Whole-genome analysis and fermentation-metabolite profiling of a cellulolytic Arthrobacter sp. FMD isolated from forest musk-deer faeces

Whole-genome analysis and fermentation-metabolite profiling of a cellulolytic Arthrobacter sp. 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FMD isolated from forest musk-deer faeces Haodong Han¹, Ruiguang Gong¹, Zhuoya Jin¹, Lili Wang¹, Bing Zhang², and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7059040/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2026 Read the published version in BMC Microbiology → Version 1 posted 14 You are reading this latest preprint version Abstract Most studies on the intestinal microbiota of the forest musk deer (Moschus berezovskii) are based on community-level sequencing, and functional characterisation of individual strains is rare. Here we isolated strain FMD from deer faeces by selecting on CMC-Na as the sole carbon source; it utilises xylose, trehalose and fructose and is positive for urease and the methyl-red test. Whole-genome sequencing yielded a 4.05 Mb chromosome (GC 64.1%, four contigs, 3 726 CDS), and phylogenomic analyses (20 core genes, ANI and SNP tree) placed the isolate in the genus Arthrobacter ; the strain was designated Arthrobacter sp . FMD. The genome encodes abundant catabolic functions, including 128 CAZymes, 27 COG and 34 KEGG genes for carbohydrate metabolism, and 54 COG and 45 KEGG genes for amino-acid metabolism, while VFDB and PHI searches indicate low pathogenic potential. Fermentation of wheat bran with Arthrobacter sp . FMD increased carboxylic acids from 21.8–33.4% and decreased fatty acyls from 24.2–10.8%. Isoquercitrin, 2-oxindole-3-acetic acid and 5-hydroxyindole-3-acetic acid were the most up-regulated metabolites, whereas Leu-Trp, 6′-O-feruloyl catalpol and Cnidioside A were the most down-regulated. Isoleucylvaline, γ-glutamyl-methionine and N²-acetylornithine showed the highest increases among amino-acid derivatives, and isovaleric, valeric, 2-hydroxy-2-methylbutyric and 2-hydroxy-3-methylbutyric acids were the predominant organic-acid products. These findings suggest that Arthrobacter sp. FMD deploys a coordinated set of hydrolases and downstream catabolic enzymes that degrade lignocellulose-derived substrates, reduce anti-nutritional factors and enrich organic acids, nucleosides and flavonoids, highlighting its potential to improve feed utilisation and gut health in the forest musk deer. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The genus Arthrobacter is composed of Gram-positive, catalase-positive, aerobic, non- sp ore-forming bacteria that are ubiquitously distributed in soil, water, air, and animal hosts [ 1 – 3 ]. They are particularly abundant in soil, where they can degrade a wide range of harmful synthetic organic compounds—including aliphatic, aromatic, and polycyclic aromatic hydrocarbons [ 4 ]. Moreover, Arthrobacter sp ecies secrete a suite of industrially valuable enzymes, including amylases, β-fructofuranosidases, and various proteases, rendering them highly versatile for biotechnological applications [ 5 ]. Furthermore, several Arthrobacter sp . possess lignocellulolytic capabilities: for example, A. woluwensis [ 6 ] and A. nitroguajacolicus [ 7 ] efficiently degrade cellulose, while certain Arthrobacter sp . strains can decompose lignin [ 8 ]. Notably, this genus exhibits robust cellulolytic activity across a broad pH and temperature sp ectrum [ 9 ]. As a wild herbivorous ruminant, forest musk deer (Moschus berezovskii; FMD) has attracted wide sp read interest in its gut microbiota due to its pivotal role in host physiological functions. However, most studies to date have focused on microbiomics sequencing[ 10 , 11 ], while investigations employing pure-culture isolation remain relatively scarce. Given the sp ecialized digestive physiology of FMD and the critical importance of lignocellulose degradation to its survival, we employed carboxymethyl cellulose sodium (CMC-Na) as the sole carbon source to selectively screen and isolate cellulolytic microorganisms from FMD fecal samples. Fortunately, we isolated a strain of Arthrobacter sp ; in this study, we further investigate its genomic features and fermentation products to elucidate its role and function in the ruminal fermentation process of the forest musk deer. Methods The Screening of Cellulose-Degrading Strains Feces of FMD stored in the laboratory were diluted at a ratio of 1 g feces per 100 mL sterile PBS to prepare a 1% bacterial su sp ension. The su sp ension was shaken at 37°C and 180 rpm for 30 min and then allowed to stand for 5 min. The supernatant was serially diluted to 10⁻⁴, 10⁻⁵, and 10⁻⁶ with sterile water. Each serial dilution was sp read, in triplicate, onto CMC-Na primary-screening agar (1% CMC-Na, 1% (NH₄)₂SO₄, 0.5% KNO₃, 0.5% Na₂CO₃, 0.01% MgSO₄·7H₂O, 5 mg kg⁻¹ FeSO₄·7H₂O, 5 mg kg⁻¹ MnSO₄·H₂O and 2% agar) and incubated at 37°C for 7 days. Colonies di sp laying distinct morphologies were re-streaked onto Congo-red cellulose agar (0.10% NaNO₃, 0.12% Na₂HPO₄, 0.09% KH₂PO₄, 0.05% MgSO₄, 0.05% KCl, 0.05% yeast extract, 0.05% acid-hydrolysed casein, 0.02% Congo red, 0.50% cellulose powder and 1.50% agar, w/v) and incubated at 30°C for 48 h, after which the diameters of the clear halos surrounding the colonies were measured. Physiological and Biochemical Characterization The Gram reaction of Arthrobacter sp . FMD was determined using a Gram Staining Kit (Solarbio Life Sciences, Beijing, China) following the manufacturer’s instructions. Smears were prepared from fresh cultures, heat-fixed, and sequentially treated with crystal violet, iodine solution, decolorizer, and safranin. The results were observed under a light microscope to determine whether the strain was Gram-positive or Gram-negative. Biochemical characteristics were determined using commercial biochemical identification tubes (Hangzhou Microbial Co., Ltd., Hangzhou, China). Tests included carbohydrate utilization (e.g., xylose, sucrose, fructose, lactose, maltose, trehalose, sorbitol, xylitol, mannitol, raffinose), enzymatic activities (e.g., urease, nitrate reduction, phenylalanine deaminase), amino acid decarboxylation (lysine, ornithine, arginine), citrate utilization, motility, indole production, hydrogen sulfide production, and methyl red (MR) test. All assays were carried out according to the manufacturer’s instructions. Tubes were incubated at 37°C for 24–48 hours, and results were evaluated based on color changes, turbidity, or precipitate formation and interpreted using standard criteria. Cultivation and genome basic analysis Arthrobacter sp . FMD was cultured in beef-extract peptone broth composed of 0.30% beef extract, 1.00% peptone and 0.50% NaCl (w/v) at 37°C and 180 rpm for 48 h before genomic DNA extraction. Genomic DNA was extracted using the bacterial genomic DNA extraction kit from Kangwei Century (Beijing, China) following the manufacturer’s protocol. The integrity of the extracted DNA was assessed by 1% agarose gel electrophoresis, while DNA purity and concentration were evaluated using a Nanodrop sp ectrophotometer and Qubit fluorometer, re sp ectively, to determine whether the samples met the quality requirements for downstream sequencing. After purification, DNA was ligated with barcodes and sequencing adapters to construct the sequencing library, which was then loaded onto a flow cell and sequenced using the Oxford Nanopore Technologies (ONT) platform. To assess potential contamination in the sequencing data, quality-controlled reads were aligned to the NCBI database using Kraken2, and the top 10 most abundant taxa were identified to evaluate the presence of non-target DNA. Clean reads, following host contamination removal, were assembled de novo using Flye. The initial assemblies were subsequently polished using Medaka. A custom script was used to evaluate whether the genome was circular; if circularity was confirmed, overlapping terminal sequences were trimmed and the genome start position was adjusted based on the predicted origin of replication or start gene to generate the finalized genome sequence. To evaluate sequencing coverage, quality-filtered reads in FASTQ format were aligned to the assembled genome using Minimap2, and per-base sequencing depth was calculated using the depth function of Samtools. A sliding window of 1000 bp was applied to calculate average sequencing depth across the genome, and depth profiles were visualized. Genome structural annotation was performed using Bakta to identify coding sequences (CDSs), transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), and non-coding RNAs (ncRNAs). The genome sequence, CDS annotations, and non-coding RNA features were integrated into a standard GenBank (GBK) format file, and a circular genome map was generated using Circos software. From innermost to outermost, the tracks of the map represent genomic coordinates, GC skew, GC content, COG classifications of CDSs on both strands, and the genomic locations of CDSs, tRNAs, and rRNAs. phylogenetic analysis In this study, we employed the VBCG [ 12 ]tool to construct a phylogenetic tree based on 20 validated bacterial core genes (VBCG). First, gene prediction was performed on the whole-genome FASTA sequences using Prodigal , identifying potential open reading frames (ORFs). Gene annotation was then conducted using HMMER to infer gene functions. Protein sequences for the 20 core genes were aligned using Muscle , and low-quality alignment regions were trimmed using Gblock . Finally, the phylogenetic tree was reconstructed using FastTree based on maximum likelihood (ML). For visualization and further analysis of the results, the iTOL (Interactive Tree Of Life) tool was used, which supports the creation, annotation, and custom di sp lay of phylogenetic trees ( https://itol.embl.de/ ). Comparative genomic and pan-genomic analysis Average nucleotide identity (ANI) was calculated using the pyani [ 13 ] package with default settings. For pan-genome and phylogenetic analyses, Data were uploaded to the IPGA v1.09 web service ( https://nmdc.cn/ipga/ ), where the Roary [ 14 ] pipeline was used to define orthologous gene clusters and generate the pan-genome profile, and a whole-genome SNP-based phylogenetic tree was constructed on the same platform. Genome annotation and functional analysis Predicted protein-coding genes were functionally annotated via the Cluster of Orthologous Groups of proteins (COG, https://www.ncbi.nlm.nih.gov/COG/ ), the Carbohydrate-Active enZymes database (CAZy, http://www.cazy.org/ ), Gene Ontology (GO, http://geneontology.org ) and the Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp ). Secondary metabolite biosynthetic gene clusters were identified using antiSMASH ( https://antismash.secondarymetabolites.org/ ). Pathogen–host interaction genes were annotated based on PHI-base ( http://www.phi-base.org/ ), virulence factors were predicted with VFDB ( http://www.mgc.ac.cn/VFs/main.htm ), and antibiotic resistance genes were screened and classified using the CARD database ( https://card.mcmaster.ca/ ). Bran Fermentation and Differential Metabolite Analysis Arthrobacter sp. FMD was first cultured in 100 mL of LB liquid medium (containing 10 g/L tryptone, 5 g/L yeast extract, and 10 g/L NaCl) at 37°C and 180 rpm until reaching the logarithmic growth phase. Subsequently, 1% (v/v) of the bacterial su sp ension was inoculated into 100 mL of fermentation medium and incubated for 7 days. The fermentation medium had the following composition: 0.10% KH₂PO₄, 0.20% ammonium tartrate, 0.05% MgSO₄·7H₂O, 