Antibiotic Susceptibility Patterns of Bacterial Isolates from Equine Clinical Submissions in Texas and Oklahoma, 2011–2024

Antibiotic Susceptibility Patterns of Bacterial Isolates from Equine Clinical Submissions in Texas and Oklahoma, 2011–2024 | 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 Short Report Antibiotic Susceptibility Patterns of Bacterial Isolates from Equine Clinical Submissions in Texas and Oklahoma, 2011–2024 Daniel Braxton Stone, Ibrahim Idris, Godwin Ohemu, Nancy Zimmerman, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9370940/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study characterized the epidemiology and antimicrobial resistance patterns of bacterial isolates recovered from equine clinical submissions in Texas and Oklahoma between 2011 and 2024. A retrospective observational analysis was conducted using bacterial culture and antimicrobial susceptibility testing records from equine samples. Data was cleaned and standardized prior to analysis. The distribution of bacterial organisms, anatomical sites of infection, antimicrobial susceptibility across major drug classes, and prevalence of multidrug resistance (MDR) were evaluated, with MDR defined as resistance to three or more antimicrobial classes. A total of 2,812 bacterial isolates were included. The most frequently identified organisms were Streptococcus equi subsp. zooepidemicus , Escherichia coli , Staphylococcus spp., Pseudomonas spp., and Klebsiella spp. Most isolates originated from reproductive, integumentary/wound, and respiratory sites. Antimicrobial susceptibility varied across organisms and drug classes, with generally high susceptibility to fluoroquinolones and aminoglycosides, and greater variability observed for β-lactams and tetracyclines. Multidrug resistance was identified in 19.0% of isolates and was more common among Gram-negative organisms. These findings highlight the diversity of antimicrobial susceptibility patterns among equine bacterial pathogens and underscore the importance of routine culture and susceptibility testing to guide therapy and support antimicrobial stewardship in equine practice. antimicrobial resistance equine bacterial infections antimicrobial susceptibility testing equine pathogens antimicrobial stewardship Figures Figure 1 Figure 2 Figure 3 Introduction Antimicrobial resistance (AMR), particularly bacterial AMR, is a growing global health threat that reduces the effectiveness of infection treatment (Ho et al., 2025 ). In veterinary medicine, antimicrobial use for prevention and treatment contributes to the emergence of resistant microorganisms, challenging stewardship efforts within the One Health framework (Ekakoro and Okafor, 2019 ). In horses, antimicrobials are commonly used to treat bacterial infections across multiple body systems (Haggett and Wilson, 2008 ). However, rising antimicrobial resistance (AMR), including multidrug-resistant (MDR) bacteria, poses significant risks to both animal and human health, as horses can act as reservoirs for zoonotic pathogens (Kabir et al., 2024 ). Inappropriate antimicrobial use—often driven by factors such as client pressure, fear of clinical deterioration, and perceived low risk—further accelerates resistance development (Hardefeldt et al., 2018 ; 2021 ). This highlights the need for improved stewardship within the equine industry. Equine bacterial infections are caused by diverse Gram-positive and Gram-negative organisms, including Streptococcus equi subsp. zooepidemicus , Staphylococcus spp., Escherichia coli , and Pseudomonas spp. (Nielsen et al., 2021 ), affecting multiple body systems. Antimicrobial susceptibility testing (AST) guides evidence-based therapy and supports stewardship in clinical practice. However, comprehensive epidemiological assessments of equine AMR remain limited, with most studies focused on specific diseases, locations, or short timeframes. As a result, long-term trends in bacterial distribution, antimicrobial susceptibility, and multidrug resistance—and their relationship with anatomical infection sites—are insufficiently understood. Therefore, this study aimed to evaluate and characterize the epidemiology and antimicrobial resistance (AMR) patterns of bacterial isolates from equine clinical samples submitted to commercial laboratories in Texas and Oklahoma from 2011 to 2024. Specifically, it describes the distribution of bacterial organisms, assesses their association with infection sites, evaluates antimicrobial susceptibility across major drug classes, and quantifies the prevalence and distribution of multidrug-resistant isolates. Methodology This study used data from equine clinical samples submitted by veterinarians for routine bacterial culture and antimicrobial susceptibility testing (AST) to commercial diagnostic laboratories in Texas and Oklahoma between 2011 and 2021. Records included bacterial identification, submission year, anatomical site, and AST results with corresponding antimicrobial agents. Data were cleaned and standardized in Microsoft Excel by removing duplicates and harmonizing anatomical sites into major system-based categories, including reproductive, integument/wound, respiratory, musculoskeletal/neurologic, gastrointestinal, urinary, ocular, otic, mammary, blood, and other/unknown. This classification enabled consistent evaluation of pathogen distribution and analysis of associations between bacterial species and infection sites. AST results, originally