Biology subjects

Hickl, O.

Publications and source records attributed to Hickl, O..

7 recordsLinked to original sources

bioRxiv2025

A prebiotic-postbiotic combination supports dietary carbohydrate-targeting functional properties in a fiber-deprived microbiota

Dietary fiber deprivation compromises gut mucosal barrier integrity by promoting microbial degradation of host mucus, a process linked to various gut-related auto immune diseases. While postbiotics are considered safer alternatives to fiber for susceptible patients, their mechanistic ef- fects on a fiber-deprived gut remain poorly understood. Here, we demonstrate in a mouse model that a fermented postbiotic, alone or in combination with a prebiotic and aloe vera, counteracted the increase of detrimental properties of the microbiota on a fiber-free diet. The supplement regimen reshaped the gut microbiota, counteracting the expansion of key mucin-degrading bac- teria, including Akkermansia muciniphila and Parabacteroides goldsteinii. Metatranscriptomic analysis revealed this compositional change corresponded to a community-wide functional pivot away from expressing mucinolytic enzymes, such as sialidases, and towards utilizing alternative substrates. These microbial shifts recapitulated the effects of dietary fiber reintroduction and translated to direct host benefits, including sustentation of the colonic mucus layer and attenu- ation of diet-induced type III immune cytokine expression. Our findings provide a mechanistic rationale for using postbiotics to functionally replace dietary fiber, offering a promising strategy to support gut homeostasis in contexts where fiber intake is limited.

microbiology↗

bioRxiv2024

Integrated multi-omics highlights alterations of gut microbiome functions in prodromal and idiopathic Parkinson's disease

Parkinsons disease (PD) is associated with gut microbiome shifts, but the functional consequences remain unclear. Here, we use an integrated multi-omics approach to compare the gut microbiomes of individuals with PD and prodromal PD as well as healthy individuals. After analyzing each omics, meta-metabolomic was selected to inform the analysis as it represents the most discriminatory and robust ome. We identified 11 metabolites that were differentially abundant between the groups, amongst which {beta}-glutamate was increased in PD and prodromal PD, and correlated with the transcriptional activities of Methanobrevibacter smithii and Clostridium spp. We identified decreases in transcripts, but not in gene abundances, related to glutamate metabolism, bile acids, chemotaxis and flagellar assembly in PD, particularly in keystone genera such as Roseburia, Agathobacter and Blautia. Our findings, integrated into the Expobiome map, reveal multifactorial microbiome alterations which converge with PD pathways. Our study highlights the importance of investigating the gut microbiomes functional dimensions to better resolve microbiome-host interactions in health and disease.

systems biology↗

bioRxiv2024

PathoFact 2.0: An Integrative Pipeline for Predicting Antimicrobial Resistance Genes, Virulence Factors, Toxins and Biosynthetic Gene Clusters in Metagenomes

SummaryAntimicrobial resistance genes (ARGs) and virulence factors (VFs) are central contributors to the global health crisis surrounding drug-resistant infections. PathoFact, a bioinformatics pipeline introduced in 2021, provides insights into ARGs, VFs, and bacterial toxins from metagenomic data. However, recent advancements in bioinformatics highlight the need for an updated version of PathoFact. We introduce PathoFact 2.0, an enhanced pipeline for improved ARG, VF, and toxin prediction. Key updates include an updated machine learning (ML) model for VF identification, a new ML model for toxin identification, expanded hidden Markov model profiles, and the antiSMASH 7.0 integration for predicting biosynthetic gene clusters. These upgrades make PathoFact 2.0 a more powerful, user-friendly platform for predicting microbiome-based pathogenicity and resistance, offering a crucial tool for better understanding and addressing the challenges posed by antimicrobial resistance and infectious diseases. Availability and ImplementationPathoFact 2.0 is available for download at https://gitlab.lcsb.uni.lu/ESB/PathoFact2/.

