BugSeq: a highly accurate cloud platform for long-read metagenomic analyses

Unveiling microbial diversity: harnessing long-read sequencing technology

Long-read sequencing has recently transformed metagenomics, enhancing strain-level pathogen characterization, enabling accurate and complete metagenome-assembled genomes, and improving microbiome taxonomic classification and profiling. These advancements are not only due to improvements in sequencing accuracy, but also happening across rapidly changing analysis methods. In this Review, we explore long-read sequencing's profound impact on metagenomics, focusing on computational pipelines for genome assembly, taxonomic characterization and variant detection, to summarize recent advancements in the field and provide an overview of available analytical methods to fully leverage long reads. We provide insights into the advantages and disadvantages of long reads over short reads and their evolution from the early days of long-read sequencing to their recent impact on metagenomics and clinical diagnostics. We further point out remaining challenges for the field such as the integration of methylation signals in sub-strain analysis and the lack of benchmarks.

Mora: abundance aware metagenomic read re-assignment for disentangling similar strains

BACKGROUND

Taxonomic classification of reads obtained by metagenomic sequencing is often a first step for understanding a microbial community, but correctly assigning sequencing reads to the strain or sub-species level has remained a challenging computational problem.

RESULTS

We introduce Mora, a MetagenOmic read Re-Assignment algorithm capable of assigning short and long metagenomic reads with high precision, even at the strain level. Mora is able to accurately re-assign reads by first estimating abundances through an expectation-maximization algorithm and then utilizing abundance information to re-assign query reads. The key idea behind Mora is to maximize read re-assignment qualities while simultaneously minimizing the difference from estimated abundance levels, allowing Mora to avoid over assigning reads to the same genomes. On simulated diverse reads, this allows Mora to achieve F1 scores comparable to other algorithms while having less runtime. However, Mora significantly outshines other algorithms on very similar reads. We show that the high penalty of over assigning reads to a common reference genome allows Mora to accurately infer correct strains for real data in the form of E. coli reads.

CONCLUSIONS

Mora is a fast and accurate read re-assignment algorithm that is modularized, allowing it to be incorporated into general metagenomics and genomics workflows. It is freely available at https://github.com/AfZheng126/MORA .

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.

Optimized bacterial community characterization through full-length 16S rRNA gene sequencing utilizing MinION nanopore technology

BACKGROUND

Accurate identification of bacterial communities is crucial for research applications, diagnostics, and clinical interventions. Although 16S ribosomal RNA (rRNA) gene sequencing is a widely employed technique for bacterial taxonomic classification, it often results in misclassified or unclassified bacterial taxa. This study sought to refine the full-length 16S rRNA gene sequencing protocol using the MinION sequencer, focusing on the V1-V9 regions. Our methodological enquiry examined several factors, including the number of PCR amplification cycles, choice of primers and Taq polymerase, and specific sequence databases and workflows employed. We used a microbial standard comprising eight bacterial strains (five gram-positive and three gram-negative) in known proportions as a validation control.

RESULTS

Based on the MinION protocol, we employed the microbial standard as the DNA template for the 16S rRNA gene amplicon sequencing procedure. Our analysis showed that an elevated number of PCR amplification cycles introduced PCR bias, and the selection of Taq polymerase and primer sets significantly affected the subsequent analysis. Bacterial identification at genus level demonstrated Pearson correlation coefficients ranging from 0.73 to 0.79 when assessed using BugSeq, Kraken-Silva and EPI2ME-16S workflows. Notably, the EPI2ME-16S workflow exhibited the highest Pearson correlation with the microbial standard, minimised misclassification, and increased alignment accuracy. At the species taxonomic level, the BugSeq workflow was superior, with a Pearson correlation coefficient of 0.92.

CONCLUSIONS

These findings emphasise the importance of careful selection of PCR settings and a well-structured analytical framework for 16S rRNA full-length gene sequencing. The results showed a robust correlation between the predicted and observed bacterial abundances at both the genus and species taxonomic levels, making these findings applicable across diverse research contexts and with clinical utility for reliable pathogen identification.

