antibiotics Article Antimicrobial Susceptibility Testing of Porcine Bacterial Pathogens: Investigating the Prospect of Testing a Representative Drug for Each Antimicrobial Family Anna Vilaró 1 , Elena Novell 1 , Vicens Enrique-Tarancon 1 , Jordi Balielles 1 , Lourdes Migura-García 2,3 and Lorenzo Fraile 4, * 1 Grup de Sanejament Porcí, 25192 Lleida, Spain; micro@gsplleida.com (A.V.); elena@gsplleida.net (E.N.); vicens@gsplleida.net (V.E.-T.); jordi@gsplleida.net (J.B.) 2 Unitat Mixta d’Investigació IRTA-UAB en Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), 08193 Bellaterra, Spain; lourdes.migura@irta.cat 3 IRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), 08193 Bellaterra, Spain 4 Departament de Ciència Animal, ETSEA, University of Lleida-Agrotecnio, 25198 Lleida, Spain * Correspondence: lorenzo.fraile@udl.cat; Tel.: +34-973-702-814 Abstract: Antimicrobial susceptibility testing is necessary to carry out antimicrobial stewardship but a limited number of drugs belonging to each antimicrobial family has to be tested for techni- cal limitations and economic resources. In this study, we have determined the minimal inhibitory concentration, using microdilution following international standards (CLSI), for 490 Actinobacillus pleuropneumoniae, 285 Pasteurella multocida, 73 Bordetella bronchiseptica, 398 Streptococcus suis and 1571 Escherichia coli strains from clinical cases collected in Spain between 2018 and 2020. The an- timicrobial susceptibility pattern was deciphered using a principal component analysis for each Citation: Vilaró, A.; Novell, E.; bacterium and a matrix correlation (high > 0.8, medium 0.5–0.8 and low < 0.5) was obtained for each Enrique-Tarancon, V.; Balielles, J.; pair of antimicrobials. No significant associations were observed between MIC patterns for different Migura-García, L.; Fraile, L. antimicrobial families, suggesting that co-selection mechanisms are not generally present in these Antimicrobial Susceptibility Testing porcine pathogens. However, a high correlation was observed between the fluroquinolones (mar- of Porcine Bacterial Pathogens: bofloxacin and enrofloxacin) for all mentioned pathogens and for ceftiofur and cefquinome for E. coli Investigating the Prospect of Testing a Representative Drug for Each and S. suis. Moreover, a significant association was also observed for tetracyclines (doxycycline and Antimicrobial Family. Antibiotics oxytetracycline) and B. bronchiseptica and tildipirosin/tulathromycin for P. multocida. These results 2022, 11, 638. https://doi.org/ suggest that generally, a representative drug per antimicrobial class cannot be selected, however, for 10.3390/antibiotics11050638 some drug–bug combinations, MIC values from one representative drug could be extrapolated to the whole antimicrobial family. Academic Editor: Linda Bester Received: 10 April 2022 Keywords: antimicrobial; susceptibility testing; MIC; porcine bacterial pathogen Accepted: 7 May 2022 Published: 10 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in 1. Introduction published maps and institutional affil- The Porcine Respiratory Disease Complex (PRDC), systemic disorders due to Strep- iations. tococcus suis (S. suis) infections and post-weaning diarrhea (PWD) are some of the most challenging diseases affecting the pig industry worldwide [1–3]. PRDC is a syndrome that results from a combination of infectious (bacteria and viruses) and non-infectious factors. Actinobacillus pleuropneumoniae (APP), Pasteurella multocida (P. multocida), Mycoplasma hyop- Copyright: © 2022 by the authors. neumoniae, Bordetella bronchiseptica (B. bronchiseptica) and Glaesserella (Haemophilus) parasuis Licensee MDPI, Basel, Switzerland. are the most common bacterial agents involved [1,4,5]. As a general approach, swine This article is an open access article preventive medicine programs should be based on applying measures to control PRDC distributed under the terms and in a cost-effective way, such as improving environmental conditions, decrease density conditions of the Creative Commons and stressors, combined with vaccination against the major viral and bacterial infectious Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ etiologic factors adapted in a case-by-case situation [1,5]. However, if such measures are 4.0/). not in place or fail, the use of antimicrobials may be necessary [4]. Antibiotics 2022, 11, 638. https://doi.org/10.3390/antibiotics11050638 https://www.mdpi.com/journal/antibiotics Antibiotics 2022, 11, 638 2 of 15 In the case of S. suis, disease outbreaks mainly occur after weaning when maternal antibodies wane. The mortality rate can be as high as 30%, but less severe manifestations of the disease include polyarthritis, meningitis, endocarditis or pneumonia during the nursery period [3,6,7]. Up to date, there is not registered vaccine for S. suis. Thus, prevention is mainly based on biosecurity, hygiene measures with all-in all-out herd management reducing the spread of the pathogen, and the use of autogenous vaccines. However, when outbreaks occur, antimicrobials are commonly prescribed, increasing the risk of emergence of antimicrobial resistance (AMR) [8,9]. Escherichia coli is the main causative agent of PWD, affecting piglets after weaning. It is an economically important enteric disease causing significant financial losses to the pig sector. PWD is characterized by a profuse diarrhea, dehydration, significant mortality and loss of body weight in surviving pigs [10,11]. When clinical signs appear, prescrip- tion of antimicrobials is the only solution to control the spread of the disease within the herd [6,7,12]. The use of antimicrobials with a therapeutic or metaphylactic purpose may be necessary to control the relevant pathogens involved in PRDC, S. suis infections and PWD [6,7,13,14]. In particular, the objective of antimicrobial therapy is to provide an effective drug to obtain a fast clinical recovery from the infection, reducing the probability of generating AMR bacteria [15]. Thus, it is essential for the veterinarian to confirm by laboratory analysis the etiology of the disease, and determine the antimicrobial susceptibility of the pathogen. In both veterinary and human medicine, antimicrobial susceptibility testing (AST) data can help to predict the clinical outcome of antimicrobial treatment [16], allowing a rational choice of the drug to treat a particular bacterial infection [17,18]. Antimicrobial suscepti- bility is usually measured by the minimum inhibitory concentration (MIC), which is the lowest concentration that stops in vitro growth of the targeted bacteria. Still, suitable clinical breakpoints (CBPs) must be available for each pair of bacterial pathogen/antimicrobial [19] to interpret the MIC data once available, and to foresee the clinical outcome after treat- ment. Unfortunately, there are limited infection-specific and host-specific CBPs for porcine pathogens, especially for infections due to S. suis and E. coli among others. This critical point makes extremely challenging to compare available data on antimicrobial resistance for porcine pathogens [6], and makes urgent the need to set up a common method to determine the antimicrobial susceptibility pattern of pathogens of veterinary interests in Europe, as recently published [20]. When optimizing the panel of antimicrobials to be tested for each targeted bacteria, one of the critical points is to establish a balanced number of antimicrobial drugs representing all of the different antimicrobial families, without hampering the laboratory routine and exceeding economic cost [19]. For some combination of bacterial species and antimicrobials, such as polymyxins and E. coli, clearly, colistin is the best representative drug of the family to test [21], but when it comes to fluoroquinolones, beta-lactams, aminoglycosides and macrolides, there are several possible drugs to select within each family, but testing all of them can exceed the load of work in routine diagnostics and make the cost of analysis unaffordable [19,20]. Hence, the aim of this study was to investigate MIC data retrieved from our diagnostic laboratory for bacterial pathogens with relevance in pig production involved in PRDC (APP, P. multocida and B. bronchiseptica), S. suis and PWD (E. coli), to determine concurrent MIC patterns between different antimicrobial families and within the same family to optimize the number of antimicrobials to be tested in a diagnostic laboratory. 2. Results 2.1. Clinical Samples From 2018 to 2020, 1060 samples were received from isowean, wean-to-finish and fattening farms suffering from clinical respiratory disease associated with the PRDC. Ad- ditionally, 496 and 1980 samples were received from sow, isowean and wean-to-finishing farms suffering clinical sings compatible with S. suis (SS) infection or PWD, respectively. In the case of sow farms, the samples were obtained from their nursery facility. Bacterial Antibiotics 2022, 11, 638 3 of 15 isolation for respiratory pathogens (APP, P. multocida (PM) and B. bronchiseptica (BB)) was possible in 80% (848/1060) of the cases, furthermore in 20%, it was possible to isolate more than one bacterial species from the same sample. Bacterial isolation of S. suis and E. coli (EC) was possible in 79.6% (398/496) and 79.3% (1571/1980) of the samples associated to systemic and digestive disorders, respectively. Finally, in 5% of the digestive samples, it was possible to isolate more than one bacterial species, generally Salmonella spp. 2.2. MIC Range, MIC50 and MIC90 for the Porcine Pathogens MIC range, MIC50 , MIC90 for 490, 285, 73, 398 and 1571 strains of APP, PM, BB, SS and EC are described in Tables 1–3, respectively. In general, a wide range of MIC was observed for each drug–bug combination with the exception of beta-lactams (amoxicillin and ceftiofur) and tiamulin for BB. Moreover, the MIC distributions were very different not only between drugs but also within each antimicrobial family for all the studied pathogens, with the exception of quinolones (enrofloxacin and marbofloxacin) for all the studied pathogens, tildipirosin and tulathromycin for PM, doxycycline and oxytetracycline for BB, amoxicillin and ampicillin for SS and ceftiofur and cefquinome for SS and EC. Table 1. Actinobacillus pleuropneumoniae (A), Pasteurella multocida (B) and Bordetella bronchiseptica (C) MIC range, MIC50 and MIC90 for 490 APP, 285 PM and 73 BB strains, respectively, isolated from respiratory clinical cases. The same color in the antimicrobial column represents member of the same antimicrobial family. A: Actinobacillus pleuropneumoniae Range MIC50 MIC90 Antimicrobial (µg/mL) (µg/mL) (µg/mL) Amoxicillin 0.06–16 0.5 8 Ceftiofur 0.06–0.25 0.06 0.06 Doxycycline 0.12–16 2 4 Enrofloxacin 0.03–4 0.06 0.5 Florfenicol 0.06–8 0.25 0.5 Marbofloxacin 0.03–4 0.03 0.5 Oxytetracycline 0.12–8 8 8 Sulfamethoxazole/trimethropim & 0.06–32 0.12 2 Tiamulin 2–32 16 16 Tildipirosin 1–64 8 8 Tilmicosin 4–64 16 16 Tulathromycin 8–64 32 64 B: Pasteurella multocida Amoxicillin 0.12–8 0.25 0.5 Ceftiofur 0.06–1 0.06 0.12 Doxycycline 0.12–16 0.5 2 Enrofloxacin 0.03–1 0.03 0.06 Florfenicol 0.12–8 0.5 0.5 Marbofloxacin 0.03–1 0.03 0.12 Oxytetracycline 0.12–8 1 8 Sulfamethoxazole/trimethropim & 0.03–8 0.12 4 Tiamulin 2–64 16 32 Tildipirosin 0.5–64 2 4 Tilmicosin 1–64 8 16 Tulathromycin 0.5–64 2 4 C: Bordetella bronchiseptica Amoxicillin 8–16 8 16 Ceftiofur 4 4 4 Doxycycline 0.12–4 1 2 Enrofloxacin 0.12–4 0.5 1 Florfenicol 1–8 4 4 Marbofloxacin 0.12–2 0.5 1 Oxytetracycline 0.25–8 2 4 Sulfamethoxazole/trimethropim & 0.06–8 4 4 Tiamulin 32–64 64 64 Tildipirosin 0.5–16 8 8 Tilmicosin 8–64 32 64 Tulathromycin 2–16 8 16 & The MIC value is showed for trimethoprim in the combination sulfamethoxazole/trimethropim. Antibiotics 2022, 11, 638 4 of 15 Table 2. Streptococcus suis MIC range, MIC50 and MIC90 for 398 strains isolated from systemic clinical cases. The same color in the antimicrobial column represents member of the same antimicrobial family. Range MIC50 MIC90 Antimicrobial (µg/mL) (µg/mL) (µg/mL) Amoxicillin 0.06–8 0.06 0.5 Ampicillin 0.06–4 0.06 0.5 Ceftiofur 0.06–4 0.25 1 Cefquinome 0.06–4 0.06 0.25 Doxycycline 0.12–16 8 16 Enrofloxacin 0.03–4 0.5 1 Florfenicol 1–8 2 4 Marbofloxacin 0.03–4 1 2 Penicillin G 0.06–8 0.25 2 Sulfamethoxazole/trimethropim & 0.06–4 1 4 Tiamulin 0.5–64 2 32 Tilmicosin 4–64 64 64 & The MIC value is showed for trimethoprim in the combination sulfamethoxazole/trimethropim. Table 3. Escherichia coli MIC range, MIC50 and MIC90 for 1571 strains isolated from digestive clinical cases. The same color in the antimicrobial column represents member of the same antimicrobial family. Range MIC50 MIC90 Antimicrobial (µg/mL) (µg/mL) (µg/mL) Amoxicillin 0.12–16 8 16 Apramycin 0.5–64 4 32 Ceftiofur 0.06–8 0.5 8 Cefquinome 0.06–8 0.12 8 Colistin 0.25–32 1 4 Enrofloxacin 0.03–4 1 4 Florfenicol 1–8 8 8 Gentamycin 0.12–64 1 16 Marbofloxacin 0.03–4 0.5 4 Neomycin 0.5–64 4 64 Spectinomycin 4–128 128 128 Sulfamethoxazole/trimethropim & 0.03–8 4 8 & The MIC value is showed for trimethoprim in the combination sulfamethoxazole/trimethropim. 2.3. Multivariate Analysis of MIC for Twelve Antimicrobials in Each Porcine Pathogen 2.3.1. Actinobacillus pleuropneumoniae (APP) The first two principal components could explain 35.4% of the variance for the MIC values of APP against all the tested antimicrobials. Sulphonamides and cephalosporins had characteristics corresponding to the most frequent MIC values (X = 0, Y = 0 in the graph) whereas quinolones (enrofloxacin and marbofloxacin) and macrolides (tilmi- cosin, tildipirosin and tulathromycin) showed the less frequent values, far from the ori- gin of the axes (Figure 1). In general, a low correlation between each pair of antimi- crobials was observed with the exception of enrofloxacin and marbofloxacin MIC val- ues (R = 0.92), and intermediate correlation (R = 0.64) for the combination tilmicosin– tildipirosin, and doxycycline–oxytetracycline (R = 0.58). APP isolates were grouped into 19 clusters (Figure S1 in Supplementary Materials) according to their MIC values but most of the isolates (61.8%) were grouped in six clusters and seven clusters included only 8.8% of the remaining isolates. axes (Figure 1). In general, a low correlation between each pair of antimicrobials was ob- served with the exception of enrofloxacin and marbofloxacin MIC values (R = 0.92), and intermediate correlation (R = 0.64) for the combination tilmicosin–tildipirosin, and doxycycline–oxytetracycline (R = 0.58). APP isolates were grouped into 19 clusters (Figure S1 in Supplementary Materials) according to their MIC values but most of the isolates Antibiotics 2022, 11, 638 5 of 15 (61.8%) were grouped in six clusters and seven clusters included only 8.8% of the remain- ing isolates. Figure1.1.Biplot Figure Biplotobtained obtainedthrough througha aprincipal principalcomponent component analysis analysis of of MICMIC values values forfor twelve twelve antimi- antimicro- crobials bials and and 490 strains 490 strains of Actinobacillus of Actinobacillus pleuropneumoniae pleuropneumoniae (APP).(APP). The antimicrobials The antimicrobials tested tested were were amoxi- amoxicillin (Amo), ceftiofur (Ceft), doxycycline (Dox), enrofloxacin (Enr), florfenicol (flo), mar- cillin (Amo), ceftiofur (Ceft), doxycycline (Dox), enrofloxacin (Enr), florfenicol (flo), marbofloxacin bofloxacin (Mar), oxytetracycline (Oxy), sulfamethoxazole/trimethoprim (Sul), tiamulin (Tia), (Mar), oxytetracycline (Oxy), sulfamethoxazole/trimethoprim (Sul), tiamulin (Tia), tildipirosin (Tild), tildipirosin (Tild), tilmicosin (Tilm) and tulathromycin (Tul). tilmicosin (Tilm) and tulathromycin (Tul). 2.3.2. Pasteurella multocida The first two principal components could explain 45.9% of the variance for the MIC values of P. multocida against all the tested antimicrobials. All the antimicrobials tested showed very variable MIC values, locating all of them far from the origin of the axes in the plot (Figure 2). In general, low correlation between each pair of antimicrobials was observed with the exception of a high association between enrofloxacin and marbofloxacin MIC values (R = 0.96) and tildipirosin and tulathromycin (R = 0.98). Tilmicosin–tildipirosin (R = 0.67), tilmicosin–tulathromycin (R = 0.68) and doxycycline–oxytetracycline (R = 0.58) exhibited intermediate correlation. Isolates of P. multocida were grouped into 15 clus- ters (Figure S2 in Supplementary Materials) according to their MIC values, but most of them (80.1%) were grouped in four clusters, whereas seven clusters included only 5% of the isolates. 