Detection of Mpox Virus Using Microbial Cell-Free DNA: The Potential of Pathogen-Agnostic Sequencing for Rapid Identification of Emerging Pathogens.

BACKGROUND: The 2022 global outbreak of Monkeypox virus (MPXV) highlighted challenges with polymerase chain reaction detection as divergent strains emerged and atypical presentations limited the applicability of swab sampling. Recommended testing in the United States requires a swab of lesions, which arise late in infection and may be unrecognized. We present MPXV detections using plasma microbial cell-free DNA (mcfDNA) sequencing. METHODS: Fifteen plasma samples from 12 case-patients were characterized through mcfDNA sequencing. Assay performance was confirmed through in silico inclusivity and exclusivity assessments. MPXV isolates were genotyped using mcfDNA, and phylodynamic information was imputed using publicly available sequences. RESULTS: MPXV mcfDNA was detected in 12 case-patients. Mpox was not suspected in 5, with 1 having documented resolution of mpox >6 months previously. Six had moderate to severe mpox, supported by high MPXV mcfDNA concentrations; 4 died. In 7 case-patients, mcfDNA sequencing detected coinfections. Genotyping by mcfDNA sequencing identified 22 MPXV mutations at 10 genomic loci in 9 case-patients. Consistent with variation observed in the 2022 outbreak, 21 of 22 variants were G > A/C > T. Phylogenetic analyses imputed isolates to sublineages arising at different time points and from different geographic locations. CONCLUSIONS: We demonstrate the potential of plasma mcfDNA sequencing to detect, quantify, and, for acute infections with high sequencing coverage, subtype MPXV using a single noninvasive test. Sequencing plasma mcfDNA may augment existing mpox testing in vulnerable patient populations or in patients with atypical symptoms or unrecognized mpox. Strain type information may supplement disease surveillance and facilitate tracking emerging pathogens.

Plasma Microbial Cell-Free DNA Sequencing from over 15,000 Patients Identified a Broad Spectrum of Pathogens.

Microbial cell-free DNA (mcfDNA) sequencing is an emerging infectious disease diagnostic tool which enables unbiased pathogen detection and quantification from plasma. The Karius Test, a commercial mcfDNA sequencing assay developed by and available since 2017 from Karius, Inc. (Redwood City, CA), detects and quantifies mcfDNA as molecules/µL in plasma. The commercial sample data and results for all tests conducted from April 2018 through mid-September 2021 were evaluated for laboratory quality metrics, reported pathogens, and data from test requisition forms. A total of 18,690 reports were generated from 15,165 patients in a hospital setting among 39 states and the District of Columbia. The median time from sample receipt to reported result was 26 h (interquartile range [IQR] 25 to 28), and 96% of samples had valid test results. Almost two-thirds (65%) of patients were adults, and 29% at the time of diagnostic testing had ICD-10 codes representing a diverse array of clinical scenarios. There were 10,752 (58%) reports that yielded at least one taxon for a total of 22,792 detections spanning 701 unique microbial taxa. The 50 most common taxa detected included 36 bacteria, 9 viruses, and 5 fungi. Opportunistic fungi (374 Aspergillus spp., 258 Pneumocystis jirovecii, 196 Mucorales, and 33 dematiaceous fungi) comprised 861 (4%) of all detections. Additional diagnostically challenging pathogens (247 zoonotic and vector-borne pathogens, 144 Mycobacterium spp., 80 Legionella spp., 78 systemic dimorphic fungi, 69 Nocardia spp., and 57 protozoan parasites) comprised 675 (3%) of all detections. This is the largest reported cohort of patients tested using plasma mcfDNA sequencing and represents the first report of a clinical grade metagenomic test performed at scale. Data reveal new insights into the breadth and complexity of potential pathogens identified.

PathoLive-Real-Time Pathogen Identification from Metagenomic Illumina Datasets.

Over the past years, NGS has become a crucial workhorse for open-view pathogen diagnostics. Yet, long turnaround times result from using massively parallel high-throughput technologies as the analysis can only be performed after sequencing has finished. The interpretation of results can further be challenged by contaminations, clinically irrelevant sequences, and the sheer amount and complexity of the data. We implemented PathoLive, a real-time diagnostics pipeline for the detection of pathogens from clinical samples hours before sequencing has finished. Based on real-time alignment with HiLive2, mappings are scored with respect to common contaminations, low-entropy areas, and sequences of widespread, non-pathogenic organisms. The results are visualized using an interactive taxonomic tree that provides an easily interpretable overview of the relevance of hits. For a human plasma sample that was spiked in vitro with six pathogenic viruses, all agents were clearly detected after only 40 of 200 sequencing cycles. For a real-world sample from Sudan, the results correctly indicated the presence of Crimean-Congo hemorrhagic fever virus. In a second real-world dataset from the 2019 SARS-CoV-2 outbreak in Wuhan, we found the presence of a SARS coronavirus as the most relevant hit without the novel virus reference genome being included in the database. For all samples, clinically irrelevant hits were correctly de-emphasized. Our approach is valuable to obtain fast and accurate NGS-based pathogen identifications and correctly prioritize and visualize them based on their clinical significance: PathoLive is open source and available on GitLab and BioConda.

Rapid, Noninvasive Diagnosis of Balamuthia mandrillaris Encephalitis by a Plasma-Based Next-Generation Sequencing Test.

Granulomatous amoebic encephalitis (GAE) caused by Balamuthia mandrillaris is a rare subacute infection with exceptionally high mortality. Diagnosis is typically made by brain biopsy or at autopsy. Detection of Balamuthia mandrillaris cell-free DNA by next-generation sequencing of plasma enabled rapid, noninvasive diagnosis in a case of amoebic encephalitis.

Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease.

Thousands of pathogens are known to infect humans, but only a fraction are readily identifiable using current diagnostic methods. Microbial cell-free DNA sequencing offers the potential to non-invasively identify a wide range of infections throughout the body, but the challenges of clinical-grade metagenomic testing must be addressed. Here we describe the analytical and clinical validation of a next-generation sequencing test that identifies and quantifies microbial cell-free DNA in plasma from 1,250 clinically relevant bacteria, DNA viruses, fungi and eukaryotic parasites. Test accuracy, precision, bias and robustness to a number of metagenomics-specific challenges were determined using a panel of 13 microorganisms that model key determinants of performance in 358 contrived plasma samples, as well as 2,625 infections simulated in silico and 580 clinical study samples. The test showed 93.7% agreement with blood culture in a cohort of 350 patients with a sepsis alert and identified an independently adjudicated cause of the sepsis alert more often than all of the microbiological testing combined (169 aetiological determinations versus 132). Among the 166 samples adjudicated to have no sepsis aetiology identified by any of the tested methods, sequencing identified microbial cell-free DNA in 62, likely derived from commensal organisms and incidental findings unrelated to the sepsis alert. Analysis of the first 2,000 patient samples tested in the CLIA laboratory showed that more than 85% of results were delivered the day after sample receipt, with 53.7% of reports identifying one or more microorganisms.

Next-generation sequencing of microbial cell-free DNA for rapid noninvasive diagnosis of infectious diseases in immunocompromised hosts.

Background: Cell-free DNA (cfDNA) sequencing has emerged as an effective laboratory method for rapid and noninvasive diagnosis in prenatal screening testing, organ transplant rejection screening, and oncology liquid biopsies but clinical experience for use of this technology in diagnostic evaluation of infections in immunocompromised hosts is limited.  Methods: We conducted an exploratory study using next-generation sequencing (NGS) for detection of microbial cfDNA in a cohort of ten immunocompromised patients with febrile neutropenia, pneumonia or intra-abdominal infection.  Results: Pathogen identification by cfDNA NGS demonstrated positive agreement with conventional diagnostic laboratory methods in 7 (70%) cases, including patients with proven/probable invasive aspergillosis, Pneumocystis jirovecii pneumonia, Stenotrophomonas maltophilia bacteremia, Cytomegalovirus and Adenovirus viremia. NGS results were discordant in 3 (30%) cases including two patients with culture negative sepsis who had undergone hematopoietic stem cell transplant in whom cfDNA testing identified the potential etiological agent of sepsis; and one kidney transplant recipient with invasive aspergillosis who had received >6 months of antifungal therapy prior to NGS testing. Conclusion: These observations support the clinical utility of measurement of microbial cfDNA sequencing from peripheral blood for rapid noninvasive diagnosis of infections in immunocompromised hosts. Larger studies are needed.

LiveKraken--real-time metagenomic classification of illumina data.

Motivation: In metagenomics, Kraken is one of the most widely used tools due to its robustness and speed. Yet, the overall turnaround time of metagenomic analysis is hampered by the sequential paradigm of wet and dry lab. In urgent experiments, it can be crucial to gain a timely insight into a dataset. Results: Here, we present LiveKraken, a real-time read classification tool based on the core algorithm of Kraken. LiveKraken uses streams of raw data from Illumina sequencers to classify reads taxonomically. This way, we are able to produce results identical to those of Kraken the moment the sequencer finishes. We are furthermore able to provide comparable results in early stages of a sequencing run, allowing saving up to a week of sequencing time on an Illumina HiSeq in High Throughput Mode. While the number of classified reads grows over time, false classifications appear in negligible numbers and proportions of identified taxa are only affected to a minor extent. Availability and implementation: LiveKraken is available at https://gitlab.com/rki_bioinformatics/LiveKraken. Supplementary information: Supplementary data are available at Bioinformatics online.

