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Article

Taxonomic and Functional Profiling of Bacterial Communities in Leather Biodegradation: Insights into Metabolic Pathways and Diversity

by
Manuela Bonilla-Espadas
1,
Marcelo Bertazzo
1,*,
Irene Lifante-Martinez
1,
Mónica Camacho
2,
Elena Orgilés-Calpena
1,
Francisca Arán-Aís
1 and
María-José Bonete
2,*
1
INESCOP, Footwear Technological Centre, 03600 Elda, Alicante, Spain
2
Grupo Biotecnología de Extremófilos, Departamento de Bioquímica y Biología Molecular y Edafología y Química Agrícola, Universidad de Alicante, 03690 San Vicente del Raspeig, Alicante, Spain
*
Authors to whom correspondence should be addressed.
Bacteria 2025, 4(3), 37; https://doi.org/10.3390/bacteria4030037
Submission received: 16 June 2025 / Revised: 8 July 2025 / Accepted: 28 July 2025 / Published: 1 August 2025
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Abstract

Leather biodegradation is a complex microbial process with increasing relevance for sustainable waste management. In this study, we investigated bacterial communities responsible for the degradation of leather treated with different tanning agents (chrome, Zeolite, Biole®) using high-throughput 16S rRNA gene sequencing and metatranscriptomic analysis. Proteobacteria, Bacteroidetes, and Patescibacteria emerged as the dominant phyla, while genera such as Acinetobacter, Pseudomonas, and Sphingopyxis were identified as key contributors to enzymatic activity and potential metal resistance. A total of 1302 enzymes were expressed across all the conditions, including 46 proteases, with endopeptidase La, endopeptidase Clp, and methionyl aminopeptidase being the most abundant. Collagen samples exhibited the highest functional diversity and total enzyme expression, whereas chrome-treated samples showed elevated protease activity, indicating selective pressure from heavy metals. Differential enzyme expression patterns were linked to both the microbial identity and tanning chemistry, revealing genus- and treatment-specific enzymatic signatures. These findings deepen our understanding of how tanning agents modulate the microbial structure and function and identify proteases with potential applications in the bioremediation and eco-innovation of leather waste processing.

1. Introduction

Leather is a durable and flexible material derived from animal hides or skins. It primarily consists of collagen fibres, which comprise approximately 90% of the protein content [1]. The remaining components include elastin, keratin, various lipids, minerals, and water. The unique three-dimensional structure of interwoven collagen fibres gives leather its strength, flexibility, and ability to resist tearing [2]. Tanning chemically modifies the collagen structure to enhance its stability and impart the desired properties [3]. Various tanning agents are employed to stabilise and preserve leather through distinct chemical mechanisms [4]. Among these, chromium salts are the most widely used mineral tanning agents, forming cross-links between collagen fibres by coordinating with carboxyl groups, resulting in highly stable and heat-resistant leather [5]. Synthetic tanning agents, such as syntans, can mimic vegetable tannins or offer specific properties by forming ionic and covalent bonds with the collagen matrix [6]. Other mineral-based tanning systems, such as Zeolite aluminosilicate-based reagents, have also been explored for their ability to stabilise collagen [7]. Although tanning significantly enhances the durability and longevity of leather products, it also affects their biodegradability and environmental impact [8]. The breakdown of leather is a complex biological process influenced by various microbial groups, with bacteria playing a crucial role [9]. These microorganisms produce specialised enzymes, such as proteases and lipases, that enable the degradation of proteins, lipids, and tanning agents present in leather [10]. Bacteria initiate a cascade of biochemical reactions through these enzymatic activities that gradually decompose the leather structure and alter its physical properties [11]. Tanning agents, especially those containing heavy metals such as chromium and aluminium, considerably influence the microbial activity during biodegradation [12]. Chromium, typically present as hexavalent chromium [Cr(VI)], can be toxic to many bacteria, inhibiting their growth and reducing their enzymatic functionality [13,14]. Similarly, aluminium-based tanning agents, known for their astringent properties, further limit microbial degradation by reducing the accessibility to proteases of the leather matrix and altering its structural integrity [15,16].
These metals may interfere with microbial metabolic pathways by binding to essential biomolecules or inducing oxidative stress [17,18]. Consequently, microbial diversity and enzymatic activity are generally lower in chrome- and aluminium-tanned leathers, favouring the survival of metal-tolerant or -resistant strains [19,20]. Microorganisms have evolved diverse mechanisms to cope with heavy-metal stress [21]. These include complexation with proteins, active efflux using membrane transporters in prokaryotes, and compartmentalisation of organelles, such as vacuoles, in eukaryotes [22]. Additional resistance strategies involve biosorption, bioleaching, biomineralisation, biotransformation, and intracellular accumulation of heavy metals [23,24]. Understanding the intricate interactions between microbial communities and leather substrates is essential for developing strategies that enhance or mitigate biodegradation, depending on the application and environmental context [25,26]. Moreover, this knowledge is crucial for advancing sustainable waste management practices in the leather industry [27]. Typically, the tanning process uses basic chromium sulphate, of which only 60–70% is absorbed by the leather. The remaining portion is discharged as effluent [28,29], resulting in high levels of Cr in the wastewater, ranging from 2.6 to 5.2 g/L after wet blue production [20].
While Cr(III) is generally less toxic, Cr(VI) poses severe environmental and health risks due to its high solubility and membrane permeability [30]. The sludge, also known as “chrome cake”, generated during wastewater treatment contains 17–20% total chromium, with approximately 0.04% present as Cr(VI) [31]. Biodegradation using microorganisms has emerged as a cost-effective and environmentally friendly approach for managing waste materials, including plastics and leather [32]. Several sustainable strategies have been developed to recycle solid tannery waste (STW), including the production of biogas, biohydrogen, biofuels, fertilisers, construction materials, and other commercial products such as adsorbents, animal feeds, proteins, fats, and enzymes [33]. Previous studies have explored the compostability and biodegradability of finished leathers [34] and identified bacterial genera that may be able to break down leather, such as Acinetobacter, Brevundimonas, and Mycolicibacterium, in soil and aquatic environments [35,36,37,38]. These bacteria are associated with enzymatic processes relevant to protein degradation. However, most investigations have been limited to taxonomic assessments, and there is still a lack of comprehensive functional profiling of these microbial communities. Specifically, the detailed metabolic pathways and enzymatic functions responsible for leather degradation remain unclear. Various approaches have been proposed to address these challenges, including the use of biosorption technologies [12], alternative tanning agents [39], and life cycle assessments [40], to improve the sustainability of tanning processes. In recent years, the industry has shown a growing interest in environmentally conscious practices and innovative waste management techniques to reduce Cr contamination and its associated environmental footprint [41]. Detailed taxonomic and functional profiling of microbial communities can provide valuable insights into the biological mechanisms underlying leather degradation, identifying new targets for biotechnological applications. Understanding the metabolic pathways and microbial interactions involved in the breakdown of collagen and leather components can contribute to the development of innovative bio-based waste management solutions. Metatranscriptomics, the high-throughput sequencing of the total RNA transcripts in an environmental sample, has emerged as a powerful approach to characterise the functional activity of microbial communities [42]. Unlike metagenomics, which reveals the genetic potential of a microbiome, metatranscriptomics captures actively expressed genes, providing real-time insights into microbial metabolism, enzymatic pathways, and ecological interactions [43]. This technique is particularly valuable in biodegradation studies as it allows for the identification of functional genes involved in the degradation of complex substrates, such as leather, and elucidates the specific metabolic activities that drive material breakdown under different environmental or chemical conditions [44].
This study aims to characterise the bacterial communities involved in leather biodegradation by examining their taxonomic composition and functional potential, integrating taxonomic and functional analyses to provide a complete, integrated understanding of the microbial activity and dynamics. This study uses high-throughput sequencing techniques to identify variations in the microbial populations and metabolic pathways under different leather treatment conditions. By combining 16S rRNA gene sequencing to identify the bacterial community composition with metatranscriptomic analysis to determine active gene expression, this approach reveals both the microbial structure and the specific metabolic pathways involved.

