BLAST-based validation of metagenomic sequence assignments

Optimizing fungal DNA extraction and purification for Oxford Nanopore untargeted shotgun metagenomic sequencing from simulated hemoculture specimens

Long-read metagenomics provides a promising alternative approach to fungal identification, circumventing methodological biases, associated with DNA amplification, which is a prerequisite for DNA barcoding/metabarcoding based on the primary fungal DNA barcode (Internal Transcribed Spacer (ITS) region). However, DNA extraction for long-read sequencing-based fungal identification poses a significant challenge, as obtaining long and intact fungal DNA is imperative. Comparing different lysis methods showed that chemical lysis with CTAB/SDS generated DNA from pure fungal cultures with high yields (ranging from 11.20 ± 0.17 µg to 22.99 ± 2.22 µg depending on the species) while preserving integrity. Evaluating the efficacy of human DNA depletion protocols demonstrated an 88.73% reduction in human reads and a 99.53% increase in fungal reads compared to the untreated yeast-spiked human blood control. Evaluation of the developed DNA extraction protocol on simulated clinical hemocultures revealed that the obtained DNA sequences exceed 10 kb in length, enabling a highly efficient sequencing run with over 80% active pores. The quality of the DNA, as indicated by the 260/280 and 260/230 ratios obtained from NanoDrop spectrophotometer readings, exceeded 1.8 and 2.0, respectively. This demonstrated the great potential of the herein optimized protocol to extract high-quality fungal DNA from clinical specimens enabling long-read metagenomics sequencing.

IMPORTANCE

A novel streamlined DNA extraction protocol was developed to efficiently isolate high molecular weight fungal DNA from hemoculture samples, which is crucial for long-read sequencing applications. By eliminating the need for labor-intensive and shear-force-inducing steps, such as liquid nitrogen grinding or bead beating, the protocol is more user-friendly and better suited for clinical laboratory settings. The automation of cleanup and extraction steps further shortens the overall turnaround time to under 6 hours. Although not specifically designed for ultra-long DNA extraction, this protocol effectively supports fungal identification through Oxford Nanopore Technology (ONT) sequencing. It yields high molecular weight DNA, resulting in longer sequence fragments that improve the number of fungal reads over human reads. Future improvements, including adaptive sampling technology, could further simplify the process by reducing the need for human DNA depletion, paving the way for more automated, bioinformatics-driven workflows.

A global perspective on the abundance, diversity and mobility of antibiotic resistance genes in Escherichia coli

Introduction

Escherichia coli (E. coli), a ubiquitous opportunistic pathogen, poses a growing threat to human health due to the increasing prevalence of antibiotic resistance. However, a comprehensive understanding of the global distribution, diversity, and transmission of antibiotic resistance genes (ARGs) in E. coli remains lacking, hindering effective strategies to combat resistance.

Methods

In this study, we analyzed 94,762 E. coli genome sequences obtained from the NCBI database using advanced bioinformatics tools. ARGs were identified by comparing sequences against a custom ARG database using BLAST. Mobile genetic element (MGE)-associated ARGs were identified by matching with ISfinder databases. Global distribution of ARGs was analyzed by clustering mobile ARG sequences with 99% genetic similarity.

Results

Our analysis revealed that 50.51% of the E. coli genome sequences contained ARGs, totaling 301,317 identified ARG sequences. These ARGs were categorized into 12 major classes and 229 subtypes. Notably, ARGs associated with multi-drug resistance (MDR), β-lactams, macrolide-lincosamide-streptogramins (MLS), tetracyclines, and aminoglycosides were particularly abundant, with the subtypes mdtK, macB, and ampC being especially prevalent. Additionally, significant differences in ARG abundance and diversity were observed across countries, with higher diversity found in high-income nations. Furthermore, 9.28% of the ARG sequences were linked to MGEs, accounting for 98.25% of all ARG subtypes. Notably, 4.20% of mobile ARGs were identified in over 20 countries, with β-lactam and aminoglycoside ARGs being the most widespread.

