{"paper_id":"4b08a40a-c0ce-48ea-aeae-596467b3bdff","body_text":"Integrated Multi-Omics Analysis Reveals Microbial Community Restructuring and Its Role in Key Carbohydrate Metabolic Pathways During Tobacco Leaf Curing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated Multi-Omics Analysis Reveals Microbial Community Restructuring and Its Role in Key Carbohydrate Metabolic Pathways During Tobacco Leaf Curing Cheng Zhang, Xiaohua Zhang, Feng Wang, Guanhui Li, Jie Ding, Yi Cao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6949781/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Nov, 2025 Read the published version in Microbial Ecology → Version 1 posted 14 You are reading this latest preprint version Abstract Microorganisms play a significant role in improving the flavor and quality of plant products. Analyzing the impact of the tobacco processing process on the microbial community structure and revealing the synergistic mechanism of microorganisms during the processing is crucial for optimizing the flavor and quality of plant products. In this study, samples were collected from four processing stages (T1: fresh leaves, T2: 42°C, T3: 54°C, T4: 68°C), and metabolite and inter-leaf microbial data of tobacco leaves were generated. A comprehensive multi-omics analysis was conducted. The study shows that the increase in temperature and the decrease in humidity during the processing lead to the reorganization of the microbial community. Brevibacterium, Staphylococcus, Aspergillus, and Ganoderma were identified as core biomarkers. Bacteria dominate in the initial degradation of starch, while fungi promote the accumulation of soluble sugars through the transformation of intermediate products. This study deepens our understanding of the role of microorganisms and their carbohydrate metabolism in the tobacco leaf processing process and proposes a new strategy for constructing regulatory models by integrating multi-omics. Microbial community Tobacco Processing Metabolomes Metabolic pathways Multi-omics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Starch-based natural organic compounds, as sustainable and renewable resources(Tao, Chen et al. 2022 ), play a critical role in the global carbon cycle and energy supply. These compounds constitute a significant portion of Earth's biomass, primarily produced by plants(Bar-On, Phillips et al. 2018 ). In the field of plant processing, the role of microorganisms is particularly important, as they can secrete various enzymes(Hebelstrup, Sagnelli et al. 2015 ), such as amylase(Zhu, Chen et al. 2023 ) and cellulase(Jiang, Peng et al. 2023 ), which can efficiently degrade the macromolecular substances in plant tissues into easily absorbed metabolic products(Jiang, Peng et al. 2023 ), significantly affecting the nutritional and flavor quality of crops such as grains(Kaur and Prasad 2021 ), vegetables(Rizo, Guillén et al. 2020 , Shang, Ye et al. 2022 ), fruits(Yuan, Wang et al. 2024 ), and tea(Feng, Deng et al. 2024 , Yang, Peng et al. 2024 ). For example, in the processing of tea and certain specific crops, microorganisms promote the formation and accumulation of aromatic compounds by secreting phenol oxidase and peroxidase enzymes(Guo, Lv et al. 2021 , Wang, Lei et al. 2024 ). When considering the processing of plant leaves, especially plant-based products, special attention is paid to the role of microorganisms in degrading complex macromolecular substances such as starch, which are not only important nutrient sources for microorganisms but also profoundly affect the final characteristics of processed products(Zhang, Mai et al. 2024 ). Starch is a difficult-to-degrade component in plant leaves, is significantly affected by processing environmental conditions (Ren, Qin et al. 2023 ). Under specific environmental conditions, microbial communities can achieve effective hydrolysis of starch macromolecules by secreting specific enzymes, such as Bacillus as a common functional microbial group, which has α-amylase activity. This results in the generation of reducing sugars, total sugars, and other intermediate products (Wardman, Bains et al. 2022 , Zhang, Mai et al. 2024 ), and further conversion into alcohols, aldehydes, acids, and esters, which are aroma components(Gong, Li et al. 2023 , Weng, Deng et al. 2024 ). This process not only enriches the flavor of the product but also promotes the complex interaction between plant-microbial microecosystems. In recent years, studies have revealed the dynamic changes in the microbial community during plant leaf processing, but most of them have focused on the description of community structure rather than functional analysis(Ding, Wei et al. 2023 ). In particular, the specific role mechanisms of core microorganisms in degrading starch and other macromolecular substances are not yet clear (Hu, Gu et al. 2021 ). Therefore, it is of great significance to explore the functions of these key microorganisms in depth and clarify their contributions to macromolecule degradation, which can optimize processing technology and improve product quality. Plants actively construct complex phyllospheric microbial consorts during processing(Santoyo 2022 ), these microorganisms closely interact with metabolites on the surface of plant leaves to jointly respond to the multiple challenges posed by the processing environment(Qin, Druzhinina et al. 2016 ). In this study, we analyzed the changes in microbial communities and metabolic functional pathways during the processing of tobacco leaves, revealed the metabolic potential of microbial communities, and explored the close relationship between microbial communities and metabolic processes during the processing, especially the potential cooperative mechanisms of microbial communities in starch degradation. The model research on exploring the interaction patterns between microbial communities and metabolites provided a new perspective for understanding the complex mechanism of microbial co-degradation of starch during the processing. In summary, this study aimed to reveal the synergistic degradation effect of tobacco processing microorganisms on starch, thereby better understanding the metabolic situation during the tobacco processing process and promoting the improvement of tobacco processing technology. 2. Materials and Methods 2.1 Sample Collection and Processing In August 2022, tobacco leaves (Nicotiana tabacum L., variety 'Yunyan 87') were collected from Fuquan City, Guizhou Province (107°14′ E, 27°02′ N) as experimental materials. During the processing procedure, a total of 4 sampling points were set up, and the temperature and relative humidity data at each sampling point were recorded. (T1: 27°C, 79%; Fresh leaves. T2: 42°C, 67%; Yellowing stage. T3: 54°C, 22%; Leaf-drying stage. T4: 68°C, 7%; Stem-drying stage) (Table. S1). At different stages, the sampling points were adjusted by means of the temperature and humidity controllers to adapt to the processing temperature and humidity conditions, and were maintained for a certain period of time in the corresponding stages to ensure the stability of temperature and humidity in the experimental environment. Meanwhile, during sample collection, the temperature and humidity at each collection time were recorded. The untreated leaves (T1) were used as the control to evaluate the effects of different temperature and relative humidity conditions during the processing on the microbial community and metabolites. At each sampling point, three biological replicates were collected for each sample, totaling 12 biological replicates. The dry bulb temperature and wet bulb temperature were recorded at each sampling time. 2.2 Organic compound measurement The collected samples were ground into powder. Approximately 0.1 g of sample was added to 1 mL of 80% ethanol for thorough homogenization. The samples were extracted at 80°C in a water bath for 30 min, and then centrifuged at 3000 rpm at room temperature for 5 min. The starch content was determined using the Solarbio assay kit (Beijing, China). The contents of sucrose, maltose, D-glucose, and D-fructose were determined according to previous studies using GC-MS (Jing, Chen et al. 2024 ). 2.3 Leaf surface microbial washing and enrichment Place approximately 20 g of fresh leaf samples or 5–10 g of dry leaf samples in a 500 mL conical flask, add 250 mL of 1% sterile PBS buffer solution, and collect microorganisms on the leaves by shaking the conical flask and centrifuging to wash off the liquid. After centrifugation, discard the supernatant and place the coarse precipitate in a -80°C freezer for subsequent sequencing work to proceed normally. (Ding, Wei et al. 2023 ). 2.4 DNA Extraction and Illumina Mi-Seq Sequencing DNA was extracted using the SDS method, and then agarose gel electrophoresis was performed to check the purity and concentration of the genomic DNA. 30ng of high-quality genomic DNA samples and corresponding fusion primers were used to configure the PCR reaction system(Ding, Wei et al. 2023 ), and the V5-V7 variable region was amplified using specific primers. The bacterial 16S rRNA gene was amplified using the 799F-1193R (AACMGGATTAGATACCCKG-ACGTCATCCCCACCTTCC) primers(Anguita-Maeso, Haro et al. 2022 ). PCR was performed using Phusion® High-Fidelity PCR Master Mix and GC Buffer (New England Biolabs, Ipswich, MA, USA) with high-fidelity and high-efficiency enzymes(Ding, Wei et al. 2023 ). The Ribosomal Database Project library was constructed. The fragment range and concentration of the library were detected using an Agilent 2100 Bioanalyzer. The qualified library was sequenced based on the size of the inserted fragment. The ITS1F and ITS2R primers were used to amplify the fungal ITS gene(Adams, Miletto et al. 2013 ), except for the annealing temperature of 55°C and 35 cycles, the PCR conditions were the same as those of the 16S rRNA gene(Sun, Wang et al. 2024 ). 2.5 Leaf surface microbial metagenome sequencing data analysis The target DNA fragments were extracted from the sequencing reads by removing the primer sequence and adapter using cutadapt v2.6. The data was filtered by setting the window length to 25 bp, and all low-quality windows were processed starting from the position where the average quality value was less than 20. Additionally, all reads with a final length reduced to 75% or less of the original read length were excluded, as well as reads containing unknown base N and low complexity reads (such as sequences with 10 consecutive identical bases). The reads were efficiently assembled into long sequences using FLASH v1.2.11(Magoč and Salzberg 2011 ) based on the overlapping regions (the minimum matching length was set to 15 bp, and an error rate of 0.1 was allowed). Pairs of reads were assembled into a single long sequence to improve the sequence's completeness and accuracy. Sequence clustering was performed using the UPARSE algorithm in USEARCH v7.0.1090_i86linux32 at a similarity threshold of 97%, which identified representative OTU sequences(Edgar 2013 ). Subsequently, UCHIME v4.2.40 was used to remove chimeric sequences that may have arisen from PCR amplification, and all processed tags were compared back to the OTU representative sequences using usearch_global, generating a comprehensive OTU abundance statistics table(Edgar, Haas et al. 2011 ). The taxonomic affiliation of each OTU was clarified by setting the confidence threshold to 0.6, and the OTU/ASV representative sequences were compared to the corresponding databases (16S used RDP (Cole, Wang et al. 2009 ), ITS used UNITE(Nilsson, Larsson et al. 2019 )). After that, all OTUs that could not be successfully annotated were removed. 2.6 Metabolite Measurement Weigh 50 µg of sample into 1.5 mL Eppendorf tubes and soak them in 800 µL of precooled extraction solution (methanol: H2O = 7:3, v/v) and 20 µL of internal standard 1 (IS1). Homogenize the samples at 50 Hz for 10 min using a woven grinding machine, then sonicate them in a water bath at 4°C for 30 min. Allow the extracts to stand at -20°C for 1 hour, then centrifuge them at 14,000 rpm for 15 min at 4°C. Filter 600 µL of the supernatant using a 0.22 µm membrane and mix 20 µL of the filtered solution from each sample with the QC sample to evaluate the repeatability and stability of the LC/MS analysis. Transfer the filtered samples and the mixed QC samples to 1.5 mL sample vials for instrument operation. Use a Hypersil GOLD aQ Dim column (1.9 µm, 2.1*100 mm, Thermo Fisher Scientific, USA) for metabolite determination. The mobile phase is a water solution containing 0.1% formic acid (A liquid) and an acetonitrile solution containing 0.1% formic acid (B liquid) that is eluted using the following gradient: 0–2 min, 5% B liquid; 2–22 min, 5%-95% B liquid; 22–27 min, 95% B liquid; 27-27.1 min, 95% B liquid-5% B liquid; 27.1–2.7 min, 5% B liquid. The flow rate is 0.3 mL/min, the column temperature is 40°C, and the injection volume is 5 µL. (Bian, Sun et al. 2023 ). The downstream data from mass spectrometry is imported into Compound Discoverer 3.3 (Thermo Fisher Scientific, USA) software, combined with the BMDB (Huadai Metabolome Database, BGI Metabolome Database), mzCloud database, and ChemSpider online database for mass spectrometry data analysis, which will result in a data matrix containing information such as metabolite peak areas and identification results. After that, the table will be further processed for information analysis. The metaX software is used to preprocess the exported data, and detailed information annotation of metabolites is performed using authoritative databases such as KEGG and HMDB, including KEGG ID, HMDB ID, classification information, and participation in KEGG metabolic pathways (Pang, Lu et al. 2024 ). 2.7 Statistical Analysis All calculations and statistical analyses were conducted in R Studio (v.4.4.1, The R Foundation for Statistical Computing, Vienna, Austria). The α diversity index was calculated to evaluate microbial community diversity, and the Bray-Curtis distance was used for similarity analysis, complemented by a non-parametric test (ANOSIM) to determine the influence of different factors on community diversity. The Venn diagram was used with the \"VennDiagram\" package. Pearson's method was used based on normalized data, and the correlation heatmap was executed using the R package \"pheatmap\". Pearson correlation analysis was conducted between biomarkers and large molecular compounds, and P values less than 0.05 were considered statistically significant. Random forest was used to establish prediction models and identify biomarkers. Differential metabolite analysis was conducted using the BGI Dr. Tom multi-omics analysis platform [ https://biosys.bgi.com//report/login ]. KEGG enrichment analysis was conducted using the Omicshare Genomics Bioinformatics Cloud Platform ( https://www.omicshare.com ). 