Unraveling metagenomics through long-read sequencing: a comprehensive review

The study of microbial communities has undergone significant advancements, starting from the initial use of 16S rRNA sequencing to the adoption of shotgun metagenomics. However, a new era has emerged with the advent of long-read sequencing (LRS), which offers substantial improvements over its predecessor, short-read sequencing (SRS). LRS produces reads that are several kilobases long, enabling researchers to obtain more complete and contiguous genomic information, characterize structural variations, and study epigenetic modifications. The current leaders in LRS technologies are Pacific Biotechnologies (PacBio) and Oxford Nanopore Technologies (ONT), each offering a distinct set of advantages. This review covers the workflow of long-read metagenomics sequencing, including sample preparation (sample collection, sample extraction, and library preparation), sequencing, processing (quality control, assembly, and binning), and analysis (taxonomic annotation and functional annotation). Each section provides a concise outline of the key concept of the methodology, presenting the original concept as well as how it is challenged or modified in the context of LRS. Additionally, the section introduces a range of tools that are compatible with LRS and can be utilized to execute the LRS process. This review aims to present the workflow of metagenomics, highlight the transformative impact of LRS, and provide researchers with a selection of tools suitable for this task.

Comparative analysis of metagenomic classifiers for long-read sequencing datasets

BACKGROUND

Long reads have gained popularity in the analysis of metagenomics data. Therefore, we comprehensively assessed metagenomics classification tools on the species taxonomic level. We analysed kmer-based tools, mapping-based tools and two general-purpose long reads mappers. We evaluated more than 20 pipelines which use either nucleotide or protein databases and selected 13 for an extensive benchmark. We prepared seven synthetic datasets to test various scenarios, including the presence of a host, unknown species and related species. Moreover, we used available sequencing data from three well-defined mock communities, including a dataset with abundance varying from 0.0001 to 20% and six real gut microbiomes.

RESULTS

General-purpose mappers Minimap2 and Ram achieved similar or better accuracy on most testing metrics than best-performing classification tools. They were up to ten times slower than the fastest kmer-based tools requiring up to four times less RAM. All tested tools were prone to report organisms not present in datasets, except CLARK-S, and they underperformed in the case of the high presence of the host's genetic material. Tools which use a protein database performed worse than those based on a nucleotide database. Longer read lengths made classification easier, but due to the difference in read length distributions among species, the usage of only the longest reads reduced the accuracy. The comparison of real gut microbiome datasets shows a similar abundance profiles for the same type of tools but discordance in the number of reported organisms and abundances between types. Most assessments showed the influence of database completeness on the reports.

CONCLUSION

The findings indicate that kmer-based tools are well-suited for rapid analysis of long reads data. However, when heightened accuracy is essential, mappers demonstrate slightly superior performance, albeit at a considerably slower pace. Nevertheless, a combination of diverse categories of tools and databases will likely be necessary to analyse complex samples. Discrepancies observed among tools when applied to real gut datasets, as well as a reduced performance in cases where unknown species or a significant proportion of the host genome is present in the sample, highlight the need for continuous improvement of existing tools. Additionally, regular updates and curation of databases are important to ensure their effectiveness.

WGS of a cluster of MDR Shigella sonnei utilizing Oxford Nanopore R10.4.1 long-read sequencing

OBJECTIVES

To utilize long-read nanopore sequencing (R10.4.1 flowcells) for WGS of a cluster of MDR Shigella sonnei, specifically characterizing genetic predictors of antimicrobial resistance (AMR).

METHODS

WGS was performed on S. sonnei isolates identified from stool and blood between September 2021 and October 2022. Bacterial DNA from clinical isolates was extracted on the MagNA Pure 24 and sequenced on the GridION utilizing R10.4.1 flowcells. Phenotypic antimicrobial susceptibility testing was interpreted based on CLSI breakpoints. Sequencing data were processed with BugSeq, and AMR was assessed with BugSplit and ResFinder.

RESULTS

Fifty-six isolates were sequenced, including 53 related to the cluster of cases. All cluster isolates were identified as S. sonnei by sequencing, with global genotype 3.6.1.1.2 (CipR.MSM5), MLST 152 and PopPUNK cluster 3. Core genome MLST (cgMLST, examining 2513 loci) and reference-based MLST (refMLST, examining 4091 loci) both confirmed the clonality of the isolates. Cluster isolates were resistant to ampicillin (blaTEM-1), trimethoprim/sulfamethoxazole (dfA1, dfrA17; sul1, sul2), azithromycin (ermB, mphA) and ciprofloxacin (gyrA S83L, gyrA D87G, parC S80I). No genomic predictors of resistance to carbapenems were identified.

CONCLUSIONS

WGS with R10.4.1 enabled rapid sequencing and identification of an MDR S. sonnei community cluster. Genetic predictors of AMR were concordant with phenotypic antimicrobial susceptibility testing.

