From defaults to databases: parameter and database choice dramatically impact the performance of metagenomic taxonomic classification tools

The use of metagenomics to enhance diagnosis of encephalitis

INTRODUCTION

Encephalitis has a broad etiology, including infectious and auto-immune causes. In infectious encephalitis, the breadth of causative organisms results in incomplete testing and low diagnostic yields.Metagenomics sequences all DNA and RNA allowing untargeted detection of all organisms in a single specimen; this is of particular use in diagnosis of encephalitis with a broad etiology.

AREAS COVERED

We review the literature and discuss metagenomics workflows, host depletion and pathogen enrichment methods, bioinformatics analysis and potential analysis of the host transcriptome to aid diagnosis. We discuss the clinical use of metagenomics for diagnosis of neurological infection including time to result, cost, quality assurance, patient cohorts in whom metagenomics adds the most value, recommended specimen types, limitations and review published cases in which metagenomics has been used to diagnose encephalitis.

EXPERT OPINION

There is good evidence for the utility of metagenomics to diagnose infection in encephalitis. Due to infections with rare, unexpected or novel pathogens, metagenomics adds most value to diagnosis in immunocompromised patients and the greatest diagnostic yield is in brain biopsies. Technical advances are needed to reduce the complexity, cost and time to result which will enable wider adoption in clinical laboratories and use as a first-line test.

Metagenomic Insights Into the Dietary Diversity of the Adaptive Radiation of Cichlid Fishes in Lake Tanganyika

Diet specialisation is a main driver of diversification in many adaptive radiations. Therefore, identifying diet items is essential to characterise trophic specialisations and to understand the dynamics of dietary adaptations. In this study, we explored the trophic niches of 56 species from the adaptive radiation of cichlid fishes in Lake Tanganyika, encompassing all major phylogenetic lineages and feeding specialisations. We employed a metagenomic sequencing approach to identify the food sources of the investigated species at high taxonomic resolution, sequencing over 400 digestive content samples from wild-caught individuals at around 50 million paired-end read depth per sample. Our analyses revealed Arthropoda, Chordata (fishes), Bacillariophyta and Streptophyta as the primary diet phyla of the Tanganyikan cichlids. Moreover, we confirmed the presence of other food sources and identified taxa not previously documented to be part of the cichlids' diet. Based on their dietary compositions, the Tanganyikan cichlids can be grouped into herbivores, invertivores, piscivores and mixed feeders. Further, we showed that trophic disparity in the radiation is shaped by rapid divergence and documented cases of dietary niche convergence. Diet composition correlated with carbon and nitrogen stable isotope values, gut length, and body morphology. Differences in diet-such as the consumption of diatoms, streptophytes and chlorophytes versus fish and arthropods-were associated with changes in body, upper oral jaw and lower pharyngeal jaw shape. Collectively, this study presents a comprehensive and detailed diet classification of the Tanganyikan cichlids, highlighting the power of metagenomic approaches in delineating dietary adaptations.

Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut

Microbes use a range of genetic codes and gene structures, yet these are often ignored during metagenomic analysis. This causes spurious protein predictions, preventing functional assignment which limits our understanding of ecosystems. To resolve this, we developed a lineage-specific gene prediction approach that uses the correct genetic code based on the taxonomic assignment of genetic fragments, removes incomplete protein predictions, and optimises prediction of small proteins. Applied to 9634 metagenomes and 3594 genomes from the human gut, this approach increased the landscape of captured expressed microbial proteins by 78.9%, including previously hidden functional groups. Optimised small protein prediction captured 3,772,658 small protein clusters, which form an improved microbial protein catalogue of the human gut (MiProGut). To enable the ecological study of a protein's prevalence and association with host parameters, we developed InvestiGUT, a tool which integrates both the protein sequences and sample metadata. Accurate prediction of proteins is critical to providing a functional understanding of microbiomes, enhancing our ability to study interactions between microbes and hosts.

