Skip to main content

Insights on bio-degumming of kenaf bast based on metagenomic and proteomics



Microbes play important roles in kanef-degumming. This study aims at identifying the key candidate microbes and proteins responsible for the degumming of kenaf bast (Hibiscus cannabinus). Kenaf bast was cut into pieces and immersed into microbia fermentation liquid collected from different sites. Fermentation liquid samples were collected at 0, 40, 110 and 150 h and then subjected to the 16S/18S rRNA sequencing analysis and isobaric tag for relative and absolute quantitation (iTRAQ) analysis. The microbial (bacterial and fungal) diversity and the differentially expressed proteins/peptides (DEPs) were identified.


With the prolonged degumming time, the weight loss rate increased, the bacterial diversity was decreased. [Weeksellaceae], Enterobacteriaceae and Moraxellaceae were rapidly increased at 0~40 h, and then decreased and were gradually replaced by Bacteroidaceae from 40 h to 150 h. Similarly, Chryseobacterium and Dysgonomonas were gradually increased at 0~110 h and then decreased; Acinetobacter and Lactococcus were increased at 0~40 h, followed by decrease. Bacteroides was the dominant genus at 150 h. Sequencing 18S rRNA-seq showed the gradually decreased Wallemia hederae and increased Codosiga hollandica during degumming. iTRAQ data analysis showed Rds1, and pyruvate kinase I was decreased and increased in the kanef-degumming, respectively. Other DEPs of ferredoxin I, superoxide dismutase and aconitatehydratase were identified to be related to the Glyoxylate and dicarboxylate metabolism (ko00630).


Bacteria including Chryseobacterium, Dysgonomonas, Acinetobacter, Lactococcus and Bacteroidesand fungi like Wallemia hederae and Codosiga hollandica are key candidate microbes for kanef degumming.


Kenaf (Hibiscus cannabinus),which contains 8–16% lignin, 53–66% cellulose, 23–35% pectin and some hemicellulose, is an annual herbaceous bast fiber crop of the genus Malvaceae [1,2,3]. It is widely planted around the world, especially in the tropical and subtropical regions, such as Asia and Latin America. Kenaf fiber is widely used as an important basic raw material in textile, manufacturing and composite fabrication due to its strong pulling force [1, 4]. However, the retting methods can influence the quality of kenaf fiber.

Retting based on the intervention of bacteria and microbia enzymes promotes the development of the textile industry via resulting in a better quality of fibers. Conventional methods for the degumming of kenaf bast included traditional natural fermentation (water retting) and chemical degumming. In comparison with the natural fermentation and chemical degumming, biological (bacterial and enzymatic) degumming presents a series of advantages including high efficiency, low pollution, low cost and high fiber quality [3, 5,6,7]. The secretion of bacteria promote the decomposition of material, which can be used for bacteria to continue to grow [6, 8]. Ideal bacterial strains for kenaf degumming should have the advantages of secreting pectinase, hemicellulose, and ligninase, but not cellulase [6,7,8].

The screening of superior bacterial strains with the activity of pectate lyase, pectinase, hemicellulase and/or ligninase and the preservation of the natural fiber structure and mechanical properties is crucial for biological degumming [7,8,9]. A series of bacterial strains have been identified with strong ability of retting or degumming, like Bacillus cereus hn1–1 [10], B. pumilus [7], B. licheniformis and B. subtilis [11] and B. tequilensis SV11-UV37 [6]. Cheng et al. [10] showed that the 10 h-degumming process by B. cereus hn1–1 produced a residual gum rate as low as 5% and the fiber rate as high as 76%. Mao et al. [12] reported that the ramie retting could be completed within 56 h by using a microbia consortium RAMCD407 plus 0.2% NaOH, with 2.84% residual gum content and 5.2 cN/dtex breaking strength of the final fiber. In addition, our previous study [7] identified that pectinase and mannanase were the key enzymes in the degumming of kenaf bast mediated by bacteria including B. pumilus, B. alcalophilus, Clostridium tertium, Brevibacillus brevis, Pectobacterium carotovora, Erwinia chrysanthemi, and Tyromyces subcaesius. All these results suggested the pivotal roles of bacteria in the degumming of kenaf bast. However, there was no systematic analysis for the alterations of bacterial secretome during degumming of kenaf bast.

This study was performed to identify the key candidate microbes and secretory proteins during the retting and degumming of kenaf bast. Alterations of microbial proteomics and community during retting and degumming of kenaf bast was detected using isobaric tags for relative and absolute quantitation (iTRAQ) and 16S/18S rRNA sequencing, respectively. These findings provide novel insights into the retting and degumming of kenaf bast.


