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Article

Integrated Transcriptomic and Proteomic Analyses Uncover the Mechanisms of Keratin Degradation in Lysobacter brunescens YQ20

by
Ming-Yue Wei
1,2,†,
Xiuzhen Gao
3,†,
Xing-Tang Zhao
1,
Yang Liu
2,
Chun-Yu Zhao
2,
Huan Li
4,
Wen-Ming An
2,
Dong-Xu Zhang
2,
Fen Zhang
2,
Qinyuan Ma
3,* and
Jia-Feng Yu
1,5,*
1
Shandong Engineering Research Center of Green and Efficient Breeding and Food Deep Processing of Featured Livestock and Poultry, Dezhou University, Dezhou 253023, China
2
College of Ecology, Resources and Environment, DeZhou University, Dezhou 253000, China
3
School of Life Sciences and Medicine, Shandong University of Technology, Zibo 255000, China
4
College of Food and Bio-Engineering, Bengbu University, Bengbu 233030, China
5
Shandong Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253000, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biology 2026, 15(4), 320; https://doi.org/10.3390/biology15040320
Submission received: 8 January 2026 / Revised: 29 January 2026 / Accepted: 6 February 2026 / Published: 12 February 2026
(This article belongs to the Section Microbiology)
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Simple Summary

Lysobacter brunescens YQ20 exhibits highly efficient wool degradation capabilities. In this study, transcriptomic and proteomic analyses were conducted to elucidate the underlying mechanisms of keratin degradation. The results showed that amino acid biosynthesis, sulfur metabolism, and keratinase production contribute to efficient wool degradation. Our results provide a theoretical basis for the biodegradation technology for wool waste, contributing to the resource utilization of textile waste.

Abstract

Several strains of Lysobacter have demonstrated keratin-degrading capabilities, positioning them as promising candidates for the degradation and utilization of wool waste. In our previous study, a novel strain, Lysobacter brunescens YQ20, exhibiting highly efficient keratin degradation capabilities, was isolated. In this study, transcriptomic and proteomic analyses were conducted to elucidate the underlying mechanisms of keratin degradation. Our findings revealed that several metabolic pathways, specifically, valine, leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; glycine, serine, and threonine metabolism; and histidine metabolism, were highly active during keratin degradation, thereby supporting the growth and metabolism of L. brunescens YQ20. Additionally, the upregulation of genes related to sulfur metabolism, cysteine and methionine metabolism, and glutathione metabolism pathways facilitated the cleavage of disulfide bonds in keratin. Moreover, keratinases identified among the differentially expressed genes and proteins (DEGs/DEPs) were classified into the S8, M14, and M28 families, whose synergistic activity contributed to the efficient hydrolysis of keratin. Collectively, these results provide valuable insights into the molecular mechanisms by which L. brunescens YQ20 contributes to keratin degradation.

1. Introduction

Wool keratin is an important potential source of natural proteins, attracting considerable attention in the textile and other fields [1]. Nevertheless, a significant amount of substandard wool fibers is discarded annually, leading to the low utilization rates of wool resources [2]. This not only results in substantial protein resource wastage but also exacerbates a range of environmental issues. However, recycling these discarded wool resources offers a dual benefit by mitigating both resource waste and environmental challenges [3].
Wool’s primary constituent, keratin, comprises roughly 95% of its total composition [4]. The stability of keratin arises from its intricate crosslinking structure formed by numerous disulfide bonds, rendering it challenging to degrade [5]. Currently, the primary methods for degrading keratin include physical means, chemical extraction, and biodegradation [6,7,8]. In most cases, high-temperature hydrolysis only yields protein and peptide mixtures with relatively low molecular weights and can damage the structure of amino acids such as methionine, lysine, and tryptophan in the product [9]. The extrusion method is simple to operate, but during the process, the drastic changes in temperature and pressure easily destroy disulfide bonds, reducing cysteine content. Additionally, it is costly, energy-intensive, and not widely used in practical applications [10]. The chemical method relies on strong acid and alkali hydrolysis or the use of organic solvents, which not only damages some amino acids in keratin but also generates large amounts of industrial wastewater. Furthermore, the process is complex, and production costs are high, presenting certain limitations in practical operation. The utilization of keratinases from microorganisms to treat keratin waste has recently emerged as an efficient, eco-friendly, and low-energy degradation technique [11].
Progress has been made in the application of microorganisms for keratin degradation. Keratinases have been identified from bacterial isolates when keratin is used as a substrate, particularly from Bacillus and Streptomyces, as well as from some species of saprophytic and parasitic fungi [12,13,14,15]. Keratin-degrading enzymes are mainly of two types, disulfide reductases and keratinases, which affect disulfide bonds and degrade protein chains. Disulfide reductases are a class of enzymes that alter the surrounding redox potential to cleave disulfide bonds, thereby increasing the solubility and accessibility of protein chains [16]. Keratinases belong to a family of hydrolases capable of promoting the hydrolysis and fragmentation of peptide bonds at different sites [17]. The microbial degradation of keratin is a complex process. Peng et al. summarized the mechanisms of keratin disulfide bond destruction, including biomembrane potential, mechanical pressure, thiolysis, and enzymatic hydrolysis [18]. The thiolysis theory was further confirmed by engineered B. licheniformis BBE11-1 and its mutants.
Based on the current study on keratin degradation, the degradation of keratin involves two steps: the cleavage of disulfide bonds and the hydrolysis of denatured proteins [19]. Peng et al. discovered that the supplementation of external sulfites could enhance keratin degradation [20]. Sulfite produced from the catabolism of cysteine can promote the hydrolysis of keratin. In the case of filamentous fungi and actinomycetes, keratin degradation initiates with the growth of hyphae on the keratin surface, which exerts force to penetrate the surface structure of feathers, followed by reduction and proteolysis [21]. Additionally, keratinases alone appear insufficient to complete the hydrolysis of the structurally complex keratin. Rahayu et al. [22] isolated Bacillus sp. MTS from Tangkuban Perahu crater Indonesia, which could produce both keratinase and disulfide reductase. The combined use of these two enzymes was more effective than using either enzyme alone. Ramnani et al. [23] (2005) found that the presence of living bacterial cells was crucial for complete keratin degradation. During degradation, cells adhered to the feather surface and provided a continuous supply of reductant to break disulfide bridges. These studies elucidate the thiolysis theory of keratin degradation. Qiu et al. [8] demonstrated that keratin belongs to at least 14 protein families, and the degradation process requires the combined action of endoproteases, exoproteases, peptidases, and disulfide reductases. However, the further understanding of keratin degradation mechanisms is still necessary.
Members of the genus Lysobacter are commonly found in natural soils and they generate volatile organic compounds and antimicrobial substances [24,25]. These substances are capable of preventing the germination of spores, enhancing plant growth, and stimulating plant defenses [26]. In addition, Lysobacter species excrete an array of hydrolytic enzymes, such as proteases, glucanases, chitinases, and cellulases [27]. In our previous study, we screened a novel keratin-degrading bacterium, Lysobacter brunescens YQ20, with effective keratin degradation capabilities. The ideal conditions for L. brunescens YQ20 to degrade wool are a pH level of 9.0 and a temperature of 37 °C, with the liquid filling 20% of the volume in an Erlenmeyer flask [28]. The yield of total amino acids from 1% wool per hour reaches 13.7 mg/L. This level is 8.6 times greater than that of Stenotrophomonas maltophilia BBE11-1, a strain noted for its superior wool-degrading capabilities [28,29]. Therefore, L. brunescens YQ20 could be an excellent candidate for the utilization of wool fiber waste from the woolen industry. However, the underlying mechanisms of keratin degradation in L. brunescens YQ20 remain poorly understood [28]. Therefore, in this study, we combined transcriptomic and proteomic analyses to identify the pathways involved in keratin degradation by Lysobacter brunescens YQ20. This work will help elucidate the keratin degradation mechanism of Lysobacter brunescens YQ20.

