Whole Genome Sequencing Reveals Environmental Pathogen Misidentification and Cross- Phylum Antimicrobial Resistance Gene Transfer in Bovine Mastitis: A Pilot Genomic Study

Whole Genome Sequencing Reveals Environmental Pathogen Misidentification and Cross- Phylum Antimicrobial Resistance Gene Transfer in Bovine Mastitis: A Pilot Genomic Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Whole Genome Sequencing Reveals Environmental Pathogen Misidentification and Cross- Phylum Antimicrobial Resistance Gene Transfer in Bovine Mastitis: A Pilot Genomic Study Amatul Muhee, Arif Pandit, Sobby Jan, Iqra Shafi Khan, Nuzhat Hassan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7079649/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jan, 2026 Read the published version in BMC Veterinary Research → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Bovine mastitis diagnosis relies predominantly on conventional microbiological methods optimized for common pathogens, potentially overlooking environmental bacteria with complex antimicrobial resistance profiles. Methods: This pilot study combined conventional identification with whole-genome sequencing (WGS) analysis of bovine mastitis isolates. A total of 330 milk samples were analyzed using standard microbiological methods, followed by comprehensive genomic characterization of two representative multidrug-resistant isolates using Illumina NovaSeq 6000 sequencing. Antimicrobial resistance gene analysis was performed using BLAST searches against the Comprehensive Antibiotic Resistance Database. Results: Of 330 samples, 202 (61.2%) tested positive for mastitis. WGS revealed critical species misidentification: one isolate initially characterized as gram-positive with Staphylococcus-like morphology was definitively identified as Stutzerimonas stutzeri through genomic analysis. Both sequenced isolates harbored extensive antimicrobial resistance gene repertoires distributed across 8-10 resistance classes, with evidence of horizontal gene transfer across bacterial orders. Phylogenetic analysis revealed resistance genes originated from Proteobacteria (61%) and Firmicutes (39%), indicating cross-phylum gene exchange. Conclusions: This pilot study demonstrates that WGS can identify bacterial species missed by conventional diagnostic methods and reveals complex horizontal gene transfer networks in mastitis-associated bacteria. The environmental pathogen S. stutzeri represents a potentially underrecognized opportunistic mastitis agent with extensive resistance potential. These findings validate the need for genomic surveillance approaches in veterinary diagnostic microbiology. bovine mastitis whole genome sequencing antimicrobial resistance Stutzerimonas stutzeri diagnostic limitations horizontal gene transfer dairy cattle veterinary microbiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Current veterinary diagnostic paradigms for bovine mastitis rely heavily on morphological and biochemical identification methods developed for common pathogens, creating systematic bias toward expected organisms while potentially missing environmental bacteria with clinical significance (Taponen and Pyörälä, 2009 ; Lippolis et al., 2017 ). The advent of whole-genome sequencing (WGS) offers unbiased species identification capabilities that can reveal the true microbial diversity in clinical infections, particularly for organisms that may exhibit atypical phenotypic characteristics in host environments. Bovine mastitis remains the most economically significant disease affecting dairy cattle worldwide, causing substantial losses through reduced milk production, increased treatment costs, and premature culling (Ruegg, 2017 ). However, the full spectrum of causative organisms may be underestimated due to limitations in conventional identification approaches (Kuehn et al., 2013 ). Traditional identification approaches, which rely on morphological characteristics, biochemical tests, and targeted PCR amplification, are optimized for common mastitis pathogens, such as Staphylococcus aureus , Escherichia coli , and Streptococcus species, potentially overlooking emerging or atypical bacterial species (Taponen and Pyörälä, 2009 ). The limitations of conventional identification methods are particularly evident when investigating complex microbial communities in mastitis-affected milk. Studies using culture-independent approaches have revealed significant microbial diversity beyond traditionally recognized mastitis pathogens, including environmental bacteria that may contribute to infection dynamics and antimicrobial resistance dissemination (G et al., 2014 ; Falentin et al., 2016 ). This hidden diversity has important implications for understanding the evolution of resistance, as environmental bacteria often serve as reservoirs for antimicrobial resistance genes that can be transferred to clinical pathogens through horizontal gene transfer mechanisms (Forsberg et al., 2012 ). Whole-genome sequencing (WGS) has emerged as a transformative tool that addresses the limitations of conventional identification and provides comprehensive insights into antimicrobial resistance mechanisms, virulence factors, and evolutionary relationships (Köser et al., 2012 ). Unlike targeted approaches, WGS enables unbiased species identification and can reveal the complete genomic context of resistance determinants, including mobile genetic elements, that facilitate the spread of resistance (Ellington et al., 2017 ). This technology has proven particularly valuable in veterinary microbiology, where accurate pathogen identification directly affects treatment decisions and resistance surveillance programs (Hendriksen et al., 2019 ). Environmental bacteria that cause mastitis may exhibit phenotypic plasticity in host-associated environments, leading to misclassification when relying solely on conventional diagnostic methods. This diagnostic challenge is particularly relevant in regions with limited access to advanced molecular diagnostic tools, where treatment decisions depend heavily on accurate pathogen identification. The widespread use of antimicrobials in dairy farming raises concerns about the evolution and spread of resistance, particularly given the potential for resistance gene transfer between diverse bacterial species sharing the same ecological niche (Jl et al., 2015) This pilot study addresses these methodological limitations by combining conventional microbiological surveillance with whole-genome sequencing analysis to evaluate diagnostic accuracy and characterize the genomic diversity of mastitis-associated bacteria The specific objectives were as follows: (1) to determine the prevalence and distribution of mastitis pathogens using conventional identification methods, (2) to perform comprehensive genomic characterization of selected isolates using whole-genome sequencing (WGS), (3) to compare conventional identification results with WGS-based species determination, (4) to characterize antimicrobial resistance (AMR) gene profiles and correlate them with phenotypic resistance patterns, and (5) to assess horizontal gene transfer events and genomic complexity in mastitis-associated bacteria. This integrated approach allows for the evaluation of both current surveillance capabilities and the potential benefits of implementing genomic technologies in veterinary diagnostic laboratories. Methods Study Design and Sample Collection This cross-sectional pilot study was conducted in Kashmir Valley, Jammu and Kashmir, India, from March 2023 to September 2023. The study protocol was approved by the Institutional Animal Ethics Committee of Sher-e-Kashmir University of Agricultural Sciences and Technology-Kashmir. Written informed consent was obtained from all participating dairy farmers. A total of 330 milk samples were aseptically collected from lactating cows using stratified random sampling from different districts of Kashmir Valley. The sample distribution included 112 samples from clinical mastitis cases at the Veterinary Clinical Complex (FVSc and AH, Shuhama), 88 samples from the Mountain Livestock Research Institute (MLRI, Manasbal), and 130 samples from district veterinary hospitals and dispensaries. Clinical mastitis was diagnosed based on udder inflammation, altered milk consistency, and a positive California Mastitis Test (CMT). Subclinical mastitis was identified using CMT, electrical conductivity measurements, pH testing, and white-side tests. The udder teats were cleaned and disinfected with 70% ethanol. The first milk streams were discarded, and approximately 15 mL of milk was collected in sterile screw-capped tubes. The samples were immediately placed on ice and transported to the laboratory within 4 h for processing. Bacterial Isolation and Identification Milk samples were cultured on 5% sheep blood agar, MacConkey agar, and mannitol salt agar plates, and incubated aerobically at 37°C for 24–48 hours. Bacterial isolates were initially identified using conventional biochemical tests including Gram staining, catalase, coagulase, and species-specific identification kits (HiMedia Laboratories, Mumbai, India). For isolates with staphylococcal morphology, molecular confirmation was attempted using species-specific PCR amplification targeting the gene of Staphylococcus aureus. However, some isolates with gram-positive and catalase-positive characteristics yielded inconsistent or negative results with species-specific primers, necessitating further molecular characterization. Additional PCR amplification was performed to target the 16S rRNA gene of E. coli and specific primers for Streptococcus dysgalactiae (Table 1 ). Table 1 PCR primers used for species-specific identification of mastitis pathogens. S.No Primer Name Primer Sequence 5’ to 3’ No. of Bases 1. S.aureus (nuc gene F) GCGATTGATGGTGATACGGTT 21 2. S.aureus (nuc gene R) AGCCAAGCCTTGACGAACTAAAGC 24 3. S.aureus (Mec A MRS1) AAAATCGATGGTAAAGGTTGGC 22 4. S.aureus (Mec A MRS2) AGTTCTGCAGTACCGGATTTGC 22 5. E.coli 16SrRNA gene(F) GACCTCGGTTTAGTTCACAGA 21 6. E.coli16SrRNAgene ® CACACGCTGACGCTGACCA 19 7. S. dysgalactiea STRD-DyI (F) GAACACGTTAGGGTCGTC 18 8. S.dysgalactiea STRD-DyII AGTATATCTTAACTAGAAAAACTATTG 27 Antimicrobial Susceptibility Testing Phenotypic antimicrobial susceptibility testing was performed using the disk diffusion method, according to the Clinical and Laboratory Standards Institute (CLSI) guidelines. Bacterial suspensions equivalent to the McFarland standard (0.5) were inoculated onto Mueller-Hinton agar plates. The antimicrobial disks tested included penicillin G (10 units), amoxicillin-clavulanic acid (30 µg), gentamicin (30 µg), cefpodoxime (10 µg), tetracycline (30 µg), streptomycin (10 µg), ceftriaxone (30 µg), enrofloxacin (10 µg), and cefotaxime (30 µg). Plates were incubated at 37°C for 18–24 hours, and inhibition zones were measured and interpreted according to the CLSI breakpoints. Multidrug resistance was defined as the resistance to three or more antimicrobials. Sample Selection for Whole Genome Sequencing From 202 mastitis-positive samples, two isolates were selected for comprehensive genomic analysis based on the following predefined criteria: (1) clinical significance from severe mastitis cases, (2) multidrug resistance to ≥ 3 antimicrobial agents, (3) distinct phenotypic characteristics warranting further investigation, (4) geographic distribution across Kashmir Valley districts, (5) evidence of complex microbial community characteristics, and (6) high-quality DNA suitable for sequencing. It should be noted that this represents a pilot genomic characterization study (0.99% of positive samples), and the findings should be interpreted within this limited scope. DNA Extraction and Quality Assessment Genomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Germany) following the manufacturer's protocol, with modifications for gram-positive bacteria. DNA concentration was quantified using a Qubit 4.0 Fluorometer (Thermo Fisher Scientific), and quality was assessed using 1.0% agarose gel electrophoresis and NanoDrop spectrophotometry. DNA samples with A260/A280 ratios > 1.8 and concentrations > 50 ng/µL were considered suitable for library preparation. Library Preparation and Whole Genome Sequencing Paired-end sequencing libraries were prepared using the Twist NGS Library Preparation Kit for Illumina following the manufacturer's protocol. The workflow includes enzymatic DNA fragmentation, end repair, A-tailing, adapter ligation, and PCR amplification. Library quality and quantity were assessed using a TapeStation 4150 (Agilent Technologies) with a High-Sensitivity D1000 ScreenTape. Whole-genome sequencing was performed on an Illumina NovaSeq 6000 platform (Unigenome, Ahmedabad, India) using 2 × 150 bp paired-end chemistry, targeting approximately 3 GB coverage per sample. Quality Control and Validation In this study, comprehensive quality control measures were implemented. Negative extraction controls (sterile water) and positive controls using the reference strains (E. coli ATCC 25922 and S. aureus ATCC 25923) were processed in each batch. Library preparation included no-template controls and sequencing runs incorporating phiX control spike-ins (1% of the reads). Post-sequencing quality assessment was performed using FastQC analysis, with > 95% of reads achieving Q30 quality scores. Contamination screening was performed using the Kraken2 database. Bioinformatics Analysis Raw sequencing reads were quality filtered using Trimmomatic v0.39 with the following parameters: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36. De novo genome assembly was performed using SPAdes v3.15.4, with an automatic k-mer selection. Assembly quality was evaluated using QUAST v5.0.2 and BUSCO v5.4.3 for completeness assessment. Genome annotation was performed using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP). Functional annotation involved BLASTp searches against the NCBI nr database (e-value ≤ 1e-5), Gene Ontology mapping using Blast2GO v5.2, and pathway analysis using the KEGG Automatic Annotation Server. Clusters of Orthologous Groups (COG) classification and Pfam domain identification were performed using the respective databases. Antimicrobial Resistance Gene Analysis Comprehensive resistome characterization was conducted using BLASTp searches against the Comprehensive Antibiotic Resistance Database (CARD) with an e-value threshold ≤ 1e-10 (Jia et al., 2017 ). Resistance genes were classified according to their mechanism and drug class. Multiple individual genes often contributed to resistance within single antimicrobial classes, and results were reported both as individual gene counts and resistance class distributions. Mobile genetic elements, including integrons, transposons, and plasmids, were identified using specialized databases and manual curation. AMR genes were mapped to their putative bacterial taxonomic origins, based on their phylogenetic distribution patterns in public databases. Each resistance gene was assigned to bacterial orders (Enterobacterales, Bacillales, Enterococcales, and Pseudomonadales) and phyla (Proteobacteria and Firmicutes) according to their predominant occurrence in the bacterial taxonomy. This approach enabled analysis of horizontal gene transfer patterns across different bacterial lineages. Comparative Genomics and Phylogenetic Analysis For taxonomic classification, 16S rRNA gene sequences were extracted from the annotated genome assemblies. Related sequences were retrieved from the NCBI GenBank database using BLASTn searches (Altschul et al., 1997 ) targeting species-specific and family-level representatives. Species assignments were considered reliable when sequences showed ≥ 97% identity with type strain sequences and were supported by phylogenetic analysis with bootstrap values ≥ 70%. For discrepancies between conventional and genomic identification, phenotypic characteristics were re-evaluated using standardized protocols. Multiple sequence alignments were constructed using DECIPHER implemented in R v4.3.0. Phylogenetic relationships were inferred using the neighbor-joining method with Kimura 2-parameter distance correction, as implemented in the ape package. Tree topology reliability was assessed using bootstrap analysis with 1,000 replicates. Phylogenetic trees were visualized using ggtree, and sequences were reliably clustered when supported by bootstrap values of ≥ 70%. Horizontal gene transfer events were assessed through comparative analysis with reference genomes and the identification of atypical GC content regions. Resistance profiles between the isolates were compared using presence/absence matrices for both resistance classes and individual gene families. Phylogenetic distances were calculated using binary distance metrics (Jaccard distance) to quantify the similarity between resistance profiles. Shared and unique resistance patterns were identified and quantified using the set theory approach. Statistical Analysis Statistical analyses were performed using the R software (v4.3.0). Descriptive statistics were calculated for prevalence data and genomic metrics. Phenotype-genotype correlation analysis included the calculation of sensitivity, specificity, positive predictive value, negative predictive value, and Cohen's kappa coefficient for agreement assessment. Fisher's exact test was used for categorical comparisons, and the Mann-Whitney U test was used for continuous variables. Multiple testing corrections were applied using the Benjamini-Hochberg false discovery rate method. Statistical significance was set at P < 0.05. Jaccard similarity coefficients were calculated to measure the degree of overlap between the resistance profiles of the isolates. The coefficient is defined as the ratio of the number of shared resistance classes to the total number of unique resistance classes across both isolates. Diversity indices and statistical comparisons were performed using R statistical software (version 4.3.0) with appropriate packages for phylogenetic and ecological analyses. Results Prevalence of Bovine Mastitis and Pathogen Distribution Of the 330 milk samples collected from dairy cattle across Kashmir Valley, 202 (61.2%; 95% CI: 55.8–66.4%) tested positive for mastitis. Clinical mastitis was detected in 152 samples (46.1%; 95% CI: 40.7–51.6%), whereas subclinical mastitis was identified in 50 samples (15.2%; 95% CI: 11.6–19.5%). The remaining 128 (38.8%) samples were obtained from healthy animals with negative mastitis screening test results. Bacterial Pathogen Distribution Clinical mastitis isolates (n = 152): Staphylococcus aureus was the predominant pathogen, isolated from 95 samples (62.5%; 95% CI: 54.4–70.1%), followed by mixed infections in 41 samples (27.0%; 95% CI: 20.2–34.7%), E. coli in 12 samples (7.9%; 95% CI: 4.3–13.4%), and Streptococcus dysgalactiae in 4 samples (2.6%; 95% CI: 0.7–6.6%). Subclinical mastitis isolates (n = 50): S. aureus maintained dominance with 35 isolates (70.0%; 95% CI: 55.4–82.1%), followed by mixed infections in nine samples (18.0%; 95% CI: 8.6–31.4%), S. dysgalactiae in four samples (8.0%; 95% CI: 2.2–19.2%), and E. coli in two samples (4.0%; 95% CI: 0.5–13.7%) (Fig. 1 ). The prevalence of S. aureus was significantly higher in subclinical mastitis than that in clinical mastitis (70.0% vs. 62.5%; Fisher's exact test, P = 0.034). Mixed infections were more common in clinical mastitis cases (27.0% vs. 18.0%; P = 0.021) (Fig. 1 ). All bacterial isolates were confirmed using biochemical tests (Hi Staph and Hi Strep identification kits) and species-specific PCR amplification. Phenotypic Antimicrobial Resistance Profiles Phenotypic antimicrobial resistance profiling was performed using the disk-diffusion method. The highest multidrug resistance patterns were as follows: Staphylococcus aureus: resistance to penicillin, tetracycline, amoxicillin-clavulanic acid, cefpodoxime, and streptomycin Streptococcus dysgalactiae: Resistance to penicillin, tetracycline, and streptomycin E. coli: Resistance to penicillin, tetracycline, amoxicillin-clavulanic acid, cefpodoxime, and strept resistance pathotypes was identified for S. aureus, including Mec A MRS1 and Mec A MRS2 genes. Genotype-Phenotype Resistance Correlation Genomic analysis revealed extensive resistance gene repertoires (287–294 genes per isolate), and phenotypic testing was limited to nine antimicrobial agents. The direct correlation between genotypic and phenotypic resistance could not be comprehensively assessed because of this limitation. Most identified resistance genes (> 90%) showed no corresponding phenotypic expression under standard testing conditions, suggesting conditional expression, silent carriage, or resistance to antimicrobials that were not included in the phenotypic panel. Whole Genome Sequencing Results Sequencing Metrics and Assembly Quality Whole-genome sequencing of C65 ( Stutzerimonas stutzeri ) and C67 ( Escherichia coli ) generated 15,037,230 paired-end reads (2.39 GB total data) for C65 and 16,749,040 reads (2.66 GB total data) for C67, with coverage of 249× and 320×, respectively. Quality control using FastQC showed > 95% reads with Q30 scores after Trimmomatic pre-processing. De novo assembly using SPAdes v3.15.4 yielded genomes assembled into 77 scaffolds for both isolates. The C65 assembly consisted of 4,442 genes, including 4,380 coding DNA sequences (CDSs), of which 4,302 encoded proteins and 78 pseudogenes. C67 contained 4,852 genes with 4,752 CDSs, of which 4,546 encoded proteins and 206 were pseudogenes (Table 2 ). Both genomes were deposited in GenBank under the accession numbers JBNYYH000000000.1 (C65) and JBNYYI000000000.1 (C67). Table 2 Genome Assembly Quality Metrics and Characteristics of Bacterial Isolates C65 and C67 Assembly Parameter C65 ( Stutzerimonas stutzeri ) C67 ( Escherichia coli ) BASIC ASSEMBLY METRICS Assembly Method SPAdes v3.15.4 SPAdes v3.15.4 Sequencing Platform Illumina NovaSeq 6000 Illumina NovaSeq 6000 Read Configuration 2 × 150 bp paired-end 2 × 150 bp paired-end Genome Coverage (×) 249.0 320.0 Total Sequencing Data (GB) 2.39 2.66 Total Raw Reads 15,037,230 16,749,040 ASSEMBLY QUALITY Number of Contigs 77 77 Estimated Genome Size (Mbp) 4.5-5.0* 4.8–5.2* Assembly Status Complete Complete GenBank Accession JBNYYH000000000.1 JBNYYI000000000.1 GENE CONTENT ANALYSIS Total Genes 4,442 4,852 Protein-coding Genes (CDSs) 4,380 4,752 CDSs with Protein Product 4,302 4,546 Protein Coding Efficiency (%) 98.2 95.7 Pseudogenes 78 206 Pseudogene Ratio (%) 1.8 4.3 RNA GENE CONTENT Total RNA Genes 62 100 rRNA Genes (5S, 16S, 23S) 1, 1, 3 7, 2, 4 Complete rRNAs 3 (5S:1, 16S:1, 23S:1) 9 (5S:5, 16S:2, 23S:2) Partial rRNAs 2 (23S) 4 (5S:2, 23S:2) tRNA Genes 53 78 ncRNA Genes 4 9 MOBILE GENETIC ELEMENTS CRISPR Arrays 1 2 Functional Annotation Coverage Functional annotation using BLASTp v2.13.0 + against the NCBI nr database yielded 4,359 proteins (98.1%) annotated for C65 and 4,462 proteins (98.2%) annotated for C67, using an e-value threshold of ≤ 1e-5. The Gene Ontology annotation coverage was 61.6% for C67 and 40.7% for C65 (Table 3 ). Table 3 Blast2GO Functional Annotation Analysis of Bovine Milk Isolates Functional Parameter C65 ( S. stutzeri ) C67 ( E. coli ) GENOME ANNOTATION OVERVIEW Total Predicted Genes 4,442 4,852 BLAST Hit Coverage 4,359 (98.1%) 4,462 (98.2%) GO Annotation Coverage 1,808 (40.7%) 2,989 (61.6%) Enzyme-Coding Genes 991 (22.3%) 1,634 (33.7%) Annotation Method Blast2GO v5.2 Blast2GO v5.2 GENE ONTOLOGY DISTRIBUTION Molecular Function Terms 2,724 5,058 Biological Process Terms 2,045 4,094 Cellular Component Terms 1,161 2,102 Total GO Terms Assigned 5,930 11,254 Average GO Terms per Gene 3.3 3.8 ENZYME CLASSIFICATION (EC) EC 1 - Oxidoreductases 241 417 EC 2 - Transferases 412 687 EC 3 - Hydrolases 361 645 EC 4 - Lyases 89 183 EC 5 - Isomerases 101 191 EC 6 - Ligases 76 118 EC 7 - Translocases 55 82 FUNCTIONAL CATEGORIES Transport-Related Genes 147 256 Metabolic Enzymes 872 1,467 Regulatory Proteins 88 140 Stress Response Genes 44 76 Signal Transduction 70 115 ANNOTATION QUALITY METRICS Hypothetical Proteins 871 (19.6%) 718 (14.8%) Well-Annotated Genes 3,571 (80.4%) 4,134 (85.2%) Functional Annotation Score* 7.2/10 8.6/10 Database Completeness Excellent Superior Suitability for Systems Biology Good Excellent Comparative Genomics and Functional Analysis Metabolic Capacity Distribution COG functional classification revealed 4,198 COG-annotated genes across 23 functional categories in C65, and 4,285 COG-annotated genes in C67 (Fig. 2 A; Table 4 ). C65 contained 334 genes involved in translation, ribosomal structure, and biogenesis (COG-J), compared to 264 genes in C67. C67 had 283 genes involved in carbohydrate metabolism (COG-G) and 221 genes in C65. Energy production and conversion (COG-C) genes, numbered 222 in C65 and 204 in C67, were identified. The Enzyme Commission classification identified 1,634 enzyme-coding genes (33.7% of protein-coding genes) in C67, compared to 991 genes (22.3%) in C65 (Fig. 2 B and Table 2 ). Table 4 COG Functional Classification Comparison Between Bovine Milk Isolates COG Category Function Description C65 Count C67 Count Difference (C65-C67) Fold Change (C65/C67) A RNA processing and modification 1 2 -1 0.50 B Chromatin structure and dynamics 0 0 0 - C Energy production and conversion 222 204 + 18 1.09 D Cell cycle control, cell division, chromosome partitioning 75 106 -31 0.71 E Amino acid transport and metabolism 318 333 -15 0.95 F Nucleotide transport and metabolism 129 112 + 17 1.15 G Carbohydrate transport and metabolism 221 283 -62 0.78 H Coenzyme transport and metabolism 235 186 + 49 1.26 I Lipid transport and metabolism 168 139 + 29 1.21 J Translation, ribosomal structure and biogenesis 334 264 + 70 1.27 K Transcription 263 276 -13 0.95 L Replication, recombination and repair 195 163 + 32 1.20 M Cell wall/membrane/envelope biogenesis 260 230 + 30 1.13 N Cell motility 55 88 -33 0.63 O Posttranslational modification, protein turnover, chaperones 188 180 + 8 1.04 P Inorganic ion transport and metabolism 262 204 + 58 1.28 Q Secondary metabolites biosynthesis, transport and catabolism 64 62 + 2 1.03 R General function prediction only 306 267 + 39 1.15 S Function unknown 194 171 + 23 1.13 T Signal transduction mechanisms 250 197 + 53 1.27 U Intracellular trafficking, secretion, and vesicular transport 59 75 -16 0.79 V Defense mechanisms 113 107 + 6 1.06 W Extracellular structures 25 31 -6 0.81 X Mobilome: prophages, transposons 69 79 -10 0.87 Y Nuclear structure 0 0 0 - Z Cytoskeleton 3 3 0 1.00 Total All functional categories 4,198 4,285 -87 0.98 Signal Transduction and Environmental Response The signal transduction mechanism (COG-T) comprised 250 genes in C65 and 197 genes in C67. The response regulator domains included 375 genes in C65 and 198 genes in C67 (Table 3 ). Pfam domain analysis identified 73 ABC transporter domains in C65 and 67 in C67 (Fig. 2 C and Table 5 ). C65 contained 37 response regulator domains, compared with 20 in C67. C67 contained 14 peptidase S8 and six fimbrial domains, whereas C65 contained 0 in both domains. Table 5 Pfam Domain Family Distribution Comparison Pfam Domain Function Description C65 Count C67 Count Difference (C65-C67) Fold Change (C65/C67) ABC_tran ATP-binding cassette transporter 73 67 + 6 1.09 Response_reg Response regulator 37 20 + 17 1.85 adh_short Short-chain dehydrogenase 18 10 + 8 1.80 HATPase_c Histidine kinase-like ATPase 16 13 + 3 1.23 MCPsignal Methyl-accepting chemotaxis protein 15 3 + 12 5.00 BPD_transp_1 Binding protein-dependent transporter 10 14 -4 0.71 Phage_integrase Phage integrase 10 11 -1 0.91 AMP-binding AMP-binding enzyme 8 11 -3 0.73 Peptidase_S8 Subtilase-type peptidase 0 14 -14 0.00 HTH_1 Helix-turn-helix DNA-binding 12 12 0 1.00 Aminotran_1_2 Aminotransferase class-I and II 11 11 0 1.00 Aldedh Aldehyde dehydrogenase 9 10 -1 0.90 GGDEF GGDEF domain 11 9 + 2 1.22 EAL EAL domain 8 9 -1 0.89 LysR_substrate LysR substrate binding 12 9 + 3 1.33 PapD_N PapD N-terminal domain 0 6 -6 0.00 Usher Usher protein 0 6 -6 0.00 Fimbrial Fimbrial protein 0 6 -6 0.00 Sugar_tr Sugar transporter 0 6 -6 0.00 HlyD HlyD family secretion protein 3 6 -3 0.50 Species Distribution and Horizontal Gene Transfer Genetic Composition Analysis Species distribution analysis revealed that C65 demonstrated Stutzerimonas stutzeri , with genetic contributions from Macrococcus and Staphylococcus species. C67 exhibited E. coli genomic architecture with contributions from Paenisporosarcina species (Fig. 3 A, Table 6 ). C65 acquires genes from at least 15 different species, including Staphylococcus aureus . C67 showed gene acquisition from the spore-forming bacteria (Fig. 3 B). Table 6 Protein Annotation Analysis and Taxonomic Distribution of Bacterial Isolates Annotation Parameter C65 ( S. stutzeri ) C67 ( E. coli ) ANNOTATION STATISTICS Total Proteins Annotated 4,220 4,462 Annotation Success Rate (%) 98.1 98.2 Database Used NCBI nr (BLASTp v2.13.0+) NCBI nr (BLASTp v2.13.0+) E-value Threshold ≤ 1e-5 ≤ 1e-5 SEQUENCE IDENTITY DISTRIBUTION High Identity (≥ 90%) 85.2% 78.9% Medium Identity (70–89%) 12.1% 18.3% Low Identity (< 70%) 2.7% 2.8% DOMINANT TAXONOMIC GROUPS Primary Species/Group Stutzerimonas stutzeri (32.6%) Enterobacteriaceae (36.7%) Secondary Species/Group Macrococcus caseolyticus (21.6%) Paenisporosarcina sp. (35.4%) Tertiary Species/Group Staphylococcus spp. (19.6%) Escherichia coli (8.2%) Potential Pathogens Detected S. aureus (6.1%) Enterobacteriaceae members Total Taxonomic Groups (> 1%) 8 6 COMMUNITY CHARACTERISTICS Community Type Environmental generalist Enteric pathogen Expected vs. Observed Species Expected: Primary (32.6%) HGT confirmed (67.4%) Expected: Minor (8.2%) Complex HGT detected (91.8%) Environmental Context Dairy-associated microbiome Dairy-associated microbiome Antimicrobial Resistance Gene Analysis AMR Gene Content Systematic screening identified 287 AMR genes in C65 (6.7% of protein-coding genes) and 294 AMR genes in C67 (6.5% of protein-coding genes) (Fig. 4 A). Drug Class Distribution C65 resistance gene distribution: peptide antibiotics (31 genes, 10.8%), glycopeptide antibiotics (29 genes, 10.1%), and macrolide antibiotics (28 genes, 9.8%). The distribution of the C67 resistance genes was as follows: tetracycline antibiotics (40 genes, 13.6%), aminoglycoside antibiotics (28 genes, 9.5%), and peptide antibiotics (27 genes, 9.2%) (Fig. 4 B, Table 7 ). Table 7 Antimicrobial Resistance Gene Analysis of Bovine Milk Isolates AMR Parameter C65 ( S. stutzeri ) C67 ( E. coli ) AMR GENE OVERVIEW Total AMR Genes Identified 287 294 AMR Gene Percentage of Protein-Coding Genes 6.7% 6.5% Database Query Method BLASTp v2.13.0+ BLASTp v2.13.0+ E-value Threshold ≤ 1e-5 ≤ 1e-5 TOP DRUG CLASS RESISTANCE Primary Drug Class Peptide antibiotics (31 genes, 10.8%) Tetracycline antibiotics (40 genes, 13.6%) Secondary Drug Class Glycopeptide antibiotics (29 genes, 10.1%) Aminoglycoside antibiotics (28 genes, 9.5%) Tertiary Drug Class Macrolide antibiotics (28 genes, 9.8%) Peptide antibiotics (27 genes, 9.2%) Tetracycline Resistance 28 genes 40 genes Aminoglycoside Resistance 26 genes 28 genes Fluoroquinolone Resistance 5 genes 9 genes Multi-Drug Resistance Patterns 14 genes 8 genes RESISTANCE MECHANISMS Antibiotic Efflux 186 genes (64.8%) 197 genes (67.0%) Antibiotic Target Alteration 49 genes (17.1%) 40 genes (13.6%) Antibiotic Target Protection 16 genes (5.6%) 16 genes (5.4%) Antibiotic Inactivation 15 genes (5.2%) 18 genes (6.1%) Reduced Permeability 7 genes (2.4%) 7 genes (2.4%) CLINICAL SIGNIFICANCE Critical Priority Resistance 21 genes 49 genes High Priority Resistance 37 genes 62 genes Treatment Impact Assessment Substantial therapeutic challenges Major treatment complications Mastitis Treatment Risk HIGH RISK HIGH RISK AMR Monitoring Priority CRITICAL CRITICAL Resistance Mechanisms Efflux-mediated resistance: C65 harbored 186 efflux genes (64.8% of AMR genes), C67 contained 197 efflux genes (67.0%). Target alterations comprised 49 genes (17.1%) in C65 and 40 genes (13.6%) in C67 (Fig. 4 C). Phylogenetic Analysis Evolutionary Positioning Phylogenetic analysis based on 16S rRNA gene sequences provided an unambiguous taxonomic classification for both bacterial isolates. Strain C-65 clustered within a well-supported clade containing authenticated Stutzerimonas stutzeri strains, demonstrating > 98% sequence identity with the type strain and other characterized members of this species. The phylogenetic tree, rooted using appropriate outgroups and supported by bootstrap values exceeding 70% at critical nodes, confirmed the placement of C-65 in the Pseudomonadaceae. (Fig. 5 ) C-67 exhibited a clear phylogenetic affiliation with Escherichia coli, forming a strongly supported monophyletic group with other E. coli strains within the Enterobacteriaceae family. Bootstrap analysis consistently supported this taxonomic assignment, with confidence values > 95% at the species-level nodes. The 16S rRNA gene sequences showed > 99% identity with E. coli type strain sequences available in public databases. (Fig. 6 ) AMR Gene Phylogeny The antimicrobial resistance genes identified in the two milk isolates represented ten distinct resistance classes. Isolate C65 harbored resistance genes across eight antimicrobial classes, whereas isolate C67 harbored resistance genes across 10 antimicrobial classes (Fig. 7 A). When considering individual resistance genes, C65 harbored 287 total AMR genes (6.7% of protein-coding genes) while C67 contained 294 total AMR genes (6.5% of protein-coding genes), with multiple genes often contributing to resistance within single antimicrobial classes. Comprehensive genomic analysis of C65 revealed a 4.75 Mb circular chromosome containing 4,442 genes, including 287 AMR genes distributed throughout the genome architecture (Fig. 8 ). Similarly, C67 demonstrated a complex genomic architecture with 4,852 total genes, including 294 AMR genes (6.5% of protein-coding genes), distributed across the Escherichia coli genome (Fig. 9 ). Both isolates were resistant to beta-lactam antibiotics, tetracyclines, aminoglycosides, sulfonamides, chloramphenicol, macrolides, glycopeptides, and quinolones. However, C67 uniquely possesses efflux pump genes and polymyxin resistance determinants. Phylogenetic mapping revealed that AMR genes originated from four distinct bacterial orders: Enterobacterales, Bacillales, Enterococcales, and Pseudomonadales (Fig. 7 B). Enterobacterales was the most prevalent source, contributing to beta-lactam, aminoglycoside, sulfonamide, and quinolone resistance. Quantitative similarity analysis using Jaccard coefficients revealed a similarity index of 0.800 between the isolates, with eight resistance classes (80%) shared and two unique to C67 (Fig. 7 C). Cross-phylum analysis demonstrated that resistance genes originated from Proteobacteria (61%) and Firmicutes (39%), indicating extensive horizontal gene transfer across the major bacterial taxonomic divisions (Fig. 7 D). The presence of resistance genes from multiple bacterial orders and phyla in single isolates provides evidence of horizontal gene transfer events, and suggests that milk-associated bacterial communities serve as reservoirs for resistance genes from diverse bacterial sources. Discussion Whole Genome Sequencing Unveils Hidden Microbial Complexity in Bovine Mastitis This pilot study provides methodological validation for the integration of whole-genome sequencing into veterinary diagnostic workflows, demonstrating critical limitations in conventional identification approaches for environmental opportunistic pathogens. The misidentification of Stutzerimonas stutzeri as a gram-positive organism illustrates how environmental bacteria may exhibit phenotypic plasticity in host-associated environments, leading to systematic diagnostic errors when relying solely on morphological and biochemical characteristics. This highlights the limitations of phenotypic identification methods and emphasizes the importance of molecular confirmation, particularly for environmental bacteria that exhibit atypical morphological characteristics in clinical samples. This discovery exemplifies the critical limitations of culture-dependent identification approaches, which remain heavily biased toward detecting expected mastitis pathogens while systematically missing environmentally derived opportunistic organisms (Ahmadi et al., 2022 ; Algharib et al., 2024 ). Recent advances in culture-independent diagnostic approaches have revealed substantial microbial diversity in mastitis-affected quarters beyond that of traditionally recognized pathogens (Rötzer et al., 2023 ). The identification of S. stutzeri in clinical mastitis represents a paradigm shift, as this environmental bacterium has been largely overlooked in veterinary diagnostics despite its documented opportunistic pathogenic potential in immunocompromised hosts. The extensive antimicrobial resistance profile of C65 (287 resistance genes comprising 6.7% of the genome) indicates that this organism is a significant resistance reservoir, potentially facilitating horizontal gene transfer within the mammary gland microbiome (Berendonk et al., 2015 ). Diagnostic Gaps and the Need for Genomic Surveillance The misidentification of isolate C65 underscores the fundamental gaps in current mastitis surveillance programs, which rely predominantly on conventional microbiological methods optimized for common pathogens (Naranjo-Lucena and Slowey, 2023 ). Traditional diagnostic workflows using selective media, biochemical tests, and species-specific polymerase chain reaction (PCR) create diagnostic blind spots that systematically exclude environmental bacteria. This bias toward "expected" pathogens may contribute to treatment failures when atypical organisms are involved, as demonstrated by recent deep learning approaches that have identified novel genomic signatures associated with mastitis susceptibility (Kotlarz et al., 2024 ). Whole metagenome sequencing studies have consistently revealed that 30% of the bacterial strains in clinical mastitis samples were previously unreported, highlighting the magnitude of unrecognized microbial diversity (Hoque et al., 2020 ). The implementation of culture-independent methods represents a critical evolution from a pathogen-focused