Startseite Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
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Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing

  • Shu Fang EMAIL logo , Chuanxiang Wang und Juan Yan
Veröffentlicht/Copyright: 23. Februar 2023
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Open Life Sciences
Aus der Zeitschrift Open Life Sciences Band 18 Heft 1

Abstract

This study aims to explore the prokaryotic microbial community structures and diversity in pit mud from different depths, and provide a theoretical basis for the liquor production and further study of pit mud. The fermented pit muds of strong-flavor liquor from Yun distillery were taken as samples. The high-throughput sequencing approach, followed by bioinformatics analyses, was used to compare the differences in the prokaryotic microbial community between pit walls and bottom represented by samples. A total of 31 bacteria phyla and 2 archaea phyla were detected. The dominant phyla in YJ-S, YJ-Z, and YJ-X (sample name) were Proteobacteria and Firmicutes, while the dominant genera in them were Acinetobacter, Aminobacterium, and Lactobacillus. YJ-Z and YJ-X were the closest in species diversity. In species richness analysis, YJ-X was the highest, followed by YJ-Z, and YJ-S was the lowest; in species uniformity analysis, YJ-S was the highest, followed by YJ-Z, and YJ-X was the lowest. The function predicted by 16S rRNA genome showed that prokaryotic microbial function in pit mud was mainly concentrated in “Carbohydrate transport and metabolism” and “Amino acid transport and metabolism.” Significant differences in prokaryotic microbial community and gene function prediction between pit walls and bottom were found in YJ-S, YJ-Z, and YJ-X (p < 0.05).

Keywords: strong-flavor liquor; high-throughput sequencing; pit mud; prokaryotic microbial community; gene function prediction

1 Introduction

China is a country with a long history of traditional liquor (baijiu) production. Baijiu is one of the world’s six major distilled liquors [1]. Liquor is mainly produced in the way of solid-state fermentation of certain crops, such as sorghum, corn, wheat, rice, and glutinous rice [2]. During the fermentation process of liquor, liquor has gradually developed with 12 kinds of different styles in the land of China, due to the differences in brewing raw materials, brewing technology, ecological environment, saccharification, distillation, and other factors in different regions of China, such as sauce-flavored, strong-flavored, light-flavored, rice-flavored, and medicinal flavors [3,4].

Among these liquors, strong-flavor liquor, which is produced by distilling fermented grains (mainly sorghum), has the strong fragrance, sweetness, and lasting aftertaste [1,2]. Strong-flavor liquor is the most popular type of liquor in China, accounting for more than half of the liquor consumption market [5,6]. During the production process of solid-state fermentation of traditional Chinese liquor, a bucket-shaped pit must be excavated underground in advance. The inner wall and bottom of the pit are coated with special fermented clay, which is called pit mud [7]. Pit mud consists of cooked sticky loess, humus soil, cooked bean cake powder, bran koji powder, yellow water, caproic acid bacteria solution, fragrant mud, yeast solution, ammonium phosphate, urea, and wine tail. The above materials are mixed and stirred in a certain proportion, then it is formed after a complex fermentation process in mother-pit. Pit mud is essential for the fermentation and production of strong-flavor liquor, due to the presence of many microorganisms, such as bacteria, archaea, and fungi [6,7]. These microorganisms, which can produce aromatic compounds for the fermentation of strong-flavor liquor, rely strongly on the suitable microecological environment provided by the pit mud [8]. Many studies have shown that the flavor of liquor is closely related to the microorganisms used for fermenting liquor raw materials in the pit mud [9]. The microbial community structure of pit mud is quite complex and closely related to the age and geographical location of the pit [4]. Different microorganisms ferment the raw materials and produce various enzymes and aromatic compounds in the liquor, forming various types of liquors with distinctive flavors [9,10].

In order to increase the output of liquor, liquor-production enterprises generally build larger pits. The pit provides a closed environment for microbial fermentation in pit mud and Daqu. The temperature in the environment is 25–32°C, the humidity is 40–45%, and the pH is 3.0–5.0 [5]. Due to the differences in the microecological environment at different locations of the pit, such as pit walls and bottom, there are differences in the microbial community abundance at different locations in the pit, which leads to differences in the quality of the fermented liquor, and affects the quality of liquor production; such studies have rarely been reported [4].