2. Materials and Methods

2.1. Strain, Medium and Culture Conditions

Lysobacter brunescens YQ20 was isolated and purified by our laboratory from sheepfolds in Tanfang Town, Weifang City, Shandong Province, China [28]. L. brunescens YQ20 was cultured in an optimized wool fermentation medium (5 g/L NaCl, 0.5 g/L KH2PO4, 1 g/L K2HPO4, 1 g/L MgSO4·7H2O, 6 g/L wool, pH 9) [28]. To simulate the influence of oxygen on wool waste degradation, varying volumes of fermentation medium (50 mL and 100 mL) were utilized in 250 mL Erlenmeyer flasks [28,30,31,32]. The aerobic group (CG) was continuously cultured at 37 °C for 108 h (h) with 50 mL of fermentation medium in a 250 mL flask. In contrast, the oxygen-limited group (EG) was cultured under the same conditions for 108 h, but with 100 mL of fermentation medium in a 250 mL flask. During the fermentation process, the absorbance of the fermentation broth from both groups was measured every 12 h. Another portion of the fermentation broth was centrifuged at 8000 rpm for 10 min at 4 °C, and the bacterial cells were collected. Then, the harvested L. brunescens YQ20 cells from the CG and EG were divided into two equal parts, one for RNA extraction and the other for protein extraction. Three biological replicates were prepared for each experimental group.

2.2. Determination of Fermentation Broth

Briefly, the fermentation broth of L. brunescens YQ20 from the CG and EG at 0 h, 12 h, 24 h, 48 h, 60 h, 72 h, 84 h, 96 h, and 108 h were boiled at 100 °C for 10 min (min), then centrifuged at 10,000× g for 15 min. The supernatant was collected and filtered through a membrane filter. The absorbance at 280 nm and 600 nm was measured using a spectrophotometer [28].

2.3. RNA Sample Preparation and Transcriptomic Analysis

The RNA sample preparation and transcriptomic analysis were performed with the methods described by Gao et al. [33]. Total RNA was extracted from the bacterial cells using TRIzol (TaKaRa Biotechnology (Dalian) Co., Ltd., Dalian, China) according to the kit instructions, and the RNA quality was assessed by Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Ribosomal RNA was digested using the Ribo-Zero™ Magnetic Kit (Bacteria, MRZB12424). Fragmentation reagent was added to break the RNA into short fragments. The fragmented RNA was used as a template to synthesize first-strand cDNA using random hexamer primers, and second-strand cDNA was synthesized by replacing dTTP with dUTP during the reaction. Different adapters were ligated, and then one strand containing dUTP was digested using UNG enzyme. The remaining single-strand cDNA was purified. The purified single-strand cDNA was then subjected to end repair, A-tailing, and sequencing adapter ligation, followed by size selection of fragments and PCR amplification. Quality control was performed on the raw data, including adapter removal, removal of low-quality reads, and trimming of low-quality bases from both the 3′ and 5′ ends. Clean reads were aligned to the L. brunescens YQ20 genome at the following URL: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA592006 (accessed on 15 January 2020) using Rockhopper2, and transcript expression levels were calculated using the RPKM method [34]. The DESeq2 package (v1.36.0) was used to normalize the gene counts for each sample, calculate fold changes, and perform significance testing of differential expression using the negative binomial (NB) distribution [35]. Differentially expressed genes were identified based on fold change and significance test.

2.4. Protein Extraction and Quantification

Protein extraction and quantification were performed as described previously [36]. Bacterial cells from the CG and EG were selected at 24 h and 84 h of fermentation and were ground into a fine powder using liquid nitrogen, and 600 μL of phenol extraction solution was added. PMSF protease inhibitor was added to obtain a final concentration of 1 mmol/L, and the mixture was ultrasonically disrupted on ice. An equal volume of phenol–Tris–HCl (pH 7.8) saturated solution was added and mixed at 4 °C for 30 min with shaking. The mixture was centrifuged at 7100× g for 10 min, and the phenol upper layer was collected. Five times the volume of pre-cooled 0.1 mol/L ammonium acetate–methanol solution was added, and the samples were precipitated overnight at −20 °C. The mixture was centrifuged at 12,000× g for 10 min, and the precipitate was collected. The precipitate was then mixed with five times the volume of cold methanol and centrifuged at 13,000 rpm for 20 min to collect the precipitate. The precipitate was resuspended and centrifuged with cold acetone, and then air-dried. The protein was dissolved by adding sample lysis buffer. The solution was centrifuged at 12,000× g for 10 min at room temperature, and the supernatant was collected. Protein concentration was determined using the bicinchoninic acid assay.

2.5. Trypsin Digestion and TMT Labeling

Trypsin digestion and TMT labeling were performed with the methods described by Ma et al. [36]. For each sample, 50 μg of protein was taken, and the samples from different groups were diluted with lysis buffer to adjust to the same concentration and volume. Dithiothreitol was added to the protein solution to obtain a final concentration of 5 mmol/L and incubated at 55 °C for 30 min, then iodoacetamide was added to a final concentration of 10 mmol/L and incubated in the dark at room temperature for 15 min. Six volumes of acetone were added to the above solution to precipitate the proteins and placed at −20 °C for at least four hours or overnight. The solution was centrifuged at 8000× g for 10 min at 4 °C to collect the precipitate, and the acetone was allowed to evaporate for 2–3 min. Then, 100 μL of 200 mmol/L TEAB was added to dissolve the precipitate, and trypsin was added at a trypsin-to-protein ratio of 1:50. The sample was incubated at 37 °C overnight. The digested samples were then vacuum freeze-dried. The freeze-dried samples were dissolved in an appropriate volume of 0.5 mol/L TEAB buffer, and the peptides were labeled according to the instructions. The labeled peptides were freeze-dried and stored at −80 °C.

2.6. High-pH Reverse-Phase HPLC Fractionation and LC-MS/MS Analysis

High-pH reverse-phase HPLC fractionation and LC-MS/MS analysis were performed with the methods described by Ma et al. [36]. Fractionation was performed using high-pH reversed-phase HPLC with an Agilent Zorbax Extend—C18 column (2.1 × 150 mm, 5 μm particles). Briefly, peptides were separated into 60 fractions using a gradient of 2–60% acetonitrile over 60 min. The peptides were then combined into 15 fractions and vacuum-dried. For LC-MS/MS analysis, peptides were dissolved in 0.1% formic acid and directly loaded onto a reverse-phase trap column (Acclaim PepMap 100, Thermo Scientific, Waltham, USA). Peptide separation was performed on a reverse-phase analytical column (Acclaim PepMap RSLC, Thermo, USA). The gradient, composed of ACN-H2O-FA (80:19.9:0.1, v/v/v), was ramped from 5% to 28% over 40 min, then from 28% to 42% over 20 min, from 42% to 90% over 5 min, and held at 90% for the final 10 min. Peptides were ionized by nano-electrospray ionization and analyzed by tandem mass spectrometry (MS/MS) using a Q Exactive mass spectrometer (Thermo, USA) coupled online to UPLC. Full peptide detection was performed in the Orbitrap at a resolution of 60,000. Peptides were selected for MS/MS using a normalized collision energy (NCE) of 36, and ion fragments were detected in the Orbitrap at a resolution of 30,000. Three biological replicates were used for each strain in the proteomic analysis. Data were analyzed using the Proteome Discoverer 2.4 software [37].

2.7. Identification of Differentially Expressed Genes (DEGs) and Proteins (DEPs)

The threshold for differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) between groups was set to |log2Fold Change| ≥1, and adjusted by Benjamini and Hochberg’s approach with a significance level of p < 0.05. DEGs and DEPs were further subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses using the OmicShare (v3.0) tools (http://www.omicshare.com/tools). GO and KEGG pathway enrichment analyses were performed based on the hypergeometric test with p < 0.05 considered as significant [38].
To compare the consistency between the proteome and transcriptome, we conducted a correlation analysis. The Pearson correlation coefficient (PCC) was calculated to evaluate the correlation between the DEGs and DEPs. The larger the PCC value, the stronger the correlation.

