Biology subjects

Laczny, C. C.

Publications and source records attributed to Laczny, C. C..

9 recordsLinked to original sources

bioRxiv2025

Acinetobacter enrichment shapes composition and function of the bacterial microbiota of field-grown tomato plants

Tomato is a staple crop and an excellent model to study host-microbiota interactions in the plant food chain. In this study we describe a lab-in-the-field approach to investigate the microbiota of field-grown tomato plants. High-throughput amplicon sequencing revealed a three-microhabitat partition, phyllosphere, rhizosphere and root interior, differentiating host-associated communities from the environmental microbiota. An individual bacterium, classified as Acinetobacter sp., emerged as a dominant member of the microbiota at the plant-soil continuum. To gain insights into the functional significance of this enrichment, we subjected rhizosphere specimens to shotgun metagenomics. Similar to the amplicon sequencing survey, a microhabitat effect defined by a set of rhizosphere-enriched functions was identified. Mobilisation of mineral nutrients, as well as adaptation to salinity and polymicrobial communities, including antimicrobial resistance genes (ARGs), emerged as a functional requirement sustaining metagenomic diversification. A metagenome-assembled genome (MAG) representative of Acinetobacter calcoaceticus was retrieved and metagenomic reads associated to this species identified a functional specialisation for plant-growth promotion traits (PGPTs), such as phosphate solubilization, siderophores production and reactive oxygen species detoxification, which were similarly represented in across tomato genotype-independent fashion. Our results revealed that the enrichment of a beneficial bacterium capable of alleviating plants abiotic stresses appears decoupled from ARGs facilitating microbiota persistence at the root-soil interface. IMPORTANCETomatoes are at centre-stage in global food security due to their high nutritional value, widespread cultivation, and versatility. Tomatoes provide essential vitamins and minerals, contribute to diverse diets, and support farmer livelihoods, making them a cornerstone of sustainable food systems. Beyond direct dietary benefits, the intricate relationship between tomatoes, their associated microbiota, and ARG is increasingly recognised. Tomato plants host diverse microbial communities in association with their organs, which influence plant health and productivity. Crop management impacts on composition and function of these communities, contributing to the prevalence of ARG in the soil and on the plants themselves. These genes can potentially transfer to human pathogens, posing a food safety and public health risk. Understanding these complex interactions is critical for developing sustainable agricultural practices capable of mitigating the impact of climatic modifications and the global threat of antimicrobial resistance.

plant biology↗

bioRxiv2024

Human gut microbiome gene co-expression network reveals a loss in taxonomic and functional diversity in Parkinson's disease

Alterations of gut microbiome structure have been observed in a panoply of human diseases including neurodegeneration. However, the ecological and functional deficits of microbiome dysbiosis have yet to be understood. Here, using integrated multi-omics (metagenomics and metatranscriptomics), we resolve microbiome gene co-expression networks in individuals with Parkinsons disease (PD) and healthy individuals. We uncover network modules with high closeness and degree centrality that represent core ecological functions, and identify key features lost in PD. More specifically, we observe a significant depletion in specific functions including secondary bile acid biosynthesis and flagellar assembly (FA) in PD. Strikingly, most hub genes, particularly those involved in bacterial microcompartments (BMCs) and FA, are predominantly found in healthy individuals. Blautia and Anaerobutyricum genera are the main contributors to these functions, showing significantly lower expression of BMC genes in PD. Additionally, we identify a strong correlation between the expression of BMC and FA genes, but also an apparent dysregulation in cross feeding between commensals in PD. Importantly, gene expression in PD was tied to reduced diversity in expressed genes, whereas in healthy individuals, higher expression levels were linked to higher diversity. Our findings reveal disruptions in key gut metabolic functions at both functional and taxonomic levels, potentially driving disease progression. Notably, we identify crucial microbiome-wide ecological features that should be restored in future gut microbiome rewilding efforts.