0.50% yeast extract, and 0.50% wheat bran (w/v). Trace-element solution was added to supply, per litre of medium, 0.001 g ZnSO₄·7H₂O, 0.0003 g MnCl₂·4H₂O, 0.0001 g CoCl₂·6H₂O, 0.0002 g NiCl₂·6H₂O, 0.0003 g Na₂MoO₄·2H₂O, 0.003 g H₃BO₃, and 0.001 g CuCl₂·2H₂O. The total volume was made up to 1000 mL with distilled water. A non-inoculated medium was used as the control group. Each treatment was conducted in triplicate. Statistical Analysis All data were expressed as mean ± standard error of the mean (SEM). Statistical analyses were performed using GraphPad Prism 10 (GraphPad Software Inc., San Diego, CA, USA). Comparisons between two groups were conducted using an unpaired Student’s t-test. A p -value < 0.05 was considered statistically significant. Result Physiological and Biochemical Characterization Arthrobacter sp . FMD tested positive for xylose, trehalose, fructose, urease, and the methyl red (MR) test. Negative results were observed for sucrose, mannose, lactose, xylitol, maltose, mannitol, dulcitol, sorbitol, raffinose, calendulol, gluconate, hydrogen sulfide production, nitrate reduction, indole production, arginine dihydrolase, citrate utilization, phenylalanine deaminase, lysine decarboxylase, ornithine decarboxylase, and motility. Characterization of the genome and phylogenetic analysis of strain Arthrobacter sp. FMD The total number of bases after quality control was 1,382,614,412 nt. The integrity of the initial genome sequence assemblies was evaluated, and the outcomes indicated a high-quality assembly, which is suitable for subsequent detailed analyses (Fig. S1 ). The whole-genome sequence of Arthrobacter sp. FMD was 4,050,930 bp with an average GC content of 64.12%. The reads were assembled into 4 contigs with an N50 of 3,882,188 bp (Fig. 1C). The total length of the predicted protein-coding genes (CDS) was 3,564,818 bp, and genome predicted 71 tRNA genes, 19 rRNA genes and 2 ncRNA genes (Fig. 1C). All the detailed parameters are shown in Table S1 . Arthrobacter sp. FMD had similar genomic GC content and genome size compared with other reported Arthrobacter sp. strains [ 15 ]. We retrieved the top five sp ecies with the highest protein-sequence similarity to Arthrobacter sp . FMD in the NCBI non-redundant (NR) protein database and supplemented them with 16 additional Arthrobacter genomes available at the complete-genome assembly level. Using the VBCG pipeline, we extracted 20 universally conserved bacterial core genes and constructed a phylogenetic tree (Fig. 1D). The analysis placed Arthrobacter sp . FMD in closest proximity to Arthrobacter sp . YC-RL1, indicating a high degree of genetic relatedness between the two strains. Comparative genomic and pan-genomic analysis From the ANIm percentage identity heatmap (Fig. 2 ), the red–orange block in the lower left indicates that the pairwise average nucleotide identities among Arthrobacter sp . YC-RL1, Arthrobacter sp . AG1021, and Arthrobacter sp . FMD all exceed 95%, confirming that they represent highly related strains. This finding is fully consistent with the phylogenetic tree, in which these three genomes cluster within the same clade. In order to di sp lay the pan-genome characteristics of 22 strains of Arthrobacter , the pan-genome characteristic curves were plotted based on the clustering results (Fig. 3 A and 3 B). The results showed that as the number of strains increased, the pan-genome showed a significant increasing trend, indicating that Arthrobacter had an open pan-genome. At the same time, as the number of strains increased, the core-genome significantly decreased. However, after the addition of six genomes, the downward trend plateaued. To study the genomic differences of the sp ecies Arthrobacter , we analyzed the distribution of core genes, non essential genes, and unique genes in each strain. The pan-genome profile with COG annotation indicates that the genomes of 22 Arthrobacter sp ecies exhibit similar GC content, with the exceptions of Arthrobacter sp . A3.2 and Arthrobacter polaris strain C1-1.1. Furthermore, compared to the other 20 Arthrobacter genomes, Arthrobacter globiformis NBRC 12137.2 and Arthrobacter crystallopoietes strain DSM 20117.1 have the largest genome size and highest gene number. At the COG level, the category "Cellular processes and signaling" contains the most shared genes, followed by "Metabolism" and "Cellular processes and signaling (Fig. 3 C). Further analysis using the UpSet plot reveals that Arthrobacter sp . FMD contains only 250 unique genes, with three strains sharing genes with it: 104 genes from Arthrobacter sp . AG1021 Ga0222388 101.1, 65 genes from Glutamicibacter protophormiae strain R912.1, and 39 genes from Arthrobacter sp . YC-RL1.1 (Fig. 3 D). The phylogenetic tree constructed based on SNP analysis exhibits similar results to those obtained from core gene-based tree construction and ANI analysis. The three strains with the closest genetic relationships were identified as Arthrobacter sp. AG1021 Ga0222388 101 1, Glutamicibacter soli strain NHPC − 3, and Glutamicibacter soli strain NHPC − 3. These findings provide additional insights into the genetic diversity among the 22 Arthrobacter strains(Fig. 3 E). Genome functional analysis results The 3,726 protein-coding genes of Arthrobacter sp . FMD were functionally classified by COG into 22 categories (Fig. 4 A), among which 27 genes fell into category Carbohydrate tran sp ort and metabolism;54 genes fell into category Amino acid tran sp ort and metabolism. The accompanying pie chart shows that dbCAN analysis identified 128 CAZyme-encoding genes, of which 45 are glycoside hydrolases (GHs) and 59 are glycosyltransferases (GTs) (Fig. 4 A). Gene Ontology (GO) annotation assigned 1,784 protein-coding genes of Arthrobacter sp . FMD to the three canonical GO domains (Fig. 4 B). Molecular function (MF) accounted for the largest share, with 871 genes, followed by biological process (BP) with 643 genes and cellular component (CC) with 270 genes. Within the CC domain, the most frequent terms were “cytoplasm,” “ribosome” and “cytosolic large ribosomal subunit”. MF assignments were dominated by “structural constituent of ribosome,” “ATP binding” and “DNA binding”. Consistent with these observations, the BP domain was enriched in “translation,” “DNA repair” and “peptidoglycan biosynthetic process. KEGG pathway annotation identified 534 genes, of which 398 mapped to the Level-1 category “Metabolism.” At the Level-2 resolution (Fig. 4 C), these genes were further distributed among “Amino acid metabolism” (45 genes), “Carbohydrate metabolism” (34 genes), and “Metabolism of other amino acids” (9 genes), whereas only five genes were assigned to “Biosynthesis of other secondary metabolites.” The analysis of pathogen-host interaction-related (PHI) genes showed that 1788 genes annotated in the PHI database were classified into eight categories, with “reduced virulence”(1052 genes) being the largest category, followed by “unaffected pathogenicity”(440 genes), “increased virulence (hypervirulence)”(132 genes), “loss of pathogenicity”(104 genes), “lethal”(24 genes), “effector (plant avirulence determinant)”( 24 genes), “chemistry target: resistance to chemical”( 7 genes) and “chemistry target: sensitivity to chemical”(4 genes ) (Fig. 5 A). Virulence factors are grouped into 14 categories according to their functions: regulation, antimicrobial activity/competitive advantage, post-translational modifications, stress survival, nutritional/metabolic factors, biofilms, immune modulation, exoenzymes, exotoxins, motility, effector delivery system, invasion, adhesion, and others. In this study, a total of 626 putative virulence genes were determined in strain Arthrobacter sp. FMD by DIAMOND analysis against the Virulence Factor Database (VFDB). The virulence factors with the highest number of annotated genes were nutritional/Metabolic factor, immune modulation, and regulation, with 230, 153, and 69 genes, re sp ectively (Fig. 5 B). A total of 314 antibiotic resistance genes of strain Arthrobacter sp . FMD were annotated in the CARD database, among which mutations in liaR (n = 19) and AbaF (n = 15) were most frequent, followed by ArlR (n = 8), fabG (n = 7), bcrA (n = 6), msbA (n = 5), and aadT (n = 4). Notably, liaR and fabG mutations confer resistance to daptomycin and triclosan, re sp ectively. Out of a total of 4 contigs of the bacterial genome, antiSMASH 8.0.1 predicted 6 BGCs on contig 1, with each region corre sp onding to a single BGC (Table 1 ). The remaining 3 contigs did not contain regions with potential secondary metabolites. Among the 6 BGCs predicted on contig 1 of the Glutamicibacter protophormiae Mb genome, 2 were annotated as terpenes, 1 as betalactone, 1 as NI-siderophore, 1 as T3PKS, and 1 as RiPP-like genes (Table 1 ). These BGCs shared homology with known secondary metabolites, including carotenoid (terpene, 68% similarity), SapB (betalactone, 41% similarity), desferrioxamine E (NI-siderophore, 72% similarity), 2-methoxy-5-methyl-6-(13-methyltetradecyl)-1,4-benzoquinone (T3PKS, 54% similarity), cattleyene (terpene-precursor, 37% similarity), and 4-formylaminooxyvinylglycine (RiPP-like, 25% similarity) (Table 1 ). While these BGCs represent potential secondary metabolites, the relatively low number of predicted gene clusters suggests that secondary metabolite production may not be a major feature of this strain's genome. It is possible that some of these genes serve other metabolic or regulatory functions, contributing to the overall physiological versatility of Arthrobacter sp. FMD. Table 1 Putative gene clusters of secondary metabolites of Arthrobacter sp. FMD using antiSMASH 8.0.1 Region Gene Type Sp an (nt) Most Similar BGCs Type Similarity a From To 1.1 terpene 323,685 348,028 carotenoid terpene 68% 1.2 betalactone 532,889 558,802 SapB ribosomal 41% 1.3 NI-siderophore 800,293 830,635 desferrioxamine E other 72% 1.4 T3PKS 2,433,016 2,474,143 2-methoxy-5-methyl-6-(13-methyltetradecyl)-1,4-benzoquinone, 2-methoxy-5-methyl-6-(13-methyltetradecyl)phenol PKS 54% 1.5 terpene-precursor 3,298,029 3,319,135 cattleyene terpene 37% 1.6 RiPP-like 3,491,688 3,503,148 4-formylaminooxyvinylglycine other 25% a The “similarity” is the percentage of homologous genes in the query and hit clusters. Bran Fermentation and Differential Metabolite Analysis The stacked bar chart illustrates the relative abundances of various metabolite super-classes before and after fermentation. Following fermentation, several metabolite super-classes showed changes in their relative abundances. Sp ecifically, carboxylic acids and derivatives increased from 21.82% before fermentation to 33.44% after fermentation. Purine nucleosides rose from 0.16–19.40%. In contrast, fatty acyls decreased from 24.18–10.81%, and organic sulfuric acids and derivatives declined from 2.85–1.52%. Other super-classes, such as steroids and steroid derivatives and pyridines and derivatives, showed minimal changes (Fig. 6 A). The volcano plot illustrates the differential metabolite abundances between conditions with the criteria of VIP > 1 and p-value < 0.05. A total of 1407 differential metabolites were identified, with 626 metabolites upregulated and 781 metabolites downregulated after fermentation. The horizontal dashed line represents the p-value = 0.05 threshold, with metabolites above the line having a p-value < 0.05 and indicating statistical significance. The vertical dashed lines corre sp ond to log₂(Fold Change) = ± 1, indicating a two-fold change in abundance. The plot shows metabolites with significant changes, with those