CS-SCORE: Rapid identification and removal of human genome contaminants from metagenomic datasets

Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools

BACKGROUND

Although the spread of antimicrobial resistance (AMR) through food and its production poses a significant concern, there is limited research on the prevalence of AMR bacteria in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance targets within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches.

RESULTS

We simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Kraken2/Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2/Bracken indicated presence of organisms not included in the synthetic metagenomes. Metaphlan3/Metaphlan4 analysis of community composition was more specific but with lower sensitivity than the Kraken2/Bracken analysis. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 (which allows read to map to multiple targets) falsely reported presence of distantly related ARGs at all isolate genome coverage levels. The presence of background microbiota in metagenomes influenced the accuracy of ARG detection by KMA, resulting in mcr-1 detection at 0.1X isolate coverage in the lettuce but not in the beef metagenome.

CONCLUSIONS

This study demonstrates accurate detection of ARGs in synthetic metagenomes using various bioinformatic methods, provided that reads from the ARG-encoding organism exceed approximately 5X isolate coverage (i.e. 0.4% of a 40 million read metagenome). While lowering thresholds for target gene detection improved sensitivity, this led to the identification of alternative ARG-alleles, potentially confounding the identification of critical ARGs in the resistome. Further advancements in sequencing technologies providing increased coverage depth or extended read lengths may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking clinically important ARGs in agri-food samples.

Background Filtering of Clinical Metagenomic Sequencing with a Library Concentration-Normalized Model

Metagenomic next-generation sequencing (mNGS) can accurately detect pathogens in clinical samples. However, wet-lab contamination constrains mNGS analysis and may result in erroneous interpretation of results. Many existing methods rely on large-scale observational microbiome studies and may not be applicable to clinical mNGS tests. By generation of a pretrained profile of common laboratory contaminants, we developed an mNGS noise-filtering model based on the inverse linear relationship between microbial sequencing reads and sample library concentration, named the background elimination and correction by library concentration-normalized (BECLEAN) model. Its efficacy was evaluated with bacteria- and yeast-spiked samples and 28 cerebrospinal fluid (CSF) specimens. The diagnostic accuracy, precision, sensitivity, and specificity of BECLEAN with reference to conventional methods and diagnosis were 92.9%, 86.7%, 100%, and 86.7%, respectively. BECLEAN led to a dramatic reduction of background noise without affecting the true-positive rate and thus can provide a time-saving and convenient tool in various clinical settings. IMPORTANCE Most of the existing methods to remove wet-lab contamination rely on large-scale observational microbiome studies and may not be applicable to clinical mNGS testing in individual cases. In clinical settings, only a handful of samples might be sequenced in a run. The lab-specific microbiome can complicate existing statistical approaches for removing contamination from small-scale clinical metagenomic sequencing data sets; thus, use of a preliminary lab-specific training set is necessary. Our study provides a rapid and accurate background-filtering tool for clinical metagenomic sequencing by generation of a pretrained profile of common laboratory contaminants. Notably, our work demonstrates that the inverse linear relationship between microbial sequencing reads and library concentration can serve to identify true contaminants and evaluate the relative abundance of a taxon in samples by comparing the observed microbial reads to the model-predicted value. Our findings extend the previously published research and demonstrate confirmatory results in clinical settings.

High-Throughput Metagenomics for Identification of Pathogens in the Clinical Settings

The application of sequencing technology is shifting from research to clinical laboratories owing to rapid technological developments and substantially reduced costs. However, although thousands of microorganisms are known to infect humans, identification of the etiological agents for many diseases remains challenging as only a small proportion of pathogens are identifiable by the current diagnostic methods. These challenges are compounded by the emergence of new pathogens. Hence, metagenomic next-generation sequencing (mNGS), an agnostic, unbiased, and comprehensive method for detection, and taxonomic characterization of microorganisms, has become an attractive strategy. Although many studies, and cases reports, have confirmed the success of mNGS in improving the diagnosis, treatment, and tracking of infectious diseases, several hurdles must still be overcome. It is, therefore, imperative that practitioners and clinicians understand both the benefits and limitations of mNGS when applying it to clinical practice. Interestingly, the emerging third-generation sequencing technologies may partially offset the disadvantages of mNGS. In this review, mainly: a) the history of sequencing technology; b) various NGS technologies, common platforms, and workflows for clinical applications; c) the application of NGS in pathogen identification; d) the global expert consensus on NGS-related methods in clinical applications; and e) challenges associated with diagnostic metagenomics are described.