Screening Host Genomic Data for Wolbachia Infections

Less than a decade ago, the production of Wolbachia genomic assemblies was tedious, time-consuming, and expensive. The production of Wolbachia genomic DNA free of contamination from host DNA, as required for Wolbachia-targeted sequencing, was then only possible after the amplification and extraction of a large amount of clonal Wolbachia DNA. However, as an endosymbiotic bacterium, Wolbachia does not grow outside the host cell environment, and large-scale recovery of the bacteria required mass rearing of their host, preferably clones of a single individual to avoid strain genetic diversity, or amplification of cell cultures infected with a single Wolbachia strain. Bacterial DNA could be separated from host DNA based on genomic size. Nowadays, the production of full Wolbachia genomes does not require the physical isolation of the bacterial strains from their respective hosts, and the bacterium is often sequenced as a by-catch of host genomic projects. Here, we provide a step-by-step protocol to (1) identify whether host genome projects contain reads from associated Wolbachia and (2) isolate/retrieve the Wolbachia reads from the rest of the sequenced material. We hope this simple protocol will support many projects aiming at studying diverse Wolbachia genome assemblies.

Nanopore-Sequencing Metabarcoding for Identification of Phytopathogenic and Endophytic Fungi in Olive (Olea europaea) Twigs

Metabarcoding approaches for the identification of plant disease pathogens and characterization of plant microbial populations constitute a rapidly evolving research field. Fungal plant diseases are of major phytopathological concern; thus, the development of metabarcoding approaches for the detection of phytopathogenic fungi is becoming increasingly imperative in the context of plant disease prognosis. We developed a multiplex metabarcoding method for the identification of fungal phytopathogens and endophytes in olive young shoots, using the MinION sequencing platform (Oxford Nanopore Technologies). Selected fungal-specific primers were used to amplify three different genomic DNA loci (ITS, beta-tubulin, and 28S LSU) originating from olive twigs. A multiplex metabarcoding approach was initially evaluated using healthy olive twigs, and further assessed with naturally infected olive twig samples. Bioinformatic analysis of basecalled reads was carried out using MinKNOW, BLAST+ and R programming, and results were also evaluated using the BugSeq cloud platform. Data analysis highlighted the approaches based on ITS and their combination with beta-tubulin as the most informative ones according to diversity estimations. Subsequent implementation of the method on symptomatic samples identified major olive pathogens and endophytes including genera such as Cladosporium, Didymosphaeria, Paraconiothyrium, Penicillium, Phoma, Verticillium, and others.

Dysbiosis of the Beneficial Gut Bacteria in Patients with Parkinson's Disease from India

Objectives

Recent advancement in understanding neurological disorders has revealed the involvement of dysbiosis of the gut microbiota in the pathophysiology of Parkinson's disease (PD). We sequenced microbial DNA using fecal samples collected from PD cases and healthy controls (HCs) to evaluate the role of gut microbiota.

Methods

Full-length bacterial 16S rRNA gene sequencing of fecal samples was performed using amplified polymerase chain reaction (PCR) products on the GridION Nanopore sequencer. Sequenced data were analyzed using web-based tools BugSeq and MicrobiomeAnalyst.

Results

We found that certain bacterial families like Clostridia UCG 014, Cristensenellaceae, and Oscillospiraceae are higher in abundance, and Lachinospiracea, Coriobacteriaceae and genera associated with short-chain fatty acid production, Faecalibacterium, Fusicatenibacter, Roseburia and Blautia, are lower in abundance among PD cases when compared with the HC. Genus Akkermansia, Dialister, Bacteroides, and Lachnospiraceae NK4A136 group positively correlated with constipation in PD.

Conclusion

Observations from this study support the other global research on the PD gut microbiome background and provide fresh insight into the gut microbial composition of PD patients from a south Indian population. We report a higher abundance of Clostridia UCG 014 group, previously not linked to PD.

