Quantitative metaproteomics reveals composition and metabolism characteristics of microbial communities in Chinese liquor fermentation starters

Analysis of dominant microorganisms and core enzymes in Qingke Baijiu Daqu by high-throughput sequencing and proteomics

Daqu plays a crucial role in Qingke Baijiu production by providing essential raw materials, microorganisms, and enzymes for fermentation. In order to further investigate the impact of microorganisms and enzymes in Qingke Baijiu Daqu, the physicochemical properties, dominant microorganisms, as well as enzyme system composition of Qingke Baijiu Daqu were studied. The results revealed 8 dominant bacterial genera and 4 dominant fungal genera in Daqu. Through the analysis of metaproteomics, 422 enzyme proteins were identified, including 121 oxidoreductases, 108 transferases, 74 hydrolases, 33 lyases, 41 isomerases, 32 ligases and 13 translocase. The activity of key enzymes in the main metabolic pathways of Daqu was measured, and it was found that the activity of saccharifying enzyme was the highest, which was the primary hydrolase of Daqu. Finally, correlation analysis showed that Erwinia was positively correlated with saccharification power. This study can provide valuable insights for controlling the quality of Daqu and Qingke Baijiu.

Revealing the Differences in Microbial Community and Quality of High-Temperature Daqu in the Southern Sichuan-Northern Guizhou Region

High-temperature Daqu is crucial to Jiang-flavor Baijiu production in the Southern Sichuan-Northern Guizhou region of China. However, the complex interplay among microorganisms, enzymes, and metabolites in the Daqu from this region requires further investigation. This study compared four high-temperature Daqu samples from this region, analyzing their physicochemical properties, enzyme activities, volatile compounds, and microbial community composition and function, and exploring the influence of microorganisms on the saccharification and aroma-formation function of Daqu in combination with correlation analysis. The microbial communities in the Daqu samples exhibited functional redundancy, with Desmospora sp. 8437 being consistently dominant (3.6-7.3%). Members of the family Bacillaceae were the principal factors contributing to the differences in starch degradation capacity, protein degradation capacity, and pyrazine content among the Daqu samples, mainly through the amylases and proteases they produce. Kroppenstedtia spp. were principal factors causing the differences in aldehyde and ketone contents, primarily via the lipid degradation enzymes they synthesize. Overall, the bacterial community composition of Daqu greatly influenced its characteristics. This study provided a theoretical basis for understanding the diversity of high-temperature Daqu in the Southern Sichuan-Northern Guizhou region.

[Microbial metaproteomics--From sample processing to data acquisition and analysis]

Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.

Critical steps in an environmental metaproteomics workflow

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non-expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high-quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single-organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

Microbial metabolism during the thermophilic phase promotes the generation of aroma substances in nongxiangxing Daqu

The thermophilic phase of Daqu fermentation is considered the key period for aroma production in Daqu, but little is known about the changes in substances during this phase. In this study, we combined a metabolomics approach with high-throughput sequencing to analyze the metabolic profiles and identify metabolism-associated microbes during the thermophilic phase of Daqu fermentation. The results revealed that the metabolic sets after 5 and 9 days of fermentation in the thermophilic phase were similar, and several amino acid and biosynthesis-related metabolic pathways were significantly enriched. In addition, pyrazines and alkanes increased and esters decreased significantly after the thermophilic phase. The metabolism of substances during the thermophilic phase involved 38 genera, and the main metabolic pathways involved were glycolysis, TCA cycle, butyric acid metabolism, and five amino acid metabolic pathways. In summary, this study points in the direction for unravelling the mechanism of aroma production in Daqu.

Increasing taxonomic and functional characterization of host-microbiome interactions by DIA-PASEF metaproteomics

Introduction

Metaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and the functional activity of microbial communities, and their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification and quantification in metaproteomics. However, DDA-MS exhibits well-known limitations in terms of depth, sensitivity, and reproducibility. Consequently, methodological improvements are required to better characterize the protein landscape of microbiomes and their interactions with the host.

Methods

We present an optimized proteomic workflow that utilizes the information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS for comprehensive metaproteomic studies in complex fecal samples of mice.

