TESIS DOCTORAL
por compendio de publicaciones
Antimicrobial resistance in the Spanish swine production: Impact of the production system and the
antimicrobial use
Resistencias antimicrobianas en la producción porcina española: impacto del sistema de producción y del uso
de antibióticos
Oscar Mencía Ares
Programa de Doctorado: Ciencias Veterinarias y de los Alimentos
Tutora: Ana M. Carvajal Urueña
Directores: Ana M. Carvajal Urueña, Pedro M. Rubio Nistal y Héctor Argüello Rodríguez
LEÓN, 2021
A mi madre y a mi padre.
A mi hermano.
A Ody.
List of publications
The present doctoral thesis by compendium of publications includes the following scientific articles for consideration:
Oscar Mencía-Ares, Héctor Argüello, Héctor Puente, Manuel Gómez-García,
Avelino Álvarez-Ordóñez, Edgar G. Manzanilla, Ana Carvajal and Pedro Rubio. (2021).
Effect of antimicrobial use and production system on Campylobacter spp., Staphylococcus spp. and Salmonella spp. resistance in Spanish swine: A cross sectional study. Zoonoses and Public Health, 68(1), 54-66. https://doi.org/10.1111/zph.12790
Oscar Mencía-Ares, Héctor Argüello, Manuel Gómez-García, Héctor Puente,
Edgar G. Manzanilla, Avelino Álvarez-Ordóñez, Ana Carvajal and Pedro Rubio. (2021).
Antimicrobial resistance in commensal Escherichia coli and Enterococcus spp. is influenced by production system, antimicrobial use and biosecurity measures on Spanish swine farms. Porcine Health Management, 7(1), 27. https://doi.org/10.1186/s40813-021- 00206-1
Oscar Mencía-Ares, Raúl Cabrera-Rubio, José Francisco Cobo-Díaz, Avelino
Álvarez-Ordóñez, Manuel Gómez-García, Héctor Puente, Paul D. Cotter, Fiona Crispie, Ana Carvajal, Pedro Rubio and Héctor Argüello. (2020). Antimicrobial use and production system shape the fecal, environmental and slurry resistomes of pig farms.
Microbiome, 8(1), 164. https://doi.org/10.1186/s40168-020-00941-7
Oscar Mencía-Ares, Maria Borowiak, Héctor Argüello, José Francisco Cobo-
Díaz, Burkhard Malorny, Avelino Álvarez-Ordóñez, Héctor Puente, Manuel Gómez- García, Ana Carvajal, Pedro Rubio and Carlus Deneke. Genomic insights into the mobilome and the resistome of sentinel microorganisms from two different swine production systems. Manuscript under preparation.
Funding and grants
This doctoral thesis was framed within the coordinated project “Reducción del uso de antibióticos en producción ganadera intensiva: programa integral de manejo y aplicación de productos alternativos”, whose reference is RTA2015-00075-C04-03, funded by Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), and whose duration was from 01/07/2017 to 31/12/2018, with Pedro M. Rubio Nistal as Principal Investigator at the University of León.
Oscar Mencía Ares held two consecutive grants to develop this doctoral thesis: Junta de Castilla y León co-financed by the European Social Fund from 04/07/2017 to 30/09/2017; and Spanish Government (Ministerio de Educación y Formación Profesional), whose reference is FPU 16/03485, from 01/10/2017 until the date of defense of the present doctoral thesis. The PhD candidate also received a grant from the Spanish Government (Ministerio de Universidades), whose reference is EST 19/00806, for a short stay in the German Federal Institute for Risk Assessment from 02/04/2021 to 25/06/2021.
Acknowledgments
Muchas gracias a todas las personas que han hecho posible esta tesis doctoral.
Summary
Antimicrobial resistance is a global threat to public health and the environment derived from the prolonged and often inappropriate use of antimicrobials in human and veterinary medicine. In food-producing animals, several factors have been described that may have an impact on the use of antimicrobials and, consequently, on antimicrobial resistance, among which we can highlight the production system and the biosecurity measures implemented on the farm. In this sense, organic and extensive Iberian pig productions are based on sustainable and eco-friendly farming systems, providing an excellent opportunity to evaluate how sustained differences in the various forms of antimicrobial use impact antimicrobial resistance, not only in the animals, but also in the farm environment.
The general objective of this doctoral thesis is to provide an in-depth characterization of antimicrobial resistance in Spanish swine production, both on intensive and organic-extensive farms. The studies carried out are developed in four chapters that address the evaluation of the potential of sentinel microorganisms in the monitoring of antimicrobial resistance and the genomic and metagenomic characterization of the resistome and mobilome of pig farms.
Sentinel bacteria recovered from organic-extensive farms consistently showed lower antimicrobial resistance than those from intensive pig herds, with antimicrobial use as the most influential factor. In addition, although no direct relationship was observed between antimicrobial use and on-farm biosecurity, certain measures, such as the application of standardized cleaning and disinfection protocols, had an apparent impact on the reduction of antimicrobial resistance in Escherichia coli and Enterococcus spp.
recovered from intensive pig herds.
The combination of phenotypic and genotypic characterization of antimicrobial resistance demonstrated the potential of Escherichia coli, Enterococcus spp. and Staphylococcus spp. as sentinel microorganisms for antimicrobial resistance surveillance in swine, as these bacteria emerged as important reservoirs of potentially mobilizable antimicrobial resistance determinants. Likewise, antimicrobial resistance monitoring in Campylobacter coli proved to be useful even though this species harbored a reduced number of antimicrobial resistance determinants in its genome. In contrast, Salmonella enterica was not a good bioindicator due to its sporadic presence in swine production and the serotype-biased antimicrobial resistance.
The genomic study of sentinel bacteria evidenced that the structure of both the resistome and mobilome was taxon-dependent, with greater similarities among those bacteria more phylogenetically related. This fact was corroborated in the metagenomic study, in which it was observed that the composition of the resistome was primarily determined by the type of sample due to changes in the predominant bacterial populations in each microecosystem. Finally, both genomic and metagenomic approaches revealed a clear interaction of the resistome and the mobilome, which was more complex on intensive farms. Overall, this doctoral thesis demonstrates that the development of antimicrobial resistance is strongly associated with mobile genetic elements and antimicrobial use.
Keywords
Antimicrobial; antimicrobial resistance: antimicrobial use; biosecurity;
Campylobacter spp.; Enterococcus spp.; Escherichia coli; mobilome; One Health; pig;
production system; resistome; Salmonella enterica; sentinel microorganism;
Staphylococcus spp.; sustainable farming
Resumen
La resistencia a los antimicrobianos es una amenaza global para la salud pública y el medioambiente derivada del uso prolongado y frecuentemente inadecuado de antibióticos en medicina humana y veterinaria. En producción animal se han descrito diversos factores que pueden repercutir en el uso de antibióticos y, en consecuencia, en las resistencias, entre los que destacan el sistema de producción y las medidas de bioseguridad implementadas en la granja. En este sentido, las producciones de cerdo ibérico ecológica y extensiva se basan en sistemas de cría sostenibles y respetuosos con el entorno, constituyendo una excelente oportunidad para evaluar cómo las diferencias sostenidas de las distintas formas de uso de antibióticos repercuten en las resistencias antimicrobianas, no solo en los animales, sino también en el ambiente de la granja.