2.3.3. Bordetella bronchiseptica The first two principal components could explain 53.4% of the variance for the MIC values against all the tested antimicrobials (Figure 3). Again, a low correlation between each pair of antimicrobials was observed with the exception of enrofloxacin and marbofloxacin MIC values (R = 0.94) and doxycycline–oxytetracycline (R = 0.84). Intermediate correlations (R = 0.5–0.7) were obtained for each pair of macrolides tested in the panel (tildipirosin, tilmicosin and tulathromycin). B. bronchiseptica isolates were grouped into eight clusters (Figure S3 in Supplementary Materials) according to their MIC values but most of the strains (73.7%) were grouped in three clusters, and three extra clusters included only 8.1% of the strains. Antibiotics 2022, 11, 638 6 of 15 Figure 2. Biplot obtained through a principal component analysis of MIC values for twelve antimicro- bials and 285 strains of Pasteurella multocida (PM). The antimicrobials tested were amoxicillin (Amo), ceftiofur (Ceft), doxycycline (Dox), enrofloxacin (Enr), florfenicol (flo), marbofloxacin (Mar), oxyte- Antibiotics 2022, 11, x FOR PEER REVIEW 7 of 16 tracycline (Oxy), sulfamethoxazole/trimethoprim (Sul), tiamulin (Tia), tildipirosin (Tild), tilmicosin (Tilm) and tulathromycin (Tul). Figure 3. Figure 3. Biplot Biplot obtained obtainedthrough throughaaprincipal principalcomponent componentanalysis analysis ofof MIC MIC values values forfor twelve twelve antimi- antimicro- crobials and 73 strains of Bordetella bronchiseptica (BB). The antimicrobials tested were amoxicillin bials and 73 strains of Bordetella bronchiseptica (BB). The antimicrobials tested were amoxicillin (Amo), (Amo), ceftiofur (Ceft), doxycycline (Dox), enrofloxacin (Enr), florfenicol (flo), marbofloxacin (Mar), ceftiofur (Ceft), doxycycline (Dox), enrofloxacin (Enr), florfenicol (flo), marbofloxacin (Mar), oxyte- oxytetracycline (Oxy), sulfamethoxazole/trimethoprim (Sul), tiamulin (Tia), tildipirosin (Tild), tracycline (Oxy), sulfamethoxazole/trimethoprim (Sul), tiamulin (Tia), tildipirosin (Tild), tilmicosin tilmicosin (Tilm) and tulathromycin (Tul). (Tilm) and tulathromycin (Tul). 2.3.4. Streptococcus suis The first two principal components could explain 53.7% of the variance for the MIC values of S. suis against all the tested antimicrobials. Tilmicosin and doxycycline had char- acteristics corresponding to the most frequent MIC values whereas quinolones (enroflox- Antibiotics 2022, 11, 638 7 of 15 2.3.4. Streptococcus suis The first two principal components could explain 53.7% of the variance for the MIC values of S. suis against all the tested antimicrobials. Tilmicosin and doxycycline had charac- teristics corresponding to the most frequent MIC values whereas quinolones (enrofloxacin and marbofloxacin) and β-lactams (penicillin G, amoxicillin, ampicillin, cefquinome and ceftiofur) showed less frequent values (Figure 4). Low correlation was observed between each pair of antimicrobials with the exception of enrofloxacin and marbofloxacin MIC values (R = 0.99), amoxicillin and ampicillin (R = 0.92) and finally, ceftiofur and cefquinome (R = 0.89). Intermediate correlation was observed for amoxicillin/ampicillin with penicillin G (R = 0.62–0.76) and with ceftiofur/cefquinome (R = 0.69–0.72). Interestingly, a corre- lation value of 0.69 and 0.47 for penicillin and ceftiofur and penicillin and cefquinome, respectively, was obtained. S. suis isolates were grouped into 14 clusters (Figure S4 in Supplementary Materials) according to their MIC values, but most of the isolates (65.3%) Antibiotics 2022, 11, x FOR PEER REVIEW 8 of 16 were grouped in four clusters and the remaining five clusters included only 5.4% of the strains. Figure 4. Figure 4. Biplot Biplot obtained obtainedthrough througha aprincipal principalcomponent component analysis analysisof of MIC values MIC for for values twelve antimi- twelve an- crobials andand timicrobials 398 strains of Streptococcus 398 strains suis. The of Streptococcus suis.antimicrobials testedtested The antimicrobials were ampicillin (Amp),(Amp), were ampicillin amox- icillin (Amo), cefquinome (Cef), ceftiofur (Ceft), doxycycline (Dox), enrofloxacin (Enr), florfenicol amoxicillin (Amo), cefquinome (Cef), ceftiofur (Ceft), doxycycline (Dox), enrofloxacin (Enr), florfeni- (flo), marbofloxacin (Mar), penicillin G, sulfamethoxazole/trimethoprim (Sul), tiamulin (Tia), and, col (flo), marbofloxacin (Mar), penicillin G, sulfamethoxazole/trimethoprim (Sul), tiamulin (Tia), and, tilmicosin (Tilm). tilmicosin (Tilm). 2.3.5. Escherichia 2.3.5. Escherichia coli coli Thefirst The firsttwo twoprincipal principalcomponents components could could explain explain 46.9% 46.9% ofof the thevariance variance for for the the MIC MIC valuesagainst values againstallallthe thetested testedantimicrobials antimicrobials(Figure (Figure5). 5).All All the the antimicrobials antimicrobialstested testedshowed showed very variable very variable MIC MIC values values locating locating all all of of them them farfar from from thethe origin origin of of the the axes axes in in the the plot, plot, particularlycephalosporins particularly cephalosporinsand andquinolones. quinolones.AAlow low correlation correlation between between each each pair pair of anti- of antimi- crobials waswas microbials observed, observed, with thethe with exception exception of enrofloxacin of enrofloxacin andand marbofloxacin marbofloxacinMIC MICvalues val- (R ues= (R 0.82) and and = 0.82) ceftiofur and and ceftiofur cefquinome cefquinome(R =(R0.92). E. coli = 0.92). isolates E. coli were isolates grouped were grouped intointo 18 clusters (Figure S5 in Supplementary) according to their MIC values. Thus, 18 clusters (Figure S5 in Supplementary) according to their MIC values. Thus, the number the number of isolates by cluster was mostly between 5% and 11% of of