3. Results 3.1 Generation of processed tobacco microbiome and metabolomics resources Both 16S rRNA sequencing and ITS sequencing were conducted to analyze tobacco phyllospheric microorganisms. A comparative analysis of metabolites in tobacco leaves at various processing stages reveals the degradation of starch macromolecules alongside the accumulation of sucrose and glucose small molecules during leaf processing, which is crucial for determining the quality of processed tobacco products. The results from GC-MS and starch measurements indicated that the starch content at stage T1 was significantly higher than that observed in the other three stages, with no significant difference between stages T2 and T4(Fig. 1 a). Conversely, the concentrations of sucrose, maltose, fructose, and glucose at T1 were lower than those at T2, T3, and T4 (Fig. 1 b, c, d, e). Notably, the concentration at stage T3 was significantly higher than that at all other stages. The impact of processing stage on compound content variation is more pronounced than self-conversion among compounds (Fig. 1 f). To ascertain whether the metabolic changes in tobacco exhibited similar specific expression patterns across different processing stages, both qualitative and quantitative analyses of the metabolomes from processed leaves were conducted. Following normalization and data correction, compounds with relative peak areas exceeding 30% were excluded from all quality control (QC) samples, resulting in a total of 41,628 metabolites identified from 16 samples. Through 16S rRNA sequencing, we obtained a total of 1,108,954 high-quality reads and identified 7,710 taxonomic operational units (OTUs) from the same set of tobacco leaf samples. Additionally, ITS sequencing yielded a total of 1,436,843 high-quality reads and identified 8,641 OTUs from these tobacco leaf samples. 3.2 Microbial communities are affected by the course of working To gain a deeper understanding of how processing influences changes in microbial communities, we analyzed the relative abundance of bacteria and fungi at both phylum and genus levels. The bacterial community during processing was predominantly composed of Firmicutes, Actinobacteria, and Bacteroidetes. Notably, the abundance of Actinobacteria and Bacteroidetes exhibited a gradual decline throughout the process, whereas that of Firmicutes showed an increasing trend(Fig. 2 a). At the genus level, Brevibacterium emerged as the dominant genus, followed by Paenibacillus and Odoribacter ; specifically, Paenibacillus species were most prevalent at T4(Fig. 2 b). In terms of fungal communities, Basidiomycota represented the dominant phylum, succeeded by Ascomycota (Fig. 2 c). At the genus level within fungi, Sampaiozyma was identified as the predominant genus with Cladosporium following closely behind (Fig. 2 d). Importantly, bacterial communities demonstrated greater responsiveness to processing compared to their fungal counterparts. To evaluate the effects of processing on microbial communities further, we assessed alterations in both structure and composition through alpha diversity metrics. The Shannon diversity index for bacteria peaked at T1 but significantly decreased by T4 (Fig. 2 e and Fig. S1 a and Table. S3, p < 0.05). while fungal community diversity experienced some reduction without significant change overall (Fig. 2 g and Fig. S1 b and Table. S2, p < 0.05). Both Chao1 and ACE richness indices for bacteria were highest at T1 but also showed significant declines by T4 (Fig. 2 f and Fig. S1 c and Table. S3, p < 0.05). Similarly, fungal community richness displayed a comparable trend with significant decreases noted (Fig. 2 h and Fig. S1 d and Table. S2, p < 0.05). Our findings indicate that processing induced shifts in both structure and diversity within tobacco phyllospheric microbial communities. Principal coordinate analysis (PCoA) revealed that PCo1 and PCo2 axes accounted for 60.37% of variation in bacterial community composition (Fig. 2 i) and 70.18% in fungal community composition respectively (Fig. 2 j). In the four stages of bacteria, no significant separation was observed in the community composition; however, there was a significant difference between T1 and the other three stages (Table. S4, p < 0.05). The fungal community composition was significantly separated and different between T1 and the other three stages (Table. S5, p < 0.05). The community composition of bacteria and fungi in T2, T3, and T4 remained relatively similar throughout the treatment process (Table. S4 and S5). 3.3 Comparative Analysis of Key Microorganisms in Various Leaf States Different processing stages selectively recruited specific groups of bacterial and fungal microorganisms on the surfaces of tobacco leaves. Utilizing a machine learning approach based on the random forest algorithm, this study identified various bacterial species present at the leaf margins across different processing stages and quantified the influence of individual bacterial species on observed variations at each node within the classification tree. The findings revealed distinct genus signatures among the top 40 leaf-associated microorganisms, reflecting recruitment patterns of both bacterial and fungal communities during different processing stages. Notably, the bacterial community exhibited greater enrichment on T2 leaves, including genera such as Alcaligenes , Virgibacillus , Oceanobacillus , Nocardiopsis , Saccharopolyspora , Mycobacterium , Streptomyces , Pseudonocardia , Georgenia , and Brevundimonas (Fig. 3 a). In contrast, the fungal community was more abundant in T1 leaves with notable genera including Eutypella , Hyphodontia , Dissoconium , Rhizoctonia , Cladosporium , Phomatospora , Alishanica , Ganoderma , Pseudopithomyces , Rachicladosporium , Acremonium , Wallemia , Aspergillus , Microascus , and Scopulariopsis (Fig. 3 c). 3.4 Comparative Analysis of Differential Metabolites in Leaves Across Different States Employing partial least squares discriminant analysis (PLS-DA), a statistical and machine learning approach, to investigate the metabolites across four groups revealed a distinct separation between fresh leaves and processed leaves, resulting in the formation of four clusters. Additionally, three clusters emerged within the processed leaves category. Notably, the processing state accounted for the largest proportion of the metabolic changes (32.47%; Fig. 4 a), demonstrating that processing conditions dominantly reshape metabolite profiles. Through the comparative analysis of differential metabolites across stages T1, T2, T3, and T4, a total of 4,985 significantly up-regulated or down-regulated metabolites were identified (Fig. 4 b). To systematically evaluate the effects of processing on metabolites, we designed a multi-stage comparison strategy. On one hand, T1 was used as the baseline control and compared with T2, T3, and T4 respectively, revealing the metabolic differences between fresh leaves (T1) and different processing stages (T2-T4; Fig. 4 c). On the other hand, the progressive stages were compared respectively, with T2 as the control to compare T3 and T4, and then with T3 as the control to compare T4, reflecting the metabolic differences among processed leaves (Fig. 4 e). Specifically, compared to stage T1, there were 1,456; 1,741; and 1,736 metabolites that exhibited up-regulation in leaf tissues at stages T2, T3, and T4 respectively. Concurrently, down-regulation was observed for 1,670; 2,195; and 2,201 metabolites in the leaf tissues at these respective stages (Fig. 4 b). Notably, however only 416 shared metabolites were detected within the processed leaf tissues from stages T2 to T4 (Fig. 4 e). In summary, the differences in metabolite expression between fresh leaf tissues and processed leaf tissues are more pronounced than those observed among processed leaves alone (Fig. 4 c and 4 e). KEGG functional enrichment analysis of common differential metabolites revealed significantly enriched functions related to carbohydrate metabolism (Fig. 4 b), including pathways such as Citrate cycle (TCA), galactose metabolism, amino and nucleotide sugar metabolism, and starch and sucrose metabolism. Furthermore, fresh leaves and processed leaves at various stages all encompassed the Citrate cycle (TCA cycle; Fig. 4 f and Fig. S2a, b, c), but there was no common carbohydrate metabolism pathway among processed leaves (Fig. 4 f and Fig. S2d, e, f). To enhance our understanding of the molecular mechanisms underlying carbohydrate metabolism, we investigated the specific expression patterns of metabolites potentially involved in this process. The results indicate that the 65 differential metabolites exhibit distinct expression profiles in fresh leaves compared to processed leaves, with 4 metabolites participating in the TCA cycle, 6 in galactose metabolism, 6 in starch and sucrose metabolism, and 16 involved in amino sugar and nucleotide sugar metabolism pathways. Notably, metabolites associated with carbohydrate metabolism demonstrate higher expression levels in fresh leaves than those found in processed leaves. Specifically, within Pathway 6 (galactose metabolism) and Pathway 8 (starch and sucrose metabolism), there are respectively 2 and 3 metabolites that display lower expression levels in fresh leaves but show increased levels upon processing (Fig .4f). To assess the influence of the microbiome on relevant metabolites in carbohydrates, mantel correlation analysis was used to look at the relationship between bacterial and fungal community pairs and differential metabolites. For example, Brevibacterium , Mycobacterium , Nocardiopsis , etc. in bacteria were significantly correlated with the expression patterns of metabolites related to metabolic pathways such as map00520, map00030, and map00500 in carbohydrates. In addition, Actinocorallia was highly correlated with the expression patterns of map00020 and related metabolites in Other metabolic pathway (Fig. 5 a). In the fungal community, Acremonium , Ganoderma and other fungi were significantly correlated with the expression patterns of related metabolites of metabolic pathways such as map00520, map00030, and map00500 in carbohydrates. In addition, fungi such as Dissoconium and Eichleriella were extremely significantly correlated with metabolic pathways such as map00520 and map00500 (Fig. 5 b). Considering the important role of starch in tobacco, PICRUSt2 was used to predict the metabolic functions of bacterial communities. Genes encoding CAZymes can indicate the tobacco biomass degradation capacity of microbial communities. Key EC taxonomic numbers involved in starch and sucrose metabolism pathways are listed. The starch degradation process contains a-amylase and glucanphosphorylase. The sucrose degradation process contains invertase and sucrosesynthase. The analysis showed that a-amylase (EC:3.2.1.1) and invertase (EC:3.2.1.26) were significantly positively correlated with sucrose accumulation, and negatively correlated with glucose 6p conversion. In addition, a-amylase (EC:3.2.1.1) was positively correlated with glucose accumulation and realization. Sucrose phosphate synthase (EC:2.4.1.14) had a significant positive correlation with D-fructose 6p accumulation, while sucrose synthase (EC:2.4.1.13) also had a high effect on glucose glucose accumulation (Fig. 5 c). To elucidate the influence of key microorganisms on related compounds, a correlation heat map analysis was employed to investigate the relationships between specific metabolites in carbohydrate metabolism and their corresponding biomarkers. The results indicated that lactose exhibited significant positive correlations with six bacterial genera, including Streptomyces , Mycobacterium , and Saccharopolyspora ; sucrose showed similar positive associations with four genera such as Nocardiopsis and Oceanobacillus ; while D-glucose was positively correlated with nine genera including Oceanobacillus and Virgibacillus (Fig. 6 a). Additionally, important metabolites like lactose, sucrose, and D-glucose were significantly positively correlated with four fungal markers including Microascus and Aspergillus (Fig. 6 b). Conversely, lactose and sucrose demonstrated negative correlations with nine genera including Rachicladosporium and Pseudopithomyces (Fig. 6 b); furthermore, D-glucose displayed a negative correlation specifically with Pseudopithomyces (Fig. 6 b). These significant correlations between keystone species and related compounds suggest their crucial roles in the enrichment or degradation processes of these compounds. 3.5 Reconstruction of Metabolic Pathways for Starch and Sucrose Degradation To enhance the understanding of the synergistic effects of bacterial members on starch and sucrose metabolism, the metabolic pathways for these compounds were reconstructed based on the expression levels of key enzymes predicted by PICRUSt2 for bacterial communities, alongside the measured concentrations of related metabolites. The number of genes corresponding to relevant enzymes and changes in compound concentrations are presented. The initial step in starch hydrolysis involves glycogen phosphorylase (EC:2.4.1.1) and amylase (EC:3.2.1.1). Analysis revealed that enzyme expression in processed leaves was significantly higher than that in fresh leaves, indicating substantial hydrolysis of starch during processing in tobacco tissues. Glycosidase (EC:3.2.1.3) and oligo-1,6-glucosidase (EC:3.2.1.10) continue to hydrolyze dextrin into D-glucose; additionally, glycosidase (EC:3.2.1.20) can also convert maltose into D-glucose as well as α-D-glucose 1p being hydrolyzed to D-glucose-6P and subsequently to D-glucose by glucose-phosphate mutase (EC:5.4.2.2). Furthermore, α-D-glucose 1p can yield sucrose when acted upon by phosphotransferase (EC:2.7.7.9), sucrose phosphate synthase (EC:2.4.1.14), sucrose synthase (EC:2.4.1.13), and sucrose phosphate phosphatase (EC:3.1.3.24). Sucrose is then hydrolyzed into glucose and fructose through the action of glycosidases (EC:3.2.1.20) and sucrases (EC:3.2.1.26), with fructose further converted into D -glucose via sequential reactions (Fig. 7 ). Enzymes within this metabolic pathway contribute to glucose and sucrose accumulation under two scenarios; specifically regarding glucose accumulation, enzymes associated with maltose, dextrin ,and fructose pathways function predominantly during Fresh and Yellowing stages facilitating both starch hydrolysis along with intermediate metabolite transformation while those involved primarily within starch and sucrose degradation processes play a significant role at Leaf-drying and Stem-drying stages promoting overall increases seen within accumulated glucoses. Sucrosynthesis remains chiefly influenced by UDP -glucoside content which serves as an intermediary product generated throughout various phases involving prior-starch breakdowns; thus expressions tied directly towards producing said UDP -glucosides observed elevated levels occurring across Leaf-drying and Stem-drying compared against earlier timeframes Fresh and Yellowing stages. The enhanced expression of enzymes associated with starch hydrolysis that produce UDP-glucose results in accelerated synthesis of UDP-glucose, thereby promoting the synthesis and accumulation of sucrose (Fig. 7 ). 