to an ecosystem-level understanding of mastitis pathogenesis, enabling the detection of bacterial communities that traditional methods systematically miss. Antimicrobial Resistance and Cross-Phylum Gene Exchange The extensive antimicrobial resistance gene repertoires identified (287–294 individual genes distributed across 8–10 resistance classes) demonstrate the complexity of resistance evolution in mixed microbial communities. The detection of resistance genes from four distinct bacterial orders (Enterobacterales, Bacillales, Enterococcales, and Pseudomonadales) within single isolates provides direct evidence for horizontal gene transfer events across major taxonomic divisions. Both isolates demonstrated extensive genomic resistomes that far exceed phenotypically expressed resistance, indicating a substantial silent resistance potential (Naushad et al., 2020 ; Naranjo-Lucena and Slowey, 2023 ). This phenomenon reflects the capacity for resistance gene activation under selective pressure, which poses significant challenges to antimicrobial stewardship in dairy farming. Phylogenetic analysis of AMR gene acquisition from four distinct bacterial orders provides compelling evidence of horizontal gene transfer events across major taxonomic divisions (Ronco et al., 2018 ). The cross-phylum distribution (61% Proteobacteria and 39% Firmicutes) demonstrated that environmental bacteria served as significant donors in resistance gene exchange networks. Recent resistome analyses have shown that horizontal gene transfer can disrupt the traditional link between the microbiome and resistome composition, explaining how distantly related species acquire similar resistance profiles to antimicrobials. Environmental Reservoirs and One Health Implications The identification of multidrug-resistant environmental bacteria in dairy cattle mastitis has profound health implications, given the potential for resistance gene transfer to human pathogens through multiple pathways (Guardabassi et al., 2020 ). Dairy environments function as critical interface zones, where bacterial communities from animals, humans, and the environment interact, creating opportunities for the dissemination of resistance across these domains. The emergence of resistance to newer antimicrobials, including ceftazidime, cefquinome, and colistin, in bovine mastitis pathogens underscores the contribution of agriculture to the global antimicrobial resistance crisis (Oliver and Murinda, 2012 ; Molineri et al., 2021 ). Recent surveillance data from multiple continents have consistently demonstrated an increasing prevalence of methicillin-resistant staphylococci and extended-spectrum beta-lactamase producers in bovine mastitis (Mostafa Abdalhamed et al., 2022 ; Yang et al., 2023 ). The mammary gland represents a unique ecological niche where environmental bacteria, commensals, and pathogens coexist under intermittent antimicrobial pressure, potentially accelerating the evolution and dissemination of resistance. Clinical and Therapeutic Implications Genomic characterization of environmental mastitis pathogens reveals virulence factors and metabolic capabilities that provide crucial insights into pathogenesis mechanisms distinct from those of traditional mastitis-causing organisms (Ashraf et al., 2022 ). Environmental bacteria that cause mastitis may respond differently to standard therapeutic protocols designed for conventional pathogens, potentially contributing to treatment failure and chronic infections. Comparative genomic studies across multiple continents have identified distinct regional variations in the strain distribution and resistance patterns, emphasizing the need for geographically tailored diagnostic and therapeutic approaches (Lippolis et al., 2017 ). The extensive resistance profiles identified suggest that empirical antibiotic therapy may be inadequate for infections caused by environmentally derived pathogens. Study Limitations and Future Directions Several limitations of this study must be acknowledged when interpreting these results. First, the extremely limited sample size (0.99% of positive samples) restricts the generalizability of the findings to a broader population of mastitis pathogens. Second, the observed species identification discrepancy may represent an isolated case rather than a systematic diagnostic limitation of the method. The search for therapeutic alternatives beyond traditional antibiotics has intensified with promising developments in herbal medicine, nanotechnology, polymers, and phototherapy, which have been shown to be effective against mastitis pathogens (Kuralkar and Kuralkar, 2021 ). The integration of rapid molecular diagnostics with genomic surveillance represents a critical advancement in precision veterinary medicine, enabling species identification and resistance profiling within clinically relevant time frames. Phylogenetic analyses based on whole-genome sequences provide unprecedented insights into the evolutionary relationships between mastitis pathogens, revealing patterns of virulence gene acquisition and resistance evolution (Crippa et al., 2023 ). Future surveillance programs should integrate metagenomic approaches with whole-genome sequencing of isolates to provide comprehensive insights into mammary gland microbiome dynamics and their roles in mastitis pathogenesis. Future studies should include larger, randomly selected sample sizes and comprehensive genotype-phenotype correlation analyses to validate these preliminary observations. Conclusions This pilot study provides preliminary evidence of the potential of whole genome sequencing to identify bacterial species that may be missed by conventional diagnostic methods for bovine mastitis. The identification of Stutzerimonas stutzeri as a misclassified environmental pathogen demonstrates the potential for systematic diagnostic errors when relying on phenotypic characteristics alone. The extensive antimicrobial resistance profiles identified, coupled with evidence of horizontal gene transfer between phylogenetically distant species, underscore the complexity of resistance evolution in dairy environments. However, these findings are based on a limited sample size (n = 2 isolates from 202 positive samples) and require validation through large-scale studies before broader conclusions regarding diagnostic limitations can be drawn. These findings support the implementation of genomic surveillance programs in veterinary medicine to improve the accuracy of pathogen identification, guide evidence-based therapy, and monitor emerging resistance. Declarations Acknowledgments The authors acknowledge the dairy farmers of the Kashmir Valley for their cooperation and participation in this study. We thank the staff at the Veterinary Clinical Complex and the Mountain Livestock Research Institute for their assistance in sample collection. We are grateful to the laboratory personnel who contributed to the bacterial isolation and identification work. The authors also acknowledge Unigenome, Ahmedabad, India, for providing whole-genome sequencing services. Ethics Approval and Consent to Participate This study was conducted in accordance with ethical guidelines for animal research. The study protocol was approved by the Institutional Animal Ethics Committee of Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir. Written informed consent was obtained from all participating dairy farmers prior to sample collection. All procedures involving animals were performed in accordance with relevant guidelines and regulations. Declaration of Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study. Consent to Publish Not applicable. Author Contributions A. Muhee : Investigation, Methodology, Data curation, formal analysis, writing – original draft, project administration, and funding acquisition. A. Pandit : Conceptualization, Methodology, Software, Formal analysis, writing – original draft, writing – review and editing, project administration, correspondence. Sobby Jan : Investigation, Data curation, Validation, Writing – review, and editing. Iqra Shafi Khan : Investigation, Data curation, formal analysis, and visualization. Nuzhat Hassan : Investigation, Methodology, Validation, Writing – review, and editing. R.A. Bhat : Resources, Supervision, Writing – review and editing, funding acquisition. M.I. Yatoo : Conceptualization, Resources, Supervision, Writing – Review and Editing Data Availability Raw sequencing data were deposited in the NCBI Sequence Read Archive under the BioProject PRJNA1048756 (accession numbers SRS25103899 and SRS25138938). The assembled genomes were submitted to GenBank under accession numbers GCA_050565245.1 and GCA_050565265.1. Funding This work was supported by the J&K Science Technology & Innovation Council (JKST&IC), Department of Science & Technology, Government of Jammu & Kashmir [grant no. JKST&IC Order no. 82 of 2021]. References Ahmadi, A., Khezri, A., Nørstebø, H., Ahmad, R., 2022. A culture-, amplification-independent, and rapid method for identification of pathogens and antibiotic resistance profile in bovine mastitis milk. Front. Microbiol. 13, 1104701. doi:10.3389/fmicb.2022.1104701 Algharib, S.A., Dawood, A.S., Huang, L., Guo, A., Zhao, G., Zhou, K., Li, C., Liu, J., Gao, X., Luo, W., Xie, S., 2024. Basic concepts, recent advances, and future perspectives in the diagnosis of bovine mastitis. J. Vet. Sci. 25, e18. doi:10.4142/jvs.23147 Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J., 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402. doi:10.1093/nar/25.17.3389 Ashraf, S., Naushad, S., Si, W., Bilal, M., Ijaz, M., Huang, H., Zhao, X., 2022. Draft Genome Sequences and Antimicrobial Resistance Genes of Five Staphylococcus aureus Strains Isolated from Bovine Milk. Microbiol. Resour. Announc. 11, e00756-22. doi:10.1128/mra.00756-22 Berendonk, T.U., Manaia, C.M., Merlin, C., Fatta-Kassinos, D., Cytryn, E., Walsh, F., Bürgmann, H., Sørum, H., Norström, M., Pons, M.-N., Kreuzinger, N., Huovinen, P., Stefani, S., Schwartz, T., Kisand, V., Baquero, F., Martinez, J.L., 2015. Tackling antibiotic resistance: the environmental framework. Nat. Rev. Microbiol. 13, 310–317. doi:10.1038/nrmicro3439 Crippa, B.L., Rodrigues, M.X., Tomazi, T., Yang, Y., de Oliveira Rocha, L., Bicalho, R.C., Silva, N.C.C., 2023. Virulence factors, antimicrobial resistance and phylogeny of bovine mastitis-associated Streptococcus dysgalactiae. J. Dairy Res. 90, 152–157. doi:10.1017/S0022029923000195 Ellington, M.J., Ekelund, O., Aarestrup, F.M., Canton, R., Doumith, M., Giske, C., Grundman, H., Hasman, H., Holden, M.T.G., Hopkins, K.L., Iredell, J., Kahlmeter, G., Köser, C.U., MacGowan, A., Mevius, D., Mulvey, M., Naas, T., Peto, T., Rolain, J.-M., Samuelsen, Ø., Woodford, N., 2017. The role of whole genome sequencing in antimicrobial susceptibility testing of bacteria: report from the EUCAST Subcommittee. Clin. Microbiol. Infect. Off. Publ. Eur. Soc. Clin. Microbiol. Infect. Dis. 23, 2–22. doi:10.1016/j.cmi.2016.11.012 Falentin, H., Rault, L., Nicolas, A., Bouchard, D.S., Lassalas, J., Lamberton, P., Aubry, J.-M., Marnet, P.-G., Le Loir, Y., Even, S., 2016. Bovine Teat Microbiome Analysis Revealed Reduced Alpha Diversity and Significant Changes in Taxonomic Profiles in Quarters with a History of Mastitis. Front. Microbiol. 7, 480. doi:10.3389/fmicb.2016.00480 Forsberg, K.J., Reyes, A., Wang, B., Selleck, E.M., Sommer, M.O.A., Dantas, G., 2012. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111. doi:10.1126/science.1220761 G, O., Ml, B., E, M., Re, R., C, F., Vs, M., Ag, T., C, S., Yh, S., Rc, B., 2014. Microbiota of cow’s milk; distinguishing healthy, sub-clinically and clinically diseased quarters. PloS One 9. doi:10.1371/journal.pone.0085904 Guardabassi, L., Butaye, P., Dockrell, D.H., Fitzgerald, J.R., Kuijper, E.J., ESCMID Study Group for Veterinary Microbiology (ESGVM), 2020. One Health: a multifaceted concept combining diverse approaches to prevent and control antimicrobial resistance. Clin. Microbiol. Infect. Off. Publ. Eur. Soc. Clin. Microbiol. Infect. Dis. 26, 1604–1605. doi:10.1016/j.cmi.2020.07.012 Hendriksen, R.S., Bortolaia, V., Tate, H., Tyson, G.H., Aarestrup, F.M., McDermott, P.F., 2019. Using Genomics to Track Global Antimicrobial Resistance. Front. Public Health 7. doi:10.3389/fpubh.2019.00242 Hoque, M.N., Istiaq, A., Clement, R.A., Gibson, K.M., Saha, O., Islam, O.K., Abir, R.A., Sultana, M., Siddiki, A.Z., Crandall, K.A., Hossain, M.A., 2020. Insights Into the Resistome of Bovine Clinical Mastitis Microbiome, a Key Factor in Disease Complication. Front. Microbiol. 11, 860. doi:10.3389/fmicb.2020.00860 Jia, B., Raphenya, A.R., Alcock, B., Waglechner, N., Guo, P., Tsang, K.K., Lago, B.A., Dave, B.M., Pereira, S., Sharma, A.N., Doshi, S., Courtot, M., Lo, R., Williams, L.E., Frye, J.G., Elsayegh, T., Sardar, D., Westman, E.L., Pawlowski, A.C., Johnson, T.A., Brinkman, F.S.L., Wright, G.D., McArthur, A.G., 2017. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 45, D566–D573. doi:10.1093/nar/gkw1004 Jl, M., Tm, C., F, B., 2015. What is a resistance gene? Ranking risk in resistomes. Nat. Rev. Microbiol. 13. doi:10.1038/nrmicro3399 Köser, C.U., Ellington, M.J., Cartwright, E.J.P., Gillespie, S.H., Brown, N.M., Farrington, M., Holden, M.T.G., Dougan, G., Bentley, S.D., Parkhill, J., Peacock, S.J., 2012. Routine use of microbial whole genome sequencing in diagnostic and public health microbiology. PLoS Pathog. 8, e1002824. doi:10.1371/journal.ppat.1002824 Kotlarz, K., Mielczarek, M., Biecek, P., Wojdak-Maksymiec, K., Suchocki, T., Topolski, P., Jagusiak, W., Szyda, J., 2024. An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem. Int. J. Mol. Sci. 25, 4715. doi:10.3390/ijms25094715 Kuehn, J.S., Gorden, P.J., Munro, D., Rong, R., Dong, Q., Plummer, P.J., Wang, C., Phillips, G.J., 2013. Bacterial Community Profiling of Milk Samples as a Means to Understand Culture-Negative Bovine Clinical Mastitis. PLOS ONE 8, e61959. doi:10.1371/journal.pone.0061959 Kuralkar, P., Kuralkar, S.V., 2021. Role of herbal products in animal production - An updated review. J. Ethnopharmacol. 278, 114246. doi:10.1016/j.jep.2021.114246 Lippolis, J.D., Holman, D.B., Brunelle, B.W., Thacker, T.C., Bearson, B.L., Reinhardt, T.A., Sacco, R.E., Casey, T.A., 2017. Genomic and Transcriptomic Analysis of Escherichia coli Strains Associated with Persistent and Transient Bovine Mastitis and the Role of Colanic Acid. Infect. Immun. 86, e00566-17. doi:10.1128/IAI.00566-17 Molineri, A.I., Camussone, C., Zbrun, M.V., Suárez Archilla, G., Cristiani, M., Neder, V., Calvinho, L., Signorini, M., 2021. Antimicrobial resistance of Staphylococcus aureus isolated from bovine mastitis: Systematic review and meta-analysis. Prev. Vet. Med. 188, 105261. doi:10.1016/j.prevetmed.2021.105261 Mostafa Abdalhamed, A., Zeedan, G.S.G., Ahmed Arafa, A., Shafeek Ibrahim, E., Sedky, D., Abdel Nabey Hafez, A., 2022. Detection of Methicillin-Resistant Staphylococcus aureus in Clinical and Subclinical Mastitis in Ruminants and Studying the Effect of Novel Green Synthetized Nanoparticles as One of the Alternative Treatments. Vet. Med. Int. 2022, 6309984. doi:10.1155/2022/6309984 Naranjo-Lucena, A., Slowey, R., 2023. Invited review: Antimicrobial resistance in bovine mastitis pathogens: A review of genetic determinants and prevalence of resistance in European countries. J. Dairy Sci. 106, 1–23. doi:10.3168/jds.2022-22267 Naushad, S., Nobrega, D.B., Naqvi, S.A., Barkema, H.W., De Buck, J., 2020. Genomic Analysis of Bovine Staphylococcus aureus Isolates from Milk To Elucidate Diversity and Determine the Distributions of Antimicrobial and Virulence Genes and Their Association with Mastitis. mSystems 5, e00063-20. doi:10.1128/mSystems.00063-20 Oliver, S.P., Murinda, S.E., 2012. Antimicrobial resistance of mastitis pathogens. Vet. Clin. North Am. Food Anim. Pract. 28, 165–185. doi:10.1016/j.cvfa.2012.03.005 Ronco, T., Klaas, I.C., Stegger, M., Svennesen, L., Astrup, L.B., Farre, M., Pedersen, K., 2018. Genomic investigation of Staphylococcus aureus isolates from bulk tank milk and dairy cows with clinical mastitis. Vet. Microbiol. 215, 35–42. doi:10.1016/j.vetmic.2018.01.003 Rötzer, V., Wenderlein, J., Wiesinger, A., Versen, F., Rauch, E., Straubinger, R.K., Zeiler, E., 2023. Bovine Udder Health: From Standard Diagnostic Methods to New Approaches—A Practical Investigation of Various Udder Health Parameters in Combination with 16S rRNA Sequencing. Microorganisms 11, 1311. doi:10.3390/microorganisms11051311 Ruegg, P.L., 2017. A 100-Year Review: Mastitis detection, management, and prevention. J. Dairy Sci. 100, 10381–10397. doi:10.3168/jds.2017-13023 Taponen, S., Pyörälä, S., 2009. Coagulase-negative staphylococci as cause of bovine mastitis- not so different from Staphylococcus aureus? Vet. Microbiol. 134, 29–36. doi:10.1016/j.vetmic.2008.09.011 Yang, F., Shi, W., Meng, N., Zhao, Y., Ding, X., Li, Q., 2023. Antimicrobial resistance and virulence profiles of staphylococci isolated from clinical bovine mastitis. Front. Microbiol. 