Bioinformatic Pipeline for Profiling Foodborne Bacterial Ecology and Resistome from Short-Read Metagenomics.

Next-generation sequencing revolutionized food safety management these last years providing access to a huge quantity of valuable data to identify, characterize, and monitor bacterial pathogens on the food chain. Shotgun metagenomics emerged as a particularly promising approach as it enables in-depth taxonomic profiling and functional investigation of food microbial communities. In this chapter, we provide a comprehensive step-by-step bioinformatical workflow to characterize bacterial ecology and resistome composition from metagenomic short-reads obtained by shotgun sequencing.

Microbial genetic potential differs among cryospheric habitats of the Damma glacier.

Climate warming has led to glacier retreat worldwide. Studies on the taxonomy and functions of glacier microbiomes help us better predict their response to glacier melting. Here, we used shotgun metagenomic sequencing to study the microbial functional potential in different cryospheric habitats, i.e. surface snow, supraglacial and subglacial sediments, subglacial ice, proglacial stream water and recently deglaciated soils. The functional gene structure varied greatly among habitats, especially for snow, which differed significantly from all other habitats. Differential abundance analysis revealed that genes related to stress responses (e.g. chaperones) were enriched in ice habitat, supporting the fact that glaciers are a harsh environment for microbes. The microbial metabolic capabilities related to carbon and nitrogen cycling vary among cryospheric habitats. Genes related to auxiliary activities were overrepresented in the subglacial sediment, suggesting a higher genetic potential for the degradation of recalcitrant carbon (e.g., lignin). As for nitrogen cycling, genes related to nitrogen fixation were more abundant in barren proglacial soils, possibly due to the presence of Cyanobacteriota in this habitat. Our results deepen our understanding of microbial processes in glacial ecosystems, which are vulnerable to ongoing global warming, and they have implications for downstream ecosystems.

Prokaryotic communities profiling of Indonesian hot springs using long-read Oxford Nanopore sequencing.

OBJECTIVES: Indonesia's location at the convergence of multiple tectonic plates results in a unique geomorphological feature with abundant hot springs. This study pioneers the metagenomic exploration of Indonesian hot springs, harbouring unique life forms despite high temperatures. The microbial community of hot springs is taxonomically versatile and biotechnologically valuable. 16s rRNA amplicon sequencing of the metagenome is a viable option for the microbiome investigation. This study utilized Oxford Nanopore's long-read 16 S rRNA sequencing for enhanced species identification, improved detection of rare members, and a more detailed community composition profile. DATA DESCRIPTION: Water samples were taken from three hot springs of the Bali, Indonesia (i) Angseri, 8.362503 S, 115.133452 E; (ii) Banjar, 8.210270 S, 114.967063 E; and (iii) Batur, 8.228806 S, 115.404829 E. BioLit Genomic DNA Extraction Kit (SRL, Mumbai, India) was used to isolate DNA from water samples. The quantity and quality of the DNA were determined using a NanoDrop™ spectrophotometer and a Qubit fluorometer (Thermo Fisher Scientific, USA). The library was created using Oxford Nanopore Technology kits, and the sequencing was done using Oxford Nanopore's GridION platform. All sequencing data was obtained in FASTQ files and filtered using NanoFilt software. This dataset is valuable for searching novel bacteria diversity and their existence.

Design and implementation of a metagenomic analytical pipeline for respiratory pathogen detection.

