Defining transcriptional signatures of susceptibility for ceftriaxone and tetracycline in Neisseria gonorrhoeae

Resistance to antibiotics is the ability of bacteria to survive in spite of the administration of drugs designated to kill them; and overuse of antibiotics over the past decades has led to a vast increase in resistance. Most infections are treated empirically, such that patients are given a recommended course of antibiotics, however these infections are not frequently tested for their antimicrobial susceptibility profiles (i.e., tested only in the case of treatment failure). This can lead to inappropriate prescription which can increase the selective pressure of antibiotics on other bacterial members of our microbiomes. Susceptibility testing would help determine the antibiotics that are the most effective in helping the prompt treatment of disease-causing organism. In this study we nominated biological signatures to serve as the foundation for developing a point-of-care (POC) diagnostic tool for antimicrobial susceptibility testing. RNA transcriptional signatures of Neisseria gonorrhoeae were generated upon drug exposure, as RNA abundance change can define susceptibility; susceptible bacteria are expected to express a stress response after drug exposure, which is absent in resistant bacteria. These profiles were then analyzed to identify Treatment-Susceptibility (TS) patterns; those transcripts differentially expressed in susceptible cells across treatment, and differentially expressed in the treatment condition. The transcripts exhibiting distinct TS pattern were investigated for each drug contrast and at two specific time points i.e., 120 minutes and 240 minutes. We identify 61 transcripts that show the TS pattern for ceftriaxone at 120 minutes and 7 at 240; and 18 transcripts that show the TS pattern for tetracycline at 120 minutes and 128 at 240. The results indicate that RNA abundance changes can reliably signify bacterial susceptibility, as susceptible isolates demonstrate a clear transcriptional response to drug exposure. This study also demonstrated that to acquire meaningful results it is crucial to test transcripts at specific timepoints, as they capture the dynamic transcriptional response of bacteria to drug exposure. By nominating these biological signatures, this study lays the groundwork for developing a rapid, RNA- based diagnostic tool. Such a tool could enhance susceptibility testing, reduce inappropriate antibiotic use, and support more targeted treatment strategies. Overall, these findings depicted the most useful transcriptional signatures, which could serve as a key component in future point-of-care (POC) devices for antimicrobial susceptibility testing.