further from the center having larger fold changes and higher statistical significance (Fig. 6 B). Bar charts di sp lay the top ten differential metabolites; upregulated amino acids, amino-acid derivatives, peptides; organic acids and organic-acid derivatives after fermentation. The top metabolites with the largest log₂(Fold Change) values include Isoquercitrin = 13.76, 2-oxindole-3-acetate = 12.89, and 5-Hydroxyindole-3-acetic acid = 12.89, while the top downregulated metabolites include Leu-Trp = -17.11, 6′-O-Feruloyl catalpol = -14.71, and Cnidioside A = -14.23 (Fig. 6 C). The top upregulated amino acids, amino-acid derivatives, and peptides include Isoleucylvaline, gamma-Glutamylmethionine, and N2-Acetylornithine (Fig. 6 D). The top upregulated organic acids and organic-acid derivatives include Isovaleric acid, Valeric acid, 2-Hydroxy-2-methylbutyric acid, and 2-Hydroxy-3-methylbutyric acid (Fig. 6 E). Discussion Due to the endangered status of FMD, few bacterial isolates have been recovered from its samples. In the present study, we isolated an Arthrobacter sp . strain from FMD feces capable of utilizing cellulose as its sole carbon source and systematically characterized its genomic functions and metabolic profile, thereby laying the groundwork for elucidating its ecological role in FMD ruminal fermentation. Members of the genus Arthrobacter have been isolated from a range of animal-derived samples: fecal material from cattle [ 16 ], Marmota himalayana [ 17 ] and Goeldi’s monkey [ 18 ]; bovine milk and uterine sp ecimens [ 19 ]; and equine reproductive tissues [ 20 ]. To our knowledge, this is the first report of a cultivable Arthrobacter from FMD fecal samples. Although Arthrobacter sp . FMD was able to grow on medium containing CMC-Na as the sole carbon source, no clearing halo was detected on Congo-red cellulose plates, presumably because the strain preferentially utilises the yeast-extract component or exhibits only weak, short-term cellulolytic activity. Average nucleotide identity (ANI) is a simple yet comprehensive metric of genomic relatedness, leveraging comparisons across thousands of conserved and lineage- sp ecific genes to achieve higher phylogenetic resolution than single-gene methods, while buffering against biases from rate heterogeneity or isolated horizontal gene transfers [ 21 ]. Single-nucleotide polymorphism (SNP)-based phylogenies sample thousands to millions of sites, yielding unmatched resolution even for near-identical strains. This genome-wide approach mitigates biases from selection or horizontal transfer of individual genes, avoids homoplasy seen with repeat-based markers, and bolsters branch support through abundant informative sites [ 22 ]. Both the phylogenetic reconstruction based on 20 concatenated core genes and ANI analysis identified Arthrobacter sp . YC-RL1.1 as the isolate’s closest relative, placing it within the genus Arthrobacter ; however, the whole-genome SNP-based phylogeny did not support YC-RL1.1 as the nearest taxon. Pan-genome analysis revealed that Arthrobacter sp . MFD exhibits gene content and genome size comparable to those of other Arthrobacter strains. It harbors a moderate number of strain‐ sp ecific genes, and among the 21 genomes surveyed, only three shared any gene clusters with Arthrobacter sp . MFD—and even then, the extent of gene sharing was minimal. The COG system assigns each orthologous group to one of 26 functional categories based on its cellular role [ 23 ]. In Arthrobacter sp . MFD, COG annotation placed 53 genes in “amino acid tran sp ort and metabolism,” while KEGG mapping identified 45 genes involved in amino acid biosynthesis and degradation—covering arginine, valine, leucine, isoleucine, and lysine pathways, among others. These findings point to extensive amino acid synthesis and metabolic capabilities in strain MFD. Moreover, COG assigned 27 genes to “carbohydrate tran sp ort and metabolism,” and CAZy annotation revealed 120 carbohydrate-active enzyme genes, highlighting a similarly rich arsenal for carbohydrate degradation and utilization. Studies have reported that members of the genus Arthrobacter generally exhibit low pathogenicity in animals [ 24 ]; however, they may possess potential pathogenicity toward plants. PHI-base annotation of Arthrobacter sp . MFD revealed several effector genes associated with anthocyanin biosynthesis, which were classified as plant avirulence determinants. These genes are linked to plant hosts including cabbage, radish, tobacco, and Arabidopsis thaliana, suggesting a possible plant-associated pathogenic capacity. However, virulence factor annotation using the VFDB database revealed that the strain harbors not only the plant toxin phytotoxin phaseolotoxin, but also multiple hemolysins, including alpha-hemolysin and beta-hemolysin/cytolysin, as well as cereulide, a toxin associated with food poisoning [ 25 ]. Using antiSMASH 8.0.1, six biosynthetic gene clusters (BGCs) were predicted, among which the NI-siderophore and terpene clusters showed relatively high similarity, with matching rates of 72% and 68%, re sp ectively. Siderophores are iron-chelating compounds produced by microorganisms under iron-limited conditions to facilitate iron uptake, with NI-siderophore representing a sp ecific type of cluster re sp onsible for the biosynthesis of nonribosomal iron-chelating molecules, and, in addition to iron, siderophores can also chelate other essential metal ions such as molybdenum, manganese, cobalt, and nickel [ 26 , 27 ]. In recent years, bacteria have been recognized as possessing the genetic potential to biosynthesize a wide array of complex terpenoids. Terpenoids represent the most structurally diverse and abundant class of natural products known to date, exhibiting remarkable and sp ecific bioactivities in various assays and in disease prevention or treatment [ 28 ]. Therefore, Arthrobacter sp . FMD may be capable of producing compounds beneficial to the host, thereby contributing to host health. To evaluate the lignocellulose utilization capability and metabolic products of Arthrobacter sp. FMD, we conducted a fermentation experiment using wheat bran as the substrate. After fermentation, the relative abundances of several metabolite super-classes changed significantly. Sp ecifically, the proportion of carboxylic acids and derivatives increased from 21.82% before fermentation to 33.44% after fermentation, while purine nucleosides rose markedly from 0.16–19.40%. In contrast, the relative abundance of fatty acyls decreased from 24.18–10.81%, and that of organic sulfuric acids and derivatives decreased from 2.85–1.52%. These results indicate that Arthrobacter sp . FMD can efficiently utilize various organic compounds in wheat bran and produces higher levels of organic acids and nucleoside metabolites during fermentation. Overall, the most strongly up-regulated metabolites were isoquercitrin, 2-oxindole-3-acetic acid, and 5-hydroxyindole-3-acetic acid, whereas Leu-Trp, 6′-O-feruloyl catalpol, and Cnidioside A showed the greatest down-regulation. Among amino acids, amino-acid derivatives, and peptides, Isoleucylvaline, γ-glutamylmethionine, and N²-acetylornithine exhibited the highest increases, while the most markedly elevated organic acids and their derivatives were isovaleric acid, valeric acid, 2-hydroxy-2-methylbutyric acid, and 2-hydroxy-3-methylbutyric acid. The metabolomic shifts observed after fermentation suggest that Arthrobacter sp . FMD expresses a coordinated suite of hydrolases and downstream catabolic enzymes that could prove advantageous in the animal gut. The sharp depletion of the dipeptide Leu-Trp, together with the concomitant enrichment of its branched-chain acid derivatives (isovaleric, valeric, 2-hydroxy-2-methylbutyric and 2-hydroxy-3-methylbutyric acids), points to an active dipeptidase/aminopeptidase system [ 29 , 30 ] followed by branched-chain aminotransferase and α-keto-acid dehydrogenase reactions [ 31 ] that funnel liberated amino acids into short-chain organic acids. Meanwhile, the pronounced decline of 6′-O-feruloyl catalpol, together with the concurrent rise in isoquercitrin and related flavonoids, suggests the activity of feruloyl esterases [ 32 ] that cleave phenolic-acid ester bonds. Moreover, the simultaneous decrease in both 6′-O-feruloyl catalpol and Cnidioside A indicates the presence of broad- sp ectrum β-glycosidases/α-L-rhamnosidases [ 33 , 34 ] capable of sequentially hydrolyzing the glycosyl moieties from flavonoid glycosides and triterpenoid saponins. In summary, Arthrobacter sp . FMD not only depolymerises lignocellulose-derived substrates during wheat-bran fermentation but also markedly reshapes the secondary-metabolite profile, depleting putative anti-nutritional compounds while enriching organic acids, nucleosides and flavonoids—changes that could improve feed utilisation and gut health. However, the present evidence remains correlative at the metabolite level: the activities of the key enzymes have yet to be measured directly, and the in-vivo effects of the newly generated metabolites still need to be verified. Declarations Data availability The whole-genome sequence of Arthrobacter sp. FMD has been deposited in the NCBI database (https://www.ncbi.nlm.nih.gov/) under Taxonomy ID 3447456. The corresponding metabolomics data are available in the OMIX database (https://ngdc.cncb.ac.cn/omix/) under accession number OMIX011123. Funding This work was supported by the Yijun County People’s Government, Shaanxi, China (grant no. K4050723310). Contributions H.H. conceived and designed the study, isolated Arthrobacter sp. FMD, carried out genome sequencing and annotation, performed the wheat-bran fermentation and metabolomics experiments, analysed all primary data and wrote the first draft of the manuscript. R.G. set up the fermentation system, prepared LC-MS samples and assisted in interpreting the metabolomics results. Z.J. conducted statistical analyses of the metabolomics data and prepared Figures 1–3. L.W. performed CAZyme prediction and phylogenomic reconstruction and reviewed the relevant manuscript sections. B.Z. collected field samples, carried out enzyme-activity assays and curated laboratory records. B.S. and Z.R. jointly supervised the project, obtained funding, verified data integrity and critically revised the manuscript; both serve as corresponding authors and accept overall responsibility for the work. All authors satisfy the BMC authorship criteria: each made a substantial contribution, approved the submitted (and any substantially modified) version and agrees to be personally accountable for their own contributions and for resolving any questions regarding the accuracy or integrity of any part of the work. Ethics declarations Ethics approval and consent to participate Ethics and Consent to Participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Busse H, Wieser M, Buczolits S. Arthrobacter. Bergey’s Manual of Systematics of Archaea and Bacteria. 2015;:1–70. Cacciari I, Lippi D. Arthrobacters: successful arid soil bacteria: a review. Arid Land Research and Management. 1987;1:1–30. Bazhanov DP, Yang K, Li H, Li C, Li J, Chen X, et al. Colonization of plant roots and enhanced atrazine degradation by a strain of Arthrobacter ureafaciens. Applied Microbiology and Biotechnology. 2017;101:6809–20. Busse H-J, Wieser M. The genus arthrobacter. In: The Prokaryotes. Springer; 2014. p. 105–32. Roy P, Kumar A. Arthrobacter. In: Beneficial microbes in agro-ecology. Elsevier; 2020. p. 3–11. Su R, Erdenedalai M, Dalai M, Batkhuu L, Chi C, Hasi S. Seasonal variation in gut microbiota composition: Comparative analysis of Siberian musk deer (Moschus moschiferus) and forest musk deer (Moschus berezovskii). 