Microbial population shifts during disturbance induced foaming in anaerobic digestion of primary and activated sludge

Foaming during anaerobic digestion (AD) of sewage sludge is poorly understood and remains an uncontrollable operational obstacle for sewage treatment systems globally, causing mechanical damage, increased hazards and reduced biogas recovery. Foams during AD commonly occur after process disturbances, such as organic loading shocks. However, it is still unclear whether these foam events are biologically driven and linked to the abundance of organisms like filamentous or hydrophobic bacteria. A time-series study was conducted, comparing digestion performance, microbial community succession, metagenomes, and metabolomes in six anaerobic continuous stirred-tank reactors (CSTRs): a control group fed normally (n = 3), and one treated group inhibited through organic shock loading of more than twice the steady state loading rate with glycerol (treatment, n = 3). As soon as microbial activity and methanogenesis recovered after inhibition, significant volumes of foam accumulated simultaneously in the reactor headspace of the three treated CSTRs. Microbial abundance profiles (16S rRNA, V3-V4) from 165 days of operation showed that filamentous or mycolic acid-producing organisms were not associated with this foam event. Shock loading led to acidification, biomass decline and microbial imbalance, contributing indirectly to the foam event. During that period, metabolomes and functional pathway abundances indicated that the stressed microbial biomass was enriched in long-chain fatty acids prior to foaming. This biomass, combined with pH changes, may have modified the physicochemical properties of sludge, leading to the fractionation of organic mass once gas production resumed. More research is needed to understand how abiotic and biotic interactions contribute to foam formation.

NGS-based Aspergillus detection in plasma and lung lavage of children with invasive pulmonary aspergillosis

In immunocompromised pediatric patients, diagnosing invasive pulmonary aspergillosis (IPA) poses a significant challenge. Next-Generation Sequencing (NGS) shows promise for detecting fungal DNA but lacks standardization. This study aims to advance towards clinical evaluation of liquid biopsy NGS for Aspergillus detection, through an evaluation of wet-lab procedures and computational analysis. Our findings support using both CHM13v2.0 and GRCh38.p14 in host-read mapping to reduce fungal false-positives. We demonstrate the sensitivity of our custom kraken2 database, cRE.21, in detecting Aspergillus species. Additionally, cell-free DNA sequencing shows superior performance to whole-cell DNA sequencing by recovering higher fractions of fungal DNA in lung fluid (bronchoalveolar lavage [BAL] fluid) and plasma samples from pediatric patients with probable IPA. In a proof-of-principle, A. fumigatus was identified in 5 out of 7 BAL fluid samples and 3 out of 5 plasma samples. This optimized workflow can advance fungal-NGS research and represents a step towards enhancing diagnostic certainty by enabling more sensitive and accurate species-level diagnosis of IPA in immunocompromised patients.

The cerebrospinal fluid virome in people with HIV: links to neuroinflammation and cognition

Despite effective HIV suppression, neuroinflammation and neurocognitive issues are prevalent in people with HIV (PWH) yet poorly understood. HIV infection alters the human virome, and virome perturbations have been linked to neurocognitive issues in people without HIV. Once thought to be sterile, the cerebrospinal fluid (CSF) hosts a recently discovered virome, presenting an unexplored avenue for understanding brain and mental health in PWH. This cross-sectional study analyzed 85 CSF samples (74 from PWH on suppressive antiretroviral therapy, and 11 from controls without HIV, CWH) through shotgun metagenomics for DNA/RNA viruses. Taxonomic composition (reads and contigs), α and β diversity, and relative abundance (RA) of prokaryotic (PV), human eukaryotic (hEV), and non-human eukaryotic viruses (nhEV) were evaluated in relation to HIV infection, markers of neuroinflammation and neurodegeneration, cognitive functions, and depressive symptoms. Sensitivity analyses and post-hoc cluster analysis on the RA of viral groups and blood-brain barrier permeability were also performed. Of 46 read-positive CSF samples, 93.5% contained PV sequences, 47.8% hEV, and 45.6% nhEV. Alpha diversity was lower in PWH versus CWH, although p>0.05. At β diversity analysis, HIV status explained 3.3% of the variation in viral composition (p=0.016). Contigs retained 13 samples positive for 8 hEV, 2 nhEV, and 6 PV. Higher RA of PV was correlated with higher CSF S100β (p=0.002) and β-Amyloid 1-42 fragment (βA-42, p=0.026), while higher RA of nhEV with poorer cognitive performance (p=0.022). Conversely, higher RA of hEV correlated with better cognition (p=0.003) and lower βA-42 (p=0.012). Sensitivity analyses in virome-positive samples only confirmed these findings. Three CSF clusters were identified and showed differences in astrocytosis, βA-42, tau protein, and cognitive functions. Participants with hEV-enriched CSF showed better cognitive performance compared to those with virus-devoid and nhEV-enriched CSF (models'p<0.05). This study provides the first comprehensive description of the CSF virome in PWH, revealing associations with neuroinflammation and cognition. These findings highlight the potential involvement of the CSF virome in brain health and inform about its composition, origin, and potential clinical implications in people with and without HIV.