reported as susceptible (S), intermediate (I), or resistant (R), were converted into a binary format for analysis: susceptible (1) and resistant (0), with intermediate results excluded. Only isolates tested against a given antimicrobial agent were included in susceptibility calculations. To evaluate susceptibility at a broader pharmacological level, antimicrobial agents were grouped into major classes, including aminoglycosides, β-lactams, fluoroquinolones, tetracyclines, macrolides, amphenicols, rifamycins, and lincosamides. This grouping enabled comparison of resistance patterns across related drugs and improved interpretation for antimicrobial stewardship. Organism–class combinations with fewer than 30 tested isolates were excluded to ensure robust estimates. Multidrug resistance (MDR) was defined as resistance to three or more antimicrobial classes. Susceptibility data were restructured to determine, for each isolate, the number of classes tested and the number to which resistance was observed. Only isolates tested against at least three classes were included, and those resistant to three or more classes were classified as MDR. Following preliminary cleaning, the dataset was imported into R (version 4.5.2) for data processing and statistical analysis using appropriate packages. After cleaning, classification, and standardization, a total of 2,812 bacterial isolates were retained for analysis. Statistical Analysis Descriptive statistics were used to summarize bacterial distribution, anatomical sites, antimicrobial susceptibility patterns, and MDR prevalence, with frequencies and percentages reported for categorical variables. Associations between bacterial species and infection sites were assessed using Pearson’s chi-square test, with Cramér’s V used to measure effect size. Differences in MDR prevalence between Gram-positive and Gram-negative organisms were also evaluated using chi-square tests. Temporal trends in MDR were analyzed using logistic regression, with MDR status as the outcome and year as the predictor. Statistical significance was set at p < 0.05. Results Isolate Submissions and Temporal Distribution A total of 2,812 bacterial isolates were analyzed. Submissions were low between 2011 and 2017, followed by a marked increase from 2018, peaking in 2021. Although submissions declined thereafter, they remained higher than in earlier years. The temporal distribution is presented in Supplementary Table 1 and illustrated in Fig. 1 . Distribution of Bacterial Organisms A diverse range of bacterial organisms was identified, with Streptococcus equi subsp. zooepidemicus being the most common (19.2%), followed by Escherichia coli (14.8%), Staphylococcus spp. (7.6%), Pseudomonas spp. (6.2%), and Klebsiella spp. (5.8%). Other frequently detected organisms included Acinetobacter spp., Streptococcus spp., Staphylococcus aureus , Enterobacter spp., and Enterococcus spp. Their distribution is presented in Fig. 2 . Temporal Trends in Bacterial Organisms Temporal patterns of bacterial isolates from equine clinical submissions (2011–2024) showed consistent detection of key species, including Streptococcus equi subsp. zooepidemicus, Escherichia coli, Staphylococcus spp., Pseudomonas spp., and Klebsiella spp. Although their relative abundances varied annually, the overall composition of dominant species remained stable. Detailed organism-specific trends are presented in Supplementary Figures S1 –S6. Anatomical Site Distribution of Isolates Bacterial isolates were recovered from multiple anatomical sites, with the reproductive system accounting for the highest proportion (29.9%), followed by integumentary/wound (23.8%) and respiratory samples (20.1%). Other categories included unknown (19.0%) and smaller proportions from musculoskeletal/neurologic, ocular, urinary, gastrointestinal, ear, blood, and mammary sites. Table 1 summarizes this distribution, while Fig. 3 illustrates bacteria–site associations. A significant association was observed (χ² = 1606, df = 270, p < 0.001), with a small-to-moderate effect size (Cramér’s V = 0.239), indicating species-specific site preferences. Table 1 Distribution of equine bacterial isolates by anatomical site (n = 2,812) Anatomical system No. (%) Reproductive 840 (29.9) Integument/wound 668 (23.8) Respiratory 565 (20.1) Other/unknown 535 (19.0) Musculoskeletal/neurologic 66 (2.3) Eye 56 (2.0) Urinary 31 (1.1) Gastrointestinal 27 (1.0) Ear 18 (0.6) Blood 4 (0.1) Mammary 2 (0.1) Total 2,812 (100) AST Patterns Antimicrobial susceptibility patterns varied across bacterial species and drug classes. Overall, fluoroquinolones and aminoglycosides showed consistently high susceptibility, whereas β-lactams and tetracyclines were more variable. Among Gram-negative organisms ( Escherichia coli , Klebsiella spp., Enterobacter spp.), resistance to β-lactams was generally lower, and susceptibility to fluoroquinolones remained high. In Gram-positive bacteria, Streptococcus dysgalactiae and Streptococcus equi subsp. zooepidemicus were highly susceptible to β-lactams, while Staphylococcus spp. showed more variable patterns. Susceptibility was categorized as high (≥ 90%), moderate (70–89%), or low (< 70%). Supplementary Tables S2 and S3 summarize organism distribution and susceptibility proportions, respectively. Class-level patterns are shown in supplementary Figs. 14 (Gram-positive) and 15 (Gram-negative). Drug-level heatmaps further highlight variability within classes (Supplementary