2020. Uwaremwe C, Li S, Chen X, Ngabire M, Shareef TME, Li J, et al. An Arthrobacter strain isolated from desert soils in the region of Shule River (China) can convert cellulose to potential biofuels. Sciences in Cold and Arid Regions. 2018;9:167–74. Jiang C, Cheng Y, Zang H, Chen X, Wang Y, Zhang Y, et al. Biodegradation of lignin and the associated degradation pathway by psychrotrophic Arthrobacter sp. C2 from the cold region of China. Cellulose. 2020;27:1423–40. Thakur V, Kumar V, Kumar S, Singh D. Diverse culturable bacterial communities with cellulolytic potential revealed from pristine habitat in Indian trans-Himalaya. Canadian Journal of Microbiology. 2018;64:798–808. JIANG F, Pengfei S, ZHANG J, Hongmei G, Haijing W, Zhenyuan C, et al. Comparative analysis of gut microbial composition and functions of forest musk deer in different breeding centres. Acta Theriologica Sinica. 2023;43:129. Su R, Erdenedalai M, Dalai M, Batkhuu L, Chi C, Hasi S. Seasonal variation in gut microbiota composition: Comparative analysis of Siberian musk deer (Moschus moschiferus) and forest musk deer (Moschus berezovskii). 2020. Tian R, Imanian B. VBCG: 20 validated bacterial core genes for phylogenomic analysis with high fidelity and resolution. Microbiome. 2023;11:247. Pritchard L, Cock P, Esen Ö. pyani v0. 2.8: average nucleotide identity (ANI) and related measures for whole genome comparisons. 2019. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MT, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31:3691–3. Ren L, Jia Y, Ruth N, Shi Y, Wang J, Qiao C, et al. Biotransformations of bisphenols mediated by a novel Arthrobacter sp. strain YC-RL1. Applied microbiology and biotechnology. 2016;100:1967–76. Kim M, Lee J-H, Kim E, Choi H, Kim Y, Lee J. Isolation of indole utilizing bacteria Arthrobacter sp. and Alcaligenes sp. from livestock waste. Indian journal of microbiology. 2016;56:158–66. Liu Y, Zhang G, Yang J, Cheng Y, Ye L, Lai X-H, et al. Arthrobacter caoxuetaonis sp. nov., Arthrobacter zhangbolii sp. nov. and Arthrobacter gengyunqii sp. nov., isolated from Marmota himalayana faeces from Qinghai-Tibet Plateau. International Journal of Systematic and Evolutionary Microbiology. 2023;73:005742. Macdonald C, Barden S, Foley S. Isolation and characterization of chitin‐degrading micro‐organisms from the faeces of Goeldi’s monkey, Callimico goeldii. Journal of applied microbiology. 2014;116:52–9. Storms V, Devriese LA, Coopman R, Schumann P, Vyncke F, Gillis M. Arthrobacter gandavensis sp. nov., for strains of veterinary origin. International journal of systematic and evolutionary microbiology. 2003;53:1881–4. Yassin A, Spröer C, Siering C, Hupfer H, Schumann P. Arthrobacter equi sp. nov., isolated from veterinary clinical material. International journal of systematic and evolutionary microbiology. 2011;61:2089–94. Arahal DR. Whole-genome analyses: average nucleotide identity. In: Methods in microbiology. Elsevier; 2014. p. 103–22. Filliol I, Motiwala AS, Cavatore M, Qi W, Hazbón MH, Bobadilla del Valle M, et al. Global phylogeny of Mycobacterium tuberculosis based on single nucleotide polymorphism (SNP) analysis: insights into tuberculosis evolution, phylogenetic accuracy of other DNA fingerprinting systems, and recommendations for a minimal standard SNP set. Journal of bacteriology. 2006;188:759–72. Galperin MY, Kristensen DM, Makarova KS, Wolf YI, Koonin EV. Microbial genome analysis: the COG approach. Briefings in bioinformatics. 2019;20:1063–70. Li S-Y, Kao C-C, Hu Y-C, Lai C-H, Jiang Y-P, Mao Y-C, et al. Arthrobacter woluwensis Bacteremia: A Clinical and Genomic Report. Pathogens. 2021;10. Meng J-N, Liu Y-J, Shen X, Wang J, Xu Z-K, Ding Y, et al. Detection of emetic Bacillus cereus and the emetic toxin cereulide in food matrices: Progress and perspectives. Trends in Food Science & Technology. 2022;123:322–33. Ahmed E, Holmström SJ. Siderophores in environmental research: roles and applications. Microbial biotechnology. 2014;7:196–208. Neilands J. Siderophores: structure and function of microbial iron transport compounds. Journal of Biological Chemistry. 1995;270:26723–6. Helfrich EJ, Lin G-M, Voigt CA, Clardy J. Bacterial terpene biosynthesis: challenges and opportunities for pathway engineering. Beilstein journal of organic chemistry. 2019;15:2889–906. Sanz Y. Aminopeptidases. Industrial enzymes: Structure, function and applications. 2007;:243–60. Taylor A. Aminopeptidases: structure and function. The FASEB journal. 1993;7:290–8. Archana, Gupta AK, Noumani A, Panday DK, Zaidi F, Sahu GK, et al. Gut microbiota derived short‐chain fatty acids in physiology and pathology: An update. Cell Biochemistry and Function. 2024;42:e4108. Samad KA, Zainol N, Yussof HW, Khushairi ZA, Mohd Sharif NSA, Mohd Syukri NS. Isolation, identification and characterization of soil bacteria for the production of ferulic acid through co-culture fermentation using banana stem waste. SN Applied Sciences. 2020;2:339. Mensitieri F, De Lise F, Strazzulli A, Moracci M, Notomista E, Cafaro V, et al. Structural and functional insights into RHA-P, a bacterial GH106 α-L-rhamnosidase from Novosphingobium sp. PP1Y. Archives of biochemistry and biophysics. 2018;648:1–11. Braune A, Blaut M. Bacterial species involved in the conversion of dietary flavonoids in the human gut. Gut microbes. 2016;7:216–34. Additional Declarations No competing interests reported. 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FMD in Congo-red cellulose agar (A); Gram Staining (B); In the circular genome map of \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD, the innermost ring marks the genomic scale, followed sequentially by rings for GC skew and GC content. The fourth and seventh rings di\u003cem\u003esp\u003c/em\u003elay the COG functional categories assigned to each CDS, while the fifth and sixth rings chart the positions of all CDS, tRNA, and rRNA genes across the genome (C); A maximum likelihood phylogenetic tree illustrating the evolutionary relationship between \u003cem\u003eArthrobacter sp. \u003c/em\u003eFMD and other \u003cem\u003eArthrobacter\u003c/em\u003e \u003cem\u003esp\u003c/em\u003eecies.Support values at each branch node represent confidence levels through 100 rounds of bootstrapping (D).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7059040/v1/daa8045f0bd531ef3f7b8279.png"},{"id":88602887,"identity":"752e3f78-b93e-43fd-80b9-3ba8d1a7b9ea","added_by":"auto","created_at":"2025-08-08 08:10:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":460646,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of ANI pairings between any two genomes strains downloaded from NCBI database.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7059040/v1/25226b88fce25cab5e0c3aae.png"},{"id":88602895,"identity":"f8ebbcfe-fcda-49a9-8414-c84058cf8d8e","added_by":"auto","created_at":"2025-08-08 08:10:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":665817,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of pan-genome between \u003cem\u003eArthrobacter\u003c/em\u003e \u003cem\u003esp\u003c/em\u003e. FMD and other 21 \u003cem\u003eArthrobacter\u003c/em\u003e strains. Curve plot of pan-genome profile (A); Curve plot of pan-genome profile (B); Pan-genome profile with COG annotation (C); Upset plot of pan-genome profile (D); whole genome SNP based phylogentic analysis (E)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7059040/v1/99856288df32d976902b0124.png"},{"id":88602892,"identity":"ad090152-ca8d-478b-8b19-97064b89edf4","added_by":"auto","created_at":"2025-08-08 08:10:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":13451702,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical legend of gene annotation classification of \u003cem\u003eArthrobacter sp. \u003c/em\u003eFMD. COG function classification (A); GO function classification (B); Histogram of KEGG (C).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7059040/v1/f8e124aa30fd775519f397c9.png"},{"id":88602885,"identity":"6570d4af-0c3d-4f37-9316-eab31ef3e226","added_by":"auto","created_at":"2025-08-08 08:10:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":10821263,"visible":true,"origin":"","legend":"\u003cp\u003ePHI function classification (A); VFDB function classification (B).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7059040/v1/577ae2d490f3284a6b7fcfe1.png"},{"id":88602884,"identity":"530e86d5-42e9-438b-be31-0edecfd30b3b","added_by":"auto","created_at":"2025-08-08 08:10:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":695448,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical legend of differential metabolite profiling before and after fermentation. Stacked bar chart of metabolite super-class abundances (A); volcano plot of MS/MS-annotated differential metabolites (B); diverging bar chart of Level-2 (class) differential metabolites (C); bar chart of the ten most up-regulated amino acids, amino-acid derivatives, and peptides (D); bar chart of the ten most up-regulated organic acids and organic-acid derivatives (E).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7059040/v1/f07b27cf6937fc5b963fa088.png"},{"id":100614662,"identity":"d5c2149d-6065-4bbc-b1b1-f4ae54c549f8","added_by":"auto","created_at":"2026-01-19 17:22:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27675786,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7059040/v1/5239fdd8-e4a0-47b7-b965-717df46c2d42.pdf"},{"id":88602877,"identity":"9161cc50-ccdc-41e6-9ecd-031aa4bd6040","added_by":"auto","created_at":"2025-08-08 08:10:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7031452,"visible":true,"origin":"","legend":"","description":"","filename":"supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7059040/v1/d356a93b2db5f36cae58fa3f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Whole-genome analysis and fermentation-metabolite profiling of a cellulolytic Arthrobacter sp. FMD isolated from forest musk-deer faeces","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe genus \u003cem\u003eArthrobacter\u003c/em\u003e is composed of Gram-positive, catalase-positive, aerobic, non-\u003cem\u003esp\u003c/em\u003eore-forming bacteria that are ubiquitously distributed in soil, water, air, and animal hosts [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. They are particularly abundant in soil, where they can degrade a wide range of harmful synthetic organic compounds—including aliphatic, aromatic, and polycyclic aromatic hydrocarbons [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, \u003cem\u003eArthrobacter sp\u003c/em\u003eecies secrete a suite of industrially valuable enzymes, including amylases, β-fructofuranosidases, and various proteases, rendering them highly versatile for biotechnological applications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, several \u003cem\u003eArthrobacter sp\u003c/em\u003e. possess lignocellulolytic capabilities: for example, \u003cem\u003eA. woluwensis\u003c/em\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and \u003cem\u003eA. nitroguajacolicus\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] efficiently degrade cellulose, while certain \u003cem\u003eArthrobacter sp\u003c/em\u003e. strains can decompose lignin [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Notably, this genus exhibits robust cellulolytic activity across a broad pH and temperature \u003cem\u003esp\u003c/em\u003eectrum [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs a wild herbivorous ruminant, forest