Biology subjects

Nicolas, J.

Publications and source records attributed to Nicolas, J..

7 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↗

bioRxiv2024

Neurophysiological Correlates of Phase-Specific Enhancement of Motor Memory Consolidation via Slow-Wave Closed-Loop Targeted Memory Reactivation

Memory consolidation can be enhanced during sleep using targeted memory reactivation (TMR) and closed-loop (CL) acoustic stimulation on the up-phase of slow oscillations (SOs). Here, we tested whether applying TMR at specific phases of the SOs (up vs. down vs. no reactivation) could influence the behavioral and neural correlates of motor memory consolidation in healthy young adults. Results showed that up- (as compared to down-) state cueing resulted in greater performance improvement. Sleep electrophysiological data indicated that up-stimulated SOs exhibited higher amplitude and greater peak-nested sigma power. Task-related functional magnetic resonance images revealed that up-state cueing strengthened activity in - and segregation of - striato-motor and hippocampal networks; and that these modulations were related to the beneficial effect of TMR on sleep features and performance. Overall, these findings highlight the potential of CL-TMR to induce phase-specific modulations of motor performance, sleep oscillations and brain responses during motor memory consolidation.

neuroscience↗

bioRxiv2023

In vitro construction and long read sequencing analysis of a 24 kb long artificial DNA sequence encoding the Universal Declaration of the Rights of Man and of the Citizen.

In absence of DNA template, the ab initio production of long double-stranded DNA molecules of predefined sequences is particularly challenging. The DNA synthesis step remains a bottleneck for many applications such as functional assessment of ancestral genes, analysis of alternative splicing or DNA-based data storage. We propose in this report a fully in vitro protocol to generate very long double-stranded DNA molecule starting from commercially available short DNA blocks in less than 3 days. This innovative application of Golden Gate assembly allowed us to streamline the assembly process to produce a 24 kb long DNA molecule storing part of the Universal Declaration of Human rights and citizens. The DNA molecule produced can be readily cloned into suitable host/vector system for amplification and selection.

synthetic biology↗

bioRxiv2022

Electrophysiological signatures of targeted memory reactivation during sleep are not accompanied by motor performance improvements in older adults

Targeted memory reactivation (TMR) during sleep enhances memory consolidation in young adults by modulating electrophysiological markers of neuroplasticity. Interestingly, older adults exhibit deficits in motor memory consolidation, an impairment that has been linked to age-related degradations in the same sleep features sensitive to TMR. We hypothesized that TMR would enhance consolidation in older adults via the modulation of these markers. Seventeen older participants were trained on a motor task involving two auditory-cued sequences. During a post-learning nap, two auditory cues were played: one associated to a learned (i.e., reactivated) sequence and one control. Performance during two delayed retests did not differ between reactivated and non-reactivated sequences. Moreover, both associated and control sounds modulated brain responses, yet there were no consistent differences between the auditory cue types. Our results collectively demonstrate that older adults do not benefit from specific reactivation of a motor memory trace by an associated auditory cue during post-learning sleep. It is possible, however, that auditory stimulation during post-learning sleep boosts motor memory consolidation in a non-specific manner.

neuroscience↗

bioRxiv2021

Sigma Oscillations Protect or Reinstate Motor Memory Depending on their Temporal Coordination with Slow Waves

Targeted memory reactivation (TMR) during post-learning sleep is known to enhance motor memory consolidation but the underlying neurophysiological processes remain unclear. Here, we confirm the beneficial effect of auditory TMR on motor performance. At the neural level, TMR enhanced slow wave (SW) characteristics. Additionally, greater TMR-related phase-amplitude coupling between slow (0.5-2 Hz) and sigma (12-16 Hz) oscillations after the SW peak was related to higher TMR effect on performance. Importantly, sounds that were not associated to learning strengthened SW-sigma coupling at the SW trough and the increase in sigma power nested in the trough of the potential evoked by these unassociated sounds was related to the TMR benefit. Altogether, our data suggest that, depending on their precise temporal coordination during post learning sleep, slow and sigma oscillations play a crucial role in either memory reinstatement or protection against irrelevant information; two processes that critically contribute to motor memory consolidation.

neuroscience↗

bioRxiv2018

Inferring biochemical reactions and metabolite structures to cope with metabolic pathway drift

Inferring genome-scale metabolic networks in emerging model organisms is challenging because of incomplete biochemical knowledge and incomplete conservation of biochemical pathways during evolution. This limits the possibility to automatically transfer knowledge from well-established model organisms. Therefore, specific bioinformatic tools are necessary to infer new biochemical reactions and new metabolic structures that can be checked experimentally. Using an integrative approach combining both genomic and metabolomic data in the red algal model Chondrus crispus, we show that, even metabolic pathways considered as conserved, like sterol or mycosporine-like amino acids (MAA) synthesis pathways, undergo substantial turnover. This phenomenon, which we formally define as \"metabolic pathway drift\", is consistent with findings from other areas of evolutionary biology, indicating that a given phenotype can be conserved even if the underlying molecular mechanisms are changing. We present a proof of concept with a new methodological approach to formalize the logical reasoning necessary to infer new reactions and new molecular structures, based on previous biochemical knowledge. We use this approach to infer previously unknown reactions in the sterol and MAA pathways.\n\nAuthor summaryGenome-scale metabolic models describe our current understanding of all metabolic pathways occuring in a given organism. For emerging model species, where few biochemical data are available about really occurring enzymatic activities, such metabolic models are mainly based on transferring knowledge from other more studied species, based on the assumption that the same genes have the same function in the compared species. However, integration of metabolomic data into genome-scale metabolic models leads to situations where gaps in pathways cannot be filled by known enzymatic reactions from existing databases. This is due to structural variation in metabolic pathways accross evolutionary time. In such cases, it is necessary to use complementary approaches to infer new reactions and new metabolic intermediates using logical reasoning, based on available partial biochemical knowledge. Here we present a proof of concept that this is feasible and leads to hypotheses that are precise enough to be a starting point for new experimental work.

systems biology↗

bioRxiv2017

De novo Clustering Nanopore Long Reads of Transcriptomics Data by Gene

Long-read sequencing currently provides sequences of several thousand base pairs. This allows to obtain complete transcripts, which offers an un-precedented vision of the cellular transcriptome.\n\nHowever the literature is lacking tools to cluster such data de novo, in particular for Oxford Nanopore Technologies reads, because of the inherent high error rate compared to short reads.\n\nOur goal is to process reads from whole transcriptome sequencing data accurately and without a reference genome in order to reliably group reads coming from the same gene. This de novo approach is therefore particularly suitable for non-model species, but can also serve as a useful pre-processing step to improve read mapping. Our contribution is both to propose a new algorithm adapted to clustering of reads by gene and a practical and free access tool that permits to scale the complete processing of eukaryotic transcriptomes.\n\nWe sequenced a mouse RNA sample using the MinION device, this dataset is used to compare our solution to other algorithms used in the context of biological clustering. We demonstrate its is better-suited for transcriptomics long reads. When a reference is available thus mapping possible, we show that it stands as an alternative method that predicts complementary clusters.

bioinformatics↗