In this study, Anhui Yun distillery Group Co., Ltd, located in Ma’anshan city, east of Anhui province, China, established some pits with 2.85 m in length, 2.65 m in width, and 2.2 m in depth for the production of strong-flavor liquor in 2003, the pit muds of the pits were cultivated in the same microorganism enriched mother-pit. Since the quality of liquor produced in these pits was not exactly the same, the pit producing the best quality liquor was selected as our research subject by consulting the distillery technician. High-throughput sequencing technologies have developed rapidly and emerged as an effective tool, both for the microbial diversity analysis of complex samples and studying metabolic pathways in recent years [11–14]. The technique of high-throughput sequencing has also been recently used for microbial community structure analysis of Chinese liquor [2,4–6,10,15]. In prokaryotic cells, the 16S rRNA genes are composed of variable regions and conserved regions, the former of which can be sequenced to identify between microbes for bacterial phylogenetic and taxonomic identification [11]. In this study, the variables V3 and V4 regions of the 16S rRNA genes were used to determine the prokaryotic microbial diversity in pit walls and bottom. Our study provides a theoretical basis for the daily maintenance of pit mud, the specifications of new pits construction, and the quality of liquor production.

2 Materials and methods

2.1 Sample collection

After the selected pit was opened and the distiller’s grains were taken out, the pit muds in 24 positions (1 g/each sample position) were taken vertically and equidistant from the fermentation pit in upper (0.2 m in depth), middle (1.2 m in depth), and bottom layer (2.2 m in depth), respectively (Figure 1). The pit muds in the same layer (eight positions/each sample layer) were transferred into a sterile bag, sealed, named as YJ-S, YJ-Z, YJ-X, and then stored at −80°C until further analysis.

Figure 1 Sampling points of mud in the pit: (a) sampling diagram and (b) sampling in the pit.
Figure 1

Sampling points of mud in the pit: (a) sampling diagram and (b) sampling in the pit.

2.2 DNA extraction

Pit mud samples needed to be transformed into pellets through pretreatment, then the resulted pellets from the three samples were performed using centrifuge following the method described by Ding et al. [16]. The pellets were subjected to DNA extraction using the commercial Mag-Bind Soil DNA Kit (Omega Bio-tek Corporation, USA) according to the manufacturer’s instruction. The concentration and purity of extracted DNAs were measured using 0.8% (w/v) agarose gel electrophoresis and a micro-spectrophotometer, respectively. The resulted DNA was diluted to a DNA concentration of 1 ng/μL and subsequently used as a template for PCR to amplify 16S rRNA genes.

2.3 PCR amplification

The V3 and V4 regions of 16S rRNA genes were amplified using the universal forward primer 341F(5′-CCCTACACGACGCTCTTCCGATCTG(barcode)CCTACGGGNGGCWGCAG-3′) and the reverse primer 805R (5′-GACTGGAGTTCCTTGGCACCCGAGAATTCCAGACTACHVGGGTATCTAATCC-3′) with a set of six-nucleotide barcodes, the primers were modified and synthesized by Sangon Biotech Company (Shanghai, China) [17,18]. The detailed PCR procedures were modified according to a method described by Tang et al. [19]. The PCR amplification conditions were as follows: 94°C for 3 min; 94°C for 30 s, 45°C for 20 s, 65°C for 30 s, five cycles; 94°C for 20 s, 55°C for 20 s, 72°C for 30 s, 20 cycles and 72°C for 5 min. The amplified PCR products were detected by 2% agarose gel electrophoresis and purified using the commercial QIAquick Gel Extraction Kit (QIAGEN Corporation, Germany). After gel purification, the PCR product was quantified using the commercial Qubit ssDNA Assay Kit (Life Technologies, USA). Finally, the purified 16S rRNA gene amplicons were to construct DNA library using TruSeq DNA PCR-Free Sample Preparation Kit (Illumina Inc, USA) and pair-end sequenced on an Illumina HiSeq2500 PE250 platform at Sangon Biotech Company (Shanghai, China).

2.4 Genome data processing

The original image data file obtained by high-throughput sequencing was transformed into the original sequence by CASAVA base recognition analysis, which was called raw reads. Cutadapt software (V1.2.1) [20] was used to remove the primer splice sequence of raw reads, pear software (V0.9.6) [21] was used to splice sequences, Prinseq software (V0.20.4) [22] was used to filter sequences, Usearch software (V5.2.236) [23] was used to remove chimeras and nonspecific amplification sequences, and finally, the qualified sequences were obtained.