musk deer (Moschus berezovskii; FMD) has attracted wide\u003cem\u003esp\u003c/em\u003eread interest in its gut microbiota due to its pivotal role in host physiological functions. However, most studies to date have focused on microbiomics sequencing[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while investigations employing pure-culture isolation remain relatively scarce. Given the \u003cem\u003esp\u003c/em\u003eecialized digestive physiology of FMD and the critical importance of lignocellulose degradation to its survival, we employed carboxymethyl cellulose sodium (CMC-Na) as the sole carbon source to selectively screen and isolate cellulolytic microorganisms from FMD fecal samples. Fortunately, we isolated a strain of \u003cem\u003eArthrobacter sp\u003c/em\u003e; in this study, we further investigate its genomic features and fermentation products to elucidate its role and function in the ruminal fermentation process of the forest musk deer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eThe Screening of Cellulose-Degrading Strains\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFeces of FMD stored in the laboratory were diluted at a ratio of 1 g feces per 100 mL sterile PBS to prepare a 1% bacterial su\u003cem\u003esp\u003c/em\u003eension. The su\u003cem\u003esp\u003c/em\u003eension was shaken at 37°C and 180 rpm for 30 min and then allowed to stand for 5 min. The supernatant was serially diluted to 10⁻⁴, 10⁻⁵, and 10⁻⁶ with sterile water. Each serial dilution was \u003cem\u003esp\u003c/em\u003eread, in triplicate, onto CMC-Na primary-screening agar (1% CMC-Na, 1% (NH₄)₂SO₄, 0.5% KNO₃, 0.5% Na₂CO₃, 0.01% MgSO₄·7H₂O, 5 mg kg⁻¹ FeSO₄·7H₂O, 5 mg kg⁻¹ MnSO₄·H₂O and 2% agar) and incubated at 37°C for 7 days. Colonies di\u003cem\u003esp\u003c/em\u003elaying distinct morphologies were re-streaked onto Congo-red cellulose agar (0.10% NaNO₃, 0.12% Na₂HPO₄, 0.09% KH₂PO₄, 0.05% MgSO₄, 0.05% KCl, 0.05% yeast extract, 0.05% acid-hydrolysed casein, 0.02% Congo red, 0.50% cellulose powder and 1.50% agar, w/v) and incubated at 30°C for 48 h, after which the diameters of the clear halos surrounding the colonies were measured.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhysiological and Biochemical Characterization\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Gram reaction of \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD was determined using a Gram Staining Kit (Solarbio Life Sciences, Beijing, China) following the manufacturer’s instructions. Smears were prepared from fresh cultures, heat-fixed, and sequentially treated with crystal violet, iodine solution, decolorizer, and safranin. The results were observed under a light microscope to determine whether the strain was Gram-positive or Gram-negative.\u003c/p\u003e\u003cp\u003eBiochemical characteristics were determined using commercial biochemical identification tubes (Hangzhou Microbial Co., Ltd., Hangzhou, China). Tests included carbohydrate utilization (e.g., xylose, sucrose, fructose, lactose, maltose, trehalose, sorbitol, xylitol, mannitol, raffinose), enzymatic activities (e.g., urease, nitrate reduction, phenylalanine deaminase), amino acid decarboxylation (lysine, ornithine, arginine), citrate utilization, motility, indole production, hydrogen sulfide production, and methyl red (MR) test. All assays were carried out according to the manufacturer’s instructions. Tubes were incubated at 37°C for 24–48 hours, and results were evaluated based on color changes, turbidity, or precipitate formation and interpreted using standard criteria.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCultivation and genome basic analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD was cultured in beef-extract peptone broth composed of 0.30% beef extract, 1.00% peptone and 0.50% NaCl (w/v) at 37°C and 180 rpm for 48 h before genomic DNA extraction. Genomic DNA was extracted using the bacterial genomic DNA extraction kit from Kangwei Century (Beijing, China) following the manufacturer’s protocol. The integrity of the extracted DNA was assessed by 1% agarose gel electrophoresis, while DNA purity and concentration were evaluated using a Nanodrop \u003cem\u003esp\u003c/em\u003eectrophotometer and Qubit fluorometer, re\u003cem\u003esp\u003c/em\u003eectively, to determine whether the samples met the quality requirements for downstream sequencing. After purification, DNA was ligated with barcodes and sequencing adapters to construct the sequencing library, which was then loaded onto a flow cell and sequenced using the Oxford Nanopore Technologies (ONT) platform.\u003c/p\u003e\u003cp\u003eTo assess potential contamination in the sequencing data, quality-controlled reads were aligned to the NCBI database using Kraken2, and the top 10 most abundant taxa were identified to evaluate the presence of non-target DNA. Clean reads, following host contamination removal, were assembled de novo using Flye. The initial assemblies were subsequently polished using Medaka. A custom script was used to evaluate whether the genome was circular; if circularity was confirmed, overlapping terminal sequences were trimmed and the genome start position was adjusted based on the predicted origin of replication or start gene to generate the finalized genome sequence.\u003c/p\u003e\u003cp\u003eTo evaluate sequencing coverage, quality-filtered reads in FASTQ format were aligned to the assembled genome using Minimap2, and per-base sequencing depth was calculated using the depth function of Samtools. A sliding window of 1000 bp was applied to calculate average sequencing depth across the genome, and depth profiles were visualized. Genome structural annotation was performed using Bakta to identify coding sequences (CDSs), transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), and non-coding RNAs (ncRNAs). The genome sequence, CDS annotations, and non-coding RNA features were integrated into a standard GenBank (GBK) format file, and a circular genome map was generated using Circos software. From innermost to outermost, the tracks of the map represent genomic coordinates, GC skew, GC content, COG classifications of CDSs on both strands, and the genomic locations of CDSs, tRNAs, and rRNAs.\u003c/p\u003e\u003cp\u003e\u003cb\u003ephylogenetic analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, we employed the \u003cem\u003eVBCG\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]tool to construct a phylogenetic tree based on 20 validated bacterial core genes (VBCG). First, gene prediction was performed on the whole-genome FASTA sequences using \u003cem\u003eProdigal\u003c/em\u003e, identifying potential open reading frames (ORFs). Gene annotation was then conducted using \u003cem\u003eHMMER\u003c/em\u003e to infer gene functions. Protein sequences for the 20 core genes were aligned using \u003cem\u003eMuscle\u003c/em\u003e, and low-quality alignment regions were trimmed using \u003cem\u003eGblock\u003c/em\u003e. Finally, the phylogenetic tree was reconstructed using \u003cem\u003eFastTree\u003c/em\u003e based on maximum likelihood (ML). For visualization and further analysis of the results, the \u003cem\u003eiTOL\u003c/em\u003e (Interactive Tree Of Life) tool was used, which supports the creation, annotation, and custom di\u003cem\u003esp\u003c/em\u003elay of phylogenetic trees ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://itol.embl.de/\u003c/span\u003e\u003cspan address=\"https://itol.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparative genomic and pan-genomic analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAverage nucleotide identity (ANI) was calculated using the pyani [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] package with default settings. For pan-genome and phylogenetic analyses, Data were uploaded to the IPGA v1.09 web service (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nmdc.cn/ipga/\u003c/span\u003e\u003cspan address=\"https://nmdc.cn/ipga/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), where the Roary [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] pipeline was used to define orthologous gene clusters and generate the pan-genome profile, and a whole-genome SNP-based phylogenetic tree was constructed on the same platform.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenome annotation and functional analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePredicted protein-coding genes were functionally annotated via the Cluster of Orthologous Groups of proteins (COG, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/COG/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/COG/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Carbohydrate-Active enZymes database (CAZy, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cazy.org/\u003c/span\u003e\u003cspan address=\"http://www.cazy.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Gene Ontology (GO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geneontology.org\u003c/span\u003e\u003cspan address=\"http://geneontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Kyoto Encyclopedia of Genes and Genomes (KEGG, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kegg.jp\u003c/span\u003e\u003cspan address=\"http://www.kegg.jp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Secondary metabolite biosynthetic gene clusters were identified using antiSMASH (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://antismash.secondarymetabolites.org/\u003c/span\u003e\u003cspan address=\"https://antismash.secondarymetabolites.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pathogen–host interaction genes were annotated based on PHI-base (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phi-base.org/\u003c/span\u003e\u003cspan address=\"http://www.phi-base.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), virulence factors were predicted with VFDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mgc.ac.cn/VFs/main.htm\u003c/span\u003e\u003cspan address=\"http://www.mgc.ac.cn/VFs/main.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and antibiotic resistance genes were screened and classified using the CARD database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://card.mcmaster.ca/\u003c/span\u003e\u003cspan address=\"https://card.mcmaster.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eBran Fermentation and Differential Metabolite Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eArthrobacter sp.\u003c/em\u003e FMD was first cultured in 100 mL of LB liquid medium (containing 10 g/L tryptone, 5 g/L yeast extract, and 10 g/L NaCl) at 37°C and 180 rpm until reaching the logarithmic growth phase. Subsequently, 1% (v/v) of the bacterial su\u003cem\u003esp\u003c/em\u003eension was inoculated into 100 mL of fermentation medium and incubated for 7 days. The fermentation medium had the following composition: 0.10% KH₂PO₄, 0.20% ammonium tartrate, 0.05% MgSO₄·7H₂O, 0.50% yeast extract, and 0.50% wheat bran (w/v). Trace-element solution was added to supply, per litre of medium, 0.001 g ZnSO₄·7H₂O, 0.0003 g MnCl₂·4H₂O, 0.0001 g CoCl₂·6H₂O, 0.0002 g NiCl₂·6H₂O, 0.0003 g Na₂MoO₄·2H₂O, 0.003 g H₃BO₃, and 0.001 g CuCl₂·2H₂O. The total volume was made up to 1000 mL with distilled water. A non-inoculated medium was used as the control group. Each treatment was conducted in triplicate.