Biology subjects

Chapleur, O.

Publications and source records attributed to Chapleur, O..

4 recordsLinked to original sources

bioRxiv2025

Comparing the performance of functional versus taxonomic metagenomics for detecting ammonia disturbances in the biogas system

Biogas is a renewable energy source with great potential, but its production is frequently hindered by process disturbances, of which a high ammonia concentration is one common cause. It is desirable that such disturbances are found as early as possible, and metagenomics data has the potential to improve this detection. This study compares functional and taxonomic aspects of metagenomics data, hypothesizing that functional data will perform better for detecting ammonia disturbances. The hypothesis was tested by metagenomic sequencing of samples from three independent studies, which followed lab-scale reactors during ammonia disturbances. The resulting sequences were used to predict protein-coding genes, which were functionally and taxonomically annotated. The read counts of these features were fitted to disturbance states and ammonia concentrations of reactor samples using regularized regression, which allowed filtering out irrelevant features even when the number of features was much larger than the number of samples. Taxonomic data had similar or better performance in detecting ammonia disturbances and in fitting ammonia concentrations, both when analyzing separate studies as well as when analyzing the combined data of the studies. Our hypothesis that functional metagenomics would outperform taxonomic metagenomics was therefore not supported. Visual abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/658511v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@43aaeforg.highwire.dtl.DTLVardef@8b5d27org.highwire.dtl.DTLVardef@190e737org.highwire.dtl.DTLVardef@3c0206_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

A deeper study on propionic acid-related inhibition during anaerobic digestion: insights on methane production and identification of biomarker candidates for an early warning

Propionic acid (HPr) accumulation is a major indicator of anaerobic digestion (AD) dysfunction, yet the relative contributions of acidity, undissociated HPr, and propionate ions (Pr-) to process inhibition remain poorly understood. We investigated these effects in mesophilic batch AD microcosms fed with municipal sewage sludge, using a comparative design involving HPr, sodium propionate (NaPr), NaCl, and HCl treatments across two series of experiments. While 20 mM HPr caused a 22% reduction in the maximal methane production rate, 81 mM HPr led to complete inhibition, with the initial pH dropping to 5.1. By contrast, 81 mM NaPr reduced methane production rate by only 40%, and 81 mM NaCl caused no inhibition, demonstrating that acidity is the dominant inhibitory factor, with Pr- exerting a secondary concentration-dependent effect. 16S rRNA gene amplicon sequencing revealed strong, compound-specific shifts in microbial community composition, affecting key functional groups including syntrophs and methanogenic archaea. The proportion of methanogens dropped from 2-3% in control reactors to less than 0.2% under 81 mM HPr, consistent with the observed methane production inhibition. Under HPr81, over 100 ASVs were differentially abundant compared to controls, a pattern largely shared with HCl-treated reactors, further confirming the predominant role of acidity. The number of differentially abundant ASVs was negatively correlated with methane production rates (R{superscript 2} = 0.97), underscoring the link between community reshaping and process impairment. These results provide a unifying framework for propionate inhibition in AD and suggest that microbial community profiling could serve as an early warning tool for process imbalance detection.

bioengineering↗

bioRxiv2020

Integrative analyses to investigate the link between microbial activity and molecules degradation during anaerobic digestion

Anaerobic digestion (AD) is a promising biological process which converts waste into sustainable energy. To fully exploit ADs capability, we need to deepen our knowledge of the microbiota involved in this complex bioprocess. High-throughput methodologies open new perspectives to investigate AD process at the molecular level, supported by recent data integration methodologies to extract relevant information. In this study, we investigated the link between microbial activity and substrate degradation in a lab-scale anaerobic co-digestion experiment, where bioreactors were fed with 9 different mixtures of three co-substrates (fish waste, sewage sludge, and grass). Samples were profiled using 16S rRNA sequencing and untargeted metabolomics. In this article, we propose a suite of multivariate tools to statistically integrate these data and identify coordinated patterns between groups of microbial and metabolic profiles specific of each co-substrate. Five main groups of features were successfully evidenced, including cadaverine degradation found to be associated with the activity of microorganisms from the order Clostridiales and the genus Methanosarcina. This study highlights the potential of data integration towards a comprehensive understanding of AD microbiota.

bioengineering↗

bioRxiv2019

A generic multivariate framework for the integration of microbiome longitudinal studies with other data types

Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g. sparsity, compositional).\n\nWe propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial communities data, and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modelling, to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

bioinformatics↗