2.5 Bioinformatics analysis of the 16S rRNA genome sequences

Usearch software contains a suite of software tools that were used to cluster all effective tags to the operational taxonomic units (OTU) based on 97% identity of the sequences. The taxonomy of each OTU representative sequence was assigned using the Ribosomal Database Project (http://rdp.cme.msu.edu/misc/resources.jsp) classifier with a minimum bootstrap threshold of 80%. Subsequently, taxonomic information at the six-level classification level domain, phylum, class, order, family, and genus was obtained [24]. Based on the results of the OTU, a Venn diagram was generated using R software (V3.2) [25] to show the number of OTU of microorganisms that were shared in the three samples and the variance among them. The Bray–Curtis algorithm was used to construct a sample clustering tree and species abundance histogram based on the abundance of different sample species. Mothur software (V1.2.7) [26] was used to calculate the five kinds of alpha diversity index, such as Shannon, ACE, Chao1, Coverage, and Simpson; R software was used to draw dilution curve and rank abundance curve. Heat maps were drawn based on unifrac distance matrix between different samples; PICRUSt software (V1.1.4) [27] was used to predict the metagenomic function of the three samples, and abundance heat maps based on the COG function and the KEGG metabolic pathway were both drawn. STAMP software (V2.1.3) [28] was used to compare with the differences of species abundance and gene functional abundance among the three samples, the 25 ranks with the lowest p value were listed in the figures, when p was less than 0.05, the species classification was marked in red. In statistical analysis, significance was determined at 95% confidence interval (p = 0.05).

2.6 Nucleic acid sequence accession number

The 16S rRNA genome sequences obtained by high-throughput sequencing in this study had been submitted to NCBI database under the accession number: SRR15900602, SRR15900603, and SRR15900777.

3 Results

3.1 OTU clustering of microorganisms in pit mud

After high-throughput sequencing of PCR amplification products, a total of 208,366 raw reads were obtained from the three samples, and 175,150 qualified reads were obtained after processing (Table 1). These 16S rRNA gene sequences were clustered into 11,806 OTUs for species analysis with 97% similarity. Among them, YJ-S had 58,282 qualified reads and a cluster of 5,071 OTUs, YJ-Z had 49,669 qualified reads and a cluster of 3,563 OTUs, YJ-X had 67,199 qualified reads and a cluster of 3,172 OTUs (Table 1). The Venn diagram can be used to count the number of shared and unique OTUs in samples. There were 464 OTUs in both YJ-S and YJ-Z, accounting for 3.93% of the total OTUs; 391 OTUs in both YJ-S and YJ-X, accounting for 3.31% of the total OTUs; and 413 OTUs in both YJ-Z and YJ-X, accounting for 3.50% of the total OTUs. There were 277 OTUs in YJ-S, YJ-Z, and YJ-X, accounting for 2.35% of the total OTUs (Figure 2). To sum up, YJ-S and YJ-Z had the highest similarity in OTU number composition, followed by YJ-Z and YJ-X, finally YJ-S and YJ-X.

Table 1

Sample sequencing information and alpha diversity of microorganism in pit mud

Sample Raw reads Qualified reads OTU Alpha diversity index
Shannon ACE Chao1 Coverage Simpson
YJ-S 73,608 58,282 5,071 3.65 90410.65 34646.12 0.93 0.17
YJ-Z 59,100 49,669 3,563 3.66 33985.33 23344.33 0.94 0.12
YJ-X 75,658 67,199 3,172 4.38 60690.04 22079.02 0.97 0.08
Figure 2 Venn diagram of number of OTUs of microorganism in pit mud.
Figure 2

Venn diagram of number of OTUs of microorganism in pit mud.