4. Discussion Metabolomics analysis revealed that the TCA cycle (citric acid cycle), starch and sucrose metabolism pathways were significantly enriched in processed leaves (Figure. 4d). The expression differences of carbohydrate metabolites between fresh leaves and processed leaves were particularly prominent (Figure. 4g). Meanwhile, starch was continuously decomposed, resulting in dynamic accumulation of substances such as D-glucose, sucrose, and maltose during the T1-T4 stages(Figure.1a and 1b and 1e), indicating that carbohydrate metabolism runs through the entire processing process. The enrichment of the above metabolic pathways suggests that the staged transformation of starch and sucrose metabolism may be driven by specific microbial functional modules. High-throughput sequencing indicates that the Firmicutes phylum, Actinobacteria phylum, and Bacteroidetes phylum are dominant in the bacterial community during the processing, and the abundance of Firmicutes phylum significantly increases at the Yellowing stage (Fig. 2 a). Previous studies have shown that an increase in temperature can promote the increase in the abundance of Firmicutes phylum (Pérez Castro, Cleland et al. 2019 ) and reduce the abundance of Actinobacteria phylum (Ghai, Mizuno et al. 2014 ), which is consistent with the results in Fig. 2 (Fig. 2 a). The Firmicutes phylum can change the sugar metabolism, amino acid metabolism, and lipid metabolism of tobacco leaves, improving the quality of tobacco leaves (Gu, Ding et al. 2010 , Zhang, Kong et al. 2023 ), and produce flavor precursor substances such as reducing sugars, amino acids, and various organic molecules (Xing, Zhang et al. 2023 ). Some genomic sequences of Actinobacteria show extraordinary potential for converting lignin-derived compounds (Ghai, Mizuno et al. 2014 ). Certain strains of Brevibacterium, which belong to the dominant Brevibacterium species in the Stem-drying stage of this study (Fig. 2 b), can significantly increase the content of sucrose, glucose, fructose, and phenolic compounds when inoculated onto plants (Ferreira, Veríssimo et al. 2024 ). The Paenibacillus (Fig. 2 b) expressing fructose diphosphate aldolase and pyruvate dehydrogenase complex components, etc., participate in the glycolysis pathway in Arabidopsis plants, providing the required carbon for the plants (Kwon, Lee et al. 2016 ). The dominant Ascomycota phylum and Basidiomycota phylum in this study play a role throughout the processing process (Fig. 2 c). Previous studies have shown that two fungal phyla detect many enzymes that promote the degradation of plant polysaccharides, such as α-galactosidase, β-glucanase, and xyloglucanase, indicating that they have the ability to degrade various plant polysaccharides extensively (Manici, Caputo et al. 2024 ). This can reduce the negative impact of stubborn macromolecular compounds such as cellulose and lignin on tobacco leaves. Cladosporium (Fig. 2 d), which belongs to the same genus as the Ascomycota, has also been used for the production of cellulase and protease (Xu, Wang et al. 2024 ). In addition, the high abundance of Cladosporium (Fig. 2 d) may also be the cause of mold contamination in the processed products (Xue, Chen et al. 2016 ). The association of the identified biomarkers Brevibacterium (Fig. 3 a) and Cladosporium (Fig. 3 b) with key metabolites revealed their core roles. The short bacillus was significantly positively correlated with d-glucose (Fig. 6 a), and it had the ability to produce amylase, which was directly related to starch degradation (Oliveira, Ramos et al. 2015 ). Cladosporium and Ganoderma were negatively correlated with lactose and sucrose, but through positive correlation metabolites such as D-glucose 6-phosphate and Sedoheptulose 7-phosphate (Fig. 6 b), they indicated that they might regulate carbon metabolism flow through the pentose phosphate pathway and gluconeogenesis pathway, promoting the generation of intermediate products (Clasquin, Melamud et al. 2011 , Liu, Yu et al. 2022 ). Moreover, Staphylococcus (Fig. 3 a) and Ganoderma (Fig. 3 b) were positively correlated with citric acid, succinic acid, fumaric acid, and cis-aconitic acid (Fig. 6 a and Fig. 6 b), which might be similar to the strategy of overexpressing MDH in cyanobacteria, by regulating the activity of specific enzymes to enhance the oxidative or reductive branches of the TCA cycle and thereby balance carbon metabolism pressure and accumulate C4 intermediates (Iijima, Watanabe et al. 2021 ). Aspergillus was positively correlated with soluble sugars (such as d-glucose; Fig. 6 b), indicating that Aspergillus drove initial degradation through the secretion of amylase and cellulase (Mojsov 2016 ). This might form a metabolic relay with the phosphorylated intermediate products of Cladosporium. Staphylococcus accelerated the energy conversion of the final products through the TCA cycle. Regarding some fungi in the study that did not report carbohydrate metabolism pathways such as Pseudopithomyces and Phoatospora (Fig. 3 b), although they were negatively correlated with glucose, lactose, and sucrose (Fig. 6 b), they were highly correlated with map00500 (starch and sucrose metabolism) (Fig. 5 b), and were significantly positively correlated with key intermediate metabolites such as D-glucose 6-phosphate, D-fructose 6-phosphate, succinic acid, and citric acid (Fig. 6 b). This might be due to the metabolic inhibition of Aspergillus (Cladosporium). When Aspergillus dominated the degradation, its antibacterial metabolites (such as isofraxidine) might inhibit the growth of competing microbial communities such as Pseudopithomyces by changing the microenvironment pH or direct toxicity (Li, Wang et al. 2024 ). Based on the correlation analysis of the biomarkers with the above metabolites and metabolic pathways, we proposed Brevibacterium, Staphylococcus, Aspergillus, Cladosporium, and Ganoderma as key biomarkers. Previous studies have shown that Brevibacterium, Staphylococcus, Aspergillus, Cladosporium and Ganoderma have significant advantages in various types of fermentation (such as fermentation of meat (Fan, Badar et al. 2025 ), Douchi fermentation (Yang, Yang et al. 2016 ), condiment fermentation (Jia, Niu et al. 2021 ), etc.) and have played different roles during the fermentation process. For example, members of the Brevibacterium genus play a role in fermentation by producing amino acids, aroma, carotenoids and antibacterial substances, which play an important role in the formation of aroma in cheese fermentation (Alexa, Cobo-Díaz et al. 2024 , Rana, Chandola et al. 2024 ). Staphylococcus participates in the hydrolysis of proteins to produce peptides and amino acids (the precursors of flavor substances) in broad bean paste and sausage fermentation (Aro Aro, Nyam-Osor et al. 2010 , Jia, Niu et al. 2021 ). Moreover, it can efficiently decompose glucose through EMP (glycolysis) pathway and HMP (phosphogluconic acid) pathway and convert it into pyruvic acid, generating organic acids such as lactic acid and acetic acid, lowering the pH value and inhibiting the growth of spoilage bacteria, dominating the degradation of carbohydrates in fermented meat, and forming an acidic base (Fan, Badar et al. 2025 ). Aspergillus is widely used in food fermentation and the production of large-scale enzymes, organic acids and bioactive compounds (Park, Jun et al. 2017 ). It can produce cellulase, pectinase, amylase and other enzymes that degrade complex polysaccharides (Mojsov 2016 ). In the fermentation of Pu'er tea, it participates in the metabolism of glycosides and polysaccharides and the production of oligosaccharides, serving as a guarantee for the rich taste (Ma, Ling et al. 2021 ). Cladosporium can secrete carbohydrate esterase (CE) and glycoside hydrolase (GH) that act on the deacetylation of plant polysaccharides (Liu, Yu et al. 2022 ), and has the ability to decompose lignin and carbohydrates in the fermentation decomposition of forest litter (Song, Tian et al. 2010 ). Ganoderma can encode multiple carbohydrate-active enzymes, including carbohydrate esterase (CE), glycoside hydrolase (GH), glycosyltransferase (GT), and polysaccharide lyase (PL), which are involved in metabolic pathways such as \"starch and sucrose metabolism\", \"glycolysis/glycogenesis\", and \"pyruvate metabolism\" (Yu, Wang et al. 2012 ). Meanwhile, Ganoderma, as a medicinal mushroom, is also used as a dietary supplement to improve the quality of steamed buns. It can degrade starch in the dough into glucose, maltose, soluble polysaccharides, etc., reducing the hardness and chewiness of the steamed buns, and improving the flavor and aroma (Guowei, Lili et al. 2019 ). Therefore, we speculate that Brevibacterium, Cladosporium and Aspergillus form the main line of starch degradation, while Ganoderma and Staphylococcus promote the generation of intermediate metabolism through the TCA cycle and phosphorylation metabolism, further participating in starch and sucrose metabolism. During the processing of plant leaves, the dynamic metabolism of starch, sucrose, and glucose is driven by microorganisms in a coordinated manner, presenting distinct phased characteristics (Figure. 7). In the initial degradation stage, the Firmicutes phylum, Paenibacillus, highly expresses amylase (EC 3.2.1.1) to cleave amylopectin into maltose (Silano, Baviera et al. 2019 ), while the Actinobacteria phylum, Streptomyces, secretes glucanase (EC 3.2.1.3) to target the α-1,6 glycosidic bonds of amylopectin and release free glucose (Wu, Dou et al. 2018 ). This process is similar to the metabolic remodeling during the saccharification stage of big koji fermentation. Under the temperature conditions of 51–53°C in the early fermentation stage, Bacillus rapidly converts the stored starch in wheat into soluble sugars through the action of amylase, providing substrates for subsequent fermentation (Zhang, Kang et al. 2024 ). It is noteworthy that the Brevibacterium genus has participated in primary degradation through the secretion of amylase (Oliveira, Ramos et al. 2015 ) at this stage, laying the foundation for its subsequent metabolic dominance (Ferreira, Veríssimo et al. 2024 ). As the processing continues, Brevibacterium catalyzes the hydrolysis of maltose into glucose (Materials, Enzymes et al. 2024) through α-glucanase (EC 3.2.1.20) and catalyzes the hydrolysis of sucrose into D-glucose and D-fructose (Lincoln and More 2017 ) through β-fructofuranosidase (EC 3.2.1.26). Meanwhile, the fungal community begins to take effect: Aspergillus, through amylase and cellulase (Mojsov 2016 ), initiates the degradation of stubborn polysaccharides, and the D-glucose generated by Aspergillus combines with D-glucose 6-phosphate produced by Cladosporium to form a metabolic relay (Răut, Călin et al. 2021 ). Additionally, Staphylococcus may convert intermediates such as citric acid and succinic acid into ATP through the TCA cycle (Iijima, Watanabe et al. 2021 , Chen, Zhao et al. 2024 ) and accumulate flavor precursors (Jia, Liu et al. 2023 ); Ganoderma promotes the accumulation of soluble sugars through glycosyltransferase (GT) and polysaccharide lyase (PL), and its application in steamed buns has been proven to optimize the final quality(Yu, Wang et al. 2012 , Guowei, Lili et al. 2019 ). Notably, Cladosporium secretes isofraxidin-like antibacterial substances to inhibit the metabolic activity of competing bacterial populations such as Pseudopithomyces, forming a chemical defense barrier to maintain its dominant position (Li, Wang et al. 2024 ). Therefore, during the processing, bacteria and fungi work together through metabolic division of labor to act on the starch and sucrose metabolic pathways. Brevibacterium and other bacteria, relying on amylase (EC 3.2.1.1) and α-glucosidase (EC 3.2.1.20), efficiently degrade large-molecular polysaccharides. Aspergillus and other fungi increase the accumulation of D-glucose 6-phosphate intermediate metabolic products. A metabolic relay division mode dominated by bacteria and assisted by fungi is formed, providing a favorable substrate foundation for subsequent tobacco leaf fermentation. 5. Conclusion This study combines metabolomics with microbial community analysis to systematically reveal the dynamic patterns of carbohydrate metabolism during plant leaf processing and the mechanisms driven by microorganisms. The research shows that an increase in temperature and a decrease in humidity during the processing process lead to a reduction in the abundance and diversity of microbial communities. The study identified Brevibacterium, Staphylococcus, Aspergillus, and Ganoderma as core biomarkers, providing candidate strains for optimizing the regulation process of tobacco. It clarifies the metabolic patterns of microbial collaboration during the regulation process of tobacco, where bacteria play a leading role in the initial degradation of starch, while fungi promote the accumulation of soluble sugars through the transformation of intermediate products. A degradation - transformation - regulation synergy model has been formed, providing a new perspective for the effective utilization of complex carbon sources. In summary, using multi-omics resources to analyze the influence of microorganisms on key carbohydrates during tobacco leaf processing and accurately understanding the interaction between microorganisms and tobacco is crucial for improving tobacco production efficiency. Declarations CRediT authorship contribution statement Kesu Wei: Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Writing-review & editing. Yang Long: Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Writing-review & editing. Cheng Zhang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing-Original Draft, Visualization. Xiaohua Zhang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing-Original Draft, Visualization. Feng Wang: Conceptualization, Funding acquisition, Project administration, Data curation, Writing-review & editing. Guanhui Li: Conceptualization, Data curation, Formal analysis, Methodology, Writing-review & editing. Jie Ding: Conceptualization, Data curation, Formal analysis, Methodology, Writing-review & editing. Yi Cao: Data curation, Supervision, Writing-review & editing. Hancheng Wang: Data curation, Supervision, Writing-review & editing. Shengjiang Wu: Data curation, Supervision, Writing-review & editing. Xianchao Shang: Data curation, Supervision, Writing-review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This study was supported by the Science and Technology Project of China Tobacco General Corporation [1102022202016/110202201019(LS-03)], the Science and Technology Project of Guizhou Science and Technology Department (QKHJC-ZK [2022] YB288), the Science and Technology Project of Guizhou Tobacco Industry Technology Center (2022XM17), the Science and Technology Project of Shandong Tobacco Industry Technology Center (202111). 