Results and discussion

We show that implementing PASEF using a DDA acquisition scheme (DDA-PASEF) increased peptide quantification up to 5 times and reached higher accuracy and reproducibility compared to previously published classical DDA and data-independent acquisition (DIA) methods. Furthermore, we demonstrate that the combination of DIA, PASEF, and neuronal-network-based data analysis, was superior to DDA-PASEF in all mentioned parameters. Importantly, DIA-PASEF expanded the dynamic range towards low-abundant proteins and it doubled the quantification of proteins with unknown or uncharacterized functions. Compared to previous classical DDA metaproteomic studies, DIA-PASEF resulted in the quantification of up to 4 times more taxonomic units using 16 times less injected peptides and 4 times shorter chromatography gradients. Moreover, 131 additional functional pathways distributed across more and even uniquely identified taxa were profiled as revealed by a peptide-centric taxonomic-functional analysis. We tested our workflow on a validated preclinical mouse model of neuropathic pain to assess longitudinal changes in host-gut microbiome interactions associated with pain - an unexplored topic for metaproteomics. We uncovered the significant enrichment of two bacterial classes upon pain, and, in addition, the upregulation of metabolic activities previously linked to chronic pain as well as various hitherto unknown ones. Furthermore, our data revealed pain-associated dynamics of proteome complexes implicated in the crosstalk between the host immune system and the gut microbiome. In conclusion, the DIA-PASEF metaproteomic workflow presented here provides a stepping stone towards a deeper understanding of microbial ecosystems across the breadth of biomedical and biotechnological fields.

Metaproteomics profiling of the microbial communities in fermentation starters (Daqu) during multi-round production of Chinese liquor

Introduction

The special flavor and fragrance of Chinese liquor are closely related to microorganisms in the fermentation starter Daqu. The changes of microbial community can affect the stability of liquor yield and quality.

Methods

In this study, we used data-independent acquisition mass spectrometry (DIA-MS) for cohort study of the microbial communities of a total of 42 Daqu samples in six production cycles at different times of a year. The DIA MS data were searched against a protein database constructed by metagenomic sequencing.

Results

The microbial composition and its changes across production cycles were revealed. Functional analysis of the differential proteins was carried out and the metabolic pathways related to the differential proteins were explored. These metabolic pathways were related to the saccharification process in liquor fermentation and the synthesis of secondary metabolites to form the unique flavor and aroma in the Chinese liquor.

Discussion

We expect that the metaproteome profiling of Daqu from different production cycles will serve as a guide for the control of fermentation process of Chinese liquor in the future.

Metaproteomic investigation of enzyme profile in daqu used for the production of Nongxiangxing baijiu

Enzymes and microbiota in daqu are essential for the brewing of Nongxiangxing baijiu. Uncover the key enzymes and functional strains in daqu is beneficial to improve the flavor and quality of Nongxiangxing baijiu. In this study, metaproteome technology was employed to determine the enzyme profiles in Nongxiangxing daqu, and strains with high saccharification activity were screened and identified. 933 proteins were identified in daqu, of which 463 belonged to enzymes, including 140 oxidoreductases, 98 transferases, 91 hydrolases, 49 ligases, 41 lyases and 27 isomerases, and hydrolase is the enzyme with the highest abundance in baijiu brewing. Among hydrolases, a total of 36 carbohydrate metabolism-related enzymes (CMEs) were identified, and 12 of them were key enzymes related to glycoside hydrolysis. Four major glycoside hydrolysis enzymes glucoamylase (EC 3.2.1.3), glucan 1,4-alpha-glucosidase (EC 3.2.1.3), glucanase (EC 3.2.1.-) and β-glucosidase (EC 3.2.1.21) were revealed, and their sources were Byssochlamys spectabilis, Lichtheimia ramosa and Thermoascus aurantiacus, respectively. Then, strains Aspergillus A2, A3, A7, Lichtheimia L1, L4, L5, and Saccharomycopsis S2, S4, S6 with high saccharifying enzyme-producing capacity were screened through culture-dependent approach. Resents presented in this study can further reveal the enzyme profiles and identify the main functional strains in daqu, which can provide theoretical support for the brewing of Nongxiangxing baijiu.