2. Materials and Methods

2.1. Assay Determining Microorganisms’ Ability to Degrade Leather (ISO:20136:2020)

A leather biodegradation test (Method B) was performed following the ISO 20136:2020 guidelines [45]. The inoculum consisted of a 50:50 mixture of tannery and municipal sewage wastewater, with one modification: 600 mL of inoculum was used instead of the standard 150 mL to enhance the microbial concentration for research purposes. The total working volume in each Erlenmeyer flask was 750 mL. Municipal wastewater was collected from the primary clarifier of a local treatment facility (Elda, Spain), representing the raw influent before secondary treatment. To reduce the turbidity while preserving the microbial diversity, the wastewater was allowed to settle under gravity, and the clarified supernatant (upper fraction) was used. Tannery wastewater was sourced directly from the tanning discharge from Curtidos Segorbe S.L. (Castellón, Spain) [46]. For each assay, 0.18–0.19 g/L of shredded leather was added to Erlenmeyer flasks containing the inoculum and minimal salts medium. Pure collagen from bovine Achilles tendons (Sigma-Aldrich®, St. Louis, MO, USA) was used as a positive control [47]. The assay was performed for 336 h (14 days) instead of 672 h (28 days) to capture the microbial and enzymatic shifts during the exponential phase of biodegradation. Three leather samples were analysed, each subjected to a distinct industrial tanning process: (i) chrome tanning using basic chromium sulphate (AV-1821), (ii) Zeolite tanning with aluminosilicate agents, and (iii) synthetic tanning with Biole®, a phenol-based syntan. All the samples were derived from bovine hides and underwent ultraviolet (UV) surface treatment for 20 min on each side before the assay to reduce the potential microbial surface contamination without affecting the tanning-induced structural integrity. The total organic carbon content of each sample was determined by elemental analysis (Table 1), enabling calculation of the theoretical maximum CO2 evolution as an indicator of biodegradation using the equation BCO2 = (mCO2/mTCO2) × 100, where BCO2 is the percentage of biodegradation (%), mCO2 is the mass of CO2 evolved from the test sample (g), corrected by subtracting the amount of CO2 from that of the negative control, and mTCO2 is the theoretical maximum mass of CO2 (g) calculated from the carbon content of the sample. The CO2 evolution during biodegradation was monitored continuously using an automated infrared (IR) detection system. This system included 2 L Erlenmeyer flasks connected via airtight tubing to a multiplexer, which sequentially directed the gas output from each flask to a CO2 sensor. A CO2-free air source was maintained using a pressure swing adsorption (PSA) unit to ensure that only CO2 generated from microbial degradation was recorded. The air flow (in L/h) and CO2 concentration (in ppm) were recorded digitally at defined intervals throughout the assay. From these readings, the CO2 air flow (mol/h) was calculated and integrated over time to determine the cumulative CO2 evolved. CO2 measurements for the negative control flasks (containing the inoculum and medium only) were subtracted from those for the test samples to correct for the background CO2. The resulting values were used to calculate the percentage of biodegradation relative to the theoretical CO2 maximum based on the elemental carbon content.

2.2. Sample Collection and Preparation

Samples were drawn at different stages of an ongoing leather biodegradation assay (ISO 20136 assay) [45]. Table 2 shows the samples collected from each Erlenmeyer flask and the times at which they were withdrawn after the start of the assay. Taxonomic profiling through 16S rRNA gene sequencing was performed without replicates for the first five samples listed in Table 2. For functional profiling, triplicate samples were prepared for each sample to estimate the variations across different media accurately. This was performed for the remaining samples in the table.
Each time, 50 mL of the sample was extracted and mixed with 100 mL of RNAprotect (QIAGEN, Hilden, Germany), followed by centrifugation at 2264× g for 10 min. The supernatant was discarded, and the samples were stored at 4 °C until all the extractions were completed. The samples were then resuspended in appropriate volumes of RNAprotect® [48] and centrifuged at 18,894 × g for 1 min, and the supernatant was discarded before further processing.