Degumming of kenaf bast and bacteria collection

The weight loss rate of kenaf bast was gradually increased with degumming, ranging from 11.72% at 40 h and 32.06% at 190 h (Table 1). The bacterial viable count, however, was primarily decreased from initial 4.2 × 107 CFU/ml to 8.7 × 106 CFU/ml at 40 h post fermentation. It was increased to the maximum 5.1 × 108 CFU/ml at 150 h, followed with a decrease. These results might suggest that the growth of bacteria had degumming function.

Table 1 Kenaf bast degumming effect during different enrichment time

General characteristics of 16S/18S rRNA sequencing

We then collected liquid samples at 0, 40, 110 and 150 h post retting and subjected to 16S/18S rRNA sequencing. A total of 167,321 and 181,887 raw reads was generated from 16S and 18S rRNA sequencing data, respectively. After removing the low-quality reads and chimera, the sequence length of trimmed reads is mostly distributed at 420 bp - 490 bp in bacteria, and the fungus sample is mostly distributed at 399 bp - 409 bp. The final rank abundance curve tends to a plateau, indicating that the sample species are richer in composition and higher in uniformity (Fig. 1). The higher species rank value of samples at 0 h (500–600) compared with of samples at 40, 110 and 140 h (200–300) indicated that the fermentation significantly decreased bacterial diversity. In addition, we found the retting significantly reduced the bacterial alpha diversity estimators like Chao 1, PD_whole_tree, Shannon and Simpson index (Table 2). In addition, retting also decreased fungal alpha diversity estimators including Chao 1 and PD_whole_tree, but increased Goods coverage (Table 2). These changes suggested retting decreased microbes viable count and bacterial diversity but increased fungal diversity.

Fig. 1
figure 1

Rank Abundance curves of 12 samples. The different color represent different samples. a . Rank Abundance curves of bacteria; b . Rank Abundance curves of fungi

Table 2 The alpha diversity of the 16S and 18S rRNA-seq

Identification of key bacteria responsible for the degumming of kenaf bast

After OTUs (operational taxonomic units) annotation, we identified the abundances (at phylum level) of Bacteroidetes (from 34.91% at 0 h to 67.75% at 150 h) and Patescibacteria (1.00 to 9.53%) were gradually increased during the degumming of kenaf bast (Additional file 1: Figure S1), which replaced the Proteobacteria. The initial abundance of Firmicutes (2.83%) was firstly increased to 15.28% at 40 h and then decreased to 3.40% at 150 h (Additional file 1: Figure S1a and b). At the family level, Sphingobacteriaceae (10.98%, Bacteroidetes), Flavobacteriaceae (9.62%), Burkholderiaceae (8.13%), and Sphingomonadaceae (6.77%) were the dominant bacteria at the initial (Fig. 2a and b). However, they were replaced by the fast-growing [Weeksellaceae] (21.55%, Bacteroidetes), Enterobacteriaceae (16.41%, Proteobacteria) and Moraxellaceae (12.13%, Proteobacteria) families at 40 h post retting. The latter bacteria were gradually replaced by the Bacteroidaceae family from 40 h to 150 h (25.89%; Fig. 2a and b). We also identified that the growth of Cytophagaceae and Chitinophagaceae families (Bacteroidetes) were inhibited by retting process. Similar changes were found in several bacterial genera. Dominant genera, including Pedobacter (9.10%), Flavobacterium (6.86%), Pseudomonas (5.97%) and Brevundimonas (5.64%) kept an equivalent level at the initial (0 h). Chryseobacterium (15.03%, [Weeksellaceae]), Acinetobacter (12.10%, Moraxellaceae) and Lactococcus (8.84%, Streptococcaceae family) grew to be the dominant bacteria at 40 h, which were then replaced by Bacteroides (25.89%) in the fermentation liquid, followed by Chryseobacterium (16.03%) and Dysgonomonas families (15.96%) (Fig. 2c and d). These changes in bacterial abundances were in response to that of the bacterial viable count in Table 1. These data showed that Acinetobacter, Chryseobacterium, Lactococcus and Bacteroidetes at genus level and [Weeksellaceae], Enterobacteriaceae, Moraxellaceae and Bacteroidaceae at family level might be key candidate bacteria responsible for the degumming of kenaf bast.