Discussion

This study provides a comprehensive overview of the global distribution and transmission of ARGs in E. coli. The high abundance of MDR and β-lactam-related ARGs, along with their widespread transmission across countries, highlights the urgent need for global surveillance and control measures. Furthermore, the strong association between ARGs and MGEs underscores the role of horizontal gene transfer in the spread of resistance. The observed variations in ARG diversity between countries suggest that socioeconomic factors, such as healthcare infrastructure and antibiotic usage patterns, significantly influence ARG prevalence. These findings are crucial for informing global strategies to mitigate the spread of antibiotic resistance and improve public health outcomes.

Using nanopore sequencing to identify bacterial infection in joint replacements: a preliminary study

This project investigates if third-generation genomic sequencing can be used to identify the species of bacteria causing prosthetic joint infections (PJIs) at the time of revision surgery. Samples of prosthetic fluid were taken during revision surgery from patients with known PJIs. Samples from revision surgeries from non-infected patients acted as negative controls. Genomic sequencing was performed using the MinION device and the rapid sequencing kit from Oxford Nanopore Technologies. Bioinformatic analysis pipelines to identify bacteria included Basic Local Alignment Search Tool, Kraken2 and MinION Detection Software, and the results were compared with standard of care microbiological cultures. Furthermore, there was an attempt to predict antibiotic resistance using computational tools including ResFinder, AMRFinderPlus and Comprehensive Antibiotic Resistance Database. Bacteria identified using microbiological cultures were successfully identified using bioinformatic analysis pipelines. Nanopore sequencing and genomic classification could be completed in the time it takes to perform joint revision surgery (2-3 h). Genomic sequencing in this study was not able to predict antibiotic resistance in this time frame, this is thought to be due to a short-read length and low read depth. It can be concluded that genomic sequencing can be useful to identify bacterial species in infected joint replacements. However, further work is required to investigate if it can be used to predict antibiotic resistance within clinically relevant timeframes.

Improving Species Level-taxonomic Assignment from 16S rRNA Sequencing Technologies

Analysis of the bacterial community from a 16S rRNA gene sequencing technologies requires comparing the reads to a reference database. The challenging task involved in annotation relies on the currently available tools and 16S rRNA databases: SILVA, Greengenes and RDP. A successful annotation depends on the quality of the database. For instance, Greengenes and RDP have not been updated since 2013 and 2016, respectively. In addition, the nature of 16S sequencing technologies (short reads) focuses mainly on the V3-V4 hypervariable region sequencing and hinders the species assignment, in contrast to whole shotgun metagenome sequencing. Here, we combine the results of three standard protocols for 16S rRNA amplicon annotation that utilize homology-based methods, and we propose a new re-annotation strategy to enlarge the percentage of amplicon sequence variants (ASV) classified up to the species level. Following the pattern (reference) method: DADA2 pipeline and SILVA v.138.1 reference database classification (Basic Protocol 1), our method maps the ASV sequences to custom nucleotide BLAST with the SILVA v.138.1 (Basic Protocol 2), and to the 16S database of Bacteria and Archaea of NCBI RefSeq Targeted Loci Project databases (Basic Protocol 3). This new re-annotation workflow was tested in 16S rRNA amplicon data from 156 human fecal samples. The proposed new strategy achieved an increase of nearly eight times the proportion of ASV classified at the species level in contrast to the reference method for the database used in the present research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Sample inference and taxonomic profiling through DADA2 algorithm. Basic Protocol 2: Custom BLASTN database creation and ASV taxonomical assignment. Basic Protocol 3: ASV taxonomical assignment using NCBI RefSeq Targeted Loci Project database. Basic Protocol 4: Definitive selection of lineages among the three methods.