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Deep Insight into the Ganoderma lucidum by Comprehensive Analysis of Its Transcriptome. PLOS ONE 7(8): e44031. doi: 10.1371/journal.pone.0044031 Yuan, X. Y., Wang, T., Sun, L. P., Qiao, Z., Pan, H. Y., Zhong, Y. J., et al. (2024). Recent advances of fermented fruits: A review on strains, fermentation strategies, and functional activities. FOOD CHEMISTRY-X 22. doi: 10.1016/j.fochx.2024.101482 Zhang, L. Y., Mai, J., Shi, J. F., Ai, K. B., He, L., Zhu, M. J., et al. (2024). Study on tobacco quality improvement and bacterial community succession during microbial co-fermentation. INDUSTRIAL CROPS AND PRODUCTS 208. doi: 10.1016/j.indcrop.2023.117889 Zhang, Q., Kong, G., Zhao, G., Liu, J., Jin, H., Li, Z., et al. (2023). Microbial and enzymatic changes in cigar tobacco leaves during air-curing and fermentation. Applied Microbiology and Biotechnology 107(18): 5789-5801. doi: 10.1007/s00253-023-12663-5 Zhang, Y. D., Kang, J. M., Han, B. Z. and Chen, X. X. (2024). Wheat-origin Bacillus community drives the formation of characteristic metabolic profile in high-temperature Daqu. LWT-FOOD SCIENCE AND TECHNOLOGY 191: 115597. doi: 10.1016/j.lwt.2023.115597 Zhu, Q., Chen, L. Q., Peng, Z., Zhang, Q. L., Huang, W. Q., Yang, F., et al. (2023). The differences in carbohydrate utilization ability between six rounds of Sauce-flavor Daqu. FOOD RESEARCH INTERNATIONAL 163: 112184. doi: 10.1016/j.foodres.2022.112184 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 12 Nov, 2025 Read the published version in Microbial Ecology → Version 1 posted Editorial decision: Revision requested 11 Aug, 2025 Reviews received at journal 11 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviews received at journal 20 Jul, 2025 Reviewers agreed at journal 28 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor assigned by journal 23 Jun, 2025 Submission checks completed at journal 23 Jun, 2025 First submitted to journal 22 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6949781\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":475872382,\"identity\":\"75cad355-ce6c-4990-9aeb-8ebe4b6f7385\",\"order_by\":0,\"name\":\"Cheng Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shandong Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Cheng\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":475872383,\"identity\":\"48548446-80d8-45e2-a5f0-4448e49a6a90\",\"order_by\":1,\"name\":\"Xiaohua Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shandong Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaohua\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":475872384,\"identity\":\"66e8d08b-4f94-4e05-a0ce-0abd414ad7d8\",\"order_by\":2,\"name\":\"Feng Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Guizhou Academy of Tobacco Science\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Feng\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":475872385,\"identity\":\"7a03527a-6e15-4958-9428-619cc0c4c6f1\",\"order_by\":3,\"name\":\"Guanhui Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Guizhou Academy of Tobacco Science\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Guanhui\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":475872386,\"identity\":\"2fa5aa82-8ba7-4e5c-8353-0f2aae04a883\",\"order_by\":4,\"name\":\"Jie Ding\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shandong Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jie\",\"middleName\":\"\",\"lastName\":\"Ding\",\"suffix\":\"\"},{\"id\":475872387,\"identity\":\"53250fbe-2d94-4de3-922b-d0abefbdc78d\",\"order_by\":5,\"name\":\"Yi Cao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Guizhou Academy of Tobacco Science\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yi\",\"middleName\":\"\",\"lastName\":\"Cao\",\"suffix\":\"\"},{\"id\":475872388,\"identity\":\"9898a889-4489-4c15-aff4-5ed0781b21f2\",\"order_by\":6,\"name\":\"Hancheng Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Guizhou Academy of Tobacco Science\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hancheng\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":475872389,\"identity\":\"8c66d5c2-1c9f-45eb-8ead-8432bb8d92c8\",\"order_by\":7,\"name\":\"Shengjiang Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Guizhou Academy of Tobacco Science\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shengjiang\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":475872390,\"identity\":\"5d5d0ea5-c1ca-4d0a-8378-e652a153161a\",\"order_by\":8,\"name\":\"Xianchao Shang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shandong Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xianchao\",\"middleName\":\"\",\"lastName\":\"Shang\",\"suffix\":\"\"},{\"id\":475872391,\"identity\":\"15f41fd4-d0d4-4273-ba30-240919e28e60\",\"order_by\":9,\"name\":\"Kesu Wei\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYFACxmYGhgobHn72xsaHH4jXciZNRrLncLOxBJHWMDMwth22MZiR3ibAQ4x6g+PNzYY/zqTxGEg+bGOQYLCT020gpOXMweZkHqBfzKUT2x4UMCQbmx0goMXsRmLzYaBfeCxnJ7YbSDAcSNxGUMv9h80Hf7Yd5jG4ebBNgocoLTcYmxN4QVpuMBKpxf5MYrMxD9Bhkj1AhoQBEX6RbD/+WPJHhY09P/vxhw8/VNjJEdSCBgxIUz4KRsEoGAWjAAcAAPBURdsiDNWnAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Guizhou Academy of Tobacco Science\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Kesu\",\"middleName\":\"\",\"lastName\":\"Wei\",\"suffix\":\"\"},{\"id\":475872392,\"identity\":\"4182eb32-448f-467f-bb11-43bbef9846dd\",\"order_by\":10,\"name\":\"Long Yang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shandong Agricultural University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Long\",\"middleName\":\"\",\"lastName\":\"Yang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-06-22 13:38:11\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6949781/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6949781/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s00248-025-02644-8\",\"type\":\"published\",\"date\":\"2025-11-12T15:57:42+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":85504980,\"identity\":\"8210ad98-f8aa-4607-b915-d21131e15797\",\"added_by\":\"auto\",\"created_at\":\"2025-06-26 15:19:04\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":116226,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCompound contents at different stages and Principal Coordinate Analysis (PCoA) analysis. (a) Bar chart of starch contents at different stages (b) Bar chart of sucrose contents at different stages (c) Bar chart of maltose contents at different stages (d) Bar chart of fructose contents at different stages (e) Bar chart of glucose contents at different stages (f) Principal Coordinate Analysis (PCoA) analysis among different compounds.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6949781/v1/efa8631a27d134a1ca523e70.png\"},{\"id\":85505006,\"identity\":\"6ecdab9b-2b1d-4ea9-a286-225ce67eca58\",\"added_by\":\"auto\",\"created_at\":\"2025-06-26 15:19:05\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":219770,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMicrobial communities were affected by processing. phylum and genus level species accumulation histogram of (a), (b) 16S rDNA and (c), (d) ITS sequenced samples. alpha diversity boxplot and wilcox test of (e), (f) 16S rDNA and (g), (h) ITS sequenced samples. Principal coordinate analysis of (i) 16S rDNA and (j) ITS sequencing samples (PCoA analysis)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6949781/v1/62eece7bc095f0cfb519a2f4.png\"},{\"id\":85504982,\"identity\":\"be31f11d-9dbb-4678-b495-1b286fbcb731\",\"added_by\":\"auto\",\"created_at\":\"2025-06-26 15:19:05\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":228122,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAcquisition of biomarkers at different processing stages. calculate the biological identifiers for 16S rDNA sequencing samples (a) based on the RF algorithm (Mean Decrease Gini). calculate the biological identifiers for ITS sequencing samples (b) based on the RF algorithm (Mean Decrease Gini)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6949781/v1/335cfc072bcdad7bce73e21b.png\"},{\"id\":85504981,\"identity\":\"90b7f49f-18ba-4c83-a273-40ea77abaabe\",\"added_by\":\"auto\",\"created_at\":\"2025-06-26 15:19:04\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":381666,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEffect of different processing stages on differential metabolites. (a) Partial least squares (PLS-DA) metabolite analysis during modulation period. (b) Differential metabolite analysis. (c) Wayne Diagram analysis of differential metabolites in T1 and T2, T1 and T3, andT1 andT4 groups. (d) KEGG enrichment analysis of different metabolites in T1 and T2, T1 and T3 and T1 and T4 groups. (e) Wayne Diagram analysis of differential metabolites in T2 and T3, T2 and T4, and T3 and T4 groups. (f) KEGG enrichment analysis of different metabolites in T2 and T3, T2 and T4, and T3 and T4 groups. (g) Expression patterns of differential metabolites involved in carbohydrate metabolism during different modulation processes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6949781/v1/ba44b06afed0a93726d6143e.png\"},{\"id\":85506129,\"identity\":\"858d27e2-0648-4137-8c00-041e626547ee\",\"added_by\":\"auto\",\"created_at\":\"2025-06-26 15:27:05\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":531056,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEffects of the microbiome on relevant metabolites in carbohydrates. mantel test correlation analysis of bacterial (a) and fungal (b) biomarkers and carbohydrate metabolic pathways (c) Correlation analysis of key ECS and related compounds involved in starch and sucrose metabolism\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6949781/v1/339bf11fd9d264f46279ac1b.png\"},{\"id\":85504996,\"identity\":\"960352ef-d083-4544-9e12-89e29ddc2f69\",\"added_by\":\"auto\",\"created_at\":\"2025-06-26 15:19:05\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":600718,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCorrelation analysis between biomarkers and carbohydrates. (a) Correlation analysis between important biomarkers of 16S rDNA sequencing samples and carbohydrates, (b) Correlation analysis between important biomarkers of ITS sequencing samples and carbohydrates.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6949781/v1/e29ce9d7d7d070e24507a960.png\"},{\"id\":85504998,\"identity\":\"46d7b81f-ef75-49d0-a8b4-c6e77e02f807\",\"added_by\":\"auto\",\"created_at\":\"2025-06-26 15:19:05\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":386604,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSchematic diagram of starch and sucrose biosynthesis\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6949781/v1/3e4983950ce6869046156d0e.png\"},{\"id\":96105337,\"identity\":\"fe2d7ec4-019d-4805-b549-a561f5affe5d\",\"added_by\":\"auto\",\"created_at\":\"2025-11-17 16:11:14\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2971029,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6949781/v1/121934fe-b195-4a46-a537-4f1e82307db0.pdf\"},{\"id\":85504984,\"identity\":\"22d028c3-1495-445e-8af2-c57ba9f5fc5e\",\"added_by\":\"auto\",\"created_at\":\"2025-06-26 15:19:05\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":990560,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryMaterial.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6949781/v1/8bf39731c74e2c950cfd4865.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Integrated Multi-Omics Analysis Reveals Microbial Community Restructuring and Its Role in Key Carbohydrate Metabolic Pathways During Tobacco Leaf Curing\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eStarch-based natural organic compounds, as sustainable and renewable resources(Tao, Chen et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), play a critical role in the global carbon cycle and energy supply. These compounds constitute a significant portion of Earth's biomass, primarily produced by plants(Bar-On, Phillips et al. \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). In the field of plant processing, the role of microorganisms is particularly important, as they can secrete various enzymes(Hebelstrup, Sagnelli et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), such as amylase(Zhu, Chen et al. \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) and cellulase(Jiang, Peng et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), which can efficiently degrade the macromolecular substances in plant tissues into easily absorbed metabolic products(Jiang, Peng et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), significantly affecting the nutritional and flavor quality of crops such as grains(Kaur and Prasad \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), vegetables(Rizo, Guill\\u0026eacute;n et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e, Shang, Ye et al. \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), fruits(Yuan, Wang et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), and tea(Feng, Deng et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e, Yang, Peng et al. \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). For example, in the processing of tea and certain specific crops, microorganisms promote the formation and accumulation of aromatic compounds by secreting phenol oxidase and peroxidase enzymes(Guo, Lv et al. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e, Wang, Lei et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). When considering the processing of plant leaves, especially plant-based products, special attention is paid to the role of microorganisms in degrading complex macromolecular substances such as starch, which are not only important nutrient sources for microorganisms but also profoundly affect the final characteristics of processed products(Zhang, Mai et al. \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eStarch is a difficult-to-degrade component in plant leaves, is significantly affected by processing environmental conditions (Ren, Qin et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Under specific environmental conditions, microbial communities can achieve effective hydrolysis of starch macromolecules by secreting specific enzymes, such as Bacillus as a common functional microbial group, which has α-amylase activity. This results in the generation of reducing sugars, total sugars, and other intermediate products (Wardman, Bains et al. \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e, Zhang, Mai et al. \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), and further conversion into alcohols, aldehydes, acids, and esters, which are aroma components(Gong, Li et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e, Weng, Deng et al. \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). This process not only enriches the flavor of the product but also promotes the complex interaction between plant-microbial microecosystems.\\u003c/p\\u003e \\u003cp\\u003eIn recent years, studies have revealed the dynamic changes in the microbial community during plant leaf processing, but most of them have focused on the description of community structure rather than functional analysis(Ding, Wei et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). In particular, the specific role mechanisms of core microorganisms in degrading starch and other macromolecular substances are not yet clear (Hu, Gu et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Therefore, it is of great significance to explore the functions of these key microorganisms in depth and clarify their contributions to macromolecule degradation, which can optimize processing technology and improve product quality.\\u003c/p\\u003e \\u003cp\\u003ePlants actively construct complex phyllospheric microbial consorts during processing(Santoyo \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), these microorganisms closely interact with metabolites on the surface of plant leaves to jointly respond to the multiple challenges posed by the processing environment(Qin, Druzhinina et al. \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). In this study, we analyzed the changes in microbial communities and metabolic functional pathways during the processing of tobacco leaves, revealed the metabolic potential of microbial communities, and explored the close relationship between microbial communities and metabolic processes during the processing, especially the potential cooperative mechanisms of microbial communities in starch degradation. The model research on exploring the interaction patterns between microbial communities and metabolites provided a new perspective for understanding the complex mechanism of microbial co-degradation of starch during the processing. In summary, this study aimed to reveal the synergistic degradation effect of tobacco processing microorganisms on starch, thereby better understanding the metabolic situation during the tobacco processing process and promoting the improvement of tobacco processing technology.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Sample Collection and Processing\\u003c/h2\\u003e \\u003cp\\u003eIn August 2022, tobacco leaves (Nicotiana tabacum L., variety 'Yunyan 87') were collected from Fuquan City, Guizhou Province (107\\u0026deg;14\\u0026prime; E, 27\\u0026deg;02\\u0026prime; N) as experimental materials. During the processing procedure, a total of 4 sampling points were set up, and the temperature and relative humidity data at each sampling point were recorded. (T1: 27\\u0026deg;C, 79%; Fresh leaves. T2: 42\\u0026deg;C, 67%; Yellowing stage. T3: 54\\u0026deg;C, 22%; Leaf-drying stage. T4: 68\\u0026deg;C, 7%; Stem-drying stage) (Table. S1). At different stages, the sampling points were adjusted by means of the temperature and humidity controllers to adapt to the processing temperature and humidity conditions, and were maintained for a certain period of time in the corresponding stages to ensure the stability of temperature and humidity in the experimental environment. Meanwhile, during sample collection, the temperature and humidity at each collection time were recorded. The untreated leaves (T1) were used as the control to evaluate the effects of different temperature and relative humidity conditions during the processing on the microbial community and metabolites. At each sampling point, three biological replicates were collected for each sample, totaling 12 biological replicates. The dry bulb temperature and wet bulb temperature were recorded at each sampling time.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Organic compound measurement\\u003c/h2\\u003e \\u003cp\\u003eThe collected samples were ground into powder. Approximately 0.1 g of sample was added to 1 mL of 80% ethanol for thorough homogenization. The samples were extracted at 80\\u0026deg;C in a water bath for 30 min, and then centrifuged at 3000 rpm at room temperature for 5 min. The starch content was determined using the Solarbio assay kit (Beijing, China). The contents of sucrose, maltose, D-glucose, and D-fructose were determined according to previous studies using GC-MS (Jing, Chen et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Leaf surface microbial washing and enrichment\\u003c/h2\\u003e \\u003cp\\u003ePlace approximately 20 g of fresh leaf samples or 5\\u0026ndash;10 g of dry leaf samples in a 500 mL conical flask, add 250 mL of 1% sterile PBS buffer solution, and collect microorganisms on the leaves by shaking the conical flask and centrifuging to wash off the liquid. After centrifugation, discard the supernatant and place the coarse precipitate in a -80\\u0026deg;C freezer for subsequent sequencing work to proceed normally. (Ding, Wei et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 DNA Extraction and Illumina Mi-Seq Sequencing\\u003c/h2\\u003e \\u003cp\\u003eDNA was extracted using the SDS method, and then agarose gel electrophoresis was performed to check the purity and concentration of the genomic DNA. 30ng of high-quality genomic DNA samples and corresponding fusion primers were used to configure the PCR reaction system(Ding, Wei et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), and the V5-V7 variable region was amplified using specific primers. The bacterial 16S rRNA gene was amplified using the 799F-1193R (AACMGGATTAGATACCCKG-ACGTCATCCCCACCTTCC) primers(Anguita-Maeso, Haro et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). PCR was performed using Phusion\\u0026reg; High-Fidelity PCR Master Mix and GC Buffer (New England Biolabs, Ipswich, MA, USA) with high-fidelity and high-efficiency enzymes(Ding, Wei et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The Ribosomal Database Project library was constructed. The fragment range and concentration of the library were detected using an Agilent 2100 Bioanalyzer. The qualified library was sequenced based on the size of the inserted fragment. The ITS1F and ITS2R primers were used to amplify the fungal ITS gene(Adams, Miletto et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), except for the annealing temperature of 55\\u0026deg;C and 35 cycles, the PCR conditions were the same as those of the 16S rRNA gene(Sun, Wang et al. \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 Leaf surface microbial metagenome sequencing data analysis\\u003c/h2\\u003e \\u003cp\\u003eThe target DNA fragments were extracted from the sequencing reads by removing the primer sequence and adapter using cutadapt v2.6. The data was filtered by setting the window length to 25 bp, and all low-quality windows were processed starting from the position where the average quality value was less than 20. Additionally, all reads with a final length reduced to 75% or less of the original read length were excluded, as well as reads containing unknown base N and low complexity reads (such as sequences with 10 consecutive identical bases). The reads were efficiently assembled into long sequences using FLASH v1.2.11(Magoč and Salzberg \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) based on the overlapping regions (the minimum matching length was set to 15 bp, and an error rate of 0.1 was allowed). Pairs of reads were assembled into a single long sequence to improve the sequence's completeness and accuracy. Sequence clustering was performed using the UPARSE algorithm in USEARCH v7.0.1090_i86linux32 at a similarity threshold of 97%, which identified representative OTU sequences(Edgar \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Subsequently, UCHIME v4.2.40 was used to remove chimeric sequences that may have arisen from PCR amplification, and all processed tags were compared back to the OTU representative sequences using usearch_global, generating a comprehensive OTU abundance statistics table(Edgar, Haas et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). The taxonomic affiliation of each OTU was clarified by setting the confidence threshold to 0.6, and the OTU/ASV representative sequences were compared to the corresponding databases (16S used RDP (Cole, Wang et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e), ITS used UNITE(Nilsson, Larsson et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e)). After that, all OTUs that could not be successfully annotated were removed.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6 Metabolite Measurement\\u003c/h2\\u003e \\u003cp\\u003eWeigh 50 \\u0026micro;g of sample into 1.5 mL Eppendorf tubes and soak them in 800 \\u0026micro;L of precooled extraction solution (methanol: H2O\\u0026thinsp;=\\u0026thinsp;7:3, v/v) and 20 \\u0026micro;L of internal standard 1 (IS1). Homogenize the samples at 50 Hz for 10 min using a woven grinding machine, then sonicate them in a water bath at 4\\u0026deg;C for 30 min. Allow the extracts to stand at -20\\u0026deg;C for 1 hour, then centrifuge them at 14,000 rpm for 15 min at 4\\u0026deg;C. Filter 600 \\u0026micro;L of the supernatant using a 0.22 \\u0026micro;m membrane and mix 20 \\u0026micro;L of the filtered solution from each sample with the QC sample to evaluate the repeatability and stability of the LC/MS analysis. Transfer the filtered samples and the mixed QC samples to 1.5 mL sample vials for instrument operation. Use a Hypersil GOLD aQ Dim column (1.9 \\u0026micro;m, 2.1*100 mm, Thermo Fisher Scientific, USA) for metabolite determination. The mobile phase is a water solution containing 0.1% formic acid (A liquid) and an acetonitrile solution containing 0.1% formic acid (B liquid) that is eluted using the following gradient: 0\\u0026ndash;2 min, 5% B liquid; 2\\u0026ndash;22 min, 5%-95% B liquid; 22\\u0026ndash;27 min, 95% B liquid; 27-27.1 min, 95% B liquid-5% B liquid; 27.1\\u0026ndash;2.7 min, 5% B liquid. The flow rate is 0.3 mL/min, the column temperature is 40\\u0026deg;C, and the injection volume is 5 \\u0026micro;L. (Bian, Sun et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe downstream data from mass spectrometry is imported into Compound Discoverer 3.3 (Thermo Fisher Scientific, USA) software, combined with the BMDB (Huadai Metabolome Database, BGI Metabolome Database), mzCloud database, and ChemSpider online database for mass spectrometry data analysis, which will result in a data matrix containing information such as metabolite peak areas and identification results. After that, the table will be further processed for information analysis. The metaX software is used to preprocess the exported data, and detailed information annotation of metabolites is performed using authoritative databases such as KEGG and HMDB, including KEGG ID, HMDB ID, classification information, and participation in KEGG metabolic pathways (Pang, Lu et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.7 Statistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eAll calculations and statistical analyses were conducted in R Studio (v.4.4.1, The R Foundation for Statistical Computing, Vienna, Austria). The α diversity index was calculated to evaluate microbial community diversity, and the Bray-Curtis distance was used for similarity analysis, complemented by a non-parametric test (ANOSIM) to determine the influence of different factors on community diversity. The Venn diagram was used with the \\\"VennDiagram\\\" package. Pearson's method was used based on normalized data, and the correlation heatmap was executed using the R package \\\"pheatmap\\\". Pearson correlation analysis was conducted between biomarkers and large molecular compounds, and P values less than 0.05 were considered statistically significant. Random forest was used to establish prediction models and identify biomarkers. Differential metabolite analysis was conducted using the BGI Dr. Tom multi-omics analysis platform [\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://biosys.bgi.com//report/login\\u003c/span\\u003e\\u003cspan address=\\\"https://biosys.bgi.com//report/login\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e]. KEGG enrichment analysis was conducted using the Omicshare Genomics Bioinformatics Cloud Platform (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.omicshare.com\\u003c/span\\u003e\\u003cspan address=\\\"https://www.omicshare.com\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Generation of processed tobacco microbiome and metabolomics resources\\u003c/h2\\u003e \\u003cp\\u003eBoth 16S rRNA sequencing and ITS sequencing were conducted to analyze tobacco phyllospheric microorganisms. A comparative analysis of metabolites in tobacco leaves at various processing stages reveals the degradation of starch macromolecules alongside the accumulation of sucrose and glucose small molecules during leaf processing, which is crucial for determining the quality of processed tobacco products. The results from GC-MS and starch measurements indicated that the starch content at stage T1 was significantly higher than that observed in the other three stages, with no significant difference between stages T2 and T4(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea). Conversely, the concentrations of sucrose, maltose, fructose, and glucose at T1 were lower than those at T2, T3, and T4 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb, c, d, e). Notably, the concentration at stage T3 was significantly higher than that at all other stages. The impact of processing stage on compound content variation is more pronounced than self-conversion among compounds (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ef).