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Fermentation process of tobacco leaves drives the specific changes of microbial community

BMC Microbiology volume 24, Article number: 534 (2024) Cite this article

Abstract

Background

The changes of microbial community on tobacco leaves are affected by several factors during fermentation. However, the relative contribution of different factors in determining microbial community is not clear. This study investigated the effects of fermentation time (fermentation for 0, 3, 6, 9 and 12 months), leaf position (middle and top tobacco leaves) and fermentation site (Longyan and Xiamen warehouses) on bacterial community of tobacco leaves using 16 S rDNA sequencing.

Results

The results demonstrated that fermentation time had a much stronger impact on bacterial diversity, composition, co-occurrence network and functional profiles than leaf position and fermentation site. With the fermentation progressed, the difference of bacterial community between middle and top tobacco leaves was gradually reduced or even disappeared. The bacterial community diversity and network complexity at three, six and nine months of fermentation were significantly lower than those at fermentation initiation. Specific bacterial genera with desired functions were recruited at different fermentation stages, such as Terribacillus, Pantoea and Franconibacter at three or six months of fermentation and Pseudomonas at nine months of fermentation. The recruited microorganisms would form biofilms on tobacco leaves and compete for polysaccharide or protein substances to accelerate the degradation of tobacco macromolecular substances.

Conclusions

In conclusion, fermentation time was an important factor in determining the composition and function of microbial community on tobacco leaves during the fermentation process.

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Background

Many raw materials in the nature, such as tea [1], cowpea [2] and tobacco [3], are not suitable for making products directly due to their high content of harmful substances, low nutrition, strong irritation and insufficient flavor. Fermentation or aging is usually employed to improve the quality of these raw materials [4]. This fermentation process can promote the degradation or transformation of macromolecular compounds and harmful substances, resulting in the formation of various flavor compounds [5]. However, fermentation is a long and complicated dynamic process, and the biological or abiotic reactions involved in this process remain unclear.

As previous studies reported, the fermentation process includes enzymatic reactions, microbial degradation, and other chemical interactions within the raw materials. Microorganisms are indispensable in the fermentation process and play an important role in the degradation of multiple macromolecular compounds [6]. In fermented tobacco leaves, a variety of microorganisms including Bacillus, Terribacillus, Pseudomonas, Enterobacter, Pantoea, and Sphingomonas were found to be the dominant populations [7, 8]. These bacteria can accelerate the degradation process of starch, cellulose, hemicellulose, protein and other refractory or harmful components in tobacco leaves through the action of the enzymes and metabolites produced by themselves, thus increasing the contents of aromatic substances such as alcohols, ketones and esters [9]. Addition of exogenous functional microorganisms such as Bacillus subtilis [10] and Pichia terricola [11] was also reported to accelerate certain compounds degradation and significantly improve the quality of tobacco. Therefore, the microbial composition and function is closely related to the product quality during the fermentation process. Revealing the variation of microbial communities and identifying the key microorganisms at different fermentation stages are vital for present studies.

It is important to note that assembly of microbial communities may begin shortly after fermentation and develop with the effects of deterministic (e.g. selection driven by biotic and abiotic stresses) and stochastic (e.g. random dispersal and drift events) processes [12]. Multiple factors including the characteristics of raw materials, fermentation time and environmental conditions should be considered in shaping microbial communities during fermentation. On the one hand, the types and contents of organic compounds contained in different raw materials are significantly different, and the difference of substrates will inevitably have selection effects on microbial species [8]. Some studies have even shown that the exact substance composition of plant leaves among different leaf positions and growing conditions differed obviously [13]. On the other hand, fermentation is a dynamic process accompanied by substance degradation and transformation that drive the microbiome assembly over time. In different stages of fermentation, there may exist different microorganisms to perform the degradation function of macromolecular compounds [14]. Besides, environmental conditions such as temperature, moisture and air were also important to affect the microbial community. The fermentation conditions of tobacco are relatively mild, with a great number of actively functional microorganisms surviving on its surface [15]. Fully managing and utilizing these factors is crutial to optimize microbial composition and improve the taste and flavor of fermented products [16]. However, the relative contribution of the above factors in shaping microbial communities during the fermentation process has not been studied.