Using nanopore sequencing to identify fungi from clinical samples with high phylogenetic resolution

The study of microbiota has been revolutionized by the development of DNA metabarcoding. This sequence-based approach enables the direct detection of microorganisms without the need for culture and isolation, which significantly reduces analysis time and offers more comprehensive taxonomic profiles across broad phylogenetic lineages. While there has been an accumulating number of researches on bacteria, molecular phylogenetic analysis of fungi still remains challenging due to the lack of standardized tools and the incompleteness of reference databases limiting the accurate and precise identification of fungal taxa. Here, we present a DNA metabarcoding workflow for characterizing fungal microbiota with high taxonomic resolution. This method involves amplifying longer stretches of ribosomal RNA operons and sequencing them using nanopore long-read sequencing technology. The resulting reads were error-polished to generate consensus sequences with 99.5-100% accuracy, which were then aligned against reference genome assemblies. The efficacy of this method was explored using a polymicrobial mock community and patient-derived specimens, demonstrating the marked potential of long-read sequencing combined with consensus calling for accurate taxonomic classification. Our approach offers a powerful tool for the rapid identification of pathogenic fungi and has the promise to significantly improve our understanding of the role of fungi in health and disease.

Genome Characterisation of the CGMMV Virus Population in Australia—Informing Plant Biosecurity Policy

The detection of cucumber green mottle mosaic (CGMMV) in the Northern Territory (NT), Australia, in 2014 led to the introduction of strict quarantine measures for the importation of cucurbit seeds by the Australian federal government. Further detections in Queensland, Western Australia (WA), New South Wales and South Australia occurred in the period 2015–2020. To explore the diversity of the current Australian CGMMV population, 35 new coding sequence complete genomes for CGMMV isolates from Australian incursions and surveys were prepared for this study. In conjunction with published genomes from the NT and WA, sequence, phylogenetic, and genetic variation and variant analyses were performed, and the data were compared with those for international CGMMV isolates. Based on these analyses, it can be inferred that the Australian CGMMV population resulted from a single virus source via multiple introductions.

Benchmarking and Validation of a Bioinformatics Workflow for Meat Species Identification Using 16S rDNA Metabarcoding

DNA-metabarcoding is becoming more widely used for routine authentication of meat-based food and feed products. Several methods validating species identification methods through amplicon sequencing have already been published. These use a variety of barcodes and analysis workflows, however, no methodical comparison of available algorithms and parameter optimization are published hitherto for meat-based products' authenticity. Additionally, many published methods use very small subsets of the available reference sequences, thereby limiting the potential of the analysis and leading to over-optimistic performance estimates. We here predict and compare the ability of published barcodes to distinguish taxa in the BLAST NT database. We then use a dataset of 79 reference samples, spanning 32 taxa, to benchmark and optimize a metabarcoding analysis workflow for 16S rDNA Illumina sequencing. Furthermore, we provide recommendations as to the parameter choices, sequencing depth, and thresholds that should be used to analyze meat metabarcoding sequencing experiments. The analysis workflow is publicly available, and includes ready-to-use tools for validation and benchmarking.

Host metabolome and faecal microbiome shows potential interactions impacted by age and weaning times in calves

BACKGROUND

Calves undergo nutritional, metabolic, and behavioural changes from birth to the entire weaning period. An appropriate selection of weaning age is essential to reduce the negative effects caused by weaning-related dietary transitions. This study monitored the faecal microbiome and plasma metabolome of 59 female Holstein calves during different developmental stages and weaning times (early vs. late) and identified the potential associations of the measured parameters over an experimental period of 140 days.

RESULTS

A progressive development of the microbiome and metabolome was observed with significant differences according to the weaning groups (weaned at 7 or 17 weeks of age). Faecal samples of young calves were dominated by bifidobacterial and lactobacilli species, while their respective plasma samples showed high concentrations of amino acids (AAs) and biogenic amines (BAs). However, as the calves matured, the abundances of potential fiber-degrading bacteria and the plasma concentrations of sphingomyelins (SMs), few BAs and acylcarnitines (ACs) were increased. Early-weaning at 7 weeks significantly restructured the microbiome towards potential fiber-degrading bacteria and decreased plasma concentrations of most of the AAs and SMs, few BAs and ACs compared to the late-weaning event. Strong associations between faecal microbes, plasma metabolites and calf growth parameters were observed during days 42-98, where the abundances of Bacteroides, Parabacteroides, and Blautia were positively correlated with the plasma concentrations of AAs, BAs and SMs as well as the live weight gain or average daily gain in calves.