El objetivo general de la presente tesis doctoral es ofrecer una descripción exhaustiva de las resistencias antimicrobianas en la producción porcina española, tanto en granjas intensivas como en las de producción ecológica-extensiva. Los estudios realizados se desarrollan en cuatro capítulos que abordan la evaluación del potencial de los microorganismos centinela en la monitorización de resistencias a los antimicrobianos y la caracterización genómica y metagenómica del resistoma y del mobiloma de las explotaciones porcinas.
Las bacterias centinela recuperadas de granjas de producción ecológica-extensiva presentaron de forma sistemática menores resistencias antimicrobianas que aquellas procedentes de explotaciones porcinas intensivas, siendo el uso de antibióticos el factor más influyente. Además, aunque no se observó una relación directa entre el uso de antibióticos y la bioseguridad en las granjas, ciertas medidas, como la aplicación de
protocolos de limpieza y desinfección adecuados, tuvieron un impacto aparente en la reducción de resistencias en Escherichia coli y Enterococcus spp. de granjas intensivas.
La combinación de la caracterización fenotípica y genotípica de las resistencias antimicrobianas demostró el potencial de Escherichia coli, Enterococcus spp. y Staphylococcus spp. en la monitorización de resistencias en porcino, ya que estas bacterias resultaron ser importantes reservorios de determinantes de resistencia potencialmente movilizables. La vigilancia de las resistencias en Campylobacter coli también se demostró útil, pese a portar un menor número de determinantes de resistencia en su genoma. Por el contrario, los resultados de Salmonella enterica indicaron que no es un buen indicador de resistencia en estas granjas debido a su presencia esporádica en porcino y que su resistencia a los antimicrobianos está condicionada por el serotipo.
El estudio genómico de las bacterias centinela demostró que la estructura tanto del resistoma como del mobiloma dependía del taxón, con mayores similitudes entre aquellas bacterias más relacionadas filogenéticamente. Este hecho fue corroborado en el estudio metagenómico, donde se observó que la composición del resistoma estaba primariamente asociada al tipo de muestra debido a cambios en las poblaciones bacterianas predominantes en cada nicho biológico. Por último, tanto el abordaje genómico como el metagenómico revelaron una clara interacción del resistoma y el mobiloma, la cual era más compleja en las granjas intensivas. En conjunto, esta tesis doctoral demuestra que el desarrollo de resistencias a los antimicrobianos está fuertemente ligado a los elementos genéticos móviles y al uso de antimicrobianos.
Palabras clave
Antibiótico; bioseguridad; Campylobacter spp.; Cerdo; Enterococcus spp.;
Escherichia coli; microorganismo centinela; mobiloma; producción sostenible;
resistencias antimicrobianas; resistoma; Salmonella enterica; Staphylococcus spp.;
sistema de producción; Una Sola Salud; uso de antibióticos
List of abbreviations
AIC: Akaike information criterion
AMEG: Antimicrobial Advice ad hoc Expert Group AMR: antimicrobial resistance
AMU: antimicrobial use
ARD: antimicrobial resistance determinant ARG: antimicrobial resistance gene
AST: antimicrobial susceptibility testing BHI: brain heart infusion
CARD: Comprehensive Antibiotic Resistance Database CDS: coding sequences
CI: confidence interval
CIA: critically important antimicrobial
CLSI: Clinical and Laboratory Standards Institute CPM: counts per million
CT: composite transposon
ECDC: European Centre for Disease Prevention and Control ECOFF: epidemiological cut-off value
EFSA: European Food Safety Authority EMA: European Medicines Agency ESBL: extended-spectrum beta-lactamase
ESVAC: European Surveillance of Veterinary Antimicrobial Consumption EU: European Union
EUCAST: European Committee on Antimicrobial Susceptibility Testing FAA: fastidious anaerobe agar
FAO: Food and Agriculture Organization of the United Nations HGT: horizontal gene transfer
IACG: Interagency Coordination Group ICE: integrative and conjugative element IME: integrative and mobilizable element IR: inverted repeat
IS: insertion sequence
ISO: International Organization for Standardization
JIACRA: Joint Interagency Antimicrobial Consumption and Resistance Analysis
LGT: lateral gene transfer
LMIC: low- and middle-income country LRT: likelihood ratio test
MALDI-TOF MS: matrix-assisted laser desorption ionization-time of flight mass spectrometry
mCCDA: modified charcoal cefoperazone deoxycholate agar MDR: multidrug-resistant
MGE: mobile genetic element MHB: Mueller-Hinton broth
MIC: minimum inhibitory concentration
MITE: miniature inverted-repeat transposable element MLP: macrolides-lincosamides-pleuromutilins
MLSP: macrolides-lincosamides-streptogramins-pleuromutilins MRSA: methicillin-resistant Staphylococcus aureus
MS: member state
NGS: next-generation sequencing
NMDS: non-metric multidimensional scaling
OECD: Organization for Economic Cooperation and Development OIE: World Organization for Animal Health
ONT: Oxford Nanopore Technologies OR: odds ratio
PBP: penicillin-binding protein PCA: principal component analysis PCR: polymerase chain reaction PCU: population correction unit
PERMANOVA: permutational multivariate analysis of variance PNS: pansusceptible
PRSS: porcine reproductive and respiratory syndrome R/I/S: resistant/intermediate/susceptible
RD: Real Decreto
TE: transposable element TSA: tryptic soy agar US: United States
UPGMA: unweighted pair group method with arithmetic mean VRE: vancomycin-resistant Enterococcus
WGS: whole genome sequencing
WGS-AST: whole genome sequencing-based antimicrobial susceptibility testing WHO: World Health Organization
WT/NWT: wild type/ non-wild type
Index
Chapter 1: Introduction
... 11. Antimicrobials and antimicrobial resistance development ... 3
1.1. General concepts ... 3
1.1.1. Antimicrobial, antibacterial and antibiotic ... 3
1.1.2. Antimicrobial and antibiotic resistance ... 3
1.2. Past, present and future of antimicrobials and antimicrobial resistance ... 4
1.2.1. The pre-antimicrobial era ... 4
1.2.2. The antimicrobial era ... 6
1.2.3. The post-antimicrobial era ... 14
1.3. The role of mobile genetic elements in the spread and dissemination of antimicrobial resistance ... 17
1.3.1. Intracellular DNA mobility ... 18
1.3.2. Intercellular DNA mobility ... 19
1.4. Molecular mechanisms of antimicrobial resistance in bacteria ... 19
1.4.1. Intrinsic resistance mechanisms ... 19
1.4.2. Acquired resistance mechanisms ... 20
1.4.2.1. Prevention of access to target ... 21
1.4.2.2. Modification of the antimicrobial target ... 22
1.4.2.3. Antimicrobial inactivation ... 23
2. Antimicrobial resistance detection methods ... 25
2.1. Phenotypic antimicrobial susceptibility testing ... 25
2.1.1. Phenotypic antimicrobial susceptibility methods ... 27
2.1.1.1 Disk diffusion method ... 27
2.1.1.2 Broth microdilution method ... 28
2.1.2. Limitations of phenotypic antimicrobial susceptibility testing ... 29
2.2. Genotypic antimicrobial resistance detection ... 30
2.2.1. Assessing antimicrobial resistance via antimicrobial resistance determinants detection ... 30
2.2.2. Application of next-generation sequencing techniques for antimicrobial resistance detection ... 31
2.2.2.1. Whole genome sequencing ... 33
2.2.2.2. Metagenomics ... 34
2.3. Genotypic prediction of the antimicrobial resistance phenotype ... 35
3. Monitoring of antimicrobial use and antimicrobial resistance in food-producing animals in the European Union ... 36
3.1. Monitoring and reduction of antimicrobial use in food-producing animals .... 36
3.1.1. Antimicrobial use in food-producing animals: towards a restrictive future ... 36
3.1.2. Monitoring of antimicrobial use in food-producing animals: the ESVAC project ... 39