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo ascertain whether the metabolic changes in tobacco exhibited similar specific expression patterns across different processing stages, both qualitative and quantitative analyses of the metabolomes from processed leaves were conducted. Following normalization and data correction, compounds with relative peak areas exceeding 30% were excluded from all quality control (QC) samples, resulting in a total of 41,628 metabolites identified from 16 samples. Through 16S rRNA sequencing, we obtained a total of 1,108,954 high-quality reads and identified 7,710 taxonomic operational units (OTUs) from the same set of tobacco leaf samples. Additionally, ITS sequencing yielded a total of 1,436,843 high-quality reads and identified 8,641 OTUs from these tobacco leaf samples.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Microbial communities are affected by the course of working\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo gain a deeper understanding of how processing influences changes in microbial communities, we analyzed the relative abundance of bacteria and fungi at both phylum and genus levels. The bacterial community during processing was predominantly composed of Firmicutes, Actinobacteria, and Bacteroidetes. Notably, the abundance of Actinobacteria and Bacteroidetes exhibited a gradual decline throughout the process, whereas that of Firmicutes showed an increasing trend(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea). At the genus level, \\u003cem\\u003eBrevibacterium\\u003c/em\\u003e emerged as the dominant genus, followed by \\u003cem\\u003ePaenibacillus\\u003c/em\\u003e and \\u003cem\\u003eOdoribacter\\u003c/em\\u003e; specifically, \\u003cem\\u003ePaenibacillus\\u003c/em\\u003e species were most prevalent at T4(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb). In terms of fungal communities, Basidiomycota represented the dominant phylum, succeeded by Ascomycota (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec). At the genus level within fungi, \\u003cem\\u003eSampaiozyma\\u003c/em\\u003e was identified as the predominant genus with \\u003cem\\u003eCladosporium\\u003c/em\\u003e following closely behind (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed). Importantly, bacterial communities demonstrated greater responsiveness to processing compared to their fungal counterparts.\\u003c/p\\u003e \\u003cp\\u003eTo evaluate the effects of processing on microbial communities further, we assessed alterations in both structure and composition through alpha diversity metrics. The Shannon diversity index for bacteria peaked at T1 but significantly decreased by T4 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ee and Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003ea and Table. S3, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). while fungal community diversity experienced some reduction without significant change overall (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eg and Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eb and Table. S2, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Both Chao1 and ACE richness indices for bacteria were highest at T1 but also showed significant declines by T4 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ef and Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003ec and Table. S3, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Similarly, fungal community richness displayed a comparable trend with significant decreases noted (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eh and Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003ed and Table. S2, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Our findings indicate that processing induced shifts in both structure and diversity within tobacco phyllospheric microbial communities.\\u003c/p\\u003e \\u003cp\\u003ePrincipal coordinate analysis (PCoA) revealed that PCo1 and PCo2 axes accounted for 60.37% of variation in bacterial community composition (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ei) and 70.18% in fungal community composition respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ej). In the four stages of bacteria, no significant separation was observed in the community composition; however, there was a significant difference between T1 and the other three stages (Table. S4, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The fungal community composition was significantly separated and different between T1 and the other three stages (Table. S5, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The community composition of bacteria and fungi in T2, T3, and T4 remained relatively similar throughout the treatment process (Table. S4 and S5).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Comparative Analysis of Key Microorganisms in Various Leaf States\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eDifferent processing stages selectively recruited specific groups of bacterial and fungal microorganisms on the surfaces of tobacco leaves. Utilizing a machine learning approach based on the random forest algorithm, this study identified various bacterial species present at the leaf margins across different processing stages and quantified the influence of individual bacterial species on observed variations at each node within the classification tree. The findings revealed distinct genus signatures among the top 40 leaf-associated microorganisms, reflecting recruitment patterns of both bacterial and fungal communities during different processing stages. Notably, the bacterial community exhibited greater enrichment on T2 leaves, including genera such as \\u003cem\\u003eAlcaligenes\\u003c/em\\u003e, \\u003cem\\u003eVirgibacillus\\u003c/em\\u003e, \\u003cem\\u003eOceanobacillus\\u003c/em\\u003e, \\u003cem\\u003eNocardiopsis\\u003c/em\\u003e, \\u003cem\\u003eSaccharopolyspora\\u003c/em\\u003e, \\u003cem\\u003eMycobacterium\\u003c/em\\u003e, \\u003cem\\u003eStreptomyces\\u003c/em\\u003e, \\u003cem\\u003ePseudonocardia\\u003c/em\\u003e, \\u003cem\\u003eGeorgenia\\u003c/em\\u003e, and \\u003cem\\u003eBrevundimonas\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea). In contrast, the fungal community was more abundant in T1 leaves with notable genera including \\u003cem\\u003eEutypella\\u003c/em\\u003e, \\u003cem\\u003eHyphodontia\\u003c/em\\u003e, \\u003cem\\u003eDissoconium\\u003c/em\\u003e, \\u003cem\\u003eRhizoctonia\\u003c/em\\u003e, \\u003cem\\u003eCladosporium\\u003c/em\\u003e, \\u003cem\\u003ePhomatospora\\u003c/em\\u003e, \\u003cem\\u003eAlishanica\\u003c/em\\u003e, \\u003cem\\u003eGanoderma\\u003c/em\\u003e, \\u003cem\\u003ePseudopithomyces\\u003c/em\\u003e, \\u003cem\\u003eRachicladosporium\\u003c/em\\u003e, \\u003cem\\u003eAcremonium\\u003c/em\\u003e, \\u003cem\\u003eWallemia\\u003c/em\\u003e, \\u003cem\\u003eAspergillus\\u003c/em\\u003e, \\u003cem\\u003eMicroascus\\u003c/em\\u003e, and \\u003cem\\u003eScopulariopsis\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ec).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Comparative Analysis of Differential Metabolites in Leaves Across Different States\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eEmploying partial least squares discriminant analysis (PLS-DA), a statistical and machine learning approach, to investigate the metabolites across four groups revealed a distinct separation between fresh leaves and processed leaves, resulting in the formation of four clusters. Additionally, three clusters emerged within the processed leaves category. Notably, the processing state accounted for the largest proportion of the metabolic changes (32.47%; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea), demonstrating that processing conditions dominantly reshape metabolite profiles.\\u003c/p\\u003e \\u003cp\\u003eThrough the comparative analysis of differential metabolites across stages T1, T2, T3, and T4, a total of 4,985 significantly up-regulated or down-regulated metabolites were identified (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb). To systematically evaluate the effects of processing on metabolites, we designed a multi-stage comparison strategy. On one hand, T1 was used as the baseline control and compared with T2, T3, and T4 respectively, revealing the metabolic differences between fresh leaves (T1) and different processing stages (T2-T4; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ec). On the other hand, the progressive stages were compared respectively, with T2 as the control to compare T3 and T4, and then with T3 as the control to compare T4, reflecting the metabolic differences among processed leaves (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ee). Specifically, compared to stage T1, there were 1,456; 1,741; and 1,736 metabolites that exhibited up-regulation in leaf tissues at stages T2, T3, and T4 respectively. Concurrently, down-regulation was observed for 1,670; 2,195; and 2,201 metabolites in the leaf tissues at these respective stages (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb). Notably, however only 416 shared metabolites were detected within the processed leaf tissues from stages T2 to T4 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ee). In summary, the differences in metabolite expression between fresh leaf tissues and processed leaf tissues are more pronounced than those observed among processed leaves alone (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ec and \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ee). KEGG functional enrichment analysis of common differential metabolites revealed significantly enriched functions related to carbohydrate metabolism (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb), including pathways such as Citrate cycle (TCA), galactose metabolism, amino and nucleotide sugar metabolism, and starch and sucrose metabolism. Furthermore, fresh leaves and processed leaves at various stages all encompassed the Citrate cycle (TCA cycle; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ef and Fig. S2a, b, c), but there was no common carbohydrate metabolism pathway among processed leaves (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ef and Fig. S2d, e, f).\\u003c/p\\u003e \\u003cp\\u003eTo enhance our understanding of the molecular mechanisms underlying carbohydrate metabolism, we investigated the specific expression patterns of metabolites potentially involved in this process. The results indicate that the 65 differential metabolites exhibit distinct expression profiles in fresh leaves compared to processed leaves, with 4 metabolites participating in the TCA cycle, 6 in galactose metabolism, 6 in starch and sucrose metabolism, and 16 involved in amino sugar and nucleotide sugar metabolism pathways. Notably, metabolites associated with carbohydrate metabolism demonstrate higher expression levels in fresh leaves than those found in processed leaves. Specifically, within Pathway 6 (galactose metabolism) and Pathway 8 (starch and sucrose metabolism), there are respectively 2 and 3 metabolites that display lower expression levels in fresh leaves but show increased levels upon processing (Fig .4f).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo assess the influence of the microbiome on relevant metabolites in carbohydrates, mantel correlation analysis was used to look at the relationship between bacterial and fungal community pairs and differential metabolites. For example, \\u003cem\\u003eBrevibacterium\\u003c/em\\u003e, \\u003cem\\u003eMycobacterium\\u003c/em\\u003e, \\u003cem\\u003eNocardiopsis\\u003c/em\\u003e, etc. in bacteria were significantly correlated with the expression patterns of metabolites related to metabolic pathways such as map00520, map00030, and map00500 in carbohydrates. In addition, \\u003cem\\u003eActinocorallia\\u003c/em\\u003e was highly correlated with the expression patterns of map00020 and related metabolites in Other metabolic pathway (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea). In the fungal community, \\u003cem\\u003eAcremonium\\u003c/em\\u003e, \\u003cem\\u003eGanoderma\\u003c/em\\u003e and other fungi were significantly correlated with the expression patterns of related metabolites of metabolic pathways such as map00520, map00030, and map00500 in carbohydrates. In addition, fungi such as \\u003cem\\u003eDissoconium\\u003c/em\\u003e and \\u003cem\\u003eEichleriella\\u003c/em\\u003e were extremely significantly correlated with metabolic pathways such as map00520 and map00500 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eb).\\u003c/p\\u003e \\u003cp\\u003eConsidering the important role of starch in tobacco, PICRUSt2 was used to predict the metabolic functions of bacterial communities. Genes encoding CAZymes can indicate the tobacco biomass degradation capacity of microbial communities. Key EC taxonomic numbers involved in starch and sucrose metabolism pathways are listed. The starch degradation process contains a-amylase and glucanphosphorylase. The sucrose degradation process contains invertase and sucrosesynthase. The analysis showed that a-amylase (EC:3.2.1.1) and invertase (EC:3.2.1.26) were significantly positively correlated with sucrose accumulation, and negatively correlated with glucose 6p conversion. In addition, a-amylase (EC:3.2.1.1) was positively correlated with glucose accumulation and realization. Sucrose phosphate synthase (EC:2.4.1.14) had a significant positive correlation with D-fructose 6p accumulation, while sucrose synthase (EC:2.4.1.13) also had a high effect on glucose glucose accumulation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ec).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo elucidate the influence of key microorganisms on related compounds, a correlation heat map analysis was employed to investigate the relationships between specific metabolites in carbohydrate metabolism and their corresponding biomarkers. The results indicated that lactose exhibited significant positive correlations with six bacterial genera, including \\u003cem\\u003eStreptomyces\\u003c/em\\u003e, \\u003cem\\u003eMycobacterium\\u003c/em\\u003e, and \\u003cem\\u003eSaccharopolyspora\\u003c/em\\u003e; sucrose showed similar positive associations with four genera such as \\u003cem\\u003eNocardiopsis\\u003c/em\\u003e and \\u003cem\\u003eOceanobacillus\\u003c/em\\u003e; while D-glucose was positively correlated with nine genera including \\u003cem\\u003eOceanobacillus\\u003c/em\\u003e and \\u003cem\\u003eVirgibacillus\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003ea). Additionally, important metabolites like lactose, sucrose, and D-glucose were significantly positively correlated with four fungal markers including \\u003cem\\u003eMicroascus\\u003c/em\\u003e and \\u003cem\\u003eAspergillus\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eb). Conversely, lactose and sucrose demonstrated negative correlations with nine genera including \\u003cem\\u003eRachicladosporium\\u003c/em\\u003e and \\u003cem\\u003ePseudopithomyces\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eb); furthermore, D-glucose displayed a negative correlation specifically with \\u003cem\\u003ePseudopithomyces\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eb). These significant correlations between keystone species and related compounds suggest their crucial roles in the enrichment or degradation processes of these compounds.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Reconstruction of Metabolic Pathways for Starch and Sucrose Degradation\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo enhance the understanding of the synergistic effects of bacterial members on starch and sucrose metabolism, the metabolic pathways for these compounds were reconstructed based on the expression levels of key enzymes predicted by PICRUSt2 for bacterial communities, alongside the measured concentrations of related metabolites. The number of genes corresponding to relevant enzymes and changes in compound concentrations are presented. The initial step in starch hydrolysis involves glycogen phosphorylase (EC:2.4.1.1) and amylase (EC:3.2.1.1). Analysis revealed that enzyme expression in processed leaves was significantly higher than that in fresh leaves, indicating substantial hydrolysis of starch during processing in tobacco tissues. Glycosidase (EC:3.2.1.3) and oligo-1,6-glucosidase (EC:3.2.1.10) continue to hydrolyze dextrin into D-glucose; additionally, glycosidase (EC:3.2.1.20) can also convert maltose into D-glucose as well as α-D-glucose 1p being hydrolyzed to D-glucose-6P and subsequently to D-glucose by glucose-phosphate mutase (EC:5.4.2.2). Furthermore, α-D-glucose 1p can yield sucrose when acted upon by phosphotransferase (EC:2.7.7.9), sucrose phosphate synthase (EC:2.4.1.14), sucrose synthase (EC:2.4.1.13), and sucrose phosphate phosphatase (EC:3.1.3.24). Sucrose is then hydrolyzed into glucose and fructose through the action of glycosidases (EC:3.2.1.20) and sucrases (EC:3.2.1.26), with fructose further converted into D -glucose via sequential reactions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eEnzymes within this metabolic pathway contribute to glucose and sucrose accumulation under two scenarios; specifically regarding glucose accumulation, enzymes associated with maltose, dextrin ,and fructose pathways function predominantly during Fresh and Yellowing stages facilitating both starch hydrolysis along with intermediate metabolite transformation while those involved primarily within starch and sucrose degradation processes play a significant role at Leaf-drying and Stem-drying stages promoting overall increases seen within accumulated glucoses. Sucrosynthesis remains chiefly influenced by UDP -glucoside content which serves as an intermediary product generated throughout various phases involving prior-starch breakdowns; thus expressions tied directly towards producing said UDP -glucosides observed elevated levels occurring across Leaf-drying and Stem-drying compared against earlier timeframes Fresh and Yellowing stages. The enhanced expression of enzymes associated with starch hydrolysis that produce UDP-glucose results in accelerated synthesis of UDP-glucose, thereby promoting the synthesis and accumulation of sucrose (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eMetabolomics analysis revealed that the TCA cycle (citric acid cycle), starch and sucrose metabolism pathways were significantly enriched in processed leaves (Figure. 4d). The expression differences of carbohydrate metabolites between fresh leaves and processed leaves were particularly prominent (Figure. 4g). Meanwhile, starch was continuously decomposed, resulting in dynamic accumulation of substances such as D-glucose, sucrose, and maltose during the T1-T4 stages(Figure.1a and 1b and 1e), indicating that carbohydrate metabolism runs through the entire processing process.\\u003c/p\\u003e \\u003cp\\u003eThe enrichment of the above metabolic pathways suggests that the staged transformation of starch and sucrose metabolism may be driven by specific microbial functional modules. High-throughput sequencing indicates that the Firmicutes phylum, Actinobacteria phylum, and Bacteroidetes phylum are dominant in the bacterial community during the processing, and the abundance of Firmicutes phylum significantly increases at the Yellowing stage (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea). Previous studies have shown that an increase in temperature can promote the increase in the abundance of Firmicutes phylum (P\\u0026eacute;rez Castro, Cleland et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) and reduce the abundance of Actinobacteria phylum (Ghai, Mizuno et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), which is consistent with the results in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea). The Firmicutes phylum can change the sugar metabolism, amino acid metabolism, and lipid metabolism of tobacco leaves, improving the quality of tobacco leaves (Gu, Ding et al. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e, Zhang, Kong et al. \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), and produce flavor precursor substances such as reducing sugars, amino acids, and various organic molecules (Xing, Zhang et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Some genomic sequences of Actinobacteria show extraordinary potential for converting lignin-derived compounds (Ghai, Mizuno et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Certain strains of Brevibacterium, which belong to the dominant Brevibacterium species in the Stem-drying stage of this study (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb), can significantly increase the content of sucrose, glucose, fructose, and phenolic compounds when inoculated onto plants (Ferreira, Ver\\u0026iacute;ssimo et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The Paenibacillus (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb) expressing fructose diphosphate aldolase and pyruvate dehydrogenase complex components, etc., participate in the glycolysis pathway in Arabidopsis plants, providing the required carbon for the plants (Kwon, Lee et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). The dominant Ascomycota phylum and Basidiomycota phylum in this study play a role throughout the processing process (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec). Previous studies have shown that two fungal phyla detect many enzymes that promote the degradation of plant polysaccharides, such as α-galactosidase, β-glucanase, and xyloglucanase, indicating that they have the ability to degrade various plant polysaccharides extensively (Manici, Caputo et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). This can reduce the negative impact of stubborn macromolecular compounds such as cellulose and lignin on tobacco leaves. Cladosporium (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed), which belongs to the same genus as the Ascomycota, has also been used for the production of cellulase and protease (Xu, Wang et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). In addition, the high abundance of Cladosporium (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed) may also be the cause of mold contamination in the processed products (Xue, Chen et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe association of the identified biomarkers Brevibacterium (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea) and Cladosporium (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb) with key metabolites revealed their core roles. The short bacillus was significantly positively correlated with d-glucose (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003ea), and it had the ability to produce amylase, which was directly related to starch degradation (Oliveira, Ramos et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Cladosporium and Ganoderma were negatively correlated with lactose and sucrose, but through positive correlation metabolites such as D-glucose 6-phosphate and Sedoheptulose 7-phosphate (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eb), they indicated that they might regulate carbon metabolism flow through the pentose phosphate pathway and gluconeogenesis pathway, promoting the generation of intermediate products (Clasquin, Melamud et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e, Liu, Yu et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Moreover, Staphylococcus (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea) and Ganoderma (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb) were positively correlated with citric acid, succinic acid, fumaric acid, and cis-aconitic acid (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003ea and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eb), which might be similar to the strategy of overexpressing MDH in cyanobacteria, by regulating the activity of specific enzymes to enhance the oxidative or reductive branches of the TCA cycle and thereby balance carbon metabolism pressure and accumulate C4 intermediates (Iijima, Watanabe et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Aspergillus was positively correlated with soluble sugars (such as d-glucose; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eb), indicating that Aspergillus drove initial degradation through the secretion of amylase and cellulase (Mojsov \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). This might form a metabolic relay with the phosphorylated intermediate products of Cladosporium. Staphylococcus accelerated the energy conversion of the final products through the TCA cycle. Regarding some fungi in the study that did not report carbohydrate metabolism pathways such as Pseudopithomyces and Phoatospora (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb), although they were negatively correlated with glucose, lactose, and sucrose (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eb), they were highly correlated with map00500 (starch and sucrose metabolism) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eb), and were significantly positively correlated with key intermediate metabolites such as D-glucose 6-phosphate, D-fructose 6-phosphate, succinic acid, and citric acid (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eb). This might be due to the metabolic inhibition of Aspergillus (Cladosporium). When Aspergillus dominated the degradation, its antibacterial metabolites (such as isofraxidine) might inhibit the growth of competing microbial communities such as Pseudopithomyces by changing the microenvironment pH or direct toxicity (Li, Wang et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Based on the correlation analysis of the biomarkers with the above metabolites and metabolic pathways, we proposed Brevibacterium, Staphylococcus, Aspergillus, Cladosporium, and Ganoderma as key biomarkers.\\u003c/p\\u003e \\u003cp\\u003ePrevious studies have shown that Brevibacterium, Staphylococcus, Aspergillus, Cladosporium and Ganoderma have significant advantages in various types of fermentation (such as fermentation of meat (Fan, Badar et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e), Douchi fermentation (Yang, Yang et al. \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), condiment fermentation (Jia, Niu et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), etc.) and have played different roles during the fermentation process. For example, members of the Brevibacterium genus play a role in fermentation by producing amino acids, aroma, carotenoids and antibacterial substances, which play an important role in the formation of aroma in cheese fermentation (Alexa, Cobo-D\\u0026iacute;az et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e, Rana, Chandola et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Staphylococcus participates in the hydrolysis of proteins to produce peptides and amino acids (the precursors of flavor substances) in broad bean paste and sausage fermentation (Aro Aro, Nyam-Osor et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e, Jia, Niu et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Moreover, it can efficiently decompose glucose through EMP (glycolysis) pathway and HMP (phosphogluconic acid) pathway and convert it into pyruvic acid, generating organic acids such as lactic acid and acetic acid, lowering the pH value and inhibiting the growth of spoilage bacteria, dominating the degradation of carbohydrates in fermented meat, and forming an acidic base (Fan, Badar et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Aspergillus is widely used in food fermentation and the production of large-scale enzymes, organic acids and bioactive compounds (Park, Jun et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). It can produce cellulase, pectinase, amylase and other enzymes that degrade complex polysaccharides (Mojsov \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). In the fermentation of Pu'er tea, it participates in the metabolism of glycosides and polysaccharides and the production of oligosaccharides, serving as a guarantee for the rich taste (Ma, Ling et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Cladosporium can secrete carbohydrate esterase (CE) and glycoside hydrolase (GH) that act on the deacetylation of plant polysaccharides (Liu, Yu et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), and has the ability to decompose lignin and carbohydrates in the fermentation decomposition of forest litter (Song, Tian et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). Ganoderma can encode multiple carbohydrate-active enzymes, including carbohydrate esterase (CE), glycoside hydrolase (GH), glycosyltransferase (GT), and polysaccharide lyase (PL), which are involved in metabolic pathways such as \\\"starch and sucrose metabolism\\\", \\\"glycolysis/glycogenesis\\\", and \\\"pyruvate metabolism\\\" (Yu, Wang et al. \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). Meanwhile, Ganoderma, as a medicinal mushroom, is also used as a dietary supplement to improve the quality of steamed buns. It can degrade starch in the dough into glucose, maltose, soluble polysaccharides, etc., reducing the hardness and chewiness of the steamed buns, and improving the flavor and aroma (Guowei, Lili et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Therefore, we speculate that Brevibacterium, Cladosporium and Aspergillus form the main line of starch degradation, while Ganoderma and Staphylococcus promote the generation of intermediate metabolism through the TCA cycle and phosphorylation metabolism, further participating in starch and sucrose metabolism.