Hostile: accurate decontamination of microbial host sequences

Strain-level variation among vaginal Lactobacillus crispatus and Lactobacillus iners as identified by comparative metagenomics

The vaginal microbiome, a relatively simple, low diversity ecosystem crucial for female health, is often dominated by Lactobacillus spp. Detailed strain-level data, facilitated by shotgun sequencing, can provide a greater understanding of the mechanisms of colonization and host-microbe interactions. We analysed 354 vaginal metagenomes from pregnant women in Ireland to investigate metagenomic community state types and strain-level variation, focusing on cell surface interfaces. Our analysis revealed multiple subspecies, with Lactobacillus crispatus and Lactobacillus iners being the most dominant. We found genes, including putative mucin-binding genes, distinct to L. crispatus subspecies. Using 337 metagenome-assembled genomes, we observed a higher number of strain-specific genes in L. crispatus related to cell wall biogenesis, carbohydrate and amino acid metabolism, many under positive selection. A cell surface glycan gene cluster was predominantly found in L. crispatus but absent in L. iners and Gardnerella vaginalis. These findings highlight strain-specific factors associated with colonisation and host-microbe interactions.

Bioinformatic approaches to blood and tissue microbiome analyses: challenges and perspectives

Advances in next-generation sequencing have resulted in a growing understanding of the microbiome and its role in human health. Unlike traditional microbiome analysis, blood and tissue microbiome analyses focus on the detection and characterization of microbial DNA in blood and tissue, previously considered a sterile environment. In this review, we discuss the challenges and methodologies associated with analyzing these samples, particularly emphasizing blood and tissue microbiome research. Key preprocessing steps-including the removal of ribosomal RNA, host DNA, and other contaminants-are critical to reducing noise and accurately capturing microbial evidence. We also explore how taxonomic profiling tools, machine learning, and advanced normalization techniques address contamination and low microbial biomass, thereby improving reliability. While it offers the potential for identifying microbial involvement in systemic diseases previously undetectable by traditional methods, this methodology also carries risks and lacks universal acceptance due to concerns over reliability and interpretation errors. This paper critically reviews these factors, highlighting both the promise and pitfalls of using blood and tissue microbiome analyses as a tool for biomarker discovery.

Evaluating methods for genome sequencing of Chlamydia trachomatis and other sexually transmitted bacteria directly from clinical swabs

Rates of bacterial sexually transmitted infections (STIs) are rising, and accessing their genomes provides information on strain evolution, circulating strains and encoded antimicrobial resistance (AMR). Notable pathogens include Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG) and Treponema pallidum (TP), globally the most common bacterial STIs. Mycoplasmoides (formerly Mycoplasma) genitalium (MG) is also a bacterial STI that is of concern due to AMR development. These bacteria are also fastidious or hard to culture, and standard sampling methods lyse bacteria, completely preventing pathogen culture. Clinical samples contain large amounts of human and other microbiota DNA. These factors hinder the sequencing of bacterial STI genomes. We aimed to overcome these challenges in obtaining whole-genome sequences and evaluated four approaches using clinical samples from Argentina (39), and Switzerland (14), and cultured samples from Finland (2) and Argentina (1). First, direct genome sequencing from swab samples was attempted through Illumina deep metagenomic sequencing, showing extremely low levels of target DNA, with under 0.01% of the sequenced reads being from the target pathogens. Second, host DNA depletion followed by Illumina sequencing was not found to produce enrichment in these very low-load samples. Third, we tried a selective long-read approach with the new adaptive sequencing from Oxford Nanopore Technologies, which also did not improve enrichment sufficiently to provide genomic information. Finally, target enrichment using a novel pan-genome set of custom SureSelect probes targeting CT, NG, TP and MG followed by Illumina sequencing was successful. We produced whole genomes from 64% of CT-positive samples, from 36% of NG-positive samples and 60% of TP-positive samples. Additionally, we enriched MG DNA to gain partial genomes from 60% of samples. This is the first publication to date to utilize a pan-genome STI panel in target enrichment. Target enrichment, though costly, proved essential for obtaining genomic data from clinical samples. These data can be utilized to examine circulating strains and genotypic resistance and guide public health strategies.