isolates by cluster was mostly between 5% and 11% of the total. the total. Antibiotics 2022,11, Antibiotics2022, 11,638 x FOR PEER REVIEW 89 of of1516 Figure5.5. Biplot Figure Biplot obtained through through aaprincipal principalcomponent componentanalysis analysis ofof MIC MICvalues forfor values twelve antimi- twelve an- crobials andand timicrobials 15711571 strains of Escherichia strains coli (EC). of Escherichia The antimicrobials coli (EC). testedtested The antimicrobials were amoxicillin (Amo), were amoxicillin apramycin (Amo), (Apr), (Apr), apramycin cefquinome (Cefq),(Cefq), cefquinome ceftiofur (Ceft),(Ceft), ceftiofur colistin (Col), (Col), colistin enrofloxacin (Enr),(Enr), enrofloxacin florfenicol flor- (flo), gentamycin (Gen), marbofloxacin (Mar), neomycin (Neo), spectinomycin (Spe) fenicol (flo), gentamycin (Gen), marbofloxacin (Mar), neomycin (Neo), spectinomycin (Spe) and and sulfameth- oxazole/trimethoprim (Sul). sulfamethoxazole/trimethoprim (Sul). 3.3.Discussion Discussion Preventivemedicine Preventive medicineprograms programsfor forlivestock livestockmust mustbe bebased basedon oncontrolling controllingdiseases diseases withmeasures with measures such such as external external and andinternal internalbiosecurity, biosecurity,use useofof vaccines, vaccines, hygiene hygiene andanddis- infection, management disinfection, management measures measures (e.g., all all (e.g., in–all outout in–all procedures) procedures) andand good husbandry good husbandry pro- tocols (e.g., protocols optimal (e.g., optimaltemperature temperatureand andventilation) ventilation)to tooptimize optimize welfare welfare of the animalsanimals[22].[22]. These Thesemeasures measureswill willbe beable abletotoreduce reducethe theuse useofofantibiotics antibioticsand andtherefore, therefore,be beone oneofofthethe drivers for the generation of AMR bacteria not only in animals, drivers for the generation of AMR bacteria not only in animals, but also in humans and but also in humans and the theenvironment environmentfollowing followingaaone-health one-healthapproach approach[23]. [23].InInany anycase, case,thetheuse useofofantibiotics antibiotics isisnecessary necessarytotoguarantee guaranteeanimal animalwelfare welfareand andgood goodveterinary veterinarypractices practicesonce oncethethedisease disease appears appearsininthe thefarm. farm.However, However, thethe optimization optimization of antibacterial of antibacterial therapy therapy is crucial in human is crucial in hu- and manveterinary medicine and veterinary in order medicine to reduce in order the selection to reduce of resistant the selection bacteria of resistant [24,25]. bacteria Our [24,25]. cluster Our cluster analysis clearly demonstrates a great variability for the MIC pattern in allthe analysis clearly demonstrates a great variability for the MIC pattern in all the porcine porcinepathogens pathogensstudied. studied.These Theseresults resultsreinforced reinforcedthe therecommendation recommendationofofdetermining determining AST ASTfor foreach eachclinical clinicalcase casein in order order to to select thethe select most suitable most suitabledrug in ain drug case-by-case a case-by-case situation situa- according to the new European legislation. As discussed tion according to the new European legislation. As discussed in our previous paperin our previous paper [17],[17], an antimicrobial an antimicrobial stewardship stewardship for for a case-by-case a case-by-case situation situation is is possible possible if ifananepidemiological epidemiological link link is proposed, meaning that within the same production system (samefarm is proposed, meaning that within the same production system (same farm ororinter- inter- related farms), the microbiological diagnosis and the determination related farms), the microbiological diagnosis and the determination of its antimicrobial of its antimicrobial susceptibility susceptibilitycouldcould bebe used used to to treat treatsimilar clinical similar cases clinical caused cases by this caused by particular this particularbacterium bacte- during a set period of time. rium during a set period of time. The Thefirst firststep steptotooptimize optimizethe theuseuseofofantibiotics antibioticsininlivestock livestockisistotocarry carryoutoutaagoodgood diagnosis including AST [19]. The existence of CBPs for each pair diagnosis including AST [19]. The existence of CBPs for each pair antimicrobial/pathogen antimicrobial/pathogen isiscritical criticaltotocorrectly correctlyforesee foreseethe theclinical clinicaloutcome outcomefor foreach eachclinical clinicalcase. case.In Inthis thisstudy, study,we we have not carried out an analysis based on the phenotype (resistant/susceptible) according have not carried out an analysis based on the phenotype (resistant/susceptible) according Antibiotics 2022, 11, 638 9 of 15 to CBPs, due to the absence of standardized CBPs for many antimicrobials and bacterial species, such as B. bronchiseptica, S. suis or E. coli [6,7,26,27]. However, we have performed a principal component analysis to decipher the MIC pattern for each included pathogen. This multivariate methodology allows analyzing relationships within a set of numerical variables, without any previous assumptions on data distribution. We have been unable to observe any association between the MIC patterns for different antimicrobial families (e.g., quinolones versus macrolides) suggesting that, co-selection mechanisms are not generally present in these porcine pathogens. However, co-selection mechanisms for some pig pathogens have been described in the literature, mainly based on the presence of plasmids harboring antimicrobial resistance genes against several antimicrobial families [28–30]. These cases could be also present in our database but our analysis, including all the isolates and antimicrobials, was unable to detect them. We have observed a high correlation and a similar MIC distribution between the MIC values for marbofloxacin and enrofloxacin, third and second generation of fluoro- quinolones, respectively, for all the studied pathogens (E. coli, S. suis, APP, P. multocida and B. bronchiseptica) suggesting that a cross-resistance mechanism could be present for fluoroquinolones and all the porcine pathogens studied. Fluoroquinolones are known to have two enzyme targets in the bacterial cell, DNA gyrase and topoisomerase IV, both involved in DNA replication. Mutations in key sites nominated quinolone resistance- determining region (QRDR) of the gyrase or topoisomerase IV can decrease the binding affinity for the antimicrobial [31]. Studies have shown that isolates with a single mutation in the QRDR to some extent are refractory to the bactericidal effect of fluoroquinolones, and in Gram-negative organisms, once a first-step mutation has reduced the susceptibility, further mutations increase the resistance and therefore the MIC value [32]. For instance, in S. suis, cross-resistance to second- and third-generation fluoroquinolones have been associated with two modifications in GyrA (at positions S81 and E85 of GyrA) and one in ParC (at position S79) [33]. Plasmid-mediated resistance and efflux pumps have also been described to confer resistance to these drugs [34]. Independently of the exact mechanisms of cross-resistance for porcine pathogens, from our data it is possible to conclude that testing one quinolone (enrofloxacin or marbofloxacin) is enough to carry out AST for this antimicrobial family and these porcine pathogens. This affirmation should be supported by other research groups using other quinolones such as danofloxacin in future studies. While it was not possible to test cross-resistance between ceftiofur and cefquinome for APP, P. multocida and B. bronchiseptica, because only ceftiofur was in the panel, a high correlation and a similar MIC distribution was observed between ceftiofur and cefquinome MIC values in E. coli and S. suis. These results suggest that the same mechanism of resistance or cross-resistance could be present between third- and fourth-generation cephalosporins for each of these two pathogens. Similar results can be extracted from data published by other researchers for E. coli [35,36]. In E. coli, resistance to cephalosporins most generally is associated with the presence of genes encoding for extended spectrum beta-lactamases (ESBL) or AmpC enzymes. Some of the most common resistance genes in livestock are variants of blaTEM , blaCTX-M , blaSHV and blaCMY [37,38]. These genes are generally located in plasmids and are known to cause cross-resistance between different cephalosporins [39]. The correlation observed between the MIC value for beta-lactams and S. suis must be carefully studied and discussed. In Streptococci, a typical route to acquire beta-lactam resistance involves variants in the penicillin-binding-protein genes (pbp) and it is necessary the joint action of many pbp variants to explain significant changes in MIC [40,41]. S. suis has three key pbp genes (pbp1A, pbp2B and pbp2X). Single point mutations in pbp2X alone have small effects on the MIC value, whereas additional mutations in the rest of pbp, taking place in a set order, may explain the high MIC value observed for some antimicrobials belonging to this family [42]. On the other hand, studies have shown that cefotaxime, a third-generation cephalosporin like ceftiofur, selectively inactivates pbp2X but not pbp2B [43]. This finding may explain the intermediate association between MIC value for the combination of penicillin and aminopenicillins and cephalosporins observed in our Antibiotics 2022, 11, 638 10 of 15 research work and supports that the mechanisms of resistance for beta-lactams could be only shared by a percentage (50–72%) of the isolates [44–46]. However, a high correlation and a similar MIC distribution has been observed between the MIC value for amoxicillin and ampicillin, agreeing perfectly with the CLSI recommendation of testing any of both drugs for Gram-positive bacteria [26]. The correlation between the MIC values for the tested tetracyclines (doxycycline and oxytetracycline) is very high for B. bronchiseptica and intermediate for the rest of bacteria tested (APP, S. suis and P. multocida). These results suggest that the mechanisms of resistance for tetracyclines could be common for most of the B. bronchiseptica strains, but these mechanisms could be only shared by a percentage (close to 50%) of the APP, S. suis and P. multocida strains. Binding site mutations in rRNA conferring tetracycline resistance are usually found in bacteria with low rRNA gene copy numbers. This could be the case for B. brochiseptica, which have three copies of the 16S rRNA. However, a plasmid-mediated mechanism such as tet(A) or tet(C) has also been described for this pathogen [14] that could provide this cross-resistant phenotype. In general, three mechanisms of resistance to tetracyclines have been well described, efflux pumps, ribosomal protection, and enzymatic inactivation of the drugs [47]. Still further studies should be addressed to identify the specific target and mechanism of action of each one of the drugs included in this family, which nowadays remain poorly understood [48]. It is especially interesting the absence