3. Result

3.1. Soluble Protein Production During Fermentation

The optical density at 280 nm correlates with the concentration of soluble protein in the fermentation broth, serving as an indicator of the keratin degradation process. The results showed that OD280 generally displayed an upward trend under both aerobic (CG) and oxygen-limited conditions (EG) (Figure 1). A detailed analysis revealed that, in the CG at 24 h, Lysobacter brunescens YQ20 displayed the most pronounced degradation trend. In contrast, in the EG, the degradation of wool by L. brunescens YQ20 had only initiated. Furthermore, the results indicated that, after 84 h, OD280 began to show a declining trend under CG. In contrast, in the EG, OD280 exhibited the most significant increase between 72 and 84 h, followed by a reduced rate of increase after 84 h. Furthermore, the OD600 showed that the bacteria are at the same growth stage at both 24 h and 84 h (Figure S1). Based on these observations, L. brunescens YQ20 cells from 24 and 84 h of fermentation were selected for subsequent transcriptomic and proteomic analyses in this study.

3.2. Analysis of RNA-Seq Transcriptomic Data

3.2.1. Transcriptome Data Assembly, Functional Annotation, and Differentially Expressed Gene (DEG) Screening

RNA was isolated from cells harvested in Figure 1 at 24 and 84 h of fermentation and selected for transcriptome sequencing. For RNA sequencing analysis, after removing low-quality reads, a total of 16,934,502 to 17,249,075 clean reads were obtained (Table S1). The Q20 and Q30 scores were 97.31–98.10% and 92.21–93.99%, respectively. Additionally, the GC content was 66.27–67.30%, and the mapping ratio was 97.62–98.56% (Table S1). These data indicate that the samples are reliable and free from exogenous contamination.
In the EG24h vs. CG24h group, 2029 differentially expressed genes (DEGs) were identified, with 1117 showing upregulation and 912 exhibiting downregulation. For the EG84h vs. CG84h group, 1682 DEGs were detected, of which 786 were upregulated and 892 were downregulated. In the EG84h vs. EG24h group, 1728 DEGs were identified, with 754 upregulated and 974 downregulated. Lastly, in the CG84h vs. CG24h group, 253 DEGs were found, with 195 upregulated and 58 downregulated (Figure 2). The detailed gene expression information for the four groups is shown in Table S2.

3.2.2. GO and KEGG Enrichments of DEGs

The differentially expressed genes (DEGs) from each group underwent Gene Ontology (GO) analysis (Table S3). These genes were primarily involved in three categories: biological processes, cellular components, and molecular functions. Notably, several significant GO terms related to “transport”, “localization”, “gene expression”, and “cellular nitrogen compound metabolic process” were identified within the biological process category (p < 0.05). In the cellular component, GO terms such as “ribosome”, “cytoplasm”, and “structural molecule activity” were highlighted. Additionally, within the molecular function category, GO terms including “metal ion binding” and “oxidoreductase activity” were revealed.
KEGG enrichment analysis revealed that amino acid metabolism pathways and sulfur metabolism pathways, which are closely related to keratin degradation, were significantly enriched in four groups. In the EG24h vs. CG24h group, the pathways of valine, leucine, and isoleucine biosynthesis; histidine metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; tyrosine metabolism; and sulfur metabolism were significantly upregulated (p < 0.05) (Figure 3a). In the EG84h vs. CG84h group, the pathways of valine, leucine, and isoleucine biosynthesis and glycine, serine, and threonine metabolism were significantly upregulated (p < 0.05) (Figure 3b). When comparing EG84h vs. EG24h, the pathways of valine, leucine, and isoleucine biosynthesis; glycine, serine, and threonine metabolism; phenylalanine metabolism; tyrosine metabolism; valine, leucine, and isoleucine degradation; histidine metabolism; and sulfur metabolism were significantly downregulated (p < 0.05) (Figure 3c). In the CG84h vs. CG24h group, the pathways of histidine metabolism and valine, leucine, and isoleucine biosynthesis were significantly upregulated (p < 0.05) (Figure 3d).

3.3. Analysis of Proteomic Data

3.3.1. Overview of Proteomics Analysis

Proteomics can reflect post-transcriptional regulation, and differential expression proteins (DEPs) were identified using TMT-based comparative proteomics. TMT analysis yielded a total of 449,400 spectra (Figure 4a), of which 134,164 were successfully matched to the database. From these identified spectra, 43,026 peptides, including 38,496 unique peptides, were obtained. Subsequent annotation led to the identification of 2935 proteins (Figure 4a). In the EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24, and CG84h vs. CG24h groups, 754 (347 upregulated, 407 downregulated), 528 (248 upregulated, 280 downregulated), 292 (198 upregulated, 94 downregulated), and 112 (76 upregulated, 36 downregulated) DEPs were detected, respectively (Figure 4b,c).

3.3.2. GO and KEGG Enrichments of DEPs

GO enrichment analysis (p < 0.05) was performed on each group (Table S4). GO enrichment analysis showed that, in biological processes, most DEPs were significantly enriched in “metabolic process”, “organic substance metabolic process”, “macromolecule metabolic process”, and “gene expression”. In the molecular function category, DEPs were primarily distributed in “nucleic acid binding” and “structural molecule activity.” In the cellular structure category, DEPs were primarily associated with “ribonucleoprotein complex”.
To further analyze the key pathways related to keratin degradation and metabolism, KEGG pathway enrichment analysis was performed on each group. In EG24h vs. CG24h, four KEGG pathways were significantly regulated, with “Tyrosine metabolism” and “Butanoate metabolism” upregulated, and “RNA degradation” and “Ribosome” downregulated (p < 0.05) (Figure 5a). In EG84h vs. CG84h, seven KEGG pathways were significantly regulated, with “Sulfur metabolism”, “Glutathione metabolism,” and “Cysteine and methionine metabolism” upregulated. In contrast, “Porphyrin and chlorophyll metabolism”, “Biosynthesis of siderophore group nonribosomal peptides”, “Ribosome”, and “Ubiquinone and other terpenoid-quinone biosynthesis” were downregulated (p < 0.05) (Figure 5b). For EG84h vs. EG24h, four KEGG pathways were significantly regulated, with “Ribosome” and “Two-component system” upregulated and “Biosynthesis of siderophore group nonribosomal peptides” and “Nitrogen metabolism” downregulated (p < 0.05) (Figure 5c). In CG84h vs. CG24h, four KEGG pathways were significantly regulated, with “Biosynthesis of siderophore group nonribosomal peptides” upregulated, and “Nitrogen metabolism”, “Folate biosynthesis”, and “Two-component system” downregulated (p < 0.05) (Figure 5d).

3.4. Integrated Transcriptomic and Proteomic Analyses

A correlation analysis was conducted between DEGs and DEPs. Among the identified DEGs in EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h, and CG84h vs. CG24h, there were 420, 255, 131, and 16 corresponding DEPs, respectively (Figure 6a,c,e,g). Of the 420 correlated DEGs-DEPs identified in EG24h vs. CG24h, 214 were co-upregulated, and 134 were co-downregulated (Figure 6b). Similarly, the majority of correlated DEGs-DEPs identified in EG84h vs. CG84h, EG84h vs. EG24h, and CG84h vs. CG24h exhibited a positively correlated regulation pattern (Figure 6d,f,h). The detailed information for the four groups is shown in Table S5. The correlation coefficients between the expression levels of DEPs and their corresponding mRNAs showed a strong correlation in the EG24h vs. CG24h group, with a value of 0.6562. The correlation coefficients between the expression levels of DEPs and their corresponding mRNAs showed a moderate correlation in the EG84h vs. CG84h and EG84h vs. EG24h groups, with values of 0.5684 and 0.5457. The correlation coefficients between the expression levels of DEPs and their corresponding mRNAs showed a weak correlation in the EG24h vs. CG24h group, with a value of 0.3716 (Figure 6b,d,f,h). These findings suggest that, while there is a general trend of correlation between gene and protein expression changes, there are also notable discrepancies, highlighting the complexity of post-transcriptional and post-translational regulation.