3.2. Harmonized monitoring of antimicrobial resistance in food-producing animals and food products in the European Union ... 40
3.2.1. Legislative framework provided by the European Union to tackle antimicrobial resistance ... 41
3.2.2. Bacterial species and antimicrobials evaluated in food-producing animals and food products ... 42
3.2.3 Characterization of antimicrobial resistance determinants through next- generation sequencing ... 47
3.3. Evaluation of the link between antimicrobial use and antimicrobial resistance from a public health perspective in the European Union ... 47
4. Antimicrobial use in pig production ... 48
5. The Spanish pig production ... 51
5.1. Characterization of the Spanish pig production: types of farms. ... 51
5.1.1. Classification according to zootechnical criteria ... 51
5.1.2. Classification according to the production system ... 52
5.2. The Spanish pig production in figures ... 53
6. Strategies to reduce the impact of antimicrobial resistance in pig production ... 56
6.1. Recommendations to reduce the need for antimicrobials ... 56
6.2. Biosecurity on pig farms ... 57
6.3. Housing conditions and management practices ... 59
6.4. Vaccination ... 61
6.5. Genetic selection for disease resistance ... 62
6.6. Alternative pig farming systems ... 63
6.7. Nutritional strategies and alternatives to antimicrobials ... 64
Chapter 2: Justification and objectives
... 67Chapter 3: Study I
... 71Chapter 4: Study II
... 75Chapter 5: Study III
... 79Chapter 6: Study IV
... 123Chapter 7: General discussion
... 1271. Antimicrobial use on a selection of Spanish pig farms ... 129
2. Antimicrobial resistance in indicator and zoonotic bacteria recovered from Spanish pig farms ... 131
3. Antimicrobial resistance surveillance in pig production using metagenomics ... 133
4. Factors associated with antimicrobial resistance in the Spanish pig production .. 135
4.1. Sample type ... 135
4.2. Antimicrobial use ... 137
4.3. Production system ... 138
4.4. Biosecurity measures ... 139
5. The role of the mobilome in the resistome of the Spanish pig farms ... 140
6. Limitations and future perspectives ... 141
Chapter 8: Conclusions/Conclusiones
... 143Chapter 9: References
... 149Chapter 10: Annexes
... 171Additional figures ... 173
Additional tables ... 182
Índice
Capítulo 1: Introducción
... 11. Antimicrobianos y resistencia a los antimicrobianos ... 3
1.1. Conceptos generales ... 3
1.1.1. Antimicrobianos, antibacterianos y antibióticos ... 3
1.1.2. Resistencia a los antimicrobianos y resistencia a los antibióticos ... 3
1.2. Pasado, presente y futuro de los antimicrobianos y la resistencia a los antimicrobianos ... 4
1.2.1. La era pre-antimicrobiana ... 4
1.2.2. La era antimicrobiana ... 6
1.2.3. La era post-antimicrobiana ... 14
1.3. El papel de los elementos genético móviles en la diseminación de la resistencia a los antimicrobianos ... 17
1.3.1. Movilización intracelular del ADN ... 18
1.3.2. Movilización intercelular del ADN ... 19
1.4. Mecanismos moleculares de resistencia a los antimicrobianos en bacterias ... 19
1.4.1. Mecanismos de resistencia intrínseca ... 19
1.4.2. Mecanismos de resistencia adquirida ... 20
1.4.2.1. Prevención del acceso a la diana molecular ... 21
1.4.2.2. Modificación de la diana molecular ... 22
1.4.2.3. Inactivación antimicrobiana ... 23
2. Métodos de detección de la resistencia a los antimicrobianos ... 25
2.1. Pruebas de sensibilidad antimicrobiana fenotípica ... 25
2.1.1. Métodos de detección de sensibilidad antimicrobiana fenotípica ... 27
2.1.1.1 Método de difusión con discos ... 27
2.1.1.2 Método de microdilución en caldo ... 28
2.1.2. Limitaciones de los métodos de detección de sensibilidad antimicrobiana fenotípica ... 29
2.2. Detección genotípica de la resistencias a los antimicrobianos ... 30
2.2.1. Evaluación de la resistencia a los antimicrobianos mediante la detección de determinantes de resistencia a los antimicrobianos ... 30
2.2.2. Aplicación de técnicas de secuenciación de nueva generación para la detección de la resistencia a los antimicrobianos ... 31
2.2.2.1. Secuenciación completa de genomas ... 33
2.2.2.2. Metagenómica ... 34
2.3. Predicción genotípica de las resistencias antimicrobianas fenotípicas ... 35
3. Monitorización del uso de antimicrobianos y de la resistencia a los antimicrobianos en animales productores de alimentos en la Unión Europea ... 36 3.1. Monitorización y reducción del uso de antimicrobianos en animales productores de alimentos ... 36 3.1.1. Uso de antimicrobianos en animales productores de alimentos: hacia un futuro restrictivo ... 36 3.1.2. Monitorización del uso de antimicrobianos en animales productores de alimentos: el proyecto ESVAC ... 39 3.2. Monitorización armonizada de la resistencia a los antimicrobianos en animales productores de alimentos y en alimentos en la Unión Europea ... 40 3.2.1. Marco legislativo previsto por la Unión Europea para hacer frente a la resistencia a los antimicrobianos ... 41 3.2.2. Especies bacterianas y antimicrobianos evaluados en animales productores de alimentos y en alimentos ... 42 3.2.3 Caracterización de la resistencia a los antimicrobianas mediante secuenciación de nueva generación ... 47 3.3. Evaluación de la asociación entre el uso de antimicrobianos y la resistencia a los antimicrobianos desde una perspectiva de salud pública en la Unión Europea ... 47 4. Uso de los antimicrobianos en producción porcina ... 48 5. La producción porcina española ... 51 5.1. Caracterización de la producción porcina española: tipos de granja ... 51 5.1.1. Clasificación según criterios zootécnicos ... 51 5.1.2. Clasificación según el sistema de producción ... 52 5.2. La producción porcina española en cifras ... 53 6. Estrategias para reducir el impacto de la resistencia a los antimicrobianos en producción porcina ... 56 6.1. Recomendaciones para reducir la necesidad del uso de antimicrobianos ... 56 6.2. La bioseguridad en las explotaciones porcinas ... 57 6.3. Condiciones de alojamiento y prácticas de manejo ... 59 6.4. Vacunación ... 61 6.5. Selección genética para la resistencia a las enfermedades ... 62 6.6. Sistemas alternativos de producción porcina ... 63 6.7. Estrategias nutricionales y alternativas a los antimicrobianos ... 64
Capítulo 2: Justificación y objetivos
... 67Capítulo 3: Estudio I
... 71Capítulo 4: Estudio II
... 75Capítulo 5: Estudio III
... 79Capítulo 6: Estudio IV
... 123Capítulo 7: Discusión general
... 127 1. Uso de antimicrobianos en una selección de granjas porcinas españolas ... 129 2. Resistencia a los antimicrobianos en bacterias indicadoras y zoonóticas aisladas de granjas porcinas españolas ... 131 3. Monitorización de la resistencia a los antimicrobianos en producción porcina mediante el uso de la metagenómica ... 133 4. Factores asociados con la resistencia a los antimicrobianos en la producción porcina española ... 135 4.1. Tipo de muestra ... 135 4.2. Uso de antimicrobianos ... 137 4.3. Sistema de producción ... 138 4.4. Medidas de bioseguridad ... 139 5. El papel del mobiloma en el resistoma de la producción porcina española... 140 6. Limitaciones y perspectivas futuras ... 141Capítulo 8: Conclusions/Conclusiones
... 143Capítulo 9: Referencias bibliográficas
... 149Capítulo 10: Anexos
... 171 Figuras adicionales ... 173 Tablas adicionales ... 182Chapter 1: Introduction
3
1. Antimicrobials and antimicrobial resistance development 1.1. General concepts
1.1.1. Antimicrobial, antibacterial and antibiotic
The word antimicrobial is derived from the Greek and refers to all agents that kill or stop the growth of microorganisms, including bacteria, fungi, protozoa and viruses. These antimicrobials are grouped according to the microbes that they act against in antibacterials, antifungals, antiprotozoals and antivirals (CDC, 2019).