Farm-to-fork changes in poultry microbiomes and resistomes in Maputo City, Mozambique

Increasing demand for poultry has spurred poultry production in low- and middle-income countries like Mozambique. Poultry may be an important source of foodborne, antimicrobial-resistant bacteria to consumers in settings with limited water, sanitation, and hygiene infrastructure. The Chicken Exposures and Enteric Pathogens in Children Exposed through Environmental Pathways (ChEEP ChEEP) study was conducted in Maputo City, Mozambique from 2019 to 2021 to quantify enteric pathogen exposures along the supply chain for commercial and local (i.e., scavenger) chicken breeds. Here, we performed metagenomic sequencing of total DNA from banked ChEEP ChEEP samples to characterize fecal and carcass microbiomes and resistome diversity between chicken breeds and along the supply chain. Fecal samples (n = 26) were collected from commercial and local chickens at production sites and markets and carcass (n = 49) and rinse bucket samples (n = 26) from markets. We conducted taxonomic profiling and identified antimicrobial resistance genes (ARGs) from metagenomic sequence data, focusing especially on potential human pathogens and "high-risk" ARGs. We estimated alpha diversity for each sample and compared by site and breed. We estimated Bray-Curtis dissimilarity between samples and examined clustering. We found that commercial and local chickens harbored distinct fecal potential pathogens and resistomes at production and market sites. Many potentially pathogenic bacteria and ARGs present in chicken fecal samples are also present on carcasses sold to consumers. Finally, commercial chicken carcasses contain high-risk ARGs that are not necessarily introduced from chicken feces. These results indicate markets are an important site of exposure to potentially pathogenic bacteria and high-risk ARGs.

IMPORTANCE

While chicken eggs and meat are a critical protein source in low-income settings, antibiotics are routinely fed to chickens with consequences for selection of antimicrobial resistance. Evaluating how poultry gut bacterial communities, including potential human pathogens and high-risk antimicrobial resistance genes, differ from farm to market could help identify where to target interventions to minimize transmission risks to human populations. In this study in Maputo City, Mozambique, we found compositional differences between commercial and local chicken breeds at production and market sites. We also found that while all potentially pathogenic bacteria and many high-risk antimicrobial resistance genes persisted from production and market through processing, some resistance genes were detected on carcass samples only after processing, suggesting human or environmental contamination is occurring within markets. Overall, our findings indicate that open-air markets may represent a critical juncture for human exposures to pathogens and antimicrobial resistance genes from poultry and poultry products.

Impact of simulation and reference catalogues on the evaluation of taxonomic profiling pipelines

Microbiome profiling tools rely on reference catalogues, which significantly affect their performance. Comparing them is, however, challenging, mainly due to differences in their native catalogues. In this study, we present a novel standardized benchmarking framework that makes such comparisons more accurate. We decided not to customize databases but to translate results to a common reference to use the tools with their native environment. Specifically, we conducted two realistic simulations of gut microbiome samples, each based on a specific taxonomic profiler, and used two different taxonomic references to project their results, namely the Genome Taxonomy Database and the Unified Human Gastrointestinal Genome. To demonstrate the importance of using such a framework, we evaluated four established profilers as well as the impact of the simulations and that of the common taxonomic references on the perceived performance of these profilers. Finally, we provide guidelines to enhance future profiler comparisons for human microbiome ecosystems: (i) use or create realistic simulations tailored to your biological context (BC), (ii) identify a common feature space suited to your BC and independent of the catalogues used by the profilers and (iii) apply a comprehensive set of metrics covering accuracy (sensitivity/precision), overall representativity (richness/Shannon) and quantification (UniFrac and/or Aitchison distance).

Managing false positives during detection of pathogen sequences in shotgun metagenomics datasets

BACKGROUND

Culture-independent diagnostic tests are gaining popularity as tools for detecting pathogens in food. Shotgun sequencing holds substantial promise for food testing as it provides abundant information on microbial communities, but the challenge is in analyzing large and complex sequencing datasets with a high degree of both sensitivity and specificity. Falsely classifying sequencing reads as originating from pathogens can lead to unnecessary food recalls or production shutdowns, while low sensitivity resulting in false negatives could lead to preventable illness.

