Biology subjects

Camus, L.

Publications and source records attributed to Camus, L..

11 recordsLinked to original sources

bioRxiv2026

Development of a continuous bioreactor to maintain stable nasal microbiomes from swab specimens and synthetic communities

BackgroundThe nasal microbiome is a collection of diverse microbial populations that inhabit the nose. Staphylococcus aureus is the most common opportunistic pathogen that colonizes the nasal mucosa, increasing the risk of invasive infections in immunocompromised and hospitalized patients. Clinicians usually prescribe antibiotics to decolonize the nasal cavities of at-risk patients from S. aureus. However, their broad antimicrobial activity can damage the resident nasal microbiome. Instead, naturally occurring compounds or resident bacteria in nasal microbiomes can effectively and safely exclude S. aureus from the nose. Cell culture and animal models have been used for nasal microbiome studies. However, their unstable microbiomes reduce the accuracy and reliability of the results. Recently, continuous bioreactors have been proposed as alternatives to these models. ResultsWe designed and operated a continuous bioreactor system to maintain stable nasal microbiomes. Next, we inoculated the bioreactor with nasal-swab specimens that we had collected from healthy volunteers. We operated the bioreactors under varying conditions (i.e., operating mode, dilution rate, temperature, pH, and medium composition), and determined the optimal conditions (continuous mode, 1 d-1, 30xlink:href=" pH 6.5, and synthetic nasal medium 3), resulting in stable microbiomes consisting of the main nasal bacterial species. The nasal microbiomes in the optimized bioreactors showed high reproducibility and resilience during a pH perturbation. Moreover, all microbiomes in the bioreactor, which were inoculated with six different nasal-swab specimens, maintained stable bacterial and metabolite compositions. In addition, we applied a synthetic microbial community (SynCom), which was derived from one of the volunteers, to demonstrate a S. aureus decolonization strategy. The bioreactor, inoculated with this SynCom, maintained a stable nasal microbiome for more than one month. Finally, different S. aureus strains that we inoculated in the SynCom showed distinct growth patterns within the otherwise stable community. ConclusionsThe continuous bioreactor enables the cultivation of stable nasal microbiomes for longer than one month by mimicking the environmental conditions of the human nose. The bioreactor is a valuable model for understanding the functions of the nasal microbiome and devising new decolonization strategies against S. aureus.

bioengineering↗

bioRxiv2026

Community composition and strain identity drive metabolic competition and Staphylococcus aureus colonization resistance in Synthetic Nasal Communities

The human nasal microbiome is a low-diversity ecosystem whose assembly principles and mechanisms of colonization resistance remain poorly understood. Staphylococcus aureus is a member of the nasal microbiome of some individuals with variable abundance. We hypothesized that nutritional competition, strain-level diversity, and nutrient availability shape community stability and the ability of commensal species to inhibit S. aureus. To test this, we constructed 50 defined synthetic communities composed of representative human nasal bacteria differing in strain and species composition and tracked their temporal dynamics, S. aureus growth, metabolic profiles, and nutritional interactions. The composition of synthetic communities with 5-10 species showed robust and reproducible dynamics and converged in one of three stable states. Synthetic communities dominated by a specific strain of Corynebacterium propinquum were highly stable and consistently excluded S. aureus. Growth curves and coculture assays showed that C. propinquum outcompetes S. aureus under poor nutritional conditions resembling the nasal environment, whereas S. aureus dominates in nutrient-rich conditions. Metabolomics analyses revealed that nutritional competition, including siderophores utilization and amino acid limitation, likely underlies this colonization resistance. These results establish a tractable synthetic community model for the human nasal microbiome and identify nutrient-dependent competition and microbial metabolite production as key drivers of community structure and pathogen exclusion.

microbiology↗

bioRxiv2025

Tyrosine availability shapes Staphylococcus aureus nasal colonization and interactions with commensal communities

Only certain individuals are nasally colonized by Staphylococcus aureus, making them more susceptible to developing endogenous infections. The role of the resident microbiota in modulating S. aureus nasal colonization remains largely unclear. Using a newly assembled nasal strain collection, we demonstrate that nasal commensals can either promote or inhibit S. aureus proliferation through strain- and community-specific effects in vitro and within gnotobiotic animals. We identified nutrient-dependent interactions as a key driver of these dynamics and found that S. aureus strains auxotrophic to tyrosine rely on amino acid-providing nasal communities to thrive in the nutrient-limited environment of the nose. Large-scale screenings revealed tyrosine dependency to affect 8% of nasal S. aureus strains and to impair S. aureus colonization. Our findings uncover a crucial factor in S. aureus physiology and its interactions as a commensal nasal species, paving the way for new decolonization strategies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/651429v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@17b6289org.highwire.dtl.DTLVardef@182249forg.highwire.dtl.DTLVardef@1324dc1org.highwire.dtl.DTLVardef@18ec3bf_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