3.2 Taxonomic annotation of microorganisms in pit mud

Both bacteria and archaea were detected in YJ-S, YJ-Z, and YJ-X. The abundance of bacteria and that of archaea in YJ-S were 99 and 1%, respectively, in YJ-Z 98% and 2%, respectively, and in YJ-X 99% and 0.7% ,respectively. Among the three samples, bacterial abundance was absolutely dominant compared with archaea. At the phylum level, a total of 33 phyla including Firmicutes, Proteobacteria, Bacteroidetes, Synergistetes, and Actinobacteria were identified in the 16S rRNA gene sequences of the three samples, and the abundances were significantly different (Figure 3; Figures S1–S3 in Supplementary 1, p < 0.05). In addition, Euryarchaeota and Crenarchaeota were both identified in all three samples, belonging to the Archaea phylum, but only the abundance of Euryarchaeota was significantly different (Figure 3; Figures S1–S3 in Supplementary 1, p < 0.05). Firmicutes was the prominent phylum in YJ-Z and YJ-X with the most abundances, while it was Proteobacteria in YJ-S (p < 0.05). At the genus level, the distribution of microbial communities in YJ-S, YJ-Z, and YJ-X was also different, and the abundance of different genera was also different. Having abundance levels of more than 1%, the main microorganisms in YJ-S were Acinetobacter (43.53%), Lysinibacillus (14.96%), Caryophanon (7.05%), Petrimonas (4.29%), Proteiniphilum (4.13%), Clostridium IV (1.79%), Solibacillus (1.5%), and Aminobacterium (1.03%), and 8.18% of the microorganisms were unclassified genus (Table 2). The main microorganisms in YJ-Z were Aminobacterium (25.04%), Clostridium IV (21.39%), Caloramator (17.01%), Lactobacillus (2.49%), Semimentibacter (1.93%), Olsenella (1.91%), Tissierella (1.6%), Syntrophimonas (1.48%), Garciella (1.25%), Syntrophaceticus (1.09%), and unclassified genus (12.75%) (Table 2). The main microorganisms in YJ-X were Lactobacillus (6.49%), Bifidobacterium (4.71%), Bacteroides (4.16%), Faecalibacterium (3.88%), Clostridium sensu stricto (3.83%), Proteiniphilum (3.55%), Syntrophomonas (3.41%), Petrimonas (2.83%), Clostridium IV (2.63%), Blautia (2.06%), Aminobacterium (1.75%), Alistipes (1.32%), Roseburia (1.14%), Burkholderia (1.07%), and unclassified genus (35.54%) (Table 2). To sum up, the numbers of genera that have an abundance of 1% or higher in YJ-S, YJ-Z, and YJ-X were 9, 10, and 14, respectively, each making up 78.28, 75.19, and 42.83% of the total abundance in the respective sample (Figure 4, Table 2). The abundances of genera mentioned above were significantly different in YJ-S, YJ-Z, and YJ-X (p < 0.05), and the p value is shown in Supplementary 2.

Figure 3 Phylum level distribution of microorganism population present in pit mud. Note: () Firmicutes; () Proteobacteria; () Bacteroidetes; () Synergistetes; () Actinobacteria; () Euryarchaeota; () unclassified; () Chloroflexi; () Verrucomicrobia; () Acidobacteria; () Tenericutes; () Cloacimonetes; () Planctomycetes; () Armatimonadetes; () Spirochaetes; () Gemmatimonadetes; () Candidatus Saccharibacteria; () Deinococcus-Thermus; () Atribacteria; () Chlamydiae; () Parcubacteria; () Nitrospirae; () Ignavibacteriae; () Lentisphaerae; () candidate division WPS-1; () Aminicenantes; () BRC1; () candidate division WPS-2; () Latescibacteria; () Thermotogae; () Elusimicrobia; () Microgenomates; () Hydrogenedentes; () Crenarchaeota.
Figure 3

Phylum level distribution of microorganism population present in pit mud. Note: () Firmicutes; () Proteobacteria; () Bacteroidetes; () Synergistetes; () Actinobacteria; () Euryarchaeota; () unclassified; () Chloroflexi; () Verrucomicrobia; () Acidobacteria; () Tenericutes; () Cloacimonetes; () Planctomycetes; () Armatimonadetes; () Spirochaetes; () Gemmatimonadetes; () Candidatus Saccharibacteria; () Deinococcus-Thermus; () Atribacteria; () Chlamydiae; () Parcubacteria; () Nitrospirae; () Ignavibacteriae; () Lentisphaerae; () candidate division WPS-1; () Aminicenantes; () BRC1; () candidate division WPS-2; () Latescibacteria; () Thermotogae; () Elusimicrobia; () Microgenomates; () Hydrogenedentes; () Crenarchaeota.