In this study, tobacco leaves in two fermentation sites with different leaf positions were used as research models. The changes in bacterial community were studied across five fermentation stages (0 month, 3 months, 6 months, 9 months and 12 months of fermentation) for 100 samples from middle and top tobacco leaves. To be specific, our objectives were to (1) reveal intricate mechanisms governing bacterial community assembly, which are interactively influenced by fermentation stages, leaf position and environmental factors of fermentation sites; and (2) unravel the temporal dynamics of microbial networks and explore the ecological functions of bacterial communities throughout the fermentation process.

Methods

Experimental design and sample collection

The tobacco plants of Nicotiana tabacum were planted in Youxi County, Fujian Province (26°11′N, 118°13′E, southern China). Tobacco leaves with good maturity (yellow-orange color) in the middle and top of the plants were collected in May, 2020. After the tobacco leaves were harvested, they underwent flue-curing in a barn according to the local flue-curing procedure. Subsequently, the middle and top tobacco leaves were placed into a 1 m3 carton (125-cm long × 100-cm wide × 80-cm high) for fermentation at Longyan warehouse and Xiamen warehouse in Fujian Province, respectively. The density of tobacco leaves was 200 kg/carton. Each treatment consisted of a minimum of ten cartons of leaves. The environmental conditions of Longyan warehouse were at 24.29℃ and 64.49% humidity, and those of Xiamen warehouse were at 25.31℃ and 61.17% humidity. After fermentation for approximately 0, 3, 6, 9, and12 months (M0, M3, M6, M9 and M12), five-point sampling methods were employed to collect tobacco leaves from a depth of 15 cm below the top surface of each carton [14]. At each time point, ten individual biological replicates were taken from ten cartons and 1.5 kg of tobacco leaves were collected from each replicate. The collected leaf samples were carefully packed in sterile plastic bags and stored at − 20ºC for further analysis.

Sample processing, DNA extraction, 16 S rDNA amplification and sequencing

The procedures of sample processing and DNA extraction were according to our previous methods [8]. After each tobacco sample were fully homogenized, 15 g leaves were weighed and put into a 1,000 mL shake flask with 350 mL of sterilized 0.1 M phosphate-buffered saline (PBS) buffer. Then, placed the shake flasks in a thermostatic oscillator and shook thoroughly at 180 rpm and 15°C for 2 h. Then the leaf residues in the eluent were removed using a sterile gauze, and the remaining eluent was centrifuged at 12,000 × g for 15 min at 4°C. Microorganisms on the tobacco leaves were obtained from the sediment.

About 0.5 g of microbial sediment were taken for microbial genomic DNA extraction using a Mag-Bind® Soil DNA Kit (Omega Biotek Inc., Doraville, GA, USA). The DNA concentration and purity were tested by electrophoresis on 1% (w/v) agarose gels. The primers 341 F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′) were used to amplify the V3-V4 variable region of the bacterial 16 S rRNA gene. PCR was conducted in 50 µL reaction system including 25 µL TaqMaster Mix (Vazyme, Piscataway, NJ, USA), 2.0 µL DNA (∼20 ng/ml), 1 µL forward/reverse primers (10 µM) and DNase-free deionized water for volume complement. The PCR reaction was performed as follows: 98°C denaturation for 30 s, 32 cycles of 98°C for 10 s, 54°C for 30s and 72°C for 45 s, with a final 10-min elongation at 72°C. The amplified products were sequenced using the Illumina HiSeq2500 platform by Novogene Technology Co. Ltd (Tianjin, China) with a paired-end strategy.

Sequencing data processing

Primer sequences and low-quality reads with a quality score (Q) below 30 were pruned. Forward and reverse 16 S rRNA gene amplicon reads were combined into full-length sequences, and the combined sequences were subjected to quality filtering (maximum expected error 0.5) using USEARCH (v10.0) [17]. DATA2 was used to assign high-quality sequences to amplicon sequence variants (ASVs) [18]. The ASVs belonging to chloroplast and mitochondrial were removed. The resultant ASVs were classified to taxonomy groups by comparing with the SILVA (v13.8) database at 97% similarity level [19].