Within antibacterials, the terms antibacterial and antibiotic are used interchangeably. Despite the purist definition of antibiotic includes only those natural substances produced by microorganisms that act against bacteria (penicillin or tetracycline), according to Davies and Davies (2010), it denotes any class of organic molecule that inhibits or kills bacteria by specific interactions with bacterial targets, regardless of the source of the particular compound. Thus, purely synthetic (sulfonamides or quinolones) and semisynthetic (methicillin or amoxicillin) compounds are also considered antibiotics.
As antibiotics are the largest and most widely characterized class of antimicrobials, these concepts will be used as synonyms throughout this doctoral thesis.
1.1.2. Antimicrobial and antibiotic resistance
Antimicrobial resistance (AMR) is a broad term that refers to the ability of the microorganisms to acquire resistance to antimicrobial compounds, encompassing resistance to antibiotic, antifungal, antiprotozoal and antiviral drugs (FAO, 2016b; WHO, 2017). Nevertheless, throughout this doctoral thesis AMR will be referred to antibiotic resistance, which occurs when bacteria initially susceptible to a specific antibiotic become
4
resistant, leading to its ineffectiveness at previously effective normal doses used for the treatment of bacterial infections.
1.2. Past, present and future of antimicrobials and antimicrobial resistance
Antimicrobials are one of the most successful chemotherapeutic strategies in the history of medicine and their introduction one hundred years ago in human and, subsequently, in veterinary medicine entailed a revolution that contributed enormously to the reduction of mortality rates due to infectious diseases (Aminov, 2010). However, different studies have evidenced that the exposure to antimicrobials, and hence the AMR development, is prior to the modern “antimicrobial era”.
1.2.1. The pre-antimicrobial era
Tetracycline traces have been detected in human skeletal remains from ancient Sudanese Nubia (350-550 AD) (Bassett et al., 1980) and bones from the late Roman period (400-500 AD) discovered in the Dakhleh Oasis, Egypt (Cook et al., 1989). These discoveries were possible due to the particularity of tetracycline compounds for inclusion into the mineral fraction of bones and teeth, providing permanent markers of metabolically active areas of tetracycline exposure (Aminov, 2010). The presence of these traces could only be explained by the incorporation of tetracyclines in the diet of these ancient people. In fact, Nelson et al. (2010) suggested that these communities could have produced gruels and beer fermentations containing Streptomyces species that provided both nutritive and pharmacological effects.
Ancient exposure to other antimicrobials is much more difficult to evidence, and its characterization could only be based on reminiscent traditional treatments that have survived from ancestral cultures, such as the use of Jordan’s red soils or remedies used
5 for millennia in traditional Chinese medicine. In this regard, Falkinham et al. (2009) observed that the skin healing properties attributed to the red soils in Jordan could be explained by the proliferation of antimicrobial-producing bacteria, including a consortium of actinomycetes, Bacillus and Lysobacter strains. In China, more than 11,000 plants have been used for centuries by herbalists as medicinal materials (Xu, 2000) and thousands of bioactive product compounds derived from these plants have been studied, including those with antimicrobial properties, such as phenolics, ethanolics or alkaloids (Carraturo et al., 2013; Kim et al., 2020; Yuen et al., 2011).
Beer, curative soils and medicinal plants are three examples of how the sustained use of certain products with antimicrobial activity during the pre-antimicrobial era contributed to a long-term selective pressure that may have led to the accumulation of antimicrobial resistance determinants (ARDs) in human-associated bacterial communities. However, these ARDs, which include both AMR genes (ARGs) and point mutations, have been detected in bacteria dating from periods prior to the use of these products by ancient civilizations. Thus, the study of microbial communities preserved in the permafrost sediments for thousands and millions of years in both Arctic (D’Costa et al., 2011) and Antarctic (Van Goethem et al., 2018) regions revealed the presence of ARGs within these microbiomes. Even, bacteria found in a cave that had been isolated for more than four million years exhibited functional ARDs (Bhullar et al., 2012).
Altogether, these studies confirm that AMR is a natural phenomenon prior to human civilization which could have its origin in harmless environmental microorganisms that have coevolved with antibiotic-producing bacteria, such as actinobacteria, leading to the development and acquisition of ARDs (Perry et al., 2016). In fact, the description of ARG-carrying transposons in ancient permafrost strains (Petrova et al., 2008; Petrova et al., 2011) evidences that these mobile genetic elements (MGEs) appeared long before the
6
use of antimicrobials, although they were rare (Mindlin & Petrova, 2017). Nevertheless, despite AMR is natural, ancient and hard wired in the microbial pangenome, the discovery and massive production of antimicrobials in the first half of the 20th century marked a milestone that changed the course of microbial evolution forever: the antimicrobial era.
1.2.2. The antimicrobial era
The beginning of the modern antimicrobial era is usually associated with names such as Paul Ehrlich, Gerhard Domagk or Alexander Fleming (Aminov, 2010; Hutchings et al., 2019). Ehrlich’s idea of a “magic bullet” which would selectively target only certain microorganisms and not the host came to his mind when working with aniline and other synthetic dyes. This idea led to a large-scale screening program in 1904 to find an active drug against syphilis, a sexually transmitted disease caused by the spirochaete Treponema pallidum, which resulted in the discovery in 1909 of a synthetic arsenic-based compound (Gelpi et al., 2015). This drug, marketed under the name Salvarsan (salvation arsenic), demonstrated efficacy in the treatment of syphilis-infected rabbits and, afterwards, in humans (Ehrlich & Hata, 1910). Subsequently, a less toxic and more soluble compound, Neosalvarsan, was introduced and became the most frequently prescribed drug until its replacement by penicillin in the 1940s (Mahoney et al., 1943). Remarkably, more than one hundred years after its discovery, its mode of action is still unknown and its chemical structure remained a mystery until 2005 (Lloyd et al., 2005).
The process standardized by Ehrlich, based on a systematic chemical modification of a lead compound and the subsequent evaluation of its activity, determined the starting point of the modern chemotherapy (Kaufmann, 2008). Thus, a bacteriologist at Bayer, Gerhard Domagk, synthesized a sulfonamide compound, sulfonamidochrysoidine, effective against streptococcal infections and commercialized as Prontosil (Otten, 1986).
