Biology subjects

Poirier, S.

Publications and source records attributed to Poirier, S..

6 recordsLinked to original sources

bioRxiv2026

Montreal Urban Observatory: research platform to monitor urban forest ecosystems for global change adaptation and health

As urban populations grow, cities are increasingly seen not only as drivers of climate change but also as critical arenas for implementing mitigation and adaptation strategies. Urban forests and green infrastructure play a vital role in shaping environmental quality and human health, yet access to these benefits remains inequitable. The Montreal Urban Observatory was designed to investigate the complex relationships among biodiversity, ecosystem functioning, and human health. Twenty-five permanent plots were established across the Island of Montreal, strategically located along gradients in vegetation cover, population density, and mean household income. Interdisciplinary research within the Observatory focuses on themes including urban forest structure and function, abiotic conditions, biodiversity, ecological interactions, natures contributions to people, related human health outcomes, and developing novel methodological approaches. The Observatory supports long-term, cross-scale monitoring and fosters collaborative research on urban social-ecological systems, thus contributing to global initiatives to enhance urban sustainability and equity through nature-based solutions. Early results confirm the relevance of the main gradients for several organisms and response variables, from microbes to trees and abiotic factors. For example, sampling all trees, public and private, within a radius of 200m centered on each plot revealed significant differences in the diversity and structure of private and public trees, including an overwhelming dominance of Thuya occidentalis not captured in commonly used public tree inventories.

ecology↗

bioRxiv2025

Season and city shape urban bioaerosol composition beyond vegetation and socioeconomic gradients

Urban vegetation varies with socio-economic gradients, as lower-income neighborhoods often host sparser and less diverse green spaces. This disparity may affect respiratory health by influencing exposure to bioaerosols. Understanding the characteristics of this aerobiome could help anticipate risks related to allergies and other respiratory conditions. Here, we hypothesized that urban vegetation cover and socio-economic status shape urban bioaerosols. We sampled bioaerosols at 65 sites across three Canadian cities of varying population size and density using an active air sampler over four months, and characterized their bacterial, fungal, and pollen composition using amplicon sequencing. Seasonal alpha diversity varied significantly for fungi and pollen. Based on beta diversity, sampling period alone explained up to 40% of pollen, 29% of fungal, and 11% of bacterial community composition variation. In contrast, vegetation cover explained only a minor portion of the variance in bioaerosol composition, and median household income, almost none. These findings provide a critical baseline for understanding the urban aerobiome and highlight the need to study how vegetation identity and diversity, rather than cover alone, may shape bioaerosol dynamics in cities. As cities grow and urban greening initiatives expand, demystifying the aerobiome dynamics becomes an urgent public health priority.

microbiology↗

bioRxiv2024

Growing a cystic fibrosis-relevant polymicrobial biofilm to probe community phenotypes.

Most in vitro models lack the capacity to fully probe bacterial phenotypes emerging from the complex interactions observed in real-life environments. This is particularly true in the context of hard-to-treat chronic and polymicrobial biofilm-based infections detected in the airways of persons with cystic fibrosis (pwCF), a multiorgan genetic disease. While multiple microbiome studies have defined the microbial compositions detected in the airway of pwCF, no in vitro models thus far had fully integrated critical cystic fibrosis (CF)-relevant lung features. Therefore, a significant knowledge-gap in our capacity to investigate the mechanisms driving the pathogenesis of mixed species CF lung infections remained. To tackle this challenge, we have built a four-species microbial community model including Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus sanguinis, and Prevotella melaninogenica grown in CF-like conditions. Through the utilization of this system, clinically relevant phenotypes such as antimicrobial (Abx) recalcitrance of several pathogens were observed and explored at the molecular level. The usefulness of this in vitro model resides in its standardized workflow that can facilitate the study of interspecies interactions in the context of chronic CF lung infections. SUMMARYIn this protocol, we describe a cystic fibrosis (CF)-lung relevant four-species polymicrobial biofilm model that can be used to explore the impact of bacterial interspecies interactions.

microbiology↗

bioRxiv2023

Holistic integration of omics data reveals the drivers that shape the ecology of microbial meat spoilage scenarios