\\u003c/p\\u003e \\u003cp\\u003eDuring the processing of plant leaves, the dynamic metabolism of starch, sucrose, and glucose is driven by microorganisms in a coordinated manner, presenting distinct phased characteristics (Figure. 7). In the initial degradation stage, the Firmicutes phylum, Paenibacillus, highly expresses amylase (EC 3.2.1.1) to cleave amylopectin into maltose (Silano, Baviera et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), while the Actinobacteria phylum, Streptomyces, secretes glucanase (EC 3.2.1.3) to target the α-1,6 glycosidic bonds of amylopectin and release free glucose (Wu, Dou et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). This process is similar to the metabolic remodeling during the saccharification stage of big koji fermentation. Under the temperature conditions of 51\\u0026ndash;53\\u0026deg;C in the early fermentation stage, Bacillus rapidly converts the stored starch in wheat into soluble sugars through the action of amylase, providing substrates for subsequent fermentation (Zhang, Kang et al. \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). It is noteworthy that the Brevibacterium genus has participated in primary degradation through the secretion of amylase (Oliveira, Ramos et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) at this stage, laying the foundation for its subsequent metabolic dominance (Ferreira, Ver\\u0026iacute;ssimo et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). As the processing continues, Brevibacterium catalyzes the hydrolysis of maltose into glucose (Materials, Enzymes et al. 2024) through α-glucanase (EC 3.2.1.20) and catalyzes the hydrolysis of sucrose into D-glucose and D-fructose (Lincoln and More \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) through β-fructofuranosidase (EC 3.2.1.26). Meanwhile, the fungal community begins to take effect: Aspergillus, through amylase and cellulase (Mojsov \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), initiates the degradation of stubborn polysaccharides, and the D-glucose generated by Aspergillus combines with D-glucose 6-phosphate produced by Cladosporium to form a metabolic relay (Răut, Călin et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Additionally, Staphylococcus may convert intermediates such as citric acid and succinic acid into ATP through the TCA cycle (Iijima, Watanabe et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e, Chen, Zhao et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) and accumulate flavor precursors (Jia, Liu et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e); Ganoderma promotes the accumulation of soluble sugars through glycosyltransferase (GT) and polysaccharide lyase (PL), and its application in steamed buns has been proven to optimize the final quality(Yu, Wang et al. \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e, Guowei, Lili et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Notably, Cladosporium secretes isofraxidin-like antibacterial substances to inhibit the metabolic activity of competing bacterial populations such as Pseudopithomyces, forming a chemical defense barrier to maintain its dominant position (Li, Wang et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTherefore, during the processing, bacteria and fungi work together through metabolic division of labor to act on the starch and sucrose metabolic pathways. Brevibacterium and other bacteria, relying on amylase (EC 3.2.1.1) and α-glucosidase (EC 3.2.1.20), efficiently degrade large-molecular polysaccharides. Aspergillus and other fungi increase the accumulation of D-glucose 6-phosphate intermediate metabolic products. A metabolic relay division mode dominated by bacteria and assisted by fungi is formed, providing a favorable substrate foundation for subsequent tobacco leaf fermentation.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThis study combines metabolomics with microbial community analysis to systematically reveal the dynamic patterns of carbohydrate metabolism during plant leaf processing and the mechanisms driven by microorganisms. The research shows that an increase in temperature and a decrease in humidity during the processing process lead to a reduction in the abundance and diversity of microbial communities. The study identified Brevibacterium, Staphylococcus, Aspergillus, and Ganoderma as core biomarkers, providing candidate strains for optimizing the regulation process of tobacco. It clarifies the metabolic patterns of microbial collaboration during the regulation process of tobacco, where bacteria play a leading role in the initial degradation of starch, while fungi promote the accumulation of soluble sugars through the transformation of intermediate products. A degradation - transformation - regulation synergy model has been formed, providing a new perspective for the effective utilization of complex carbon sources.\\u003c/p\\u003e\\n\\u003cp\\u003eIn summary, using multi-omics resources to analyze the influence of microorganisms on key carbohydrates during tobacco leaf processing and accurately understanding the interaction between microorganisms and tobacco is crucial for improving tobacco production efficiency.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eCRediT authorship contribution statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eKesu Wei: Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Writing-review \\u0026amp; editing. Yang Long: Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Writing-review \\u0026amp; editing. Cheng Zhang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing-Original Draft, Visualization. Xiaohua Zhang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing-Original Draft, Visualization. Feng Wang: Conceptualization, Funding acquisition, Project administration, Data curation, Writing-review \\u0026amp; editing. Guanhui Li: Conceptualization, Data curation, Formal analysis, Methodology, Writing-review \\u0026amp; editing. Jie Ding: Conceptualization, Data curation, Formal analysis, Methodology, Writing-review \\u0026amp; editing. Yi Cao: Data curation, Supervision, Writing-review \\u0026amp; editing. Hancheng Wang: Data curation, Supervision, Writing-review \\u0026amp; editing. Shengjiang Wu: Data curation, Supervision, Writing-review \\u0026amp; editing. Xianchao Shang: Data curation, Supervision, Writing-review \\u0026amp; editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of Competing Interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was supported by the Science and Technology Project of China Tobacco General Corporation [1102022202016/110202201019(LS-03)], the Science and Technology Project of Guizhou Science and Technology Department (QKHJC-ZK [2022] YB288), the Science and Technology Project of Guizhou Tobacco Industry Technology Center (2022XM17), the Science and Technology Project of Shandong Tobacco Industry Technology Center (202111). The authors would like to express their gratitude to the Supercomputing Center of Shandong Agricultural University for the technical support provided.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eData will be made available on request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAdams, R. I., Miletto, M., Taylor, J. W. and Bruns, T. D. (2013). Dispersal in microbes: fungi in indoor air are dominated by outdoor air and show dispersal limitation at short distances. The ISME Journal 7(7): 1262-1273. doi: 10.1038/ismej.2013.28\\u003c/li\\u003e\\n\\u003cli\\u003eAlexa, E. A., Cobo-D\\u0026iacute;az, J. F., Renes, E., O\\u0026acute;Callaghan, T. F., Kilcawley, K., Mannion, D., et al. (2024). 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LWT-FOOD SCIENCE AND TECHNOLOGY 192. doi: 10.1016/j.lwt.2023.115707\\u003c/li\\u003e\\n\\u003cli\\u003eYu, G.-J., Wang, M., Huang, J., Yin, Y.-L., Chen, Y.-J., Jiang, S., et al. (2012). Deep Insight into the Ganoderma lucidum by Comprehensive Analysis of Its Transcriptome. PLOS ONE 7(8): e44031. doi: 10.1371/journal.pone.0044031\\u003c/li\\u003e\\n\\u003cli\\u003eYuan, X. Y., Wang, T., Sun, L. P., Qiao, Z., Pan, H. Y., Zhong, Y. J., et al. (2024). Recent advances of fermented fruits: A review on strains, fermentation strategies, and functional activities. FOOD CHEMISTRY-X 22. doi: 10.1016/j.fochx.2024.101482\\u003c/li\\u003e\\n\\u003cli\\u003eZhang, L. Y., Mai, J., Shi, J. F., Ai, K. B., He, L., Zhu, M. J., et al. (2024). Study on tobacco quality improvement and bacterial community succession during microbial co-fermentation. INDUSTRIAL CROPS AND PRODUCTS 208. doi: 10.1016/j.indcrop.2023.117889\\u003c/li\\u003e\\n\\u003cli\\u003eZhang, Q., Kong, G., Zhao, G., Liu, J., Jin, H., Li, Z., et al. (2023). Microbial and enzymatic changes in cigar tobacco leaves during air-curing and fermentation. Applied Microbiology and Biotechnology 107(18): 5789-5801. doi: 10.1007/s00253-023-12663-5\\u003c/li\\u003e\\n\\u003cli\\u003eZhang, Y. D., Kang, J. M., Han, B. Z. and Chen, X. X. (2024). Wheat-origin Bacillus community drives the formation of characteristic metabolic profile in high-temperature Daqu. LWT-FOOD SCIENCE AND TECHNOLOGY 191: 115597. doi: 10.1016/j.lwt.2023.115597\\u003c/li\\u003e\\n\\u003cli\\u003eZhu, Q., Chen, L. Q., Peng, Z., Zhang, Q. L., Huang, W. Q., Yang, F., et al. (2023). The differences in carbohydrate utilization ability between six rounds of Sauce-flavor Daqu. FOOD RESEARCH INTERNATIONAL 163: 112184. doi: 10.1016/j.foodres.2022.112184\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"microbial-ecology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"meco\",\"sideBox\":\"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)\",\"snPcode\":\"248\",\"submissionUrl\":\"https://submission.nature.com/new-submission/248/3\",\"title\":\"Microbial Ecology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Microbial community, Tobacco Processing, Metabolomes, Metabolic pathways, Multi-omics analysis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6949781/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6949781/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eMicroorganisms play a significant role in improving the flavor and quality of plant products. Analyzing the impact of the tobacco processing process on the microbial community structure and revealing the synergistic mechanism of microorganisms during the processing is crucial for optimizing the flavor and quality of plant products. In this study, samples were collected from four processing stages (T1: fresh leaves, T2: 42\\u0026deg;C, T3: 54\\u0026deg;C, T4: 68\\u0026deg;C), and metabolite and inter-leaf microbial data of tobacco leaves were generated. A comprehensive multi-omics analysis was conducted. The study shows that the increase in temperature and the decrease in humidity during the processing lead to the reorganization of the microbial community. Brevibacterium, Staphylococcus, Aspergillus, and Ganoderma were identified as core biomarkers. Bacteria dominate in the initial degradation of starch, while fungi promote the accumulation of soluble sugars through the transformation of intermediate products. This study deepens our understanding of the role of microorganisms and their carbohydrate metabolism in the tobacco leaf processing process and proposes a new strategy for constructing regulatory models by integrating multi-omics.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Integrated Multi-Omics Analysis Reveals Microbial Community Restructuring and Its Role in Key Carbohydrate Metabolic Pathways During Tobacco Leaf Curing\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-06-26 15:19:00\",\"doi\":\"10.21203/rs.3.rs-6949781/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-08-12T01:23:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-08-11T20:23:50+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"316910617380103236451039812702548538976\",\"date\":\"2025-08-06T04:33:38+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"229193687171390661594123629271847743729\",\"date\":\"2025-08-05T15:58:10+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"15181620364874331395751019795161654187\",\"date\":\"2025-08-05T15:53:10+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-07-21T02:42:24+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"262431179443590754386154009036747251865\",\"date\":\"2025-06-28T10:48:02+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"141429565180610667361739263075293355454\",\"date\":\"2025-06-26T14:02:25+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"213225779264328827208189780673968873858\",\"date\":\"2025-06-24T15:56:02+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"295003632396285577556227429844305936422\",\"date\":\"2025-06-24T14:20:24+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-06-24T12:54:31+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-06-24T03:29:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-06-24T03:29:36+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Microbial Ecology\",\"date\":\"2025-06-22T13:26:24+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"microbial-ecology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"meco\",\"sideBox\":\"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)\",\"snPcode\":\"248\",\"submissionUrl\":\"https://submission.nature.com/new-submission/248/3\",\"title\":\"Microbial Ecology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"fdd37071-3824-468c-aa7e-a11622de7968\",\"owner\":[],\"postedDate\":\"June 26th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-17T16:07:36+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6949781\",\"link\":\"https://doi.org/10.1007/s00248-025-02644-8\",\"journal\":{\"identity\":\"microbial-ecology\",\"isVorOnly\":false,\"title\":\"Microbial Ecology\"},\"publishedOn\":\"2025-11-12 15:57:42\",\"publishedOnDateReadable\":\"November 12th, 2025\"},\"versionCreatedAt\":\"2025-06-26 15:19:00\",\"video\":\"\",\"vorDoi\":\"10.1007/s00248-025-02644-8\",\"vorDoiUrl\":\"https://doi.org/10.1007/s00248-025-02644-8\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6949781\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6949781\",\"identity\":\"rs-6949781\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}