However, Prontosil seemed to be only the precursor for the active drug with antimicrobial
7 activity, sulphanilamide (Tréfouël et al., 1935). The fact that this compound largely used in the dye industry was off patent and cheap to produce, led to the mass production of sulfonamide antimicrobials by many companies (Aminov, 2017). It converted these drugs into the first truly effective and broad spectrum antimicrobials in clinical use (Hutchings et al., 2019), and whose use is still widely spread in human and veterinary medicine.
Nevertheless, these compounds were rapidly superseded by the penicillin introduction in clinical medicine.
The serendipitous discovery of penicillin by Alexander Fleming in 1928 due to a contaminated Petri dish with Penicillium (Fleming, 1929) entailed a revolution in the antimicrobial field. However, it was not until 12 years later, in 1940, when the Oxford team led by Howard Florey and Ernest Chain purified penicillin in enough quantities for clinical testing (Abraham & Chain, 1942), triggering its mass production and distribution.
In 1945, Dorothy Hodgkin solved the beta-lactam structure of penicillin (Hodgkin, 1949), which enabled the development of semi-synthetic antimicrobials derived from this drug to bypass penicillin resistance (Hutchings et al., 2019).
The Fleming’s antimicrobial screening method, based on inhibition zones in lawns of bacteria on agar plates, required less time and resources than those based on animal disease models (Aminov, 2017). This method, together with the discovery of antimicrobial compounds derived from microbes, such as penicillin, led to Selman Waksman to start a systematic screening of antibiotic-producer microorganisms in the late 1930s, identifying soil-dwelling actinomycetes as prolific producers of these compounds. Thus, Waksman discovered many antibiotics from these bacteria, such as the aminoglycosides neomycin and streptomycin in Streptomyces genus (Waksman et al., 2010). These studies began the golden age of antimicrobial discovery, from the 1940s to the 1960s, when most classes of antimicrobials were characterized (Figure 1) (Hutchings
8
et al., 2019). From the 1970s onwards, the number of antimicrobial classes discovered declined dramatically and most of the latest commercialized antibiotics are derived from known classes (Katz & Baltz, 2016).
Figure 1. Timeline showing when new antimicrobial classes were introduced in clinic. Antimicrobials are colored depending on their origin: green: actinomycetes; blue: other bacteria; purple: fungi; and orange:
synthetic. Below the timeline there is information relating to antimicrobial discovery and antimicrobial resistance with public health relevance. Antibiosis refers to an antagonistic association between two or more microorganisms that is detrimental to at least one of them, mainly due to the secretion of antimicrobial compounds. MRSA: methicillin-resistant Staphylococcus aureus NP: natural products; UN united Nations;
VRSA: vancomycin-resistant Staphylococcus aureus. ©Hutchings et al. (2019).
9 Throughout the last one hundred years more than thirty different antimicrobial classes with bacterial, fungal or synthetic origin have been discovered (Table 1; Figure 1). Most of them are still used in human and/or veterinary medicine. Nevertheless, the massive overuse of these drugs during the last decades has jeopardized the usefulness of antimicrobials due to the rapid development and spread of AMR to most of the antimicrobial classes (Figure 1).
Fleming was the first to warn about the potential resistance to penicillin if it was underdosed, but were Abraham and Chain (1940) who demonstrated that bacteria could destroy penicillin by enzymatic degradation. Erythromycin was introduced in Boston City Hospital in 1950s as an alternative to overcome penicillin resistance in Staphylococcus aureus; however, it was completely withdrawn in less than one year due to a 70%
resistance rate to this antimicrobial in S. aureus isolates. The same was observed with chlortetracycline or chloramphenicol and, subsequently, with most other antibiotics (Finland, 1979). In few years, many pathogenic bacteria became multidrug-resistant (MDR), which was defined as acquired non-susceptibility to at least one agent in three or more antimicrobial classes (Magiorakos et al., 2012). Thus, the term “superbug” was developed to refer to MDR microorganisms, such as nosocomial Escherichia coli, Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae or Clostridium difficile, which cause increased morbidity and mortality since ARDs endow high levels of resistance to the antimicrobials recommended for their treatment (Davies & Davies, 2010).
In summary, this century of antimicrobial misuse has made AMR one of the largest threats to global health and food security, drifting the world into a new period in which this situation will have to be dealt with: the post-antimicrobial era.
10
Table 1. Antimicrobial classes and their authorization for veterinary use in the European Union (EU). Adapted from Hutchings et al. (2019).
Antimicrobial class
a
Discovery reported
Introduced
clinically Example Molecular target
b
Categorization for veterinary use in the EU
Antibiotics from actinomycetesAminoglycosides 1944 1946 Gentamicin Protein synthesis: 30S
ribosomal subunit
cCategory C
Tetracyclines 1948 1948 Chlortetracycline Protein synthesis: 30S
ribosomal subunit
dCategory D
Phenicols 1947 1949 Florfenicol Protein synthesis: 50S
ribosomal subunit Category C
Macrolides 1952 1952 Erythromycin Protein synthesis: 50S
ribosomal subunit Category C
Tuberactinomycins 1951 1953 Viomycin Protein synthesis: 30S and 50S
ribosomal subunits Category A
Glycopeptides 1954 1958 Vancomycin Cell wall synthesis: D-Ala-D-
Ala termini of lipid II Category A
Lincosamides 1962 1963 Lincomycin Protein synthesis: 50S
ribosomal subunit Category C
Rifamycins 1959 1963 Rifampicin Nucleic acid synthesis: RNA
polymerase
eCategory A
Cycloserines 1955 1964 Cycloserine
Cell wall synthesis: inhibition of alanine racemase and D-
alanine-D-alanine ligase
Category A
Streptogramins 1953 1965 Quinupristin Protein synthesis: 50S
ribosomal subunit Category A
Phosphonates 1969 1971 Fosfomycin Cell wall synthesis: MurA
(UDP-GlcNAc-3- Category A
11 enolpyruvyltransferase)
inhibition
Carbapenems 1976 1985 Meropenem Cell wall synthesis: penicillin-
binding proteins Category A
Lipopeptides 1987 2003 Daptomycin Cell wall: cell membrane
disruption Category A
Lipiarmycins 1975 2011 Fidaxomicin Nucleic acid synthesis: RNA
polymerase Category A
Antibiotics from other bacteria
Polypeptides 1939 1941 Gramicidin A
Cell wall: forms ion channels that increase the permeability of the bacterial cell membrane
Category A
Bacitracin 1945 1948 Bacitracin
Cell wall synthesis: inhibition of dephosphorylation of C55-
isoprenyl pyrophosphate
Category D
Polymyxins 1950 1959 Colistin Cell wall: cell membrane
disruption Category B
Mupirocin 1971 1985 Mupirocin Protein synthesis: isoleucyl t-
RNA synthetase Category A
Monobactams 1981 1986 Monobactam Cell wall synthesis: penicillin-
binding proteins Category A
Antibiotics from fungi
Penicillins 1929 1943 Amoxicillin Cell wall synthesis: penicillin-
binding proteins
f
Fusidanes 1958 1962 Fusidic acid Protein synthesis: elongation
factor G Category D
12
Enniatins 1953 1963 Enniatin B Cell wall: cell membrane
disruption Category A
Cephalosporins 1948 1964 Ceftiofur Cell wall synthesis: penicillin-
binding proteins
g
Pleuromutilins 1951 2007 Valnemulin Protein synthesis: 50S
ribosomal subunit Category C
Synthetic antibiotics
Arsphenamines 1907 1910 Salvarsan Not known No longer available
Sulfonamides 1932 1936 Sulfamethoxazole
Folate synthesis: inhibition of dihydropteroate
synthetase
Category D
Salicylates 1902 1943 4-Aminosalicylic
acid
Folate synthesis: prodrug that inhibits dihydrofolate
reductase
Category A
Sulfones 1908 1945 Dapsone
Folate synthesis: inhibition of dihydropteroate
synthetase
Category A
Pyridinamides 1952 1952 Isoniazid
Cell wall: prodrug that inhibits the synthesis of
mycolic acids
Category A
Nitrofurans 1945 1953 Furaltadone DNA synthesis: DNA
damage Category D
Azoles 1959 1960 Metronidazole DNA synthesis: DNA
damage Category D
Fluoroquinolones 1962 1962 Enrofloxacin
DNA synthesis: inhibition of DNA gyrase, and
topoisomerase IV
Category B
13
Diaminopyrimidines 1950 1962 Trimethoprim Folate synthesis: inhibition
of dihydrofolate reductase Category D
Ethambutol 1962 1962 Ethambutol Cell wall: arabinosyl
transferase inhibition Category A
Thioamides 1956 1965 Ethionamide
Cell wall: prodrug that inhibits the synthesis of
mycolic acids
Category A
Phenazines 1954 1969 Clofazimine DNA synthesis: binds to
guanine bases Category A
Oxazolidinones 1987 2000 Linezolid Protein synthesis: 50S
ribosomal subunit Category A
Diarylquinolines 2004 2012 Bedaquiline ATP synthesis: proton
pump inhibition Category A
aDiscovery reported refers to the year of the first report in literature
bCategorization of antimicrobials by the EMA (2019a) for their use in veterinary medicine in the EU: Category D, prudent, first line treatments; Category C, caution, when there are no antimicrobials clinically effective in Category D; Category B, restrict, only when there are no antimicrobials clinically effective in Categories C or D;
Category A, avoid, not authorized;
cCategory D for espectinomycin
dCategory A for glycylcyclines, such as tigecycline
eCategory C for rifaximin
fCategory D for natural penicillins, aminopenicillins without beta-lactamase inhibitors and anti-staphylococcal penicillins; Category C for aminopenicillins in combination with beta-lactamase inhibitors; Category A for the rest of penicillins.
gCategory C for first- and second-generation and cephamyxins; Category B for third- and fourth-generation without beta-lactamase inhibitors; Category A for the rest of cephalosporins.
14
1.2.3. The post-antimicrobial era
The current alarming levels of AMR worldwide and regardless of the degree of development of countries highlights the urgent need to develop measures to tackle this threat in order to protect humans, animals and the environment against the spread of these superbugs, and to ensure the usefulness of the available antimicrobials.
The Organization for Economic Cooperation and Development (OECD) reports that in some member countries 35% of human infections are caused by microbes resistant to prescribed antimicrobials. This percentage increases in low- and middle-income countries (LMICs) to 80-90% for some pathogens (OECD, 2018). Consequently, despite the true magnitude of AMR is not fully known, it is estimated that around 700,000 people die annually directly due to resistant pathogens (O’neill, 2014), but the future is far from promising. In countries where resistance can be measured accurately, the OECD predicts that around 2.4 million people could die throughout the next 30 years in Europe, North America and Australia due to AMR if no new policies are implemented (OECD, 2018).
This figure could increase worldwide, including in LMICs, reaching more than 10 million global deaths every year by 2050 (World Bank, 2017). However, this will not only take human lives, but there is also the economic impact which has been estimated at over US$3.5 billion per year (OECD, 2018). In fact, despite the current cost of measures against AMR has been estimated in nearly US$9 billion per year (World Bank, 2017), if investments and actions are delayed, a higher global cost will be paid to cope with the impact of this uncontrolled threat (IACG, 2019).
15 Since the drivers and impact of AMR involve humans, animals, plants, food and the environment, and all of them are interconnected (Figure 2), a One Health approach is essential to address this issue from multiple perspectives (IACG, 2019). To achieve this goal, the World Health Organization (WHO) launched the Global Action Plan on Antimicrobial Resistance in 2015, which encouraged the development of National Antimicrobial Resistance Action Plans (WHO, 2015). To support national implementation of these measures and contribute to the development of a One Health strategy, a joint framework of the WHO, the Food and Agriculture Organization of the United Nations (FAO) and the World Organization for Animal Health (OIE) was established (FAO, OIE, & WHO, 2019). Nevertheless, current efforts to implement these national plans are still too slow and must be accelerated in every country if we want to revert the situation.
Figure 2. One Health approach to the emergence and spread of antimicrobial-resistant bacteria.
Adapted from the Korea Disease Control and Prevention Agency. Retrieved from http://www.cdc.go.kr/contents.es?mid=a50301040000.
16
In general terms, the action plans include five strategic objectives: i) to improve awareness and understanding of AMR; ii) to develop AMR monitoring and surveillance programs; iii) to reduce the incidence of infections; iv) to optimize antimicrobial use (AMU); and v) to promote sustainable development and increase the investment in preventive and therapeutic tools (WHO, 2015). These items are achievable in developed countries and some middle-income countries, but not in the other countries. Many LMICs have structural problems that should be addressed, such as access to drinking water, sanitation or hygiene in healthcare facilities, or improvement in waste management and environmental protection, which jeopardize the success of the measures against AMR. As a matter of fact, different studies have estimated that the inadequate access to antimicrobials in these countries causes more than 6 million deaths annually, including children who die of preventable diarrhea, sepsis and pneumonia (Daulaire et al., 2015;
Laxminarayan et al., 2016; Rochford et al., 2018). That is why the Interagency Coordination Group (IACG) on Antimicrobial Resistance recommends to guarantee the success of the Sustainable Development Goals in order to ensure a coordinate global One Health response to AMR (IACG, 2019).
The implementation of policies to tackle AMR should be supported by the development of new antimicrobial compounds, thus ensuring a holistic approach in the fight against this hazard. The recent discovery of new antibiotic-producing strains from unexplored environments, together with the new tools of genome mining and heterologous pathway expression, have reinvigorated the identification of new antimicrobial classes, drawing a more promising future (Hutchings et al., 2019). In some cases, genomic data suggest the presence of “cryptic” biosynthetic gene clusters that are not yielded under standard laboratory conditions and, therefore, its culture should simulate its complex natural environment (Rutledge & Challis, 2015; Zhang et al., 2019).
17 This is how, for example, the antibacterial peptide teixobactin was extracted from Eleftheria terrae (Ling et al., 2015). Altogether, this highlights the relevance of soil environments for antimicrobial discovery.
Unfortunately, most large pharmaceutical companies have abandoned the discovery and development of new antimicrobial compounds due to the huge restrictions on AMU and lack of incentives. This task has been passed to public research groups and small companies, slowing down the process (Hutchings et al., 2019; IACG, 2019). This is why, to increase the success rate in the discovery and commercialization of new drugs, incentives and new investments should be assigned to pharmaceutical companies (Årdal et al., 2018; OECD, 2018).