2.3. RNA Extraction and Quality Control

RNA isolation was performed according to the protocol provided in the QIAGEN® RNA QIAsymphony kit [49]. This procedure included mechanical cell lysis and enzymatic treatment. The quality and concentration of the extracted RNA were assessed using a NanoDrop spectrophotometer. Samples designated for transcriptomic analysis were extracted in triplicate, and the QIAGEN® RNAse MinElute kit [50] was used to clean and concentrate RNA from low-volume samples. A subsequent NanoDrop analysis was performed to measure the RNA concentration and quality.

2.4. 16S and Transcriptomic Library Preparation

Amplicon libraries specific to bacterial DNA were prepared to capture the taxonomic profiles of the samples. DNA (50 ng) was extracted using mechanical cell lysis combined with enzymatic treatment following the QIAsymphony RNA kit protocol (Qiagen) to ensure efficient extraction and was subsequently amplified using Illumina’s 16S Metagenomic Sequencing Library Preparation protocol (Illumina 15044223 Rev. B). Amplification was performed using a two-step PCR protocol targeting the hypervariable V3–V4 region of the 16S rRNA gene, enabling precise differentiation between the bacterial taxa [51]. In the first amplification step, primers were designed with a dual structure: (1) a universal linker sequence to facilitate the incorporation of indices and sequencing primers using the Nextera XT Index kit (Illumina, Inc. San Diego, CA, USA) [52] and (2) universal primers targeting the V3–V4 region of the 16S rRNA gene [51]. The second amplification step incorporated the index sequences necessary for library preparation. The resulting 16S libraries were quantified using fluorometry with the Quant-iT PicoGreen dsDNA Assay (Thermo Fisher Scientific, Madrid, Spain) [53] to ensure an adequate quality and quantity for downstream applications. Quality control of the libraries was further verified using automated electrophoresis on the Agilent TapeStation system (Agilent Technologies, Santa Clara, CA, USA), ensuring that the libraries met the required standards for high-quality sequencing. RNA depletion was performed using RiboZero-Plus (Illumina, Inc., San Diego, CA, USA) [54] and a custom probe for metatranscriptomic libraries. This step effectively removed the majority of the ribosomal RNA molecules, enriching the mRNA content of the samples to enhance the sequencing efficiency and functional analysis [54].

2.5. 16S and Transcriptomic Library Sequencing

Once sequencing libraries for the 16S products had been prepared, they were loaded onto the MiSeq platform (Illumina, Inc., San Diego, CA, USA) using a 300 bp × 2 paired-end design. Transcriptomic libraries were sequenced using the NovaSeq 6000 platform (Illumina Inc., San Diego, CA, USA), using a 150 bp × 2 paired-end design. This setup provided a significantly higher number of reads of improved quality. Sequencing was completed within approximately 56 h. Image analysis, base calling, and data quality control were performed on the MiSeq platform using the MiSeq Control Software (MCS v3.1).

2.6. 16S Analysis

Raw sequencing data were first processed to remove low-quality reads and chimeric sequences. Paired-end reads were merged using the PEAR program [55] and the SILVA v.138 database [56]. Quality filtering was performed with Reformat, eliminating reads shorter than 200 nucleotides or with a quality score below 20. Amplicon Sequence Variants (ASVs) were generated using DADA2 [57], which models sequencing errors to infer true biological sequences while removing chimeric sequences. Taxonomic assignments for the resulting ASVs were conducted using a hybrid approach: first, BLASTN [58] was used against the NCBI 16S ribosomal RNA database to assign the taxonomy. For sequences with less than 97% identity, the assignments were refined using the NBAYES classifier along with the SILVA v.138 database [59]. This combined strategy ensured robust and precise taxonomic classification of the bacterial communities in the samples. Alpha diversity analysis was performed by first normalising all the samples to 107,703 reads using rarefaction, implemented using the Phyloseq package in R [60]. Diversity indices were then calculated using the vegan package in R [61], specifically computing the Shannon and Simpson indices, as well as the species richness, for each sample. These analyses provided standardised metrics for within-sample microbial diversity assessment across the conditions.

2.7. Metatranscriptomic Analysis

Metatranscriptomic samples were subjected to a quality filtering process, in which sequences shorter than 50 nucleotides and those with a quality score below 20 were removed. Functional annotation of the metatranscriptomic data was performed using HUMAnN3 [62], which utilises the UniRef protein database [63]. This database includes various functional protein domains, enabling the identification of known proteins and predicting the functions in organisms not explicitly represented in the database. Specific functional annotations were obtained following annotation with UniRef to provide a comprehensive view of the functional state of each sample.

3. Results

3.1. Assay Determining Microorganisms’ Ability to Degrade Leather (ISO:20136:2020)

The biodegradation results for the leather samples described in Section 2.1 are shown in Figure 1, which shows the biodegradation assay. These biodegradation curves show the percentage of leather biodegradation throughout the assay (14 days); the times shown in Table 2 (Section 2.2) correspond to the times (h) in this graph. There were three phases of leather biodegradation—the initial (65 h), exponential (113 h), and final (240 h) phases—observed in all the samples with varying biodegradation percentages. Collagen was used as a positive control because it was fully degraded in approximately 30 days. At the final stage of the assay, 336 h after the start of the assay, the biodegradation percentages for collagen, chrome, Zeolite, and Biole were 56%, 2%, 41%, and 15%, respectively. It is important to note that the assay was conducted over 14 days, which is half the typical duration (28 days) for standard biodegradation tests. Therefore, the measured biodegradation values likely underestimate the full potential of the samples to be biodegraded.

3.2. 16S and Transcriptomic Library Sequencing

The resulting RNA concentration and quality, assessed using Nanodrop, are presented in Table S1 in the Supplementary Materials Section. The RNA concentration was found to be optimal for all the amplicon and transcriptomic samples. The RNA concentration of the constructed libraries, measured using PicoGreen, was optimal for all the amplicons and transcriptomic samples, enabling library sequencing. The sequencing results are shown in Table S2 in the Supplementary Materials Section. In all the analysed transcriptomic samples, the number of reads exceeded 20,000,000, with an average of 45 million reads per sample.