Chlamydia trachomatis genomes from rectal samples: description of a new clade comprising ompA-genotype L4 from Argentina

Whole-genome analysis has provided insights into the evolution of Chlamydia trachomatis and, recently, into circulating strains that cause lymphogranuloma venereum (LGV). A large LGV outbreak of a new ompA-genotype, L2b, was first reported in Europe in the early 2000s, primarily affecting men who have sex with men (MSM), and then expanded globally. More recent work shows that this outbreak is diversifying into variants of described ompA-genotypes, with the same L2b genomic backbone. This study extends the investigation of LGV cases to Argentina and Finland. In 2017, an LGV outbreak was described in Argentina characterized by distinct genomic features shown by both ompA-genotyping and Multi-Locus Sequence Typing (MLST) analysis. We have obtained whole-genome sequences from cultured isolates and clinical samples via SureSelect (Agilent) target enrichment. Based on ompA and phylogenetic analyses, we describe further diversity within the ompA-genotype L2b clade, illustrating the transmission dynamics in Argentina and Finland. A key finding is that of a novel clade of Argentinian samples, characterized by a proposed new ompA-genotype L4. Additionally, we present the genome sequence of a non-LGV strain associated with anorectal proctitis. These findings contribute to the investigation of LGV evolution, particularly with the presence of the novel L4 lineage, and provide insights into genomic diversity and transmission dynamics of C. trachomatis.

Incomplete human reference genomes can drive false sex biases and expose patient-identifying information in metagenomic data

As next-generation sequencing technologies produce deeper genome coverages at lower costs, there is a critical need for reliable computational host DNA removal in metagenomic data. We find that insufficient host filtration using prior human genome references can introduce false sex biases and inadvertently permit flow-through of host-specific DNA during bioinformatic analyses, which could be exploited for individual identification. To address these issues, we introduce and benchmark three host filtration methods of varying throughput, with concomitant applications across low biomass samples such as skin and high microbial biomass datasets including fecal samples. We find that these methods are important for obtaining accurate results in low biomass samples (e.g., tissue, skin). Overall, we demonstrate that rigorous host filtration is a key component of privacy-minded analyses of patient microbiomes and provide computationally efficient pipelines for accomplishing this task on large-scale datasets.

Benchmarking short-read metagenomics tools for removing host contamination

BACKGROUND

The rapid evolution of metagenomic sequencing technology offers remarkable opportunities to explore the intricate roles of microbiome in host health and disease, as well as to uncover the unknown structure and functions of microbial communities. However, the swift accumulation of metagenomic data poses substantial challenges for data analysis. Contamination from host DNA can substantially compromise result accuracy and increase additional computational resources by including nontarget sequences.

RESULTS

In this study, we assessed the impact of computational host DNA decontamination on downstream analyses, highlighting its importance in producing accurate results efficiently. We also evaluated the performance of conventional tools like KneadData, Bowtie2, BWA, KMCP, Kraken2, and KrakenUniq, each offering unique advantages for different applications. Furthermore, we highlighted the importance of an accurate host reference genome, noting that its absence negatively affected the decontamination performance across all tools.

CONCLUSIONS

Our findings underscore the need for careful selection of decontamination tools and reference genomes to enhance the accuracy of metagenomic analyses. These insights provide valuable guidance for improving the reliability and reproducibility of microbiome research.

Incomplete human reference genomes can drive false sex biases and expose patient-identifying information in metagenomic data

As next-generation sequencing technologies produce deeper genome coverages at lower costs, there is a critical need for reliable computational host DNA removal in metagenomic data. We find that insufficient host filtration using prior human genome references can introduce false sex biases and inadvertently permit flow-through of host-specific DNA during bioinformatic analyses, which could be exploited for individual identification. To address these issues, we introduce and benchmark three host filtration methods of varying throughput, with concomitant applications across low biomass samples such as skin and high microbial biomass datasets including fecal samples. We find that these methods are important for obtaining accurate results in low biomass samples (e.g., tissue, skin). Overall, we demonstrate that rigorous host filtration is a key component of privacy-minded analyses of patient microbiomes and provide computationally efficient pipelines for accomplishing this task on large-scale datasets.

Investigating Anthrax-Associated Virulence Genes among Archival and Contemporary Bacillus cereus Group Genomes

Bacillus anthracis causes anthrax through virulence factors encoded on two plasmids. However, non-B. anthracis organisms within the closely related, environmentally ubiquitous Bacillus cereus group (BCG) may cause an anthrax-like disease in humans through the partial adoption of anthrax-associated virulence genes, challenging the definition of anthrax disease. To elucidate these phenomena and their evolutionary past, we performed whole-genome sequencing on non-anthracis BCG isolates, including 93 archival (1967-2003) and 5 contemporary isolates (2019-2023). We produced annotated genomic assemblies and performed a pan-genome analysis to identify evidence of virulence gene homology and virulence gene acquisition by linear inheritance or horizontal gene transfer. At least one anthrax-associated virulence gene was annotated in ten isolates. Most homologous sequences in archival isolates showed evidence of pseudogenization and subsequent gene loss. The presence or absence of accessory genes, including anthrax-associated virulence genes, aligned with the phylogenetic structure of the BCG core genome. These findings support the hypothesis that anthrax-associated virulence genes were inherited from a common ancestor in the BCG and were retained or lost across different lineages, and contribute to a growing body of work informing public health strategies related to anthrax surveillance and identification.