RESULTS

We used simulated and published shotgun sequencing datasets containing Salmonella-derived reads to explore the appearance and mitigation of false positive results using the popular taxonomic annotation softwares Kraken2 and Metaphlan4. Using default parameters, Kraken2 is sensitive but prone to false positives, while Metaphlan4 is more specific but unable to detect Salmonella at low abundance. We then developed a bioinformatic pipeline for identifying and removing reads falsely identified as Salmonella by Kraken2 while retaining high sensitivity. Carefully considering software parameters and database choices is essential to avoiding false positive sample calls. With well-chosen parameters plus additional steps to confirm the taxonomic origin of reads, it is possible to detect pathogens with very high specificity and sensitivity.

Impact of database choice and confidence score on the performance of taxonomic classification using Kraken2

Accurate taxonomic classification is essential to understanding microbial diversity and function through metagenomic sequencing. However, this task is complicated by the vast variety of microbial genomes and the computational limitations of bioinformatics tools. The aim of this study was to evaluate the impact of reference database selection and confidence score (CS) settings on the performance of Kraken2, a widely used k-mer-based metagenomic classifier. In this study, we generated simulated metagenomic datasets to systematically evaluate how the choice of reference databases, from the compact Minikraken v1 to the expansive nt- and GTDB r202, and different CS (from 0 to 1.0) affect the key performance metrics of Kraken2. These metrics include classification rate, precision, recall, F1 score, and accuracy of true versus calculated bacterial abundance estimation. Our results show that higher CS, which increases the rigor of taxonomic classification by requiring greater k-mer agreement, generally decreases the classification rate. This effect is particularly pronounced for smaller databases such as Minikraken and Standard-16, where no reads could be classified when the CS was above 0.4. In contrast, for larger databases such as Standard, nt and GTDB r202, precision and F1 scores improved significantly with increasing CS, highlighting their robustness to stringent conditions. Recovery rates were mostly stable, indicating consistent detection of species under different CS settings. Crucially, the results show that a comprehensive reference database combined with a moderate CS (0.2 or 0.4) significantly improves classification accuracy and sensitivity. This finding underscores the need for careful selection of database and CS parameters tailored to specific scientific questions and available computational resources to optimize the results of metagenomic analyses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-024-00178-0.

High Prevalence of a Novel Circovirus in the European Hedgehog (Erinaceus europaeus), a Common Species in Decline

Hedgehog (Erinaceus europaeus) declines in western Europe have been associated with the emergence of Hedgehog diphtheric disease (HDD), with a probable multifactorial, yet unidentified etiology. We used metagenomic sequencing of cell-free DNA (cfDNA) in hedgehog blood to identify possible causes of HDD. We detected a novel circovirus species in the European hedgehog, providing the first record of a circovirus within the mammalian order Eulipotyphla. The novel circovirus genome exhibits the characteristic circovirus structure, including a functional replicase (REP) and capsid (CAP) gene. Phylogenetic analysis placed all four detected genomes in a monophyletic clade, most closely related to sequences isolated from dogs. Subsequent PCR-based screening of 188 hedgehog liver samples demonstrated a high prevalence (61%) of this circovirus in hedgehogs brought to wildlife rescue centers, however, without any significant association with HDD. Since circoviruses are well known to interfere with host immunity across mammalian and avian taxa, the high level of circovirus detection in hedgehogs warrants further research into the role of this novel virus in hedgehog health.

A novel barcoded nanopore sequencing workflow of high-quality, full-length bacterial 16S amplicons for taxonomic annotation of bacterial isolates and complex microbial communities