Fig. 2
figure 2

The relative abundance of the dominant bacterial family and genus. a and b, the stacked and linear figure of the relative abundance of 12 bacterial families (relative abundance > 1%) during the degumming of kenaf bast, respectively. c and d, the stacked and linear figure of the relative abundance of 9 bacterial genera (relative abundance > 1%) during the degumming of kenaf bast, respectively

Identification of key fungi responsible for the degumming of kenaf bast

As expected, fungal abundances were also changed in response to degumming. All fungi were mainly dominanted by 2 phyla: Opisthokonta (98.73%) and SAR (1.24%). The relative abundance of Opisthokonta subkingdom was gradually decreased to 85.09%, and replaced by SAR phylum (14.61% at 150 h; Fig. 3a). The dominant fungal families Incertae Sedis (61.11 to 8.10%) and Pezizomycotina (18.73 to 3.89%) were replaced by Dipodascaceae (5.98 to 53.08%) and some other fungi such as Bulleribasidiaceae, Craspedida, Chrysophyceae, etc. (Fig. 3b). At genus level, the results showed that Wallemia (60.43%) and Eurotiomycetes (18.55%) were the dominant fungi (Fig. 3c). As for specific species, the dominant positions of Wallemia hederae (60.33%) at the initial, but decreased at 40 h (36.97%), 110 h (12.12%) and 150 h (7.50%) (Fig. 3d). The relative abundance of Codosiga hollandica species was increased from 0.16% at 0 h to 2.42% at 150 h.

Fig. 3
figure 3

The relative abundance of the dominant fungal family. a to d, the stacked figure of the relative abundance of dominate fungal at phylum, family, genus and species level during the degumming of kenaf bast, respectively

Microbia secretomics analysis and identification of candidate proteins or peptides

We then performed the secretomics analysis to identify the candidate proteins which might be responsible for biological degumming of kenaf bast, since there are significant changes in the relative abundance of bacteria and fungi. A total of 197 proteins, including 67 DEPs were identified (Additional file 2: Table S1). Clustering analysis showed the distinct expression patterns of these proteins in the samples (Fig. 4). We identified the significantly down regulated Rds1 protein peptides (including I4YCX5 and R9AEW5, A1DDU4, A0A0S7E3J2, A0A0J5SQP1, Q4WVL1, B0Y1F6, A0A084BN00 and A0A0K8L4F0), superoxide dismutase peptides (J1ACL6 and A0A0Q9DZS2), and the upregulated peptides of pyruvate kinase I (A0A0A2W3C3), lipoprotein (A0A0N7K9K4), ferredoxin I (I4JHJ0), thioredoxin (A0A088F1E4, A0A0M2Y158 and A0A0M3C9P8). A0A0A2W3C3 was enriched into the pathways including glucagon signaling pathway (ko04922) and pyruvate metabolism (ko0492). A peptide of aconitatehydratase (aconitase, ACO), which is related to the glyoxylate and dicarboxylate metabolism (ko00630), was decreased at 40 h and then increased at 110 and 150 h post retting compared with 0 h (Table 3). Most of the other peptides were annotated with transporter activities (Additional file 2: Table S1).

Fig. 4
figure 4

The heatmap of the 64 differentially expressed proteins/peptides. Red and blue represents the high and low expression, respectively. _1 and 2 represent biological repeat 1 and 2 in each group, respectively

Table 3 Several differentially expressed proteins in during degumming

Among the other non-DEPs, we identified that the peptide of Aldehyde dehydrogenase family protein (A0A160F3I4), Aspartate aminotransferase (A0A0A2VU16) and 6-phosphogluconate dehydrogenase (L8X2A2). The L8X2A2 was identified to be related with pentose phosphate pathway.