3.3. 16S rRNA Gene Sequence Analysis of Bacterial Communities

The taxonomic assignment of the representative sequences (ASVs) followed a mixed strategy to ensure broader and more precise classification, as described in Section 2.7. This hybrid approach successfully assigned the taxonomy at the family level for 90% of ASVs and at the genus level for 78% of ASVs. Figure 2 and Figure 3 present the bacterial profiles of the analysed samples at the phylum and family levels, respectively, along with the beta diversity index (local contribution to the beta diversity, LCBD). The LCBD index identifies the diversity patterns among samples, with higher values indicating markedly distinct bacterial compositions [64]. Among the nine phyla with a relative abundance greater than 1%, the most prevalent were Proteobacteria (44 ± 8%), Bacteroidetes (17 ± 5%), and Patescibacteria (15 ± 5%). Notably, the “Inoculum” sample represented the initial microbial community introduced in all the experimental conditions, serving as a baseline reference. As the biodegradation assay progressed, the microbial communities in each leather sample (collagen, chrome, Zeolite, and Biole) diverged from this initial inoculum, reflecting microbial selection and adaptation driven by the different chemical properties of the leather samples and tanning agents. This microbial shift highlights the influence of the substrate composition on shaping the structure and diversity of bacterial communities during the biodegradation process.
Alphaproteobacteria and Gammaproteobacteria were generally present in similar proportions, except in the inoculum, where Alphaproteobacteria were predominant. Notable taxa included Lysobacter brunescens in the Zeolite and Biole samples, Pararheinheimera in collagen, and Acinetobacter in nearly all the samples, which is of interest due to its protease activity, relevant to leather tanning. Additionally, Pseudomonas, known for its protein-degrading potential, accounted for 2% of the collagen sample [65]. Patescibacteria, a phylum of small-sized bacteria with reduced genomes [66], accounted for 10–20% of all the samples. These bacteria, often referred to as nanobacteria or Candidate Phyla Radiation (CPR) groups, exhibit unique metabolic capabilities and function as symbionts within microbial communities [66]. Key taxa included Candidatus Campbellbacteria, Saccharimonadales, and Candidatus Pacebacteria, although the taxonomic precision remained limited due to the phylum’s underrepresentation in databases. Bacteroidetes, another dominant phylum, includes the family Flavobacteriaceae, which is known for its diverse carbohydrate metabolism and habitats in marine, soil, and freshwater environments [67]. Despite their prominence, only a few genera, such as the Mariniflexile sp. and marine NS9 groups, were consistently identified across the samples. Given the low representation of bacteria isolated from aquatic and terrestrial environments in the existing databases, achieving precise taxonomic assignment at the genus level remains challenging. Despite this limitation, a genus-level profile was successfully obtained for the analysed bacterial communities, as shown in Figure 3. The bar plots illustrate the relative abundance of bacterial genera across different sample groups, highlighting the taxonomic composition within each condition.
In the collagen sample, the microbial community was enriched in Pararheinheimera and Lysobacter. In contrast, the chrome, Zeolite, and Biole samples exhibited greater diversity in their genus compositions, with the presence of genera such as Pseudomonas, Flavobacterium, and Mesorhizobium. Sphingopyxis is known for its ability to survive in extreme conditions and degrade various xenobiotics and environmental contaminants [68]. Acinetobacter, known for its metabolic versatility [13], was present across multiple sample types, suggesting its potential role in microbial interactions within these environments. The local contribution to the beta diversity (LCBD) values, represented by the sizes of the dots at the bottom of each bar plot, indicate the uniqueness of each microbial community compared with the others. The inoculum sample exhibited a distinct composition, potentially reflecting its role as a baseline microbial population before exposure to the experimental conditions.

3.4. Alpha Diversity

The alpha diversity was assessed using the Shannon and Simpson indices and species richness to evaluate the microbial diversity within each sample (shown in Figure S1 in the Supplementary Materials Section). These metrics were computed using the vegan package in R [61], which enables organism abundance relationship analysis. The Shannon index quantifies the species biodiversity, ranging from 0 (indicating a single species) to higher values as the diversity increases [69]. The Simpson index (λ) measures the probability that two randomly selected individuals belong to the same species, with lower values indicating species dominance and higher values reflecting a more evenly distributed community [70]. Additionally, the species richness estimates the total number of species within a sample, increasing proportionally with the sample size [61]. All the samples were normalised to 107,703 reads using rarefaction implemented using the Phyloseq package in R [71] to ensure comparability. The results showed that the inoculum and chrome samples exhibited higher alpha diversity, whereas collagen and Biole had lower diversity levels. Simpson’s index supported these findings, reinforcing the dominance of specific taxa in the samples with lower diversity. However, due to the lack of sample replicates, statistical comparisons between the groups could not be performed, and the alpha diversity values should be interpreted as descriptive rather than indicative of significant differences [72,73].