New insights into past and future invasion of Drosophila suzukii using novel genomic resources and statistical methods to combine individual and pool sequencing data

Global change is accelerating biological invasions, making it crucial to understand how species adapt in new environments to improve management strategies. Genomic data provide valuable insights into adaptation through Genotype-Environment Association (GEA) studies, which identify genes and biological processes tied to invasion success, and through geometric Genomic Offset (gGO) statistics, which estimate genetic (mal)adaptation to new environments. Here, we investigate genetic adaptation in the invasive pest Drosophila suzukii using novel genomic resources and statistical methods. We use a new chromosome-level genome assembly and data from 37 populations, combining publicly available and newly generated pooled and individual sequencing data, analyzed with an enhanced version of BayPass software, tailored for such hybrid datasets. First, we identify genomic regions showing genetic differentiation between native and invasive populations. Then, using a GEA with 29 environmental covariates, we estimate the gGO between the source environments and the invaded areas, shedding light on the potential adaptive challenges D. suzukii faced during previous invasions. In addition, we estimate gGO for geographical areas not yet invaded to predict future invasion risks, and identify regions from which preadapted populations may originate. Our results reveal numerous genomic regions associated with the invasive status from genome scans. However, when considering broader patterns of adaptation to specific environmental variables through gGO analyses, we find that D. suzukii populations likely faced only limited adaptive challenges across their major invasion range, while certain uninvaded regions still remain at high risk of future invasion. Our study offers significant insights into D. suzukii adaptation and provides a practical population genomics framework to predict biological invasions, applicable to various species.

evolutionary biology↗

bioRxiv2024

Predicting species invasiveness with genomic data: is Genomic Offset related to establishment probability?

Predicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so-called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species, but have still rarely been used in this context. Here, we evaluated the relationship between GO and the estab-lishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which the-oretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public real data from the crop pest species Bactrocera tryoni, a fruit fly native from Northern Australia, we computed GO between "source" populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the estab-lishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g. propagule pressure or admixture) will need further investigation.

evolutionary biology↗

bioRxiv2023

Genomic reconstruction of the successful establishment of a feralized bovine population on the subantarctic island of Amsterdam

The feral cattle of the subantarctic island of Amsterdam provide an outstanding case study of a large mammalian population that was established by a handful of founders and thrived within a few generations in a seemingly inhospitable environment. Here, we investigated the genetic history and composition of this population using genotyping and sequencing data. Our inference showed an intense but brief founding bottleneck around the late 19th century and revealed contributions from European taurine and Indian Ocean zebu in the founder ancestry. Comparative analysis of whole genome sequences further revealed a moderate reduction in genetic diversity despite high levels of inbreeding. The brief and intense bottleneck was associated with high levels of drift, a flattening of the site frequency spectrum and a slight relaxation of purifying selection on mildly deleterious variants. However, we did not observe any significant purge of highly deleterious variants. Interestingly, the populations success in the harsh environment can be attributed to pre-adaptation from their European taurine ancestry, suggesting no strong bioclimatic challenge, and also contradicting evidence for insular dwarfism. Genome scan for footprints of selection uncovered a majority of candidate genes related to nervous system function, likely reflecting rapid feralization driven by behavioral changes and complex social restructuring. This unprecedented case study provides valuable insights into rapid population establishment, feralization, and genetic adaptation in challenging environments. It highlights the importance of preserving the unique genetic legacies of feral populations and raises ethical questions in the eyes of conservation efforts.

evolutionary biology↗

bioRxiv2023

Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum

Genome-scale metabolic models (GEMs) are organism specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. The present work combines in silico and in vitro approaches to create and curate strain-specific genome-scale metabolic models of Corynebacterium striatum. Approaches towards modeling rarely studied organisms are scarce. We introduce a cost-effective and easy experimental protocol which can be adapted to other organisms as well. Furthermore, the comparability of growth kinetics and in silico growth rates is discussed. This work introduces five newly created strain-specific genome-scale metabolic models (GEMs) of high quality, satisfying all contemporary standards and requirements. All these models have been benchmarked using the community standard test suite Metabolic Model Testing (MEMOTE) and experimentally validated by laboratory experiments conducted specifically for this purpose. For the curation of those models, the software infrastructure refineGEMs was developed to work on these models in parallel and to comply with the quality standards for GEMs. The model predictions were confirmed by experimental data and a new comparison metric based on the doubling time was developed to quantify bacterial growth. Future modeling projects can rely on the proposed software, which is independent of specific environmental conditions. The validation approach based on the growth rate calculation is now accessible and closely aligned with biological questions. The curated models are freely available via BioModels and a GitHub repository and can be used. The open-source software refineGEMs is available from https://github.com/draeger-lab/refinegems.

