed by the identification of 33 differential metabolites (key metabolites) filtered using the criteria of variable importance in projection >1 and < 0.001. According to the microbiological results, was the dominant bacteria phylum in three samples. was the dominant class in MT1(26.84 %) and MT2(36.54 %), MT3 is (25.66 %). And was the dominant family per the abundance analysis, MTI was 24.32 %, MT2 and MT3 were 33.71 % and 24.40 % respectively. Three samples dominant fungi phylum were , and dominant fungi family were . showed a significant positive connection with various fatty acyls, according to correlation analyses between dominant microorganisms (genus level) and key metabolites. Fatty acyls and showed a positive correlation, but had the reverse relation. These findings offer a theoretical framework for future studies on the impact of metabolites on Baijiu quality during fermentation.

References

Yoon S, Ha S, Kwon S, Lim J, Kim Y, Seo H . Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol. 2016; 67(5):1613-1617. PMC: 5563544. DOI: 10.1099/ijsem.0.001755. View

Wani A, Ahmad S, Americo-Pinheiro J, Tizro N, Singh R . Building the taxonomic profile of the Riniaie Marwah hot spring of Kishtwar in Jammu and Kashmir: the first high-throughput sequencing-based metagenome study. Iran J Microbiol. 2023; 15(6):723-733. PMC: 10751607. DOI: 10.18502/ijm.v15i6.14132. View

Kopustinskiene D, Jakstas V, Savickas A, Bernatoniene J . Flavonoids as Anticancer Agents. Nutrients. 2020; 12(2). PMC: 7071196. DOI: 10.3390/nu12020457. View

Xiao C, Yang Y, Lu Z, Chai L, Zhang X, Wang S . Daqu microbiota exhibits species-specific and periodic succession features in Chinese baijiu fermentation process. Food Microbiol. 2021; 98:103766. DOI: 10.1016/j.fm.2021.103766. View

Mohapatra D, Patel A, Kar A, Deshpande S, Tripathi M . Effect of different processing conditions on proximate composition, anti-oxidants, anti-nutrients and amino acid profile of grain sorghum. Food Chem. 2018; 271:129-135. DOI: 10.1016/j.foodchem.2018.07.196. View

Wang Y, He Y, Liu Y, Wang D . Analyzing Volatile Compounds of Young and Mature Fruit by HS-SPME-GC-MS and rOAV. Foods. 2023; 12(1). PMC: 9818226. DOI: 10.3390/foods12010059. View

Muth M, Rothkotter S, Paprosch S, Schmid R, Schnitzlein K . Competition of Thermomyces lanuginosus lipase with its hydrolysis products at the oil-water interface. Colloids Surf B Biointerfaces. 2016; 149:280-287. DOI: 10.1016/j.colsurfb.2016.10.019. View

Gumel A, Annuar M . Thermomyces lanuginosus lipase-catalyzed synthesis of natural flavor esters in a continuous flow microreactor. 3 Biotech. 2017; 6(1):24. PMC: 4711288. DOI: 10.1007/s13205-015-0355-9. View

Iwata K, Maeda M, Kashiwagi Y, Maehashi K, Yoshikawa J . Isolation of Zygosaccharomyces siamensis kiy1 as a novel arabitol-producing yeast and its arabitol production. AMB Express. 2023; 13(1):76. PMC: 10349796. DOI: 10.1186/s13568-023-01581-4. View

10.

Zang X, Monge M, Fernandez F . Mass Spectrometry-Based Non-targeted Metabolic Profiling for Disease Detection: Recent Developments. Trends Analyt Chem. 2020; 118:158-169. PMC: 7430701. DOI: 10.1016/j.trac.2019.05.030. View

11.

Edison A, Colonna M, Gouveia G, Holderman N, Judge M, Shen X . NMR: Unique Strengths That Enhance Modern Metabolomics Research. Anal Chem. 2020; 93(1):478-499. DOI: 10.1021/acs.analchem.0c04414. View

12.

Gu F, Chen Y, Fang Y, Wu G, Tan L . Contribution of Bacillus Isolates to the Flavor Profiles of Vanilla Beans Assessed through Aroma Analysis and Chemometrics. Molecules. 2015; 20(10):18422-36. PMC: 6331939. DOI: 10.3390/molecules201018422. View

13.

Caporaso J, Kuczynski J, Stombaugh J, Bittinger K, Bushman F, Costello E . QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010; 7(5):335-6. PMC: 3156573. DOI: 10.1038/nmeth.f.303. View

14.

Du Y, Xin W, Xia Y, Zhu M, Qin J, Pan Z . Analysis of fermentation control factors on volatile compounds of primary microorganisms in Jiang-flavor Daqu. J Food Biochem. 2022; 46(10):e14277. DOI: 10.1111/jfbc.14277. View

15.

Yi Z, Jin Y, Xiao Y, Chen L, Tan L, Du A . Unraveling the Contribution of High Temperature Stage to Jiang-Flavor Daqu, a Liquor Starter for Production of Chinese Jiang-Flavor Baijiu, With Special Reference to Metatranscriptomics. Front Microbiol. 2019; 10:472. PMC: 6423406. DOI: 10.3389/fmicb.2019.00472. View

16.

Wani A, Akhtar N, Sher F, Navarrete A, Americo-Pinheiro J . Microbial adaptation to different environmental conditions: molecular perspective of evolved genetic and cellular systems. Arch Microbiol. 2022; 204(2):144. DOI: 10.1007/s00203-022-02757-5. View

17.

Du H, Wang X, Zhang Y, Xu Y . Exploring the impacts of raw materials and environments on the microbiota in Chinese Daqu starter. Int J Food Microbiol. 2019; 297:32-40. DOI: 10.1016/j.ijfoodmicro.2019.02.020. View

18.

Smith C, Want E, OMaille G, Abagyan R, Siuzdak G . XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006; 78(3):779-87. DOI: 10.1021/ac051437y. View

19.

Campbell J, Morgan-Kiss R, Cronan Jr J . A new Escherichia coli metabolic competency: growth on fatty acids by a novel anaerobic beta-oxidation pathway. Mol Microbiol. 2003; 47(3):793-805. DOI: 10.1046/j.1365-2958.2003.03341.x. View

20.

Wang Q, Wang C, Xiang X, Xu H, Han G . Analysis of microbial diversity and succession during fermentation using high-throughput sequencing technology. Eng Life Sci. 2022; 22(7):495-504. PMC: 9288988. DOI: 10.1002/elsc.202200015. View