Statistical analysis

The statistical analyses were carried out in R environment (v.4.2.1) [20]. Alpha-diversity indexes including community richness indexes (Chao1 index and observed OUTs) and community diversity indexes (Shannon index and Simpson diversity) were calculated based on the normalized ASV table in QIIME2 and then visualized in box plots. Kruskal-Wallis rank-sum test was used to compare the significant difference between microbial alpha indexes of fermented leaves with different leaf positions or different fermentation time. The beta-diversity of bacterial community was estimated based on Bray-Curtis distance matrices and then visualized using Principal coordinate analysis (PCoA) plots. The Adonis function in the “vegan” package was examined by PERMANOVA (Permutational Multivariate Analysis of Variance) [21] to evaluate the effects of fermentation stage, leaf position and fermentation site on the bacterial community. The βNTI (beta Nearest Taxon Index) and RCbray (Bray–Curtis-based Raup-Crick Index) were calculated to assess the relative contribution of determinism and stochasticity in microbial community assembly [22]. According to the values of βNTI and RCbray, deterministic and stochastic processes could be divided into five ecological processes, including heterogeneous selection (βNTI < − 2), homogeneous selection (βNTI > + 2), dispersal limitation (|βNTI|< 2 and RCbray > 0.95), homogenizing dispersal (|βNTI|< 2 and RCbray < – 0.95), and undominated (|βNTI|< 2 and |RCbray|< 0.95) [23, 24].

Differential analysis of the relative abundance of ASVs cross different groups was calculated using the DESeq2 R package [25] and visualized using volcano plots. LefSe (Linear discriminant analysis Effect Size) analysis for bacterial genera was performed to explore the marker genera in samples with different leaf position or different fermentation time. For genera with an average abundance greater than 0.1% in all samples, abundances were normalized to the sum of the values per sample in 1 million and then subjected to LDA. Under one-against-all comparison mode, genera with α less than 0.05 and an LDA (Linear discriminant analysis) score more than 4 was defined as significantly different members from other samples. Bacterial functional profiles were predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) [26] and then annotated by referring to the Kyoto Encyclopedia of Genes and the Genomes (KEGG) database.

Co-occurrence network analysis

Co-occurrence networks were constructed by computing multiple abundance correlations based on ASV level matrices (the average relative abundance > 0.1% and ) using the R package “ggClusterNet” [27]. To be specific, the ASVs presented in at least 6 out of the 10 biological replicates and showed statistically significant correlations (P < 0.05, Pearson’s correlations with a magnitude > 0.7 or < − 0.7) were used to perform network analysis. The topological properties of co-occurrence networks were also obtained using “ggClusterNet” R package. Network visualization was carried out in Gephi software (version 0.9.2). Keystone species were identified based on the values of within-module connectivity (Zi) and among-module connectivity (Pi) according to a previous study [28]. Nodes (ASVs) could be classified into four categories, including peripheral nodes (Zi ≤ 2.5 and Pi ≤ 0.62), connectors (Zi ≤ 2.5 and Pi > 0.62), module hubs (Zi > 2.5 and Pi ≤ 0.62), and network hubs (Zi > 2.5 and Pi > 0.62) [29].

Results

Bacterial community assembly on tobacco leaves was affected by fermentation time and leaf position

After quality filtration, a total of 11,011,441 high-quality sequences were retained in 200 samples and these reads were assigned into 10,227 ASVs. The ASV table was then normalized to 24,000 reads in each sample for the analysis of microbial diversity and composition. PCoA ordinations and PERMANOVA analysis showed that the assembly of bacterial community on tobacco leaves during the fermentation process was determined by fermentation time, leaf position and fermentation site to varying degrees (Fig. 1). The PCoA plot showed the two axes (PCo1 and PCo2) explained 42.82% of the variance and the significant dissimilarity of the whole bacterial community (Figs. 1A and S1). The results of PERMANOVA revealed that fermentation time explained the largest variation in the whole bacterial community (49.54%), followed by leaf position (7.24%) and fermentation site (2.25%) (Fig. 1A and Table S1). In the early stage of fermentation (M0, M3 and M6), the assembly of bacterial community was mainly explained by leaf position (34.30%, 43.36% and 66.07%, respectively). The relative contribution of fermentation site in shaping microbial community was varied (13.41–41.60%) with fermentation stages (Fig. 1B and Table S2).