3.5. Analysis of DEGs/DEPs in the Amino Acid Metabolism

The KEGG analysis indicated that many DEGs/DEPs related to amino acid metabolism and sulfur metabolism were affected by the keratin degradation. Consequently, we characterized the DEGs/DEPs involved in amino acid metabolism and sulfur metabolism.
A total of nine DEGs were identified in the valine, leucine, and isoleucine biosynthesis pathway, including dihydroxy-acid dehydratase (ilvD), ketol-acid reductoisomerase (ilvC), acetolactate synthase 2 catalytic subunit (ilvG), branched-chain amino acid aminotransferase (ilvE), 2-isopropylmalate synthase (leuA), 3-isopropylmalate dehydratase large subunit (leuC), isopropylmalate isomerase (leuD), and isocitrate dehydrogenase (leuB). All nine DEGs were upregulated in the EG24h vs. CG24h comparison (Figure 7a). Four DEGs, specifically ilvD, ilvC, ilvG, and ilvE, were upregulated in the EG84h vs. CG84h group. In contrast, all nine DEGs were downregulated in the EG84h vs. EG24h comparison (Figure 7a). In the phenylalanine, tyrosine, and tryptophan biosynthesis pathway, a total of eight DEGs/DEPs were identified, including tryptophan synthase subunit alpha (trpA), tryptophan synthase subunit beta (trpB), tyrosinase (mepA), 3-deoxy-7-phosphoheptulonate synthase class II (aroH), maleylacetoacetate isomerase (maiA), shikimate dehydrogenase (aroE), 3-dehydroquinate dehydratase (aroQ), and 4-hydroxyphenylpyruvate dioxygenase (hpd). All eight DEGs and two DEPs (mepA and maiA) were upregulated in the EG24h vs. CG24h group (Figure 7b). In the EG84h vs. CG84h group, five DEGs (trpA, trpB, aroH, maiA, aroQ) and three DEPs (trpB, mepA, maiA) were upregulated (Figure 7b). In the EG84h vs. EG24h group, five DEGs/DEPs (mepA, aroE, hpd, aroQ) were expressed, with DEGs mepA, aroE, and hpd downregulated, and DEG aroQ and DEP trpA upregulated (Figure 7b).
In the glycine, serine, and threonine metabolism pathway, six DEGs/DEPs were identified, including phosphoserine phosphatase 1 (pspA), phosphoglycerate dehydrogenase (serA), threonine dehydratase (tdcB), homoserine kinase (thrB), threonine synthase (thrC), and D-glycerate 3-kinase (GLYK) (Figure 7c). These DEGs and the DEP GLYK were upregulated in the EG24h vs. CG24h group, while DEPs serA and thrC were downregulated (Figure 7c). Two DEGs/DEPs, serA and thrC, were expressed in the EG84h vs. CG84h group, with DEG serA upregulated and DEG thrC and DEP thrC downregulated (Figure 7c). Four DEGs/DEPs, pspA, tdcB, thrB, and thrC, were expressed in the CG84h vs. CG24h group, with DEGs pspA, tdcB, thrC, and DEP thrB upregulated (Figure 7c). In the histidine metabolism pathway, a total of nine DEGs/DEPs were identified, including ATP phosphoribosyltransferase (hisG), histidinol dehydrogenase (hisD), histidinol-phosphate transaminase (hisC), bifunctional imidazole glycerol-phosphate dehydratase (hisB), imidazole glycerol phosphate synthase (hisH), 1-(5-phosphoribosyl)-5-((5-phosphoribosylamino)methylideneamino)imidazole-4-carboxamide isomerase (hisA), bifunctional phosphoribosyl-AMP cyclohydrolase (hisI), histidine ammonia-lyase (hutH), and urocanate hydratase (hutU) (Figure 7d). These nine DEGs and DEPs were upregulated in the EG24h vs. CG24h group, while DEPs hisG, hisD, and hisB were downregulated (Figure 7d). In the EG84h vs. CG84h group, six DEGs/DEPs were expressed, where DEGs hisH, hisA, and hisI, as well as DEPs hisG, hisD, and hisB, were downregulated (Figure 7d). In the EG84h vs. EG24h group, six DEGs (hisG, hisD, hisC, hisB, hutH, and hutU) were downregulated. In the CG84h vs. CG24h group, six DEGs (hisD, hisC, hisB, hisH, hisA, and hisI) were upregulated (Figure 7d).

3.6. Analysis of DEGs/DEPs in Sulfide Metabolism

Sulfide typically refers to H2S, HS−, etc. In the presumed keratin degradation model, the disulfide bond reduction system (such as glutathione-dependent reductases) first attacks the disulfide bond network of keratin [39]. After cleavage, free sulfides or sulfur-containing peptides may be released. The primary challenge in efficient keratin degradation lies in the abundant disulfide crosslinks between keratin molecules. It is necessary to reduce these disulfide bonds to weaken the rigid structure. We also identified DEGs/DEPs related to sulfide metabolism and associated pathways. In the sulfur metabolism pathway, four DEGs were identified, including phosphoadenosine phosphosulfate reductase (cysH), sulfite reductase subunit beta (cysI), and two sulfite reductase (NADPH) flavoprotein alpha-component (cysJ) (Figure 8a). These four DEGs were upregulated in the EG24h vs. CG24h group but downregulated in the EG84h vs. EG24h group (Figure 8a). In the cysteine and methionine metabolism pathway, seven DEGs/DEPs were identified, including acireductone dioxygenase (mtnD), two methionine synthases (metH), homoserine dehydrogenase (hsd), homoserine O-acetyltransferase (metX), O-succinylhomoserine (thiol)-lyase (metB), and cysteine dioxygenase (cdo1) (Figure 8b). Six DEGs, including mtnD, two metH, hsd, metX, and metB, and two DEPs, hsd and metB, were upregulated in the EG24h vs. CG24h group (Figure 8b). In the EG84h vs. CG84h group, five DEGs/DEPs were expressed. The DEGs mtnD, hsd, metB, and cdo1 and the DEP hsd were upregulated, while the DEG metH was downregulated (Figure 8b). In the EG84h vs. EG24h group, six DEGs were expressed, where two metH, hsd, metX, and metB were downregulated, while cdo1 was upregulated (Figure 8b). In the CG84h vs. CG24h group, one DEP, metB, was downregulated. In the glutathione metabolism pathway, seven DEGs/DEPs were identified, including two glutathione peroxidases (gpo), one gamma-glutamyltransferase (ggt), and four glutathione S-transferases (gst) (Figure 8c). Among them, four DEGs (one gpo, one ggt, two gst) and three DEPs (one gpo, one ggt, one gst) were upregulated in the EG24h vs. CG24h group. In the EG84h vs. CG84h group, five DEGs (one gpo, four gst) and four DEPs (two gpo, one ggt, one gst) were upregulated. Three DEGs were expressed in the EG84h vs. EG24h group, where one ggt was downregulated and two gst were upregulated. One DEP, gst, was upregulated in the EG84h vs. EG24h group.

3.7. Analysis of DEGs/DEPs of Potential Proteases for Keratin Decomposition

A total of twenty-two keratinases were identified among the DEGs/DEPs. Among these keratinases, fourteen were classified within the S8 family, including five serine proteases and nine subtilisin-like serine proteases. Six carboxypeptidases belonged to the M14 family, and two peptidase S1s belonged to the M28 family (Table 1). It was found that all serine protease and subtilisin-like serine protease DEGs/DEPs were upregulated in the EG24 vs. CG24 group. Most serine protease and subtilisin-like serine protease DEGs/DEPs were upregulated in the EG84 vs. CG84 group, while they showed downregulation in the EG84 vs. EG24 and CG84 vs. CG24 groups. Similarly, carboxypeptidase and peptidase S1 DEGs/DEPs showed a general trend of upregulation in the EG24 vs. CG24 and EG84 vs. CG84 groups, whereas most of them were not expressed in the EG84 vs. EG24 and CG84 vs. CG24 groups. Overall, most keratinase DEGs/DEPs were upregulated in the EG24 vs. CG24 and EG84 vs. CG84 groups, while most were either not expressed or downregulated in the EG84 vs. EG24 and CG84 vs. CG24 groups. In this study, seven DEGs/DEPs were associated with siderophores, including two TonB2 proteins (tonB2), one Fe3+ dicitrate transport protein (fecA), one TonB-dependent receptor (fyuA), and three bacterioferritin (bfr) (Figure S1). The seven DEGs and two bfr DEPs were upregulated in the EG24h vs. CG24h group, while most remains unchanged or downregulated in the EG84 vs. CG84, EG84 vs. EG24, and CG84 vs. CG24 groups.