CONCLUSION

The present study reported that weaning at 17 weeks of age was beneficial due to higher growth rate of late-weaned calves during days 42-98 and a quick adaptability of microbiota to weaning-related dietary changes during day 112, suggesting an age-dependent maturation of the gastrointestinal tract. However, the respective plasma samples of late-weaned calves contained several metabolites with differential concentrations to the early-weaned group, suggesting a less abrupt but more-persistent effect of dietary changes on host metabolome compared to the microbiome.

MTSv: rapid alignment-based taxonomic classification and high-confidence metagenomic analysis

As the size of reference sequence databases and high-throughput sequencing datasets continue to grow, it is becoming computationally infeasible to use traditional alignment to large genome databases for taxonomic classification of metagenomic reads. Exact matching approaches can rapidly assign taxonomy and summarize the composition of microbial communities, but they sacrifice accuracy and can lead to false positives. Full alignment tools provide higher confidence assignments and can assign sequences from genomes that diverge from reference sequences; however, full alignment tools are computationally intensive. To address this, we designed MTSv specifically for alignment-based taxonomic assignment in metagenomic analysis. This tool implements an FM-index assisted q-gram filter and SIMD accelerated Smith-Waterman algorithm to find alignments. However, unlike traditional aligners, MTSv will not attempt to make additional alignments to a TaxID once an alignment of sufficient quality has been found. This improves efficiency when many reference sequences are available per taxon. MTSv was designed to be flexible and can be modified to run on either memory or processor constrained systems. Although MTSv cannot compete with the speeds of exact k-mer matching approaches, it is reasonably fast and has higher precision than popular exact matching approaches. Because MTSv performs a full alignment it can classify reads even when the genomes share low similarity with reference sequences and provides a tool for high confidence pathogen detection with low off-target assignments to near neighbor species.

Drastic reduction of false positive species in samples of insects by intersecting the default output of two popular metagenomic classifiers

The use of high-throughput sequencing to recover short DNA reads of many species has been widely applied on biodiversity studies, either as amplicon metabarcoding or shotgun metagenomics. These reads are assigned to taxa using classifiers. However, for different reasons, the results often contain many false positives. Here we focus on the reduction of false positive species attributable to the classifiers. We benchmarked two popular classifiers, BLASTn followed by MEGAN6 (BM) and Kraken2 (K2), to analyse shotgun sequenced artificial single-species samples of insects. To reduce the number of misclassified reads, we combined the output of the two classifiers in two different ways: (1) by keeping only the reads that were attributed to the same species by both classifiers (intersection approach); and (2) by keeping the reads assigned to some species by any classifier (union approach). In addition, we applied an analytical detection limit to further reduce the number of false positives species. As expected, both metagenomic classifiers used with default parameters generated an unacceptably high number of misidentified species (tens with BM, hundreds with K2). The false positive species were not necessarily phylogenetically close, as some of them belonged to different orders of insects. The union approach failed to reduce the number of false positives, but the intersection approach got rid of most of them. The addition of an analytic detection limit of 0.001 further reduced the number to ca. 0.5 false positive species per sample. The misidentification of species by most classifiers hampers the confidence of the DNA-based methods for assessing the biodiversity of biological samples. Our approach to alleviate the problem is straightforward and significantly reduced the number of reported false positive species.