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll data were expressed as mean ± standard error of the mean (SEM). Statistical analyses were performed using GraphPad Prism 10 (GraphPad Software Inc., San Diego, CA, USA). Comparisons between two groups were conducted using an unpaired Student’s t-test. A \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cb\u003ePhysiological and Biochemical Characterization\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD tested positive for xylose, trehalose, fructose, urease, and the methyl red (MR) test. Negative results were observed for sucrose, mannose, lactose, xylitol, maltose, mannitol, dulcitol, sorbitol, raffinose, calendulol, gluconate, hydrogen sulfide production, nitrate reduction, indole production, arginine dihydrolase, citrate utilization, phenylalanine deaminase, lysine decarboxylase, ornithine decarboxylase, and motility.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharacterization of the genome and phylogenetic analysis of strain\u003c/b\u003e \u003cb\u003eArthrobacter sp.\u003c/b\u003e \u003cb\u003eFMD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe total number of bases after quality control was 1,382,614,412 nt. The integrity of the initial genome sequence assemblies was evaluated, and the outcomes indicated a high-quality assembly, which is suitable for subsequent detailed analyses (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The whole-genome sequence of \u003cem\u003eArthrobacter sp. FMD\u003c/em\u003e was 4,050,930 bp with an average GC content of 64.12%. The reads were assembled into 4 contigs with an N50 of 3,882,188 bp (Fig.\u0026nbsp;1C). The total length of the predicted protein-coding genes (CDS) was 3,564,818 bp, and genome predicted 71 tRNA genes, 19 rRNA genes and 2 ncRNA genes (Fig.\u0026nbsp;1C). All the detailed parameters are shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. \u003cem\u003eArthrobacter sp. FMD\u003c/em\u003e had similar genomic GC content and genome size compared with other reported \u003cem\u003eArthrobacter sp.\u003c/em\u003e strains [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe retrieved the top five \u003cem\u003esp\u003c/em\u003eecies with the highest protein-sequence similarity to \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD in the NCBI non-redundant (NR) protein database and supplemented them with 16 additional \u003cem\u003eArthrobacter\u003c/em\u003e genomes available at the complete-genome assembly level. Using the VBCG pipeline, we extracted 20 universally conserved bacterial core genes and constructed a phylogenetic tree (Fig.\u0026nbsp;1D). The analysis placed \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD in closest proximity to \u003cem\u003eArthrobacter sp\u003c/em\u003e. YC-RL1, indicating a high degree of genetic relatedness between the two strains. \u003c/p\u003e\u003cp\u003e\u003cb\u003eComparative genomic and pan-genomic analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFrom the ANIm percentage identity heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the red–orange block in the lower left indicates that the pairwise average nucleotide identities among \u003cem\u003eArthrobacter sp\u003c/em\u003e. YC-RL1, \u003cem\u003eArthrobacter sp\u003c/em\u003e. AG1021, and \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD all exceed 95%, confirming that they represent highly related strains. This finding is fully consistent with the phylogenetic tree, in which these three genomes cluster within the same clade.\u003c/p\u003e\u003cp\u003eIn order to di\u003cem\u003esp\u003c/em\u003elay the pan-genome characteristics of 22 strains of \u003cem\u003eArthrobacter\u003c/em\u003e, the pan-genome characteristic curves were plotted based on the clustering results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The results showed that as the number of strains increased, the pan-genome showed a significant increasing trend, indicating that \u003cem\u003eArthrobacter\u003c/em\u003e had an open pan-genome. At the same time, as the number of strains increased, the core-genome significantly decreased. However, after the addition of six genomes, the downward trend plateaued. To study the genomic differences of the \u003cem\u003esp\u003c/em\u003eecies \u003cem\u003eArthrobacter\u003c/em\u003e, we analyzed the distribution of core genes, non essential genes, and unique genes in each strain.\u003c/p\u003e\u003cp\u003eThe pan-genome profile with COG annotation indicates that the genomes of 22 \u003cem\u003eArthrobacter sp\u003c/em\u003eecies exhibit similar GC content, with the exceptions of \u003cem\u003eArthrobacter sp\u003c/em\u003e. A3.2 and \u003cem\u003eArthrobacter polaris\u003c/em\u003e strain C1-1.1. Furthermore, compared to the other 20 \u003cem\u003eArthrobacter\u003c/em\u003e genomes, \u003cem\u003eArthrobacter globiformis\u003c/em\u003e NBRC 12137.2 and \u003cem\u003eArthrobacter crystallopoietes\u003c/em\u003e strain DSM 20117.1 have the largest genome size and highest gene number. At the COG level, the category \"Cellular processes and signaling\" contains the most shared genes, followed by \"Metabolism\" and \"Cellular processes and signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Further analysis using the UpSet plot reveals that \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD contains only 250 unique genes, with three strains sharing genes with it: 104 genes from \u003cem\u003eArthrobacter sp\u003c/em\u003e. AG1021 Ga0222388 101.1, 65 genes from \u003cem\u003eGlutamicibacter protophormiae\u003c/em\u003e strain R912.1, and 39 genes from \u003cem\u003eArthrobacter sp\u003c/em\u003e. YC-RL1.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The phylogenetic tree constructed based on SNP analysis exhibits similar results to those obtained from core gene-based tree construction and ANI analysis. The three strains with the closest genetic relationships were identified as \u003cem\u003eArthrobacter sp.\u003c/em\u003e AG1021 Ga0222388 101 1, \u003cem\u003eGlutamicibacter soli\u003c/em\u003e strain NHPC − 3, and \u003cem\u003eGlutamicibacter soli\u003c/em\u003e strain NHPC − 3. These findings provide additional insights into the genetic diversity among the 22 \u003cem\u003eArthrobacter\u003c/em\u003e strains(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenome functional analysis results\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 3,726 protein-coding genes of \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD were functionally classified by COG into 22 categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), among which 27 genes fell into category Carbohydrate tran\u003cem\u003esp\u003c/em\u003eort and metabolism;54 genes fell into category Amino acid tran\u003cem\u003esp\u003c/em\u003eort and metabolism. The accompanying pie chart shows that dbCAN analysis identified 128 CAZyme-encoding genes, of which 45 are glycoside hydrolases (GHs) and 59 are glycosyltransferases (GTs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eGene Ontology (GO) annotation assigned 1,784 protein-coding genes of \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD to the three canonical GO domains (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Molecular function (MF) accounted for the largest share, with 871 genes, followed by biological process (BP) with 643 genes and cellular component (CC) with 270 genes. Within the CC domain, the most frequent terms were “cytoplasm,” “ribosome” and “cytosolic large ribosomal subunit”. MF assignments were dominated by “structural constituent of ribosome,” “ATP binding” and “DNA binding”. Consistent with these observations, the BP domain was enriched in “translation,” “DNA repair” and “peptidoglycan biosynthetic process. KEGG pathway annotation identified 534 genes, of which 398 mapped to the Level-1 category “Metabolism.” At the Level-2 resolution (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), these genes were further distributed among “Amino acid metabolism” (45 genes), “Carbohydrate metabolism” (34 genes), and “Metabolism of other amino acids” (9 genes), whereas only five genes were assigned to “Biosynthesis of other secondary metabolites.”\u003c/p\u003e\u003cp\u003eThe analysis of pathogen-host interaction-related (PHI) genes showed that 1788 genes annotated in the PHI database were classified into eight categories, with “reduced virulence”(1052 genes) being the largest category, followed by “unaffected pathogenicity”(440 genes), “increased virulence (hypervirulence)”(132 genes), “loss of pathogenicity”(104 genes), “lethal”(24 genes), “effector (plant avirulence determinant)”( 24 genes), “chemistry target: resistance to chemical”( 7 genes) and “chemistry target: sensitivity to chemical”(4 genes ) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eVirulence factors are grouped into 14 categories according to their functions: regulation, antimicrobial activity/competitive advantage, post-translational modifications, stress survival, nutritional/metabolic factors, biofilms, immune modulation, exoenzymes, exotoxins, motility, effector delivery system, invasion, adhesion, and others. In this study, a total of 626 putative virulence genes were determined in strain \u003cem\u003eArthrobacter sp.\u003c/em\u003e FMD by DIAMOND analysis against the Virulence Factor Database (VFDB). The virulence factors with the highest number of annotated genes were nutritional/Metabolic factor, immune modulation, and regulation, with 230, 153, and 69 genes, re\u003cem\u003esp\u003c/em\u003eectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eA total of 314 antibiotic resistance genes of strain \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD were annotated in the CARD database, among which mutations in liaR (n = 19) and AbaF (n = 15) were most frequent, followed by ArlR (n = 8), fabG (n = 7), bcrA (n = 6), msbA (n = 5), and aadT (n = 4). Notably, liaR and fabG mutations confer resistance to daptomycin and triclosan, re\u003cem\u003esp\u003c/em\u003eectively.\u003c/p\u003e\u003cp\u003eOut of a total of 4 contigs of the bacterial genome, antiSMASH 8.0.1 predicted 6 BGCs on contig 1, with each region corre\u003cem\u003esp\u003c/em\u003eonding to a single BGC (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The remaining 3 contigs did not contain regions with potential secondary metabolites. Among the 6 BGCs predicted on contig 1 of the Glutamicibacter protophormiae Mb genome, 2 were annotated as terpenes, 1 as betalactone, 1 as NI-siderophore, 1 as T3PKS, and 1 as RiPP-like genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These BGCs shared homology with known secondary metabolites, including carotenoid (terpene, 68% similarity), SapB (betalactone, 41% similarity), desferrioxamine E (NI-siderophore, 72% similarity), 2-methoxy-5-methyl-6-(13-methyltetradecyl)-1,4-benzoquinone (T3PKS, 54% similarity), cattleyene (terpene-precursor, 37% similarity), and 4-formylaminooxyvinylglycine (RiPP-like, 25% similarity) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). While these BGCs represent potential secondary metabolites, the relatively low number of predicted gene clusters suggests that secondary metabolite production may not be a major feature of this strain's genome. It is possible that some of these genes serve other metabolic or regulatory functions, contributing to the overall physiological versatility of \u003cem\u003eArthrobacter sp.\u003c/em\u003e FMD.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePutative gene clusters of secondary metabolites of \u003cem\u003eArthrobacter sp.