4. Discussion

Our previous study screened a novel keratin-degrading bacterium, L. brunescens YQ20, which exhibited effective keratin degradation capabilities [28]. To understand the molecular regulatory mechanisms underlying keratin degradation and identify key genes and pathways at both the transcriptional and protein levels, based on the time-course analysis of soluble protein production in the fermentation broth, bacterial samples from EG24h, EG84h, CG24h and CG84h were selected for RNA-Seq transcriptomics and proteomics analysis (Figure 1). During the keratin degradation process, numerous DEGs (Figure 2, Table S2) and DEPs (Figure 4) were obtained. Correlation analysis revealed a low correlation coefficient between the expression levels of all DEPs and their corresponding mRNAs, indicating extensive post-transcriptional and translational regulation (Figure 6). It is widely acknowledged that transcriptomic and proteomic data frequently exhibit a limited correlation, as mRNA levels are not definitive proxies for protein abundance [33,40,41]. This discrepancy arises from several factors: proteins may undergo secretion, temporary degradation, or exist at concentrations below the detection threshold of conventional proteomics [33]. Moreover, proteins generally possess longer half-lives than mRNAs and are subject to extensive regulation at post-transcriptional, translational, and post-translational stages, further decoupling their levels from transcriptional activity [42]. Although the expression of some genes and proteins is not directly correlated, these variations are complementary and necessary for a comprehensive understanding of L. brunescens YQ20’s ability to degrade wool. KEGG analysis results revealed that many DEGs/DEPs involved in amino acid metabolism and sulfur metabolism were influenced by the duration of keratin degradation (Figure 3 and Figure 5). Additionally, a set of DEGs and DEPs associated with proteases involved in keratin degradation were identified in this study. Unfortunately, some DEPs could not obtain GO or KEGG annotations through databases (Table S4). These proteins may represent strain-specific gene products or proteins whose functions have not yet been characterized. In the future, research can be conducted through gene-knockout or functional studies.

4.1. Amino Acid Metabolism in Keratin Degradation

Keratin is rich in various amino acids, including cysteine, arginine, serine, proline, valine, leucine, glutamic acid, and glycine, among others [8]. Studies have shown that the early stages of inoculation on feather-based media can also activate the intermediate synthesis of pyruvate-derived amino acids, including valine, leucine, and isoleucine [43]. In our previous study, the composition of amino acids was detected in the fermentation broth of L. brunescens YQ20 after keratin degradation. During the degradation of wool waste, 15 types of amino acids were discovered, with a total content of free amino acids reaching 5.4 × 103 mg/L [28]. In the EG24h vs. CG24h group, the DEGs related to valine, leucine, and isoleucine biosynthesis (ilvD, ilvC, ilvG, ilvE, leuA, leuC, leuD, leuB), phenylalanine, tyrosine, and tryptophan biosynthesis (trpA, trpB, mepA, aroH, maiA, aroE, aroQ, hpd), glycine, serine, and threonine metabolism (pspA, serA, tdcB, thrB, thrC, GLYK), and histidine metabolism (hisG, hisD, hisC, hisB, hisH, hisA, hisI, hutH, hutU) all exhibited upregulated expression (Figure 7a–d). Additionally, three amino acid metabolism-related proteins (mepA, maiA, GLYK) were also upregulated in the EG24h vs. CG24h group (Figure 7b). In contrast, the majority of genes related to these amino acid metabolic pathways were downregulated in the EG84h vs. EG24h comparison (Figure 7a–d). Moreover, some amino acid metabolism-related DEPs/DEGs were also identified in the EG84h vs. EG24h group. The citric acid cycle is a series of chemical reactions that release stored energy as ATP and carbon dioxide. A previous study indicated that arginine and glutamate can produce alpha-ketoglutarate for the citric acid cycle [39]. Similarly, other amino acids can be transformed into succinyl-CoA, fumarate, or oxaloacetate, serving as intermediates of the citric acid cycle [39]. These findings suggest that amino acid metabolism is markedly active during keratin degradation, facilitating the synthesis of intermediates for aromatic amino acids, histidine, and pyruvate-derived amino acids, which are essential for supporting cellular growth and metabolic processes. Studies have shown that the histidine biosynthesis pathway is widespread and highly conserved in bacteria [44].
Furthermore, some studies indicated that, after the degradation of keratin, soluble peptides and amino acids are generated [45]. The biosynthesized amino acids such as valine, leucine, isoleucine, phenylalanine, tyrosine, and tryptophan can quickly convert into proteins and other cellular components, supporting bacterial reproduction and producing more degradation enzymes. In addition, amino acids can enter the TCA cycle through deamination and be thoroughly oxidized to produce a large amount of ATP, which provides energy for energy-consuming processes such as keratinase synthesis, secretion, and active transport [11]. Additionally, studies have shown that histidine acts as an intracellular antioxidant and a multifunctional cellular physiological protector [46]. We observed that genes related to L-histidine biosynthesis (hisD, hisC, hisB, hisH, hisA, hisI) were significantly upregulated in the CG84h vs. CG24h group (Figure 7d). Compared to CG24h, a large amount of metabolic products accumulated in the medium in the CG84h group, and histidine, as a multifunctional protector, likely played a role in antioxidative cell protection. Therefore, the upregulation of these amino acids may also reflect general growth, stress response, or nitrogen assimilation. Further work is needed to verify the role of these amino acids.

4.2. Sulfite Metabolic Pathway for Breaking Down Disulfide Bonds

Keratin, rich in disulfide bonds, is notoriously resistant to hydrolysis by common proteases, with the key step in its degradation being the breaking of these disulfide bonds [47]. Sulfite refers to a sulfur-containing oxyanion (such as SO32−) that participates in disulfide bond reduction reactions. It has been reported that disulfide reductases can degrade the disulfide bonds in keratin [48]. In this study, the upregulated expression of DEGs such as phosphoadenosine phosphosulfate reductase (cysH), sulfite reductase subunit beta (cysI), and sulfite reductase subunit alpha (cysJ) in the EG24h vs. CG24h group (Figure 8a) suggests that L. brunescens YQ20 produces sulfides and disulfide reductases that directly target these disulfide bonds. A previous study has shown that Bacillus licheniformis BBE11-1 can metabolize cysteine to generate sulfite, which has been proven to disrupt disulfide bonds [20]. In this study, we also explored the cysteine and methionine metabolism pathways. Several upregulated DEGs/DEPs were identified in both the EG24h vs. CG24h and EG84h vs. CG84h groups, including metB, metX, metH, mtnD, hsd, cysK, and cdo1. The upregulation of metB, metX, metH, mtnD, and hsd facilitates the synthesis of L-Homocysteine, which is further converted into L-cysteine. L-cysteine, under the action of cdo1, generates secreted sulfite that reduces the disulfide bonds in keratin, thereby enhancing feather hydrolysis (Figure 8b). Several reports indicated that glutathione (GSH) can promote keratin degradation by activating disulfide reductases [49]. ggt is a periplasmic enzyme that hydrolyzes GSH to produce cysteine–glycine. The GGT-GSH system modifies the feather substrate by reducing disulfide bonds, making it more susceptible to keratinase attack, thereby enhancing protein release [50]. The decrease in oxygen availability directly leads to the weakening of the electron transport chain function, causing the shift in redox potential towards reduction. ROS is produced under oxygen-limited conditions. Cysteine synthesis and other sulfur metabolism pathways are one of the most significant pathways in response to superoxide stress [51]. Glutathione, through the action of gst, can utilize the highly nucleophilic thiol group on the cysteine residue of glutathione to bind with electrophilic target substances [52]. In addition, gpo plays a central role in glutathione metabolism. gpo uses glutathione (GSH) as a reducing agent to reduce peroxides, and GSH is oxidized to glutathione disulfide (GSSG) in this process. Subsequently, glutathione reductase (GR) reduces GSSG back to GSH, completing the recycling of glutathione. In this study, upregulated DEGs/DEPs including gpo, ggt, and gst were identified in both the EG24h vs. CG24h and EG84h vs. CG84h groups, suggesting that the GSH production may not only be involved in disulfide bond reduction, but also may play a key role in maintaining intracellular redox balance (Figure 8c). In addition, the upregulation of the sulfite metabolic pathway and specific protease families have been reported as a specific feature of keratin degradation, and its expression is highly substrate-dependent [39]. Therefore, we infer that the upregulation of these pathways is more likely to be directly associated with the degradation process of wool keratin, and this inference needs to be ultimately confirmed through precise substrate control experiments in the future.
In summary, the degradation of keratin, hindered by its abundance of disulfide bonds, necessitates the cleavage of these bonds as a crucial step. Our findings, in line with previous reports, suggest that L. brunescens YQ20 employs a combination of disulfide reductases, sulfite production, and potentially GSH-mediated pathways to achieve this, ultimately facilitating the hydrolysis of keratin-rich materials such as wool.