Table 2

Abundance (%) of major genera (≥1%) from each pit mud sample

Genus YJ-S (%) Genus YJ-Z (%) Genus YJ-X (%)
Acinetobacter 43.53 Aminobacterium 25.04 Lactobacillus 6.49
Lysinibacillus 14.96 Clostridium IV 21.39 Bifidobacterium 4.71
Caryophanon 7.05 Caloramator 17.01 Bacteroides 4.16
Petrimonas 4.29 Lactobacillus 2.49 Faecalibacterium 3.88
Proteiniphilum 4.13 Sedimentibacter 1.93 Clostridium sensu stricto 3.83
Clostridium IV 1.79 Olsenella 1.91 Proteiniphilum 3.55
Solibacillus 1.50 Tissierella 1.60 Syntrophomonas 3.41
Aminobacterium 1.03 Syntrophomonas 1.48 Petrimonas 2.83
Garciella 1.25 Clostridium IV 2.63
Syntrophaceticus 1.09 Blautia 2.06
Aminobacterium 1.75
Alistipes 1.32
Roseburia 1.14
Burkholderia 1.07
Figure 4 Genus level distribution of microorganism population present in pit mud. Note: () unclassified; () Acinetobacter; () Aminobacterium; () Clostridium IV; () Caloramator; () Lysinibacillus; () Lactobacillus; () Proteiniphilum; () Petrimonas; () Caryophanon; () Syntrophomonas; () Clostridium sensu stricto; () Bifidobacterium; () Bacteroides; () Faecalibacterium; () Sedimentibacter; () Tissierella; () Blautia; () Olsenella; () Garciella; () Alistipes; () Syntrophaceticus; () Clostridium III; () Solibacillus; () Roseburia; () Burkholderia; () Clostridium XlVa; () Lutispora; () Methanoculleus; () Gemmiger; () Methanomassiliicoccus; () Rummeliibacillus; () Bacillus; () Sporanaerobacter; () Collinsella; () Anaerostipes; () Methanobacterium; () Oscillibacter; () Lachnospiracea_incertae_sedis; () Tepidimicrobium; () Intestinimonas; () Barnesiella; () Parabacteroides; () Klebsiella; () Fusicatenibacter; () Ralstonia; () Pelotomaculum; () Tepidanaerobacter; () Anaerovorax; () other.
Figure 4

Genus level distribution of microorganism population present in pit mud. Note: () unclassified; () Acinetobacter; () Aminobacterium; () Clostridium IV; () Caloramator; () Lysinibacillus; () Lactobacillus; () Proteiniphilum; () Petrimonas; () Caryophanon; () Syntrophomonas; () Clostridium sensu stricto; () Bifidobacterium; () Bacteroides; () Faecalibacterium; () Sedimentibacter; () Tissierella; () Blautia; () Olsenella; () Garciella; () Alistipes; () Syntrophaceticus; () Clostridium III; () Solibacillus; () Roseburia; () Burkholderia; () Clostridium XlVa; () Lutispora; () Methanoculleus; () Gemmiger; () Methanomassiliicoccus; () Rummeliibacillus; () Bacillus; () Sporanaerobacter; () Collinsella; () Anaerostipes; () Methanobacterium; () Oscillibacter; () Lachnospiracea_incertae_sedis; () Tepidimicrobium; () Intestinimonas; () Barnesiella; () Parabacteroides; () Klebsiella; () Fusicatenibacter; () Ralstonia; () Pelotomaculum; () Tepidanaerobacter; () Anaerovorax; () other.

3.3 Microbial diversity in the pit mud

In the analysis of alpha diversity, diversity index, dilution curve, and rank abundance curve are important tools for the analysis of species diversity in different samples. The results from the Shannon index showed YJ-X (4.38) > YJ-Z (3.66) > YJ-S (3.65), and Simpson index showed YJ-X (0.08) < YJ-Z (0.12) < YJ-S (0.17) (Table 1). In microbial diversity between pit walls and bottom, the sample with the richest abundance was YJ-X, followed by YJ-Z and finally YJ-S (Figure 5). The rank abundance curve showed the species evenness: the highest being YJ-S, followed by YJ-Z and YJ-X (Figure 6). In the analysis of beta diversity, the weighted unifrac distance matrix between the two samples was used to draw a heat map for measuring the difference of the two samples. YJ-Z and YJ-X were the most similar in microbial diversity, while YJ-S and YJ-Z were the most different (Figure 7).

Figure 5 Dilution cure analysis of samples.
Figure 5

Dilution cure analysis of samples.

Figure 6 Rank abundance cure analysis of samples.
Figure 6

Rank abundance cure analysis of samples.

Figure 7 Distance heatmap of samples.
Figure 7

Distance heatmap of samples.