\u003c/em\u003e FMD using antiSMASH 8.0.1\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGene Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSp\u003c/em\u003ean (nt)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMost Similar BGCs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSimilarity\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTo\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eterpene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e323,685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e348,028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ecarotenoid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eterpene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebetalactone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e532,889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e558,802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSapB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eribosomal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e41%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNI-siderophore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e800,293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e830,635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edesferrioxamine E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eother\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3PKS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,433,016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,474,143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2-methoxy-5-methyl-6-(13-methyltetradecyl)-1,4-benzoquinone, 2-methoxy-5-methyl-6-(13-methyltetradecyl)phenol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePKS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eterpene-precursor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,298,029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,319,135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ecattleyene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eterpene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e37%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRiPP-like\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,491,688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,503,148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4-formylaminooxyvinylglycine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eother\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eThe “similarity” is the percentage of homologous genes in the query and hit clusters.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBran Fermentation and Differential Metabolite Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe stacked bar chart illustrates the relative abundances of various metabolite super-classes before and after fermentation. Following fermentation, several metabolite super-classes showed changes in their relative abundances. \u003cem\u003eSp\u003c/em\u003eecifically, carboxylic acids and derivatives increased from 21.82% before fermentation to 33.44% after fermentation. Purine nucleosides rose from 0.16–19.40%. In contrast, fatty acyls decreased from 24.18–10.81%, and organic sulfuric acids and derivatives declined from 2.85–1.52%. Other super-classes, such as steroids and steroid derivatives and pyridines and derivatives, showed minimal changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The volcano plot illustrates the differential metabolite abundances between conditions with the criteria of VIP \u0026gt; 1 and p-value \u0026lt; 0.05. A total of 1407 differential metabolites were identified, with 626 metabolites upregulated and 781 metabolites downregulated after fermentation. The horizontal dashed line represents the p-value = 0.05 threshold, with metabolites above the line having a p-value \u0026lt; 0.05 and indicating statistical significance. The vertical dashed lines corre\u003cem\u003esp\u003c/em\u003eond to log₂(Fold Change) = ± 1, indicating a two-fold change in abundance. The plot shows metabolites with significant changes, with those further from the center having larger fold changes and higher statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eBar charts di\u003cem\u003esp\u003c/em\u003elay the top ten differential metabolites; upregulated amino acids, amino-acid derivatives, peptides; organic acids and organic-acid derivatives after fermentation. The top metabolites with the largest log₂(Fold Change) values include Isoquercitrin = 13.76, 2-oxindole-3-acetate = 12.89, and 5-Hydroxyindole-3-acetic acid = 12.89, while the top downregulated metabolites include Leu-Trp = -17.11, 6′-O-Feruloyl catalpol = -14.71, and Cnidioside A = -14.23 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The top upregulated amino acids, amino-acid derivatives, and peptides include Isoleucylvaline, gamma-Glutamylmethionine, and N2-Acetylornithine (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The top upregulated organic acids and organic-acid derivatives include Isovaleric acid, Valeric acid, 2-Hydroxy-2-methylbutyric acid, and 2-Hydroxy-3-methylbutyric acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDue to the endangered status of FMD, few bacterial isolates have been recovered from its samples. In the present study, we isolated an \u003cem\u003eArthrobacter sp\u003c/em\u003e. strain from FMD feces capable of utilizing cellulose as its sole carbon source and systematically characterized its genomic functions and metabolic profile, thereby laying the groundwork for elucidating its ecological role in FMD ruminal fermentation.\u003c/p\u003e\u003cp\u003eMembers of the genus \u003cem\u003eArthrobacter\u003c/em\u003e have been isolated from a range of animal-derived samples: fecal material from cattle [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], Marmota himalayana [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Goeldi\u0026rsquo;s monkey [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]; bovine milk and uterine \u003cem\u003esp\u003c/em\u003eecimens [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; and equine reproductive tissues [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To our knowledge, this is the first report of a cultivable \u003cem\u003eArthrobacter\u003c/em\u003e from FMD fecal samples. Although \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD was able to grow on medium containing CMC-Na as the sole carbon source, no clearing halo was detected on Congo-red cellulose plates, presumably because the strain preferentially utilises the yeast-extract component or exhibits only weak, short-term cellulolytic activity.\u003c/p\u003e\u003cp\u003eAverage nucleotide identity (ANI) is a simple yet comprehensive metric of genomic relatedness, leveraging comparisons across thousands of conserved and lineage-\u003cem\u003esp\u003c/em\u003eecific genes to achieve higher phylogenetic resolution than single-gene methods, while buffering against biases from rate heterogeneity or isolated horizontal gene transfers [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Single-nucleotide polymorphism (SNP)-based phylogenies sample thousands to millions of sites, yielding unmatched resolution even for near-identical strains. This genome-wide approach mitigates biases from selection or horizontal transfer of individual genes, avoids homoplasy seen with repeat-based markers, and bolsters branch support through abundant informative sites [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Both the phylogenetic reconstruction based on 20 concatenated core genes and ANI analysis identified \u003cem\u003eArthrobacter sp\u003c/em\u003e. YC-RL1.1 as the isolate\u0026rsquo;s closest relative, placing it within the genus \u003cem\u003eArthrobacter\u003c/em\u003e; however, the whole-genome SNP-based phylogeny did not support YC-RL1.1 as the nearest taxon.\u003c/p\u003e\u003cp\u003ePan-genome analysis revealed that \u003cem\u003eArthrobacter sp\u003c/em\u003e. MFD exhibits gene content and genome size comparable to those of other \u003cem\u003eArthrobacter\u003c/em\u003e strains. It harbors a moderate number of strain‐\u003cem\u003esp\u003c/em\u003eecific genes, and among the 21 genomes surveyed, only three shared any gene clusters with \u003cem\u003eArthrobacter sp\u003c/em\u003e. MFD\u0026mdash;and even then, the extent of gene sharing was minimal.\u003c/p\u003e\u003cp\u003eThe COG system assigns each orthologous group to one of 26 functional categories based on its cellular role [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In \u003cem\u003eArthrobacter sp\u003c/em\u003e. MFD, COG annotation placed 53 genes in \u0026ldquo;amino acid tran\u003cem\u003esp\u003c/em\u003eort and metabolism,\u0026rdquo; while KEGG mapping identified 45 genes involved in amino acid biosynthesis and degradation\u0026mdash;covering arginine, valine, leucine, isoleucine, and lysine pathways, among others. These findings point to extensive amino acid synthesis and metabolic capabilities in strain MFD. Moreover, COG assigned 27 genes to \u0026ldquo;carbohydrate tran\u003cem\u003esp\u003c/em\u003eort and metabolism,\u0026rdquo; and CAZy annotation revealed 120 carbohydrate-active enzyme genes, highlighting a similarly rich arsenal for carbohydrate degradation and utilization. Studies have reported that members of the genus \u003cem\u003eArthrobacter\u003c/em\u003e generally exhibit low pathogenicity in animals [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; however, they may possess potential pathogenicity toward plants. PHI-base annotation of \u003cem\u003eArthrobacter sp\u003c/em\u003e. MFD revealed several effector genes associated with anthocyanin biosynthesis, which were classified as plant avirulence determinants. These genes are linked to plant hosts including cabbage, radish, tobacco, and Arabidopsis thaliana, suggesting a possible plant-associated pathogenic capacity. However, virulence factor annotation using the VFDB database revealed that the strain harbors not only the plant toxin phytotoxin phaseolotoxin, but also multiple hemolysins, including alpha-hemolysin and beta-hemolysin/cytolysin, as well as cereulide, a toxin associated with food poisoning [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Using antiSMASH 8.0.1, six biosynthetic gene clusters (BGCs) were predicted, among which the NI-siderophore and terpene clusters showed relatively high similarity, with matching rates of 72% and 68%, re\u003cem\u003esp\u003c/em\u003eectively. Siderophores are iron-chelating compounds produced by microorganisms under iron-limited conditions to facilitate iron uptake, with NI-siderophore representing a \u003cem\u003esp\u003c/em\u003eecific type of cluster re\u003cem\u003esp\u003c/em\u003eonsible for the biosynthesis of nonribosomal iron-chelating molecules, and, in addition to iron, siderophores can also chelate other essential metal ions such as molybdenum, manganese, cobalt, and nickel [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In recent years, bacteria have been recognized as possessing the genetic potential to biosynthesize a wide array of complex terpenoids. Terpenoids represent the most structurally diverse and abundant class of natural products known to date, exhibiting remarkable and \u003cem\u003esp\u003c/em\u003eecific bioactivities in various assays and in disease prevention or treatment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD may be capable of producing compounds beneficial to the host, thereby contributing to host health.\u003c/p\u003e\u003cp\u003eTo evaluate the lignocellulose utilization capability and metabolic products of \u003cem\u003eArthrobacter sp.