The use of omics data for monitoring the microbial flow of fresh meat products along a production line and the development of spoilage prediction tools from these data is a promising but challenging task. In this context, we produced a large multivariate dataset (over 600 samples) obtained on the production lines of two similar types of fresh meat products (poultry and raw pork sausages).We describe a full analysis of this dataset in order to decipher how the spoilage microbial ecology of these two similar products may be shaped differently depending on production parameter characteristics. Our strategy involved a holistic approach to integrate unsupervised and supervised statistical methods on multivariate data (OTU-based microbial diversity; metabolomic data of volatile organic compounds; sensory measurements; growth parameters), and a specific selection of potential uncontrolled (initial microbiota composition) or controlled (packaging type; lactate concentration) drivers. Our results demonstrate that the initial microbiota, which is shown to be very different between poultry and pork sausages, has a major impact on the spoilage scenarios and on the effect that a downstream parameter such as packaging type has on the overall evolution of the microbial community. Depending on the process, we also show that specific actions on the pork meat (such as deboning and defatting) elicit specific food spoilers such as Dellaglioa algida, which becomes dominant during storage. Finally, ecological network reconstruction allowed us to map six different metabolic pathways involved in the production of volatile organic compounds involved in spoilage. We were able connect them to the different bacterial actors and to the influence of packaging type in an overall view. For instance, our results demonstrate a new role of Vibrionaceae in isopropanol production, and of Latilactobacillus fuchuensis and Lactococcus piscium in methanethiol/disylphide production. We also highlight a possible commensal behavior between Leuconostoc carnosum and Latilactobacillus curvatus around 2,3-butanediol metabolism. We conclude that our holistic approach combined with large-scale multi-omic data was a powerful strategy to prioritize the role of production parameters, already known in the literature, that shape the evolution and/or the implementation of different meat spoilage scenarios.

microbiology↗

bioRxiv2021

The role of microbial diversity in the formation of soil organic matter quality and persistence

The largest terrestrial carbon sink on earth is soil carbon stocks. As the climate changes, the rate at which the Earths climate warms depends in part on the persistence of soil organic carbon. Microbial turnover forms the backbone of soil organic matter (SOM) formation and it has been recently proposed that SOM molecular complexity is a key driver of stability. Despite this, the links between microbial diversity, chemical complexity and biogeochemical nature of soil organic matter remain missing. Here we used a model soil system to test the hypothesis that more diverse microbial communities generate more stable soil organic matter. We inoculated microbial communities of varying diversities into an model soil matrix amended with simple carbon, and measured the thermal stability of the resultant soil organic matter. Using a novel data analysis approach with Rock-Eval(R) ramped thermal analysis, we found that microbial community diversity drives the chemical fingerprint of soil organic matter. Bacteria-only and low diversity communities lead to less chemically-diverse and more thermally-labile soil carbon pools than highly diverse communities. Our results provide direct evidence for a link between microbial diversity, molecular complexity and SOM stability. This evidence demonstrates the benefits of managing soils for maximum biological diversity as a means of building persistent SOM stocks. ClassificationBiological Sciences: Ecology

ecology↗

bioRxiv2020

Integrating independent microbial studies to build predictive models of anaerobic digestion inhibition

Anaerobic digestion (AD) is a microbial process that can efficiently degrade organic waste into renewable energies such as methane-rich biogas. However, the underpinning microbial mechanisms are highly vulnerable to a wide range of inhibitory compounds, leading to process failure and economic losses. High-throughput sequencing technologies enable the identification of microbial indicators of digesters inhibition and can provide new insights into the key phylotypes at stake during AD process. But yet, current studies have used different inocula, substrates, geographical sites and types of reactors, resulting in indicators that are not robust or reproducible across independent studies. In addition, such studies focus on the identification of a single microbial indicator that is not reflective of the complexity of AD. Our study proposes the first analysis of its kind that seeks for a robust signature of microbial indicators of phenol and ammonia inhibitions, whilst leveraging on 4 independent in-house and external AD microbial studies. We applied a recent multivariate integrative method on two-in-house studies to identify such signature, then predicted the inhibitory status of samples from two datasets with more than 90% accuracy. Our study demonstrates how we can efficiently analyze existing studies to extract robust microbial community patterns, predict AD inhibition, and deepen our understanding of AD towards better AD microbial management. HighlightsO_LIRobust biomarkers of AD inhibition were tagged by integrating independent 16S studies C_LIO_LIIncrease of the Clostridiales relative abundance is an early warning of AD inhibition C_LIO_LICloacimonetes is associated with good performance of biomethane production C_LIO_LIMultivariate model predicts ammonia inhibition with 90% accuracy in external data C_LI

microbiology↗