Fig. 1
figure 1

The changes of bacterial community diversity on tobacco leaves during the fermentation process. A Principal coordinate analysis (PCoA) showing the effects of fermentation time, leaf position and fermentation site on the structures of bacterial community on tobacco leaves. B PCoA showing the effects of leaf position and fermentation site on the structures of bacterial community at different fermentation stages. Asterisks denote significant differences between samples from top and middle leaves (one-way ANOVA followed by a post hoc Tukey test, ***P < 0.001). C Shannon index and Chao1 showing the effects of fermentation time and leaf position on the α-diversity of bacterial community on tobacco leaves

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Four alpha diversity indices including Shannon index, Chao1 index, the observed OTUs and Simpson diversity were calculated (Figs. 1C, S2 and S3). It was found that the bacterial alpha diversity was significantly affected (Kruskal-Wallis; P < 0.01) by fermentation time. With the progress of fermentation, the bacterial alpha diversity showed a trend of reducing first (M3, M6 and M9) and then increasing (M12) (Fig. S3). For different leaf positions, we found the community diversity of top leaves was significantly higher than that of middle leaves at the beginning of fermentation. However, the difference was gradually reduced or even disappeared in the fermentation process (Figs. 1C and S2).

The calculation results of deterministic (|βNTI|≥ 2) and stochastic (|βNTI|< 2) processes showed that the βNTI values decreased significantly during the first 9 months of the fermentation process (P < 0.01) (Fig. 2A), indicating that bacterial community assembly altered from stochastic to deterministic processes. Null model analysis was further performed to seek the community assembly processes over different fermentation stages (Fig. 2B). A higher relative contribution of stochastic processes primarily associated with homogenizing dispersal and undominated (drift and/or diversification) was found in M0 (91.73% for middle leaves and 81.93% for top leaves) and M12 (80.99% for middle leaves and 86.51% for top leaves) bacterial communities. Conversely, bacterial communities in M3 (88.22% for middle leaves and 85.56% for top leaves), M6 (88.45% for middle leaves and 92.46% for top leaves) and M9 (86.91% for middle leaves and 89.56% for top leaves) were driven by deterministic processes mainly belonging to homogeneous selection. Totally, stochastic process was the primary contributor to bacterial community assembly at the early (M0) and late (M12) stages and deterministic process exerted a greater influence on community in the middle stages (M3, M6 and M9) of tobacco fermentation.

Fig. 2
figure 2

Deterministic and stochastic processes in bacterial community assembly during the fermentation process. A Relative contribution of determinism and stochasticity on bacterial community assembly on tobacco leaves during the fermentation process based on the β-Nearest Taxon Index (βNTI) values. B The relative importance of five ecological processes on bacterial community at different fermentation stages

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Different marker microbes were detected at different fermentation stages

Taxonomic classification showed that the bacterial community on fermented tobacco leaves was mainly composed of Proteobacteria (relative abundance ranging from 39.27 to 94.97%) and Firmicutes (relative abundance ranging from 2.86 to 59.10%) at the phylum level (Fig. 3A). In the early stages of fermentation (M0, M3 and M6), the content of Firmicutes was more abundant on middle leaves than that on top leaves, but Proteobacteria presented a contrary trend (Fig. 3A). The taxonomic composition at the genus level showed a significant variation with the fermentation process (Fig. 3B). LefSe analysis was conducted at the genus level to determine the enrichment/depletion pattern between middle and top leaves (Fig. S4) or among different fermentation time (Fig. 4) with an LDA score threshold of 4. We found that the relative abundance of Terribacillus and Bacillus on middle leaves were much higher in most fermentation stages (M0, M3, M6 and M9), but Sphingomonas and Pantoea were more abundant on top leaves (Fig. S4), which indicated the potential marker genera on middle and top tobacco leaves during fermentation.