4.3. Proteases for Keratin Degradation

Keratinases are mainly classified as serine proteases (S) or metalloproteases (M). Currently known keratinases belong to at least 14 different protease families, i.e., S1, S8, S9, S10, S16, M3, M4, M14, M16, M28, M32, M36, M38, and M55, with S8 family keratinases produced by the chymotrypsin subfamily being dominant [8]. Previous studies on Bacillus sp. 8A6 have shown that the expression of key keratinase genes and their associated disulfide reduction activity are strongly induced in the presence of keratin substrates, while they remain at a basal level in media rich in readily available carbon and nitrogen sources [43]. This strongly suggests substrate specificity in this type of metabolic pathway. In this study, keratinases identified among the DEPs/DEGs were mainly classified as serine proteases and/or metalloproteases (Table 1). Among these, 14 keratinases belong to the S8 family, including 5 serine proteases and 9 subtilisin-like serine proteases. All serine protease and subtilisin-like serine protease DEGs/DEPs were upregulated in the EG24 vs. CG24 group, and most were also upregulated in the EG84 vs. CG84 group. S8 family keratinases possess serine-binding sites, allowing them to effectively act on keratin [53]. Additionally, serine proteases can collaborate with disulfide reductases. For instance, the Stenotrophomonas sp. strain D-1 isolated from deer hair follicles produces two extracellular enzymes: serine protease and disulfide bond reductase. Compared to single-keratinase treatment, the combination of these two enzymes enhances the keratinolytic rate by 50-fold [54]. The M14 and M28 families belong to the class of exocellular keratinases. In this study, six carboxypeptidases from the M14 family and two peptidase S1s from the M28 family were identified. All members of the M14 family contain the His-Xaa-Xaa-Glu motif, which catalyzes the hydrolytic removal of a single C-terminal amino acid from polypeptide chains [55]. Exopeptidases (M28) can work with endoproteases (S8) and oligopeptidases (M3) to degrade keratin [56]. Similarly, the majority of carboxypeptidase and peptidase S1 DEGs/DEPs exhibited an upregulated expression trend in both EG24 vs. CG24 and EG84 vs. CG84 comparisons, while they remained largely unexpressed in the EG84 vs. EG24 and CG84 vs. CG24 groups. The upregulation of these serine proteases (S) or metalloproteases (M) in the EG24 vs. CG24h and EG84 vs. CG84h groups may highlight the significant potential of L. brunescens YQ20 in degrading wool. In addition, studies have shown that proteases can directly participate in the oxygen-limited sensing signaling pathway [57]. Transcription factors are activated under oxygen-limited conditions and can directly bind to the promoter regions of protease genes or indirectly induce protease expression by regulating downstream sigma factors [58]. Meanwhile, quantitative proteomics analysis shows that bacteria differentially express protease systems under different oxygen-limited conditions to maintain proteomic homeostasis [59]. Therefore, the upregulation of the keratinase family observed in this study is likely to reflect an integrated stress and adaptation strategy of L. brunescens YQ20 in oxygen-limited environments.
Siderophores sequester metal ions and make them available for the organism, which may affect keratinase function and overall metabolic processes [60]. tonB2, as a component of the TonB system, is responsible for transferring the energy from proton motive force (PMF) to outer membrane receptors, facilitating the transmembrane transport of substrates such as siderophores [61]. Studies have shown that the expression of the TonB system is regulated by iron ions, with the expression of the tonB2 gene being upregulated when iron ion concentrations are low [62]. fecA binds to iron dicitrate in the periplasm and then transfers it to the TonB-dependent transporter, which spans the outer membrane [63]. fyuA is a TonB-dependent β-barrel outer membrane receptor that can recognize and bind to siderophores, assisting bacteria in acquiring iron [64]. bfr is an iron storage protein found in a variety of prokaryotic organisms, capable of forming a hydrous oxide mineral core of iron in its central cavity. Studies have shown that the deletion of the bfr gene in P. aeruginosa severely impairs the organism’s ability to accumulate iron [65]. In the EG24h vs. CG24h group, the DEGs tonB2, fecA, fyuA, and bfr were upregulated, while most of them remains downregulated or unchanged in other groups (Figure S2). These results suggest that, in the early stage of inoculating the strain in the wool medium, siderophores may affect keratinase function by maintaining iron homeostasis.
Based on our current findings, we can only speculate about the underlying keratin degradation mechanisms of L. brunescens YQ20 at the transcriptional and proteomic levels. In future work, direct functional verification, such as keratinase activity assay, sulfite or thiol quantification, and zymogram analysis or protease inhibition, is needed to elucidate the roles of these candidate pathways and enzymes.

5. Conclusions

In this study, integrated transcriptomic and proteomic analyses were employed to investigate the potential pathways involved in keratin degradation by L. brunescens YQ20. The putative mechanism is shown in Figure 9. The result revealed that L. brunescens YQ20 upregulated numerous mRNAs and proteins related to amino acid metabolism, sulfite metabolism, and keratinase synthesis during keratin degradation. The upregulation of DEGs/DEPs related to valine, leucine, and isoleucine biosynthesis, phenylalanine, tyrosine, and tryptophan biosynthesis, glycine, serine, and threonine metabolism, and histidine metabolism likely promote the growth and metabolism of the strain. Additionally, the upregulation of DEPs/DEGs related to disulfide reductases, cysteine and methionine metabolism, and glutathione (GSH) metabolism may help to break the disulfide bonds abundant in keratin. Furthermore, the upregulated expression of keratinase DEPs/DEGs belonging to the S8, M14, and M28 families indicates their putative role in the proteolytic cleavage of keratin polypeptides. We emphasize that this model is inferred from systematic expression correlations and provides a strong foundation for future biochemical and genetic validation of the specific functions of these candidate enzymes and pathways.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology15040320/s1, Figure S1: Time-course analysis of OD600 during fermentation of Lysobacter brunescens YQ20. CG, aerobic condition; EG, oxygen-limited condition; Figure S2: DEGs/DEPs associated with siderophores; Table S1: Statistics of sequencing production and mapping ratio; Table S2: Gene expression information for the four groups; Table S3: Gene Ontology analysis for the four groups; Table S4: GO enrichment analysis for the four groups; Table S5: Correlated DEGs-DEPs identified in four groups.