3.5. Metatranscriptomic Analysis

A total of 1302 enzymes were detected across all the samples, with the most abundant being EC 2.7.7.6, DNA-directed RNA polymerase, an enzyme that catalyses the synthesis of RNA using a DNA template [74]; EC 5.2.1.8, peptidylprolyl isomerase, an enzyme that catalyses the cis–trans isomerisation of prolyl peptide bonds [75]; EC 3.4.21.53, endopeptidase La, an ATP-dependent serine proteinase; EC 3.4.21.92, an endopeptidase Clp which hydrolyses proteins to small peptides; and EC 1.16.3.1, ferroxidase, a multicopper oxidase which plays a crucial role in iron homeostasis by catalysing the oxidation of Fe(II) to Fe(III) [76] (Figure 4).
Among these, 446 enzymes were shared across all the samples, whereas 32 enzymes were present exclusively in Biole, Zeolite, and collagen, indicating their absence in the chromium-treated samples (Figure 5). Additionally, a group of 15 enzymes was found to be common to all the samples except chrome and Biole, suggesting that these enzymes may be associated with microbial activity specific to certain tanning conditions and could be inhibited by the presence of heavy metals such as chromium. Moreover, 53 enzymes were unique to collagen, and 10 were exclusive to Zeolite, highlighting distinct functional profiles in these environments.
Given that tanned leather is primarily composed of proteins (~50%), along with water (~10%), dichloromethane-soluble material (~11%), water-washable material (~3%), and non-water-washable tanning agents (~24%) [34,77], further analysis was conducted to identify and classify the proteases in the metatranscriptomic dataset. The protein composition of tanned leather includes approximately 90% collagen, 6% keratin, 3% albumin, and 0.9% elastin [78], making proteases particularly relevant. Of the detected enzymes, 46 were classified as proteases (EC 3.4.–), as shown in Figure 6. The most abundant proteases included EC 3.4.21.53, endopeptidase La (37 ± 6%), which hydrolyses proteins in the presence of ATP; EC 3.4.21.92, endopeptidase Clp, which hydrolyses proteins and small peptides in the presence of ATP and Mg2+ (16.5 ± 1.3%); and EC 3.4.11.18, methionyl aminopeptidase, responsible for the cleavage of N-terminal methionine residues (10.9 ± 1.7%) [79].

3.5.1. Alpha Diversity

To assess the functional diversity, the Shannon and Simpson indices were calculated for the different sample groups shown in Figure S2. The collagen samples, followed by Zeolite and Biole, exhibited the highest functional diversity indices, which contrasts with the results of the taxonomic analysis, where collagen showed the lowest bacterial diversity (Figure 4). A similar pattern was observed, although with lower values, when analysing the alpha diversity and considering only proteases, as shown in Figure S3.

3.5.2. Beta Diversity

The beta diversity provides insights into the functional differences between microbial communities. To assess it, a dissimilarity matrix was calculated based on Bray–Curtis distances and visualised using Principal Coordinate Analysis (PCoA) (Figure 7). The x-axis (PC1) accounted for 71% of the variability among the samples, while the y-axis (PC2) explained 16%. In this analysis, clear groupings were observed among samples belonging to the same experimental group, indicating distinct functional profiles across the conditions.
Taxonomic analysis of protease-associated enzymes revealed that the genera Aminobacter, Sphingopyxis, Pseudomonas, and Mesorhizobium contributed the most significantly to the enzymatic profile. Proteases from Sphingopyxis were consistently found across all the sample groups, whereas those from Aminobacter were absent from the chrome sample. The DESeq2 program was used to identify 242 significantly differentially expressed enzymes (functional markers) across the sample groups. A subset of these enzymes, specifically those related to protein degradation, was selected to examine the potential contributors to leather degradation, as illustrated in Figure 8. The DESeq2 results indicated that most peptidases were predominantly expressed in the chrome, Zeolite, and Biole samples, whereas collagen exhibited lower expression levels. The only enzyme with higher expression in the collagen sample was EC 3.4.16.4, a carboxypeptidase that removes D-alanine residues from bacterial cell wall precursor proteins [80]. This enzyme is primarily expressed in Pseudomonas sp. TC11 [81]. The most pronounced differences in the differential expression were observed between the collagen and other sample groups. The only peptidase found to be underexpressed in the chrome sample compared to all the other groups, except the collagen sample, which exhibited even lower expression levels, was the HslU-HslV peptidase. This enzyme functions as a proteolytic complex responsible for degrading damaged cytoplasmic proteins [82] and is mainly expressed by Aminobacter aminovorans [83].

