Functional and genomic characterization of ligninolytic Bacillus strains for improved jute fibre delignification

Abstract

Lignin, the second most abundant biopolymer on Earth, imparting rigidity to plant cell walls but poses a challenge in the processing of lignocellulosic biomass such as jute (Corchorus spp.). Compared to other bast fibers such as flax and ramie, jute fiber has a very high lignin content (13.3% to 15%), hindering its use in the textile sector. Although chemical methods for lignin degradation are widely used, they are environmentally damaging and economically unfeasible. Consequently, bacterial lignin-degrading enzymes, especially those derived from Bacillus species, have attracted interest owing to their resilience and adaptability. This study investigated three Bacillus strains—LJRB1 (Bacillus subtilis), LJRB2 (Bacillus amyloliquefaciens), and LJRB3 (Bacillus licheniformis)–which were identified for their efficient lignin-degrading capabilities. Comprehensive characterization of these strains was performed, including growth condition optimization, enzyme profiling (MnP and LiP), and genomic analysis via whole-genome sequencing to identify key ligninolytic genes. These strains, if combined with previously proven high-efficiency pectinolytic strains (PJRB 1–3) will enhance the microbial retting consortium by simultaneously reducing the fiber lignin content and retting time. These findings lay the foundation for the development of a next-generation microbial retting formulation that is eco-friendly, cost-effective, and industry-oriented, potentially enhancing the quality and marketability of jute fiber for high-end applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-026-05165-7.

Introduction

Lignin, the second most abundant biopolymer on Earth, naturally exists as a fundamental component of lignocellulose, linked with cellulose and hemicellulose in plants, providing stiffness and structural integrity. In plant cell walls, lignin also serves as a framework that restricts the accessibility of cellulose and hemicellulose for downstream processing [18]. The proportion of lignin in jute fiber ranges from 13.3% to 15% [9, 38], whereas that of other well-known bast fibers, such as flax and ramie, is less than 5% [43]. The elevated lignin content in jute fibers limits their application as a textile material. Retting is a complex process that plays a critical role in determining the final quality of the jute fiber. To obtain superior-grade low-lignin fibers suitable for the textile industry, improved jute retting methods with better lignification are urgently required. Fiber weakening is the result of excessive retting, whereas poor separation is the result of suboptimal retting. Datta et al. [15] examined the sequential stages of microbial interaction and colonization, microbial interactions, and systematic breakdown of pectic substances in jute bark under both controlled and traditional water-retting conditions. Biorefining plant biomass is a sustainable method that utilizes biological processes (e.g., ligninolytic microbes, as well as lignin-degrading enzymes) to enhance the value of lignocellulosic materials, focusing on lignin degradation [18]. Chemical processes such as pyrolysis, hydrogenation, selective oxidation, and breakdown, which are commonly used to biorefine plant biomass, can have significant negative environmental effects. To find an environmentally friendly and sustainable option, current research on delignification methods has focused mainly on the development and use of bacterial extracellular enzymes.

Owing to its bulky three-dimensional branched structure (containing several phenolic compounds) and the presence of various ether and C–C bonds [53], lignin is resistant to degradation by conventional hydrolytic enzymes, which are typically effective against linear carbohydrate polymers and short-branched polysaccharides [26]. The two main types of enzymes involved in lignin degradation are lignin-modifying enzymes and lignin-degrading auxiliary enzymes. Laccase and heme-containing peroxidases, including lignin peroxidase (LiP), manganese peroxidase (MnP), versatile peroxidase (VP), and feruloyl esterase, constitute lignin-modifying enzymes, together referred to as ligninolytic enzymes [33, 53]. The main lignin-modifying enzymes recently used in the delignification of bast fibers, such as jute, are lignin peroxidases (LiPs), manganese peroxidases (MnPs), and laccases [39]. Each enzyme plays a distinct role in breaking down the complex lignin structure in an eco-friendly way [18]. Auxiliary enzymes required for complete ligninolysis include glucose oxidase, cellobiose dehydrogenase, aryl alcohol oxidases, and pyranose 2-oxidase [22]. Lignin-modifying enzymes modify the structure of lignin through various chemical reactions (mainly oxidation), making it more accessible for further breakdown.

Hydrogen peroxide (H₂O₂) is necessary for lignin peroxidase (LiP, EC 1.11.1.14) to catalyze the oxidation of non-phenolic lignin molecules and mineralize recalcitrant aromatic compounds [14]. Manganese Peroxidase (MnP, EC 1.11.1.13) oxidizes Mn²⁺ to Mn³⁺ (in the presence of H₂O₂), which then acts as a diffusible oxidant and oxidizes phenolic structures in lignin, resulting in lignin breakdown [14].

Laccases (EC 1.10.3.2) are multicopper oxidases that catalyze the one-electron oxidation of phenolic lignin compounds using molecular oxygen as the electron acceptor, producing water as a by-product [45].

Barai et al. [3] reported that five Bacillus species, identified based on metabolic fingerprinting patterns using the Biolog system (Biolog Inc., Hayward, USA), exhibited superior lignin-degrading potential among a broad range of screened isolates. The application of these selected strains to jute fiber resulted in substantial lignin reduction, ranging from 3.88% to a maximum of 21.97%, compared to untreated controls.

The observed delignification was accompanied by a decrease in fiber strength (maximum 20.9 g/tex) relative to the control (22.2 g/tex), while fiber fineness improved, as indicated by a reduction in tex values to 2.5 compared to the control value of 2.85.

Enzymatic assays further confirmed their ligninolytic capabilities, particularly in terms of manganese peroxidase (MnP) and lignin peroxidase (LiP) activities. The highest MnP activity recorded was 482 U L⁻¹ for isolate no.L10 (LJRB2), whereas isolate no. L9 (LJRB1) demonstrated a maximum LiP activity of 615.6 U L⁻¹ in a comparable incubation period. Comprehensive characterization of these five promising isolates, including qualitative ligninolytic enzyme screening using plate assays with Azure B and ABTS, was conducted, in which they were selected from a total of twenty bacterial isolates. The delignification potential of these isolates has also been experimentally validated. In that study, the same three bacterial isolates were designated as L9 (LJRB1), L10 (LJRB2), and L30 (LJRB3). A comprehensive set of assays was performed to evaluate delignification performance, including dye decolorization assays (Azure B and ABTS), quantitative enzyme activity analyses (lignin peroxidase and manganese peroxidase), and direct assessment of delignification efficiency through lignin content reduction following bacterial treatment of fibers. In addition, fiber quality parameters, such as tensile strength and fineness, were measured to determine the practical impact of bacterial treatment.

This study meticulously examined and comprehensively characterized three distinct ligninolytic Bacillus species, identified as more promising among the previously isolated five strains, focusing on their growth parameters, retention of ligninolytic enzyme-producing ability (LiP, MnP and Laccase) while reviving the isolates and genetic characteristics via a whole-genomee sequencing.

