Weakest link dynamics predict apparent antibiotic interactions in a model cross-feeding community

Abstract

13With the growing global threat of antimicrobial resistance, novel strategies are required for 14 combatting resistant pathogens. Combination therapy, wherein multiple drugs are used to treat an 15 infection, has proven highly successful in the treatment of cancer and HIV. However, this 16 practice has proven challenging for the treatment of bacterial infections due to difficulties in 17 selecting the correct combinations and dosages. An additional challenge in infection treatment is 18 the polymicrobial nature of many infections, which may respond to antibiotics differently than a 19 monoculture pathogen. This study tests whether patterns of antibiotic interactions (synergy, 20 antagonism, or independence/additivity) in monoculture can be used to predict antibiotic 21 interactions in an obligate cross-feeding co-culture. Using our previously described weakest link 22 hypothesis, we hypothesized antibiotic interactions in co-culture based on the interactions we 23 observed in monoculture. We then compared our predictions to observed antibiotic interactions 24 in co-culture. We tested the interactions between ten previously identified antibiotic 25 combinations using checkerboard assays. Although our antibiotic combinations interacted 26 differently than predicted in our monocultures, our monoculture results were generally sufficient 27 to predict co-culture patterns based solely on the weakest link hypothesis. These results suggest 28 that combination therapy for cross-feeding multispecies infections may be successfully designed 29 based on antibiotic interaction patterns for their component species. 30 31 independent mutation is required for resistance to each drug (8, 9). This approach, of using 49 multiple drugs to target multiple essential targets, has also been used in cancer chemotherapy to 50 manage drug-resistant and genetically heterogeneous tumors (10, 11). In cases of bacterial 51 infections, multidrug therapy has been adopted in only a few specific infections, such as 52 treatment for drug sensitive tuberculosis (2). However, clinical trials of combination therapy in 53 the treatment of bacterial infections in patients have been limited. Choosing the correct drug 54 combination is difficult (12, 13), and efficacy has been mixed (14, 15). A greater understanding 55 4 of the mechanisms driving effective combination therapy are therefore required for successful 56 clinical implementation. 57The success of combination therapy is affected by interactions between drugs, wherein the 58 activity and effectiveness of one drug is impacted by the presence or absence of another (16). 59There are several mechanisms by which antibiotics may synergize (work more effectively or at 60 lower doses together than separately) or antagonize (work less effectively or at higher doses 61 together than separately). While the precise nature of these interactions depends on the drugs and 62 the bacterial species being targeted, some general mechanisms have been described for different 63 classes of antibiotics (17). Synergi...

Positive interactions within and between populations decrease the likelihood of evolutionary rescue

“…Next, several experimental evolution studies involving bacterial mutualisms have found that while some populations evolve a more efficient division of labour and elevated growth rates, other replicate populations experience unexplained collapse, leading to extinction of both interacting species 34,35 . Finally, a recent study exploring the adaptability of metabolically co-dependent bacterial populations in the presence of antibiotics showed that codependency between two species results in a limitation of their adaptation ability by the least adaptable, "weakest link" strain 36 . Thus, once a species is more adapted to the environment than its partner, it can not further increase in fitness until it's cooperator evolves to a similar fitness.…”