Biology subjects

Maron, B.

Publications and source records attributed to Maron, B..

5 recordsLinked to original sources

bioRxiv2026

Cross-Resistance Limits the Ability of Antimicrobial Peptide Combinations to Delay Resistance Evolution

AimsAntimicrobial peptide (AMP) combinations have been proposed to delay resistance evolution, but it remains unclear what properties of a peptide pair determine whether a combination reduces resistance evolution relative to its component AMPs used alone. One suggested factor is mode of action, yet this has rarely been tested experimentally. In the current study we have asked whether mode of action or physicochemical similarity between peptides better predicts which combinations delay resistance. MethodsWe evolved Staphylococcus aureus with six AMPs with reported membrane-targeting and intracellular-targeting activity, individually and in all 15 pairwise combinations. We quantified resistance evolution, cross-resistance and fitness costs across the full AMP panel, and performed whole-genome sequencing on 126 evolved lineages. ResultsResistance varied across AMPs and correlated with peptide chain length, not mode of action. Cross-resistance was associated with physicochemical similarity, and similar peptides selected for overlapping mutations. Most combinations reduced resistance relative to single-AMP treatment, but those whose components shared cross-resistance were less effective, channeling evolution into convergent trajectories that resolve both selective pressures at once. Notably, mode of action did not predict combination outcome. ConclusionsCross-resistance, not mode of action, is a key factor in determining AMP combination efficacy. Physicochemical distance between peptides may serve as a practical predictor for cross-resistance, enabling selection of AMP combinations that are more likely to constrain resistance evolution.

microbiology↗

bioRxiv2026

Trustworthy ML/AI for Aging Clocks: Preventing Systematic Prediction Bias in Biological Age Estimation

Machine learning (ML)- and artificial intelligence (AI)-based aging clocks are increasingly used to quantify physiological and molecular aging from omics and medical imaging data as distinct from chronological age. Here, we characterize a fundamental but underappreciated statistical limitation of commonly used ML/AI regression models for continuous outcomes: systematic prediction bias and its propagation to downstream association estimates. This issue becomes more challenging when the true outcome, biological age, is latent and therefore unobserved during ML/AI model training. We demonstrate that systematic prediction bias can distort and, in some cases, even reverse downstream association analyses that use aging clocks as ML/AI-predicted outcomes to assess their associations with exposures or clinical factors. For example, it can produce spurious associations suggesting that older predicted brain age is linked to better cognitive performance, or that older epigenetic age is associated with better kidney function. To address this problem, we introduce a principled and broadly applicable ML/AI regression framework based on constrained optimization, yielding better calibrated aging-clock estimates and valid downstream inference.

bioinformatics↗

bioRxiv2025

Targeting NEDD9-SH3 with a Covalent Peptide Controls Endothelial Phenotype

Src homology 3 (SH3) proteins regulate numerous fibroproliferative pathophenotypes including pulmonary arterial hypertension (PAH) but are challenging to target therapeutically. We innovated a peptidomimetic that occupies the canonical focal adhesion kinase (FAK) binding site on the SH3 domain of the neural precursor cell expressed, developmentally down-regulated 9 (NEDD9) protein, a pro-PAH regulator. Peptidomimetic derivatization with a bromoacetamide group alkylated a NEDD9 cysteine positioned uniquely among SH3 domains (Cys18), which stabilized the RT loop, prevented FAK binding, and inhibited human pulmonary artery endothelial cell (HPAEC) migration. When linked to a thalidomide moiety, the peptide showed degrader activity of NEDD9 protein and, therefore, we next investigated therapeutic application of NEDD9 inhibition. In HPAECs, si-NEDD9 downregulated sulfatase-1, which increased podosome rosette formation and cell migration via 6-O-desulfation of glycocalyx-forming heparan sulfate proteoglycans, and reversed vascular remodeling and PAH in vivo. Whereas sulfatase-1 overexpression decreased pulmonary endothelial podosome formation, cell migration, and tube formation and increased collagen III synthesis, sulfatase-1 knockdown prevented fibroproliferative remodeling and pulmonary hypertension in PAH in vivo. These data leverage cysteinyl thiol reactivity to establish an SH3 domain-targeting structure-validated covalent peptide and identify two convergent mechanisms through NEDD9 that control endothelial phenotype, including reverse remodeling via sulfatase-1 transcriptional control. Overall, this study advances an SH3-specific therapeutic approach with relevance to PAH and other fibroproliferative pathophenotypes.

molecular biology↗

bioRxiv2024

The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures

The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial Peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance. Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution in vitro to that of single sequence AMPs, using the ESKAPE pathogen Pseudomonas aeruginosa (P. aeruginosa) as a model Gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to form biofilms. P. aeruginosa was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied, and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined. Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved in vitro, further providing an alternative to classic antibiotics worth pursuing.

microbiology↗

bioRxiv2022

Combination treatment can hinder the evolution of resistance to antimicrobial peptides

Antibiotic resistant microbial pathogens are becoming a major threat to human health. Therefore, there is an urgent need to develop new alternatives to conventional antibiotics. One such promising alternative is antimicrobial peptides (AMPs), which are produced by virtually all organisms and typically inhibit bacteria via membrane disruption. However, previous studies demonstrated that bacteria can rapidly develop AMP resistance. Here, we study whether combination therapy, known to be able to inhibit the evolution of resistance to conventional antibiotics, can also hinder the evolution of AMP resistance. To do so, we evolved the opportunistic pathogen S. aureus in the presence of individual AMP, AMP pairs, and a combinatorial antimicrobial peptide library. Treatment with some AMP pair indeed hindered the evolution of resistance compared with individual AMPs. In particular, resistance to pairs was delayed when resistance to the individual AMPs came at a cost of impaired bacterial growth, and did not confer cross-resistance to other tested AMPs. The lowest level of resistance evolved during treatment with the combinatorial antimicrobial peptide library termed random antimicrobial peptide mixture, which contains more than a million different peptides. A better understanding of how AMP combinations affect the evolution of resistance is a crucial step in order to design resistant proof AMPs cocktails that will offer a sustainable treatment option for antibiotic resistant pathogens.

evolutionary biology↗