4. Discussion

This study provides a comprehensive taxonomic and functional characterisation of the bacterial communities involved in the biodegradation of leather subjected to different tanning agents. The high sequencing depth obtained, exceeding 100,000 reads for 16S rRNA amplicons and 20 million reads for metatranscriptomic libraries, enabled robust analysis of both the microbial diversity and functional gene expression. Taxonomic profiling revealed that Proteobacteria, Bacteroidetes, and Patescibacteria dominated across all the conditions, consistent with their established roles in wastewater and environmental biodegradation systems [84]. Notably, Proteobacteria are frequently the most abundant phylum in wastewater treatment plants [85], even compared to taxa such as Acinetobacter and Pseudomonas, which are also prominent in contaminated soils and rhizospheres, where they exhibit metabolic versatility and tolerance to environmental stressors [86,87,88]. In this study, the microbial community composition shifted in response to the chemical nature of the leather substrate, indicating that tanning agents such as chromium, Zeolite, and Biole play a key role in microbial selection. The consistent presence of Acinetobacter and Pseudomonas across the treatments suggests that these genera may serve as core functional members of the leather-degrading microbiome, particularly due to their known enzymatic capabilities [86]. For example, Acinetobacter species have been shown to degrade collagen and reduce the Cr(VI) levels in tannery effluents across a range of pH and temperature conditions [89,90]. Pseudomonas species also exhibit significant proteolytic activity and biodegradative potential, although their performance varies depending on the chemical complexity of the substrate [91,92,93]. Functional analysis of metatranscriptomic data revealed the expression of 1302 enzymes, of which 446 were shared across all the samples. Notably, 46 proteases (EC 3.4.-) were identified as being potentially involved in leather biodegradation, with the key enzymes including endopeptidase La (EC 3.4.21.53), endopeptidase Clp (EC 3.4.21.92), and methionyl aminopeptidase (EC 3.4.11.18). These proteases are widely recognised for their roles in protein hydrolysis and are plausible contributors to the breakdown of collagen and keratin, the main structural proteins in leather. Chrome-treated samples exhibited the highest cumulative protease expression, suggesting that metal-tolerant taxa may selectively upregulate proteolytic pathways to adapt to inhibitory environments. However, specific enzymatic pathways, such as that for the HslU HslV complex (EC 3.4.25.2), were underexpressed in the chrome-treated conditions, indicating that chromium may simultaneously suppress other cellular degradation mechanisms [94].
In contrast, collagen-only samples exhibited the most significant overall enzymatic diversity, albeit with less protease-specific dominance. This indicates that while the metabolic activity is broadly elevated in untreated substrates, it may not be focused solely on protein degradation. Alpha diversity indices supported this distinction: collagen samples showed higher functional diversity (Shannon and Simpson) but lower taxonomic richness, emphasising the utility of metatranscriptomic analysis in capturing functionally active microbial processes.
Comparative enzyme profiling revealed the existence of condition-specific functional signatures. While 446 enzymes were universally present, others were exclusive to specific samples: 53 enzymes were unique to collagen, 10 to Zeolite, and 32 were shared among the collagen, Zeolite, and Biole samples and yet were absent in the chrome-treated samples. These patterns strongly suggest that chromium exerts a selective inhibitory effect on specific metabolic pathways. A separate subset of 15 enzymes was found in all the samples except chrome and Biole, implying further suppression under synthetic or heavy-metal-rich tanning conditions. This trend was confirmed by Principal Coordinate Analysis, which showed clear functional separation by the tanning type.
To refine the link between the microbial taxa and enzymatic function, we examined genus-specific expression of proteases (Supplementary Figures S4–S7). In Aminobacter, the most expressed proteases were endopeptidase La (EC 3.4.21.53), HslU HslV peptidase (EC 3.4.25.2), and endopeptidase Clp (EC 3.4.21.92), with no detectable expression in the chrome-treated samples and the highest expression under Zeolite conditions. This pattern suggests a strong sensitivity to chromium and an ability to adapt to less chemically aggressive environments. Sphingopyxis maintained protease expression under all the conditions, dominated by repressor LexA (EC 3.4.21.88), methionyl aminopeptidase (EC 3.4.11.18), and endopeptidase Clp, with maximal expression under the chrome and Zeolite treatments, indicating enhanced tolerance or enzymatic resilience. Pseudomonas showed strong expression of endopeptidase La, peptidase Do (EC 3.4.21.107), and HslU HslV peptidase, but with discontinuous detection: no expression was observed in Biole-treated samples, collagen at 65 h and 113 h, chrome at 113 h and 240 h, and Zeolite at 65 h and 240 h. These fluctuations may reflect transcriptional regulation, stress-induced suppression, or time-specific niche adaptation. Mesorhizobium expressed the same three enzymes but predominantly in collagen samples, with limited activity under all the tanned conditions. This suggests that the genus may be functionally relevant only in chemically mild substrates.
The breakdown of collagen and leather components during biodegradation involves intricate metabolic pathways, primarily driven by enzymatic activity and chemical reactions. The degradation of collagen, a central component in leather, occurs through both enzymatic and non-enzymatic processes [95]. These proteases hydrolyse the peptide bonds within collagen, keratin, and other leather proteins, leading to structural disintegration [96]. Additionally, genera such as Acinetobacter, Pseudomonas, Aminobacter, and Sphingopyxis contributed significantly to protease expression, highlighting their roles in protein degradation and potential metal resistance mechanisms, particularly under chrome tanning conditions. Microbial interactions, including synergistic enzymatic activities and metabolic cross-feeding, likely enhance the degradation efficiency by enabling complementary substrate breakdown and nutrient sharing within the microbial community [97]. Understanding these metabolic pathways and microbial consortia provides a basis for developing bio-based waste management solutions, such as tailored microbial treatments or enzyme-based technologies, to accelerate leather waste degradation, facilitate resource recovery, and reduce the environmental impact in line with circular bioeconomy strategies.
Altogether, these results reinforce the idea that an interplay of the taxonomic structure, functional capacity, and chemical environment shapes leather biodegradation. Chromium, in particular, exerts a dual effect, stimulating some proteolytic responses while inhibiting others. Moreover, the expression of specific proteases is tightly linked to both the microbial identity and tanning chemistry. Future studies should incorporate enzyme activity assays, metaproteomic profiling, and longer degradation timeframes to validate transcriptomic inferences and better characterise the synergistic roles of microbial consortia in collagen decomposition.

5. Conclusions

This study provides novel and integrative insights into the taxonomic composition and functional potential of bacterial communities involved in the biodegradation of leather processed with different tanning agents. Using high-throughput sequencing and metatranscriptomic profiling, we identified dominant phyla including Proteobacteria, Bacteroidetes, and Patescibacteria, with Acinetobacter and Pseudomonas emerging as potential key players in collagen degradation and metal tolerance. Functional annotation revealed the expression of 1302 enzymes, among which 46 were classified as proteases likely involved in the breakdown of structural proteins such as collagen and keratin. Collagen-only samples displayed the highest overall enzymatic diversity, while chrome-treated leathers showed elevated levels of specific proteases, indicating that tanning agents exert a selective influence on the microbial activity. Genus-level protease expression analysis confirmed that the enzymatic profiles were not uniformly distributed but were shaped by both the microbial identity and tanning chemistry. These findings not only enhance our understanding of microbially driven leather degradation but also highlight proteases as potential biotechnological targets for sustainable waste management and eco-innovative leather processing.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/bacteria4030037/s1, Table S1: Concentration (ng/µL), purity ratios (A260/280 and A260/230), and RNA integrity number equivalent (RINe) for DNA and RNA samples extracted under different conditions. Samples labelled with “16S rRNA” correspond to RNA extracted for transcriptomic analysis. Table S2: Sequencing results for the constructed libraries, detailing the sample reference, read orientation, total number of raw sequences obtained, average read length, total sequenced bases measured in megabases (Mb), and average quality score for the forward reads (R1). Samples labelled with “16S rRNA” correspond to RNA extracted for transcriptomic analysis. Figure S1. Boxplot of the alpha diversity at the Amplicon Sequence Variant (ASV) level based on the studied variables. The displayed parameters correspond to the richness, Simpson index (Simpson), and Shannon index. Figure S2: Boxplot of the alpha diversity for the functional expression profile of the analysed samples. The displayed parameters correspond to the Simpson index (Simpson) and the Shannon index (Shannon). Figure S3: Boxplot of the alpha diversity for the functional expression profile of the proteases present in the analysed samples. The displayed parameters correspond to the Simpson index (Simpson) and the Shannon index (Shannon). Figure S4. Normalised expression (copies per million, cpm) of protease-encoding transcripts attributed to the genus Aminobacter in leather samples subjected to different tanning treatments (Biole, collagen, chrome, Zeolite) and sampled at 65 h, 113 h, and 240 h. The “Others” category includes enzymes with relative abundances below the inclusion threshold. Figure S5. Normalised expression (copies per million, cpm) of protease-encoding transcripts attributed to the genus Sphingopyxis in leather samples subjected to different tanning treatments (Biole, collagen, chrome, Zeolite) and sampled at 65 h, 113 h, and 240 h. The “Others” category includes enzymes with relative abundances below the inclusion threshold. Figure S6. Normalised expression (copies per million, cpm) of protease-encoding transcripts attributed to the genus Pseudomonas in leather samples subjected to different tanning treatments (Biole, collagen, chrome, Zeolite) and sampled at 65 h, 113 h, and 240 h. The “Others” category includes enzymes with relative abundances below the inclusion threshold. Figure S7. Normalised expression (copies per million, cpm) of protease-encoding transcripts attributed to the genus Mesorhizobium in leather samples subjected to different tanning treatments (Biole, collagen, chrome, Zeolite) and sampled at 65 h, 113 h, and 240 h. The “Others” category includes enzymes with relative abundances below the inclusion threshold.