\u003c/em\u003e FMD, we conducted a fermentation experiment using wheat bran as the substrate. After fermentation, the relative abundances of several metabolite super-classes changed significantly. \u003cem\u003eSp\u003c/em\u003eecifically, the proportion of carboxylic acids and derivatives increased from 21.82% before fermentation to 33.44% after fermentation, while purine nucleosides rose markedly from 0.16\u0026ndash;19.40%. In contrast, the relative abundance of fatty acyls decreased from 24.18\u0026ndash;10.81%, and that of organic sulfuric acids and derivatives decreased from 2.85\u0026ndash;1.52%. These results indicate that \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD can efficiently utilize various organic compounds in wheat bran and produces higher levels of organic acids and nucleoside metabolites during fermentation. Overall, the most strongly up-regulated metabolites were isoquercitrin, 2-oxindole-3-acetic acid, and 5-hydroxyindole-3-acetic acid, whereas Leu-Trp, 6\u0026prime;-O-feruloyl catalpol, and Cnidioside A showed the greatest down-regulation. Among amino acids, amino-acid derivatives, and peptides, Isoleucylvaline, γ-glutamylmethionine, and N\u0026sup2;-acetylornithine exhibited the highest increases, while the most markedly elevated organic acids and their derivatives were isovaleric acid, valeric acid, 2-hydroxy-2-methylbutyric acid, and 2-hydroxy-3-methylbutyric acid. The metabolomic shifts observed after fermentation suggest that \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD expresses a coordinated suite of hydrolases and downstream catabolic enzymes that could prove advantageous in the animal gut. The sharp depletion of the dipeptide Leu-Trp, together with the concomitant enrichment of its branched-chain acid derivatives (isovaleric, valeric, 2-hydroxy-2-methylbutyric and 2-hydroxy-3-methylbutyric acids), points to an active dipeptidase/aminopeptidase system [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] followed by branched-chain aminotransferase and α-keto-acid dehydrogenase reactions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] that funnel liberated amino acids into short-chain organic acids. Meanwhile, the pronounced decline of 6\u0026prime;-O-feruloyl catalpol, together with the concurrent rise in isoquercitrin and related flavonoids, suggests the activity of feruloyl esterases [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] that cleave phenolic-acid ester bonds. Moreover, the simultaneous decrease in both 6\u0026prime;-O-feruloyl catalpol and Cnidioside A indicates the presence of broad-\u003cem\u003esp\u003c/em\u003eectrum β-glycosidases/α-L-rhamnosidases [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] capable of sequentially hydrolyzing the glycosyl moieties from flavonoid glycosides and triterpenoid saponins.\u003c/p\u003e\u003cp\u003eIn summary, \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD not only depolymerises lignocellulose-derived substrates during wheat-bran fermentation but also markedly reshapes the secondary-metabolite profile, depleting putative anti-nutritional compounds while enriching organic acids, nucleosides and flavonoids\u0026mdash;changes that could improve feed utilisation and gut health. However, the present evidence remains correlative at the metabolite level: the activities of the key enzymes have yet to be measured directly, and the in-vivo effects of the newly generated metabolites still need to be verified.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole-genome sequence of \u003cem\u003eArthrobacter\u003c/em\u003e sp. FMD has been deposited in the NCBI database (https://www.ncbi.nlm.nih.gov/) under Taxonomy ID 3447456. The corresponding metabolomics data are available in the OMIX database (https://ngdc.cncb.ac.cn/omix/) under accession number OMIX011123.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Yijun County People\u0026rsquo;s Government, Shaanxi, China (grant no. K4050723310).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.H. conceived and designed the study, isolated Arthrobacter sp. FMD, carried out genome sequencing and annotation, performed the wheat-bran fermentation and metabolomics experiments, analysed all primary data and wrote the first draft of the manuscript. R.G. set up the fermentation system, prepared LC-MS samples and assisted in interpreting the metabolomics results. Z.J. conducted statistical analyses of the metabolomics data and prepared Figures 1\u0026ndash;3. L.W. performed CAZyme prediction and phylogenomic reconstruction and reviewed the relevant manuscript sections. B.Z. collected field samples, carried out enzyme-activity assays and curated laboratory records. B.S. and Z.R. jointly supervised the project, obtained funding, verified data integrity and critically revised the manuscript; both serve as corresponding authors and accept overall responsibility for the work. All authors satisfy the BMC authorship criteria: each made a substantial contribution, approved the submitted (and any substantially modified) version and agrees to be personally accountable for their own contributions and for resolving any questions regarding the accuracy or integrity of any part of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics and Consent to Participate Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBusse H, Wieser M, Buczolits S. Arthrobacter. 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Canadian Journal of Microbiology. 2018;64:798\u0026ndash;808.\u003c/li\u003e\n\u003cli\u003eJIANG F, Pengfei S, ZHANG J, Hongmei G, Haijing W, Zhenyuan C, et al. Comparative analysis of gut microbial composition and functions of forest musk deer in different breeding centres. Acta Theriologica Sinica. 2023;43:129.\u003c/li\u003e\n\u003cli\u003eSu R, Erdenedalai M, Dalai M, Batkhuu L, Chi C, Hasi S. Seasonal variation in gut microbiota composition: Comparative analysis of Siberian musk deer (Moschus moschiferus) and forest musk deer (Moschus berezovskii). 2020.\u003c/li\u003e\n\u003cli\u003eTian R, Imanian B. VBCG: 20 validated bacterial core genes for phylogenomic analysis with high fidelity and resolution. Microbiome. 2023;11:247.\u003c/li\u003e\n\u003cli\u003ePritchard L, Cock P, Esen \u0026Ouml;. pyani v0. 2.8: average nucleotide identity (ANI) and related measures for whole genome comparisons. 2019.\u003c/li\u003e\n\u003cli\u003ePage AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MT, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31:3691\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eRen L, Jia Y, Ruth N, Shi Y, Wang J, Qiao C, et al. Biotransformations of bisphenols mediated by a novel Arthrobacter sp. strain YC-RL1. Applied microbiology and biotechnology. 2016;100:1967\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eKim M, Lee J-H, Kim E, Choi H, Kim Y, Lee J. Isolation of indole utilizing bacteria Arthrobacter sp. and Alcaligenes sp. from livestock waste. Indian journal of microbiology. 2016;56:158\u0026ndash;66.\u003c/li\u003e\n\u003cli\u003eLiu Y, Zhang G, Yang J, Cheng Y, Ye L, Lai X-H, et al. 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Arthrobacter woluwensis Bacteremia: A Clinical and Genomic Report. Pathogens. 2021;10.\u003c/li\u003e\n\u003cli\u003eMeng J-N, Liu Y-J, Shen X, Wang J, Xu Z-K, Ding Y, et al. Detection of emetic Bacillus cereus and the emetic toxin cereulide in food matrices: Progress and perspectives. Trends in Food Science \u0026amp; Technology. 2022;123:322\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eAhmed E, Holmstr\u0026ouml;m SJ. Siderophores in environmental research: roles and applications. Microbial biotechnology. 2014;7:196\u0026ndash;208.\u003c/li\u003e\n\u003cli\u003eNeilands J. Siderophores: structure and function of microbial iron transport compounds. Journal of Biological Chemistry. 1995;270:26723\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eHelfrich EJ, Lin G-M, Voigt CA, Clardy J. Bacterial terpene biosynthesis: challenges and opportunities for pathway engineering. Beilstein journal of organic chemistry. 2019;15:2889\u0026ndash;906.\u003c/li\u003e\n\u003cli\u003eSanz Y. Aminopeptidases. Industrial enzymes: Structure, function and applications. 2007;:243\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eTaylor A. Aminopeptidases: structure and function. The FASEB journal. 1993;7:290\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eArchana, Gupta AK, Noumani A, Panday DK, Zaidi F, Sahu GK, et al. Gut microbiota derived short‐chain fatty acids in physiology and pathology: An update. Cell Biochemistry and Function. 2024;42:e4108.\u003c/li\u003e\n\u003cli\u003eSamad KA, Zainol N, Yussof HW, Khushairi ZA, Mohd Sharif NSA, Mohd Syukri NS. Isolation, identification and characterization of soil bacteria for the production of ferulic acid through co-culture fermentation using banana stem waste. SN Applied Sciences. 2020;2:339.\u003c/li\u003e\n\u003cli\u003eMensitieri F, De Lise F, Strazzulli A, Moracci M, Notomista E, Cafaro V, et al. Structural and functional insights into RHA-P, a bacterial GH106 \u0026alpha;-L-rhamnosidase from Novosphingobium sp. PP1Y. Archives of biochemistry and biophysics. 2018;648:1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eBraune A, Blaut M. Bacterial species involved in the conversion of dietary flavonoids in the human gut. Gut microbes. 2016;7:216\u0026ndash;34.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7059040/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7059040/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMost studies on the intestinal microbiota of the forest musk deer (Moschus berezovskii) are based on community-level sequencing, and functional characterisation of individual strains is rare. Here we isolated strain FMD from deer faeces by selecting on CMC-Na as the sole carbon source; it utilises xylose, trehalose and fructose and is positive for urease and the methyl-red test. Whole-genome sequencing yielded a 4.05 Mb chromosome (GC 64.1%, four contigs, 3 726 CDS), and phylogenomic analyses (20 core genes, ANI and SNP tree) placed the isolate in the genus \u003cem\u003eArthrobacter\u003c/em\u003e; the strain was designated \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD. The genome encodes abundant catabolic functions, including 128 CAZymes, 27 COG and 34 KEGG genes for carbohydrate metabolism, and 54 COG and 45 KEGG genes for amino-acid metabolism, while VFDB and PHI searches indicate low pathogenic potential. Fermentation of wheat bran with \u003cem\u003eArthrobacter sp\u003c/em\u003e. FMD increased carboxylic acids from 21.8\u0026ndash;33.4% and decreased fatty acyls from 24.2\u0026ndash;10.8%. Isoquercitrin, 2-oxindole-3-acetic acid and 5-hydroxyindole-3-acetic acid were the most up-regulated metabolites, whereas Leu-Trp, 6\u0026prime;-O-feruloyl catalpol and Cnidioside A were the most down-regulated. Isoleucylvaline, γ-glutamyl-methionine and N\u0026sup2;-acetylornithine showed the highest increases among amino-acid derivatives, and isovaleric, valeric, 2-hydroxy-2-methylbutyric and 2-hydroxy-3-methylbutyric acids were the predominant organic-acid products. These findings suggest that \u003cem\u003eArthrobacter sp.\u003c/em\u003e FMD deploys a coordinated set of hydrolases and downstream catabolic enzymes that degrade lignocellulose-derived substrates, reduce anti-nutritional factors and enrich organic acids, nucleosides and flavonoids, highlighting its potential to improve feed utilisation and gut health in the forest musk deer.\u003c/p\u003e","manuscriptTitle":"Whole-genome analysis and fermentation-metabolite profiling of a cellulolytic Arthrobacter sp. 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