Material and methods

Scanning electron microscopy

Based on their higher delignification capacity, three different strains of Bacillus identified by the BIOLOG microbial identification system as Bacillus subtilis (LJRB1), Bacillus amyloliquefaciens (LJRB2), and Bacillus licheniformis (LJRB3) were further studied for their growth parameters, biochemical properties, and whole genome sequences. While light microscopy after Gram staining only dictated the shape and gram characteristics of the strains, high-resolution SEM imaging was performed for a more detailed and intricate view of the bacterial cells. All three bacterial cultures were freshly grown to the log phase in nutrient broth media and then centrifuged to retain the pellet. Each bacterial pellet was then washed (sequentially with Na-P buffer, glutaraldehyde, and again with Na-P buffer), dehydrated with different ethanol concentrations, and preserved in 70% ethanol until use. After a final wash with 80% ethanol, stubs were prepared by applying adhesive tape and then adding the dried bacterial sample on the tape. The specimens were subsequently examined using a scanning electron microscope (Zeiss Gemini SEM 450 and Gemini 2; Carl Zeiss AG) after gold coating.

Optimization of ideal conditions for growth

To determine the optimum growth parameters, specifically ideal for growth during the logarithmic phase, LJRB strains were cultured at different pH (6.5, 7.5, and 8.5) and temperature (25 °C, 30 °C, 35 °C, and 40 °C) ranges. The optical density (OD) of the culture medium was measured at 590 nm after a 24-h incubation.

Assessment of pectin, xylan and cellulose degradation capabilities

The three isolates were grown separately (using the spot plate method) on agar plates containing pectin, xylan, and cellulose as the sole carbon source. After 48 h of incubation period, pectin agar plates (1% pectin, 1% yeast extract, 0.37% NaCl, 2.5% agar) were flooded with 2% Cetrimide [13], xylan agar plates (0.5% xylan, 0.5% yeast extract, 0.5% NaCl, 2% agar) were flooded with Congo red (0.1%) for 15 min and rinsed with 1 M NaCl solution [17]; and in order to observe the substrate hydrolysis zone surrounding the colonies, CMC agar plates (0.04% KH₂PO₄, 0.02% MgSO₄•7H₂O, 0.01% K₂HPO₄, 0.01% NaCl, 0.04% yeast extract, 1% carboxymethyl cellulose, and 1.5% agar) were also rinsed with 1 M NaCl solution following a 15-min treatment with 0.1% Congo red [41].

Qualitative tests to evaluate the production of lignin-modifying enzymes

The three selected bacterial strains (freshly grown on mineral salt medium) were spot plated on mineral salt medium (K₂HPO₄ 1 g/L, KH₂PO₄ 1 g/L, (NH₄)₂SO₄ 2 g/L, MnSO₄ 0.02 g/L, FeSO₄ 0.05 g/L, CaCl₂ 0.1 g/L, MgSO₄ 0.2 g/L, and peptone 5 g/L), supplemented with 0.01% Azure-B for qualitative assay of lignin peroxidase production and 0.01% phenol red for manganese peroxidase production assay [44]. The plates were subsequently incubated at 32 °C for 5 days and evaluated for ligninolytic enzyme activity. The decolorization surrounding the colonies was indicative of lignin peroxidase activity, whereas a change in medium color from light orange to dark red was taken as evidence of manganese peroxidase production.

The isolated bacterial colonies were grown (by spot plate method) on LBM (lignin-modifying enzyme basal medium, composed (g/L) of KH₂PO₄:1, C₄H₁₂N₂O₆:0.5, CuSO₄.5H₂O:0.001, Fe(SO₄)₃:0.001, MgSO₄.7H₂O:0.5, CaCl₂.2H₂O:0.01, yeast extract:0.01, MnSO₄.H₂O:0.001, 20% glucose solution:10 ml/L, and 1.6% agar powder) agar plates containing 0.1% ABTS (2, 2'-azino-bis 3-ethylbenzothiazoline-6-sulphonic acid) to detect the production of extracellular laccase [34]. Plates were incubated at 34ºC for 7 days and checked for the development of any blue or green colour around the colonies.

Extraction of DNA and preparation of libraries

Genomic DNA from bacteria was isolated with the DNeasy Ultraclean Microbial Kit and evaluated spectrophotometrically (Nanodrop 1000 instrument). Before preparing the sequencing library, a 1% agarose gel was used to check the quality of the DNA samples and the A260/230 ratio was measured to ensure that library preparation met QC standards. The NEBNext Ultra II DNA Library Prep Kit (NEB: E7103) from New England Biolabs (NEB) was used to prepare the library. Using a DNA HS test kit (Thermofisher: Q32851) and Qubit 4.0 fluorometer (Thermofisher: Q33238), the finished library was checked according to the manufacturer's protocol. Agilnet high-sensitivity D1000 screentapes (Agilent # 5067–5582) on a TapeStation 4150 was used to determine the library's insert size, as per the manufacturer’s instructions. Only libraries that passed quality control were used for sequencing as paired-end 2 × 150 bp using Illumina NovaSeq 6000, which yielded approximately 4 GB of sequencing data each.

De novo assembly, contig ordering, and scaffolding

Two distinct assemblers were used for de novo genome assembly: Unicycler [52] and SPAdes v3.15.0 [48]. All assembly tools were used with the default k-mer specified. Although assemblies were generated using both methods, Unicycler's contigs were the only ones retained for all subsequent analyses due to their greater continuity and comprehensiveness.

Scaffolding and gap filling were performed using two key tools: LINKS [50] for scaffolding and DegapSeq (Galaxy web server, Europe) [11] for gap filling, to enhance the quality of the Unicycler assembly. This allowed the conversion of contigs to longer scaffolds and decreased the number of assembly gaps, helping the overall completeness of the genome assembly.

Taxonomic classification based on genomes

The Microbial Genomes Atlas (MiGA) tool was used to perform taxonomic classification using whole-genome data [40]. MiGA offers a comprehensive platform for genome-scale taxonomic classification by employing the NCBI non-redundant bacterial genome database. Subsequent analyses utilized the Type (Strain) Genome Server (TYGS) [30], a reputable tool for genome-centric phylogenetic classification, to ensure accurate strain identification. Strain taxonomic identification was also performed using GTDB-tk tools [10]. To thoroughly investigate the genomic variation in the selected strains, pan-genome analysis was performed using the Roary pipeline [35].