Author Contributions

Conceptualization, J.-F.Y. and Q.M.; methodology, X.-T.Z., X.G. and M.-Y.W.; software, M.-Y.W.; validation, Y.L., H.L., X.G., W.-M.A., D.-X.Z., F.Z. and C.-Y.Z.; formal analysis, M.-Y.W.; investigation, M.-Y.W.; resources, J.-F.Y. and Q.M.; data curation, M.-Y.W.; writing—original draft preparation, M.-Y.W.; writing—review and editing, Y.L., H.L. and X.G.; visualization, M.-Y.W.; supervision, J.-F.Y. and Q.M.; project administration, M.-Y.W.; funding acquisition, J.-F.Y., M.-Y.W., Y.L. and H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Key R&D Program of Shandong Province, China, grant number 2023LZGCQY020; Shandong Provincial Major Agricultural Technology Collaborative Promotion Program, grant number SDNYXTTG-2025-49; Research Foundation of Dezhou University, grant number 2023xjrc119; Shandong Provincial Natural Science Foundation, grant number ZR2023QC311; National Natural Science Foundation of China, grant number 32501457; Shandong Provincial Natural Science Foundation, grant number ZR2025QC172; and Talent Introduction Project of Bengbu University, grant number, 2024YYX12QD.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the Supplementary Material. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Time-course analysis of soluble protein production during fermentation of Lysobacter brunescens YQ20. CG, aerobic condition; EG, oxygen-limited condition.
Figure 1. Time-course analysis of soluble protein production during fermentation of Lysobacter brunescens YQ20. CG, aerobic condition; EG, oxygen-limited condition.
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Figure 2. Summary of differentially expressed genes (DEGs). (a) Numbers of DEGs in EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h, and CG84h vs. CG24h, respectively. The number of upregulated, downregulated and differentially expressed genes are indicated in red, green, and blue colors. (b) Venn diagrams of total DEGs. The EG24h vs. CG24h group, EG84h vs. CG84h group, CG84h vs. CG24h group, and EG84h vs. EG24h group are indicated by yellow, red, purple and green.
Figure 2. Summary of differentially expressed genes (DEGs). (a) Numbers of DEGs in EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h, and CG84h vs. CG24h, respectively. The number of upregulated, downregulated and differentially expressed genes are indicated in red, green, and blue colors. (b) Venn diagrams of total DEGs. The EG24h vs. CG24h group, EG84h vs. CG84h group, CG84h vs. CG24h group, and EG84h vs. EG24h group are indicated by yellow, red, purple and green.
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Figure 3. KEGG pathway enrichment analysis of DEGs. The figure presents the KEGG pathways that were significantly enriched (p < 0.05) across four comparisons: (a) EG24h vs. CG24h, (b) EG84h vs. CG84h, (c) EG84h vs. EG24h, and (d) CG84h vs. CG24h. A higher enrichment score corresponds to a more substantial significance of pathway enrichment. Pathways marked in red indicate upregulation, while those in green denote downregulation.
Figure 3. KEGG pathway enrichment analysis of DEGs. The figure presents the KEGG pathways that were significantly enriched (p < 0.05) across four comparisons: (a) EG24h vs. CG24h, (b) EG84h vs. CG84h, (c) EG84h vs. EG24h, and (d) CG84h vs. CG24h. A higher enrichment score corresponds to a more substantial significance of pathway enrichment. Pathways marked in red indicate upregulation, while those in green denote downregulation.
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Figure 4. Overview of proteomic data. (a) Summary of MS/MS spectra and identified differentially accumulated proteins (DEPs). (b) Numbers of DEPs in EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h and CG84h vs. CG24h, respectively. The upregulated, downregulated and the total number of differentially expressed proteins are indicated by red, green and blue. (c) Venn diagrams of total DEPs. The EG24h vs. CG24h group, EG84h vs. CG84h group, CG84h vs. CG24h group, and EG84h vs. EG24h group are indicated by yellow, red, purple and green.
Figure 4. Overview of proteomic data. (a) Summary of MS/MS spectra and identified differentially accumulated proteins (DEPs). (b) Numbers of DEPs in EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h and CG84h vs. CG24h, respectively. The upregulated, downregulated and the total number of differentially expressed proteins are indicated by red, green and blue. (c) Venn diagrams of total DEPs. The EG24h vs. CG24h group, EG84h vs. CG84h group, CG84h vs. CG24h group, and EG84h vs. EG24h group are indicated by yellow, red, purple and green.
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Figure 5. KEGG pathway enrichment analysis of DEPs. The figure presents the KEGG pathways that were significantly enriched (p < 0.05) across four comparisons: (a) EG24h vs. CG24h, (b) EG84h vs. CG84h, (c) EG84h vs. EG24h, and (d) CG84h vs. CG24h. A higher enrichment score corresponds to a more substantial significance of pathway enrichment. Pathways marked in red indicate upregulation, while those in green denote downregulation.
Figure 5. KEGG pathway enrichment analysis of DEPs. The figure presents the KEGG pathways that were significantly enriched (p < 0.05) across four comparisons: (a) EG24h vs. CG24h, (b) EG84h vs. CG84h, (c) EG84h vs. EG24h, and (d) CG84h vs. CG24h. A higher enrichment score corresponds to a more substantial significance of pathway enrichment. Pathways marked in red indicate upregulation, while those in green denote downregulation.
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Figure 6. Correlation of DEPs and their corresponding mRNAs. (a,c,e,g) Venn diagrams showing the numbers of associated DEGs-DEPs identified in the EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h and CG84h vs. CG24h groups, respectively. (b,d,f,h) Correlation between the DEGs and DEPs in the EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h and CG84h vs. CG24h groups, respectively. The horizontal axis represented the protein expression level, and the vertical axis denoted the mRNA expression level. Red plot and blue plot: same trend of DEPs and DEGs; green plot and yellow plot: opposite trend of DEPs and DEGs, and all data were log2-transformed. “FC” represents fold change.
Figure 6. Correlation of DEPs and their corresponding mRNAs. (a,c,e,g) Venn diagrams showing the numbers of associated DEGs-DEPs identified in the EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h and CG84h vs. CG24h groups, respectively. (b,d,f,h) Correlation between the DEGs and DEPs in the EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h and CG84h vs. CG24h groups, respectively. The horizontal axis represented the protein expression level, and the vertical axis denoted the mRNA expression level. Red plot and blue plot: same trend of DEPs and DEGs; green plot and yellow plot: opposite trend of DEPs and DEGs, and all data were log2-transformed. “FC” represents fold change.
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Figure 7. Changes in DEGs/DEPs associated with amino acid metabolism in the groups of EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h and CG84h vs. CG24h when L. brunescens YQ20 grew in wool medium: (a) valine, leucine, and isoleucine biosynthesis; (b) phenylalanine, tyrosine and tryptophan biosynthesis; (c) glycine, serine and threonine metabolism; (d) histidine metabolism. Red represents upregulated DEGs or DEPs, while green represents downregulated DEGs or DEPs. Gray blocks indicate that the genes were not detected as DEGs or DEPs by RNA-seq or TMT-based quantitative proteome analysis.
Figure 7. Changes in DEGs/DEPs associated with amino acid metabolism in the groups of EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h and CG84h vs. CG24h when L. brunescens YQ20 grew in wool medium: (a) valine, leucine, and isoleucine biosynthesis; (b) phenylalanine, tyrosine and tryptophan biosynthesis; (c) glycine, serine and threonine metabolism; (d) histidine metabolism. Red represents upregulated DEGs or DEPs, while green represents downregulated DEGs or DEPs. Gray blocks indicate that the genes were not detected as DEGs or DEPs by RNA-seq or TMT-based quantitative proteome analysis.
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Figure 8. Changes in DEGs/DEPs associated with sulfide metabolism in the groups of EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h and CG84h vs. CG24h when L. brunescens YQ20 grew in wool medium. (a) Sulfur metabolism; (b) cysteine and methionine metabolism; (c) glutathione metabolism. Red represents upregulated DEGs or DEPs, while green represents downregulated DEGs or DEPs. Gray blocks indicate that the genes were not detected as DEGs or DEPs by RNA-seq or TMT-based quantitative proteome analysis.
Figure 8. Changes in DEGs/DEPs associated with sulfide metabolism in the groups of EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h and CG84h vs. CG24h when L. brunescens YQ20 grew in wool medium. (a) Sulfur metabolism; (b) cysteine and methionine metabolism; (c) glutathione metabolism. Red represents upregulated DEGs or DEPs, while green represents downregulated DEGs or DEPs. Gray blocks indicate that the genes were not detected as DEGs or DEPs by RNA-seq or TMT-based quantitative proteome analysis.
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Figure 9. A putative model for keratin degradation in Lysobacter brunescens YQ20. The heatmap is represented from left to right as EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h, and CG84h vs. CG24h. Upregulated DEGs/DEPs are shown in red color. Downregulated DEGs/DEPs are shown in green color. Unchanged DEGs/DEPs are shown in gray color. APS, adenosine 5′-phosphosulfate; PAPS, 3′-phosphoadenylsulfate; aroE, shikimate dehydrogenase; aroH, 3-deoxy-7-phosphoheptulonate synthase class II; aroQ, 3-dehydroquinate dehydratase; cdo1, cysteine dioxygenase; cysH, phosphoadenosine phosphosulfate reductase; cysI, sulfite reductase subunit beta; cysJ, sulfite reductase (NADPH) flavoprotein alpha-component; ggt, gamma-glutamyltransferase; gpo, glutathione peroxidase; gst, glutathione S-transferase; GLYK, D-glycerate 3-kinase; hisA, 1-(5-phosphoribosyl)-5-((5-phosphoribosylamino)methylideneamino)imidazole-4-carboxamide isomerase; hisB, bifunctional imidazole glycerol-phosphate dehydratase; hisC, histidinol-phosphate transaminase; hisD, histidinol dehydrogenase; hisG, ATP phosphoribosyltransferase; hisH, imidazole glycerol phosphate synthase; hisI, bifunctional phosphoribosyl-AMP cyclohydrolase; hpd, 4-hydroxyphenylpyruvate dioxygenase; hsd, homoserine dehydrogenase; hutH, histidine ammonia-lyase; hutU, urocanate hydratase; ilvD, dihydroxy-acid dehydratase; ilvC, ketol-acid reductoisomerase; ilvG, acetolactate synthase 2 catalytic subunit; ilvE, branched-chain amino acid aminotransferase; leuA, 2-isopropylmalate synthase; leuC, 3-isopropylmalate dehydratase large subunit; leuD, isopropylmalate isomerase; leuB, isocitrate dehydrogenase; maiA, maleylacetoacetate isomerase; metB, O-succinylhomoserine (thiol)-lyase; metH, methionine synthase; metX, homoserine O-acetyltransferase; mtnD, acireductone dioxygenase; pspA, phosphoserine phosphatase 1; serA, phosphoglycerate dehydrogenase; tdcB, threonine dehydratase; thrB, homoserine kinase; thrC, threonine synthase; trpA, tryptophan synthase subunit alpha; trpB, tryptophan synthase subunit beta.
Figure 9. A putative model for keratin degradation in Lysobacter brunescens YQ20. The heatmap is represented from left to right as EG24h vs. CG24h, EG84h vs. CG84h, EG84h vs. EG24h, and CG84h vs. CG24h. Upregulated DEGs/DEPs are shown in red color. Downregulated DEGs/DEPs are shown in green color. Unchanged DEGs/DEPs are shown in gray color. APS, adenosine 5′-phosphosulfate; PAPS, 3′-phosphoadenylsulfate; aroE, shikimate dehydrogenase; aroH, 3-deoxy-7-phosphoheptulonate synthase class II; aroQ, 3-dehydroquinate dehydratase; cdo1, cysteine dioxygenase; cysH, phosphoadenosine phosphosulfate reductase; cysI, sulfite reductase subunit beta; cysJ, sulfite reductase (NADPH) flavoprotein alpha-component; ggt, gamma-glutamyltransferase; gpo, glutathione peroxidase; gst, glutathione S-transferase; GLYK, D-glycerate 3-kinase; hisA, 1-(5-phosphoribosyl)-5-((5-phosphoribosylamino)methylideneamino)imidazole-4-carboxamide isomerase; hisB, bifunctional imidazole glycerol-phosphate dehydratase; hisC, histidinol-phosphate transaminase; hisD, histidinol dehydrogenase; hisG, ATP phosphoribosyltransferase; hisH, imidazole glycerol phosphate synthase; hisI, bifunctional phosphoribosyl-AMP cyclohydrolase; hpd, 4-hydroxyphenylpyruvate dioxygenase; hsd, homoserine dehydrogenase; hutH, histidine ammonia-lyase; hutU, urocanate hydratase; ilvD, dihydroxy-acid dehydratase; ilvC, ketol-acid reductoisomerase; ilvG, acetolactate synthase 2 catalytic subunit; ilvE, branched-chain amino acid aminotransferase; leuA, 2-isopropylmalate synthase; leuC, 3-isopropylmalate dehydratase large subunit; leuD, isopropylmalate isomerase; leuB, isocitrate dehydrogenase; maiA, maleylacetoacetate isomerase; metB, O-succinylhomoserine (thiol)-lyase; metH, methionine synthase; metX, homoserine O-acetyltransferase; mtnD, acireductone dioxygenase; pspA, phosphoserine phosphatase 1; serA, phosphoglycerate dehydrogenase; tdcB, threonine dehydratase; thrB, homoserine kinase; thrC, threonine synthase; trpA, tryptophan synthase subunit alpha; trpB, tryptophan synthase subunit beta.
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Table 1. Potential proteases for keratin decomposition.
Table 1. Potential proteases for keratin decomposition.
EG24 vs. CG24EG84 vs. CG84EG84 vs. EG24CG84 vs. CG24
Gene IDFamilyEnzymesLog2 FC
(G)		Log2 FC
(P)		Log2 FC
(G)		Log2 FC
(P)		Log2 FC
(G)		Log2 FC
(P)		Log2 FC
(G)		Log2 FC
(P)
S8
YQ20_0516 serine protease11.362.242.443.78−9.401.43--
YQ20_0707 serine protease4.402.243.503.09−1.21---
YQ20_2093 serine protease8.402.68-3.64−7.65---
YQ20_3010 serine protease7.752.35-1.83−6.38---
YQ20_3285 serine protease6.121.57-2.15−8.46---
YQ20_0112 subtilisin-like serine protease4.371.12−2.251.55−2.25---
YQ20_0510 subtilisin-like serine protease3.581.344.512.294.51---
YQ20_0512 subtilisin-like serine protease4.411.402.311.992.31---
YQ20_0514 subtilisin-like serine protease7.923.033.452.773.45---
YQ20_1257 subtilisin-like serine protease3.371.85----−1.20-
YQ20_2824 subtilisin-like serine protease5.331.79-2.40----
YQ20_2871 subtilisin-like serine protease2.691.35−1.422.06−1.42---
YQ20_3010 subtilisin-like serine protease7.752.35-1.83----
YQ20_3889 subtilisin-like serine protease5.161.90−1.261.18−1.26---
M14
YQ20_0502 carboxypeptidase7.361.49-1.16−7.66---
YQ20_0503 carboxypeptidase6.86---−7.07---
YQ20_0911 carboxypeptidase2.051.06--−1.66---
YQ20_2175 carboxypeptidase3.461.40--−3.94---
YQ20_2363 carboxypeptidase5.27--2.13−4.69---
YQ20_3096 carboxypeptidase5.15---−5.41---
M28
YQ20_0492 peptidase S16.742.19-2.05−7.00---
YQ20_2780 peptidase S14.872.58-3.33−5.402.31--
FC, fold change; G, gene; P, protein; “-” indicates the corresponding alteration in protein expression or gene expression was not detected.
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Wei, M.-Y.; Gao, X.; Zhao, X.-T.; Liu, Y.; Zhao, C.-Y.; Li, H.; An, W.-M.; Zhang, D.-X.; Zhang, F.; Ma, Q.; et al. Integrated Transcriptomic and Proteomic Analyses Uncover the Mechanisms of Keratin Degradation in Lysobacter brunescens YQ20. Biology 2026, 15, 320. https://doi.org/10.3390/biology15040320

