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Andre, G.

Publications and source records attributed to Andre, G..

2 recordsLinked to original sources

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↗

A deep learning approach for improved detection of homologous recombination deficiency from shallow genomic profiles

Homologous Recombination Deficiency (HRD) is a predictive biomarker of poly-ADP ribose polymerase 1 inhibitors (PARPi) response. Most HRD detection methods are based on genome wide enumeration of scarring events and require deep genome sequence profiles (> 30x). The cost and workflow-specific biases introduced by these genome profiling methods currently limits clinical adoption of HRD testing. We introduce the Genomic Integrity Index (GII), a Convolutional Neuronal Network, that leverages features from low pass (1x) Whole Genome Sequencing data to distinguish HRD positive and negative samples. In a cohort of 230 ovarian and breast cancer, we found GII supports accurate stratification of samples yielding results that are highly concordant with state-of-the-art HRD detection methods (0.865<AUC<0.996) which require 50x deeper coverage. We conclude that the deep learning framework supporting GII allows accurate detection of HRD from shallow genome profiles, reducing biases and data generation costs making it uniquely suited for clinical applications.

cancer biology↗