The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota

BACKGROUND

Autism spectrum disorders (ASD) are associated with dysregulation of the microbiota-gut-brain axis, changes in microbiota composition as well as in the fecal, serum, and urine levels of microbial metabolites. Yet a causal relationship between dysregulation of the microbiota-gut-brain axis and ASD remains to be demonstrated. Here, we hypothesized that the microbial metabolite p-Cresol, which is more abundant in ASD patients compared to neurotypical individuals, could induce ASD-like behavior in mice.

RESULTS

Mice exposed to p-Cresol for 4 weeks in drinking water presented social behavior deficits, stereotypies, and perseverative behaviors, but no changes in anxiety, locomotion, or cognition. Abnormal social behavior induced by p-Cresol was associated with decreased activity of central dopamine neurons involved in the social reward circuit. Further, p-Cresol induced changes in microbiota composition and social behavior deficits could be transferred from p-Cresol-treated mice to control mice by fecal microbiota transplantation (FMT). We also showed that mice transplanted with the microbiota of p-Cresol-treated mice exhibited increased fecal p-Cresol excretion, compared to mice transplanted with the microbiota of control mice. In addition, we identified possible p-Cresol bacterial producers. Lastly, the microbiota of control mice rescued social interactions, dopamine neurons excitability, and fecal p-Cresol levels when transplanted to p-Cresol-treated mice.

CONCLUSIONS

The microbial metabolite p-Cresol induces selectively ASD core behavioral symptoms in mice. Social behavior deficits induced by p-Cresol are dependant on changes in microbiota composition. Our study paves the way for therapeutic interventions targeting the microbiota and p-Cresol production to treat patients with ASD. Video abstract.

Transcriptomics of the Rooibos (Aspalathus linearis) Species Complex

Rooibos (Aspalathus linearis), widely known as a herbal tea, is endemic to the Cape Floristic Region of South Africa (SA). It produces a wide range of phenolic compounds that have been associated with diverse health promoting properties of the plant. The species comprises several growth forms that differ in their morphology and biochemical composition, only one of which is cultivated and used commercially. Here, we established methodologies for non-invasive transcriptome research of wild-growing South African plant species, including (1) harvesting and transport of plant material suitable for RNA sequencing; (2) inexpensive, high-throughput biochemical sample screening; (3) extraction of high-quality RNA from recalcitrant, polysaccharide- and polyphenol rich plant material; and (4) biocomputational analysis of Illumina sequencing data, together with the evaluation of programs for transcriptome assembly (Trinity, IDBA-Trans, SOAPdenovo-Trans, CLC), protein prediction, as well as functional and taxonomic transcript annotation. In the process, we established a biochemically characterized sample pool from 44 distinct rooibos ecotypes (1–5 harvests) and generated four in-depth annotated transcriptomes (each comprising on average ≈86,000 transcripts) from rooibos plants that represent distinct growth forms and differ in their biochemical profiles. These resources will serve future rooibos research and plant breeding endeavours.

Benchmarking Metagenomics Tools for Taxonomic Classification

Metagenomic sequencing is revolutionizing the detection and characterization of microbial species, and a wide variety of software tools are available to perform taxonomic classification of these data. The fast pace of development of these tools and the complexity of metagenomic data make it important that researchers are able to benchmark their performance. Here, we review current approaches for metagenomic analysis and evaluate the performance of 20 metagenomic classifiers using simulated and experimental datasets. We describe the key metrics used to assess performance, offer a framework for the comparison of additional classifiers, and discuss the future of metagenomic data analysis.