Gene prediction and annotations

Prokka [42] on the Galaxy server was used to annotate draft genomes. The web tool Rapid Annotation Using Subsystem Technology (RAST) annotation server (http://rast.theseed.org/FIG/rast.cgi) was used to annotate gene functions [5]. The Operon-mapper (https://biocomputo.ibt.unam.mx/operon_mapper/) [46] tools were employed to analyze the arrangement of protein-coding genes into operons. Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology assignments and KEGG pathway predictions were used to functionally annotate the gene using the KEGG Automatic Annotation Server (KAAS) [31] following the bi-directional best hit (BBH) method. Using eggNOG-mapper v2 [7] (http://eggnog-mapper.embl.de), genomes were analyzed for clusters of orthologous groups (COGs) annotations. Carbohydrate-active enzymes (CAZy) were annotated using the dbCAN 3 meta server (http://bcb.unl.edu/dbCAN2/blast.php), an automated online platform for annotating carbohydrate-active enzymes and their substrates. Antibiotic resistance of three genome was assessed through CARD (Comprehensive Antibiotic Resistance Database) web tools (https://card.mcmaster.ca/analyze/rgi).

Results

Scanning electron microscopy

High-resolution scanning electron microscopy revealed that all LJRB strains exhibit a rod-shaped morphology. Additionally, a limited number of flagella were observed as extracellular structures associated with each strain (Fig. 1).

Fig. 1.

SEM of three LJRB strains: a LJRB1 at 39.44 KX; b LJRB2 at 27.17 KX; c LJRB3 at 28.32 KX, Single bacterium representation of ligninolytic bacterial strains: d LJRB1 at 34.87 KX; e LJRB2 at 30.78 KX; f LJRB3 at 55.10 KX. SEM micrographs revealed the presence of hair-like appendages (flagella), suggesting the occurrence of extracellular filamentous structures: g LJRB1, h LJRB2, i LJRB3

Confirmation of ideal conditions for growth

When the growth of the isolates were recorder, all strains exhibited OD values < 1.0 across the tested pH range, indicating suboptimal growth at 25 °C temperature. Statistical analyses were performed using one-way ANOVA in GraphPad Prism 8.0. The analysis revealed significant differences in growth among the three isolates across temperatures within each pH level, with p < 0.0001 at 25 °C, p < 0.005 at 30 °C, p < 0.0001 at 35 °C, and p < 0.001 at 40 °C. Additionally, for each isolate, growth variation across different pH levels at fixed temperature was assessed. For LJRB1, significant differences were observed at all temperatures (25 °C: p < 0.0001; 30 °C: p < 0.005; 35 °C: p < 0.001; 40 °C: p < 0.0001). LJRB2 showed significant variation at 25 °C and 40 °C (p < 0.0001), whereas the differences at 30 °C and 35 °C were not significant. In contrast, LJRB3 levels differed significantly across all temperatures (25 °C: p < 0.0001; 30 °C: p < 0.0001; 35 °C: p < 0.0001; 40 °C: p < 0.0001). All three strains demonstrated satisfactory growth at 30 °C and 35 °C across pH 6.5, 7.5, and 8.5, with OD values ≥ 1.5. However, for simultaneous cultivation under uniform conditions relevant for experimental standardization or potential field applications it is essential to identify a common optimum. Among the tested conditions, pH 7.5 and 35 °C supported consistently high and comparable growth (OD ≥ 1.5) across all strains, and can therefore be considered as the ideal condition for growth. In contrast, growth was moderately reduced at 40 °C, with OD values falling below 1.5 across all tested pH levels, indicating that this temperature is less favourable for optimal growth of the LJRB strains (Fig. 2).

Fig. 2.

Graphical representations of standarization of optimum growth conditions (pH & temperature) of the ligninolytic isolates: a Growth of the isolates at 25⁰c: All LJRBs show ˂ 1 OD irrespective of the pH values (6.5, 7.5, 8.5). b Growth of the isolates at 30⁰c: The OD values of the strains are ≤ 1.5 in different pH values. c Growth of the isolates at 35⁰c: The observed OD values of the strains are ≥ 1.5 in the different pH range. d Growth of the isolates at 40⁰c: The isolates show OD values ˂1.5 throughout all the pH ranges

Assessment of pectin, xylan, and cellulose degradation capabilities

No discernible hydrolysis (halo) zones were observed for any of the isolates on the pectin agar plates, indicating the absence of pectinase activity. Similarly, the absence of hydrolysis zones on xylan agar plates suggests that the isolates do not exhibit xylanase activity. In contrast, cellulase activity was indicated by the formation of distinct halo zones surrounding the bacterial colonies on CMC agar plates. However, the low potency index (zone diameter/colony diameter) suggested limited cellulolytic activity, indicating a restricted capacity of these isolates to degrade plant cellulose (Fig. 3).

Fig. 3.

Screening for pectin, Xylan & Cellulose degrading properties of the ligninolytic isolates. a Pectin Agar plates showed no (or negligible) hydrolyzed zone for any isolate after flooding the grown (on Pectin Agar Media by spot plate method) colony with 2% Cetrimide, which indicates that the LJRBs don’t have pectinase activity. b Xylan Agar plates also showed no hydrolyzed zone for any isolate after flooding the grown (on Xylan Agar Media by streak plate method) colony with 0.1% congo red & then washing with 1M NaCl solution, which indicates they don’t have xylanase activity. c CMC Agar plates showed hydrolyzed (halo) zone around each isolate (Carboxymethylcellulose Agar Media by spot plate method) after treating with 0.1% congo red & then washing with 1M NaCl solution

Qualitative tests to assess the synthesis of lignin-modifying enzymes

Decolourization of Azure B dye containing plates around each isolate indicated the production of lignin peroxidase. Similarly, the transition of phenol red–supplemented media from light orange to deep red confirmed manganese peroxidase activity. In contrast, no blue or green coloration was observed in ABTS-containing agar following seven days of incubation using the spot plate method, indicating the absence of extracellular laccase production in all isolates (Fig. 4).

Fig. 4.

Qualitative assays for determination of the lignin modifying enzyme productions by the LJRB strains. a Ligninolytic isolate (taken from fresh culture grown on Mineral salt medium [MSM]) showing decolorization of Azure-B dye around bacterial growth on azure-B agar (MSM + 0.01% Azure-B) plate indicates the production of lignin peroxidase (LiP). b Colour change of phenol red from light orange to deep red upon bacterial growth on phenol red agar plate (MSM+ 0.01% phenol red) indicates the production of manganese peroxidase (MnP). c Transparent ABTS agar plates (Lignin Modifying Enzyme Basal Medium supplemented with 0.1% ABTS) inoculated with LJRB strains, using fresh cultures grown on MSM, showed no blue or green colour development after 7 days of incubation at 34 °C, indicating absence of laccase activity

Assembly and detailed characterization of the genomes of LJRB strains

The three LJRB strains were subjected to whole-genome shotgun sequencing, yielding approximately 2.5 GB to 4.0 GB of data, which achieved nearly 100X genome coverage. The pair-end sequencing, with a mean read length of 150 bp, generated approximately 14.5 to 16.0 million reads, reaching an average Phred score of 38.8. Two distinct tools, SPAdes and Unicycler, were used to merge approximately 14.5 to 14.9 million reads after applying a quality filter (Q > 30). After testing two assembly methods, Unicycler chose the assemblies with a kmer of 95 for further analysis. The results were consistent, and the genome sizes were similar to those of other reported Bacillus species. LJRB1, LJRB2, and LJRB3 had main assemblies of 4,075,305, 4,074,525, and 4,132,528 base pairs, respectively. Their N50 values were 40,539 bp, 992,857 bp, and 992,857 bp.