OBJECTIVE: We developed an in-house bioinformatics pipeline to improve the detection of respiratory pathogens in metagenomic sequencing data. This pipeline addresses the need for short-time analysis, high accuracy, scalability, and reproducibility in a high-performance computing environment. RESULTS: We evaluated our pipeline using ninety synthetic metagenomes designed to simulate nasopharyngeal swab samples. The pipeline successfully identified 177 out of 204 respiratory pathogens present in the compositions, with an average processing time of approximately 4 min per sample (processing 1 million paired-end reads of 150 base pairs). For the estimation of all the 470 taxa included in the compositions, the pipeline demonstrated high accuracy, identifying 420 and achieving a correlation of 0.9 between their actual and predicted relative abundances. Among the identified taxa, 27 were significantly underestimated or overestimated, including only three clinically relevant pathogens. We also validated the pipeline by applying it to a clinical dataset from a study on metagenomic pathogen characterization in patients with acute respiratory infections and successfully identified all pathogens responsible for the diagnosed infections. These findings underscore the pipeline's effectiveness in pathogen detection and highlight its potential utility in respiratory pathogen surveillance.

Strain-resolved de-novo metagenomic assembly of viral genomes and microbial 16S rRNAs.

BACKGROUND: Metagenomics is a powerful approach to study environmental and human-associated microbial communities and, in particular, the role of viruses in shaping them. Viral genomes are challenging to assemble from metagenomic samples due to their genomic diversity caused by high mutation rates. In the standard de Bruijn graph assemblers, this genomic diversity leads to complex k-mer assembly graphs with a plethora of loops and bulges that are challenging to resolve into strains or haplotypes because variants more than the k-mer size apart cannot be phased. In contrast, overlap assemblers can phase variants as long as they are covered by a single read. RESULTS: Here, we present PenguiN, a software for strain resolved assembly of viral DNA and RNA genomes and bacterial 16S rRNA from shotgun metagenomics. Its exhaustive detection of all read overlaps in linear time combined with a Bayesian model to select strain-resolved extensions allow it to assemble longer viral contigs, less fragmented genomes, and more strains than existing assembly tools, on both real and simulated datasets. We show a 3-40-fold increase in complete viral genomes and a 6-fold increase in bacterial 16S rRNA genes. CONCLUSION: PenguiN is the first overlap-based assembler for viral genome and 16S rRNA assembly from large and complex metagenomic datasets, which we hope will facilitate studying the key roles of viruses in microbial communities. Video Abstract.

A single amplified genome catalog reveals the dynamics of mobilome and resistome in the human microbiome.

BACKGROUND: The increase in metagenome-assembled genomes (MAGs) has advanced our understanding of the functional characterization and taxonomic assignment within the human microbiome. However, MAGs, as population consensus genomes, often aggregate heterogeneity among species and strains, thereby obfuscating the precise relationships between microbial hosts and mobile genetic elements (MGEs). In contrast, single amplified genomes (SAGs) derived via single-cell genome sequencing can capture individual genomic content, including MGEs. RESULTS: We introduce the first substantial SAG dataset (bbsag20) from the human oral and gut microbiome, comprising 17,202 SAGs above medium-quality without co-assembly. This collection unveils a diversity of bacterial lineages across 312 oral and 647 gut species, demonstrating different taxonomic compositions from MAGs. Moreover, the SAGs showed cellular-level evidence of the translocation of oral bacteria to the gut. We also identified broad-host-range MGEs harboring antibiotic resistance genes (ARGs), which were not detected in the MAGs. CONCLUSIONS: The difference in taxonomic composition between SAGs and MAGs indicates that combining both methods would be effective in expanding the genome catalog. By connecting mobilomes and resistomes in individual samples, SAGs could meticulously chart a dynamic network of ARGs on MGEs, pinpointing potential ARG reservoirs and their spreading patterns in the microbial community. Video Abstract.

Metagenome quality metrics and taxonomical annotation visualization through the integration of MAGFlow and BIgMAG.