14. doi:10.3389/fmicb.2023.1190790 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Jan, 2026 Read the published version in BMC Veterinary Research → Version 1 posted Editorial decision: Revision requested 07 Dec, 2025 Reviews received at journal 28 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviewers invited by journal 16 Jul, 2025 Editor invited by journal 11 Jul, 2025 Editor assigned by journal 10 Jul, 2025 Submission checks completed at journal 10 Jul, 2025 First submitted to journal 09 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7079649","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486452279,"identity":"cebe52d2-50cb-4432-bb38-2b3628d1b0d3","order_by":0,"name":"Amatul Muhee","email":"","orcid":"","institution":"FVSc \u0026 AH, Sher E Kashmir University of Agricultural Sciences and Technology of Kashmir","correspondingAuthor":false,"prefix":"","firstName":"Amatul","middleName":"","lastName":"Muhee","suffix":""},{"id":486452280,"identity":"68ebb9ae-c106-4b70-8dc3-96f8122aa2fd","order_by":1,"name":"Arif Pandit","email":"data:image/png;base64,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","orcid":"","institution":"Mountain Livestock Research Institute, Sher E Kashmir University of Agricultural Sciences and Technology of Kashmir","correspondingAuthor":true,"prefix":"","firstName":"Arif","middleName":"","lastName":"Pandit","suffix":""},{"id":486452282,"identity":"9b3df6d0-f33d-476d-883d-aee3fa551919","order_by":2,"name":"Sobby Jan","email":"","orcid":"","institution":"FVSc \u0026 AH, Sher E Kashmir University of Agricultural Sciences and Technology of Kashmir","correspondingAuthor":false,"prefix":"","firstName":"Sobby","middleName":"","lastName":"Jan","suffix":""},{"id":486452284,"identity":"5d97aee0-f554-4bc3-a2e7-1b68ef2f7306","order_by":3,"name":"Iqra Shafi Khan","email":"","orcid":"","institution":"FVSc \u0026 AH, Sher E Kashmir University of Agricultural Sciences and Technology of Kashmir","correspondingAuthor":false,"prefix":"","firstName":"Iqra","middleName":"Shafi","lastName":"Khan","suffix":""},{"id":486452286,"identity":"2dba1de9-954b-47bb-b976-2e7cb6bab4e6","order_by":4,"name":"Nuzhat Hassan","email":"","orcid":"","institution":"FVSc \u0026 AH, Sher E Kashmir University of Agricultural Sciences and Technology of Kashmir","correspondingAuthor":false,"prefix":"","firstName":"Nuzhat","middleName":"","lastName":"Hassan","suffix":""},{"id":486452288,"identity":"1e0b377a-d9ef-4e68-9ba5-5ac7953489ea","order_by":5,"name":"R. A. Bhat","email":"","orcid":"","institution":"FVSc \u0026 AH, Sher E Kashmir University of Agricultural Sciences and Technology of Kashmir","correspondingAuthor":false,"prefix":"","firstName":"R.","middleName":"A.","lastName":"Bhat","suffix":""},{"id":486452289,"identity":"757c9367-b7b4-4a24-aad4-228bdba4fb32","order_by":6,"name":"M. I. Yatoo","email":"","orcid":"","institution":"FVSc \u0026 AH, Sher E Kashmir University of Agricultural Sciences and Technology of Kashmir","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"I.","lastName":"Yatoo","suffix":""}],"badges":[],"createdAt":"2025-07-09 04:23:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7079649/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7079649/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12917-025-05280-z","type":"published","date":"2026-01-14T16:29:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87065778,"identity":"a512d0bc-521b-46df-9b60-e8f1822ed506","added_by":"auto","created_at":"2025-07-18 18:21:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57901,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of major bacterial pathogens isolated from clinical mastitis (n=152, dark grey bars) and subclinical mastitis (n=50, orange bars) samples. The data are presented as percentages with 95% confidence intervals (error bars). Statistical significance was determined using the Fisher's exact test. *P \u0026lt; 0.05, significant differences between the clinical and subclinical mastitis groups. Staphylococcus aureus was significantly more prevalent in subclinical mastitis (P = 0.034), whereas mixed infections were more common in clinical mastitis cases (P = 0.021). Sample size: clinical mastitis, n=152; subclinical mastitis, n=50.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7079649/v1/93261764dff3e67a689bb89b.png"},{"id":87065457,"identity":"24c0f461-9270-4b12-9bd0-333228eab260","added_by":"auto","created_at":"2025-07-18 18:13:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":497912,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Clusters of Orthologous Groups (COG) functional classification showing gene distribution across 23 functional categories. Blue bars represent C65 (S. stutzeri), red bars represent C67 (E. coli). (B) Enzyme Commission (EC) classification displaying enzymatic capacity distribution. Total enzyme counts: C65 = 991 (22.3% of protein-coding genes), C67 = 1,634 (33.7% of protein-coding genes). EC classes: 1 = oxidoreductases, 2 = transferases, 3 = hydrolases, 4 = lyases, 5 = isomerases, 6 = ligases, 7 = translocases. (C) Top Pfam domain families comparison showing functional domain abundance. Domain counts are displayed horizontally with C65 (blue) and C67 (red) representing the number of domains identified in each isolate.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7079649/v1/9116ad59b0683e786a4f29cc.png"},{"id":87065458,"identity":"5214fe6d-4dd8-4743-bc15-e4e9d3e35f32","added_by":"auto","created_at":"2025-07-18 18:13:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":358566,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Species contribution patterns showing percentage distribution of genomic content by taxonomic origin. C65 (S. stutzeri) and C67 (E. coli) isolates display distinct taxonomic compositions with color-coded species groups. (B) Horizontal gene transfer networks illustrating donor species and acquired gene numbers. Bar lengths represent the number of acquired genes from each donor species, categorized by transfer type: dairy-environment (blue), enteric (orange), environmental (green), pathogen-associated (red), and spore-former (purple). Gene acquisition numbers are shown for each donor species with percentages indicating relative contribution to total acquired genes.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7079649/v1/139c1edeb444a5f0ad8485a9.png"},{"id":87066498,"identity":"718644b1-f96c-45ec-8d95-a070c97ebd39","added_by":"auto","created_at":"2025-07-18 18:37:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":411569,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Total antimicrobial resistance (AMR) gene content identified through BLAST analysis against the Comprehensive Antibiotic Resistance Database (CARD). C65 harbored 287 AMR genes (6.7% of protein-coding genes), C67 contained 294 AMR genes (6.5% of protein-coding genes). (B) Drug class distribution showing the top antimicrobial resistance gene categories. Numbers indicate gene counts with percentages of total AMR genes for each class. Blue bars = C65 (S. stutzeri), red bars = C67 (E. coli). (C) Resistance mechanisms distribution categorized by functional mechanism. Efflux-mediated resistance dominated both isolates (\u0026gt;64% of AMR genes), followed by target alteration, inactivation, protection, and reduced permeability mechanisms.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7079649/v1/6386020276a87812b8ae30f6.png"},{"id":87065782,"identity":"fa0169cc-e97d-4308-92e5-703f433140f7","added_by":"auto","created_at":"2025-07-18 18:21:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":880957,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic analysis of Stutzerimonas stutzeri strain C-65 based on 16S rRNA gene sequences.\u003c/p\u003e\n\u003cp\u003eA neighbor-joining phylogenetic tree was constructed using Kimura 2-parameter distance correction with 1,000 bootstrap replicates. Strain C-65 (highlighted in red box) clustered within a well-supported clade containing authenticated S. stutzeri strains with \u0026gt;98% sequence identity to type strain sequences. Bootstrap values ≥70% are shown at major nodes, indicating statistical support for the tree topology. The tree was rooted in appropriate outgroups from the Pseudomonadaceae family. The scale bar represents evolutionary distance. GenBank accession numbers are provided for all the reference sequences used in the phylogenetic reconstruction.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7079649/v1/80352a2aadc243301811ab8f.png"},{"id":87065784,"identity":"609b695e-e5aa-40e8-967f-6a9de41743a8","added_by":"auto","created_at":"2025-07-18 18:21:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1001371,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic positioning of Escherichia coli strain C-67 within the Enterobacteriaceae family.\u003c/p\u003e\n\u003cp\u003eA 16S rRNA gene-based phylogenetic tree was constructed using the neighbor-joining method with the Kimura 2-parameter distance model and 1,000 bootstrap replicates. Strain C-67 (highlighted in the red box) exhibits clear phylogenetic affiliation with E. coli strains, forming a strongly supported monophyletic group with bootstrap values \u0026gt;95% at species-level nodes. The strain showed \u0026gt;99% sequence identity with E. coli type strain sequences. Tree topology was supported by bootstrap analysis, with confidence values displayed at the critical nodes. The scale bar indicates evolutionary distance units.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7079649/v1/12b96118fcf7723b253135aa.png"},{"id":87065779,"identity":"614b243d-1dba-47e0-8d6c-42b91ea9411d","added_by":"auto","created_at":"2025-07-18 18:21:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":448800,"visible":true,"origin":"","legend":"\u003cp\u003e(A) AMR gene distribution by resistance class showing shared and unique resistance patterns between isolates C65 and C67. Both isolates harbor resistance genes across 10 distinct antimicrobial classes with high similarity (Jaccard coefficient = 0.800). (B) Bacterial order distribution of AMR gene origins based on phylogenetic mapping. Heat map intensity represents gene count contribution from each bacterial order: Enterobacterales, Bacillales, Enterococcales, and Pseudomonadales. (C) Resistance profile similarity analysis using Jaccard coefficients. Shared resistance classes (80%, green bar) versus unique resistance patterns (20%, blue bar for C67-specific). (D) Cross-phylum analysis of AMR gene origins showing distribution between Proteobacteria (61%) and Firmicutes (39%), indicating extensive horizontal gene transfer across major bacterial taxonomic divisions.\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7079649/v1/9fef2424be15dbdaf2fe3bad.png"},{"id":87065468,"identity":"e6174010-f48c-4eba-baee-8a191f39909c","added_by":"auto","created_at":"2025-07-18 18:13:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":250637,"visible":true,"origin":"","legend":"\u003cp\u003eThe circular representation displays the complete 4.75 Mb genome containing 4,442 genes with 287 antimicrobial resistance (AMR) genes distributed throughout the chromosome. Tracks from outside to inside represent: (1) genome coordinates in megabases (Mb); (2) GC content percentage shown as a blue line with peaks and valleys indicating regions of varying nucleotide composition; (3) forward strand genes (+) color-coded by Clusters of Orthologous Groups (COG) functional categories; (4) reverse strand genes (-) with corresponding COG color coding; (5) AMR genes highlighted by clinical importance (red = critical, orange = high priority, yellow = medium priority); (6) RNA features including rRNA operons (dark blue), tRNA genes (teal), and pseudogenes (light blue); and (7) pseudogenes marked in the innermost track. The genome achieved 249× sequencing coverage. COG functional categories are represented by distinct colors: translation/ribosomal (334 genes, blue), amino acid metabolism (318 genes, green), general function (306 genes, yellow), transcription (263 genes, brown), inorganic ion transport (262 genes, purple), and cell wall/membrane biogenesis (260 genes, gray). The high density of AMR genes throughout the chromosome indicates extensive resistance potential against multiple antimicrobial classes.\u003c/p\u003e","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7079649/v1/ee4324150a12c38a1dbfd851.png"},{"id":87065465,"identity":"c8b57837-657c-4f6b-8e8c-aa5912ed4c72","added_by":"auto","created_at":"2025-07-18 18:13:56","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":253070,"visible":true,"origin":"","legend":"\u003cp\u003eThe circular representation illustrates the complete 5.0 Mb genome containing 4,852 genes with 294 antimicrobial resistance (AMR) genes distributed across the chromosome. Track organization follows the same pattern as Figure 8: (1) genome coordinates in megabases; (2) GC content percentage fluctuations shown as a blue line; (3) forward strand genes (+) with COG functional color coding; (4) reverse strand genes (-) with corresponding colors; (5) AMR genes categorized by clinical importance levels; (6) RNA features including rRNA operons, tRNA genes, and pseudogenes; and (7) innermost pseudogene track. Sequencing achieved 320× coverage depth. COG functional distribution shows: amino acid metabolism (333 genes, green), carbohydrate metabolism (283 genes, orange), transcription (276 genes, brown), general function (267 genes, yellow), translation/ribosomal (264 genes, blue), and cell wall/membrane (230 genes, gray). The extensive AMR gene complement (294 genes, 6.5% of protein-coding genes) demonstrates significant resistance potential, with genes distributed throughout the genome architecture rather than clustered in specific regions, suggesting multiple acquisition events and chromosomal integration of resistance determinants.\u003c/p\u003e","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7079649/v1/33f67220e60ac4b108e964cc.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Whole Genome Sequencing Reveals Environmental Pathogen Misidentification and Cross- Phylum Antimicrobial Resistance Gene Transfer in Bovine Mastitis: A Pilot Genomic Study","fulltext":[{"header":"Background","content":"\u003cp\u003eCurrent veterinary diagnostic paradigms for bovine mastitis rely heavily on morphological and biochemical identification methods developed for common pathogens, creating systematic bias toward expected organisms while potentially missing environmental bacteria with clinical significance (Taponen and Pyörälä, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lippolis et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The advent of whole-genome sequencing (WGS) offers unbiased species identification capabilities that can reveal the true microbial diversity in clinical infections, particularly for organisms that may exhibit atypical phenotypic characteristics in host environments.\u003c/p\u003e\u003cp\u003eBovine mastitis remains the most economically significant disease affecting dairy cattle worldwide, causing substantial losses through reduced milk production, increased treatment costs, and premature culling (Ruegg, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the full spectrum of causative organisms may be underestimated due to limitations in conventional identification approaches (Kuehn et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Traditional identification approaches, which rely on morphological characteristics, biochemical tests, and targeted PCR amplification, are optimized for common mastitis pathogens, such as \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e species, potentially overlooking emerging or atypical bacterial species (Taponen and Pyörälä, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe limitations of conventional identification methods are particularly evident when investigating complex microbial communities in mastitis-affected milk. Studies using culture-independent approaches have revealed significant microbial diversity beyond traditionally recognized mastitis pathogens, including environmental bacteria that may contribute to infection dynamics and antimicrobial resistance dissemination (G et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Falentin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This hidden diversity has important implications for understanding the evolution of resistance, as environmental bacteria often serve as reservoirs for antimicrobial resistance genes that can be transferred to clinical pathogens through horizontal gene transfer mechanisms (Forsberg et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhole-genome sequencing (WGS) has emerged as a transformative tool that addresses the limitations of conventional identification and provides comprehensive insights into antimicrobial resistance mechanisms, virulence factors, and evolutionary relationships (Köser et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Unlike targeted approaches, WGS enables unbiased species identification and can reveal the complete genomic context of resistance determinants, including mobile genetic elements, that facilitate the spread of resistance (Ellington et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This technology has proven particularly valuable in veterinary microbiology, where accurate pathogen identification directly affects treatment decisions and resistance surveillance programs (Hendriksen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEnvironmental bacteria that cause mastitis may exhibit phenotypic plasticity in host-associated environments, leading to misclassification when relying solely on conventional diagnostic methods. This diagnostic challenge is particularly relevant in regions with limited access to advanced molecular diagnostic tools, where treatment decisions depend heavily on accurate pathogen identification. The widespread use of antimicrobials in dairy farming raises concerns about the evolution and spread of resistance, particularly given the potential for resistance gene transfer between diverse bacterial species sharing the same ecological niche (Jl et al., 2015)\u003c/p\u003e\u003cp\u003eThis pilot study addresses these methodological limitations by combining conventional microbiological surveillance with whole-genome sequencing analysis to evaluate diagnostic accuracy and characterize the genomic diversity of mastitis-associated bacteria\u003c/p\u003e\u003cp\u003eThe specific objectives were as follows: (1) to determine the prevalence and distribution of mastitis pathogens using conventional identification methods, (2) to perform comprehensive genomic characterization of selected isolates using whole-genome sequencing (WGS), (3) to compare conventional identification results with WGS-based species determination, (4) to characterize antimicrobial resistance (AMR) gene profiles and correlate them with phenotypic resistance patterns, and (5) to assess horizontal gene transfer events and genomic complexity in mastitis-associated bacteria. This integrated approach allows for the evaluation of both current surveillance capabilities and the potential benefits of implementing genomic technologies in veterinary diagnostic laboratories.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Sample Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis cross-sectional pilot study was conducted in Kashmir Valley, Jammu and Kashmir, India, from March 2023 to September 2023. The study protocol was approved by the Institutional Animal Ethics Committee of Sher-e-Kashmir University of Agricultural Sciences and Technology-Kashmir. Written informed consent was obtained from all participating dairy farmers.\u003c/p\u003e\u003cp\u003eA total of 330 milk samples were aseptically collected from lactating cows using stratified random sampling from different districts of Kashmir Valley. The sample distribution included 112 samples from clinical mastitis cases at the Veterinary Clinical Complex (FVSc and AH, Shuhama), 88 samples from the Mountain Livestock Research Institute (MLRI, Manasbal), and 130 samples from district veterinary hospitals and dispensaries. Clinical mastitis was diagnosed based on udder inflammation, altered milk consistency, and a positive California Mastitis Test (CMT). Subclinical mastitis was identified using CMT, electrical conductivity measurements, pH testing, and white-side tests.\u003c/p\u003e\u003cp\u003eThe udder teats were cleaned and disinfected with 70% ethanol. The first milk streams were discarded, and approximately 15 mL of milk was collected in sterile screw-capped tubes. The samples were immediately placed on ice and transported to the laboratory within 4 h for processing.