of cross-resistance between families with high number of drugs such as aminoglycosides and macrolides. Thus, resistance mechanisms are complex and differ between aminoglycoside molecules and between bacterial species, and generally, there is less cross-resistance when compared with other classes of antimicro- bials [49]. In the case of macrolides, the level of cross-resistance depends on the mechanisms conferring the resistance. Thus, it is well known that modification of the ribosomal tar- get probably confers cross-resistance to macrolides and lincosamides as a whole family, whereas efflux pumps and enzymatic inactivation do not and, as a consequence, a lot of variability in cross-resistance is expected [50]. This result implies that it is not possible to select “target” drugs for macrolides and aminoglycosides as surrogate markers for all the family with the only exception of tildipirosin/tulathromycin and P. multocida. This result could be extremely relevant to set up European programs for surveillance of antimicrobial resistance of porcine pathogens [20]. 4. Materials and Methods 4.1. Clinical Samples Between 2018 and 2020, samples were taken from diseased or recently deceased pigs from farms showing acute clinical signs of respiratory tract infections, clinical symptoms compatible with S. suis infections (nervous symptoms and arthritis) or pigs showing diarrhea that had not been exposed to antimicrobial treatment for, at least, 15 days prior to sampling. Thus, the sampled animals were between 3 and 24 weeks old for animals showing overt respiratory symptoms with or without depression and/or hyperthermia (>39.8 ◦ C). For each clinical case, samples of lungs of two recently deceased pigs (<12 h) were submitted under refrigeration to the laboratory. On the other hand, the sampled animals were between 3 and 12 weeks old for animals showing clinical symptoms compatible with S. suis infections (nervous symptoms and arthritis) and pigs showing diarrhea. In the case of S. suis infections, whole blood, cerebrospinal fluid or articular fluid were obtained from sick or recently deceased animals (<12 h). Finally, intestinal content obtained from humanly euthanized animals or watery diarrhea from sick pigs were obtained from animals showing digestive symptoms during the post-weaning period. In both cases, the samples were submitted under refrigeration to the laboratory and processed during the following 24 h after collection. Antibiotics 2022, 11, 638 11 of 15 4.2. Bacterial Isolation and Identification Clinical specimens were cultured aseptically onto blood agar (Columbia agar with 5% Sheep blood, 254005 BD), chocolate agar (GC II agar with IsoVitaleX, 254060, BD or blood Agar No. 2 Base, 257011, BD) and MacConkey agar (4016702, Biolife Italiana Srl) and incubated at 35 ± 2 ◦ C in aerobic conditions with 5–10% CO2 for 24–48 h to address the isolation of respiratory and systemic pathogens. Finally, for the isolation of digestive pathogens, specimens were cultured aseptically onto Blood agar, MacConkey agar and Xylose-Lysine-Desoxycholate Agar (XLD, CM0469, Oxoid). The plates were incubated at 35 ± 2 ◦ C in aerobic conditions for 24 h. Identification of isolates for respiratory pathogens (APP, P. multocida and B. bronchisep- tica), S. suis and digestive pathogens was carried out by matrix-assisted laser desorption ionization-time of flight (MALDI-TOF Biotyper System, Bruker Daltonics, Bremen, Germany) as previously described [17]. Individual strains were stored at −80 ◦ C in brain heart infu- sion (CM1135, Oxoid) with 30% of glycerol (G9012, Sigma-aldrich, St. Louis, MO, USA). 4.3. Antimicrobial Susceptibility Testing MIC values were determined using the broth microdilution method by means of cus- tomized 96-well microtiter plates (Sensititre, Trek diagnostic Systems Inc., East Grinstead, UK) containing a total of twelve and seven–eight antibiotics/concentrations, respectively, in accordance with the recommendations presented by the Clinical and Laboratory Stan- dards Institute [26,27]. This antimicrobial panel was selected to represent commonly used compounds for treatment of pig diseases in practice. Bacteria were thawed, cultured on chocolate agar or blood agar, and incubated at 35 ±2 ◦ C in ambient air (or with 5–10% CO2 for APP) for 18–24 h. Three to five colonies were picked and emulsified in demineralized water (or Cation Adjusted Muëller–Hinton Broth (CAMHB) for APP and S. suis) to obtain a turbidity of 0.5 McFarland standard (Sensititre™ nephelometer V3011). Suspensions were further diluted in CAMHB for E. coli, CAMHB or Cation Adjusted Mueller–Hinton Broth with 2.5–5% Lysed Horse Blood for P. multocida and B. bronchiseptica, Cation Adjusted Mueller Hinton Broth with 2.5–5% Lysed Horse Blood (CAMHB + LHB) for S. suis and Veterinary Fastidious Medium (VFM) or Mueller–Hinton Fastidious broth with Yeast (MHF-Y) for APP to reach a final inoculum concentration of 5 × 105 cfu/mL. Then, the Sensititre panel was reconstituted by adding 100 µL/well of the inoculum. Plates containing E. coli and B. bronchiseptica isolates were incubated at 35 ± 2 ◦ C for 16–20 h, P. multocida isolates were incubated at 35 ± 2 ◦ C for 18–24 h and S. suis isolates were incubated at 35 ± 2 ◦ C for 20–24 h. In the case of APP isolates, plates were covered with a perforated seal and incubated at 35 ± 2 ◦ C with 5–10% CO2 for 20–24 h. The antibiotic panels were read manually using Sensititre™ Vizion (V2021) and the MIC value was established as the lowest drug concentration inhibiting visible growth. For each strain tested, a colony count and a purity check were performed following CLSI and manufacturer recommendations. Moreover, quality control strains were also included. Thus, Actinobacillus pleuropneumoniae (ATCC 27090™), Escherichia coli (ATCC 25922™), Streptococcus pneumoniae (ATCC 49619™) and Enterococcus faecalis (ATCC 29212™) were included as quality control following CLSI recommendations [26,27]. The MICs of the quality control strains had to be within acceptable CLSI ranges to accept the results obtained in the laboratory. 