References of "Galata, Valentina 50039538"
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See detailFunctional meta-omics provide critical insights into long- and short-read assemblies
Galata, Valentina UL; Busi, Susheel Bhanu UL; Kunath, Benoît UL et al

in Briefings in Bioinformatics (2021)

Real-world evaluations of metagenomic reconstructions are challenged by distinguishing reconstruction artifacts from genes and proteins present in situ. Here, we evaluate short-read-only, long-read-only ... [more ▼]

Real-world evaluations of metagenomic reconstructions are challenged by distinguishing reconstruction artifacts 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. We demonstrate how different assembly approaches 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 assess the metagenomic data-based protein predictions. Our findings pave the way for critical assessments of metagenomic reconstructions. We propose a reference-independent solution, which exploits the synergistic effects of multi-omic data integration for the in situ study of microbiomes using long-read sequencing data. [less ▲]

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See detailPathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data
de Nies, Laura UL; Lopes, Sara; Busi, Susheel Bhanu UL et al

in Microbiome (2021)

Background Pathogenic microorganisms cause disease by invading, colonizing, and damaging their host. Virulence factors including bacterial toxins contribute to pathogenicity. Additionally, antimicrobial ... [more ▼]

Background Pathogenic microorganisms cause disease by invading, colonizing, and damaging their host. Virulence factors including bacterial toxins contribute to pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is essential to identify virulence factors and antimicrobial resistance genes in situ. At present, there is a clear lack of computational approaches to simultaneously identify these factors in metagenomic datasets. Results Here, we present PathoFact, a tool for the contextualized prediction of virulence factors, bacterial toxins, and antimicrobial resistance genes with high accuracy (0.921, 0.832 and 0.979, respectively) and specificity (0.957, 0.989 and 0.994). We evaluate the performance of PathoFact on simulated metagenomic datasets and perform a comparison to two other general workflows for the analysis of metagenomic data. PathoFact outperforms all existing workflows in predicting virulence factors and toxin genes. It performs comparably to one pipeline regarding the prediction of antimicrobial resistance while outperforming the others. We further demonstrate the performance of PathoFact 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 are hypothesized to play a role. In each case, we identify virulence factors and AMR genes which differentiated between the case and control groups, thereby revealing novel gene associations with the studied diseases. Conclusion PathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, bacterial toxins, and antimicrobial resistance genes in metagenomic data. Additionally, our tool 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, PathoFact’s modules for virulence factors, toxins, and antimicrobial resistance genes can be applied independently, thereby making it a flexible and versatile tool. PathoFact, its models, and databases are freely available at https://pathofact.lcsb.uni.lu. [less ▲]

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See detailGlacier-fed stream biofilms harbour diverse resistomes and biosynthetic gene clusters 2021.11.18.469141
Busi, Susheel Bhanu UL; de Nies, Laura UL; Pramateftaki, Paraskevi et al

E-print/Working paper (2021)

Background Antimicrobial resistance (AMR) is a universal phenomenon whose origins lay in natural ecological interactions such as competition within niches, within and between micro- to higher-order ... [more ▼]

Background Antimicrobial resistance (AMR) is a universal phenomenon whose origins lay in natural ecological interactions such as competition within niches, within and between micro- to higher-order organisms. However, the ecological and evolutionary processes shaping AMR need to be better understood in view of better antimicrobial stewardship. Resolving antibiotic biosynthetic pathways, including biosynthetic gene clusters (BGCs), and corresponding antimicrobial resistance genes (ARGs) may therefore help in understanding the inherent mechanisms. However, to study these phenomena, it is crucial to examine the origins of AMR in pristine environments with limited anthropogenic influences. In this context, epilithic biofilms residing in glacier-fed streams (GFSs) are an excellent model system to study diverse, intra- and inter-domain, ecological crosstalk.Results We assessed the resistomes of epilithic biofilms from GFSs across the Southern Alps (New Zealand) and the Caucasus (Russia) and observed that both bacteria and eukaryotes encoded twenty-nine distinct AMR categories. Of these, beta-lactam, aminoglycoside, and multidrug resistance were both abundant and taxonomically distributed in most of the bacterial and eukaryotic phyla. AMR-encoding phyla included Bacteroidota and Proteobacteria among the bacteria, alongside Ochrophyta (algae) among the eukaryotes. Additionally, BGCs involved in the production of antibacterial compounds were identified across all phyla in the epilithic biofilms. Furthermore, we found that several bacterial genera (Flavobacterium, Polaromonas, etc.) including representatives of the superphylum Patescibacteria encode both ARGs and BGCs within close proximity of each other, thereby demonstrating their capacity to simultaneously influence and compete within the microbial community.Conclusions Our findings highlight the presence and abundance of AMR in epilithic biofilms within GFSs. Additionally, we identify their role in the complex intra- and inter-domain competition and the underlying mechanisms influencing microbial survival in GFS epilithic biofilms. We demonstrate that eukaryotes may serve as AMR reservoirs owing to their potential for encoding ARGs. We also find that the taxonomic affiliation of the AMR and the BGCs are congruent. Importantly, our findings allow for understanding how naturally occurring BGCs and AMR contribute to the epilithic biofilms mode of life in GFSs. Importantly, these observations may be generalizable and potentially extended to other environments which may be more or less impacted by human activity.Competing Interest StatementThe authors have declared no competing interest.AMRAntimicrobial resistanceARGsAntimicrobial resistance gene(s)BGCBiosynthetic gene clustersCACaucasusCPRCandidate Phyla radiationGFSsGlacier-fed stream(s)GLGlacierIRS-RSisoleucyl-tRNA synthetase - high resistanceIMPIntegrate Meta-Omics PipelineKEGGKyoto Encyclopedia of Genes and GenomesMAGsMetagenome-assembled genome(s)NRPSNon-ribosomal peptide synthetasesPKSPolyketide synthases (type I and type II)RiPPsPost-translationally modified peptide(s)SASouthern Alps [less ▲]

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