bioinformatics↗

bioRxiv2022

UniFuncNet: a flexible network annotation framework

SummaryFunctional annotation is an integral part in the analysis of organisms, as well as of multi-species communities. A common way to integrate such information is using biological networks. However, current data integration network tools are heavily dependent on a single source of information, which might strongly limit the amount of relevant data contained within the network. Here we present UniFuncNet, a network annotation framework that dynamically integrates data from multiple biological databases, thereby enabling data collection from various sources based on user preference. This results in a flexible and comprehensive data retrieval framework for network based analyses of omics data. Importantly, UniFuncNets data integration methodology allows for the output of a non-redundant composite network and associated metadata. In addition, a workflow exporting UniFuncNets output to the graph database management system Neo4j was implemented, which allows for efficient querying and analysis. AvailabilitySource code is available at https://github.com/PedroMTQ/UniFuncNet.

bioinformatics↗

bioRxiv2022

Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by multi-omic analysis

BackgroundAlterations of the gut microbiome have been linked to multiple chronic diseases. However, the drivers of such changes remain largely unknown. The oral cavity acts as a major route of exposure to exogenous factors including pathogens, and processes therein may affect the communities in the subsequent compartments of the gastrointestinal tract. Here, we perform strain-resolved, integrated multi-omic analyses of saliva and stool samples collected from eight families with multiple cases of type 1 diabetes mellitus (T1DM). ResultsWe identified distinct oral microbiota mostly reflecting competition between streptococcal species. More specifically, we found a decreased abundance of the commensal Streptococcus salivarius in the oral cavity of T1DM individuals, which is linked to its apparent competition with the pathobiont Streptococcus mutans. The decrease in S. salivarius in the oral cavity was also associated with its decrease in the gut as well as higher abundances in facultative anaerobes including Enterobacteria. In addition, we found evidence of gut inflammation in T1DM as reflected in the expression profiles of the Enterobacteria as well as in the human gut proteome. Finally, we were able to follow transmitted strain-variants from the oral cavity to the gut at the metagenomic, metatranscriptomic and metaproteomic levels, highlighting not only the transfer, but also the activity of the transmitted taxa along the gastrointestinal tract. ConclusionsAlterations of the oral microbiome in the context of T1DM impact the microbial communities in the lower gut, in particular through the reduction of "oral-to-gut" transfer of Streptococcus salivarius. Our results indicate that the observed oral-cavity-driven gut microbiome changes may contribute towards the inflammatory processes involved in T1DM. Through the integration of multi-omic analyses, we resolve strain-variant "mouth-to-gut" transfer in a disease context.

microbiology↗

bioRxiv2021

binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets

The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces complete and pure metagenome-assembled genomes (MAG) from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms or is highly competitive with commonly-used and state- of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, non-linear dimension reduction of genomic signatures, as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared to seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete (>95% pure, >90% complete) and high-quality (>90% pure, >70% complete) genomes from simulated data sets from the Critical Assessment of Metagenome Interpretation (CAMI) initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome (MIMAG) standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method.

bioinformatics↗

bioRxiv2020

Mantis: flexible and consensus-driven genome annotation

BackgroundThe past decades have seen a rapid development of the (meta-)omics fields, producing an unprecedented amount of data. Through the use of well-characterized datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation allows the identification of regions of interest (i.e. domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, some challenges remain, specifically in terms of speed, flexibility, and reproducibility. In the era of big data it also becomes increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, thus overcoming some limitations in overly relying on computationally generated data. ResultsWe implemented a protein annotation tool - Mantis, which uses text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for total customization of the reference data used, adaptable, and reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which led to an average 0.038 increase in precision when compared to sequence-wide annotation. Mantis is fast, annotating an average genome in 25-40 minutes, whilst also outputting high-quality annotations (average coverage 81.4%, average precision 0.892). ConclusionsMantis is a protein function annotation tool that produces high-quality consensusdriven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license available at https://github.com/PedroMTQ/mantis.

bioinformatics↗