Due to recent improvements, Nanopore sequencing has become a promising method for experiments relying on amplicon sequencing. We describe a flexible workflow to generate and annotate high-quality, full-length 16S rDNA amplicons. We evaluated it for two applications, namely, (i) identification of bacterial isolates and (ii) species-level profiling of microbial communities. We assessed the identification of single bacterial isolates by sequencing, using a set of barcoded full-length 16S rRNA gene primer pairs (pair A), on 47 isolates encompassing multiple genera and compared those results with matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS)-based identification. Species-level community profiling was tested with two sets of barcoded full-length 16S primer pairs (A and B) and compared to the results obtained with shotgun Illumina sequencing using 27 stool samples. We developed a Nextflow pipeline to retain high-quality reads and taxonomically annotate them. We found high agreement between our workflow and MALDI-TOF data for isolate identification (positive predictive value = 0.90, Cramér's V = 0.857, and Theil's U = 0.316). For species-level community profiling, we found strong correlations (rs > 0.6) of alpha diversity indices between the two primer sets and Illumina sequencing. At the community level, we found significant but small differences when comparing sequencing techniques. Finally, we found a moderate to strong correlation when comparing the relative abundances of individual species (average rs = 0.6 and 0.533 for primers A and B). Despite identified shortcomings, the proposed workflow enabled accurate identification of single bacterial isolates and prominent features in microbial communities, making it a worthwhile alternative to MALDI-TOF MS and Illumina sequencing.IMPORTANCEA quick, robust, simple, and cost-effective method to identify bacterial isolates and communities in each sample is indispensable in the fields of microbiology and infection biology. Recent technological advances in Oxford Nanopore Technologies sequencing make this technique an attractive option considering the adaptability, portability, and cost-effectiveness of the platform, even with small sequencing batches. Here, we validated a flexible workflow to identify bacterial isolates and characterize bacterial communities using the Oxford Nanopore Technologies sequencing platform combined with the most recent v14 chemistry kits. For bacterial isolates, we compared our nanopore-based approach to matrix-assisted laser desorption ionization-time of flight mass spectrometry-based identification. For species-level profiling of complex bacterial communities, we compared our nanopore-based approach to Illumina shotgun sequencing. For reproducibility purposes, we wrapped the code used to process the sequencing data into a ready-to-use and self-contained Nextflow pipeline.

Comparative evaluation of soil DNA extraction kits for long read metagenomic sequencing

Metagenomics has been transformative in our understanding of the diversity and function of soil microbial communities. Applying long read sequencing to whole genome shotgun metagenomics has the potential to revolutionise soil microbial ecology through improved taxonomic classification, functional characterisation and metagenome assembly. However, optimisation of robust methods for long read metagenomics of environmental samples remains undeveloped. In this study, Oxford Nanopore sequencing using samples from five commercially available soil DNA extraction kits was compared across four soil types, in order to optimise read length and reproducibility for comparative long read soil metagenomics. Average extracted DNA lengths varied considerably between kits, but longer DNA fragments did not translate consistently into read lengths. Highly variable decreases in the length of resulting reads from some kits were associated with poor classification rate and low reproducibility in microbial communities identified between technical repeats. Replicate samples from other kits showed more consistent conversion of extracted DNA fragment size into read length and resulted in more congruous microbial community representation. Furthermore, extraction kits showed significant differences in the community representation and structure they identified across all soil types. Overall, the QIAGEN DNeasy PowerSoil Pro Kit displayed the best suitability for reproducible long-read WGS metagenomic sequencing, although further optimisation of DNA purification and library preparation may enable translation of higher molecular weight DNA from other kits into longer read lengths. These findings provide a novel insight into the importance of optimising DNA extraction for achieving replicable results from long read metagenomic sequencing of environmental samples.

Benchmarking bacterial taxonomic classification using nanopore metagenomics data of several mock communities

Taxonomic classification is crucial in identifying organisms within diverse microbial communities when using metagenomics shotgun sequencing. While second-generation Illumina sequencing still dominates, third-generation nanopore sequencing promises improved classification through longer reads. However, extensive benchmarking studies on nanopore data are lacking. We systematically evaluated performance of bacterial taxonomic classification for metagenomics nanopore sequencing data for several commonly used classifiers, using standardized reference sequence databases, on the largest collection of publicly available data for defined mock communities thus far (nine samples), representing different research domains and application scopes. Our results categorize classifiers into three categories: low precision/high recall; medium precision/medium recall, and high precision/medium recall. Most fall into the first group, although precision can be improved without excessively penalizing recall with suitable abundance filtering. No definitive 'best' classifier emerges, and classifier selection depends on application scope and practical requirements. Although few classifiers designed for long reads exist, they generally exhibit better performance. Our comprehensive benchmarking provides concrete recommendations, supported by publicly available code for reassessment and fine-tuning by other scientists.

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data

The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and analyzing the deluge of sequencing data for precise taxonomic classification remains a formidable challenge. Existing classification approaches primarily fall into two categories, database-based methods and machine learning methods, each presenting its own set of challenges and advantages. On this basis, the aim of our study was to conduct a comparative analysis between these two methods while also investigating the merits of integrating multiple database-based methods. Through an in-depth comparative study, we evaluated the performance of both methodological categories in taxonomic classification by utilizing simulated data sets. Our analysis revealed that database-based methods excel in classification accuracy when backed by a rich and comprehensive reference database. Conversely, while machine learning methods show superior performance in scenarios where reference sequences are sparse or lacking, they generally show inferior performance compared with database methods under most conditions. Moreover, our study confirms that integrating multiple database-based methods does, in fact, enhance classification accuracy. These findings shed new light on the taxonomic classification of high-throughput sequencing data and bear substantial implications for the future development of computational biology. For those interested in further exploring our methods, the source code of this study is publicly available on https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator. Additionally, a dedicated webpage showcasing our collected database, data sets, and various classification software can be found at http://lab.malab.cn/~tqz/project/taxonomic/.

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.

Phage-inclusive profiling of human gut microbiomes with Phanta

Due to technical limitations, most gut microbiome studies have focused on prokaryotes, overlooking viruses. Phanta, a virome-inclusive gut microbiome profiling tool, overcomes the limitations of assembly-based viral profiling methods by using customized k-mer-based classification tools and incorporating recently published catalogs of gut viral genomes. Phanta's optimizations consider the small genome size of viruses, sequence homology with prokaryotes and interactions with other gut microbes. Extensive testing of Phanta on simulated data demonstrates that it quickly and accurately quantifies prokaryotes and viruses. When applied to 245 fecal metagenomes from healthy adults, Phanta identifies ~200 viral species per sample, ~5× more than standard assembly-based methods. We observe a ~2:1 ratio between DNA viruses and bacteria, with higher interindividual variability of the gut virome compared to the gut bacteriome. In another cohort, we observe that Phanta performs equally well on bulk versus virus-enriched metagenomes, making it possible to study prokaryotes and viruses in a single experiment, with a single analysis.

Ten common issues with reference sequence databases and how to mitigate them

Metagenomic sequencing has revolutionized our understanding of microbiology. While metagenomic tools and approaches have been extensively evaluated and benchmarked, far less attention has been given to the reference sequence database used in metagenomic classification. Issues with reference sequence databases are pervasive. Database contamination is the most recognized issue in the literature; however, it remains relatively unmitigated in most analyses. Other common issues with reference sequence databases include taxonomic errors, inappropriate inclusion and exclusion criteria, and sequence content errors. This review covers ten common issues with reference sequence databases and the potential downstream consequences of these issues. Mitigation measures are discussed for each issue, including bioinformatic tools and database curation strategies. Together, these strategies present a path towards more accurate, reproducible and translatable metagenomic sequencing.

Traditional potato tillage systems in the Peruvian Andes impact bacterial diversity, evenness, community composition, and functions in soil microbiomes

The soil microbiome, a crucial component of agricultural ecosystems, plays a pivotal role in crop production and ecosystem functioning. However, its response to traditional tillage systems in potato cultivation in the Peruvian highlands is still far from understood. Here, ecological and functional aspects of the bacterial community were analyzed based on soil samples from two traditional tillage systems: 'chiwa' (minimal tillage) and 'barbecho' (full tillage), in the Huanuco region of the Peruvian central Andes. Similar soil bacterial community composition was shown for minimal tillage system, but it was heterogeneous for full tillage system. This soil bacterial community composition under full tillage system may be attributed to stochastic, and a more dynamic environment within this tillage system. 'Chiwa' and 'barbecho' soils harbored distinct bacterial genera into their communities, indicating their potential as bioindicators of traditional tillage effects. Functional analysis revealed common metabolic pathways in both tillage systems, with differences in anaerobic pathways in 'chiwa' and more diverse pathways in 'barbecho'. These findings open the possibilities to explore microbial bioindicators for minimal and full tillage systems, which are in relationship with healthy soil, and they can be used to propose adequate tillage systems for the sowing of potatoes in Peru.