Figures S7–S12). MDR Prevalence and temporal trend MDR analysis included isolates tested against at least three antimicrobial classes. Of 2,688 eligible isolates, 512 (19.0%) were classified as MDR, with prevalence varying by species. The highest MDR proportions were observed in Citrobacter , Klebsiella , and Enterobacter spp., while it was relatively low in streptococci (Supplementary Table S5). MDR was more common in Gram-negative (23.3%) than Gram-positive (14.9%) isolates, a statistically significant difference (χ² = 30.24, df = 1, p = 3.81 × 10⁻⁸). Annual MDR prevalence fluctuated over time without a clear trend (Supplementary Figure S13). Discussion This study provides a longitudinal assessment of the epidemiology and AMR patterns of equine bacterial isolates in Texas and Oklahoma (2011–2024). Among 2,812 isolates, the most common species were Streptococcus equi subsp. zooepidemicus , Escherichia coli , Staphylococcus spp., Pseudomonas spp., and Klebsiella spp., with most samples originating from reproductive, integumentary/wound, and respiratory sites. Susceptibility varied across organisms and drug classes, with generally high susceptibility to fluoroquinolones and aminoglycosides, and more variable responses to β-lactams and tetracyclines. MDR was identified in about 20% of isolates and was significantly more common in Gram-negative bacteria. The high prevalence of Streptococcus equi subsp. zooepidemicus in this study is consistent with previous reports identifying it as a common equine pathogen, particularly in respiratory and reproductive infections (Nocera et al., 2021 ). As an opportunistic commensal of the respiratory and genital mucosa, it is frequently associated with conditions such as endometritis and respiratory disease (Mohamed et al., 2025 ). Similar patterns have been reported in large-scale surveillance studies, where group C streptococci, especially S. zooepidemicus , are among the most prevalent isolates (Léon et al., 2020 ). Gram-negative bacteria, including Escherichia coli , Pseudomonas spp., and Klebsiella spp., were consistently identified across the study period and are commonly associated with wound, respiratory, and uterine infections, particularly in hospitalized or immunocompromised horses (Hallowell et al., 2024 ; Pottier et al., 2022 ). Previous studies also highlight E. coli as a major Gram-negative isolate in equine samples (Albihn et al., 2003 ). The association between bacterial species and anatomical sites supports pathogen tropism in equine infections. Streptococci were primarily linked to respiratory and reproductive sites, while Gram-negative organisms were more frequently associated with wounds and reproductive infections, consistent with earlier reports (Adams et al ., 2014). AST results showed marked variability across bacterial species and antimicrobial classes. Fluoroquinolones and aminoglycosides demonstrated consistently high susceptibility, whereas β-lactams and tetracyclines were more variable. These findings align with previous studies reporting strong efficacy of fluoroquinolones and aminoglycosides against Gram-negative equine pathogens (Isgren et al., 2025). Reduced β-lactam susceptibility among Enterobacterales reflects known trends, likely driven by antimicrobial pressure and the emergence of ESBL-producing strains (Steinman and Navon-Venezia, 2020 ). Similarly, lower tetracycline susceptibility is consistent with documented increases in resistance, particularly in E. coli (Moura et al., 2013 ). In contrast, Gram-positive streptococci, including S. dysgalactiae and S. equi subsp. zooepidemicus , remained highly susceptible to β-lactams, supporting their continued use as first-line treatments for equine streptococcal infections (Veiga et al., 2024 ). MDR was identified in ~ 19% of isolates and was significantly more common in Gram-negative bacteria, consistent with their ability to acquire resistance genes via mobile genetic elements (Kumavath et al., 2025 ). Previous studies also report increasing MDR E. coli and other Gram-negative infections in horses (De Lagarde et al., 2019 ), with evidence that horses can carry resistant organisms such as ESBL-producing E. coli and MRSA (Maddox et al., 2012 ). This highlights the risk of escalating resistance, particularly with inappropriate antimicrobial use. These findings emphasize the importance of routine culture and AST to guide therapy and support antimicrobial stewardship. However, limitations include potential selection bias from diagnostic submissions, variability in AST panels, and lack of data on prior treatment or clinical outcomes, which may influence resistance patterns. Conclusion In conclusion, a limited number of pathogens, including Streptococcus equi subsp. zooepidemicus , Escherichia coli , Staphylococcus spp., and other Gram-negative organisms, account for most equine bacterial infections. Susceptibility varied across organisms and drug classes, with MDR more common in Gram-negative bacteria. These findings highlight the need for routine AST, improved antimicrobial stewardship, and continued surveillance, alongside future studies incorporating genetic data, antimicrobial use, and clinical outcomes. Declarations Funding This work was supported in part by a USDA-APHIS AMR Dashboard Cooperative Agreement. The findings are those of the authors and do not necessarily reflect the views of USDA or APHIS. Mention of trade names does not imply endorsement. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Daniel Braxton Stone contributed to study conceptualization and data acquisition. Ibrahim Idris conducted data analysis, interpreted the results, and drafted the manuscript. Godwin Ohemu, Nancy Zimmerman, Marcelo Schmidt, and Tianxi Ji contributed to data management and manuscript revision. Babafela Awosile contributed to study design, supervision, data interpretation, and critical revision. All authors approved the final manuscript. Data Availability The datasets analysed during the current study are not publicly available due to institutional and data-use restrictions but are available from the corresponding author on reasonable requests. Ethics Approval This study was a retrospective observational analysis of laboratory data and did not involve direct interaction with animals. No ethical approval was required. 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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-9370940","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":625181437,"identity":"ce9276a2-e89f-455f-ba7a-5b3ba1738b1d","order_by":0,"name":"Daniel Braxton Stone","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Braxton","lastName":"Stone","suffix":""},{"id":625181439,"identity":"ebc03281-a853-44e7-972d-33b790d9a5f5","order_by":1,"name":"Ibrahim Idris","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Ibrahim","middleName":"","lastName":"Idris","suffix":""},{"id":625181441,"identity":"b2dfdc83-ed4b-4b1c-94af-3cf6ec5708a1","order_by":2,"name":"Godwin Ohemu","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Godwin","middleName":"","lastName":"Ohemu","suffix":""},{"id":625181442,"identity":"715492a9-8620-449f-bc1c-d5ac402de802","order_by":3,"name":"Nancy Zimmerman","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Nancy","middleName":"","lastName":"Zimmerman","suffix":""},{"id":625181444,"identity":"429d2fbd-3a13-40f6-8c54-3435ff0712b9","order_by":4,"name":"Marcelo Schmidt","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Marcelo","middleName":"","lastName":"Schmidt","suffix":""},{"id":625181446,"identity":"870f8acc-17ea-4d84-8503-cb4a22cecdd1","order_by":5,"name":"Tianxi Ji","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Tianxi","middleName":"","lastName":"Ji","suffix":""},{"id":625181447,"identity":"72aef2ee-50bd-4718-9041-76b8c430401c","order_by":6,"name":"Babafela Awosile","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie3PMUvDQBjG8bcE4hLI+pSi+Qp3HJRAK/0qCYF2SdUxQ8EDIY5d+z2EzAmFmyKu2SS7SsUlgxRziILDpavD/beXux93L5HN9g9bkEeUUUjk6JGF5H8fOEbCZU9qAtGZ1AQ0zp1hwsof4pV6BjF1gvD7x+pQZggIKW+7G1yI560CZbNYmnbxrhKUNbhEKoTHIKbKcUH1yki4TNnkPcdIYl1M+l3iQpNRvjcS9vQiuuqIhQxei3HHcPuQa3IcIE06RSUR61fQfyxiribSTPjubRmWCkmO6w+9C9+pRISRWgnj+v5635Sb+eUWy7jtPueBf1e1zWEzOzeR39w/U3Tqus1ms9kG+wLP4VU/fmsIfgAAAABJRU5ErkJggg==","orcid":"","institution":"Texas Tech University","correspondingAuthor":true,"prefix":"","firstName":"Babafela","middleName":"","lastName":"Awosile","suffix":""},{"id":625181448,"identity":"205b1ef6-d1b1-4f17-b377-dc6a6ba33ee2","order_by":7,"name":"Babafela Awosile","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Babafela","middleName":"","lastName":"Awosile","suffix":""}],"badges":[],"createdAt":"2026-04-09 16:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9370940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9370940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707491,"identity":"45d022bd-fff0-48d0-b4b3-b4447a5f2535","added_by":"auto","created_at":"2026-04-24 09:20:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201811,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual distribution of equine bacterial isolates submitted for culture and AST in Texas and Oklahoma, 2011–2024 (n = 2,812).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9370940/v1/35d25678f715691c00182f6f.png"},{"id":107643523,"identity":"20872a62-58bb-485f-8f52-6bb6d789f257","added_by":"auto","created_at":"2026-04-23 13:52:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":567425,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of bacterial organisms isolated from equine clinical submissions (n = 2,812). Bars represent the number of isolates for each bacterial organism, with percentages shown in parentheses.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9370940/v1/466ca3aae15851e7b99f0053.png"},{"id":107643524,"identity":"012ecf3a-df85-413b-bf74-fb5bcf32aa1e","added_by":"auto","created_at":"2026-04-23 13:52:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":682914,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing the distribution of equine bacterial isolates across anatomical site categories. Rows represent bacterial organisms and columns represent anatomical systems. Color intensity corresponds to the number of isolates recovered from each anatomical site for each bacterial species.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9370940/v1/7795c2471241f277c020f729.png"},{"id":108866927,"identity":"82ff8d61-fc3b-49cd-b09a-e63095528f2e","added_by":"auto","created_at":"2026-05-09 11:11:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1676458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9370940/v1/83c5f732-003a-4a2d-bda8-8724506b1dc5.pdf"},{"id":107643521,"identity":"70c0384c-6222-425a-9726-f22d8df9973d","added_by":"auto","created_at":"2026-04-23 13:52:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1868437,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9370940/v1/7c764959c14f8fb8ae9d6903.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Antibiotic Susceptibility Patterns of Bacterial Isolates from Equine Clinical Submissions in Texas and Oklahoma, 2011–2024","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntimicrobial resistance (AMR), particularly bacterial AMR, is a growing global health threat that reduces the effectiveness of infection treatment (Ho et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In veterinary medicine, antimicrobial use for prevention and treatment contributes to the emergence of resistant microorganisms, challenging stewardship efforts within the One Health framework (Ekakoro and Okafor, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn horses, antimicrobials are commonly used to treat bacterial infections across multiple body systems (Haggett and Wilson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, rising antimicrobial resistance (AMR), including multidrug-resistant (MDR) bacteria, poses significant risks to both animal and human health, as horses can act as reservoirs for zoonotic pathogens (Kabir et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Inappropriate antimicrobial use\u0026mdash;often driven by factors such as client pressure, fear of clinical deterioration, and perceived low risk\u0026mdash;further accelerates resistance development (Hardefeldt et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This highlights the need for improved stewardship within the equine industry.\u003c/p\u003e \u003cp\u003eEquine bacterial infections are caused by diverse Gram-positive and Gram-negative organisms, including \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e spp., \u003cem\u003eEscherichia coli\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e spp. (Nielsen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), affecting multiple body systems. Antimicrobial susceptibility testing (AST) guides evidence-based therapy and supports stewardship in clinical practice. However, comprehensive epidemiological assessments of equine AMR remain limited, with most studies focused on specific diseases, locations, or short timeframes. As a result, long-term trends in bacterial distribution, antimicrobial susceptibility, and multidrug resistance\u0026mdash;and their relationship with anatomical infection sites\u0026mdash;are insufficiently understood.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to evaluate and characterize the epidemiology and antimicrobial resistance (AMR) patterns of bacterial isolates from equine clinical samples submitted to commercial laboratories in Texas and Oklahoma from 2011 to 2024. Specifically, it describes the distribution of bacterial organisms, assesses their association with infection sites, evaluates antimicrobial susceptibility across major drug classes, and quantifies the prevalence and distribution of multidrug-resistant isolates.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study used data from equine clinical samples submitted by veterinarians for routine bacterial culture and antimicrobial susceptibility testing (AST) to commercial diagnostic laboratories in Texas and Oklahoma between 2011 and 2021. Records included bacterial identification, submission year, anatomical site, and AST results with corresponding antimicrobial agents.\u003c/p\u003e \u003cp\u003eData were cleaned and standardized in Microsoft Excel by removing duplicates and harmonizing anatomical sites into major system-based categories, including reproductive, integument/wound, respiratory, musculoskeletal/neurologic, gastrointestinal, urinary, ocular, otic, mammary, blood, and other/unknown. This classification enabled consistent evaluation of pathogen distribution and analysis of associations between bacterial species and infection sites.\u003c/p\u003e \u003cp\u003eAST results, originally reported as susceptible (S), intermediate (I), or resistant (R), were converted into a binary format for analysis: susceptible (1) and resistant (0), with intermediate results excluded. Only isolates tested against a given antimicrobial agent were included in susceptibility calculations.\u003c/p\u003e \u003cp\u003eTo evaluate susceptibility at a broader pharmacological level, antimicrobial agents were grouped into major classes, including aminoglycosides, β-lactams, fluoroquinolones, tetracyclines, macrolides, amphenicols, rifamycins, and lincosamides. This grouping enabled comparison of resistance patterns across related drugs and improved interpretation for antimicrobial stewardship. Organism\u0026ndash;class combinations with fewer than 30 tested isolates were excluded to ensure robust estimates.\u003c/p\u003e \u003cp\u003eMultidrug resistance (MDR) was defined as resistance to three or more antimicrobial classes. Susceptibility data were restructured to determine, for each isolate, the number of classes tested and the number to which resistance was observed. Only isolates tested against at least three classes were included, and those resistant to three or more classes were classified as MDR.\u003c/p\u003e \u003cp\u003eFollowing preliminary cleaning, the dataset was imported into R (version 4.5.2) for data processing and statistical analysis using appropriate packages. After cleaning, classification, and standardization, a total of 2,812 bacterial isolates were retained for analysis.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to summarize bacterial distribution, anatomical sites, antimicrobial susceptibility patterns, and MDR prevalence, with frequencies and percentages reported for categorical variables. Associations between bacterial species and infection sites were assessed using Pearson\u0026rsquo;s chi-square test, with Cram\u0026eacute;r\u0026rsquo;s V used to measure effect size. Differences in MDR prevalence between Gram-positive and Gram-negative organisms were also evaluated using chi-square tests. Temporal trends in MDR were analyzed using logistic regression, with MDR status as the outcome and year as the predictor. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIsolate Submissions and Temporal Distribution\u003c/h2\u003e \u003cp\u003eA total of 2,812 bacterial isolates were analyzed. Submissions were low between 2011 and 2017, followed by a marked increase from 2018, peaking in 2021. Although submissions declined thereafter, they remained higher than in earlier years. The temporal distribution is presented in Supplementary Table\u0026nbsp;1 and illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDistribution of Bacterial Organisms\u003c/h3\u003e\n\u003cp\u003eA diverse range of bacterial organisms was identified, with \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e being the most common (19.2%), followed by \u003cem\u003eEscherichia coli\u003c/em\u003e (14.8%), \u003cem\u003eStaphylococcus\u003c/em\u003e spp. (7.6%), \u003cem\u003ePseudomonas\u003c/em\u003e spp. (6.2%), and \u003cem\u003eKlebsiella\u003c/em\u003e spp. (5.8%). Other frequently detected organisms included \u003cem\u003eAcinetobacter\u003c/em\u003e spp., \u003cem\u003eStreptococcus\u003c/em\u003e spp., \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e spp., and \u003cem\u003eEnterococcus\u003c/em\u003e spp. Their distribution is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTemporal Trends in Bacterial Organisms\u003c/h3\u003e\n\u003cp\u003eTemporal patterns of bacterial isolates from equine clinical submissions (2011\u0026ndash;2024) showed consistent detection of key species, including \u003cem\u003eStreptococcus equi subsp. zooepidemicus, Escherichia coli, Staphylococcus spp., Pseudomonas spp., and Klebsiella spp.\u003c/em\u003e Although their relative abundances varied annually, the overall composition of dominant species remained stable. Detailed organism-specific trends are presented in Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u0026ndash;S6.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnatomical Site Distribution of Isolates\u003c/h2\u003e \u003cp\u003eBacterial isolates were recovered from multiple anatomical sites, with the reproductive system accounting for the highest proportion (29.9%), followed by integumentary/wound (23.8%) and respiratory samples (20.1%). Other categories included unknown (19.0%) and smaller proportions from musculoskeletal/neurologic, ocular, urinary, gastrointestinal, ear, blood, and mammary sites. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes this distribution, while Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates bacteria\u0026ndash;site associations. A significant association was observed (χ\u0026sup2; = 1606, df\u0026thinsp;=\u0026thinsp;270, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a small-to-moderate effect size (Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;0.239), indicating species-specific site preferences.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eDistribution of equine bacterial isolates by anatomical site (n\u0026thinsp;=\u0026thinsp;2,812)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnatomical system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReproductive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e840 (29.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntegument/wound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e668 (23.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e565 (20.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e535 (19.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusculoskeletal/neurologic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMammary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.1)\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\u003e2,812 (100)\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 \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAST Patterns\u003c/h3\u003e\n\u003cp\u003eAntimicrobial susceptibility patterns varied across bacterial species and drug classes. Overall, fluoroquinolones and aminoglycosides showed consistently high susceptibility, whereas β-lactams and tetracyclines were more variable. Among Gram-negative organisms (\u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e spp., \u003cem\u003eEnterobacter\u003c/em\u003e spp.), resistance to β-lactams was generally lower, and susceptibility to fluoroquinolones remained high. In Gram-positive bacteria, \u003cem\u003eStreptococcus dysgalactiae\u003c/em\u003e and \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e were highly susceptible to β-lactams, while \u003cem\u003eStaphylococcus\u003c/em\u003e spp. showed more variable patterns.\u003c/p\u003e \u003cp\u003eSusceptibility was categorized as high (\u0026ge;\u0026thinsp;90%), moderate (70\u0026ndash;89%), or low (\u0026lt;\u0026thinsp;70%). Supplementary Tables S2 and S3 summarize organism distribution and susceptibility proportions, respectively. Class-level patterns are shown in supplementary Figs.\u0026nbsp;14 (Gram-positive) and 15 (Gram-negative). Drug-level heatmaps further highlight variability within classes (Supplementary Figures S7\u0026ndash;S12).\u003c/p\u003e\n\u003ch3\u003eMDR Prevalence and temporal trend\u003c/h3\u003e\n\u003cp\u003eMDR analysis included isolates tested against at least three antimicrobial classes. Of 2,688 eligible isolates, 512 (19.0%) were classified as MDR, with prevalence varying by species. The highest MDR proportions were observed in \u003cem\u003eCitrobacter\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e, and \u003cem\u003eEnterobacter\u003c/em\u003e spp., while it was relatively low in streptococci (Supplementary Table S5). MDR was more common in Gram-negative (23.3%) than Gram-positive (14.9%) isolates, a statistically significant difference (χ\u0026sup2; = 30.24, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;3.81 \u0026times; 10⁻⁸). Annual MDR prevalence fluctuated over time without a clear trend (Supplementary Figure S13).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a longitudinal assessment of the epidemiology and AMR patterns of equine bacterial isolates in Texas and Oklahoma (2011\u0026ndash;2024). Among 2,812 isolates, the most common species were \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e spp., \u003cem\u003ePseudomonas\u003c/em\u003e spp., and \u003cem\u003eKlebsiella\u003c/em\u003e spp., with most samples originating from reproductive, integumentary/wound, and respiratory sites. Susceptibility varied across organisms and drug classes, with generally high susceptibility to fluoroquinolones and aminoglycosides, and more variable responses to β-lactams and tetracyclines. MDR was identified in about 20% of isolates and was significantly more common in Gram-negative bacteria.\u003c/p\u003e \u003cp\u003eThe high prevalence of \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e in this study is consistent with previous reports identifying it as a common equine pathogen, particularly in respiratory and reproductive infections (Nocera et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As an opportunistic commensal of the respiratory and genital mucosa, it is frequently associated with conditions such as endometritis and respiratory disease (Mohamed et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similar patterns have been reported in large-scale surveillance studies, where group C streptococci, especially \u003cem\u003eS. zooepidemicus\u003c/em\u003e, are among the most prevalent isolates (L\u0026eacute;on et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGram-negative bacteria, including \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e spp., and \u003cem\u003eKlebsiella\u003c/em\u003e spp., were consistently identified across the study period and are commonly associated with wound, respiratory, and uterine infections, particularly in hospitalized or immunocompromised horses (Hallowell et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pottier et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous studies also highlight \u003cem\u003eE. coli\u003c/em\u003e as a major Gram-negative isolate in equine samples (Albihn et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe association between bacterial species and anatomical sites supports pathogen tropism in equine infections. Streptococci were primarily linked to respiratory and reproductive sites, while Gram-negative organisms were more frequently associated with wounds and reproductive infections, consistent with earlier reports (Adams \u003cem\u003eet al\u003c/em\u003e., 2014).\u003c/p\u003e \u003cp\u003eAST results showed marked variability across bacterial species and antimicrobial classes. Fluoroquinolones and aminoglycosides demonstrated consistently high susceptibility, whereas β-lactams and tetracyclines were more variable. These findings align with previous studies reporting strong efficacy of fluoroquinolones and aminoglycosides against Gram-negative equine pathogens (Isgren et al., 2025). Reduced β-lactam susceptibility among Enterobacterales reflects known trends, likely driven by antimicrobial pressure and the emergence of ESBL-producing strains (Steinman and Navon-Venezia, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, lower tetracycline susceptibility is consistent with documented increases in resistance, particularly in \u003cem\u003eE. coli\u003c/em\u003e (Moura et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, Gram-positive streptococci, including \u003cem\u003eS. dysgalactiae\u003c/em\u003e and \u003cem\u003eS. equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e, remained highly susceptible to β-lactams, supporting their continued use as first-line treatments for equine streptococcal infections (Veiga et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMDR was identified in ~\u0026thinsp;19% of isolates and was significantly more common in Gram-negative bacteria, consistent with their ability to acquire resistance genes via mobile genetic elements (Kumavath et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Previous studies also report increasing MDR \u003cem\u003eE. coli\u003c/em\u003e and other Gram-negative infections in horses (De Lagarde et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with evidence that horses can carry resistant organisms such as ESBL-producing \u003cem\u003eE. coli\u003c/em\u003e and MRSA (Maddox et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This highlights the risk of escalating resistance, particularly with inappropriate antimicrobial use.\u003c/p\u003e \u003cp\u003eThese findings emphasize the importance of routine culture and AST to guide therapy and support antimicrobial stewardship. However, limitations include potential selection bias from diagnostic submissions, variability in AST panels, and lack of data on prior treatment or clinical outcomes, which may influence resistance patterns.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, a limited number of pathogens, including \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e spp., and other Gram-negative organisms, account for most equine bacterial infections. Susceptibility varied across organisms and drug classes, with MDR more common in Gram-negative bacteria. These findings highlight the need for routine AST, improved antimicrobial stewardship, and continued surveillance, alongside future studies incorporating genetic data, antimicrobial use, and clinical outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by a USDA-APHIS AMR Dashboard Cooperative Agreement. The findings are those of the authors and do not necessarily reflect the views of USDA or APHIS. Mention of trade names does not imply endorsement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDaniel Braxton Stone contributed to study conceptualization and data acquisition. Ibrahim Idris conducted data analysis, interpreted the results, and drafted the manuscript. Godwin Ohemu, Nancy Zimmerman, Marcelo Schmidt, and Tianxi Ji contributed to data management and manuscript revision. Babafela Awosile contributed to study design, supervision, data interpretation, and critical revision. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are not publicly available due to institutional and data-use restrictions but are available from the corresponding author on reasonable requests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a retrospective observational analysis of laboratory data and did not involve direct interaction with animals. No ethical approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHo CS, Wong CTH, Aung TT et al (2025) Antimicrobial resistance: a concise update. Lancet Microbe 6:100947. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.lanmic.2024.07.010\u003c/span\u003e\u003cspan address=\"10.1016/j.lanmic.2024.07.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEkakoro JE, Okafor CC (2019) Antimicrobial use practices of veterinary clinicians at a veterinary teaching hospital in the United States. 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Equine Vet J 44:289\u0026ndash;296. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2042-3306.2011.00441.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2042-3306.2011.00441.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"antimicrobial resistance, equine bacterial infections, antimicrobial susceptibility testing, equine pathogens, antimicrobial stewardship","lastPublishedDoi":"10.21203/rs.3.rs-9370940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9370940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study characterized the epidemiology and antimicrobial resistance patterns of bacterial isolates recovered from equine clinical submissions in Texas and Oklahoma between 2011 and 2024. A retrospective observational analysis was conducted using bacterial culture and antimicrobial susceptibility testing records from equine samples. Data was cleaned and standardized prior to analysis. The distribution of bacterial organisms, anatomical sites of infection, antimicrobial susceptibility across major drug classes, and prevalence of multidrug resistance (MDR) were evaluated, with MDR defined as resistance to three or more antimicrobial classes. A total of 2,812 bacterial isolates were included. The most frequently identified organisms were \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e spp., \u003cem\u003ePseudomonas\u003c/em\u003e spp., and \u003cem\u003eKlebsiella\u003c/em\u003e spp. Most isolates originated from reproductive, integumentary/wound, and respiratory sites. Antimicrobial susceptibility varied across organisms and drug classes, with generally high susceptibility to fluoroquinolones and aminoglycosides, and greater variability observed for β-lactams and tetracyclines. Multidrug resistance was identified in 19.0% of isolates and was more common among Gram-negative organisms. These findings highlight the diversity of antimicrobial susceptibility patterns among equine bacterial pathogens and underscore the importance of routine culture and susceptibility testing to guide therapy and support antimicrobial stewardship in equine practice.\u003c/p\u003e","manuscriptTitle":"Antibiotic Susceptibility Patterns of Bacterial Isolates from Equine Clinical Submissions in Texas and Oklahoma, 2011–2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 13:52:25","doi":"10.21203/rs.3.rs-9370940/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f1d5cdfc-b191-4134-95f3-783008be735a","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-09T10:52:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T16:33:12+00:00","index":45,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T11:10:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 13:52:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9370940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9370940","identity":"rs-9370940","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}