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Sub-minimum inhibitory concentrations (sub-MICs) of antibiotics are frequently encountered in vivo, particularly at sites of infection and on indwelling medical devices. However, their influence on biofilm formation is controversial, with evidence of both stimulatory and inhibitory effects. Clarifying these responses may reveal novel therapeutic opportunities for managing nosocomial biofilm-related infections.<\/jats:p>\n                    <jats:p>\n                      This study aims to investigate the effects of three sub-MIC concentrations (25%, 50%, and 75% of MIC) of five antibiotic classes on biofilm formation in\n                      <jats:italic>Pseudomonas aeruginosa<\/jats:italic>\n                      ,\n                      <jats:italic>Escherichia coli<\/jats:italic>\n                      , and\n                      <jats:italic>Klebsiella pneumoniae<\/jats:italic>\n                      using two distinct biofilm quantification methods.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      In this study, the effects of 5 different antimicrobial agents, namely, azithromycin, gentamicin, ciprofloxacin, doxycycline, and imipenem, at different sub-MIC concentrations (12.5%, 25%, and 50% of MIC) were tested on 5 different clinical isolates of\n                      <jats:italic>P. aeruginosa<\/jats:italic>\n                      ,\n                      <jats:italic>E. coli<\/jats:italic>\n                      , and\n                      <jats:italic>K. pneumoniae<\/jats:italic>\n                      . Clinical isolates were exposed to sub-MIC levels of antibiotics in a standardized biofilm assay, with statistical comparisons conducted to determine significant biofilm modulation using crystal violet (CV) staining to measure total biomass accumulation and quantitative PCR (qPCR) to assess qPCR-derived genome-equivalent DNA abundance within biofilms. qPCR detects DNA originating from viable, dead, damaged, or lysed cells, as well as extracellular DNA (eDNA).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Across the 75 tested conditions (5 antibiotics \u00d7 3 sub-MIC concentrations \u00d7 5 clinical isolates per species), sub-inhibitory antibiotic exposure produced pronounced, species- and target-dependent biofilm responsiveness varied by species:\n                      <jats:italic>P. aeruginosa<\/jats:italic>\n                      demonstrated the highest biomass modulation (62% CV significance),\n                      <jats:italic>E. coli<\/jats:italic>\n                      exhibited the greatest molecular responsiveness (55% qPCR significance), and\n                      <jats:italic>K. pneumoniae<\/jats:italic>\n                      showed lower overall modulation (47% CV, 32% qPCR significance). Imipenem exerted the highest biomass suppression in\n                      <jats:italic>P. aeruginosa<\/jats:italic>\n                      (80% CV vs. 13% qPCR inhibition), whereas doxycycline predominantly reduced genome equivalents in\n                      <jats:italic>P. aeruginosa<\/jats:italic>\n                      (73% qPCR vs. 7% CV inhibition). In\n                      <jats:italic>E. coli<\/jats:italic>\n                      , azithromycin produced peak biomass inhibition (73% CV vs. 7% qPCR inhibition), while gentamicin led to molecular suppression (60% qPCR vs. 27% CV inhibition). In\n                      <jats:italic>K. pneumoniae<\/jats:italic>\n                      , azithromycin and ciprofloxacin directed to biomass inhibition (both 40% CV), while azithromycin produced the highest qPCR reduction (33% qPCR). Bidirectional modulation was widespread across drug classes. Azithromycin acted as a primary inducer in\n                      <jats:italic>P. aeruginosa<\/jats:italic>\n                      (80% CV, 67% qPCR induction), while imipenem shifted from a biomass inhibitor in\n                      <jats:italic>P. aeruginosa<\/jats:italic>\n                      (80% CV inhibition) to a primary inducer in\n                      <jats:italic>K. pneumoniae<\/jats:italic>\n                      (47% CV, 60% qPCR induction). Unidirectional responses were drug- and method-specific, such as imipenem in\n                      <jats:italic>P. aeruginosa<\/jats:italic>\n                      CV (80% inhibition) and doxycycline in\n                      <jats:italic>P. aeruginosa<\/jats:italic>\n                      qPCR (73% inhibition).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates that sub-MIC exposure produces heterogeneous, antibiotic-, species-, isolate-, concentration-, and method-dependent biofilm responses that diverge between structural matrix accumulation and genomic abundance. Therefore, our findings highlight that sub-MIC exposures demand a multi-assay diagnostic framework to accurately evaluate biofilm-associated drug responses.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12866-026-05620-5","type":"journal-article","created":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T09:23:33Z","timestamp":1790414613000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond lethal doses: sub-MIC levels of multi-class antibiotics induce species- and isolate-dependent biofilm inhibition in gram-negative pathogens"],"prefix":"10.1186","volume":"26","author":[{"given":"Raghda","family":"Elawady","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2923-1027","authenticated-orcid":false,"given":"Aliaa G.","family":"Aboulela","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3610-4592","authenticated-orcid":false,"given":"Ahmed N.","family":"Amer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8530-4879","authenticated-orcid":false,"given":"Abeer A.","family":"Ghazal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9038-6597","authenticated-orcid":false,"given":"Ahmed","family":"Gaballah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,26]]},"reference":[{"issue":"11","key":"5620_CR1","doi-asserted-by":"publisher","first-page":"14002","DOI":"10.3390\/ijms131114002","volume":"13","author":"UU Nwodo","year":"2012","unstructured":"Nwodo UU, Green E, Okoh AI. 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