Fig. 3
figure 3

The changes of bacterial composition on tobacco leaves during the fermentation process. A The changes of bacterial composition at the phylum level. B The changes of bacterial composition at the genus level

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Considering the constant changes of microorganisms during fermentation, the specific microorganisms of middle and top leaves in different fermentation stages were excavated at the genus level. Compared with other fermentation stages, the genera enriched on middle (Enterobacter and Kosakonia) and top leaves (Sphingomonas, Stenotrophomons, Methylobacterium-Methylorubrum and Pseudocitrobacter) in M0 were significantly different (Fig. 4A). However, as the fermentation process progressed, the microorganisms enriched in the middle and top leaves tended to be consistent in the same fermentation stage, indicating that the enriched microorganisms may play an important role in the corresponding stage. For example, Franconibacter was enriched in M6, Pseudomonas was enriched in M9 and Xylella was enriched in M12, for both middle and top leaves. Besides, Terribacillus and Pantoea were enriched in the early stage of fermentation, while the enrichment periods in middle and top leaves were different (Fig. 4A).

Differential analysis of ASVs confirmed the constantly variation of microorganisms (Fig. 4B). Compared with the original bacterial community (M0), there were 63, 67, 72 and 88 ASVs enriched in M3, M6, M9 and M12 for middle leaves, and 48, 60, 49 and 85 ASVs enriched in M3, M6, M9 and M12 for top leaves. Notably, a large number of ASVs were depleted in M3 and M6 for both middle (143 and 128, respectively) and top (215 and 190, respectively) leaves when compared with those in M0, which indicated the selection and filtration for microorganisms on the surface of tobacco leaves during the fermentation process.

Fig. 4
figure 4

The enrichment and depletion patterns of bacterial composition during the fermentation process. A The LEfSe analysis with an LDA score threshold of 4 showing the specific bacterial genera of middle and top tobacco leaves at different fermentation stages. B The volcano plots showing the enriched or depleted bacterial ASVs of middle and top tobacco leaves at different fermentation stages (M3, M6, M9 and M12) compared with the original bacterial community (M0)

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The complexity and stability of microbial co-occurrence networks were changed during fermentation

To assess the impact of fermentation time on potential interaction dynamics within the bacterial community, co-occurrence networks between bacteria at different fermentation stages were constructed (Fig. 5). The results showed that bacterial network patterns shifted clearly across five fermentation stages (Fig. 5A). The complexity (nodes and edges) and stability (the proportion of negative connections) of bacterial networks of middle and top leaves presented the same trend during the fermentation process, but the networks of top leaves were more connected and complex than those of middle leaves at the same fermentation stage (Fig. 5B). Specifically, bacterial networks of M3, M6 and M9 showed significantly lower connections (edges) than the networks of M0 and M12 for both top and middle leaves (Fig. 5B, Tables S3 and S4). However, the proportion of negative interactions which represented the network stability were significantly higher in M3 (49.21% and 44.99% for middle and top leaves) and M6 (45.04% and 44.21% for middle and top leaves) (Fig. 5B, Tables S3 and S4).

According to the within-module connectivity (Zi) and among-module connectivity (Pi) of nodes, they could be further defined as peripherals, connectors, module hubs, and network hubs (Fig. 5C). Two module hubs (M12) and 14 connectors (2 in M0, 9 in M3 and 3 in M12) were detected in middle leaves, but only two module hubs (1 in M0 and 1 in M9) and two connectors (1 in M0 and 1 in M9) were detected in top leaves (Fig. 5C). All these module hubs and connectors belonged to Alphaproteobacteria (5) or Gammaproteobacteria (14) except for ASV1 (belonging to Bacilli) in M0 (Table S5). The degree of module hubs and connectors in middle leaves ranged from 2 to 25, but that of top leaves was significantly higher (ranging from 12 to 59) (Fig. 5D and E). For middle leaves, we found that 6 connectors (ASV82, ASV50, ASV94, ASV 12, ASV9 and ASV60) belonging to Enterobacteriaceae and Moraxellaceae in M3 and 2 connectors belonging to Sphingomonas in M12 (ASV541 and ASV 86) (Fig. 5D) were enriched members in M3 and M12 (Fig. 4B), respectively. For top leaves, a connector (ASV9) belonging to Enterobacteriaceae (Fig. 5E) was also an enriched member in M9 (Fig. 4B).