3.4 Gene functional diversity of microorganisms in pit mud

In COG analysis, the gene functional abundance ranked the top five in the three samples: “General function prediction only” followed by “Amino acid transport and metabolism,” “Function unknown,” “Transcription” and “Translation, ribosomal structure and biogenesis” in YJ-S; “General function prediction only” followed by “Carbohydrate transport and metabolism,” “Amino acid transport and metabolism,” “Transcription” and “function unknown” in YJ-Z; “General function prediction only” followed by “Carbohydrate transport and metabolism,” “Transcription,” “Amino acid transport and metabolism” and “Function unknown” in YJ-X (Figure 8, Table 3). And the abundances of gene functional were significantly different (Figures S4–S6 in Supplementary 1, p < 0.05).

Figure 8 Heatmap of gene functional predictions of samples based on COG.
Figure 8

Heatmap of gene functional predictions of samples based on COG.

Table 3

Abundance of top five COG classification of microorganism in pit mud

YJ-S Abundance YJ-Z Abundance YJ-X Abundance
General function prediction only 1265413 General function prediction only 2064464 General function prediction only 1620397
Amino acid transport and metabolism 922062 Carbohydrate transport and metabolism 1615582 Carbohydrate transport and metabolism 1271587
Function unknown 881872 Amino acid transport and metabolism 1518171 Transcription 1194181
Transcription 790853 Transcription 1426763 Amino acid transport and metabolism 1158436
Translation, ribosomal structure and biogenesis 698184 Function unknown 1420056 Function unknown 1095976

In the analysis of KEGG metabolic pathway, the gene functional abundance ranks in the top five: in YJ-S it was “Amino acid Metabolism” followed by “Membrane Transport,” “Carbohydrate Metabolism,” “Replication and Repair” and “Energy Metabolism.” In YJ-Z it was “Membrane Transport” followed by “Carbohydrate Metabolism,” “Amino Acid Metabolism,” “Replication and Repair” and “Translation.” In YJ-X it was “Membrane Transport” followed by “Carbohydrate Metabolism,” “Amino Acid Metabolism,” “Replication and Repair” and “Translation” (Figure 9, Table 4). And the abundances of gene function were significantly different (Figures S7–S9 in Supplementary 1, p < 0.05).

Figure 9 Heatmap of gene functional predictions of samples based on KEGG.
Figure 9

Heatmap of gene functional predictions of samples based on KEGG.

Table 4

Abundance of top five KEGG metabolic pathways of microorganism in pit mud

YJ-S Abundance YJ-Z Abundance YJ-X Abundance
Amino acid metabolism 1245752 Membrane transport 2156445 Membrane transport 1765974
Membrane transport 1234878 Carbohydrate metabolism 1584091 Carbohydrate metabolism 2080906
Carbohydrate metabolism 1077914 Amino acid metabolism 1871697 Amino acid metabolism 1360333
Replication and repair 883471 Replication and repair 1677025 Replication and repair 1211644
Energy metabolism 654153 Translation 1142174 Translation 803222