SWGTS-a platform for stream-based host DNA depletion

MOTIVATION

Microbial sequencing data from clinical samples is often contaminated with human sequences, which have to be removed prior to sharing. Existing methods for human read removal, however, are applicable only after the target dataset has been retrieved in its entirety, putting the recipient at least temporarily in control of a potentially identifiable genetic dataset with potential implications under regulatory frameworks such as the GDPR. In some instances, the ability to carry out stream-based host depletion as part of the data transfer process may be preferable.

RESULTS

We present SWGTS, a client-server application for the transfer and stream-based host depletion of sequencing reads. SWGTS enforces a robust upper bound on the maximum amount of human genetic data from any one client held in memory at any point in time by storing all incoming sequencing data in a limited-size, client-specific intermediate processing buffer, and by throttling the rate of incoming data if it exceeds the speed of host depletion carried out on the SWGTS server in the background. SWGTS exposes a HTTP-REST interface, is implemented using docker-compose, Redis and traefik, and requires less than 8 Gb of RAM for deployment. We demonstrate high filtering accuracy of SWGTS; incoming data transfer rates of up to 1.65 megabases per second in a conservative configuration; and mitigation of re-identification risks by the ability to limit the number of SNPs present on a popular population-scale genotyping array covered by reads in the SWGTS buffer to a low user-defined number, such as 10 or 100.

AVAILABILITY AND IMPLEMENTATION

SWGTS is available on GitHub: https://github.com/AlBi-HHU/swgts (https://doi.org/10.5281/zenodo.10891052). The repository also contains a jupyter notebook that can be used to reproduce all the benchmarks used in this article. All datasets used for benchmarking are publicly available.

Long-Read Nanopore-Based Sequencing of Anelloviruses

Routinely used metagenomic next-generation sequencing (mNGS) techniques often fail to detect low-level viremia (<104 copies/mL) and appear biased towards viruses with linear genomes. These limitations hinder the capacity to comprehensively characterize viral infections, such as those attributed to the Anelloviridae family. These near ubiquitous non-pathogenic components of the human virome have circular single-stranded DNA genomes that vary in size from 2.0 to 3.9 kb and exhibit high genetic diversity. Hence, species identification using short reads can be challenging. Here, we introduce a rolling circle amplification (RCA)-based metagenomic sequencing protocol tailored for circular single-stranded DNA genomes, utilizing the long-read Oxford Nanopore platform. The approach was assessed by sequencing anelloviruses in plasma drawn from people who inject drugs (PWID) in two geographically distinct cohorts. We detail the methodological adjustments implemented to overcome difficulties inherent in sequencing circular genomes and describe a computational pipeline focused on anellovirus detection. We assessed our protocol across various sample dilutions and successfully differentiated anellovirus sequences in conditions simulating mixed infections. This method provides a robust framework for the comprehensive characterization of circular viruses within the human virome using the Oxford Nanopore.

Pangenome databases improve host removal and mycobacteria classification from clinical metagenomic data

BACKGROUND

Culture-free real-time sequencing of clinical metagenomic samples promises both rapid pathogen detection and antimicrobial resistance profiling. However, this approach introduces the risk of patient DNA leakage. To mitigate this risk, we need near-comprehensive removal of human DNA sequences at the point of sequencing, typically involving the use of resource-constrained devices. Existing benchmarks have largely focused on the use of standardized databases and largely ignored the computational requirements of depletion pipelines as well as the impact of human genome diversity.

RESULTS

We benchmarked host removal pipelines on simulated and artificial real Illumina and Nanopore metagenomic samples. We found that construction of a custom kraken database containing diverse human genomes results in the best balance of accuracy and computational resource usage. In addition, we benchmarked pipelines using kraken and minimap2 for taxonomic classification of Mycobacterium reads using standard and custom databases. With a database representative of the Mycobacterium genus, both tools obtained improved specificity and sensitivity, compared to the standard databases for classification of Mycobacterium tuberculosis. Computational efficiency of these custom databases was superior to most standard approaches, allowing them to be executed on a laptop device.

CONCLUSIONS

Customized pangenome databases provide the best balance of accuracy and computational efficiency when compared to standard databases for the task of human read removal and M. tuberculosis read classification from metagenomic samples. Such databases allow for execution on a laptop, without sacrificing accuracy, an especially important consideration in low-resource settings. We make all customized databases and pipelines freely available.