Fig. 5
figure 5

The co-occurrence networks of bacterial community on tobacco leaves during the fermentation process. A Bacterial co-occurrence networks on tobacco leaves during the fermentation process. Nodes represent ASVs. The size of each node represents connections with other nodes and the colors of nodes represent different phyla. The edges between the nodes indicate strong and significant (P < 0.01) correlations. A blue line indicates a positive interaction, while a red line indicates a negative interaction. B Number of positive and negative interactions in bacterial networks at different fermentation stages. C The scatter plot of within-module connectivity (Zi) and among-module connectivity (Pi) showing the topological role of each ASV. D The number and types (positive or negative) of edges showing the interactions of module hubs and connectors on middle leaves with other ASVs. The nodes marked in red were bacteria that are significantly enriched at the corresponding fermentation stage compared with the original bacterial community (M0). E The number and types (positive or negative) of edges showing the interactions of module hubs and connectors on top leaves with other ASVs

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The functional metabolisms of microbial community were changed during fermentation

Metagenomes of bacterial communities were predicted using PICRUSt2 and then annotated by referring to the KEGG database. Shannon index based on KOs (KEGG Orthologs) showed that the KO diversity was significantly affected (Kruskal-Wallis; P < 0.01) by fermentation time (Figs. S5 and 6A). The KO diversities of M3, M6 and M9 were significantly lower than that of M0 (Fig. S5). For different leaf positions, we found the KO diversity of M3 and M12 was significantly lower (P < 0.05) in top leaves than that in middle leaves, but KO diversity of M9 showed a contrary trend (Fig. 6A). PCoA analysis for KO category indicated that there were significant differences in community functions at different fermentation stages (Figs. 6B and S6). The results of PERMANOVA revealed that fermentation time (51.68%) and leaf position (11.27%) were important factors in affecting community functions (Fig. 6B).

Fig. 6
figure 6

PICRUSt predicted metagenome functions of bacterial community on tobacco leaves at KO level. A Functional diversity of KO genes in bacterial community on top and middle leaves at different fermentation stages. Asterisks denote significant differences between samples from top and middle leaves (one-way ANOVA followed by a post hoc Tukey test, *P < 0.05; **P < 0.01). B PCoA analysis showing the effects of fermentation time, leaf position and fermentation site on the distribution of KO functional genes in bacterial community on tobacco leaves. C Heatmap showing the relative abundance of functional genes involved in carbohydrate metabolism, amino acid metabolism, cell motility, nucleotide metabolism and cell community-prokaryotes

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Several metabolic pathways including carbohydrate metabolism, amino acid metabolism, cell motility and cell community-prokaryotes were enriched in M3, M6 and M9 (Fig. 6C). However, the specific pathways enriched in M3 and M6 were significantly different from those in M9 (Figs. S7, S8, S9 and S10). For example, for carbohydrate metabolism, genes involved in galactose metabolism (K02080, K10984, K10985, K10986 and K12112), C5-branched dibasic acid metabolism (K18292, K18290 and K18288) and pentose and glucuronate interconversions (K08092, K16849 and K13874) were more abundant in M3 and M6, but genes involved in butanoate metabolism (K01799) and glyoxylate and dicarboxylate metabolism (K00281, K00124 and K00127) were more abundant in M9 (Figs. 6C and S7). For amino caid metabolism, genes involved in arginine and proline metabolism, tryptophan metabolism, histidine metabolism and lysine biosynthesis were more abundant in M9 (Figs. 6C and S8). For cell motility and cell community-prokaryotes, the genes involved bacterial chemotaxis, flagellar assembly, quorum sensing and biofilm formation in M3, M6 and M9 were much higher than M0 and M12 (Figs. 6C, S9 and S10).

Discussion

The microbial community on tobacco leaves were affected by several factors such as the characteristics of raw materials, fermentation time and environmental conditions during the fermentation process. However, it is not clear that which factor plays the dominant role in shaping microbial communities during the fermentation process. In present study, our results demonstrated that fermentation time had a much stronger impact on bacterial community assembly than leaf position and fermentation site (Fig. 1A). The important effect of fermentation time on the bacterial community assembly was consistent with previous studies which showed the dynamic variation of microbial community across fermentation process of tobacco leaves [12, 30]. In studies of other systems, experimental sites frequently influenced the microbial community structure and function [31]. Conversely, the fermentation site showed little effect on bacterial community (Figs. 1 and 6), and the reason could be attributed to the fact that tobacco leaves were often fermented under relatively stable conditions with the temperature of 20℃~30℃ and the relative humidity of 65%~75% [32].