The degumming of kenaf bast is a process mediated by dynamic change of microbes. Using the 16S/18S rRNA sequencing, we identified the changed bacterial and fungal abundance during the degumming of kenaf bast (0~ 150 h). In the fermentation liquid, the growth of Cytophagaceae and Chitinophagaceae was inhibited during the degumming of kenaf bast. Many bacteria genera played crucial roles in in the degumming process of kenaf bast, such as Bacteroides, Chryseobacterium, Dysgonomonas, Acinetobacter, and Lactococcus, of which the abundance were greatly changed with degumming treatment. Similarly, some fungi also participated in the degumming process of kenaf bast including Pezizomycotina, Dipodascaceae, Codosiga hollandica, and Incertae Sedis. The abundance of subdivided Wallemia and Eurotiomycetes genera were dramatically reduced in the process of dealkylation and fermentation. And the increased Dipodascaceae family might promote the degumming of kenaf bast. A series of Bacillus strains has been identified to be ramie- or kanef-degumming strains, like B. cereus hn1–1 [10], B. pumilus [7], B. licheniformis and B. subtilis [11] and B. tequilensis SV11-UV37 [6]. In addition, our previous study [7] showed that seven bacterial strains belonging to the species including B. pumilus, B. alcalophilus, C. tertium, Brevibacillus brevis, Pectobacterium carotovora, Erwinia chrysanthemi and Tyromyces sub caesius were the key in strains for the degumming of kenaf bast. Other reports also showed the ability of B. licheniformis, Paenibacillus macerans, C. tertium, B. tequilensis and B. vulgatusor the proteases and pectinolytic enzymes derived from these strains for degumming fiber, wool and wood [6, 7, 13, 14]. For instance, enzymatic treatment is an acceptable method of intervention among the methods for wool treatment for breaking down the surface structure [14]. Serine proteases are the most common commercial proteases derived from Bacillus strains.

For the degumming of plant fibers, some researchers had isolated proteases, xylanases and pectate lyases from the bacteria like Acinetobacter spp. (> 1 species of the genus) [15] and B. cereus [16] and fungi including Extremophilic fungi [17,18,19]. Researchers also identified the lignin degrading role of Pseudomonas, Lactococcus and Acinetobacter strains in hemp, ramie and mechanical pulp [20,21,22,23]. For instance, Hu et al. [23] observed that abundances of Pseudomonas and Acinetobacter were increased to the highest at 36 h post retting and decreased subsequently. In particular, the finding about Acinetobacter and Lactococcus was consistent with our results, which was increased to 12.09 and8.84% at 40 h and then decreased to 4.10 and 0.84% at 150 h. The dynamic changes of these bacteria during the degumming of kanef bast suggested their crucial roles in degrading kanef.

Kanef-degumming is a dynamic process of bacterial adaptation and growth. The initial stage is characterized by decreased bacterial richness and diversity [24]. We determined the decreased bacterial viable count at the 40 h post retting, followed by increased bacterial viable count but not bacterial richness and diversity. Our present study presented a cluster of anaerobic Bacteroidaceae members like Bacteroides, Chryseobacterium and Dysgonomonas, played crucial roles in the degumming of kanef bast, especially in the late stage. Cytophagaceae was initially inhibited, which might guarantee the fiber structure. The rapid growth of anaerobic Bacteroidaceae bacteria changed bacterial diversity. Xylan and pentose (including xylose) are main components of hemicellulose in plants [25]. The degradation of hemicellulose into oligomers and sugarsis a metabolic property shared by sugar-fermenting Bacteroides [26,27,28,29]. The increased abundance of these Bacteroidaceae members might suggest the accumulation of their substrates derived from the early stage fermentation from aerobic bacteria like Acinetobacter and Lactococcus or the changed environments.

In addition, we also identified the down regulation of several peptides of Rds1 during the degumming of kanef bast. Rds1 a stress-responsible protein, which could be depressed by starving from glucose, ammonium, phosphate, exposuring to carbon dioxide and high temperature [30]. The down regulation of it was theoretically in line with the hypothesis that the starvation of sugar and oxygen of early retting stage. What’s more, the identification of the gradually decreased halophilic Wallemia hederae and increased turfgrass pathogen in the fermentation liquid might suggest the deterioration of fermentation. Codosiga hollandica.


In conclusion, we identified a cluster of key bacteria responsible for the degumming of kanef bast. We identified that the growth of Cytophagaceae was initially inhibited at the early stage of degumming for kenaf bast. The up-and-down change in the abundance of Acinetobacter and Lactococcus (Streptococcaceae) and the gradually increased growth of Bacteroides, Chryseobacterium, Dysgonomonas characterized the degumming process. In addition, we also identified the increased Codosiga hollandica and decreased Wallemia hederae fungus family during degumming for 150 h. Secretory proteomics analysis showed Rds1, pyruvate kinase I and aconitatehydratase peptides were changed during the degumming of kanef bast. These findings provide evidence on the crucial roles of these microbes in the degumming of kenaf bast.


Bacteria collection and degumming of kenaf bast

Humus samples (50 g) were collected from Sanya, China. Water samples (100 ml) were collected from a conventional retting pond (50 cm away from the water surface) in Xiaoshan, Zhejiang, China. Soil samples (50 g) were collected from continuous cropping soil of Kenaf in Xiaoshan. Soil and humus samples were diluted into 100 ml bacteria free water (autoclave at 121 °C for 20 min), filtered and then mixed with the above water samples.