4.4. Data Analysis The results of the sensitivity tests are presented as MIC distributions (MIC range, MIC50 and MIC90 ) and these were determined for each species–antimicrobial combination. Moreover, a principal component analysis (PCA) was carried out. This multivariate tech- nique has been used to decipher the antimicrobial susceptibility pattern for each bacterium taking into account the MIC values for all the drugs. PCA also provides information about correlations between variables with matrix correlations. The correlation between MIC Antibiotics 2022, 11, 638 12 of 15 values for each pair of antimicrobials was classified as high (>0.8), intermediate (0.5–0.8) and low (<0.5) for each bacterium. Finally, a constellation plot was also generated using between-group linkage via Ward’s hierarchical clustering that allows generating clusters of strains of each studied pathogen according to their antimicrobial susceptibility testing for all the antimicrobials together. All the data analysis was carried out with JMP® , Version 13 (SAS Institute Inc., Cary, NC, USA, 1989–2019). 5. Conclusions In general, antimicrobial susceptibility testing results from one drug are not represen- tative from the whole antimicrobial family, however, for specific drug–bug combinations, MIC values from one drug may be extrapolated to the whole family. This is the case of fluoroquinolones (marbofloxacin and enrofloxacin) and all porcine pathogens tested herein, for ceftiofur and cefquinome in combination with E. coli and S. suis, ampicillin and amoxicillin with S suis, tetracyclines (doxycycline and oxytetracycline) with B. bronchiseptica and tildipirosin/tulathromycin with P. multocida. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/antibiotics11050638/s1, Figure S1. Constellation plot of the 490 strains of Actinobacillus pleuropneumoniae (APP) after a hierar-chical clustering analysis of MIC values for amoxicillin (Amo), ceftiofur (Ceft), doxycycline (Dox), enrofloxacin (Enr), florfenicol (flo), marbofloxacin (Mar), oxytetracycline (Oxy), sulfamethoxazole/trimethoprim (Sul), tiamulin (Tia), tildipirosin (Tild), tilmicosin (Tilm) and tulathromycin (Tul); Figure S2. Constellation plot of the 285 strains of Pasteurella multocida (PM) after a hierarchical clustering analysis of MIC values for amox- icillin (Amo), ceftiofur (Ceft), doxycycline (Dox), enrofloxacin (Enr), florfenicol (flo), marbofloxacin (Mar), oxytetracycline (Oxy), sulfamethoxazole/trimethoprim (Sul), tiamulin (Tia), tildipirosin (Tild), tilmicosin (Tilm) and tulathromycin (Tul); Figure S3. Constellation plot of the 78 strains of Bordetella bronchiseptica (BB) after a hierarchical clustering analysis of MIC values for amoxicillin (Amo), ceftio- fur (Ceft), doxycycline (Dox), enrofloxacin (Enr), florfenicol (flo), marbofloxacin (Mar), oxytetracycline (Oxy), sulfamethoxazole/trimethoprim (Sul), tiamulin (Tia), tildipirosin (Tild), tilmicosin (Tilm) and tu-lathromycin (Tul); Figure S4. Constellation plot of the 398 strains of Streptococcus suis after a hierarchical clustering analysis of MIC values for ampicillin (Amp), amoxicillin (Amo), cefquinome (Cef), ceftiofur (Ceft), doxycycline (Dox), enrofloxacin (Enr), florfenicol (flo), marbofloxacin (Mar), Penicillin G, sulfamethoxazole/trimethoprim (Sul), tiamulin (Tia), and, tilmicosin (Tilm); Figure S5. Constellation plot of the 1571 strains of Escherichia coli (EC) after a hierarchical clustering analysis of MIC values for amoxicillin (Amo), apramycin (Apr), cefquinome (Cefq), ceftiofur (Ceft), colistin (Col), enrofloxacin (Enr), florfenicol (flo), gentamycin (Gen), marbofloxacin (Mar), neomycin (Neo), spectinomycin (Spe) and sulfamethoxazole/trimethoprim (Sul). Author Contributions: Conceptualization, A.V. and L.F.; methodology, A.V., E.N., V.E.-T. and J.B.; formal analysis, L.F.; writing—original draft preparation, L.F., L.M.-G. and A.V.; writing—review and editing, L.F., L.M.-G. and A.V.; funding acquisition, E.N. and V.E.-T. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable because this study does not involve humans or animals. All the samples were sent for diagnosis due to clinical cases. Informed Consent Statement: Written informed consent has been obtained from veterinarians to publish this paper. This informed consent is signed whereas filling the information about the case. Data Availability Statement: The data presented in this study are available on reasonable request from the corresponding author. The data are not publicly available due to confidentiality issues related with clinical cases. Acknowledgments: This study was carried out with the support of the Porcine Sanitation Group of Lleida, Spain (Grup de Sanejament Porcí—GSP) in collaboration with swine practitioners working in the field. This centre colaborates with OIE for the Research and Control of Emerging and Re-emerging Swine Diseases in Europe. Antibiotics 2022, 11, 638 13 of 15 Conflicts of Interest: The authors declare no conflict of interest. References 1. Gebhardt, J.T.; Tokach, M.D.; Dritz, S.S.; DeRouchey, J.M.; Woodworth, J.C.; Goodband, R.D.; Henry, S.C. 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