4 Discussion

In recent years, microbial diversity in different types of fermented foods, such as fermented seafood, kimchi, traditional fermented mustard, traditional Myanmar fermented tea leaves, and traditional fermented vegetables, were all investigated using rRNA gene sequencing technology [13,14,29–31]. Based on the analysis of coverage index (≥0.93, Table 1) and dilution curve (Figure 5), all microorganisms in the three samples were covered relatively comprehensive, which could truly reflect the prokaryotic microbial community differences between pit walls and bottom. The results showed that YJ-Z and YJ-X were the most similar in terms of microbial diversity. However, due to the differences in vertical height and the microecological environment, the prokaryotic microbial community structure and abundance in YJ-Z and YJ-X were different. At the phylum level, the dominant phylum of YJ-Z and YJ-X was Firmicutes, while in YJ-S it was Proteobacteria. Previous studies also showed that Firmicutes and Bacteroidetes were the dominant phyla in pit muds [2,6,32–34]. Firmicutes can produce endophytic spores and resist extreme environments, degrade volatile fatty acids such as butyrate and its analogs, and is represented mainly by the Clostridia and Bacilli [10]. Although the relative abundance of Firmicutes, Proteobacteria, Bacteroidetes, and Synergistetes differed among YJ-S, YJ-Z, and YJ-X, they were still the key components of the microbiota in the pit mud of Yun liquor. And the similar results were also reported in the pit mud of Yibin liquor [2]. Bacteroidetes was the major contributor to the synthesis of terpenes [32]. In general, at increased depth within the pit mud, the relative abundance of Firmicutes and Actinobacteria shows an upward trend. At the genus level, the dominant genus of the three samples were also different. For example, Aminobacterium was the dominant genus in YJ-Z, with an abundance of 25.04%; while in YJ-S and YJ-X, Aminobacterium’s abundance was much lower, being 1.03% and 1.75%, respectively. Aminobacterium is a gram-negative, single arrangement, slightly curved rod, no spore formation, non-motile, and strictly anaerobic mesophilic bacteria. It can increase the content of ammonium nitrogen in pit mud by amino acid fermentation, which was first found in the sludge of the wastewater treatment tank of the dairy factory, and the model species of this bacterium was Aminobacterium colombense [35,36]. In this study, the abundance of Aminobacterium was the highest in YJ-Z, which may be related to the anaerobic environment in the middle of the pit and the fermentation temperature suitable for the growth of Aminobacterium. Aminobacterium and Sedimentibacter can ferment amino acids to generate ammonium nitrogen through metabolism, which could provide nitrogen sources for the growth of other microbes [10]. In YJ-X, the abundance of unclassified genus was as high as 35.54%, the dominant genus was Lactobacillus, and its abundance was only 6.49%; the abundance of Lactobacillus in YJ-Z was also as low as 2.49%; the abundance is the lowest in YJ-S, being only 0.03%. Lactobacillus is a gram-positive, rod-shaped, non-spore producing, facultative anaerobic, and fermenting with glucose to produce lactic acid. Lactic acid is further converted to ethyl acetate through enzymatic reaction, which increases the mellow smell of liquor. The repulsion relationship Lactobacillus has with other bacteria contributes to the production of caproic acid, which inhibits the growth of bacteriocin by pathogenic microorganisms and mold rot microorganisms, maintaining the quality of liquor fermentation [37,38]. Lactobacillus was accounted for the most in YJ-X, which was consistent with the reported viewpoints of Gao et al. [6]. Lactobacillus was critical for liquor brewing, also as a dominant genus among liquor pit muds of several flavor types [2]. Acinetobacter was the dominant genus in YJ-S, and the abundance was as high as 43.53%. Acinetobacter is a gram-negative, oxidase negative, and non-motile aerobic bacterium, which is widely distributed in soil and water, and even found in hot spring with an optimum growth temperature between 33 and 35°C [39,40]. Acinetobacter is the main microorganism for brewing maotai flavor and light flavor liquor by balancing the taste of alcohol and coordinating the effect of alcohol through the fermentation of glucose and other carbohydrates. In this study, Acinetobacter accounted for the main proportion in the upper part of the pit, indicating that YJ-S was partially similar to maotai flavor and light flavor in pit mud. The abundance of Acinetobacter in YJ-Z and YJ-X was very low, 0.14 and 0.51%, respectively. This may be caused by the fact that YJ-Z and YJ-X are further from the ground and have less contaction with oxygen, causing them to have different microecological environments than YJ-S. Aminobacterium, Syntrophomonas, and Petrimonas play a positive role in the maturation of pit mud, which indicate that the tested pit mud samples are in the mature state [10]. Based on the abundance of major genera in pit muds, the mature state of YJ-Z was more than YJ-S and YJ-X.