Recentrifuge: Robust comparative analysis and contamination removal for metagenomics

Metagenomic sequencing is becoming widespread in biomedical and environmental research, and the pace is increasing even more thanks to nanopore sequencing. With a rising number of samples and data per sample, the challenge of efficiently comparing results within a specimen and between specimens arises. Reagents, laboratory, and host related contaminants complicate such analysis. Contamination is particularly critical in low microbial biomass body sites and environments, where it can comprise most of a sample if not all. Recentrifuge implements a robust method for the removal of negative-control and crossover taxa from the rest of samples. With Recentrifuge, researchers can analyze results from taxonomic classifiers using interactive charts with emphasis on the confidence level of the classifications. In addition to contamination-subtracted samples, Recentrifuge provides shared and exclusive taxa per sample, thus enabling robust contamination removal and comparative analysis in environmental and clinical metagenomics. Regarding the first area, Recentrifuge's novel approach has already demonstrated its benefits showing that microbiomes of Arctic and Antarctic solar panels display similar taxonomic profiles. In the clinical field, to confirm Recentrifuge's ability to analyze complex metagenomes, we challenged it with data coming from a metagenomic investigation of RNA in plasma that suffered from critical contamination to the point of preventing any positive conclusion. Recentrifuge provided results that yielded new biological insight into the problem, supporting the growing evidence of a blood microbiota even in healthy individuals, mostly translocated from the gut, the oral cavity, and the genitourinary tract. We also developed a synthetic dataset carefully designed to rate the robust contamination removal algorithm, which demonstrated a significant improvement in specificity while retaining a high sensitivity even in the presence of cross-contaminants. Recentrifuge's official website is www.recentrifuge.org. The data and source code are anonymously and freely available on GitHub and PyPI. The computing code is licensed under the AGPLv3. The Recentrifuge Wiki is the most extensive and continually-updated source of documentation for Recentrifuge, covering installation, use cases, testing, and other useful topics.

Metagenomic Next-Generation Sequencing Reveals Individual Composition and Dynamics of Anelloviruses during Autologous Stem Cell Transplant Recipient Management

Over recent years, there has been increasing interest in the use of the anelloviruses, the major component of the human virome, for the prediction of post-transplant complications such as severe infections. Due to an important diversity, the comprehensive characterization of this viral family over time has been poorly studied. To overcome this challenge, we used a metagenomic next-generation sequencing (mNGS) approach with the aim of determining the individual anellovirus profile of autologous stem cell transplant (ASCT) patients. We conducted a prospective pilot study on a homogeneous patient cohort regarding the chemotherapy regimens that included 10 ASCT recipients. A validated viral mNGS workflow was used on 108 plasma samples collected at 11 time points from diagnosis to 90 days post-transplantation. A complex interindividual variability in terms of abundance and composition was noticed. In particular, a strong sex effect was found and confirmed using quantitative PCR targeting torque teno virus, the most abundant anellovirus. Interestingly, an important turnover in the anellovirus composition was observed during the course of the disease revealing a strong intra-individual variability. Although more studies are needed to better understand anellovirus dynamics, these findings are of prime importance for their future use as biomarkers of immune competence.

Current progress and future opportunities in applications of bioinformatics for biodefense and pathogen detection: report from the Winter Mid-Atlantic Microbiome Meet-up, College Park, MD, January 10, 2018

The Mid-Atlantic Microbiome Meet-up (M3) organization brings together academic, government, and industry groups to share ideas and develop best practices for microbiome research. In January of 2018, M3 held its fourth meeting, which focused on recent advances in biodefense, specifically those relating to infectious disease, and the use of metagenomic methods for pathogen detection. Presentations highlighted the utility of next-generation sequencing technologies for identifying and tracking microbial community members across space and time. However, they also stressed the current limitations of genomic approaches for biodefense, including insufficient sensitivity to detect low-abundance pathogens and the inability to quantify viable organisms. Participants discussed ways in which the community can improve software usability and shared new computational tools for metagenomic processing, assembly, annotation, and visualization. Looking to the future, they identified the need for better bioinformatics toolkits for longitudinal analyses, improved sample processing approaches for characterizing viruses and fungi, and more consistent maintenance of database resources. Finally, they addressed the necessity of improving data standards to incentivize data sharing. Here, we summarize the presentations and discussions from the meeting, identifying the areas where microbiome analyses have improved our ability to detect and manage biological threats and infectious disease, as well as gaps of knowledge in the field that require future funding and focus.