Metagenomic profiling of viral and microbial communities from the pox lesions of lumpy skin disease virus and sheeppox virus-infected hosts

Introduction

It has been recognized that capripoxvirus infections have a strong cutaneous tropism with the manifestation of skin lesions in the form of nodules and scabs in the respective hosts, followed by necrosis and sloughing off. Considering that the skin microbiota is a complex community of commensal bacteria, fungi and viruses that are influenced by infections leading to pathological states, there is no evidence on how the skin microbiome is affected during capripoxvirus pathogenesis.

Methods

In this study, shotgun metagenomic sequencing was used to investigate the microbiome in pox lesions from hosts infected with lumpy skin disease virus and sheep pox virus.

Results

The analysis revealed a high degree of variability in bacterial community structures across affected skin samples, indicating the importance of specific commensal microorganisms colonizing individual hosts. The most common and abundant bacteria found in scab samples were Fusobacterium necrophorum, Streptococcus dysgalactiae, Helcococcus ovis and Trueperella pyogenes, irrespective of host. Bacterial reads belonging to the genera Moraxella, Mannheimia, Corynebacterium, Staphylococcus and Micrococcus were identified.

Discussion

This study is the first to investigate capripox virus-associated changes in the skin microbiome using whole-genome metagenomic profiling. The findings will provide a basis for further investigation into capripoxvirus pathogenesis. In addition, this study highlights the challenge of selecting an optimal bioinformatics approach for the analysis of metagenomic data in clinical and veterinary practice. For example, direct classification of reads using a kmer-based algorithm resulted in a significant number of systematic false positives, which may be attributed to the peculiarities of the algorithm and database selection. On the contrary, the process of de novo assembly requires a large number of target reads from the symbiotic microbial community. In this work, the obtained sequencing data were processed by three different approaches, including direct classification of reads based on k-mers, mapping of reads to a marker gene database, and de novo assembly and binning of metagenomic contigs. The advantages and disadvantages of these techniques and their practicality in veterinary settings are discussed in relation to the results obtained.

HiTaxon: a hierarchical ensemble framework for taxonomic classification of short reads

Motivation

Whole microbiome DNA and RNA sequencing (metagenomics and metatranscriptomics) are pivotal to determining the functional roles of microbial communities. A key challenge in analyzing these complex datasets, typically composed of tens of millions of short reads, is accurately classifying reads to their taxa of origin. While still performing worse relative to reference-based short-read tools in species classification, ML algorithms have shown promising results in taxonomic classification at higher ranks. A recent approach exploited to enhance the performance of ML tools, which can be translated to reference-dependent classifiers, has been to integrate the hierarchical structure of taxonomy within the tool's predictive algorithm.

Results

Here, we introduce HiTaxon, an end-to-end hierarchical ensemble framework for taxonomic classification. HiTaxon facilitates data collection and processing, reference database construction and optional training of ML models to streamline ensemble creation. We show that databases created by HiTaxon improve the species-level performance of reference-dependent classifiers, while reducing their computational overhead. In addition, through exploring hierarchical methods for HiTaxon, we highlight that our custom approach to hierarchical ensembling improves species-level classification relative to traditional strategies. Finally, we demonstrate the improved performance of our hierarchical ensembles over current state-of-the-art classifiers in species classification using datasets comprised of either simulated or experimentally derived reads.

Availability and implementation

HiTaxon is available at: https://github.com/ParkinsonLab/HiTaxon.

A metatranscriptomics strategy for efficient characterization of the microbiome in human tissues with low microbial biomass

The high background of host RNA poses a major challenge to metatranscriptome analysis of human samples. Hence, metatranscriptomics has been mainly applied to microbe-rich samples, while its application in human tissues with low ratio of microbial to host cells has yet to be explored. Since there is no computational workflow specifically designed for the taxonomic and functional analysis of this type of samples, we propose an effective metatranscriptomics strategy to accurately characterize the microbiome in human tissues with a low ratio of microbial to host content. We experimentally generated synthetic samples with well-characterized bacterial and host cell compositions, and mimicking human samples with high and low microbial loads. These synthetic samples were used for optimizing and establishing the workflow in a controlled setting. Our results show that the integration of the taxonomic analysis of optimized Kraken 2/Bracken with the functional analysis of HUMAnN 3 in samples with low microbial content, enables the accurate identification of a large number of microbial species with a low false-positive rate, while improving the detection of microbial functions. The effectiveness of our metatranscriptomics workflow was demonstrated in synthetic samples, simulated datasets, and most importantly, human gastric tissue specimens, thus providing a proof of concept for its applicability on mucosal tissues of the gastrointestinal tract. The use of an accurate and reliable metatranscriptomics approach for human tissues with low microbial content will expand our understanding of the functional activity of the mucosal microbiome, uncovering critical interactions between the microbiome and the host in health and disease.

MRGM: an enhanced catalog of mouse gut microbial genomes substantially broadening taxonomic and functional landscapes

Mouse gut microbiome research is pivotal for understanding the human gut microbiome, providing insights into disease modeling, host-microbe interactions, and the dietary influence on the gut microbiome. To enhance the translational value of mouse gut microbiome studies, we need detailed and high-quality catalogs of mouse gut microbial genomes. We introduce the Mouse Reference Gut Microbiome (MRGM), a comprehensive catalog with 42,245 non-redundant mouse gut bacterial genomes across 1,524 species. MRGM marks a 40% increase in the known taxonomic diversity of mouse gut microbes, capturing previously underrepresented lineages through refined genome quality assessment techniques. MRGM not only broadens the taxonomic landscape but also enriches the functional landscape of the mouse gut microbiome. Using deep learning, we have elevated the Gene Ontology annotation rate for mouse gut microbial proteins from 3.2% with orthology to 60%, marking an over 18-fold increase. MRGM supports both DNA- and marker-based taxonomic profiling by providing custom databases, surpassing previous catalogs in performance. Finally, taxonomic and functional comparisons between human and mouse gut microbiota reveal diet-driven divergences in their taxonomic composition and functional enrichment. Overall, our study highlights the value of high-quality microbial genome catalogs in advancing our understanding of the co-evolution between gut microbes and their host.

Microbial hitchhikers harbouring antimicrobial-resistance genes in the riverine plastisphere

BACKGROUND

The widespread nature of plastic pollution has given rise to wide scientific and social concern regarding the capacity of these materials to serve as vectors for pathogenic bacteria and reservoirs for Antimicrobial Resistance Genes (ARG). In- and ex-situ incubations were used to characterise the riverine plastisphere taxonomically and functionally in order to determine whether antibiotics within the water influenced the ARG profiles in these microbiomes and how these compared to those on natural surfaces such as wood and their planktonic counterparts.

RESULTS

We show that plastics support a taxonomically distinct microbiome containing potential pathogens and ARGs. While the plastisphere was similar to those biofilms that grew on wood, they were distinct from the surrounding water microbiome. Hence, whilst potential opportunistic pathogens (i.e. Pseudomonas aeruginosa, Acinetobacter and Aeromonas) and ARG subtypes (i.e. those that confer resistance to macrolides/lincosamides, rifamycin, sulfonamides, disinfecting agents and glycopeptides) were predominant in all surface-related microbiomes, especially on weathered plastics, a completely different set of potential pathogens (i.e. Escherichia, Salmonella, Klebsiella and Streptococcus) and ARGs (i.e. aminoglycosides, tetracycline, aminocoumarin, fluoroquinolones, nitroimidazole, oxazolidinone and fosfomycin) dominated in the planktonic compartment. Our genome-centric analysis allowed the assembly of 215 Metagenome Assembled Genomes (MAGs), linking ARGs and other virulence-related genes to their host. Interestingly, a MAG belonging to Escherichia -that clearly predominated in water- harboured more ARGs and virulence factors than any other MAG, emphasising the potential virulent nature of these pathogenic-related groups. Finally, ex-situ incubations using environmentally-relevant concentrations of antibiotics increased the prevalence of their corresponding ARGs, but different riverine compartments -including plastispheres- were affected differently by each antibiotic.

CONCLUSIONS

Our results provide insights into the capacity of the riverine plastisphere to harbour a distinct set of potentially pathogenic bacteria and function as a reservoir of ARGs. The environmental impact that plastics pose if they act as a reservoir for either pathogenic bacteria or ARGs is aggravated by the persistence of plastics in the environment due to their recalcitrance and buoyancy. Nevertheless, the high similarities with microbiomes growing on natural co-occurring materials and even more worrisome microbiome observed in the surrounding water highlights the urgent need to integrate the analysis of all environmental compartments when assessing risks and exposure to pathogens and ARGs in anthropogenically-impacted ecosystems. Video Abstract.