(Table 1, Fig. 5a, b, c).

Table 1.

Metrics and characteristics of LJRB genomic sequences

Feature	LJRB 1		LJRB 2		LJRB 3
No of Reads	32979140		17279850		14765646
Primary assemblies
Unicycler
Assembly length	4075305			4074525		4132528
N 50	992857			992857		40539
SPAdes
Assembly length	4073286			4075094		4201442
N 50	1057556			997777		41645
Final assembly following scaffolding and contig reordering
Scaffold No	25	25					198
Total Bases	4,074,599 bp	4074592 bp					4,131,606 bp
Maximum scaffold length	1057511	1057512					130023
N 50	992,684	992,684					40,589
GC %	43.87	43.87					37.71

Fig. 5.

The circular genome of (a) LJRB 1; b LJRB 2; c LJRB 3 Bacteria exhibits a genome size of 4 Mb. The composition of the highlighted rings, beginning with the outermost and progressing to the center, is as follows. Ring 1: CARD (Comprehensive Antibiotic Resistance Database) RGI outcome on the positive strand; Ring 2: PHASTES annotation; Ring 3: CDS feature; Ring 4: Contigs; Ring 5: GC skew; Ring 6: GC content; Ring 7: CARD RGI outcome on the negative strand. The data were generated with the Proksee Genome Analysis Server (https://proksee.ca/)

To enhance the quality of genome assemblies, contigs were assembled into longer scaffolds and arranged in accordance with the available complete genomes of the most closely related strains. With maximal sizes of 1,057,511 bp, 1,057,512 bp, and 130,023 bp, respectively, the finished scaffolded and reorganised genome assemblies of LJRB1, LJRB2, and LJRB3 contained 25, 25, and 197 scaffolds. LJRB1, LJRB2, and LJRB3 had final genome sizes of 4,074,599, 4,074,592, and 4,131,606 base pairs, respectively, with GC contents of 43.87%, 43.87%, and 37.71%.

Genome-level resolution of taxonomic identities

The TYGS server tools were used to confirm the taxonomic classifications of the three LJRB strains. The following strains' identities were confirmed by genomic evidence: Bacillus stercoris (LJRB1), Bacillus stercoris (LJRB2), and Lysinibacillus capsici (LJRB3). The three query strains were categorised with the corresponding species using the Genome BLAST Distance Phylogeny (GBDP) method of TYGS, which placed them into 13 species groups (Fig. 6a, b).

Fig. 6.

Phylogenetic study of the LJRB strain obtained from the Type (Strain) Genome Server (TYGS). Phylogenomic tree of (a) LJRB1, LJRB2; b LJRB3 generated utilizing the Genome BLAST Distance Phylogeny (GBDP) approach from TYGS. Three query strains were classified and arranged into separate species groupings. Branch lengths are represented in scales of GBDP distance computation, while the numbers above the branches denote GBDP pseudo-bootstrap support values obtained from 100 replications

Genome-based taxonomic classification of the three assembled genomes (LJRB 1, LJRB 2, and LJRB 3) performed using GTDB-tk tools also demonstrated clear affiliation within the phylum Bacillota. Both LJRB 1 and LJRB 2 were consistently assigned to the genus Bacillus, with species-level identification as Bacillus stercoris. This classification was strongly supported by high Average Nucleotide Identity (ANI) values of 98.47% and alignment fraction (AF) values of 0.947–0.948 against the reference genome GCF_000738015.1. The placement and classification results were congruent, as both topology-based phylogenetic placement and ANI-based methods yielded identical species assignments. Additionally, multiple closely related genomes, including Bacillus subtilis, Bacillus inaquosorum, and Bacillus spizizenii, showed lower ANI values (< 95%), further confirming that LJRB 1 and LJRB2 belong specifically to Bacillus stercoris. The multiple sequence alignment (MSA) coverage for both genomes was high (96.19%), indicating robust genome completeness and reliable classification.

In contrast, LJRB 3 was assigned to a different lineage within the same phylum, classified under the genus Lysinibacillus, with species-level identification as Lysinibacillus capsici. This assignment was supported by an ANI value of 97.15% and an AF of 0.923 against the reference genome GCF_003367505.1. Similar to the previous genomes, both phylogenetic placement and ANI-based classification were in agreement. Other related species, such as Lysinibacillus boronitolerans and Lysinibacillus fusiformis, exhibited ANI values below the 95% species threshold, supporting the distinct classification of LJRB 3 as Lysinibacillus capsici.

The Roary analysis identified approximately 4000 gene clusters shared across the three genomes, as shown in Fig. 7a. The distribution indicates that most genes are present across all genomes (core genome), with very limited variation in gene counts among strains. The phylogenetic tree constructed from core gene alignments shows that LJRB1 and LJRB2 cluster closely together, indicating a recent common ancestor. LJRB3 branches separately, suggesting it is more evolutionarily divergent. The accompanying gene presence–absence heatmap (Roary matrix) supports this, with dark blue regions indicating shared gene clusters and light/grey regions indicating absence or variation.

Fig. 7.

Pangenome analysis of the three bacteria through Roary pipeline, a Gene cluster distribution among the three genomes. The plot illustrates the quantity of genes common among the strains, signifying a substantial fraction of genes preserved across all genomes (core genome). The x-axis denotes the quantity of genomes containing each gene cluster, whilst the y-axis signifies the number of gene clusters; b Phylogenetic tree of the core genome and gene presence-absence matrix for the three strains (LJRB1, LJRB2, and LJRB3). The phylogenetic tree was developed using concatenated core gene alignments, depicting the evolutionary relationships among the strains. The heatmap illustrates the presence-absence matrix of the Roary gene, with coloured cells denoting the presence of gene clusters and lighter or blank areas signifying absence. The clustering pattern indicates genetic similarity, with LJRB1 and LJRB2 exhibiting greater relatedness than LJRB3; c Pangenome composition illustrating the distribution of core, shell, and cloud genes. The pie chart demonstrates that most genes are part of the core genome, whereas accessory (shell) and unique (cloud) genes are minimally represented, signifying a closed pangenome organization with restricted genomic diversity

(Fig. 7).

Genome annotation of lignin-degrading bacteria

Prokka was used to annotate the genomic assemblies of LJRB1, LJRB2, and LJRB3 with meticulous precision. LJRB3 has the most coding sequences in its genome (4046), followed by LJRB1 (4035) and LJRB2 (4032). With 74 tRNA coding genes, LJRB3 has the most tRNA coding genes compared to 71 and 69 in LJRB1 and LJRB2, respectively.

The RASTtk functional annotation software, which delivers precise subsystem-level annotations and gene count estimations, was employed to study the genomes of LJRB1, LJRB2, and LJRB3. There are 4225, 4220, and 4241 genes in LJRB1, LJRB2, and LJRB3 genomes respectively, including both RNA and protein-coding genes. 1271 genes, or 28% of LJRB1, are categorised into 337 subsystems based on the RASTtk seed subsystem annotations. Similarly, 1246 genes, or 28% of LJRB2, are spread across 342 subsystems, while 1267 genes, or 28% of LJRB3, are spread across 345 subsystems. With 289 genes in LJRB1, 286 in LJRB2, and 264 in LJRB3, the category "Amino acid and derivatives" had the highest gene counts of all the subsystem categories that were examined, followed by 'Carbohydrates' category with 240, 237, and 182 in LJRB1, LJRB2 and LJRB3 respectively (Table 2, Fig. 8a,8b,8c).

Table 2.

Annotation of LJRB genomes via the RAST annotation server

Feature	LJRB1	LJRB2	LJRB3
CDS	4225	4220	4241
tRNA	66	66	74
rRNA	3	3	3

Fig. 8.

Distribution of subsystem categories in the genomes of LJRB strains as identified using RASTtk annotations. a LJRB1; b LJRB2; c LJRB3. The characteristics and extent of each LJRB genome are depicted in pie charts. The bars on the left indicate the percentage of protein inclusion or exclusion within the subsystems. The green bars represent included proteins, whereas the blue bar indicates proteins excluded from the subsystems

The LJRB genomes show a broad range of antimicrobial resistance genes. All isolates carry efflux pumps and glycopeptide resistance clusters, making them resilient against disinfectants and drugs like vancomycin. Specific strains (LJRB 1 and 2) harbor aminoglycoside and macrolide resistance genes, while LJRB 3 has a distinct macrolide mechanism. Together, these findings highlight a diverse set of shared and unique resistance strategies across the isolates, underscoring their strong potential to withstand multiple antibiotic classes.

Characterization of orthologous genes in LJRB strains

Between 88.87% and 91.35% of the protein-coding genes were classified utilising the COG database, based on the annotation of clusters of orthologous groups (COG) for the LJRB genomes. The majority of the genes (36.54% to 38.81%) in the COG functions were related to "metabolism," with "amino acid transport and metabolism (E)" accounting for the largest functional category with 333 to 339 genes. Approximately 18% of the COG categories were classified as "information storage and processing," and 16.5% were classified as "cellular processes and signalling." In particular, this category included ribosomal structure and biogenesis (J175–186), transcription (K295–318), signal transduction processes (T105–108), replication, recombination, and repair (L129–149), and cell wall/membrane/envelope biogenesis (M168–186). Furthermore, the majority of protein-coding genes associated with metabolic processes can be clarified through KEGG pathway enrichment analysis (Supplementary Fig. 1).

The three main categories of metabolic genes are involved in vitamin, cofactor, and carbohydrate metabolism. About three genes are linked to the production and metabolism of glycans in the LJRB 2 and LJRB 3 strains. The metabolism and biodegradation of xenobiotics constitute an important gene group in KEGG analysis. LJRB1 and LJRB3 had one and nine genes in this category, respectively, whereas LJRB2 had none. The majority of KEGG classifications across all three LJRB genomes encompass protein families related to genetic information processing (roughly 5–22 genes), signalling, and cellular processes (approximately 0–4 genes), as well as genes relevant to metabolism. OrthoVenn analysis of the protein sequences revealed that the three strains possess 2,567 single-copy gene clusters, 1,036 orthologous clusters (comprising at least two species), and 3,603 clusters. All three species shared 1818 ortholog clusters, and at least two genomes shared 2195 of them. A single genome contained 51 gene clusters (Supplementary Fig. 2a,2b,2c).

Enzyme family involved in lignin degradation: Important CAZy groups and genes

Examining the genome-wide distribution of genes encoding CAZymes is essential because LJRB strains are specifically selected for their capacity to degrade lignin. Both LJRB 1 and LJRB2 contained 128 CAZymes, whereas LJRB3 had only 54 CAZymes. AA 1, AA 3, AA 4, AA 6, and AA 7 are the most common CAZy classes identified to degrade lignins. The glycoside hydrolase family 28 (GH28) was the most common among the CAZy classifications, with number of sequences varying from 13 to 56 in several LJRB strains. LJRB2 had the most GHs of the three strains—56, while LJRB1 and LJRB3 had 46 and 13 GHs, respectively. A single copy of AA1 was found in both the LJRB1 and LJRB2 strains when the Auxiliary Activity CAZy family, which encodes lignin-oxidizing enzymes like laccase/p-diphenol:oxygen oxidoreductase (AA1, EC 1.10.3.2), was examined. There were two copies of AA1 in the LJRB 3 genome. One crucial member of the CAZy family is vanillyl-alcohol oxidase (AA 4, EC 1.1.3.38), which facilitates the conversion of certain phenolic compounds with side chains at the para-position of the aromatic ring. LJRB1 and LJRB2 had between 15 and 19 CEs, while LJRB3 had exactly 13 CEs.

Consensus CAZy predictions are based on HotPep, Diamond BLAST hits, and HMMER. Glycoside hydrolases are represented by GHs, glycosyl transferases by GTs, polysaccharide lyases by PLs, carbohydrate esterases by CEs, auxiliary activities by AAs, and carbohydrate-binding modules by CBMs.

(Table 3).

Table 3.

Comparative genome-wide distribution of CAZYs in the LJRB genomes

Bacterial species and strain	Total CAZy	Major CAZy catagories						Ligninolytic coding CAZy
		GT	GH	PL	CH	AA	CBM
LJRB1	128	39	46	7	15	4	13	AA1, AA4, AA6, AA7
LJRB2	128	39	56	7	19	4	13	AA1, AA4, AA6, AA7
LJRB3	54	20	13	0	13	7	1	AA1, AA3, AA4, AA7

Discussion

Jute fibre, also known as the "golden fibre," is widely utilised in various industries, offering affordability, environmental friendliness, and biodegradability. The production of sacks, bags, ropes, mats, carpets, geotextiles, and composites are among its main uses. Due to its low carbon footprint and sustainability, jute has recently garnered increased attention as a sustainable alternative to synthetic fibers in paper pulp, eco-packaging materials, and automobile interiors [47]. However, lignin, a complex phenolic compound that gives jute fibre its stiffness and rigidity, is present in large concentrations in the fibre by nature. Although lignin provides the fiber with structural strength, it also makes it coarse, stiff, and less appropriate for uses requiring textile grade [28]. The softness, dyeability, and spinning performance of the fibre are all diminished by a high lignin level. Therefore, to increase the softness, fineness, and tensile strength of fibres, it is essential to reduce the amount of lignin using biological, chemical, or enzymatic methods [24]. This enhances jute's quality and market value, while also increasing its use in premium products such as clothing, furniture, and cutting-edge composite materials. To sum up, reducing the amount of lignin in jute fibre is crucial to maximizing its economic potential and advancing it as a sustainable alternative in various industries.