Kenaf bast was collected from Xianghongma No. 1 plants in Changsha, China. The samples were cut into pieces (3 cm) and then immersed into bacteria mixture (10 g: 5 ml) with supplementation of 100 ml bacteria free water. For the degumming of kenaf bast, samples were maintained on an orbital shaker at 30 °C, pH 7.0, for 30 min. Then the fermentation liquid samples were collected at 0, 40, 110, 150 and 190 h post degumming and used for further analysis. Each experiment was done in triplicates.

Determination of kenaf bast weight loss rate and viable count of bacteria

The weight loss rate of the kenaf bast samples in each condition was calculated according to the following formula: weight loss rate (%) = [initial weight (10 g)-final weight (g) of kenaf bast]/initial weight (10 g) of kenaf bast × 100%. Total viable count was quantified traditionally using the colony-forming units (CFUs) after incubation on nutrient broth solid media (pH 7.0) for 0–190 h.

DNA extraction and 16S and 18S ribosomal RNA gene sequencing

DNA extraction was performed using a PowerSoil™ DNA Isolation Kit (MOBIO laboratories, San Diego, Carlsbad, California, USA). The concentration and purity of the DNA was measured by agarose gel electrophoresis. The 16S ribosomal DNA (rDNA) gene V3-V4 region of the bacteria was amplified by PCR with bar coded primers (343F: 5′- TACGGRAGGCAGCAG − 3′ and 798R: 5′- AGGGTATCTAATCCT-3′), using FastPfu Polymerase (TransStart, Beijing, China). The PCR primers for the 18S rDNA of fungus were NS1: 5′- GTAGTCATATGCTTGTCTC − 3′ and NS8: 5′- TCCGCAGGTTCACCTACGGA − 3′. Reaction parameters were: 95 °C for 5 min, followed by 30 cycles of 95 °C 30 s, 52 °C 45 s (16S) or 1 min (18S), 72 °C 1 min, and the final step of 72 °C for 10 min. The amplicons of 16S and 18S rDNA were purified by an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, California, USA). After repeating the above steps (amplification and purification), the concentration of final purified amplicons was detected by Qubit 2.0 (Thermo Fisher Scientific, Walthan, Massachusetts, USA). At the end, the samples were pooled and subjected to an Illumina MiSeq Instrument (Illumina, San Diego, California, USA) in Shanghai OE Biotech. Co., Ltd. with 350 bp paired-end sequencing.

Data processing

Raw data from different samples were identified based on the unique barcode. The primer sequences were removed and data were trimmed using U-Search software [31]. FLASH v1.2.7 software ( was used for merging paired-end reads and the counting of reads [32]. The chimeric sequences were removed using UCHIME ( [33]. Sequences were clustered into OTUs by QIIME (v1.8.0, [34] according to the minimal 97% similarity. Through matching to the Silva database ( [35], the taxonomic information for each OTU was obtained. Alpha and beta diversities were analyzed to determine differences among groups in terms of species complexity by QIIME (v1.8.0) software.

Protein extraction, digestion, and iTRAQ labeling

Fermentation liquid samples (30 ml) were collected at 0 h, 40 h, 110 h and 150 h, and then centrifuged at 1500 g for 10 min in an Eppendorf centrifuge (Eppendorf, San Diego, California, USA). The supernatants were collected and filtered through a 0.22 μm membrane. Samples were then diluted into precooled TCA/acetone (1:9) solutions (1: 4 v/v) and then stored at − 20 °C overnight. Pellets were collected by centrifugation (Sigma Aldrich, Schnelldorf, Germany) at 17000 g for 30 min, followed by washing with precooled acetone (90%) for three times. The precipitate was air-dried and then dissolved in sodium dodecyl sulfonate lysate supplementing (Beyotime, Shanghai, China) with protease inhibitor cocktail (P8340, Sigma, USA) on a homogenizer (Hai Shu Ke Sheng, Ningbo, Zhejiang, China). The crude precipitates were collected by centrifugation at 12000 g for 10 min at 4 °C (Sigma Aldrich, Schnelldorf, Germany). The supernatant was selected after sonication by centrifugation (12,000 g for 15 min) for twice. Finally, the supernatant was stored at − 80 °C for further use. The concentration of protein was measured using BCA method [36], with BCA Protein Assay Kit (Thermo Scientific Dionex, San Jose, USA). The integrity of the extracted protein was detected by SDS-PAGE [37].