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBacterial Isolation and Identification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMilk samples were cultured on 5% sheep blood agar, MacConkey agar, and mannitol salt agar plates, and incubated aerobically at 37°C for 24–48 hours. Bacterial isolates were initially identified using conventional biochemical tests including Gram staining, catalase, coagulase, and species-specific identification kits (HiMedia Laboratories, Mumbai, India). For isolates with staphylococcal morphology, molecular confirmation was attempted using species-specific PCR amplification targeting the gene of Staphylococcus aureus. However, some isolates with gram-positive and catalase-positive characteristics yielded inconsistent or negative results with species-specific primers, necessitating further molecular characterization. Additional PCR amplification was performed to target the 16S rRNA gene of E. coli and specific primers for Streptococcus dysgalactiae (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003ePCR primers used for species-specific identification of mastitis pathogens.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS.No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimer Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrimer Sequence 5’ to 3’\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo. of Bases\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS.aureus (nuc gene F)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGCGATTGATGGTGATACGGTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS.aureus (nuc gene R)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAGCCAAGCCTTGACGAACTAAAGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS.aureus (Mec A MRS1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAAAATCGATGGTAAAGGTTGGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS.aureus (Mec A MRS2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAGTTCTGCAGTACCGGATTTGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE.coli 16SrRNA gene(F)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGACCTCGGTTTAGTTCACAGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE.coli16SrRNAgene ®\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCACACGCTGACGCTGACCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS. dysgalactiea STRD-DyI (F)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGAACACGTTAGGGTCGTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS.dysgalactiea STRD-DyII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAGTATATCTTAACTAGAAAAACTATTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAntimicrobial Susceptibility Testing\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePhenotypic antimicrobial susceptibility testing was performed using the disk diffusion method, according to the Clinical and Laboratory Standards Institute (CLSI) guidelines. Bacterial suspensions equivalent to the McFarland standard (0.5) were inoculated onto Mueller-Hinton agar plates. The antimicrobial disks tested included penicillin G (10 units), amoxicillin-clavulanic acid (30 µg), gentamicin (30 µg), cefpodoxime (10 µg), tetracycline (30 µg), streptomycin (10 µg), ceftriaxone (30 µg), enrofloxacin (10 µg), and cefotaxime (30 µg). Plates were incubated at 37°C for 18–24 hours, and inhibition zones were measured and interpreted according to the CLSI breakpoints. Multidrug resistance was defined as the resistance to three or more antimicrobials.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample Selection for Whole Genome Sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFrom 202 mastitis-positive samples, two isolates were selected for comprehensive genomic analysis based on the following predefined criteria: (1) clinical significance from severe mastitis cases, (2) multidrug resistance to ≥ 3 antimicrobial agents, (3) distinct phenotypic characteristics warranting further investigation, (4) geographic distribution across Kashmir Valley districts, (5) evidence of complex microbial community characteristics, and (6) high-quality DNA suitable for sequencing. It should be noted that this represents a pilot genomic characterization study (0.99% of positive samples), and the findings should be interpreted within this limited scope.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDNA Extraction and Quality Assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenomic DNA was extracted using the DNeasy Blood \u0026amp; Tissue Kit (Qiagen, Germany) following the manufacturer's protocol, with modifications for gram-positive bacteria. DNA concentration was quantified using a Qubit 4.0 Fluorometer (Thermo Fisher Scientific), and quality was assessed using 1.0% agarose gel electrophoresis and NanoDrop spectrophotometry. DNA samples with A260/A280 ratios \u0026gt; 1.8 and concentrations \u0026gt; 50 ng/µL were considered suitable for library preparation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLibrary Preparation and Whole Genome Sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePaired-end sequencing libraries were prepared using the Twist NGS Library Preparation Kit for Illumina following the manufacturer's protocol. The workflow includes enzymatic DNA fragmentation, end repair, A-tailing, adapter ligation, and PCR amplification. Library quality and quantity were assessed using a TapeStation 4150 (Agilent Technologies) with a High-Sensitivity D1000 ScreenTape. Whole-genome sequencing was performed on an Illumina NovaSeq 6000 platform (Unigenome, Ahmedabad, India) using 2 × 150 bp paired-end chemistry, targeting approximately 3 GB coverage per sample.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuality Control and Validation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, comprehensive quality control measures were implemented. Negative extraction controls (sterile water) and positive controls using the reference strains (E. coli ATCC 25922 and S. aureus ATCC 25923) were processed in each batch. Library preparation included no-template controls and sequencing runs incorporating phiX control spike-ins (1% of the reads). Post-sequencing quality assessment was performed using FastQC analysis, with \u0026gt; 95% of reads achieving Q30 quality scores. Contamination screening was performed using the Kraken2 database.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBioinformatics Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRaw sequencing reads were quality filtered using Trimmomatic v0.39 with the following parameters: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36. De novo genome assembly was performed using SPAdes v3.15.4, with an automatic k-mer selection. Assembly quality was evaluated using QUAST v5.0.2 and BUSCO v5.4.3 for completeness assessment.\u003c/p\u003e\u003cp\u003eGenome annotation was performed using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP). Functional annotation involved BLASTp searches against the NCBI nr database (e-value ≤ 1e-5), Gene Ontology mapping using Blast2GO v5.2, and pathway analysis using the KEGG Automatic Annotation Server. Clusters of Orthologous Groups (COG) classification and Pfam domain identification were performed using the respective databases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAntimicrobial Resistance Gene Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eComprehensive resistome characterization was conducted using BLASTp searches against the Comprehensive Antibiotic Resistance Database (CARD) with an e-value threshold ≤ 1e-10 (Jia et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Resistance genes were classified according to their mechanism and drug class. Multiple individual genes often contributed to resistance within single antimicrobial classes, and results were reported both as individual gene counts and resistance class distributions. Mobile genetic elements, including integrons, transposons, and plasmids, were identified using specialized databases and manual curation. AMR genes were mapped to their putative bacterial taxonomic origins, based on their phylogenetic distribution patterns in public databases. Each resistance gene was assigned to bacterial orders (Enterobacterales, Bacillales, Enterococcales, and Pseudomonadales) and phyla (Proteobacteria and Firmicutes) according to their predominant occurrence in the bacterial taxonomy. This approach enabled analysis of horizontal gene transfer patterns across different bacterial lineages.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparative Genomics and Phylogenetic Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor taxonomic classification, 16S rRNA gene sequences were extracted from the annotated genome assemblies. Related sequences were retrieved from the NCBI GenBank database using BLASTn searches (Altschul et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) targeting species-specific and family-level representatives. Species assignments were considered reliable when sequences showed ≥ 97% identity with type strain sequences and were supported by phylogenetic analysis with bootstrap values ≥ 70%. For discrepancies between conventional and genomic identification, phenotypic characteristics were re-evaluated using standardized protocols. Multiple sequence alignments were constructed using DECIPHER implemented in R v4.3.0. Phylogenetic relationships were inferred using the neighbor-joining method with Kimura 2-parameter distance correction, as implemented in the ape package. Tree topology reliability was assessed using bootstrap analysis with 1,000 replicates. Phylogenetic trees were visualized using ggtree, and sequences were reliably clustered when supported by bootstrap values of ≥ 70%. Horizontal gene transfer events were assessed through comparative analysis with reference genomes and the identification of atypical GC content regions. Resistance profiles between the isolates were compared using presence/absence matrices for both resistance classes and individual gene families. Phylogenetic distances were calculated using binary distance metrics (Jaccard distance) to quantify the similarity between resistance profiles. Shared and unique resistance patterns were identified and quantified using the set theory approach.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using the R software (v4.3.0). Descriptive statistics were calculated for prevalence data and genomic metrics. Phenotype-genotype correlation analysis included the calculation of sensitivity, specificity, positive predictive value, negative predictive value, and Cohen's kappa coefficient for agreement assessment. Fisher's exact test was used for categorical comparisons, and the Mann-Whitney U test was used for continuous variables. Multiple testing corrections were applied using the Benjamini-Hochberg false discovery rate method. Statistical significance was set at P \u0026lt; 0.05. Jaccard similarity coefficients were calculated to measure the degree of overlap between the resistance profiles of the isolates. The coefficient is defined as the ratio of the number of shared resistance classes to the total number of unique resistance classes across both isolates. Diversity indices and statistical comparisons were performed using R statistical software (version 4.3.0) with appropriate packages for phylogenetic and ecological analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003ePrevalence of Bovine Mastitis and Pathogen Distribution\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOf the 330 milk samples collected from dairy cattle across Kashmir Valley, 202 (61.2%; 95% CI: 55.8\u0026ndash;66.4%) tested positive for mastitis. Clinical mastitis was detected in 152 samples (46.1%; 95% CI: 40.7\u0026ndash;51.6%), whereas subclinical mastitis was identified in 50 samples (15.2%; 95% CI: 11.6\u0026ndash;19.5%). The remaining 128 (38.8%) samples were obtained from healthy animals with negative mastitis screening test results.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBacterial Pathogen Distribution\u003c/b\u003e\u003c/p\u003e\u003cp\u003eClinical mastitis isolates (n\u0026thinsp;=\u0026thinsp;152): Staphylococcus aureus was the predominant pathogen, isolated from 95 samples (62.5%; 95% CI: 54.4\u0026ndash;70.1%), followed by mixed infections in 41 samples (27.0%; 95% CI: 20.2\u0026ndash;34.7%), E. coli in 12 samples (7.9%; 95% CI: 4.3\u0026ndash;13.4%), and Streptococcus dysgalactiae in 4 samples (2.6%; 95% CI: 0.7\u0026ndash;6.6%).\u003c/p\u003e\u003cp\u003eSubclinical mastitis isolates (n\u0026thinsp;=\u0026thinsp;50): S. aureus maintained dominance with 35 isolates (70.0%; 95% CI: 55.4\u0026ndash;82.1%), followed by mixed infections in nine samples (18.0%; 95% CI: 8.6\u0026ndash;31.4%), S. dysgalactiae in four samples (8.0%; 95% CI: 2.2\u0026ndash;19.2%), and E. coli in two samples (4.0%; 95% CI: 0.5\u0026ndash;13.7%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe prevalence of S. aureus was significantly higher in subclinical mastitis than that in clinical mastitis (70.0% vs. 62.5%; Fisher's exact test, P\u0026thinsp;=\u0026thinsp;0.034). Mixed infections were more common in clinical mastitis cases (27.0% vs. 18.0%; P\u0026thinsp;=\u0026thinsp;0.021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll bacterial isolates were confirmed using biochemical tests (Hi Staph and Hi Strep identification kits) and species-specific PCR amplification.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhenotypic Antimicrobial Resistance Profiles\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePhenotypic antimicrobial resistance profiling was performed using the disk-diffusion method. The highest multidrug resistance patterns were as follows:\u003c/p\u003e\u003cp\u003eStaphylococcus aureus: resistance to penicillin, tetracycline, amoxicillin-clavulanic acid, cefpodoxime, and streptomycin\u003c/p\u003e\u003cp\u003eStreptococcus dysgalactiae: Resistance to penicillin, tetracycline, and streptomycin\u003c/p\u003e\u003cp\u003eE. coli: Resistance to penicillin, tetracycline, amoxicillin-clavulanic acid, cefpodoxime, and strept resistance pathotypes was identified for S. aureus, including Mec A MRS1 and Mec A MRS2 genes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenotype-Phenotype Resistance Correlation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenomic analysis revealed extensive resistance gene repertoires (287\u0026ndash;294 genes per isolate), and phenotypic testing was limited to nine antimicrobial agents. The direct correlation between genotypic and phenotypic resistance could not be comprehensively assessed because of this limitation. Most identified resistance genes (\u0026gt;\u0026thinsp;90%) showed no corresponding phenotypic expression under standard testing conditions, suggesting conditional expression, silent carriage, or resistance to antimicrobials that were not included in the phenotypic panel.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhole Genome Sequencing Results\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSequencing Metrics and Assembly Quality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhole-genome sequencing of C65 (\u003cem\u003eStutzerimonas stutzeri\u003c/em\u003e) and C67 (\u003cem\u003eEscherichia coli\u003c/em\u003e) generated 15,037,230 paired-end reads (2.39 GB total data) for C65 and 16,749,040 reads (2.66 GB total data) for C67, with coverage of 249\u0026times; and 320\u0026times;, respectively. Quality control using FastQC showed\u0026thinsp;\u0026gt;\u0026thinsp;95% reads with Q30 scores after Trimmomatic pre-processing.\u003c/p\u003e\u003cp\u003eDe novo assembly using SPAdes v3.15.4 yielded genomes assembled into 77 scaffolds for both isolates. The C65 assembly consisted of 4,442 genes, including 4,380 coding DNA sequences (CDSs), of which 4,302 encoded proteins and 78 pseudogenes. C67 contained 4,852 genes with 4,752 CDSs, of which 4,546 encoded proteins and 206 were pseudogenes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both genomes were deposited in GenBank under the accession numbers JBNYYH000000000.1 (C65) and JBNYYI000000000.1 (C67).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenome Assembly Quality Metrics and Characteristics of Bacterial Isolates C65 and C67\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssembly Parameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC65 (\u003cem\u003eStutzerimonas stutzeri\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC67 (\u003cem\u003eEscherichia coli\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBASIC ASSEMBLY METRICS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssembly Method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPAdes v3.15.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSPAdes v3.15.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSequencing Platform\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIllumina NovaSeq 6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIllumina NovaSeq 6000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRead Configuration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 \u0026times; 150 bp paired-end\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 \u0026times; 150 bp paired-end\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenome Coverage (\u0026times;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e249.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e320.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Sequencing Data (GB)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Raw Reads\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15,037,230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16,749,040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eASSEMBLY QUALITY\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Contigs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstimated Genome Size (Mbp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.5-5.0*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.8\u0026ndash;5.2*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssembly Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComplete\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComplete\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenBank Accession\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eJBNYYH000000000.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eJBNYYI000000000.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGENE CONTENT ANALYSIS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4,442\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4,852\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein-coding Genes (CDSs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4,380\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4,752\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCDSs with Protein Product\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4,302\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4,546\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein Coding Efficiency (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e98.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e95.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudogenes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e206\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudogene Ratio (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRNA GENE CONTENT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal RNA Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erRNA Genes (5S, 16S, 23S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1, 1, 3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e7, 2, 4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplete rRNAs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3 (5S:1, 16S:1, 23S:1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e9 (5S:5, 16S:2, 23S:2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePartial rRNAs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2 (23S)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4 (5S:2, 23S:2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etRNA Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003encRNA Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMOBILE GENETIC ELEMENTS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRISPR Arrays\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional Annotation Coverage\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFunctional annotation using BLASTp v2.13.0\u0026thinsp;+\u0026thinsp;against the NCBI nr database yielded 4,359 proteins (98.1%) annotated for C65 and 4,462 proteins (98.2%) annotated for C67, using an e-value threshold of \u0026le;\u0026thinsp;1e-5. The Gene Ontology annotation coverage was 61.6% for C67 and 40.7% for C65 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBlast2GO Functional Annotation Analysis of Bovine Milk Isolates\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunctional Parameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC65 (\u003cem\u003eS. stutzeri\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC67 (\u003cem\u003eE. coli\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGENOME ANNOTATION OVERVIEW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Predicted Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4,442\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4,852\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBLAST Hit Coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4,359 (98.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4,462 (98.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGO Annotation Coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1,808 (40.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2,989 (61.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnzyme-Coding Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e991 (22.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1,634 (33.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnotation Method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlast2GO v5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlast2GO v5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGENE ONTOLOGY DISTRIBUTION\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular Function Terms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2,724\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e5,058\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiological Process Terms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2,045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4,094\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCellular Component Terms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1,161\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2,102\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal GO Terms Assigned\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e5,930\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e11,254\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage GO Terms per Gene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eENZYME CLASSIFICATION (EC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC 1 - Oxidoreductases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e241\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e417\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC 2 - Transferases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e412\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e687\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC 3 - Hydrolases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e361\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e645\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC 4 - Lyases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e89\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e183\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC 5 - Isomerases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e101\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e191\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC 6 - Ligases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e118\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC 7 - Translocases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFUNCTIONAL CATEGORIES\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransport-Related Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e147\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e256\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolic Enzymes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e872\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1,467\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegulatory Proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e140\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStress Response Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSignal Transduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e70\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e115\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eANNOTATION QUALITY METRICS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothetical Proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e871 (19.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e718 (14.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWell-Annotated Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3,571 (80.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4,134 (85.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunctional Annotation Score*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e7.2/10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e8.6/10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDatabase Completeness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eExcellent\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSuperior\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuitability for Systems Biology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGood\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eExcellent\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparative Genomics and Functional Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMetabolic Capacity Distribution\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCOG functional classification revealed 4,198 COG-annotated genes across 23 functional categories in C65, and 4,285 COG-annotated genes in C67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). C65 contained 334 genes involved in translation, ribosomal structure, and biogenesis (COG-J), compared to 264 genes in C67. C67 had 283 genes involved in carbohydrate metabolism (COG-G) and 221 genes in C65. Energy production and conversion (COG-C) genes, numbered 222 in C65 and 204 in C67, were identified. The Enzyme Commission classification identified 1,634 enzyme-coding genes (33.7% of protein-coding genes) in C67, compared to 991 genes (22.3%) in C65 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCOG Functional Classification Comparison Between Bovine Milk Isolates\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOG Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFunction Description\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC65 Count\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC67 Count\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDifference (C65-C67)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFold Change (C65/C67)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRNA processing and modification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChromatin structure and dynamics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnergy production and conversion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e222\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e204\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u0026thinsp;18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCell cycle control, cell division, chromosome partitioning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAmino acid transport and metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e318\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e333\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.95\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNucleotide transport and metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCarbohydrate transport and metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e221\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e283\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoenzyme transport and metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLipid transport and metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTranslation, ribosomal structure and biogenesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e334\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e264\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u0026thinsp;70\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.27\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTranscription\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReplication, recombination and repair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCell wall/membrane/envelope biogenesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCell motility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePosttranslational modification, protein turnover, chaperones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInorganic ion transport and metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e262\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e204\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u0026thinsp;58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary metabolites biosynthesis, transport and catabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral function prediction only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFunction unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignal transduction mechanisms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e250\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e197\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u0026thinsp;53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.27\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntracellular trafficking, secretion, and vesicular transport\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefense mechanisms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExtracellular structures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMobilome: prophages, transposons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNuclear structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCytoskeleton\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAll functional categories\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4,198\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4,285\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.98\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSignal Transduction and Environmental Response\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe signal transduction mechanism (COG-T) comprised 250 genes in C65 and 197 genes in C67. The response regulator domains included 375 genes in C65 and 198 genes in C67 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePfam domain analysis identified 73 ABC transporter domains in C65 and 67 in C67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). C65 contained 37 response regulator domains, compared with 20 in C67. C67 contained 14 peptidase S8 and six fimbrial domains, whereas C65 contained 0 in both domains.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePfam Domain Family Distribution Comparison\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePfam Domain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFunction Description\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC65 Count\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC67 Count\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDifference (C65-C67)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFold Change (C65/C67)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eABC_tran\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATP-binding cassette transporter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResponse_reg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResponse regulator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eadh_short\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShort-chain dehydrogenase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHATPase_c\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHistidine kinase-like ATPase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMCPsignal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMethyl-accepting chemotaxis protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBPD_transp_1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBinding protein-dependent transporter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhage_integrase\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhage integrase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAMP-binding\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAMP-binding enzyme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePeptidase_S8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubtilase-type peptidase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHTH_1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHelix-turn-helix DNA-binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAminotran_1_2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAminotransferase class-I and II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAldedh\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAldehyde dehydrogenase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGGDEF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGDEF domain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEAL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEAL domain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLysR_substrate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLysR substrate binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePapD_N\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePapD N-terminal domain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUsher\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUsher protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFimbrial\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFimbrial protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSugar_tr\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSugar transporter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHlyD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHlyD family secretion protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpecies Distribution and Horizontal Gene Transfer\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic Composition Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSpecies distribution analysis revealed that C65 demonstrated \u003cem\u003eStutzerimonas stutzeri\u003c/em\u003e, with genetic contributions from \u003cem\u003eMacrococcus\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e species. C67 exhibited \u003cem\u003eE. coli\u003c/em\u003e genomic architecture with contributions from \u003cem\u003ePaenisporosarcina\u003c/em\u003e species (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). C65 acquires genes from at least 15 different species, including \u003cem\u003eStaphylococcus aureus\u003c/em\u003e. C67 showed gene acquisition from the spore-forming bacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eProtein Annotation Analysis and Taxonomic Distribution of Bacterial Isolates\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnotation Parameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC65 (\u003cem\u003eS. stutzeri\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC67 (\u003cem\u003eE. coli\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANNOTATION STATISTICS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Proteins Annotated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4,220\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4,462\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnotation Success Rate (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e98.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e98.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDatabase Used\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNCBI nr (BLASTp v2.13.0+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNCBI nr (BLASTp v2.13.0+)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE-value Threshold\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1e-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1e-5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSEQUENCE IDENTITY DISTRIBUTION\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Identity (\u0026ge;\u0026thinsp;90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e85.2%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e78.9%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium Identity (70\u0026ndash;89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e12.1%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e18.3%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Identity (\u0026lt;\u0026thinsp;70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2.7%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.8%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDOMINANT TAXONOMIC GROUPS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary Species/Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStutzerimonas stutzeri\u003c/em\u003e \u003cb\u003e(32.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eEnterobacteriaceae (36.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary Species/Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e \u003cb\u003e(21.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ePaenisporosarcina\u003c/em\u003e \u003cb\u003esp. (35.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary Species/Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus\u003c/em\u003e \u003cb\u003espp. (19.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e \u003cb\u003e(8.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotential Pathogens Detected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eS. aureus\u003c/em\u003e \u003cb\u003e(6.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eEnterobacteriaceae members\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Taxonomic Groups (\u0026gt;\u0026thinsp;1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCOMMUNITY CHARACTERISTICS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommunity Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eEnvironmental generalist\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eEnteric pathogen\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpected vs. Observed Species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eExpected: Primary (32.6%)\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;br\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eHGT confirmed (67.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eExpected: Minor (8.2%)\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;br\u0026thinsp;\u0026gt;\u0026thinsp;\u003cb\u003eComplex HGT detected (91.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironmental Context\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eDairy-associated microbiome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eDairy-associated microbiome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAntimicrobial Resistance Gene Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAMR Gene Content\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSystematic screening identified 287 AMR genes in C65 (6.7% of protein-coding genes) and 294 AMR genes in C67 (6.5% of protein-coding genes) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDrug Class Distribution\u003c/b\u003e\u003c/p\u003e\u003cp\u003eC65 resistance gene distribution: peptide antibiotics (31 genes, 10.8%), glycopeptide antibiotics (29 genes, 10.1%), and macrolide antibiotics (28 genes, 9.8%). The distribution of the C67 resistance genes was as follows: tetracycline antibiotics (40 genes, 13.6%), aminoglycoside antibiotics (28 genes, 9.5%), and peptide antibiotics (27 genes, 9.2%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAntimicrobial Resistance Gene Analysis of Bovine Milk Isolates\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMR Parameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC65 (\u003cem\u003eS. stutzeri\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC67 (\u003cem\u003eE. coli\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMR GENE OVERVIEW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal AMR Genes Identified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e287\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e294\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMR Gene Percentage of Protein-Coding Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e6.7%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6.5%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDatabase Query Method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBLASTp v2.13.0+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBLASTp v2.13.0+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE-value Threshold\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1e-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1e-5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTOP DRUG CLASS RESISTANCE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary Drug Class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePeptide antibiotics (31 genes, 10.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eTetracycline antibiotics (40 genes, 13.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary Drug Class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGlycopeptide antibiotics (29 genes, 10.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eAminoglycoside antibiotics (28 genes, 9.5%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary Drug Class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMacrolide antibiotics (28 genes, 9.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ePeptide antibiotics (27 genes, 9.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTetracycline Resistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e28 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e40 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAminoglycoside Resistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e26 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e28 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFluoroquinolone Resistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e5 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e9 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMulti-Drug Resistance Patterns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e14 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e8 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRESISTANCE MECHANISMS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntibiotic Efflux\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e186 genes (64.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e197 genes (67.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntibiotic Target Alteration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e49 genes (17.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e40 genes (13.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntibiotic Target Protection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e16 genes (5.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e16 genes (5.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntibiotic Inactivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e15 genes (5.2%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e18 genes (6.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReduced Permeability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e7 genes (2.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e7 genes (2.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCLINICAL SIGNIFICANCE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCritical Priority Resistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e21 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e49 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Priority Resistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e37 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e62 genes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment Impact Assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eSubstantial therapeutic challenges\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eMajor treatment complications\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMastitis Treatment Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eHIGH RISK\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHIGH RISK\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMR Monitoring Priority\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCRITICAL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eCRITICAL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eResistance Mechanisms\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEfflux-mediated resistance: C65 harbored 186 efflux genes (64.8% of AMR genes), C67 contained 197 efflux genes (67.0%). Target alterations comprised 49 genes (17.1%) in C65 and 40 genes (13.6%) in C67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhylogenetic Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvolutionary Positioning\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePhylogenetic analysis based on 16S rRNA gene sequences provided an unambiguous taxonomic classification for both bacterial isolates. Strain C-65 clustered within a well-supported clade containing authenticated Stutzerimonas stutzeri strains, demonstrating\u0026thinsp;\u0026gt;\u0026thinsp;98% sequence identity with the type strain and other characterized members of this species. The phylogenetic tree, rooted using appropriate outgroups and supported by bootstrap values exceeding 70% at critical nodes, confirmed the placement of C-65 in the Pseudomonadaceae. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eC-67 exhibited a clear phylogenetic affiliation with Escherichia coli, forming a strongly supported monophyletic group with other E. coli strains within the Enterobacteriaceae family. Bootstrap analysis consistently supported this taxonomic assignment, with confidence values\u0026thinsp;\u0026gt;\u0026thinsp;95% at the species-level nodes. The 16S rRNA gene sequences showed\u0026thinsp;\u0026gt;\u0026thinsp;99% identity with E. coli type strain sequences available in public databases. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAMR Gene Phylogeny\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe antimicrobial resistance genes identified in the two milk isolates represented ten distinct resistance classes. Isolate C65 harbored resistance genes across eight antimicrobial classes, whereas isolate C67 harbored resistance genes across 10 antimicrobial classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). When considering individual resistance genes, C65 harbored 287 total AMR genes (6.7% of protein-coding genes) while C67 contained 294 total AMR genes (6.5% of protein-coding genes), with multiple genes often contributing to resistance within single antimicrobial classes. Comprehensive genomic analysis of C65 revealed a 4.75 Mb circular chromosome containing 4,442 genes, including 287 AMR genes distributed throughout the genome architecture (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Similarly, C67 demonstrated a complex genomic architecture with 4,852 total genes, including 294 AMR genes (6.5% of protein-coding genes), distributed across the Escherichia coli genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Both isolates were resistant to beta-lactam antibiotics, tetracyclines, aminoglycosides, sulfonamides, chloramphenicol, macrolides, glycopeptides, and quinolones. However, C67 uniquely possesses efflux pump genes and polymyxin resistance determinants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePhylogenetic mapping revealed that AMR genes originated from four distinct bacterial orders: Enterobacterales, Bacillales, Enterococcales, and Pseudomonadales (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Enterobacterales was the most prevalent source, contributing to beta-lactam, aminoglycoside, sulfonamide, and quinolone resistance. Quantitative similarity analysis using Jaccard coefficients revealed a similarity index of 0.800 between the isolates, with eight resistance classes (80%) shared and two unique to C67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eCross-phylum analysis demonstrated that resistance genes originated from Proteobacteria (61%) and Firmicutes (39%), indicating extensive horizontal gene transfer across the major bacterial taxonomic divisions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). The presence of resistance genes from multiple bacterial orders and phyla in single isolates provides evidence of horizontal gene transfer events, and suggests that milk-associated bacterial communities serve as reservoirs for resistance genes from diverse bacterial sources.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eWhole Genome Sequencing Unveils Hidden Microbial Complexity in Bovine Mastitis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis pilot study provides methodological validation for the integration of whole-genome sequencing into veterinary diagnostic workflows, demonstrating critical limitations in conventional identification approaches for environmental opportunistic pathogens. The misidentification of Stutzerimonas stutzeri as a gram-positive organism illustrates how environmental bacteria may exhibit phenotypic plasticity in host-associated environments, leading to systematic diagnostic errors when relying solely on morphological and biochemical characteristics. This highlights the limitations of phenotypic identification methods and emphasizes the importance of molecular confirmation, particularly for environmental bacteria that exhibit atypical morphological characteristics in clinical samples. This discovery exemplifies the critical limitations of culture-dependent identification approaches, which remain heavily biased toward detecting expected mastitis pathogens while systematically missing environmentally derived opportunistic organisms (Ahmadi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Algharib et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecent advances in culture-independent diagnostic approaches have revealed substantial microbial diversity in mastitis-affected quarters beyond that of traditionally recognized pathogens (R\u0026ouml;tzer et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The identification of S. stutzeri in clinical mastitis represents a paradigm shift, as this environmental bacterium has been largely overlooked in veterinary diagnostics despite its documented opportunistic pathogenic potential in immunocompromised hosts. The extensive antimicrobial resistance profile of C65 (287 resistance genes comprising 6.7% of the genome) indicates that this organism is a significant resistance reservoir, potentially facilitating horizontal gene transfer within the mammary gland microbiome (Berendonk et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiagnostic Gaps and the Need for Genomic Surveillance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe misidentification of isolate C65 underscores the fundamental gaps in current mastitis surveillance programs, which rely predominantly on conventional microbiological methods optimized for common pathogens (Naranjo-Lucena and Slowey, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Traditional diagnostic workflows using selective media, biochemical tests, and species-specific polymerase chain reaction (PCR) create diagnostic blind spots that systematically exclude environmental bacteria. This bias toward \"expected\" pathogens may contribute to treatment failures when atypical organisms are involved, as demonstrated by recent deep learning approaches that have identified novel genomic signatures associated with mastitis susceptibility (Kotlarz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhole metagenome sequencing studies have consistently revealed that 30% of the bacterial strains in clinical mastitis samples were previously unreported, highlighting the magnitude of unrecognized microbial diversity (Hoque et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The implementation of culture-independent methods represents a critical evolution from a pathogen-focused to an ecosystem-level understanding of mastitis pathogenesis, enabling the detection of bacterial communities that traditional methods systematically miss.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAntimicrobial Resistance and Cross-Phylum Gene Exchange\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe extensive antimicrobial resistance gene repertoires identified (287\u0026ndash;294 individual genes distributed across 8\u0026ndash;10 resistance classes) demonstrate the complexity of resistance evolution in mixed microbial communities. The detection of resistance genes from four distinct bacterial orders (Enterobacterales, Bacillales, Enterococcales, and Pseudomonadales) within single isolates provides direct evidence for horizontal gene transfer events across major taxonomic divisions. Both isolates demonstrated extensive genomic resistomes that far exceed phenotypically expressed resistance, indicating a substantial silent resistance potential (Naushad et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Naranjo-Lucena and Slowey, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This phenomenon reflects the capacity for resistance gene activation under selective pressure, which poses significant challenges to antimicrobial stewardship in dairy farming.\u003c/p\u003e\u003cp\u003ePhylogenetic analysis of AMR gene acquisition from four distinct bacterial orders provides compelling evidence of horizontal gene transfer events across major taxonomic divisions (Ronco et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The cross-phylum distribution (61% Proteobacteria and 39% Firmicutes) demonstrated that environmental bacteria served as significant donors in resistance gene exchange networks. Recent resistome analyses have shown that horizontal gene transfer can disrupt the traditional link between the microbiome and resistome composition, explaining how distantly related species acquire similar resistance profiles to antimicrobials.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnvironmental Reservoirs and One Health Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe identification of multidrug-resistant environmental bacteria in dairy cattle mastitis has profound health implications, given the potential for resistance gene transfer to human pathogens through multiple pathways (Guardabassi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Dairy environments function as critical interface zones, where bacterial communities from animals, humans, and the environment interact, creating opportunities for the dissemination of resistance across these domains. The emergence of resistance to newer antimicrobials, including ceftazidime, cefquinome, and colistin, in bovine mastitis pathogens underscores the contribution of agriculture to the global antimicrobial resistance crisis (Oliver and Murinda, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Molineri et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecent surveillance data from multiple continents have consistently demonstrated an increasing prevalence of methicillin-resistant staphylococci and extended-spectrum beta-lactamase producers in bovine mastitis (Mostafa Abdalhamed et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The mammary gland represents a unique ecological niche where environmental bacteria, commensals, and pathogens coexist under intermittent antimicrobial pressure, potentially accelerating the evolution and dissemination of resistance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical and Therapeutic Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenomic characterization of environmental mastitis pathogens reveals virulence factors and metabolic capabilities that provide crucial insights into pathogenesis mechanisms distinct from those of traditional mastitis-causing organisms (Ashraf et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Environmental bacteria that cause mastitis may respond differently to standard therapeutic protocols designed for conventional pathogens, potentially contributing to treatment failure and chronic infections. Comparative genomic studies across multiple continents have identified distinct regional variations in the strain distribution and resistance patterns, emphasizing the need for geographically tailored diagnostic and therapeutic approaches (Lippolis et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The extensive resistance profiles identified suggest that empirical antibiotic therapy may be inadequate for infections caused by environmentally derived pathogens.