There have been reports of using physical techniques, such as steam explosion, and chemical techniques, including metal catalysis, hydrothermal methods, solvents, in addition of acid or base, to pretreat lignocellulose and eliminate lignin, thereby decreasing the crystallinity of cellulose [19, 23]. But they require energy or large amounts of chemicals, which have many hazardous effects on the environment. Consequently, there is increasing interest in microbial-mediated lignin degradation as a more sustainable alternative. Several studies [4, 25] have demonstrated the potential of microorganisms to biochemically convert lignin into value-added compounds, highlighting their promise in environmentally benign lignocellulosic bioprocessing.

This highlights importance of identifying and characterising the lignin degrading bacterial isolates. Details of lignin-degrading bacterial consortium screened from the sludge of a reed pond have been published [49].

However, many new ligninolytic enzymes may still be discovered, and the bacterial ligninolytic potential remains largely untapped [32]. Compared to the traditional chemical methods, bacterial enzyme mediated delignification offer an effective sustainable option, specifically in terms increased selectivity, high thermostability and less dependency on mediators [27, 51].

Optimum growth conditions

After observing the growth of ligninolytic isolates in different sets of pH and temperature, it can be concluded that they can properly grow in nearly similar conditions like that of the pectinolytics (i.e. temperature of 35ºC or above & pH ranging from 7.5–8.0). So they can be accommodated together in same working condition, if we need to apply our findings for the betterment of jute fibre production in future.

Pectinase, xylanase, cellulase and lignin modifying enzymes production

Three of the Gram-positive ligninolytic bacterial isolates exhibited no detectable pectinase or xylanase activity, indicating that they are unlikely to degrade pectic components following initial breakdown by pectinolytic jute retting bacterial community. Furthermore, the absence of xylanase activity suggests that these isolates do not target xylan, a key hemicellulosic component associated with both cellulose and lignin. As xylan plays a crucial role in maintaining cell wall recalcitrance and structural integrity [12], its preservation minimizes the risk of damage to plant fibres under both synthetic (e.g., enzymatic or chemical treatments) and natural (biotic and abiotic) stress conditions.

Although the ligninolytic strains demonstrated the ability to degrade cellulose, their potency index indicated relatively low cellulolytic activity. This suggests that their impact on cellulose is limited and does not significantly compromise plant structural integrity. Consequently, these isolates are well-suited for applications requiring selective lignin removal without substantial degradation of cellulose. Moreover, given the structural interlinkage between cellulose, xylan, and lignin, the persistence of xylan further reduces the likelihood of extensive cellulose degradation.

Lignin-modifying enzymes production

Bacterial growth on lignin or its derivatives does not necessarily reflect true ligninolytic potential; therefore, dye decolourization assays were used as a rapid qualitative indicator of enzyme activity [2, 8, 21, 44]. In this study, decolourization of Azure B and phenol red suggested the presence of lignin peroxidase (LiP) and manganese peroxidase (MnP), respectively, whereas no colour change in ABTS indicated the absence of extracellular laccase activity. These observations are consistent with our previous findings [3], where the quantitative activities of LiP and MnP were established. Given this, the present qualitative assays serve only as confirmatory evidence of enzyme production in the revived cultures. Detailed enzymatic characterization has not been repeated here to avoid redundancy, and readers are referred to the earlier study for comprehensive quantitative analysis.

The genome assemblies and annotations of LJRB strains delineate their genomic architecture and unique characteristics

The genome size and GC content influence a microbe's ability to adapt to different environmental conditions. The versatility of Bacillus species is demonstrated by the availability of complete genome sequences of Bacillus and Lysinibacillus at NCBI, which span a range of 79% (from 3.42 Mb to 6.13 Mb). The species studied has a GC content range of 43.71% to 37.71%. Consistent with the expected genome length and GC content, the three Bacillus LJRB strains—each with a 4.1 Mb genome—were assembled into contigs in this study. LJRB1 and LJRB2 exhibited higher GC content than LJRB3, which was aligned with the average GC content of 46.3% for the selected Bacillus species. The LJRB strains were similar to the corresponding Bacillus species in terms of gene content, the number of tRNA sequences, and other features, including phage-like traits and insertion sequences. It is known that bacterial insertion sequences impact genomic plasticity and adaptability. To understand how these traits influence genomic plasticity and adaptability in a challenging environment such as jute-retting water, it is essential to examine the insertion sites identified in the LJRB genomes.

Genome annotation using Prokka revealed significant genomic similarity among LJRB1, LJRB2, and LJRB3, with moderate discrepancies in coding regions and tRNA genes. LJRB3 had the most coding sequences and tRNA genes, indicating marginally increased genomic complexity and possible metabolic diversity relative to LJRB1 and LJRB2. The analogous gene counts among the three genomes suggest a conserved genomic structure, indicating a tight evolutionary relationship among the strains.

Functional annotation using RASTtk revealed that all three genomes share a comparable functional profile, with roughly 28% of genes assigned to subsystems. The gene distribution across subsystems was notably consistent, while LJRB3 showed a slightly greater number of subsystems, suggesting greater functional potential. The prevalence of genes associated with "Amino acids and derivatives" and "Carbohydrates" underscores the metabolic proficiency of these strains in nutrient uptake and energy metabolism, perhaps enhancing their ecological flexibility and functional roles within their surroundings. The annotation results indicate that LJRB1, LJRB2, and LJRB3 possess a conserved core genome with minor functional differentiation.

These isolates carry a wide range of antimicrobial resistance genes, making them highly adaptable against multiple drug classes. Efflux pumps (qacG, qacJ) strengthen survival in disinfectant-heavy environments [6, 36], while aminoglycoside resistance genes (aadK, ANT(6)) point to strong streptomycin resistance [37]. Ribosomal protection mechanisms (msr(G), vmlR) further expand resistance to macrolides and other ribosome-targeting antibiotics. Altogether, these findings suggest that these strains might have shared many resistance genes through horizontal transfers from other bacteria in the niche extreme environments.

LJRB strains display distinct patterns of CAZYs in their genomes

One key finding from the whole-genome sequencing of the LJRB strains was the identification of several significant carbohydrate-degrading genes. Earlier, Datta et al. [16] had reported that in pectinolytic Bacillus strains (PJRB), the dominant CAZy families were PL1, PL9, and GH28. In contrast, the present study reveals a distinct profile in LJRB strains. Genome-wide analysis for CAZys showed that LJRB1 and LJRB2 each had a relatively high number of CAZyme-encoding genes, while LJRB3 had fewer. The greater number of CAZymes in LJRB1 and LJRB2 indicates these strains might have a stronger enzymatic ability to break down plant biomass. Among the different CAZy classes, Glycoside Hydrolases (GHs) were the most common, with LJRB2 containing the largest number of GH family enzymes. Glycoside hydrolases break glycosidic bonds in polysaccharides like cellulose, hemicellulose, and pectin, which are essential components of plant cell walls. The second-most-abundant carbohydrate-active enzyme families belong to the auxiliary activity (AA) groups, specifically AA1, AA3, AA4, AA6, and AA7.