The quantified samples were then digested according to the filter aided sample preparation procedure as previously described [38]. In brief, 100 μg of protein was precipitated by precooled acetone (1:5 v/v) at − 20 °C for 1 h, centrifuged at 16000 g for 10 min at 4 °C, and vacuum freeze-dried. Protein precipitation was prepared using an iTRAQ kit (Applied Biosystems, Carlsbad, California, USA) following the manufacturer’s instructions. The marked samples were then mixed, dried and then subjected to separation and identification.

2D-LC-MS/MS analysis

The freeze-dried sample was dissolved in 110 μL of the mobile phase A solution. Peptide separation was performed on an Agilent 1200 HPLC (Agilent Technologies, Foster City, California, USA) with the Narrow-Bore column (2.1 mm × 150 mm × 5 μm), analytical guard column (4.6 × 12.5 mm, 5-Micron), flow rate of 0.3 ml/min, at 210 nm and 280 nm in Shanghai Luming Biotech. Co., Ltd. Reverse phase chromatographic analyses were performed using Nano-RPLC Buffer A (Applied Biosystems), PepMap100C18 column (75 μm × 20 mm, 3 μm, NanoViper; Thermo Scientific Dionex, San Jose, USA) with the mobile phase B increased from 5 to 35% in 70 min. The Q Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific, Bremen, Germany; nano-electrospray ionization, 1.6 kV, 250 °C) was used for data-dependent acquisition according to the previously reported method [39].

Protein identification and quantification

The raw proteomics data in the format of .raw was aligned to UniProt database ( using Maxquant (Version; Thermo Fisher Scientific). Proteins and peptides with fold discovery rate < 0.01 were retained as for further identification of differentially expressed peptides/proteins (DEPs). The significant different proteins between groups were identified with the threshold of T-test p value ≤0.05 and fold change (FC) ≥ 1.2.

Bioinformatics analysis

For annotation of the DEPs, Gene Ontology (GO, and Kyoto Encyclopedia of Genes and Genomes (KEGG, databases were used for the gene functions prediction. The GO classifications of molecular function, biological process and cellular component and the pathways significantly related to these DEPs were identified with the criteria of p < 0.05.

Statistical analysis

Data were expressed as the mean ± standard deviation. The SPSS 22.0 software was employed for the statistical analysis. One-way ANOVA test was performed to analyze the differences. Comparison of differences between groups was detected using t-test. The p-value < 0.05 was considered as significantly difference.

Availability of data and materials

The original data were uploaded to SRA database ( and the BioProject ID is PRJNA562024.





Colony-forming units


Differentially expressed proteins/peptides


Fold change


Gene Ontology


Isobaric tag for relative and absolute quantitation


Kyoto Encyclopedia of Genes and Genomes


Operational taxonomic units (OTUs)


Ribosomal DNA


  1. Ramesh M. Kenaf (Hibiscus cannabinus L.) fibre based bio-materials: A review on processing and properties. Prog Mater Sci. 2016;78:1–92.

    Article  Google Scholar 

  2. Ayadi R, Hanana M, Mzid R, Hamrouni L, Khouja M, Salhi HA. Hibiscus cannabinus L.–kenaf: a review paper. J Nat Fibers. 2017;14:466–84.

    CAS  Google Scholar 

  3. Ververis C, Christodoulakis N, Santas R, Santas P, Georghiou K. Effects of municipal sludge and treated waste water on biomass yield and fiber properties of kenaf (Hibiscus cannabinus L.). Ind Crop Prod. 2016;84:7–12.

    Article  Google Scholar 

  4. Zaleha M, Mahzan S, Fitri M, Kamarudin K, Eliza Y, Tobi AM. Wave velocity characteristic for Kenaf natural fibre under impact damage. IOP Conf. Ser. Mater Sci Eng. 2017;165:012018.

  5. Zhao D, Pan C, Ping W, Ge J. Degumming crude enzyme produced by Bacillus cereus HDYM-02 and its application in flax retting. BioResources. 2018;13:5213–24.

    CAS  Google Scholar 

  6. Chiliveri SR, Koti S, Linga VR. Retting and degumming of natural fibers by pectinolytic enzymes produced from Bacillus tequilensis SV11-UV37 using solid state fermentation. SpringerPlus. 2016;5:1–17.