Evaluation of methods for detecting human reads in microbial sequencing datasets

Sequencing data from host-associated microbes can often be contaminated by the body of the investigator or research subject. Human DNA is typically removed from microbial reads either by subtractive alignment (dropping all reads that map to the human genome) or by using a read classification tool to predict those of human origin, and then discarding them. To inform best practice guidelines, we benchmarked eight alignment-based and two classification-based methods of human read detection using simulated data from 10 clinically prevalent bacteria and three viruses, into which contaminating human reads had been added. While the majority of methods successfully detected >99 % of the human reads, they were distinguishable by variance. The most precise methods, with negligible variance, were Bowtie2 and SNAP, both of which misidentified few, if any, bacterial reads (and no viral reads) as human. While correctly detecting a similar number of human reads, methods based on taxonomic classification, such as Kraken2 and Centrifuge, could misclassify bacterial reads as human, although the extent of this was species-specific. Among the most sensitive methods of human read detection was BWA, although this also made the greatest number of false positive classifications. Across all methods, the set of human reads not identified as such, although often representing <0.1 % of the total reads, were non-randomly distributed along the human genome with many originating from the repeat-rich sex chromosomes. For viral reads and longer (>300 bp) bacterial reads, the highest performing approaches were classification-based, using Kraken2 or Centrifuge. For shorter (c. 150 bp) bacterial reads, combining multiple methods of human read detection maximized the recovery of human reads from contaminated short read datasets without being compromised by false positives. A particularly high-performance approach with shorter bacterial reads was a two-stage classification using Bowtie2 followed by SNAP. Using this approach, we re-examined 11 577 publicly archived bacterial read sets for hitherto undetected human contamination. We were able to extract a sufficient number of reads to call known human SNPs, including those with clinical significance, in 6 % of the samples. These results show that phenotypically distinct human sequence is detectable in publicly archived microbial read datasets.

PriLive: privacy-preserving real-time filtering for next-generation sequencing

Motivation

In next-generation sequencing, re-identification of individuals and other privacy-breaching strategies can be applied even for anonymized data. This also holds true for applications in which human DNA is acquired as a by-product, e.g. for viral or metagenomic samples from a human host. Conventional data protection strategies including cryptography and post-hoc filtering are only appropriate for the final and processed sequencing data. This can result in an insufficient level of data protection and a considerable time delay in the further analysis workflow.

Results

We present PriLive, a novel tool for the automated removal of sensitive data while the sequencing machine is running. Thereby, human sequence information can be detected and removed before being completely produced. This facilitates the compliance with strict data protection regulations. The unique characteristic to cause almost no time delay for further analyses is also a clear benefit for applications other than data protection. Especially if the sequencing data are dominated by known background signals, PriLive considerably accelerates consequent analyses by having only fractions of input data. Besides these conceptual advantages, PriLive achieves filtering results at least as accurate as conventional post-hoc filtering tools.

Availability and implementation

PriLive is open-source software available at https://gitlab.com/rki_bioinformatics/PriLive.

Supplementary information

Supplementary data are available at Bioinformatics online.

Insights into the human oral microbiome

Human oral cavity harbors the second most abundant microbiota after the gastrointestinal tract. The expanded Human Oral Microbiome Database (eHOMD) that was last updated on November 22, 2017, contains the information of approximately 772 prokaryotic species, where 70% is cultivable, and 30% belong to the uncultivable class of microorganisms along with whole genome sequences of 482 taxa. Out of 70% culturable species, 57% have already been assigned to their names. The 16S rDNA profiling of the healthy oral cavity categorized the inhabitant bacteria into six broad phyla, viz. Firmicutes, Actinobacteria, Proteobacteria, Fusobacteria, Bacteroidetes and Spirochaetes constituting 96% of total oral bacteria. These hidden oral micro-inhabitants exhibit a direct influence on human health, from host's metabolism to immune responses. Altered oral microflora has been observed in several diseases such as diabetes, bacteremia, endocarditis, cancer, autoimmune disease and preterm births. Therefore, it becomes crucial to understand the oral microbial diversity and how it fluctuates under diseased/perturbed conditions. Advances in metagenomics and next-generation sequencing techniques generate rapid sequences and provide extensive information of inhabitant microorganisms of a niche. Thus, the retrieved information can be utilized for developing microbiome-based biomarkers for their use in early diagnosis of oral and associated diseases. Besides, several apex companies have shown keen interest in oral microbiome for its diagnostic and therapeutic potential indicating a vast market opportunity. This review gives an insight of various associated aspects of the human oral microbiome.

Viromes As Genetic Reservoir for the Microbial Communities in Aquatic Environments: A Focus on Antimicrobial-Resistance Genes

Despite studies of viromes isolated from aquatic environments are becoming increasingly frequent, most of them are limited to the characterization of viral taxonomy. Bacterial reads in viromes are abundant but the extent to which this genetic material is playing a role in the ecology of aquatic microbiology remains unclear. To this aim, we developed of a useful approach for the characterization of viral and microbial communities of aquatic environments with a particular focus on the identification of microbial genes harbored in the viromes. Virus-like particles were isolated from water samples collected across the Lambro River, from the spring to the high urbanized Milan area. The derived viromes were analyzed by shotgun metagenomic sequencing looking for the presence, relative abundance of bacterial genes with particular focus on those genes involved in antimicrobial resistance mechanisms. Antibiotic and heavy metal resistance genes have been identified in all virome samples together with a high abundance of reads assigned to cellular processes and signaling. Virome data compared to those identified in the microbiome isolated from the same sample revealed differences in terms of functional categories and their relative abundance. To verify the role of aquatic viral population in bacterial gene transfer, water-based mesocosms were perturbed or not perturbed with a low dose of tetracycline. The results obtained by qPCR assays revealed variation in abundance of tet genes in the virome and microbiome highlighting a relevant role of viral populations in microbial gene mobilization.

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.