Biology subjects

Rognan, D.

Publications and source records attributed to Rognan, D..

3 recordsLinked to original sources

bioRxiv2024

An anti-virulence drug targeting the evolvability protein Mfd protects against infections with antimicrobial resistant ESKAPE pathogens

The increased incidence of antibiotic resistance and declining discovery of new antibiotics have created a global health crisis, especially for the treatment of infections caused by Gram-negative bacteria. Here, we identify and characterize a molecule, NM102, that displays antimicrobial activity exclusively in the context of infection. NM102 inhibits the activity of the non-essential Mutation Frequency Decline (Mfd) protein by competing with ATP binding to its active site. Inhibition of Mfd by NM102 sensitizes pathogenic bacteria to the host immune response and blocks infections with clinically- relevant Klebsiella pneumoniae and Pseudomonas aeruginosa, without inducing host toxicity. Finally, NM102 inhibits the function of Mfd as a mutation and evolvability factor, thus reducing the bacterial capacity to develop antimicrobial resistance. These data provide a potential roadmap to expand the arsenal of drugs to combat antimicrobial resistance. HighlightO_LINM102 is a "first in class" molecule specifically targeting the active site of the bacterial Mfd protein C_LIO_LINM102 has a new mode of action: it inhibits Mfd function during immune stress response C_LIO_LINM102 also inhibits Mfd evolvability function and thereby decreases bacterial resistance to known antibiotics C_LIO_LINM102 effectively treats Gram-negative infections in animal models C_LIO_LINM102 is efficient against clinically relevant resistant bacteria and provides an increased efficacy in combination with the {beta}-lactam meropenem C_LI

microbiology↗

bioRxiv2023

FLT3 signaling inhibition preserves opioid analgesia while abrogating tolerance and hyperalgesia

Opioid analgesia is counteracted on chronic use by tolerance and hyperalgesia inducing dose escalation and life-threatening overdoses. Mu opiate receptors (MOR) expressed in primary sensory neurons were recently found to control tolerance and hyperalgesia, but the underlying mechanisms remained elusive. Here we show that genetic inactivation of fms-like tyrosine kinase receptor 3 (FLT3) receptor in sensory neurons abrogates morphine tolerance and hyperalgesia by preventing MOR-induced hyperactivation of the cAMP signaling pathway and subsequent excitatory adaptive processes. Moreover, the specific FLT3 inhibitor BDT001 potentiates morphine analgesia in acute and chronic pain models, without aggravating morphine adverse effects, and reverses tolerance and hyperalgesia once installed. Thus, FLT3 appears as a key regulator of the MOR signaling pathway and its pharmacological blockade shows promise to enhance chronic opioid analgesic efficacy.

neuroscience↗

bioRxiv2021

Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor revealed by binding site image processing

Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We recently described a new computational approach (ProCare), inspired by numerical image processing, to identify local similarities in fragment-based subpockets. During the validation of the method, we unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor necrosis factor alpha (TNF-) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT inhibitors indeed bind to soluble human TNF- trimer. Interestingly, the herein disclosed similarity could be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is uniquely suited to identify local and unobvious similarities among totally unrelated targets. AUTHOR SUMMARYComputational comparison of binding sites in proteins can provide insights on potential unrelated proteins that may bind to similar ligands. However, accurate prediction of binding site similarity requires powerful methods, ideally able to detect even local similarities. We herewith applied a recently developed computer vision method to identify an unexpected binding site similarity between two totally unrelated proteins that was confirmed experimentally by in vitro biophysical binding assays. Considering more precisely local similarities can therefore efficiently guide drug discovery, notably to repurpose existing drug candidates.

bioinformatics↗