  7. Duan S, Cheng L, Liu Z, Feng X, Zheng K, Peng Y. Diversity and characteristics of Kenaf Bast degumming microbial resources. J Nat Fibers. 2018;15:799–807.

    Article  CAS  Google Scholar 

  8. Saha M, Rana RS, Adhikary B, Mitra S. Screening of bacterial strains for pectate lyase production and detection of optimal growth conditions for enhanced enzyme activity. J Appl Nat Sci. 2017;9:370–4.

    Article  CAS  Google Scholar 

  9. Ramesh D, Ayre BG, Webber CL, D'Souza NA. Dynamic mechanical analysis, surface chemistry and morphology of alkali and enzymatic retted kenaf fibers. Text Res J. 2015;85:2059–70.

    Article  CAS  Google Scholar 

  10. Cheng L, Wang Q, Feng X, Duan S, Yang Q, Zheng K, Liu Z, Liu Z, Peng Y. Screening a bacterium and its effect on the biological degumming of ramie and kenaf. Sci Agr. 2018;75:375–80.

    Article  CAS  Google Scholar 

  11. Donaghy JA, Levett PN, Haylock RW. Changes in microbia populations during anaerobic flax retting. J Appl Microbiol. 2010;69:634–41.

    Google Scholar 

  12. Mao K, Chen H, Qi H, Qiu Z, Zhang L, Zhou J. Visual degumming process of ramie fiber using a microbia consortium RAMCD407. Cellulose. 2019;26:3513–28.

    Article  CAS  Google Scholar 

  13. Shinde S. Significance of microbiological tests in technical textiles. Man-Made Textiles in India. 2010;53:241–9.

  14. McDevitt JP, Winkler J: Method for enzymatic treatment of wool. In: Google Patents; 2000.

  15. Salwan R, Sharma V, Pal M, Kasana RC, Yadav SK, Gulati A. Heterologous expression and structure-function relationship of low-temperature and alkaline active protease from Acinetobacter sp. IHB B 5011 (MN12). Int J Biol Macromol. 2018;107:567–74.

    Article  CAS  Google Scholar 

  16. Kohli P, Gupta R. Application of calcium alginate immobilized and crude pectin lyase from Bacillus cereus in degumming of plant fibres. Biocatal Biotransfor. 2019;37:341–8.

  17. Salwan R, Sharma V. Proteases from Extremophilic Fungi: a tool for white biotechnology: 3rd ISNPS. France: Avignon; 2016.

    Google Scholar 

  18. Gundala PB, Chinthala P. Extremophilic pectinases. In: Extremophilic Enzymatic Processing of Lignocellulosic Feedstocks to Bioenergy: Springer; 2017. p. 155–80.

  19. Polizeli M, Rizzatti A, Monti R, Terenzi H, Jorge JA, Amorim D. Xylanases from fungi: properties and industrial applications. Appl Microbiol Biot. 2005;67:577–91.

    Article  CAS  Google Scholar 

  20. Tuomela M, Hatakka A, Raiskila S, Vikman M, Itävaara M. Biodegradation of radiolabelled synthetic lignin (14 C-DHP) and mechanical pulp in a compost environment. Appl Microbiol Biot. 2021;55:492–9.

  21. Ribeiro A, Pochart P, Day A, Mennuni S, Bono P, Baret J-L, Spadoni J-L, Mangin I. Microbia diversity observed during hemp retting. Appl Microbiol Biot. 2015;99:4471–84.

    Article  CAS  Google Scholar 

  22. Wang Q. Chen H-g, fang G, Chen A-q, Yuan P, Liu J-s. isolation of Bacillus cereus P05 and Pseudomonas sp. X12 and their application in the ramie retting. Ind Crop Prod. 2017;97:518–24.

    Article  CAS  Google Scholar 

  23. Hu Q, Zhang J, Xu C, Li C, Liu S. The dynamic microbiota profile during pepper (Piper nigrum L.) peeling by solid-state fermentation. Curr Microbiol. 2017;74:739–46.

    Article  CAS  Google Scholar 

  24. Zhao D, Liu P, Pan C, Du R, Ping W, Ge J. Bacterial succession and metabolite changes during flax (Linum usitatissimum L.) retting with Bacillus cereus HDYM-02. Sci Rep. 2016;6:31812.