In this study, PICRUSt software was used to predict macrogenomic information from 16S rRNA gene of the three samples, and COG database and KEGG database were used to analyze microorganism gene COG classification and KEGG metabolic pathway in YJ-S, YJ-Z, and YJ-X, respectively. Since YJ-S, YJ-Z, and YJ-X were located in different microecological environments, their microorganism gene COG classification and KEGG metabolic pathway abundance were different from each other. In COG analysis, “General function prediction only” was in the first position in the three samples. The abundance of “Amino acid transport and metabolism” was decreased with the decrease of the vertical distance of the sample in the pit, which were in the second, third, and fourth positions in YJ-S, YJ-Z, and YJ-X, respectively. The function of “Amino acid transport and metabolism” may be related to the contact amount of oxygen. Further down the pit, the oxygen contact amount decreases, so the abundance of “Amino acid transport and metabolism” decreases. The abundance of “Function unknown” was the highest in YJ-S, and the abundance ranking was the same in YJ-Z and YJ-X. This may be due to the fact that YJ-S was closer to the ground and had more exposure to oxygen. “Carbohydrate transport and metabolism,” “Amino acid transport and metabolism,” and “Transcription” were the main genes function in YJ-S, YJ-Z, and YJ-X, which were consistent with the conclusion of Wang et al. [41] in studying the microbial community structure and gene function of high-temperature Daqu liquor in a factory in Xiangyang, Hubei Province. In KEGG analysis, “Amino Acid Metabolism” was in the first position in YJ-S, and in the third position in both YJ-Z and YJ-X. This may be due to YJ-S having a closer vertical distance to the ground, more contact with oxygen, more aerobic bacteria in pit mud, and more bacteria using amino acid fermentation. “Membrane Transport” and “Carbohydrate Transport and Metabolism” were in the second and third positions in YJ-Z and YJ-X, indicating that there may be a large number of cellulose degrading enzyme genes in the macrogenomes of the two samples, and a large number of cellulose degrading enzymes synthesized by bacteria were transported outside the cells to degrade cellulose in fermented grains. “Translation” was present at the fifth position in both YJ-Z and YJ-X, after the fifth position in YJ-S. It showed that the enzyme synthesis and metabolism of microorganism, and the characteristics that are beneficial to grain fermentation, in YJ-Z and YJ-X were stronger than those in YJ-S. “Carbohydrate Metabolism” and “Amino Acid Metabolism” were arranged at the second and third positions in YJ-Z and YJ-X, respectively, and the first and third positions in YJ-S, indicating that the gene functions of pit mud microorganism were mainly concentrated in carbohydrate metabolism and amino acid metabolism, which was consistent with the predicted function of “Amino acid transport and metabolism” and that of “Carbohydrate transport and metabolism.” The prediction was based on COG database in this study.

5 Conclusion

The study above showed that prokaryotic microbial community structures and diversity, and gene function prediction is significantly different in pit walls from those in the bottom. The dominant phyla in YJ-S, YJ-Z, and YJ-X were Proteobacteria and Firmicutes, and the dominant genera in them were Acinetobacter, Aminobacterium, and Lactobacillus. In species diversity analysis, YJ-Z and YJ-X were the closest; in species richness analysis, the order was YJ-X > YJ-Z > YJ-S; in species uniformity analysis, the order was YJ-S > YJ-Z > YJ-X. The prokaryotic microbial function in pit mud was mainly concentrated in “Carbohydrate transport and metabolism” and “Amino acid transport and metabolism.” These scientific data provide a theoretical basis for the future maintenance of pit mud, the specifications of building new pits, the input of fermentation material, and the quality of liquor production. In future, the study can be used as a groundwork for revealing the relationship among physicochemical properties, microbiome, and volatiles of pit mud.

  1. Funding information: This study was financially supported by the school level key construction discipline of food science and engineering in Chaohu University (grant no. kj16zdjsxk02) and the project of Chaohu University (grant no. kj21kctd03).

  2. Author contributions: Shu Fang participated in the experiments design, data analysis, and drafted the manuscript. Chuanxiang Wang provided these pit mud samples. Juan Yan participated in the experiment operation. All authors have read and approved the final manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-09-24
Revised: 2022-12-25
Accepted: 2023-01-14
Published Online: 2023-02-23

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  135. Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility
  136. Catharanthus roseus (L.) G. Don counteracts the ampicillin resistance in multiple antibiotic-resistant Staphylococcus aureus by downregulation of PBP2a synthesis
  137. Combination of a bronchogenic cyst in the thoracic spinal canal with chronic myelocytic leukemia
  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0571
Schlagwörter für diesen Artikel
strong-flavor liquor; high-throughput sequencing; pit mud; prokaryotic microbial community; gene function prediction
Creative Commons
BY 4.0

Artikel in diesem Heft

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  78. Inhibition of TAZ impairs the migration ability of melanoma cells
  79. Molecular complexity analysis of the diagnosis of Gitelman syndrome in China
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  129. Knockdown of DLK4 inhibits non-small cell lung cancer tumor growth by downregulating CKS2
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  133. Clinical analysis of severe Chlamydia psittaci pneumonia: Case series study
  134. Bioinformatics analysis to identify potential biomarkers for the pulmonary artery hypertension associated with the basement membrane
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  136. Catharanthus roseus (L.) G. Don counteracts the ampicillin resistance in multiple antibiotic-resistant Staphylococcus aureus by downregulation of PBP2a synthesis
  137. Combination of a bronchogenic cyst in the thoracic spinal canal with chronic myelocytic leukemia
  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
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  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
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  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
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  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
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  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
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  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
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© 2025 De Gruyter Brill
Heruntergeladen am 6.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/biol-2022-0571/html
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