Threat Net: A Metagenomic Surveillance Network for Biothreat Detection and Early Warning

Early detection of novel pathogens can prevent or substantially mitigate biological incidents, including pandemics. Metagenomic next-generation sequencing (mNGS) of symptomatic clinical samples may enable detection early enough to contain outbreaks, limit international spread, and expedite countermeasure development. In this article, we propose a clinical mNGS architecture we call "Threat Net," which focuses on the hospital emergency department as a high-yield surveillance location. We develop a susceptible-exposed-infected-removed (SEIR) simulation model to estimate the effectiveness of Threat Net in detecting novel respiratory pathogen outbreaks. Our analysis serves to quantify the value of routine clinical mNGS for respiratory pandemic detection by estimating the cost and epidemiological effectiveness at differing degrees of hospital coverage across the United States. We estimate that a biological threat detection network such as Threat Net could be deployed across hospitals covering 30% of the population in the United States. Threat Net would cost between $400 million and $800 million annually and have a 95% chance of detecting a novel respiratory pathogen with traits of SARS-CoV-2 after 10 emergency department presentations and 79 infections across the United States. Our analyses suggest that implementing Threat Net could help prevent or substantially mitigate the spread of a respiratory pandemic pathogen in the United States.

No Isolate, No Problem: Using a Novel Insertion Sequence PCR to Link Rats to Human Shigellosis Cases in an Underserved Urban Community

During an investigation into a cluster of Shigella flexneri serotype 2a cases in an underserved community, we assessed the relatedness of human and rat S. flexneri isolates utilizing a novel PCR targeting insertion sites (IS-PCR) of mobile elements in the Shigella genome characteristic of the cluster strain. Whole-genome sequences of S. flexneri (n = 50) associated with the cluster were analyzed. De novo genome assemblies were analyzed by a Geneious V10.2.6 motif search, and two unique IS were identified in all human Shigella sequences of the local cluster. Hydrolysis probe PCR assays were designed to detect these sequences consisting of forward and reverse primers to amplify across each insertion site and a hydrolysis probe spanning the insertion site. IS-PCR was performed for three Shigella PCR-positive culture-negative rat intestine specimens from this community. Both insertion sites were detected in the de novo genome assemblies of all clinical S. flexneri isolates (n = 50). Two of the three PCR-positive culture-negative rat samples were positive for both unique ISs identified in the human S. flexneri isolates, suggesting that the rat Shigella species strains were closely related to the human strains in the cluster. The cycle threshold (Ct) values were >35, indicating that the bacterial load was very low in the rat samples. Two unique IS were identified in clinical isolates from a community S. flexneri cluster. Both IS targets were identified in PCR-positive (Shigella spp.), culture-negative rat tissue and clinical isolates from humans, indicating relatedness. IMPORTANCE This article describes a novel molecular method to show relatedness between bacterial infections, which may not be able to grow in the laboratory due to treatment with antibiotics or for bacteria requiring unique conditions to grow well. Uniquely, we applied this technique to Shigella isolates from human cases associated with a local cluster in an underserved community, as well as rat samples from the same community. We believe that this novel approach can serve as a complementary method to support outbreak/cluster investigation for Shigella spp.

Understanding the impact of spinal cord injury on the microbiota of healthy skin and pressure injuries

Pressure injuries (PI) are a common issue among individuals with spinal cord injury (SCI), especially in the sitting areas of the body. Considering the risk of infections occurring to PI during the wound healing process, the skin microbiome is likely to be a source of bacteria. We investigated the relationship between skin and PI microbiomes, and assessed any correlation with clinically relevant outcomes related to PI. Samples were isolated from SCI patients undergoing reconstructive surgery of PI, severity grades III and IV. DNA samples from skin and PI were analysed using 16S rRNA gene sequencing. Our results showed disparities in microbiome composition between skin and PI. The skin had lower diversity, while PI showed increased bacterial homogeneity as the severity grade progressed. The skin bacterial composition varied based on its location, influenced by Cutibacterium. Compositional differences were identified between PI grades III and IV, with clusters of bacteria colonizing PI, characterized by Pseudomonas, Proteus and Peptoniphilus. The skin and PI microbiomes were not affected by the level of the SCI. Our study highlights the differences in the microbiome of skin and PI in SCI patients. These findings could be used to target specific bacteria for PI treatment in clinical practice.