    Article  CAS  Google Scholar 

  25. Saha BC. Hemicellulose bioconversion. J ind microbiol biot. 2003;30:279–91.

    Article  CAS  Google Scholar 

  26. Matte A, Forsberg CW, Gibbins AMV. Enzymes associated with metabolism of xylose and other pentoses by Prevotella (Bacteroides) ruminicola strains, Selenomonas ruminantium D, and Fibrobacter succinogenes S85. Can J Microbiol. 1992;38:370–6.

    Article  CAS  Google Scholar 

  27. Van Maris AJ, Winkler AA, Kuyper M, De Laat WT, Van Dijken JP, Pronk JT. Development of efficient xylose fermentation in Saccharomyces cerevisiae: xylose isomerase as a key component: Biofuels. Springer; 2007. p. 179–204.

  28. Moysés D, Reis V, Almeida J, Moraes L, Torres F. Xylose fermentation by Saccharomyces cerevisiae: challenges and prospects. Int J Mol Sci. 2016;17:207.

    Article  Google Scholar 

  29. Solden LM, Hoyt DW, Collins WB, Plank JE, Daly RA, Hildebrand E, Beavers TJ, Wolfe R, Nicora CD, Purvine SO. New roles in hemicellulosic sugar fermentation for the uncultivated Bacteroidetes family BS11. ISME J. 2017;11:691.

    Article  CAS  Google Scholar 

  30. Ludin KM, Hilti N, Schweingruber ME. Schizosaccharomyces pombe rds1, an adenine-repressible gene regulated by glucose, ammonium, phosphate, carbon dioxide and temperature. Mol Gen Genet. 1995;248:439–45.

    Article  CAS  Google Scholar 

  31. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.

    Article  CAS  Google Scholar 

  32. Deepak R, Shengdar QT, Cyd K, Jennifer AF, Jeffry DS, Keith J. FLASH assembly of TALENs for high-throughput genome editing. Nat Biotechnol. 2012;30:460–5.

    Article  Google Scholar 

  33. Edgar RC, Haas BJ, Clemente JC, Christopher Q, Rob K. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200.

    Article  CAS  Google Scholar 

  34. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. QIIME allows analysis of high-throughput community sequencing data. Bioinformatics. 2010;7:335–6.

    CAS  Google Scholar 

  35. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acid Res. 2012;41:D590–6.

    Article  Google Scholar 

  36. Smith PK, Krohn RI, Hermanson GT, Mallia AK, Gartner FH, Provenzano MD, Fujimoto EK, Goeke NM, Olson BJ, Klenk DC. Measurement of protein using bicinchoninic acid. Anal Biochem. 1985;150:76–85.

    Article  CAS  Google Scholar 

  37. Giovanni C, Maurizio B, Luca M, Laura S, Gian Marco G, Barbara C, Paola O, Luciano Z, Pier GR. Blue silver: a very sensitive colloidal Coomassie G-250 staining for proteome analysis. Electrophoresis. 2010;25:1327–33.

  38. Wisniewski J, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6:359–62.

  39. Yu H, Wang X, Xu J, Ma Y, Zhang S, Yu D, Fei D, Muhammad A. iTRAQ-based quantitative proteomics analysis of molecular mechanisms associated with Bombyx mori (Lepidoptera) larval midgut response to BmNPV in susceptible and near-isogenic strains. J Proteome. 2017;165:S1874391917302105.

Download references




National Natural Science Foundation of China (No. 31871675 and 31700438); China Agriculture Research System (No. CARS-16-E22); Chinese Agricultural Science and Technology Innovation Project (No. ASTIP-IBFC08); Natural Science Foundation of Hunan Province (No. 2019JJ40331 and 2019JJ50711). The funder had no direct role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.

Author information

Authors and Affiliations



DSW summarized the sequencing data, performed the data analysis and prepared the original manuscript. DSW and CLF attended discussion and revised MS. FXY attended data re-interpreting discussion. DSW, CLF, FXY, YQ, LZY and ZK prepared the plant material and attended the transcriptomes data analyses and discussion. DSW and PYD designed the experiments, provided research platform, performed the pathway enrichment analysis, and revised the manuscript. All authors approved the final manuscript.

Corresponding author

Correspondence to Yuan De Peng.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1 Figure S1.

The relative abundance of the dominant bacterial phyla. a and b, the stacked and linear figure of the relative abundance of 8 phyla during the degumming of kenaf bast, respectively.

Additional file 2 Table S1.

The list of the differentially expressed proteins/peptides in the degumming of the kenaf bast.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Duan, S., Cheng, L., Feng, X. et al. Insights on bio-degumming of kenaf bast based on metagenomic and proteomics. BMC Genomics 21, 121 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: