24 July 2025

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What is cross-resistance?

From a strictly microbiological perspective, cross-resistance refers to a phenomenon in which resistance to different classes of antimicrobial agents arises due to a shared or overlapping resistance mechanism.1,2 Mechanisms such as broad-spectrum antibiotic-inactivating enzymes, ribosomal target-modifying enzymes, ribosomal mutations, and multidrug efflux pumps exemplify how a single bacterial resistance mechanism can reduce the efficacy of multiple, structurally unrelated antimicrobial agents.3 For example, macrolide, lincosamide, and streptogramin B (MLSB) antibiotics have distinct chemical structures but share a similar mode of action. They inhibit protein synthesis by binding to adenine residues at positions 2058 or 2059 within domain V of the 23S rRNA subunit. Methylation of the A2058 residue in 23S rRNA, mediated by erm genes, or point mutations at position 2059, alters the antibiotic binding site and leads to cross-resistance to macrolides, lincosamides, and streptogramin B (MLSB phenotype) in Gram-positive cocci.4 Another example is the acquisition of the cfr (chloramphenicol–florfenicol resistance) gene, which encodes an rRNA methyltransferase that confers resistance to five antibiotic classes, including phenicols, lincosamides, oxazolidinones, pleuromutilins and streptogramin A (PhLOPSA phenotype). PhLOPSA antibiotics bind near the peptidyl transferase center at A2503; however, cfr-mediated methylation at this site disrupts their binding.5 In Gram-negative bacteria, cross-resistance can also arise through mechanisms such as the production of AAC(6′)-Ib-cr, which is an aminoglycoside acetyltransferase that not only confers resistance to amikacin but also reduces the activity of ciprofloxacin by N-acetylating the amino nitrogen on its piperazinyl substituent.6 Other mechanisms include multidrug efflux pumps, which expel a broad range of antimicrobial agents in addition to disinfectants, heavy metals, and dyes; porin mutations that reduce antibiotic intake affecting a wide range of drugs; and the production of β-lactamases capable of hydrolyzing multiple β-lactam antibiotics, thereby contributing to cross-resistance within the β-lactam class.7,8

Unlike the microbiological concept of cross-resistance, based on defined mechanistic links, these epidemiological associations may be context-dependent and not universally applicable.

Cross-resistance in an epidemiological context

The term cross-resistance has also been used in a broader epidemiological context to describe situations where resistance to one antimicrobial agent is associated with resistance to others from different classes. For instance, cross-resistance between beta-lactams and macrolides is frequently observed in Streptococcus pneumoniae isolates resistant to penicillin.9 Widely disseminated multidrug-resistant S. pneumoniae clones often accumulate multiple resistance mechanisms, contributing to their successful spread.9,10 Another example involves Gram-negative bacilli harboring mobile genetic elements such as integrons, which carry distinct resistance genes organized as gene cassettes.11 Numerous studies have described class B carbapenemase genes located within class 1 integrons, often alongside genes encoding aminoglycoside-modifying enzymes.11,12 Acquisition of such integrons may lead to cross-resistance across different antimicrobial classes.

My concern is that patterns observed in a single institution or population could be misinterpreted as generalizable, potentially leading to inaccurate assumptions in settings with different epidemiological profiles.

To better understand and predict cross-resistance, statistical analyses have been employed to identify these associations.13-15 However, factors such as prior antibiotic exposure, invasive procedures, hospital stays as well as specific patient characteristics can significantly influence observed resistance patterns, underscoring the context-dependent nature of these findings.13-15 While I understand that such analyses aim to support clinicians in selecting the most appropriate empiric therapy based on local data, I remain cautious about adopting this broader definition. Unlike the microbiological concept of cross-resistance, based on defined mechanistic links, these epidemiological associations may be context-dependent and not universally applicable. My concern is that patterns observed in a single institution or population could be misinterpreted as generalizable, potentially leading to inaccurate assumptions in settings with different epidemiological profiles.

Collateral sensitivity

In addition to cross-resistance, the concept of collateral sensitivity has been introduced. From a microbiological standpoint, collateral sensitivity can be defined as an evolutionary trade-off in bacteria, where resistance to one antibiotic leads to increased susceptibility to another antibiotic.16 In some studies, it is also described as a see-saw effect.17 A clear example is observed with mutations in genes encoding β-lactamases. These mutations can cause structural changes in the β-lactamase protein, particularly near its active site, which may alter its interaction with various β-lactams. For instance, in Pseudomonas aeruginosa, specific mutations in Pseudomonas-derived cephalosporinases (PDC, also referred to as AmpC) can result in resistance to ceftolozane-tazobactam and ceftazidime-avibactam, while simultaneously increasing susceptibility to imipenem.18 In contrast, the term collateral sensitivity has also been adopted in epidemiological contexts.14,15,19 Zwep and colleagues developed and implemented a statistical approach to detect collateral sensitivity and collateral resistance using large-scale clinical surveillance data. Utilizing minimum inhibitory concentration (MIC) data from 419 Escherichia coli strains tested against 20 antibiotics from the PATRIC database, their conditional t-test method identified 14 instances of collateral sensitivity and 178 potential collateral resistance interactions. The strongest collateral sensitivity effect was observed with ertapenem increasing susceptibility to cefazolin, while cefazolin demonstrated multiple collateral sensitivity associations with other antibiotics.19 Similar to the broader use of cross-resistance, the use of collateral sensitivity is based on statistical associations rather than defined molecular mechanisms. While epidemiological studies may provide useful insights into local resistance trends and therapeutic options, I believe applying the term “collateral sensitivity” in this context can be misleading. As with cross-resistance, epidemiologically derived patterns may not be generalizable across different healthcare settings or geographic regions. Moreover, the level of expression of resistance genes or the specific underlying mechanisms is often unknown and not directly measurable in surveillance data. This unpredictability is particularly pronounced in Gram-negative bacteria, where resistance phenotypes may not correlate reliably with the presence of specific genes due to complex regulatory and permeability factors.3,7,8 To avoid confusion, it may be helpful to distinguish between the microbiological and epidemiological usages of both cross-resistance and collateral sensitivity. One possible solution could be the addition of an E (for epidemiological) when referring to context-specific, data-driven associations. This would help clarify for healthcare professionals whether a given resistance pattern arises from mechanistic understanding or statistical inference.

While both cross-resistance and collateral sensitivity are valuable concepts in the study of antimicrobial resistance, their interpretation should be carefully framed within their respective contexts.

Conclusion

In summary, while both cross-resistance and collateral sensitivity are valuable concepts in the study of antimicrobial resistance, their interpretation should be carefully framed within their respective contexts. The microbiological definitions, grounded in mechanistic insights, offer a robust foundation for understanding resistance at the molecular level. In contrast, epidemiological uses of these terms, though potentially informative for guiding empirical therapy and mitigating antimicrobial resistance, should be clearly distinguished to avoid overgeneralization and misinterpretation. As resistance patterns continue to evolve across different clinical and geographical settings, maintaining this conceptual distinction will be crucial for developing effective surveillance strategies, guiding antimicrobial stewardship, and supporting clinical decision-making.