The challenges of AMR are complex and require a One Health approach, but they are not insurmountable. A coordinated global intervention could preserve the current antimicrobials, protect the future from MDR pathogens and, hence, help to save millions of lives. Nevertheless, actions must not be delayed.
1.3. The role of mobile genetic elements in the spread and dissemination of antimicrobial resistance
The massive misuse of antimicrobials has accelerated the selection and spread of MDR bacteria throughout the last century due to the emergence of point mutations and the acquisition and mutation of preexisting ARGs, as mentioned above. Mutations are a common cause of AMR by promoting its vertical transmission, but horizontal gene transfer (HGT) events are posing the greatest challenge by being the main mediators of AMR among bacteria (Gillings, 2013; Woodford & Ellington, 2007). The capture, accumulation and dissemination of genes that confer resistance to antimicrobials is
18
mainly determined by the action of MGEs, which refers to the elements that promote and enable intracellular and intercellular DNA mobility (Partridge et al., 2018).
1.3.1. Intracellular DNA mobility
Intracellular mobilization of ARGs can be possible mainly due to the presence of transposable elements (TEs) and integrons. TEs represent a group of MGEs that are capable of integrate into host DNA and move almost randomly to new locations on the same or different DNA molecule within a single cell (Vandecraen et al., 2017). Insertion sequences (ISs) are well-characterized TEs that are composed of a transposase gene flanked by two inverted repeats (IRs), which are able to modulate gene expression and promote mobility by forming, for instance, composite transposons (CTs). CTs are constituted by a region bounded by two copies of the same or related IS that can be transposed as a single unit. Unit transposons (UTs) are TEs flanked by IRs that carry a transposase together with accessory genes and/or additional TEs. Miniature inverted- repeat transposable elements (MITEs) are non-autonomous TEs derived from ISs or UTs that have undergone a deletion in their core genes but have retained the IRs and can constitute CT-like structures. Other TEs, such as integrative and conjugative elements (ICEs) and integrative and mobilizable elements (IMEs), are larger components capable of contributing to the intercellular dissemination of ARGs through conjugation (Johansson et al., 2021; Partridge et al., 2018).
Integrons use site-specific recombination to move ARGs between defined sites.
ARGs are inserted into the integrons in gene cassettes, which are small MGEs that include one or two genes that are expressed thanks to the promoter provided by the integron sequence. Multiple cassettes may be inserted into the same integron to create a cassette array, which could confer MDR to the bacteria (Partridge et al., 2018).
19 1.3.2. Intercellular DNA mobility
Intercellular dissemination of ARGs occurs by the three canonical mechanisms of HGT: conjugation (mediated by plasmids and ICEs), transduction (mediated by bacteriophages) and transformation (uptake of extracellular DNA) (von Wintersdorff et al., 2016). Among them, conjugation through plasmids is the most frequent and efficient due to their capacity to carry and integrate other MGEs and ARGs associated with these elements (Shintani et al., 2015). ICEs are also self-transmissible by conjugation, but the difference is their integration into the host chromosomal DNA, allowing replication during cell division, although replication of excised elements has also been demonstrated (Carraro & Burrus, 2015).
In general terms, the success in the rapid evolution and propagation of ARDs among different bacterial taxa can be explained by the interaction between the different types of MGEs, since it is the synergistic effect of their properties which contributes to the adaptation of bacteria to hostile environments.
1.4. Molecular mechanisms of antimicrobial resistance in bacteria
Bacteria are not uniformly resistant or susceptible to all antimicrobials, as there are molecular mechanisms that can modify their AMR genotype and, consequently, their phenotype. These AMR mechanisms can be acquired via point mutations or HGT events, but can also be inherent to the bacterial species due to their intrinsic resistance.
1.4.1. Intrinsic resistance mechanisms
The intrinsic resistance of a bacterial taxon to an antimicrobial can be defined as the ability to resist the action of the drug thanks to inherent structural or functional characteristics, independently of previous antimicrobial exposure (Cox & Wright, 2013).
20
The main bacterial mechanism for intrinsic resistance is the inability of the drug to get access to the specific target. This is the situation of Gram-negative bacteria, which are intrinsically resistant to many antimicrobials due to their inability to penetrate the outer membrane, as it happens with glycopeptides or lipopeptides (Carpenter & Chambers, 2004; Zgurskaya et al., 2015). Another possibility is the lack of the target where the antimicrobial exerts its function, as it occurs with polymyxins in Gram-positive bacteria, whose target is the absent outer membrane (WHO, 2018).
The screening of novel drug combinations has demonstrated that intrinsic resistance mechanisms can be overcome and so increase the spectrum of activity of certain antimicrobials beyond those bacterial taxa they are prescribed for (Blair et al., 2015). The disruption of the outer membrane in Gram-negative pathogens, mediated by chelators, peptides or small organic compounds, could contribute to overcome many determinants of antimicrobial inactivation, turning into effective antimicrobials traditionally used against Gram-positive bacteria, such as rifamycins and macrolides (MacNair & Brown, 2020).
1.4.2. Acquired resistance mechanisms
Bacteria initially susceptible to a specific antimicrobial can acquire ARDs either by point mutations or by intercellular dissemination of ARG-containing MGEs, as described in section 1.3. Acquired AMR can be mediated by several mechanisms, which fall into three main categories: i) minimization of the intracellular concentration of the antimicrobial; ii) modification of the antimicrobial target; and iii) inactivation of the antimicrobial.
21 1.4.2.1. Prevention of access to target
Reduced permeability through porins
In those bacteria with large outer membranes, the entrance of hydrophilic substances into the cell is mediated by porin channels that regulate their diffusion (Blair et al., 2015).
Reducing the number of porins or replacing them with more selective channels are the main ways to reduce the permeability for drug influx (Reygaert, 2018). Thus, beta- lactams resistance, which is usually mediated by enzymatic degradation, has been associated to reduced porin production in the absence of beta-lactamases in some Gram- negative pathogens, such as E. coli (Choi & Lee, 2019; Ziervogel & Roux, 2013), K.
pneumoniae (Sugawara et al., 2016) or Pseudomonas aeruginosa (Okamoto et al., 2001).
Besides, the selective pressure caused by antimicrobial exposure determines the accumulation of mutations in porins, as it has been demonstrated for carbapenem resistance in Enterobacter aerogenes (Lavigne et al., 2013) or E. coli (Tängdén et al., 2013).
Increased transport through efflux pumps
Bacterial efflux pumps actively transport out of the cell a wide range of substances, such as antimicrobials, heavy metals, quorum sensing signals or bacterial metabolites, among others (Blanco et al., 2016). Certain efflux pumps can be substrate-specific, as the Tet pumps which are active against tetracyclines (Roberts, 1996), but most of them transport structurally different compounds and are known as MDR efflux pumps (Blair et al., 2015).
All bacteria carry multiple chromosomally-located genes that encode efflux pumps.
These efflux pumps can be constitutively expressed, conferring intrinsic resistance to certain antimicrobials (Reygaert, 2018), or induced in response to environmental stimuli, as the indole induction of the acrAB genes in Salmonella spp. (Nikaido et al., 2012) and
22
E. coli (Hirakawa et al., 2005) that occurs during infection. Moreover, mutations in regulatory regions encoded alongside the efflux pump genes can lead to their overexpression and so to a high-level resistance to antimicrobials that were initially effective (Blair et al., 2015).