UltraSEQ, a Universal Bioinformatic Platform for Information-Based Clinical Metagenomics and Beyond

Applied metagenomics is a powerful emerging capability enabling the untargeted detection of pathogens, and its application in clinical diagnostics promises to alleviate the limitations of current targeted assays. While metagenomics offers a hypothesis-free approach to identify any pathogen, including unculturable and potentially novel pathogens, its application in clinical diagnostics has so far been limited by workflow-specific requirements, computational constraints, and lengthy expert review requirements. To address these challenges, we developed UltraSEQ, a first-of-its-kind accurate and scalable metagenomic bioinformatic tool for potential clinical diagnostics and biosurveillance utility. Here, we present the results of the evaluation of our novel UltraSEQ pipeline using an in silico-synthesized metagenome, mock microbial community data sets, and publicly available clinical data sets from samples of different infection types, including both short-read and long-read sequencing data. Our results show that UltraSEQ successfully detected all expected species across the tree of life in the in silico sample and detected all 10 bacterial and fungal species in the mock microbial community data set. For clinical data sets, even without requiring data set-specific configuration setting changes, background sample subtraction, or prior sample information, UltraSEQ achieved an overall accuracy of 91%. Furthermore, as an initial demonstration with a limited patient sample set, we show UltraSEQ's ability to provide antibiotic resistance and virulence factor genotypes that are consistent with phenotypic results. Taken together, the above-described results demonstrate that the UltraSEQ platform offers a transformative approach for microbial and metagenomic sample characterization, employing a biologically informed detection logic, deep metadata, and a flexible system architecture for the classification and characterization of taxonomic origin, gene function, and user-defined functions, including disease-causing infections. IMPORTANCE Traditional clinical microbiology-based diagnostic tests rely on targeted methods that can detect only one to a few preselected organisms or slow, culture-based methods. Although widely used today, these methods have several limitations, resulting in rates of cases of an unknown etiology of infection of >50% for several disease types. Massive developments in sequencing technologies have made it possible to apply metagenomic methods to clinical diagnostics, but current offerings are limited to a specific disease type or sequencer workflow and/or require laboratory-specific controls. The limitations associated with current clinical metagenomic offerings result from the fact that the backend bioinformatic pipelines are optimized for the specific parameters described above, resulting in an excess of unmaintained, redundant, and niche tools that lack standardization and explainable outputs. In this paper, we demonstrate that UltraSEQ uses a novel, information-based approach that enables accurate, evidence-based predictions for diagnosis as well as the functional characterization of a sample.

Benchmarking microbial DNA enrichment protocols from human intestinal biopsies

Shotgun metagenomic sequencing is a powerful tool for studying bacterial communities in their natural habitats or sites of infection, without the need for cultivation. However, low microbial signals in metagenomic sequencing can be overwhelmed by host DNA contamination, resulting in decreased sensitivity for microbial read detection. Several commercial kits and other methods have been developed to enrich bacterial sequences; however, these assays have not been tested extensively for human intestinal tissues yet. Therefore, the objective of this study was to assess the effectiveness of various wet-lab and software-based approaches for depleting host DNA from microbiome samples. Four different microbiome DNA enrichment methods, namely the NEBNext Microbiome DNA Enrichment kit, Molzym Ultra-Deep Microbiome Prep, QIAamp DNA Microbiome kit, and Zymo HostZERO microbial DNA kit, were evaluated, along with a software-controlled adaptive sampling (AS) approach by Oxford Nanopore Technologies (ONT) providing microbial signal enrichment by aborting unwanted host DNA sequencing. The NEBNext and QIAamp kits proved to be effective in shotgun metagenomic sequencing studies, as they efficiently reduced host DNA contamination, resulting in 24% and 28% bacterial DNA sequences, respectively, compared to <1% in the AllPrep controls. Additional optimization steps using further detergents and bead-beating steps improved the efficacy of less efficient protocols but not of the QIAamp kit. In contrast, ONT AS increased the overall number of bacterial reads resulting in a better bacterial metagenomic assembly with more bacterial contigs with greater completeness compared to non-AS approaches. Additionally, AS also allowed for the recovery of antimicrobial resistance markers and the identification of plasmids, demonstrating the potential utility of AS for targeted sequencing of microbial signals in complex samples with high amounts of host DNA. However, ONT AS resulted in relevant shifts in the observed bacterial abundance, including 2 to 5 times more Escherichia coli reads. Furthermore, a modest enrichment of Bacteroides fragilis and Bacteroides thetaiotaomicron was also observed with AS. Overall, this study provides insight into the efficacy and limitations of various methods for reducing host DNA contamination in human intestinal samples to improve the utility of metagenomic sequencing.

