Hidden suppressive interactions are common in higher-order drug combinations
Natalie Ann Lozano-Huntelman,
April Zhou,
Elif Tekin,
Mauricio Cruz-Loya,
Bjørn Østman,
Sada Boyd,
Van M. Savage,
Pamela Yeh
Affiliations
Natalie Ann Lozano-Huntelman
Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA
April Zhou
Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA; Computational and Systems Biology, University of California, Los Angeles, 90095, USA
Elif Tekin
Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA
Mauricio Cruz-Loya
Computational and Systems Biology, University of California, Los Angeles, 90095, USA
Bjørn Østman
Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA
Sada Boyd
Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA
Van M. Savage
Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA; Computational and Systems Biology, University of California, Los Angeles, 90095, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
Pamela Yeh
Ecology and Evolutionary Biology, University of California, Los Angeles, 90095, USA; Santa Fe Institute, Santa Fe, NM 87501, USA; Corresponding author
Summary: The rapid increase of multi-drug resistant bacteria has led to a greater emphasis on multi-drug combination treatments. However, some combinations can be suppressive—that is, bacteria grow faster in some drug combinations than when treated with a single drug. Typically, when studying interactions, the overall effect of the combination is only compared with the single-drug effects. However, doing so could miss “hidden” cases of suppression, which occur when the highest order is suppressive compared with a lower-order combination but not to a single drug. We examined an extensive dataset of 5-drug combinations and all lower-order—single, 2-, 3-, and 4-drug—combinations. We found that a majority of all combinations—54%—contain hidden suppression. Examining hidden interactions is critical to understanding the architecture of higher-order interactions and can substantially affect our understanding and predictions of the evolution of antibiotic resistance under multi-drug treatments.