All strains have AA1-family multicopper oxidases (p-diphenol: oxygen oxidoreductases). LJRB1 & 2 each have one AA1 gene, while LJRB3 has two. Laccases oxidise phenolic lignin units, generating radicals that cleave lignin bonds. The extra laccase in LJRB3 suggests it can initiate more extensive lignin oxidation. Bacterial laccases (e.g. Bacillus LMCOs) are known to oxidise lignin-model compounds and even produce vanillin as a breakdown product [20, 29]. All genomes possess AA4 genes (vanillyl alcohol oxidases), which convert lignin-derived vanillyl alcohol and related phenolics into the corresponding aldehydes. This reaction is a critical step in directing lignin-derived aromatic monomers into downstream catabolism. The presence of AA4 indicates that each strain can process methoxylated phenolics, such as guaiacol derivatives [20, 29]. AA6 family enzymes (NADH: quinone oxidoreductases) are present in all strains. These reduce quinones to hydroquinones, which protect the cell from quinone toxicity and also modulate lignin breakdown intermediates. Their presence indicates the strains can handle reactive lignin-derived quinones [29]. Genes for AA3 (GMC oxidoreductases) and AA7 (glucooligosaccharide oxidases) were also identified. AA3 enzymes (such as aryl-alcohol oxidases) generate H₂O₂ for peroxidase enzymes, while AA7 enzymes produce H₂O₂ from sugars. H₂O₂ can drive Fenton chemistry or feed peroxidases (though no class II peroxidase genes were annotated), aiding oxidative lignin degradation [29]. As noted, LJRB1 & 2 have extensive GH repertoires. Families GH3, GH5, GH13, etc. degrade cellulose, hemicellulose and pectin. GH28 (pectate lyases/polygalacturonases) are especially abundant (46–56 copies) in LJRB1 & 2, enabling breakdown of pectin-rich middle lamella. This would loosen plant cell walls, exposing lignin. GH18 chitinases or GH16 laminarinases could also contribute to general carbohydrate turnover. Although LJRB3 has fewer GHs, it still possesses families targeting cellulose/hemicellulose (e.g. GH5 in smaller numbers), supporting some polysaccharide degradation [1]. Compared to LJRB3LJRB1 and LJRB2 have more CEs, which remove acetyl or feruloyl groups from hemicellulose/pectin, thereby also liberating lignin-carbohydrate complexes.

Differentiation of functional and genomic characteristics among LJRB strains

Although the LJRB strains share a significant core genome, they exhibit distinct functional and genomic divergence, as indicated by differences in COG category distributions, KEGG pathway compositions, and orthologous gene clustering. The prevalence of metabolism-associated genes in all genomes indicates a common metabolic framework; however, variations in particular metabolic pathways highlight strain-specific specialization.

Differences in genes linked to transcription, replication, and cell envelope biogenesis indicate distinct regulatory and structural capabilities among the strains, potentially affecting growth, stress responses, and environmental adaptability. The strain-specific enrichment of glycan metabolism genes in LJRB2 and LJRB3 suggests a possible difference in cell surface architecture or substrate utilization strategies.

The varying abundance of xenobiotic biodegradation genes underscores functional dissociation, with LJRB3 showing a significantly higher gene count in this region than the other strains. This indicates improved metabolic adaptability in managing intricate or hazardous substances. Orthologous cluster analysis substantiates these findings by identifying a conserved genomic core alongside distinct gene clusters that probably contribute to strain-specific characteristics.

The data collectively indicate that although the LJRB strains maintain conserved metabolic and informational structures, functional divergence, influenced by strain-specific gene composition and pathway enrichment, may underlie their ecological distinctiveness and adaptive capacity.

Comparative genomic analysis of the three LJRB strains (LJRB1, LJRB2, and LJRB3) revealed several genomic features linked to metabolic versatility and plant biomass degradation. Functional annotation using the COG database showed that a large proportion of the predicted protein-coding genes could be assigned to known orthologous groups. Among the COG functional categories, genes related to metabolism comprised the largest share, suggesting that these strains possess diverse metabolic abilities that may aid their survival in complex lignocellulosic environments. Within this category, amino acid transport and metabolism formed the most significant subgroup, indicating a strong capacity for nutrient uptake and utilisation. This metabolic flexibility is common in microorganisms involved in plant biomass breakdown, where various substrates and aromatic intermediates are produced during lignin degradation.

Alongside metabolic genes, approximately 18% of the annotated genes were linked to information storage and processing, including ribosomal structure and biogenesis, transcription, and replication-related functions. These genes are vital for maintaining cellular processes and for ensuring effective adaptation to environmental stressors encountered during lignocellulose degradation. Similarly, around 16.5% of the genes were involved in cellular processes and signaling, such as signal transduction mechanisms and cell wall or membrane biogenesis. These categories suggest that the LJRB strains possess regulatory networks capable of responding to environmental cues and regulating enzyme production during plant biomass breakdown.

The KEGG pathway analysis further supported the metabolic versatility of the LJRB genomes by identifying genes involved in carbohydrate metabolism, cofactor and vitamin metabolism, and xenobiotic biodegradation. Carbohydrate metabolism pathways are particularly important in microorganisms associated with plant biomass decomposition because lignocellulosic materials contain a complex mixture of polysaccharides and aromatic polymers. The detection of genes involved in glycan biosynthesis and metabolism in LJRB2 and LJRB3 suggests these strains may be able to process plant-derived carbohydrates. Moreover, genes involved in xenobiotic biodegradation were identified in LJRB1 and LJRB3, with LJRB3 harbouring the highest number. Xenobiotic degradation pathways often overlap with aromatic compound metabolism, suggesting that these strains may possess enzymatic systems capable of transforming lignin-derived aromatic intermediates generated during lignin depolymerization. This metabolic potential is consistent with the ecological role of bacteria involved in lignocellulose degradation and may contribute to the breakdown of complex aromatic compounds in plant biomass [54].

All strains have complete glycolysis, the TCA cycle, and the pentose phosphate pathway (as expected). Differences emerge in secondary metabolism: LJRB3 encodes 9 genes in xenobiotic/aromatic degradation pathways (benzoate, aminobenzoate, etc.), whereas LJRB1 encodes 1, and LJRB2 encodes none. LJRB3 also has genes for nicotinate/nicotinamide metabolism (5 genes) not found in LJRB1 and LJRB2. These expansions indicate LJRB3 is primed to catabolize diverse aromatic compounds. This mirrors findings in other Bacillus – for example, B. cereus AH7-7 had 64 genes in aromatic pathways (phenylalanine, aminobenzoate) and multiple monooxygenases [54] Our KEGG analysis suggests that LJRB3 could similarly funnel lignin-derived aromatics (such as vanillin and ferulate) into central metabolism, a key step in delignification.