References

  1. Colclough A, Corander J, Sheppard SK, Bayliss SC, Vos M (2019) Patterns of cross-resistance and collateral sensitivity between clinical antibiotics and natural antimicrobials. Evol Appl. 12(5):878-887.
  2. Sanders CC, Sanders WE Jr, Goering RV, Werner V (1984) Selection of multiple antibiotic resistance by quinolones, beta-lactams, and aminoglycosides with special reference to cross-resistance between unrelated drug classes. Antimicrob Agents Chemother. 26(6):797-801.
  3. Urban-Chmiel R, Marek A, Stępień-Pyśniak D, Wieczorek K, Dec M, Nowaczek A et al. (2022) Antibiotic Resistance in Bacteria-A Review. Antibiotics (Basel). 11(8):1079.
  4. Nor Amdan NA, Shahrulzamri NA, Hashim R, Mohamad Jamil N (2024) Understanding the evolution of macrolides resistance: A mini review. J Glob Antimicrob Resist. 38:368-375.
  5. Long KS, Poehlsgaard J, Kehrenberg C, Schwarz S, Vester B (2006) The Cfr rRNA methyltransferase confers resistance to Phenicols, Lincosamides, Oxazolidinones, Pleuromutilins, and Streptogramin A antibiotics. Antimicrob Agents Chemother. 50(7):2500-5.
  6. Robicsek A, Strahilevitz J, Jacoby GA, Macielag M, Abbanat D, Park CH et al. (2006) Fluoroquinolone-modifying enzyme: a new adaptation of a common aminoglycoside acetyltransferase. Nat Med. 83-8.
  7. Blanco P, Hernando-Amado S, Reales-Calderon JA, Corona F, Lira F, Alcalde-Rico M et al. (2016) Bacterial Multidrug Efflux Pumps: Much More Than Antibiotic Resistance Determinants. Microorganisms. 4(1):14.
  8. Bush K (2018) Past and Present Perspectives on β-Lactamases. Antimicrob Agents Chemother. 62(10):e01076-18. 
  9. Appelbaum PC (2000) Microbiological and pharmacodynamic considerations in the treatment of infection due to antimicrobial-resistant Streptococcus pneumoniae. Clin Infect Dis. 31 Suppl 2:S29-34.
  10. Klugman KP (2002) The successful clone: the vector of dissemination of resistance in Streptococcus pneumoniae. J Antimicrob Chemother. 50 Suppl S2:1-5.  
  11. Mendes RE, Toleman MA, Ribeiro J, Sader HS, Jones RN, Walsh TR (2004) Integron carrying a novel metallo-beta-lactamase gene, blaIMP-16, and a fused form of aminoglycoside-resistant gene aac(6′)-30/aac(6′)-Ib’: report from the SENTRY Antimicrobial Surveillance Program. Antimicrob Agents Chemother. 48(12):4693-702.
  12. Di Pilato V, Pollini S, Rossolini GM( 2014) Characterization of plasmid pAX22, encoding VIM-1 metallo-β-lactamase, reveals a new putative mechanism of In70 integron mobilization. J Antimicrob Chemother. 69(1):67-71.
  13. Cherny SS, Nevo D, Baraz A, Baruch S, Lewin-Epstein O, Stein GY et al. (2021) Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling. J Antimicrob Chemother. 76(1):239-248.
  14. Zilberberg MD, Nathanson BH, Sulham K, Shorr AF (2020) Antimicrobial Susceptibility and Cross-Resistance Patterns among Common Complicated Urinary Tract Infections in U.S. Hospitals, 2013 to 2018. Antimicrob Agents Chemother. 64(8):e00346-20.
  15. Cherny SS, Chowers M, Obolski U (2023) Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source. Commun Med (Lond). 3(1):61.
  16. Roemhild R, Andersson DI (2021) Mechanisms and therapeutic potential of collateral sensitivity to antibiotics. PLoS Pathog. 17(1):e1009172.
  17. Barbosa C, Römhild R, Rosenstiel P, Schulenburg H (2019) Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa. Elife. 8:e51481.
  18. Cabot G, Kim K, Mark BL, Oliver A, Khajehpour M (2023) Biochemical Insights into Imipenem Collateral Susceptibility Driven by ampCMutations Conferring Ceftolozane/Tazobactam Resistance in Pseudomonas aeruginosa. Antimicrob Agents Chemother. 67(2):e0140922.
  19. Zwep LB, Haakman Y, Duisters KLW, Meulman JJ, Liakopoulos A, van Hasselt JGC (2021) Identification of antibiotic collateral sensitivity and resistance interactions in population surveillance data. JAC Antimicrob Resist. 3(4):dlab175.

Ana Cristina Gales is a leading Brazilian physician and researcher specializing in infectious diseases and antimicrobial resistance. She is an associate professor in the Division of Infectious Diseases at the Escola Paulista de Medicina, Universidade Federal de São Paulo (EPM-UNIFESP), and co-directs the Antimicrobial Resistance Institute of São Paulo (ARIES), which focuses on combating drug-resistant pathogens through research and innovation.

Beyond her academic and research endeavors, Ana is deeply committed to public health, providing care to patients within Brazil’s Unified Health System (SUS), demonstrating her dedication to accessible and high-quality healthcare.

The author declares that they do not have any relationships or affiliations that could be construed as a potential conflict of interest.