Author Contributions

M.B.-E.: Methodology, investigation, formal analysis, validation, data curation, writing—original draft preparation. I.L.-M.: Methodology, data curation. M.C.: Writing—review and editing; resources. E.O.-C.: Supervision, project administration. F.A.-A.: Supervision, project administration. M.B.: Conceptualization, supervision, writing—review and editing. M.-J.B.: Conceptualization, supervision, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was co-financed by the European Union through the European Regional Development Fund as part of the Operational Programme of the Valencian Community 2014–2020 and the BIOREQ project, grant number IMDEEA/2021/11, and the University of Alicante, Project UAIND21-02B.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Figure 1. Biodegradation of different leather samples and collagen over 336 h, expressed as the % of CO2 evolution. The percentage of biodegradation was calculated based on the cumulative CO2 evolved from each sample, determined via infrared (IR) detection and normalised to the theoretical maximum CO2 that could be produced based on the carbon content of the sample (as per ISO 20136:2020, Method B). All the values were corrected by subtracting the CO2 values from those for the negative control (inoculum only). The x-axis shows the time in hours, corresponding to the times in Table 2. Phases of the biodegradation curve (initial, exponential, and deceleration) are annotated. Collagen was purchased from Sigma-Aldrich (St. Louis, MO, USA) and used as a positive control sample.
Figure 1. Biodegradation of different leather samples and collagen over 336 h, expressed as the % of CO2 evolution. The percentage of biodegradation was calculated based on the cumulative CO2 evolved from each sample, determined via infrared (IR) detection and normalised to the theoretical maximum CO2 that could be produced based on the carbon content of the sample (as per ISO 20136:2020, Method B). All the values were corrected by subtracting the CO2 values from those for the negative control (inoculum only). The x-axis shows the time in hours, corresponding to the times in Table 2. Phases of the biodegradation curve (initial, exponential, and deceleration) are annotated. Collagen was purchased from Sigma-Aldrich (St. Louis, MO, USA) and used as a positive control sample.
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Figure 2. Bar plots representing the taxonomic profiles and LCBD values of the bacterial communities in the analysed samples at the phylum level. The “Inoculum” sample corresponds to the microbial inoculum used in the biodegradation assay. All the samples shown are those that underwent 16S rRNA gene-based taxonomic profiling, as listed in Table 2. The “Others” category includes bacterial taxa without annotations at the phylum level and/or that exhibited a low relative abundance across the samples.
Figure 2. Bar plots representing the taxonomic profiles and LCBD values of the bacterial communities in the analysed samples at the phylum level. The “Inoculum” sample corresponds to the microbial inoculum used in the biodegradation assay. All the samples shown are those that underwent 16S rRNA gene-based taxonomic profiling, as listed in Table 2. The “Others” category includes bacterial taxa without annotations at the phylum level and/or that exhibited a low relative abundance across the samples.
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Figure 3. Bar plots representing the taxonomic profiles and LCBD values of the bacterial communities in the analysed samples at the genus level. The “Inoculum” sample corresponds to the microbial inoculum used in the biodegradation assay. All the samples shown are those that underwent 16S rRNA gene-based taxonomic profiling, as listed in Table 2. The “Others” category includes bacterial taxa without annotations at the phylum level and/or that exhibited a low relative abundance across the samples.
Figure 3. Bar plots representing the taxonomic profiles and LCBD values of the bacterial communities in the analysed samples at the genus level. The “Inoculum” sample corresponds to the microbial inoculum used in the biodegradation assay. All the samples shown are those that underwent 16S rRNA gene-based taxonomic profiling, as listed in Table 2. The “Others” category includes bacterial taxa without annotations at the phylum level and/or that exhibited a low relative abundance across the samples.
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Figure 4. Normalised expression counts per million (cpm) for the total enzymes detected in the analysed samples. Sample numbers corresponding to each leather type are provided in Table 2. The 30 most abundant enzymes are individually annotated, while enzymes with a lower relative abundance are grouped under the category “Others” for clarity.
Figure 4. Normalised expression counts per million (cpm) for the total enzymes detected in the analysed samples. Sample numbers corresponding to each leather type are provided in Table 2. The 30 most abundant enzymes are individually annotated, while enzymes with a lower relative abundance are grouped under the category “Others” for clarity.
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Figure 5. Distribution of uniquely and commonly expressed enzymes among leather samples subjected to different tanning treatments. Each vertical bar represents the intersection size, that is, the number of enzymes either uniquely expressed in a single treatment condition or shared among the specific combination of samples indicated by the connected dots below each bar (x-axis). The x-axis shows each unique combination of sample groups, while the y-axis shows the corresponding count of enzymes detected for that combination. The zeros along the x-axis indicate combinations where no shared enzymes were detected. The enzyme presence was determined based on metatranscriptomic analysis of the microbial communities. This representation enables comparison of functional overlaps and specificities across chrome-, Zeolite-, and Biole®-tanned leathers, as well as untreated collagen, throughout the biodegradation assay.
Figure 5. Distribution of uniquely and commonly expressed enzymes among leather samples subjected to different tanning treatments. Each vertical bar represents the intersection size, that is, the number of enzymes either uniquely expressed in a single treatment condition or shared among the specific combination of samples indicated by the connected dots below each bar (x-axis). The x-axis shows each unique combination of sample groups, while the y-axis shows the corresponding count of enzymes detected for that combination. The zeros along the x-axis indicate combinations where no shared enzymes were detected. The enzyme presence was determined based on metatranscriptomic analysis of the microbial communities. This representation enables comparison of functional overlaps and specificities across chrome-, Zeolite-, and Biole®-tanned leathers, as well as untreated collagen, throughout the biodegradation assay.
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Figure 6. Normalised expression counts per million (cpm) of the proteases detected in the analysed samples. Sample codes corresponding to each leather type are detailed in Table 2. The 31 most abundant proteases are individually annotated, while those with a lower relative abundance are grouped under the category “Others” for improved readability.
Figure 6. Normalised expression counts per million (cpm) of the proteases detected in the analysed samples. Sample codes corresponding to each leather type are detailed in Table 2. The 31 most abundant proteases are individually annotated, while those with a lower relative abundance are grouped under the category “Others” for improved readability.
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Figure 7. Principal Coordinate Analysis (PCoA) showing enzyme-level differences between the analysed groups, based on the Bray–Curtis distance matrix.
Figure 7. Principal Coordinate Analysis (PCoA) showing enzyme-level differences between the analysed groups, based on the Bray–Curtis distance matrix.
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Figure 8. Summary heatmap of functional marker identification using DESeq2. Enzyme codes highlighted in red indicate higher expression in the first compared group, whereas those highlighted in blue indicate higher expression in the second compared group. The statistical significance levels were as follows: (.) p < 0.1, (*) p < 0.05, (**) p < 0.01.
Figure 8. Summary heatmap of functional marker identification using DESeq2. Enzyme codes highlighted in red indicate higher expression in the first compared group, whereas those highlighted in blue indicate higher expression in the second compared group. The statistical significance levels were as follows: (.) p < 0.1, (*) p < 0.05, (**) p < 0.01.
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Table 1. Elemental composition (% C, % N, % H, % S) of the leather samples used in this study. Collagen was purchased from Sigma-Aldrich® (St. Louis, MO, USA) and used as the positive control.
Table 1. Elemental composition (% C, % N, % H, % S) of the leather samples used in this study. Collagen was purchased from Sigma-Aldrich® (St. Louis, MO, USA) and used as the positive control.
SampleTanning Type% 12C% N% H% S
CollagenNone51.010.37.00.9
M3 chrome AV-1821Chrome-tanned (heavy metal-based)43.27.82.40.0
M6 ZeoliteZeolite-tanned (mineral-based)42.26.22.30.0
M7 BioleSyntan-tanned (organic phenol-based)45.27.52.50.0
Table 2. Overview of samples collected during the leather biodegradation assay (ISO 20136), including the withdrawal times and analyses conducted for taxonomic and functional profiling. An inoculum ratio of 50:50 was the initial inoculum ratio used for the biodegradation assay. Samples marked with “rRNA” were the taxonomic profiling samples.
Table 2. Overview of samples collected during the leather biodegradation assay (ISO 20136), including the withdrawal times and analyses conducted for taxonomic and functional profiling. An inoculum ratio of 50:50 was the initial inoculum ratio used for the biodegradation assay. Samples marked with “rRNA” were the taxonomic profiling samples.
Erlenmeyer PositionSampleSample NumberTime (h)
-50:50 inoculum ratio2206450
10Collagen—16S rRNA22064965
11Chrome—16S rRNA22065365
12Zeolite—16S rRNA22065765
13Biole—16S rRNA22066165
10Collagen—65 h220646C65
11Chrome—65 h220650C65
12Zeolite—65 h220654C65
13Biole—65 h220658C65
10Collagen—113 h220647C113
11Chrome—113 h220651C113
12Zeolite—113 h220655C113
13Biole—113 h220659C113
10Collagen—240 h220648C240
11Chrome—240 h220652C240
12Zeolite—240 h220656C240
13Biole—240 h220660C240
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Bonilla-Espadas, M.; Bertazzo, M.; Lifante-Martinez, I.; Camacho, M.; Orgilés-Calpena, E.; Arán-Aís, F.; Bonete, M.-J. Taxonomic and Functional Profiling of Bacterial Communities in Leather Biodegradation: Insights into Metabolic Pathways and Diversity. Bacteria 2025, 4, 37. https://doi.org/10.3390/bacteria4030037

AMA Style

Bonilla-Espadas M, Bertazzo M, Lifante-Martinez I, Camacho M, Orgilés-Calpena E, Arán-Aís F, Bonete M-J. Taxonomic and Functional Profiling of Bacterial Communities in Leather Biodegradation: Insights into Metabolic Pathways and Diversity. Bacteria. 2025; 4(3):37. https://doi.org/10.3390/bacteria4030037

Chicago/Turabian Style

Bonilla-Espadas, Manuela, Marcelo Bertazzo, Irene Lifante-Martinez, Mónica Camacho, Elena Orgilés-Calpena, Francisca Arán-Aís, and María-José Bonete. 2025. "Taxonomic and Functional Profiling of Bacterial Communities in Leather Biodegradation: Insights into Metabolic Pathways and Diversity" Bacteria 4, no. 3: 37. https://doi.org/10.3390/bacteria4030037

APA Style

Bonilla-Espadas, M., Bertazzo, M., Lifante-Martinez, I., Camacho, M., Orgilés-Calpena, E., Arán-Aís, F., & Bonete, M.-J. (2025). Taxonomic and Functional Profiling of Bacterial Communities in Leather Biodegradation: Insights into Metabolic Pathways and Diversity. Bacteria, 4(3), 37. https://doi.org/10.3390/bacteria4030037

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