Agnostic Sequencing for Detection of Viral Pathogens

The advent of next-generation sequencing (NGS) technologies has expanded our ability to detect and analyze microbial genomes and has yielded novel molecular approaches for infectious disease diagnostics. While several targeted multiplex PCR and NGS-based assays have been widely used in public health settings in recent years, these targeted approaches are limited in that they still rely on a priori knowledge of a pathogen's genome, and an untargeted or unknown pathogen will not be detected. Recent public health crises have emphasized the need to prepare for a wide and rapid deployment of an agnostic diagnostic assay at the start of an outbreak to ensure an effective response to emerging viral pathogens. Metagenomic techniques can nonspecifically sequence all detectable nucleic acids in a sample and therefore do not rely on prior knowledge of a pathogen's genome. While this technology has been reviewed for bacterial diagnostics and adopted in research settings for the detection and characterization of viruses, viral metagenomics has yet to be widely deployed as a diagnostic tool in clinical laboratories. In this review, we highlight recent improvements to the performance of metagenomic viral sequencing, the current applications of metagenomic sequencing in clinical laboratories, as well as the challenges that impede the widespread adoption of this technology.

Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets

BACKGROUND

Long-read shotgun metagenomic sequencing is gaining in popularity and offers many advantages over short-read sequencing. The higher information content in long reads is useful for a variety of metagenomics analyses, including taxonomic classification and profiling. The development of long-read specific tools for taxonomic classification is accelerating, yet there is a lack of information regarding their relative performance. Here, we perform a critical benchmarking study using 11 methods, including five methods designed specifically for long reads. We applied these tools to several mock community datasets generated using Pacific Biosciences (PacBio) HiFi or Oxford Nanopore Technology sequencing, and evaluated their performance based on read utilization, detection metrics, and relative abundance estimates.

RESULTS

Our results show that long-read classifiers generally performed best. Several short-read classification and profiling methods produced many false positives (particularly at lower abundances), required heavy filtering to achieve acceptable precision (at the cost of reduced recall), and produced inaccurate abundance estimates. By contrast, two long-read methods (BugSeq, MEGAN-LR & DIAMOND) and one generalized method (sourmash) displayed high precision and recall without any filtering required. Furthermore, in the PacBio HiFi datasets these methods detected all species down to the 0.1% abundance level with high precision. Some long-read methods, such as MetaMaps and MMseqs2, required moderate filtering to reduce false positives to resemble the precision and recall of the top-performing methods. We found read quality affected performance for methods relying on protein prediction or exact k-mer matching, and these methods performed better with PacBio HiFi datasets. We also found that long-read datasets with a large proportion of shorter reads (< 2 kb length) resulted in lower precision and worse abundance estimates, relative to length-filtered datasets. Finally, for classification methods, we found that the long-read datasets produced significantly better results than short-read datasets, demonstrating clear advantages for long-read metagenomic sequencing.

CONCLUSIONS

Our critical assessment of available methods provides best-practice recommendations for current research using long reads and establishes a baseline for future benchmarking studies.

Detection of antiviral drug resistance in patients with congenital cytomegalovirus infection using long-read sequencing: a retrospective observational study

BACKGROUND

Congenital human cytomegalovirus (cCMV) infection can cause sensorineural hearing loss and neurodevelopmental disabilities in children. Ganciclovir and valganciclovir (GCV/VGCV) improve long-term audiologic and neurodevelopmental outcomes for patients with cCMV infection; however, antiviral drug resistance has been documented in some cases. Long-read sequencing can be used for the detection of drug resistance mutations. The objective of this study was to develop full-length analysis of UL97 and UL54, target genes with mutations that confer GCV/VGCV resistance using long-read sequencing, and investigate drug resistance mutation in patients with cCMV infection.

METHODS

Drug resistance mutation analysis was retrospectively performed in 11 patients with cCMV infection treated with GCV/VGCV. UL97 and UL54 genes were amplified using blood DNA. The amplicons were sequenced using a long-read sequencer and aligned with the reference gene. Single nucleotide variants were detected and replaced with the reference sequence. The replaced sequence was submitted to a mutation resistance analyzer, which is an open platform for drug resistance mutations.

RESULTS

Two drug resistance mutations (UL54 V823A and UL97 A594V) were found in one patient. Both mutations emerged after 6 months of therapy, where viral load increased. Mutation rates subsided after cessation of GCV/VGCV treatment.

CONCLUSIONS

Antiviral drug resistance can emerge in patients with cCMV receiving long-term therapy. Full-length analysis of UL97 and UL54 via long-read sequencing enabled the rapid and comprehensive detection of drug resistance mutations.