The community assembly (Fig. 1) and microbial co-occurrence networks (Fig. 5) of M3, M6 and M9 were significantly different from those of M0 and M12. The significantly reduced taxonomic diversity (Fig. S3), functional diversity (Fig. 6) and network complexity (Fig. 5) in M3, M6 and M9 indicated that tobacco leaves exerted a strong selection effect to recruit and filter specific bacterial taxa and functions during the fermentation process. Fermentation of tobacco leaves was usually accompanied by the degradation of macromolecular compounds and the generation of low-molecular-weight metabolites [33]. Therefore, the selection effect on bacterial composition might be related to the dynamic changes of substances on tobacco leaves [16], which was similar to the screening process of root exudates for microorganisms during plant growth and development [24]. The above results provide additional evidence in support of the fermentation time determining bacterial community assembly regardless different tobacco materials and environment conditions.

For different leaf positions, we found the community diversity (Fig. 1A) and composition (Fig. 4A) were significantly between top and middle leaves at the beginning of fermentation, but the difference was gradually reduced or even disappeared in M3, M6 and M9. For both middle and top leaves, Terribacillus, Pantoea and Franconibacter were enriched in M3 or M6, Pseudomonas was enriched in M9 and Xylella was enriched in M12 (Fig. 4A). These bacterial genera played important roles in the degradation and transformation of different compounds, such as macromolecular organic substances, nicotine and aromatic compounds [8, 16]. For example, Terribacillus, Franconibacter and Pseudomonas were related to the generation of favor components and may affect the sensory quality of tobacco leaves [34]. Pseudomonas were also well known as important contributors to nicotine degradation, as reported in Pseudomonas plecoglossicida TND35 [35], Pseudomonas stutzeri ZCJ [36] and Pseudomonas sp. HF-1 [37]. These results suggested specific bacterial genera with desired functions were enriched at the same fermentation stage even if the raw materials of tobacco leaves were different. This is in line with the expectation of ecological process that bacterial community assembly shifted from stochastic to deterministic processes (Fig. 2). Homogeneous selection often occurred when some common stressor driven two communities to be convergent [38, 39]. Based on the great contribution of homogeneous selection in M3, M6 and M9, it could be speculated that the same environmental selection pressure (fermentation time) leaded to similar community structures [40].

The microbial interactions are important to maintain the stability and function of the fermentation ecosystem during the fermentation process of foods [41]. Here, our study revealed that bacterial co-occurrence networks of M3, M6 and M9 for both middle and top tobacco leaves showed significantly lower connections than the networks of M0 and M12 (Fig. 5, Tables S3 and S4), which might result in the low bacterial diversity of tobacco leaves in M3, M6 and M9 (Fig. 1C). When nutrients were abundant, competing microbial groups could coexist stably, but in the case of nutrient restriction, some microbes may be eliminated in the competition process, thus reducing the community diversity [42]. The microbial interactions in the natural ecosystems could be categorized into positive and negative patterns based on nutrients, material, information, and space [28]. Here, the proportions of negative interactions between bacteria in M3 and M6 networks were significantly higher (Fig. 5B, Tables S3 and S4), indicating that most bacteria in M3 and M6 might be mutually exclusive in the tobacco fermentation ecosystem. The result was consistent with other studies on microbial interactions in the fermentation of tobacco [14] and tea [43]. The exclusion relationship between bacteria on tobacco leaves might be due to the competition between them for resource and space. On the one hand, during the fermentation process, the types of organic substances in tobacco leaves were constantly changing, and microorganisms had to compete for limited and specific nutrient resources to ensure their growth and reproduction [44]. On the other hand, most of the microorganisms on tobacco leaves were aerobic. In order to occupy favorable space on the surface of tobacco leaves, there must be a competitive relationship between different microorganisms.

Dynamic changes of microbial functional spectrum can provide useful information for exploring the relationships between microbial community and chemical composition on tobacco leaves during the fermentation process [30]. Consistent with changes in taxonomic diversity (Figs. 1C, S2 and S3) and network characteristics (Fig. 5) of bacterial community, the functional profiles of bacterial community in M3, M6 and M9 was also significantly different from those in M0 and M12. Genes involved in carbohydrate metabolism and amino acid metabolism were more abundant in M3, M6 and M9 (Fig. 6). The carbohydrates and nitrogenous compounds in tobacco leaves are important sources of nutrition for microorganisms [45]. The contents of protein, starch, cellulose, lignin and pectin in tobacco leaves can reach more than 50% [8]. Here, our results showed that microbial genes related to carbohydrate metabolism increased significantly in M3 and M6, and microbial genes related to amino acid metabolism increased significantly in M9, which indicated that microorganisms were mainly involved in the degradation of polysaccharides in the early stages of fermentation (M3 and M6), and then played vital roles in the degradation of proteins. Meanwhile, we found microbiome functional genes involved in cell motility (bacterial chemotaxis and flagellar assembly) and cell community-prokaryotes (quorum sensing and biofilm formation) were enriched in M3, M6 and M9 (Figs. 6, S9 and S10). Just as plants attract beneficial microbes by releasing specific root exudates when facing disease [46], the organic substances in tobacco leaves acted as signaling molecules that recruited some desired microorganisms to accumulate on its surface. During this process, microorganisms could sense external chemical signals and change the direction of bacterial flagella rotation by regulating the activity of their signaling system (e.g. methyl-accepting chemotaxis protein) [47], thus exhibiting chemotaxis behavior towards or away from the chemicals. The enriched bacteria on tobacco leaves inevitably interact with each other, often forming multispecies biofilms [48]. Quorum sensing (QS) is an important microbial interaction in the food fermentation ecosystem, and can modulate several phenotypes such as biofilm formation, tolerance to acid stress, bacteriocin production, competence, morphological modifications, motility, and so on [49]. The activities and interactions of functional microorganisms in biofilms are of great significance to the quality of fermented tobacco leaves.

Conclusions

Collectively, our study demonstrated that fermentation time had a much stronger impact on bacterial community assembly than leaf position and fermentation site. The bacterial community assembly, microbial co-occurrence networks and microbial functional profiles of M3, M6 and M9 were significantly different from those of M0 and M12. Specific bacterial genera were selected to perform desired functions at different fermentation stages with the metabolic changes of on tobacco leaves.

Data availability

Sequence data that support the findings of this study have been deposited in the Genome Sequence Archive in National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences (https://bigd.big.ac.cn/gsa), under accession number CRA016119.

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Acknowledgements

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Funding

This work was supported by the Science and Technology Project of CNTC (110202102017) and the Science and Technology Project of China Tobacco Fujian Industrial Co., Ltd. (FJZYHZJH2022007).

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  1. Jiemeng Tao and Shanyi Chen contributed equally to this work.

Authors and Affiliations

  1. China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China

    Jiemeng Tao, Chen Wang, Yalong Xu, Peijian Cao & Qiansi Chen

  2. Technology Center, China Tobacco Fujian Industrial Co., Ltd, Xiamen, 361000, China

    Shanyi Chen, Zhenkun Jiang, Enren Zhang, Hui Liang, Ning Ding, Mingqian Zhang & Wei He

  3. Beijing Life Science Academy, Beijing, 102200, China

    Jiemeng Tao, Chen Wang, Yalong Xu, Peijian Cao & Qiansi Chen

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  1. Jiemeng Tao
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Contributions

J.T., W.H. and Q.C. designed the study. J.T. and S.C. wrote the manuscript. J.T., S.C. and Z.J. performed the experiments. E.Z., H.L. and Y.X. conducted the statistical and bioinformatics analysis. P.C., W.H. and Q.C. contributed to conceptualization and funding acquisition. C.W., N.D. and M.Z. were involved in the revision of the manuscript. All authors reviewed the manuscript.

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Correspondence to Wei He or Qiansi Chen.

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Tao, J., Chen, S., Jiang, Z. et al. Fermentation process of tobacco leaves drives the specific changes of microbial community. BMC Microbiol 24, 534 (2024). https://doi.org/10.1186/s12866-024-03702-w

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