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

Altered infective competence of the human gut microbiome in COVID-19

ObjectivesInfections with SARS-CoV-2 have a pronounced impact on the gastrointestinal tract and its resident microbiome. Clear differences between severe cases of infection and healthy individuals have been reported, including the loss of commensal taxa. We aimed to understand if microbiome alterations including functional shifts are unique to severe cases or a common effect of COVID-19. DesignWe used high-resolution systematic multi-omic analyses to profile the gut microbiome in asymptomatic-to-moderate COVID-19 individuals compared to a control group. ResultsWe found a striking increase in the overall abundance and expression of both virulence factors and antimicrobial resistance genes in COVID-19. Importantly, these genes are encoded and expressed by commensal taxa from families such as Acidaminococcaceae and Erysipelatoclostridiaceae, which we found to be enriched in COVID-19 positive individuals. We also found an enrichment in the expression of a betaherpesvirus and rotavirus C genes in COVID-19 positive individuals compared to healthy controls. ConclusionOur analyses identified an altered and increased infective competence of the gut microbiome in COVID-19 patients.

microbiology↗

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

Synergistic effect of short- and long-read sequencing on functional meta-omics

Real-world evaluations of metagenomic reconstructions are challenged by distinguishing reconstruction artefacts from genes and proteins present in situ. Here, we evaluate short-read-only, long-read-only, and hybrid assembly approaches on four different metagenomic samples of varying complexity and demonstrate how they affect gene and protein inference which is particularly relevant for downstream functional analyses. For a human gut microbiome sample, we use complementary metatranscriptomic, and metaproteomic data to evaluate the metagenomic data-based protein predictions. Our findings pave the way for critical assessments of metagenomic reconstructions and we propose a reference-independent solution based on the synergistic effects of multi-omic data integration for the in situ study of microbiomes using long-read sequencing data.

microbiology↗

bioRxiv2020

PathoFact: A pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data

BackgroundPathogenic microorganisms cause disease by invading, colonizing and damaging their host. Virulence factors including bacterial toxins contribute to their pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is therefore essential to identify virulence factors and antimicrobial resistance genes in metagenomic datasets. At present, there is a clear lack of computational approaches to simultaneously identifying these factors. Here we present PathoFact, a tool for the contextualized prediction of virulence factors and antimicrobial resistance genes in metagenomic data. ResultsPathoFact predicts virulence factors, bacterial toxins and antimicrobial resistance genes with high accuracy (0.92, 0.83 and 0.99) and specificity (0.96, 0.99 and 0.98), respectively. The performance of PathoFact was furthermore demonstrated on three publicly available case-control metagenomic datasets representing an actual infection as well as chronic diseases in which either pathogenic potential or bacterial toxins were predicted to play a role. With PathoFact, we identified virulence factors (including toxins) and antimicrobial resistance genes, and identified signature genes which differentiated between the disease and control groups. ConclusionPathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, toxins and antimicrobial resistance genes in metagenomic data. Additionally, PathoFact combines the prediction of these pathogenicity factors with the identification of mobile genetic elements. This provides further depth to the analysis by considering the genomic context of the pertinent genes. Furthermore, each module (virulence factors, toxin and antimicrobial resistance genes) of PathoFact is also a standalone component, making it a flexible and versatile tool. PathoFact is freely available online at https://git-r3lab.uni.lu/laura.denies/PathoFact.

microbiology↗

bioRxiv2018

Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates

Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data (11,087 newly sequenced whole genomes) and culture-based resistance profiles (10,991 of 11,087 isolates were comprehensively tested against 22 antibiotics in total) of clinical isolates including 18 main species spanning a time period of 30 years. Species and drug specific resistance patterns could be observed including increasing resistance rates for Acinetobacter baumannii to carbapenems and for Escherichia coli to fluoroquinolones. Species-level pan-genomes were constructed to reflect the genetic repertoire of the respective species such as conserved essential genes and known resistance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed to infer gene-drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into a resource, GEAR-base, available for academic research use free of charge at https://gear-base.com.

genomics↗