Long-Reads-Based Metagenomics in Clinical Diagnosis With a Special Focus on Fungal Infections

Identification of the causative infectious agent is essential in the management of infectious diseases, with the ideal diagnostic method being rapid, accurate, and informative, while remaining cost-effective. Traditional diagnostic techniques rely on culturing and cell propagation to isolate and identify the causative pathogen. These techniques are limited by the ability and the time required to grow or propagate an agent in vitro and the facts that identification based on morphological traits are non-specific, insensitive, and reliant on technical expertise. The evolution of next-generation sequencing has revolutionized genomic studies to generate more data at a cheaper cost. These are divided into short- and long-read sequencing technologies, depending on the length of reads generated during sequencing runs. Long-read sequencing also called third-generation sequencing emerged commercially through the instruments released by Pacific Biosciences and Oxford Nanopore Technologies, although relying on different sequencing chemistries, with the first one being more accurate both platforms can generate ultra-long sequence reads. Long-read sequencing is capable of entirely spanning previously established genomic identification regions or potentially small whole genomes, drastically improving the accuracy of the identification of pathogens directly from clinical samples. Long-read sequencing may also provide additional important clinical information, such as antimicrobial resistance profiles and epidemiological data from a single sequencing run. While initial applications of long-read sequencing in clinical diagnosis showed that it could be a promising diagnostic technique, it also has highlighted the need for further optimization. In this review, we show the potential long-read sequencing has in clinical diagnosis of fungal infections and discuss the pros and cons of its implementation.

Surveillance of Listeria monocytogenes: Early Detection, Population Dynamics, and Quasimetagenomic Sequencing during Selective Enrichment

In this study, we addressed different aspects regarding the implementation of quasimetagenomic sequencing as a hybrid surveillance method in combination with enrichment for early detection of Listeria monocytogenes in the food industry. Different experimental enrichment cultures were used, comprising seven L. monocytogenes strains of different sequence types (STs), with and without a background microbiota community. To assess whether the proportions of the different STs changed over time during enrichment, the growth and population dynamics were assessed using dapE colony sequencing and dapE and 16S rRNA amplicon sequencing. There was a tendency of some STs to have a higher relative abundance during the late stage of enrichment when L. monocytogenes was enriched without background microbiota. When coenriched with background microbiota, the population dynamics of the different STs was more consistent over time. To evaluate the earliest possible time point during enrichment that allows the detection of L. monocytogenes and at the same time the generation of genetic information that enables an estimation regarding the strain diversity in a sample, quasimetagenomic sequencing was performed early during enrichment in the presence of the background microbiota using Oxford Nanopore Technologies Flongle and Illumina MiSeq sequencing. The application of multiple displacement amplification (MDA) enabled detection of L. monocytogenes (and the background microbiota) after only 4 h of enrichment using both applied sequencing approaches. The MiSeq sequencing data additionally enabled the prediction of cooccurring L. monocytogenes strains in the samples.

IMPORTANCE We showed that a combination of a short primary enrichment combined with MDA and Nanopore sequencing can accelerate the traditional process of cultivation and identification of L. monocytogenes. The use of Illumina MiSeq sequencing additionally allowed us to predict the presence of cooccurring L. monocytogenes strains. Our results suggest quasimetagenomic sequencing is a valuable and promising hybrid surveillance tool for the food industry that enables faster identification of L. monocytogenes during early enrichment. Routine application of this approach could lead to more efficient and proactive actions in the food industry that prevent contamination and subsequent product recalls and food destruction, economic and reputational losses, and human listeriosis cases.

Next- and Third-Generation Sequencing Outperforms Culture-Based Methods in the Diagnosis of Ascitic Fluid Bacterial Infections of ICU Patients

OBJECTIVES

Infections of the ascitic fluid are serious conditions that require rapid diagnosis and treatment. Ascites is often accompanied by other critical pathologies such as gastrointestinal bleeding and bowel perforation, and infection increases the risk of mortality in intensive care patients. Owing to a relatively low success rate of conventional culture methods in identifying the responsible pathogens, new methods may be helpful to guide antimicrobial therapy and to refine empirical regimens. Here, we aim to assess outcomes and to identify responsible pathogens in ascitic fluid infections, in order to improve patients' care and to guide empirical therapy.

METHODS

Between October 2019 and March 2021, we prospectively collected 50 ascitic fluid samples from ICU patients with suspected infection. Beside standard culture-based microbiology methods, excess fluid underwent DNA isolation and was analyzed by next- and third-generation sequencing (NGS) methods.

RESULTS

NGS-based methods had higher sensitivity in detecting additional pathogenic bacteria such as E. faecalis and Klebsiella in 33 out of 50 (66%) ascitic fluid samples compared with culture-based methods (26%). Anaerobic bacteria were especially identified by sequencing-based methods in 28 samples (56%), in comparison with only three samples in culture. Analysis of clinical data showed a correlation between sequencing results and various clinical parameters such as peritonitis and hospitalization outcomes.

CONCLUSIONS

Our results show that, in ascitic fluid infections, NGS-based methods have a higher sensitivity for the identification of clinically relevant pathogens than standard microbiological culture diagnostics, especially in detecting hard-to-culture anaerobic bacteria. Patients with such infections may benefit from the use of NGS methods by the possibility of earlier and better targeted antimicrobial therapy, which has the potential to lower the high morbidity and mortality in critically ill patients with ascitic bacterial infection.

Nanopore metagenomic sequencing for detection and characterization of SARS-CoV-2 in clinical samples

OBJECTIVES

The COVID-19 pandemic has underscored the need for rapid novel diagnostic strategies. Metagenomic Next-Generation Sequencing (mNGS) may allow for the detection of pathogens that can be missed in targeted assays. The goal of this study was to assess the performance of nanopore-based Sequence-Independent Single Primer Amplification (SISPA) for the detection and characterization of SARS-CoV-2.

METHODS

We performed mNGS on clinical samples and designed a diagnostic classifier that corrects for barcode crosstalk between specimens. Phylogenetic analysis was performed on genome assemblies.

RESULTS

Our assay yielded 100% specificity overall and 95.2% sensitivity for specimens with a RT-PCR cycle threshold value less than 30. We assembled 10 complete, and one near-complete genomes from 20 specimens that were classified as positive by mNGS. Phylogenetic analysis revealed that 10/11 specimens from British Columbia had a closest relative to another British Columbian specimen. We found 100% concordance between phylogenetic lineage assignment and Variant of Concern (VOC) PCR results. Our assay was able to distinguish between the Alpha and Gamma variants, which was not possible with the current standard VOC PCR being used in British Columbia.

CONCLUSIONS

This study supports future work examining the broader feasibility of nanopore mNGS as a diagnostic strategy for the detection and characterization of viral pathogens.

Cloud Computing Enabled Big Multi-Omics Data Analytics

High-throughput experiments enable researchers to explore complex multifactorial diseases through large-scale analysis of omics data. Challenges for such high-dimensional data sets include storage, analyses, and sharing. Recent innovations in computational technologies and approaches, especially in cloud computing, offer a promising, low-cost, and highly flexible solution in the bioinformatics domain. Cloud computing is rapidly proving increasingly useful in molecular modeling, omics data analytics (eg, RNA sequencing, metabolomics, or proteomics data sets), and for the integration, analysis, and interpretation of phenotypic data. We review the adoption of advanced cloud-based and big data technologies for processing and analyzing omics data and provide insights into state-of-the-art cloud bioinformatics applications.

Next-generation sequencing for cytomegalovirus antiviral resistance genotyping in a clinical virology laboratory

INTRODUCTION

The identification of CMV antiviral drug resistance (AVDR) is a critical diagnostic test for immunocompromised patients with CMV infection and a failure of virologic response on optimal antiviral treatment. We developed a next-generation sequencing (NGS) assay for CMV AVDR testing and compared the AVDR mutations identified by NGS to Sanger sequencing.

METHODS

Retrospective review of CMV AVDR testing requests for UL97 and UL54 at our laboratory from 2014 to 2019 was conducted. NGS was performed on the MinION and compared to Sanger sequencing performed at the national reference laboratory. Analysis of the sequences was completed with a novel cloud bioinformatics platform (BugSeq).

RESULTS

Twenty patient samples previously characterized were included for study on the MinION. NGS captured all of the CMV AVDR mutations identified by Sanger, and identified additional mutations in UL97 and/or UL54 in 8/13 (62%) of the samples. An analysis of the depth of coverage at which we no longer detected minority single nucleotide variants (SNVs) detected in the original data was conducted, estimating a recall of 95% at 1800 fold coverage.

CONCLUSION

NGS utilizing MinION technology for the detection of CMV AVDR mutations identified additional minority variants in UL97 and UL54 as compared with Sanger sequencing. Through the application of a bioinformatics pipeline available online, our NGS process eliminates barriers associated with the use of the MinION and NGS in clinical laboratories.