Orthologous gene cluster analysis using OrthoVenn revealed a high level of genomic similarity among the three LJRB strains while also highlighting distinct genetic features. The identification of single-copy gene clusters shared across the strains indicates a conserved core genome that likely supports fundamental cellular and metabolic functions. Furthermore, orthologous clusters comprising at least two genomes were identified, indicating substantial functional overlap among the strains. A total of ortholog clusters were shared by all three strains, suggesting that these bacteria possess a common set of genes that may contribute to lignocellulose degradation and environmental adaptation. However, the presence of unique gene clusters in individual genomes indicates that each strain also contains strain-specific genes that may confer specialized metabolic capabilities or ecological adaptations. Such genomic diversity may explain differences in enzymatic profiles and lignin-degradation efficiency among strains. Functional categorization shows metabolism dominates all three genomes. Notably, “amino acid transport and metabolism” is the largest single COG category, reflecting a generalist nutritional role. “Carbohydrate metabolism” genes (plentiful in RAST) are especially numerous in LJRB1 and LJRB2. All strains have ample genes for transcription/translation, cell wall biogenesis, and signal transduction, indicating robust adaptability. These general metabolic capabilities, combined with the specific ligninolytic enzymes above, equip the strains for plant biomass niches.

The genome-level accuracy of the LJRB strains indicates taxonomic connections with two Bacillus species and one Lysinibacillus species

One of the main goals of the current genome sequencing study is to determine the final taxonomic classification of the three promising ligninolytic bacterial strains. In bacterial taxonomy research, whole-genome sequence-based taxonomy—which employs core genome alignment and Average Nucleotide Identity (ANI) values—is generally considered more accurate than traditional methods. This study clearly identified three Bacillus species using genome-based taxonomy analysis: LJRB1 as Bacillus stercoris, LJRB2 as Bacillus stercoris, and LJRB3 as Lysinibacillus capsici.

The present genome-based taxonomic analysis highlights the effectiveness of combining phylogenetic topology and ANI metrics for accurate bacterial classification. The high ANI values (> 98%) observed for LJRB 1 and LJRB 2 relative to Bacillus stercoris clearly exceed the commonly accepted species delineation threshold of 95%, confirming their assignment to the same species. The high alignment fraction further indicates substantial genomic similarity and coverage, suggesting that these genomes are not only closely related but also likely represent strains of Bacillus stercoris. The presence of several closely related Bacillus species with ANI values below the threshold reinforces the discriminatory power of ANI in resolving species-level taxonomy within this complex genus.

The classification of LJRB 3 as Lysinibacillus capsici demonstrates phylogenetic diversity among the analyzed samples despite their shared phylum (Bacillota). The ANI value of 97.15% supports its placement within the species boundary, while the comparatively lower AF value (0.923) and MSA percentage (93.61%) may indicate minor genomic divergence or incomplete assembly relative to the reference genome. Nevertheless, the congruence between phylogenetic placement and ANI-based classification provides strong confidence in this assignment.

Overall, the agreement between topology-based and ANI-based methods across all samples underscores the robustness of the classification pipeline. The absence of conflicting signals or warnings further validates the reliability of the results. These findings suggest that LJRB 1 and LJRB 2 likely represent closely related or possibly clonal strains of Bacillus stercoris, while LJRB 3 represents a distinct species within the Lysinibacillus genus. Such diversity may reflect ecological or functional variation among the sampled bacterial communities, warranting further investigation into their genomic features and potential roles in the studied environment.

The prevalence of the core genome indicates that these three bacterial strains possess a remarkably stable genetic structure. This exemplifies the characteristics of closely related strains that inhabit comparable ecological niches or exhibit minimal evolutionary divergence. Fundamental biological processes are upheld Genomic innovation and horizontal gene acquisition are both quite restricted. The analysis of evolutionary relationships indicates.

LJRB1 and LJRB2 exhibit a close relationship, presumably originating from a recent shared lineage.LJRB3 exhibits a degree of divergence. The scarcity of accessory and unique genes suggests a closed pangenome structure, The species exhibits restricted horizontal gene transfer. The gene content exhibits a notable degree of stability among different strains. Given that a majority of genes are part of the core genome, it is probable that, The strains exhibit comparable metabolic pathways and physiological functions. The functional differences among strains can be nuanced and governed by the level of gene expression focus on regulatory elements instead of gene content.

The evolutionary pattern identified in this context that significant preservation (core genome predominance) and Restricted genomic adaptability. There is a moderate level of phylogenetic divergence observed between LJRB3 and the combined groups of LJRB1 and LJRB2.This indicates that the progression in these strains is influenced more by point mutations and minor variations instead of extensive gene gain or deletion.

Conclusion

In conclusion, this study presents a comprehensive functional and genomic characterization of three ligninolytic Bacillus strains capable of improving jute fibre delignification. By optimizing growth conditions, profiling lignin-modifying enzyme activities, and conducting whole-genome analyses, the research demonstrates the potential of these strains—LJRB1, LJRB2, and LJRB3—as robust candidates for eco-friendly microbial retting processes. The integration of these strains into a microbial consortium lays the groundwork for the development of next-generation retting formulations that can reduce both retting time and lignin content in jute fiber without adversely impacting essential plant components. These findings contribute valuable insights into bacterial delignification strategies and support the advancement of sustainable, high-quality jute fibre production for diverse industrial applications.

Abbreviations

ANI

Average nucleotide identity

CARD

The comprehensive antibiotic resistance database

COG

Culter of orthologue Groups

GBDP

Genome BLAST distance phylogeny

LJRB

Lignin jute retting bacteria

KEGG

Kyoto Encyclopedia of genes and genomes

RAST

Rapid annotations using subsystems technology

TYGS

Type strain genome server

Authors’ contributions

Shrestha Barai: Investigation, Formal analysis, Writing – original draft. Bijan Majumdar: Project administration, Resources, Funding acquisition, Writing – review & editing. Pranit Mukherjee: Investigation, Methodology, Visualisation, Formal analysis. Data curation, Writing – review & editing, Visualisation, and Formal analysis. Lipi Chattopadhyay: Investigation, Formal analysis, Writing – review & editing, Writing – original draft, Validation, Supervision, Software, Gouranga Kar: Resources, Project administration, Funding acquisition, Writing – review & editing. Subhojit Datta: Conceptualisation, Methodology, Formal analysis, Writing – original draft, review & editing.

Funding

No, this research did not receive any funding.

Data availability

Genome sequences of LJRB1, LJRB2, and LJRB3 are accessible at NCBI under the following accession nos.: LJRB1 (BioSample Accession SAMN43932786, SRA accession SRR30842638); LJRB2 (BioSample Accession SAMN43932787, SRA accession SRR30842637), and LJRB3 (BioSample Accession SAMN43932788, SRA accession SRR30842636).

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.