Biology subjects

Ramette, A.

Publications and source records attributed to Ramette, A..

6 recordsLinked to original sources

bioRxiv2026

PCR-free, targeted genomic sequencing using Dynamically optimized reference Adaptive Sampling (DORAS)

Whole genome sequencing (WGS) has become a cornerstone of clinical microbiology, enabling comprehensive analysis of microbial genome diversity. However, WGS is often computationally intensive and time-consuming when applied to specific applications like multilocus sequence typing (MLST), where only a subset of genes is only needed for typing. This study evaluates the potential of adaptive sampling (AS), a software-based solution available on Oxford Nanopore Technologies (ONT) devices, to optimize sequencing runs for MLST by reducing the production of unnecessary reads falling outside of the target areas. We demonstrate that AS, when used directly with the target gene sequences, does not reach sufficient target coverage when compared to WGS baseline sequencing due to inefficient read recruitment. Thus, we developed a novel, PCR-free approach, termed Dynamically Optimized Reference Adaptive Sampling (DORAS), which streamlines gene-specific enrichment by targeting genomic regions of interest and their genomic vicinity. DORAS first determines the genomic context of regions of interest for each sample, and then dynamically adjusts the length of the reference sequences during live sequencing. Consensus sequences are periodically constructed and evaluated for taxonomic classification. We demonstrate that full MLST profiles can be obtained in approximately half the time required for whole-genome sequencing to achieve 30X coverage (3 vs. 6 h), with no additional hands-on library preparation time. Validation on clinical isolates from hospital outbreaks belonging to Corynebacterium diphtheriae, vancomycin-resistant Enterococci, and routine clinical E. coli isolates, demonstrated the consistent retrieval of MLST types as compared to standard WGS methods. DORAS thus offers a cost-effective, efficient solution for routine surveillance and outbreak investigations based on MLST types in the clinical setting.

genomics↗

bioRxiv2025

Resolving the CES1 Genomic Locus with Cas9-Directed Targeted Long-Read Sequencing for Precision Pharmacogenomics

Carboxylesterase 1 (CES1) is the primary hepatic hydrolase in humans, crucial for the metabolism of ester-containing drugs and endogenous lipids. However, the CES1 genomic region is difficult to resolve because of adjacent highly homologous pseudogenes and the presence of large structural variants. These complexities often cause read misalignment and inaccurate variant calling with conventional short-read sequencing, hindering reliable pharmacogenomic analyses. To overcome these limitations, we employed an optimized, PCR-free Oxford Nanopore Technologies (ONT) sequencing method, Cas9directed targeted sequencing (nCATS), to characterize the targeted region of up to 76 kb, including CES1, CES1P1 or CES1A2, and their intergenic regions. This approach uses Cas9 to selectively enrich and sequence long native DNA fragments, while avoiding amplification-induced artefacts. Long-read sequencing was performed in 23 human blood samples and the HepG2 hepatoblastoma cell line enabling high-resolution mapping to the CES1 locus. We uncover five previously unrecognized main CES1 haplotypes and report that many single nucleotide variants (SNVs) in public databases are likely artefacts caused by short-read misalignment. Additionally, we identify long inverted repeats (LIRs) flanking a fragile genomic site within the region, which may form DNA hairpins and contribute to structural plasticity at the locus. This study demonstrates the utility of long-read sequencing for resolving complex genomic regions such as CES1, allowing comprehensive detection of structural variants and haplotype-resolved SNVs. Our findings provide improved reference sequences and deeper insight into CES1 diversity, with significant implications for future pharmacogenetic research and the development of personalized treatment strategies involving CES1-metabolized medications.

genetics↗

bioRxiv2025

Unveiling the Terra Cognita of Sequence Spaces using Cartesian Projection of Asymmetric Distances

Visualizing relationships within massive biological datasets remains a significant challenge, particularly as sequence length and volume increase. We introduce CAPASYDIS (Cartesian Projections of Asymmetric Distances), a scalable approach designed to map the explored regions of a given sequence space. Unlike traditional dimensionality reduction methods, CAPASYDIS calculates asymmetric distances which account for both the position and type of sequence variations. It projects sequences into a fixed, low-dimensional coordinate system, termed a "seqverse", where each sequence occupies a permanent location. This design allows for the instant mapping of new sequences without the need to recalculate the global structure, transforming sequence analysis from a relative comparison into navigation on a standardized map. We applied this method to a large rRNA sequence dataset spanning the three domains of life. Our results demonstrate that the sequences of Bacteria, Archaea, and Eukaryota occupy spatially distinct regions characterized by fundamentally different shapes and patterns of variation. Furthermore, the resulting seqverses retain high amount of taxonomic information, when analyzed from broad domain levels to single-base differences. Overall, CAPASYDIS provides a reproducible, scalable framework for defining the boundaries and topography of biological sequence universes.

bioinformatics↗

bioRxiv2024

Component specific responses of the microbiomes to common chemical stressors in the human food chain

Along a food chain, microbiomes occur in each component and often contribute to the functioning or the health of their host or environment. One Health emphasizes the connectivity of each components health. Chemical stress typically causes dysbiotic microbiomes, but it remains unclear whether chemical stressors consistently affect the microbiomes along food chain components. Here, we systematically challenged a model food chain, including water, sediments, soil, plants, and animals, with three chemical stresses consisting of arsenic (a toxic trace element), benzoxazinoids (an abundant bioactive plant metabolites), and terbuthylazine (an herbicide typically found along a human food chain). The analysis of 1,064 microbiome profiles for commonalities and differences in their stress responses indicated that chemical stressors decreased microbiome diversity in soil and animal, but not in the other microbiomes. In response to stress, all food chain communities strongly shifted in their composition, generally becoming compositionally more similar to each other. In addition, we observed stochastic effects in host-associated communities (plant, animal). Dysbiotic microbiomes were characterized by different sets of bacteria, which responded specifically to the three chemical stressors. Microbial co-occurrence patterns significantly shifted with either decreased (water, sediment, plant, animal) or increased (soil) network sparsity and numbers of keystone taxa following stress treatments. This suggested major re-distribution of the roles that specific taxa may have, with the community stability of plant and animal microbiomes being the most affected by chemical stresses. Overall, we observed stress- and component-specific responses to chemical stressors in microbiomes along the model food chain, which could have implications on food chain health.

microbiology↗

bioRxiv2019

Evaluation of primer pairs for microbiome profiling across a food chain from soils to humans within the One Health framework

The "One Health" framework emphasizes the ecological relationships between soil, plant, animal and human health. Microbiomes play important roles in these relationships, as they modify the health and performance of the different compartments and influence the transfer of energy, matter and chemicals between them. Standardized methods to characterize microbiomes along food chains are, however, currently lacking. To address this methodological gap, we evaluated the performance of DNA extractions kits and commonly recommended primer pairs targeting different hypervariable regions (V3-V4, V4, V5-V6, V5-V6-V7) of the 16S rRNA gene, on microbiome samples along a model food chain, including soils, maize roots, cattle rumen, and cattle and human faeces. We also included faeces from gnotobiotic mice colonized with defined bacterial taxa and mock communities to confirm the robustness of our molecular and bioinformatic approaches on these defined low microbial diversity samples. Based on Amplicon Sequence Variants, the primer pair 515F-806R led to the highest estimates of species richness and diversity in all sample types and offered maximum diversity coverage of reference databases in in silico primer analysis. The influence of the DNA extraction kits was negligible compared to the influence of the choice of primer pairs. Comparing microbiomes using 515F-806R revealed that soil and root samples have the highest estimates of species richness and inter-sample variation. Species richness decreased gradually along the food chain, with the lowest richness observed in human faeces. Primer pair choice directly influenced the estimation of community changes (beta diversity) within and across compartments and may give rise to preferential detection of specific taxa. This work demonstrates why a standardized approach is necessary to analyse microbiomes within and between source compartments along food chains in the context of the One Health framework.

microbiology↗

bioRxiv2019

A sample-to-report solution for taxonomic identification of cultured bacteria in the clinical setting based on nanopore sequencing

Amplicon sequencing of 16S rRNA gene is commonly used for the identification of bacterial isolates in diagnostic laboratories, and mostly relies on the Sanger sequencing method. The latter, however, suffers from a number of limitations with the most significant being the inability to resolve mixed amplicons when closely related species are co-amplified from a mixed culture. This often leads to either increased turnover time or absence of usable sequence data. Short-read NGS technologies could address the mixed amplicon issue, but would lack both cost efficiency at low throughput and fast turnaround times. Nanopore sequencing developed by Oxford Nanopore Technologies (ONT) could solve those issues by enabling flexible number of samples per run and adjustable sequencing time. Here we report on the development of a standardized laboratory workflow combined with a fully automated analysis pipeline LORCAN (Long Read Consensus ANalysis), which together provide a sample-to-report solution for amplicon sequencing and taxonomic identification of the resulting consensus sequences. Validation of the approach was conducted on a panel of reference strains and on clinical samples consisting of single or mixed rRNA amplicons associated with various bacterial genera by direct comparison to the corresponding Sanger sequences. Additionally, artificial read mixtures of closely related species were used to assess LORCANs behaviour when dealing with samples with known cross-contamination level. We demonstrate that by combining ONT amplicon sequencing results with LORCAN, the accuracy of Sanger sequencing can be closely matched (>99.6% sequence identity) and that mixed samples can be resolved at the single base resolution level. The presented approach has the potential to significantly improve the flexibility, reliability and availability of amplicon sequencing in diagnostic settings.

microbiology↗