AMA Style

Wei M-Y, Gao X, Zhao X-T, Liu Y, Zhao C-Y, Li H, An W-M, Zhang D-X, Zhang F, Ma Q, et al. Integrated Transcriptomic and Proteomic Analyses Uncover the Mechanisms of Keratin Degradation in Lysobacter brunescens YQ20. Biology. 2026; 15(4):320. https://doi.org/10.3390/biology15040320

Chicago/Turabian Style

Wei, Ming-Yue, Xiuzhen Gao, Xing-Tang Zhao, Yang Liu, Chun-Yu Zhao, Huan Li, Wen-Ming An, Dong-Xu Zhang, Fen Zhang, Qinyuan Ma, and et al. 2026. "Integrated Transcriptomic and Proteomic Analyses Uncover the Mechanisms of Keratin Degradation in Lysobacter brunescens YQ20" Biology 15, no. 4: 320. https://doi.org/10.3390/biology15040320

APA Style

Wei, M.-Y., Gao, X., Zhao, X.-T., Liu, Y., Zhao, C.-Y., Li, H., An, W.-M., Zhang, D.-X., Zhang, F., Ma, Q., & Yu, J.-F. (2026). Integrated Transcriptomic and Proteomic Analyses Uncover the Mechanisms of Keratin Degradation in Lysobacter brunescens YQ20. Biology, 15(4), 320. https://doi.org/10.3390/biology15040320

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