Some efflux pump genes have been included in MGEs and constitute an important hazard as this resistance mechanism could be disseminated to clinically relevant pathogens. This is the case of the novel plasmid-encoded resistance-nodulation-division efflux pump gene cluster, named as tmexCD1-toprJ1, which is widespread among K.
pneumoniae isolated from food-producing animals and confers resistance to tetracyclines, including tigecycline, and reduced susceptibility to many other antimicrobials (Lv et al., 2020).
1.4.2.2. Modification of the antimicrobial target
Most antimicrobials bind specifically with high affinity to their target, altering its normal activity. Therefore, a major resistance mechanism consists on the modification of the target structure where the antimicrobial exerts its function, enabling the target to carry out its normal function. This can be achieved through specific mutations in the genes encoding the target protein, leading to structural changes, or through target protection, which is mediated by the modification of the target without mutational changes (Blair et al., 2015).
In Gram-positive bacteria, the main mechanism of beta-lactam resistance occurs via alterations in the structure and/or the number of penicillin-binding proteins (PBPs), which are transpeptidases involved in the construction of peptidoglycan in the cell wall (Sun et al., 2014). PBPs active site is the target of beta-lactams, leading to enzyme inactivation and
23 and hence the cell wall disruption (de Sousa Oliveira et al., 2016). Therefore, modified PBPs enable to overcome the inhibition of native PBPs in the presence of the antimicrobials. Among the different types of modified PBPs, the most alarming is PBP2a, which confers resistance to penicillins and cephalosporins and is genetically encoded in the plasmid-located mecA gene, or the homologous mecB and mecC genes (Becker et al., 2014). Such is the case of the methicillin-resistant S. aureus (MRSA), whose phenotype is conferred by the acquisition of the staphylococcal cassette chromosome mec (SCCmec), which carries the mecA gene or the increasingly frequent mecC gene (Paterson et al., 2014).
The ribosome is one of the main antibiotic targets in the bacterial cell and different strategies have been developed by bacteria to overcome their action. Thus, for instance, the erythromycin ribosome methylase (erm) family of ARGs protect the 16S rRNA with the methylation of the drug-binding site, thus conferring resistance to macrolides, lincosamides and streptogramins (Varaldo et al., 2009). Another example is the cfr gene family, which encodes a 23S rRNA methyltransferase that confers cross-resistance to phenicols, lincosamides, oxazolidinones, pleuromutilins and streptogramin A (PhLOPSA
phenotype) (Guerin et al., 2020). Both erm and cfr genes are frequently located in MGEs and constitute a threat for their dissemination in clinically relevant pathogens, such as the widely spread erm(B) ARG integrated in MDR genomic islands in Campylobacter (Qin et al., 2014).
1.4.2.3. Antimicrobial inactivation
The two main ways in which bacteria inactivate antimicrobials are the enzymatic degradation of the antibiotic and the transference of a chemical group to the drug.
24
Inactivation by enzymatic degradation
Thousands of enzymes have been described to be involved in the hydrolysis of antimicrobials from different classes. Among them, it is worth mentioning the tetX gene, which encodes a flavin-dependent monooxygenase that confers resistance to tetracyclines (Yang et al., 2004), or the widely characterized group of beta-lactamases, which includes different subclasses that can degrade different antibiotics within this class.
The production of beta-lactamases is the most common resistance mechanism developed by Gram-negative bacteria to face the action of beta-lactams, which includes enzymes against penicillins, cephalosporins, carbapenems and monobactams, among others (Tooke et al., 2019). The early described beta-lactamases, active against penicillins and first-generation cephalosporins, were rapidly replaced by the dissemination of extended-spectrum beta-lactamases (ESBL), which were firstly characterized in Enterobacteriaceae in 1983 (Knothe et al., 1983), coinciding with the introduction of third-generation cephalosporins in the clinical practice. Among ESBL, blaCTX-M genes, whose origin is found in chromosomally encoded genes of Kluyvera species (Rossolini et al., 2008), are a good example of the rapid spread and dissemination of beta-lactamases in a relatively short period of time in food-producing animals and clinical isolates.
As a result of the increased resistance to third-generation cephalosporins in clinically relevant pathogens, carbapenems were introduced in clinical use, which led to the development and dissemination of carbapenemases (Blair et al., 2015). Furthermore, different studies have detected plasmid-located ARGs encoding for ESBLs and carbapenemases enzymes in Gram-negative bacteria, such as E. coli, P. aeruginosa or Acinetobacter baumanii, with serious clinical implications as these isolates are resistant to all beta-lactams (Lynch et al., 2013; Woodford et al., 2011).
25 Inactivation by transference of a chemical group
The enzymatic transference of a chemical group to an antimicrobial compound can prevent the antibiotic from binding to its target protein. These enzymes are mainly involved in the transference of acyl, phosphate, nucleotidyl or adenyl chemical groups and constitute an important resistance mechanism against a wide range of antimicrobials, including fluoroquinolones, phenicols or aminoglycosides (Reygaert, 2018; Wright, 2005).
The most representative example of this resistance mechanism are the aminoglycoside-modifying enzymes, which are grouped into three main classes:
acetyltransferases, phosphotransferases and nucleotidyltransferases. These transferases, which are generally encoded in MGEs, confer high-level resistance to aminoglycosides and, depending on the enzyme involved, the aminoglycosides affected can vary (Ramirez
& Tolmasky, 2010).
2. Antimicrobial resistance detection methods
AMR detection is essential in the optimization and preservation of the currently available antimicrobials, since it provides information about drug susceptibility and guidance on the appropriate use of antimicrobials. Different methods have been implemented to achieve this goal and can be grouped into phenotypic or genotypic depending on the information provided.
2.1. Phenotypic antimicrobial susceptibility testing
Phenotypic AMR detection is based on antimicrobial susceptibility testing (AST) methods, which are defined as microbiological procedures whereby a pure culture of a microorganism is grown in the presence of an antimicrobial agent (van Belkum et al., 2020). These methods provide qualitative information about the susceptibility of a
26
specific pathogen to an antibiotic at defined concentrations and, in some cases, quantitative information through the minimum inhibitory concentration (MIC), which is the lowest antimicrobial concentration that inhibits the growth of a microorganism.
Most AST methods and their interpretation are carried out following guidelines provided by international organizations, highlighting the International Organization for Standardization (ISO), the Clinical and Laboratory Standards Institute (CLSI) from United States (US) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST). Despite many similarities and agreements among these organizations, there is still a lack of harmonization in the recommendations for the implementation of AST methods and the interpretation of the results (Mercer et al., 2020). Therefore, to avoid inconsistencies throughout this doctoral thesis we will follow the guidelines provided by EUCAST.
The clinical breakpoints defined in these guidelines are interpretative criteria for the determination of the optimal dose of antimicrobials for treating the infection caused by a specific pathogen (Mercer et al., 2020). These breakpoints are determined based on information regarding the microorganism (MIC values and AMR mechanisms), the antimicrobial (route of administration, dosage and pharmacokinetic and pharmacodynamic properties) and clinical data (EUCAST, 2019). According to EUCAST (2021a), these breakpoints are categorized into susceptible (S), which indicates a high probability of therapeutic success using a standard dosing regimen of the antibiotic;
intermediate (I) or susceptible with increased exposure, defined as high probability of therapeutic success with an increased dosing regimen or an increased concentration at the site of infection; and resistant (R), indicative of low probability of therapeutic success.
These clinical breakpoints are not permanent and can undergo changes based on new investigations.