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Limitations and Future Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral limitations of this study must be acknowledged when interpreting these results. First, the extremely limited sample size (0.99% of positive samples) restricts the generalizability of the findings to a broader population of mastitis pathogens. Second, the observed species identification discrepancy may represent an isolated case rather than a systematic diagnostic limitation of the method.\u003c/p\u003e\u003cp\u003eThe search for therapeutic alternatives beyond traditional antibiotics has intensified with promising developments in herbal medicine, nanotechnology, polymers, and phototherapy, which have been shown to be effective against mastitis pathogens (Kuralkar and Kuralkar, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The integration of rapid molecular diagnostics with genomic surveillance represents a critical advancement in precision veterinary medicine, enabling species identification and resistance profiling within clinically relevant time frames.\u003c/p\u003e\u003cp\u003ePhylogenetic analyses based on whole-genome sequences provide unprecedented insights into the evolutionary relationships between mastitis pathogens, revealing patterns of virulence gene acquisition and resistance evolution (Crippa et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Future surveillance programs should integrate metagenomic approaches with whole-genome sequencing of isolates to provide comprehensive insights into mammary gland microbiome dynamics and their roles in mastitis pathogenesis. Future studies should include larger, randomly selected sample sizes and comprehensive genotype-phenotype correlation analyses to validate these preliminary observations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis pilot study provides preliminary evidence of the potential of whole genome sequencing to identify bacterial species that may be missed by conventional diagnostic methods for bovine mastitis. The identification of Stutzerimonas stutzeri as a misclassified environmental pathogen demonstrates the potential for systematic diagnostic errors when relying on phenotypic characteristics alone. The extensive antimicrobial resistance profiles identified, coupled with evidence of horizontal gene transfer between phylogenetically distant species, underscore the complexity of resistance evolution in dairy environments. However, these findings are based on a limited sample size (n\u0026thinsp;=\u0026thinsp;2 isolates from 202 positive samples) and require validation through large-scale studies before broader conclusions regarding diagnostic limitations can be drawn.\u003c/p\u003e\u003cp\u003eThese findings support the implementation of genomic surveillance programs in veterinary medicine to improve the accuracy of pathogen identification, guide evidence-based therapy, and monitor emerging resistance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the dairy farmers of the Kashmir Valley for their cooperation and participation in this study. We thank the staff at the Veterinary Clinical Complex and the Mountain Livestock Research Institute for their assistance in sample collection. We are grateful to the laboratory personnel who contributed to the bacterial isolation and identification work. The authors also acknowledge Unigenome, Ahmedabad, India, for providing whole-genome sequencing services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with ethical guidelines for animal research. The study protocol was approved by the Institutional Animal Ethics Committee of Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir. Written informed consent was obtained from all participating dairy farmers prior to sample collection. All procedures involving animals were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Muhee\u003c/strong\u003e: Investigation, Methodology, Data curation, formal analysis, writing \u0026ndash; original draft, project administration, and funding acquisition. \u003cstrong\u003eA. Pandit\u003c/strong\u003e: Conceptualization, Methodology, Software, Formal analysis, writing \u0026ndash; original draft, writing \u0026ndash; review and editing, project administration, correspondence. \u003cstrong\u003eSobby Jan\u003c/strong\u003e: Investigation, Data curation, Validation, Writing \u0026ndash; review, and editing. \u003cstrong\u003eIqra Shafi Khan\u003c/strong\u003e: Investigation, Data curation, formal analysis, and visualization. \u003cstrong\u003eNuzhat Hassan\u003c/strong\u003e: Investigation, Methodology, Validation, Writing \u0026ndash; review, and editing. \u003cstrong\u003eR.A. Bhat\u003c/strong\u003e: Resources, Supervision, Writing \u0026ndash; review and editing, funding acquisition. \u003cstrong\u003eM.I. Yatoo\u003c/strong\u003e: Conceptualization, Resources, Supervision, Writing \u0026ndash; Review and Editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequencing data were deposited in the NCBI Sequence Read Archive under the BioProject PRJNA1048756 (accession numbers SRS25103899 and SRS25138938). The assembled genomes were submitted to GenBank under accession numbers GCA_050565245.1 and GCA_050565265.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the J\u0026amp;K Science Technology \u0026amp; Innovation Council (JKST\u0026amp;IC), Department of Science \u0026amp; Technology, Government of Jammu \u0026amp; Kashmir [grant no. JKST\u0026amp;IC Order no. 82 of 2021].\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmadi, A., Khezri, A., N\u0026oslash;rsteb\u0026oslash;, H., Ahmad, R., 2022. A culture-, amplification-independent, and rapid method for identification of pathogens and antibiotic resistance profile in bovine mastitis milk. Front. Microbiol. 13, 1104701. doi:10.3389/fmicb.2022.1104701\u003c/li\u003e\n\u003cli\u003eAlgharib, S.A., Dawood, A.S., Huang, L., Guo, A., Zhao, G., Zhou, K., Li, C., Liu, J., Gao, X., Luo, W., Xie, S., 2024. Basic concepts, recent advances, and future perspectives in the diagnosis of bovine mastitis. J. Vet. Sci. 25, e18. doi:10.4142/jvs.23147\u003c/li\u003e\n\u003cli\u003eAltschul, S.F., Madden, T.L., Sch\u0026auml;ffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J., 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389\u0026ndash;3402. doi:10.1093/nar/25.17.3389\u003c/li\u003e\n\u003cli\u003eAshraf, S., Naushad, S., Si, W., Bilal, M., Ijaz, M., Huang, H., Zhao, X., 2022. Draft Genome Sequences and Antimicrobial Resistance Genes of Five Staphylococcus aureus Strains Isolated from Bovine Milk. Microbiol. Resour. Announc. 11, e00756-22. doi:10.1128/mra.00756-22\u003c/li\u003e\n\u003cli\u003eBerendonk, T.U., Manaia, C.M., Merlin, C., Fatta-Kassinos, D., Cytryn, E., Walsh, F., B\u0026uuml;rgmann, H., S\u0026oslash;rum, H., Norstr\u0026ouml;m, M., Pons, M.-N., Kreuzinger, N., Huovinen, P., Stefani, S., Schwartz, T., Kisand, V., Baquero, F., Martinez, J.L., 2015. Tackling antibiotic resistance: the environmental framework. Nat. Rev. Microbiol. 13, 310\u0026ndash;317. doi:10.1038/nrmicro3439\u003c/li\u003e\n\u003cli\u003eCrippa, B.L., Rodrigues, M.X., Tomazi, T., Yang, Y., de Oliveira Rocha, L., Bicalho, R.C., Silva, N.C.C., 2023. Virulence factors, antimicrobial resistance and phylogeny of bovine mastitis-associated Streptococcus dysgalactiae. J. Dairy Res. 90, 152\u0026ndash;157. doi:10.1017/S0022029923000195\u003c/li\u003e\n\u003cli\u003eEllington, M.J., Ekelund, O., Aarestrup, F.M., Canton, R., Doumith, M., Giske, C., Grundman, H., Hasman, H., Holden, M.T.G., Hopkins, K.L., Iredell, J., Kahlmeter, G., K\u0026ouml;ser, C.U., MacGowan, A., Mevius, D., Mulvey, M., Naas, T., Peto, T., Rolain, J.-M., Samuelsen, \u0026Oslash;., Woodford, N., 2017. The role of whole genome sequencing in antimicrobial susceptibility testing of bacteria: report from the EUCAST Subcommittee. Clin. Microbiol. Infect. Off. Publ. Eur. Soc. Clin. Microbiol. Infect. Dis. 23, 2\u0026ndash;22. doi:10.1016/j.cmi.2016.11.012\u003c/li\u003e\n\u003cli\u003eFalentin, H., Rault, L., Nicolas, A., Bouchard, D.S., Lassalas, J., Lamberton, P., Aubry, J.-M., Marnet, P.-G., Le Loir, Y., Even, S., 2016. Bovine Teat Microbiome Analysis Revealed Reduced Alpha Diversity and Significant Changes in Taxonomic Profiles in Quarters with a History of Mastitis. Front. Microbiol. 7, 480. doi:10.3389/fmicb.2016.00480\u003c/li\u003e\n\u003cli\u003eForsberg, K.J., Reyes, A., Wang, B., Selleck, E.M., Sommer, M.O.A., Dantas, G., 2012. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107\u0026ndash;1111. doi:10.1126/science.1220761\u003c/li\u003e\n\u003cli\u003eG, O., Ml, B., E, M., Re, R., C, F., Vs, M., Ag, T., C, S., Yh, S., Rc, B., 2014. Microbiota of cow\u0026rsquo;s milk; distinguishing healthy, sub-clinically and clinically diseased quarters. PloS One 9. doi:10.1371/journal.pone.0085904\u003c/li\u003e\n\u003cli\u003eGuardabassi, L., Butaye, P., Dockrell, D.H., Fitzgerald, J.R., Kuijper, E.J., ESCMID Study Group for Veterinary Microbiology (ESGVM), 2020. One Health: a multifaceted concept combining diverse approaches to prevent and control antimicrobial resistance. Clin. Microbiol. Infect. Off. Publ. Eur. Soc. Clin. Microbiol. Infect. Dis. 26, 1604\u0026ndash;1605. doi:10.1016/j.cmi.2020.07.012\u003c/li\u003e\n\u003cli\u003eHendriksen, R.S., Bortolaia, V., Tate, H., Tyson, G.H., Aarestrup, F.M., McDermott, P.F., 2019. Using Genomics to Track Global Antimicrobial Resistance. Front. Public Health 7. doi:10.3389/fpubh.2019.00242\u003c/li\u003e\n\u003cli\u003eHoque, M.N., Istiaq, A., Clement, R.A., Gibson, K.M., Saha, O., Islam, O.K., Abir, R.A., Sultana, M., Siddiki, A.Z., Crandall, K.A., Hossain, M.A., 2020. Insights Into the Resistome of Bovine Clinical Mastitis Microbiome, a Key Factor in Disease Complication. Front. Microbiol. 11, 860. doi:10.3389/fmicb.2020.00860\u003c/li\u003e\n\u003cli\u003eJia, B., Raphenya, A.R., Alcock, B., Waglechner, N., Guo, P., Tsang, K.K., Lago, B.A., Dave, B.M., Pereira, S., Sharma, A.N., Doshi, S., Courtot, M., Lo, R., Williams, L.E., Frye, J.G., Elsayegh, T., Sardar, D., Westman, E.L., Pawlowski, A.C., Johnson, T.A., Brinkman, F.S.L., Wright, G.D., McArthur, A.G., 2017. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 45, D566\u0026ndash;D573. doi:10.1093/nar/gkw1004\u003c/li\u003e\n\u003cli\u003eJl, M., Tm, C., F, B., 2015. What is a resistance gene? Ranking risk in resistomes. Nat. Rev. Microbiol. 13. doi:10.1038/nrmicro3399\u003c/li\u003e\n\u003cli\u003eK\u0026ouml;ser, C.U., Ellington, M.J., Cartwright, E.J.P., Gillespie, S.H., Brown, N.M., Farrington, M., Holden, M.T.G., Dougan, G., Bentley, S.D., Parkhill, J., Peacock, S.J., 2012. Routine use of microbial whole genome sequencing in diagnostic and public health microbiology. PLoS Pathog. 8, e1002824. doi:10.1371/journal.ppat.1002824\u003c/li\u003e\n\u003cli\u003eKotlarz, K., Mielczarek, M., Biecek, P., Wojdak-Maksymiec, K., Suchocki, T., Topolski, P., Jagusiak, W., Szyda, J., 2024. An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p \u0026gt;\u0026gt; n Problem. Int. J. Mol. Sci. 25, 4715. doi:10.3390/ijms25094715\u003c/li\u003e\n\u003cli\u003eKuehn, J.S., Gorden, P.J., Munro, D., Rong, R., Dong, Q., Plummer, P.J., Wang, C., Phillips, G.J., 2013. Bacterial Community Profiling of Milk Samples as a Means to Understand Culture-Negative Bovine Clinical Mastitis. PLOS ONE 8, e61959. doi:10.1371/journal.pone.0061959\u003c/li\u003e\n\u003cli\u003eKuralkar, P., Kuralkar, S.V., 2021. Role of herbal products in animal production - An updated review. J. Ethnopharmacol. 278, 114246. doi:10.1016/j.jep.2021.114246\u003c/li\u003e\n\u003cli\u003eLippolis, J.D., Holman, D.B., Brunelle, B.W., Thacker, T.C., Bearson, B.L., Reinhardt, T.A., Sacco, R.E., Casey, T.A., 2017. Genomic and Transcriptomic Analysis of Escherichia coli Strains Associated with Persistent and Transient Bovine Mastitis and the Role of Colanic Acid. Infect. Immun. 86, e00566-17. doi:10.1128/IAI.00566-17\u003c/li\u003e\n\u003cli\u003eMolineri, A.I., Camussone, C., Zbrun, M.V., Su\u0026aacute;rez Archilla, G., Cristiani, M., Neder, V., Calvinho, L., Signorini, M., 2021. Antimicrobial resistance of Staphylococcus aureus isolated from bovine mastitis: Systematic review and meta-analysis. Prev. Vet. Med. 188, 105261. doi:10.1016/j.prevetmed.2021.105261\u003c/li\u003e\n\u003cli\u003eMostafa Abdalhamed, A., Zeedan, G.S.G., Ahmed Arafa, A., Shafeek Ibrahim, E., Sedky, D., Abdel Nabey Hafez, A., 2022. Detection of Methicillin-Resistant Staphylococcus aureus in Clinical and Subclinical Mastitis in Ruminants and Studying the Effect of Novel Green Synthetized Nanoparticles as One of the Alternative Treatments. Vet. Med. Int. 2022, 6309984. doi:10.1155/2022/6309984\u003c/li\u003e\n\u003cli\u003eNaranjo-Lucena, A., Slowey, R., 2023. Invited review: Antimicrobial resistance in bovine mastitis pathogens: A review of genetic determinants and prevalence of resistance in European countries. J. Dairy Sci. 106, 1\u0026ndash;23. doi:10.3168/jds.2022-22267\u003c/li\u003e\n\u003cli\u003eNaushad, S., Nobrega, D.B., Naqvi, S.A., Barkema, H.W., De Buck, J., 2020. Genomic Analysis of Bovine Staphylococcus aureus Isolates from Milk To Elucidate Diversity and Determine the Distributions of Antimicrobial and Virulence Genes and Their Association with Mastitis. mSystems 5, e00063-20. doi:10.1128/mSystems.00063-20\u003c/li\u003e\n\u003cli\u003eOliver, S.P., Murinda, S.E., 2012. Antimicrobial resistance of mastitis pathogens. Vet. Clin. North Am. Food Anim. Pract. 28, 165\u0026ndash;185. doi:10.1016/j.cvfa.2012.03.005\u003c/li\u003e\n\u003cli\u003eRonco, T., Klaas, I.C., Stegger, M., Svennesen, L., Astrup, L.B., Farre, M., Pedersen, K., 2018. Genomic investigation of Staphylococcus aureus isolates from bulk tank milk and dairy cows with clinical mastitis. Vet. Microbiol. 215, 35\u0026ndash;42. doi:10.1016/j.vetmic.2018.01.003\u003c/li\u003e\n\u003cli\u003eR\u0026ouml;tzer, V., Wenderlein, J., Wiesinger, A., Versen, F., Rauch, E., Straubinger, R.K., Zeiler, E., 2023. Bovine Udder Health: From Standard Diagnostic Methods to New Approaches\u0026mdash;A Practical Investigation of Various Udder Health Parameters in Combination with 16S rRNA Sequencing. Microorganisms 11, 1311. doi:10.3390/microorganisms11051311\u003c/li\u003e\n\u003cli\u003eRuegg, P.L., 2017. A 100-Year Review: Mastitis detection, management, and prevention. J. Dairy Sci. 100, 10381\u0026ndash;10397. doi:10.3168/jds.2017-13023\u003c/li\u003e\n\u003cli\u003eTaponen, S., Py\u0026ouml;r\u0026auml;l\u0026auml;, S., 2009. Coagulase-negative staphylococci as cause of bovine mastitis- not so different from Staphylococcus aureus? Vet. Microbiol. 134, 29\u0026ndash;36. doi:10.1016/j.vetmic.2008.09.011\u003c/li\u003e\n\u003cli\u003eYang, F., Shi, W., Meng, N., Zhao, Y., Ding, X., Li, Q., 2023. Antimicrobial resistance and virulence profiles of staphylococci isolated from clinical bovine mastitis. Front. Microbiol. 14. doi:10.3389/fmicb.2023.1190790\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":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"bovine mastitis, whole genome sequencing, antimicrobial resistance, Stutzerimonas stutzeri, diagnostic limitations, horizontal gene transfer, dairy cattle, veterinary microbiology","lastPublishedDoi":"10.21203/rs.3.rs-7079649/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7079649/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Bovine mastitis diagnosis relies predominantly on conventional microbiological methods optimized for common pathogens, potentially overlooking environmental bacteria with complex antimicrobial resistance profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This pilot study combined conventional identification with whole-genome sequencing (WGS) analysis of bovine mastitis isolates. A total of 330 milk samples were analyzed using standard microbiological methods, followed by comprehensive genomic characterization of two representative multidrug-resistant isolates using Illumina NovaSeq 6000 sequencing. Antimicrobial resistance gene analysis was performed using BLAST searches against the Comprehensive Antibiotic Resistance Database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Of 330 samples, 202 (61.2%) tested positive for mastitis. WGS revealed critical species misidentification: one isolate initially characterized as gram-positive with Staphylococcus-like morphology was definitively identified as Stutzerimonas stutzeri through genomic analysis. Both sequenced isolates harbored extensive antimicrobial resistance gene repertoires distributed across 8-10 resistance classes, with evidence of horizontal gene transfer across bacterial orders. Phylogenetic analysis revealed resistance genes originated from Proteobacteria (61%) and Firmicutes (39%), indicating cross-phylum gene exchange.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This pilot study demonstrates that WGS can identify bacterial species missed by conventional diagnostic methods and reveals complex horizontal gene transfer networks in mastitis-associated bacteria. The environmental pathogen S. stutzeri represents a potentially underrecognized opportunistic mastitis agent with extensive resistance potential. These findings validate the need for genomic surveillance approaches in veterinary diagnostic microbiology.\u003c/p\u003e","manuscriptTitle":"Whole Genome Sequencing Reveals Environmental Pathogen Misidentification and Cross- Phylum Antimicrobial Resistance Gene Transfer in Bovine Mastitis: A Pilot Genomic Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 18:13:51","doi":"10.21203/rs.3.rs-7079649/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-08T04:43:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T12:19:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102992337179033868020078761094338302143","date":"2025-11-08T16:46:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T21:50:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276994355499417413813084929563299215084","date":"2025-08-13T15:05:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108876057256318113615820526966833916631","date":"2025-07-27T01:47:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T15:23:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-11T11:35:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-10T09:46:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-10T09:42:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2025-07-09T04:19:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"51b36405-d5fc-46fb-b054-12670cab4034","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:06:35+00:00","versionOfRecord":{"articleIdentity":"rs-7079649","link":"https://doi.org/10.1186/s12917-025-05280-z","journal":{"identity":"bmc-veterinary-research","isVorOnly":false,"title":"BMC Veterinary Research"},"publishedOn":"2026-01-14 16:29:58","publishedOnDateReadable":"January 14th, 2026"},"versionCreatedAt":"2025-07-18 18:13:51","video":"","vorDoi":"10.1186/s12917-025-05280-z","vorDoiUrl":"https://doi.org/10.1186/s12917-025-05280-z","workflowStages":[]},"version":"v1","identity":"rs-7079649","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7079649","identity":"rs-7079649","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}