systems biology↗

bioRxiv2020

Trophic cooperation promotes bacterial survival of Staphylococcus aureus and Pseudomonas aeruginosa

In the context of infection, Pseudomonas aeruginosa and Staphylococcus aureus are frequently co-isolated, particularly in cystic fibrosis (CF) patients. Within lungs, the two pathogens exhibit a range of competitive and coexisting interactions. In the present study, we explored the impact of S. aureus on the physiology of P. aeruginosa in the context of coexistence. Transcriptomic analyses showed that S. aureus significantly and specifically affects the expression of numerous genes involved in P. aeruginosa carbon and amino acid metabolism. In particular, 65% of the strains presented considerable overexpression of the genes involved in the acetoin catabolic (aco) pathway. We demonstrated that acetoin is (i) produced by clinical S. aureus strains, (ii) detected in sputa from CF patients, and (iii) involved in P. aeruginosas aco system induction. Furthermore, acetoin is catabolized by P. aeruginosa, a metabolic process that improves the survival of both pathogens by providing a new carbon source for P. aeruginosa and avoiding the toxic accumulation of acetoin on S. aureus. Due to its beneficial effects on both bacteria, acetoin catabolism could testify to the establishment of trophic cooperation between S. aureus and P. aeruginosa in the CF lung environment, thus promoting their persistence.

microbiology↗

bioRxiv2019

NorK, a novel norfloxacin efflux pump in Staphylococcus aureus

A novel efflux pump similar to Nor efflux pumps and designated as NorK was identified in Staphylococcus aureus. It contributes to norfloxacin resistance and presents a high level of expression in different strains. Its expression is not regulated by MgrA, unlike other genes of the nor gene family, suggesting that NorK could be important in the absence of expression of other nor genes.

microbiology↗

bioRxiv2019

Coexistence with Pseudomonas aeruginosa alters Staphylococcus aureus transcriptome, antibiotic resistance and internalization into epithelial cells

Cystic fibrosis (CF) is the most common life-threatening genetic disease among Caucasians. CF patients suffer from chronic lung infections due to the presence of thick mucus, caused by cftr gene dysfunction. The two most commonly found bacteria in the mucus of CF patients are Staphylococcus aureus and Pseudomonas aeruginosa. It is well known that early-infecting P. aeruginosa strains produce anti-staphylococcal compounds and inhibit S. aureus growth. More recently, it has been shown that late-infecting P. aeruginosa strains develop commensal-like/coexistence interaction with S. aureus. The aim of this study was to decipher the impact of P. aeruginosa strains on S. aureus. RNA sequencing analysis showed 77 genes were specifically dysregulated in the context of competition and 140 genes in the context of coexistence in the presence of P. aeruginosa. In coexistence, genes encoding virulence factors and proteins involved in carbohydrates, lipids, nucleotides and amino acids metabolism were downregulated. On the contrary, several transporter family encoding genes were upregulated. In particular, several antibiotic pumps belonging to the Nor family were upregulated: tet38, norA and norC, leading to an increase in antibiotic resistance of S. aureus when exposed to tetracycline and ciprofloxacin and an enhanced internalization rate within epithelial pulmonary cells. This study shows that coexistence with P. aeruginosa affects the S. aureus transcriptome and virulence.

microbiology↗

bioRxiv2017

Underwater hyperspectral classification of deep sea corals exposed to a toxic compound

Coral reefs around the world are under threat due to anthropogenic impacts on the environment. It is therefore important to develop methods to monitor the status of the reefs and detect changes in the health condition of the corals at an early stage before severe damage occur. In this work, we evaluate underwater hyperspectral imaging as a method to detect changes in health status of both orange and white color morphs of the coral species Lophelia pertusa.\n\nDiffering health status was achieved by exposing 66 coral samples to the toxic compound 2-methylnaphthalene in concentrations of 0 mg L-1 to 3.5 mg L-1. A machine learning model was utilized to classify corals according to lethal concentration (LC) levels LC5 (5 % mortality) and LC25 (25 % mortality), solely based on their reflectance spectra. All coral samples were classified to correct concentration group. This is a first step towards developing a remote sensing technique able to assess environmental impact on deep-water coral habitats over larger areas.

biochemistry↗