Background: Building Metagenome-Assembled Genomes (MAGs) from highly complex metagenomics datasets encompasses a series of steps covering from cleaning the sequences, assembling them to finally group them into bins. Along the process, multiple tools aimed to assess the quality and integrity of each MAG are implemented. Nonetheless, even when incorporated within end-to-end pipelines, the outputs of these pieces of software must be visualized and analyzed manually lacking integration in a complete framework. Methods: We developed a Nextflow pipeline (MAGFlow) for estimating the quality of MAGs through a wide variety of approaches (BUSCO, CheckM2, GUNC and QUAST), as well as for annotating taxonomically the metagenomes using GTDB-Tk2. MAGFlow is coupled to a Python-Dash application (BIgMAG) that displays the concatenated outcomes from the tools included by MAGFlow, highlighting the most important metrics in a single interactive environment along with a comparison/clustering of the input data. Results: By using MAGFlow/BIgMAG, the user will be able to benchmark the MAGs obtained through different workflows or establish the quality of the MAGs belonging to different samples following the divide and rule methodology. Conclusions: MAGFlow/BIgMAG represents a unique tool that integrates state-of-the-art tools to study different quality metrics and extract visually as much information as possible from a wide range of genome features.

Metagenome sequencing and 982 microbial genomes from Kermadec and Diamantina Trenches sediments.

Deep-sea trenches representing an intriguing ecosystem for exploring the survival and evolutionary strategies of microbial communities in the highly specialized deep-sea environments. Here, 29 metagenomes were obtained from sediment samples collected from Kermadec and Diamantina trenches. Notably, those samples covered a varying sampling depths (from 5321 m to 9415 m) and distinct layers within the sediment itself (from 0~40 cm in Kermadec trench and 0~24 cm in Diamantina trench). Through metagenomic binning process, we reconstructed 982 metagenome assembled genomes (MAGs) with completeness >60% and contamination <5%. Within them, completeness of 351 MAGs were >90%, while an additional 331 were >80%. Phylogenomic analysis for the MAGs revealed nearly all of them were distantly related to known cultivated isolates. The abundant bacterial MAGs affiliated to phyla of Proteobacteria, Planctomycetota, Nitrospirota, Acidobacteriota, Actinobacteriota, and Chlorofexota, while the abundant archaeal phyla affiliated with Nanoarchaeota and Thermoproteota. These results provide a dataset available for further interrogation of diversity, distribution and ecological function of deep-sea microbes existed in the trenches.

Towards predicting the geographical origin of ancient samples with metagenomic data.

Reconstructing the history-such as the place of birth and death-of an individual sample is a fundamental goal in ancient DNA (aDNA) studies. However, knowing the place of death can be particularly challenging when samples come from museum collections with incomplete or erroneous archives. While analyses of human DNA and isotope data can inform us about the ancestry of an individual and provide clues about where the person lived, they cannot specifically trace the place of death. Moreover, while ancient human DNA can be retrieved, a large fraction of the sequenced molecules in ancient DNA studies derive from exogenous DNA. This DNA-which is usually discarded in aDNA analyses-is constituted mostly by microbial DNA from soil-dwelling microorganisms that have colonized the buried remains post-mortem. In this study, we hypothesize that remains of individuals buried in the same or close geographic areas, exposed to similar microbial communities, could harbor more similar metagenomes. We propose to use metagenomic data from ancient samples' shotgun sequencing to locate the place of death of a given individual which can also help to solve cases of sample mislabeling. We used a k-mer-based approach to compute similarity scores between metagenomic samples from different locations and propose a method based on dimensionality reduction and logistic regression to assign a geographical origin to target samples. We apply our method to several public datasets and observe that individual samples from closer geographic locations tend to show higher similarities in their metagenomes compared to those of different origin, allowing good geographical predictions of test samples. Moreover, we observe that the genus Streptomyces commonly infiltrates ancient remains and represents a valuable biomarker to trace the samples' geographic origin. Our results provide a proof of concept and show how metagenomic data can also be used to shed light on the place of origin of ancient samples.

CAIM: coverage-based analysis for identification of microbiome.

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic approach. In this study, we developed a new bioinformatics tool, coverage-based analysis for identification of microbiome (CAIM), for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count-based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consistently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similarity of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and 44 primary liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.