HiLive: real-time mapping of illumina reads while sequencing.

Motivation: Next Generation Sequencing is increasingly used in time critical, clinical applications. While read mapping algorithms have always been optimized for speed, they follow a sequential paradigm and only start after finishing of the sequencing run and conversion of files. Since Illumina machines write intermediate output results, HiLive performs read mapping while still sequencing and thereby drastically reduces crucial overall sample analysis time, e.g. in precision medicine. Methods: We present HiLive as a novel real time read mapper that implements a k-mer based alignment strategy. HiLive continuously reads intermediate BCL files produced by Illumina sequencers and then extends initial k-mer matches by increasingly produced data from the sequencer. Results: We applied HiLive on real human transcriptome data to show that final read alignments are reported within few minutes after the end of a full Illumina HiSeq 1500 run, while already the necessary conversion to FASTQ files as the standard input to current read mapping methods takes roughly five times as long. Further, we show on simulated and real data that HiLive has comparable accuracy to recent read mappers. Availability and Implementation: HiLive and its source code are freely available from https://gitlab.com/SimonHTausch/HiLive . Contact: renardB@rki.de. Supplementary information: Supplementary data are available at Bioinformatics online.

DUDes: a top-down taxonomic profiler for metagenomics.

MOTIVATION: Species identification and quantification are common tasks in metagenomics and pathogen detection studies. The most recent techniques are built on mapping the sequenced reads against a reference database (e.g. whole genomes, marker genes, proteins) followed by application-dependent analysis steps. Although these methods have been proven to be useful in many scenarios, there is still room for improvement in species and strain level detection, mainly for low abundant organisms. RESULTS: We propose a new method: DUDes, a reference-based taxonomic profiler that introduces a novel top-down approach to analyze metagenomic Next-generation sequencing (NGS) samples. Rather than predicting an organism presence in the sample based only on relative abundances, DUDes first identifies possible candidates by comparing the strength of the read mapping in each node of the taxonomic tree in an iterative manner. Instead of using the lowest common ancestor we propose a new approach: the deepest uncommon descendent. We showed in experiments that DUDes works for single and multiple organisms and can identify low abundant taxonomic groups with high precision. AVAILABILITY AND IMPLEMENTATION: DUDes is open source and it is available at http://sf.net/p/dudes SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: renardB@rki.de.

Metagenomic profiling of known and unknown microbes with microbeGPS.

Microbial community profiling identifies and quantifies organisms in metagenomic sequencing data using either reference based or unsupervised approaches. However, current reference based profiling methods only report the presence and abundance of single reference genomes that are available in databases. Since only a small fraction of environmental genomes is represented in genomic databases, these approaches entail the risk of false identifications and often suggest a higher precision than justified by the data. Therefore, we developed MicrobeGPS, a novel metagenomic profiling approach that overcomes these limitations. MicrobeGPS is the first method that identifies microbiota in the sample and estimates their genomic distances to known reference genomes. With this strategy, MicrobeGPS identifies organisms down to the strain level and highlights possibly inaccurate identifications when the correct reference genome is missing. We demonstrate on three metagenomic datasets with different origin that our approach successfully avoids misleading interpretation of results and additionally provides more accurate results than current profiling methods. Our results indicate that MicrobeGPS can enable reference based taxonomic profiling of complex and less characterized microbial communities. MicrobeGPS is open source and available from https://sourceforge.net/projects/microbegps/ as source code and binary distribution for Windows and Linux operating systems.

Pipasic: similarity and expression correction for strain-level identification and quantification in metaproteomics.

MOTIVATION: Metaproteomic analysis allows studying the interplay of organisms or functional groups and has become increasingly popular also for diagnostic purposes. However, difficulties arise owing to the high sequence similarity between related organisms. Further, the state of conservation of proteins between species can be correlated with their expression level, which can lead to significant bias in results and interpretation. These challenges are similar but not identical to the challenges arising in the analysis of metagenomic samples and require specific solutions. RESULTS: We introduce Pipasic (peptide intensity-weighted proteome abundance similarity correction) as a tool that corrects identification and spectral counting-based quantification results using peptide similarity estimation and expression level weighting within a non-negative lasso framework. Pipasic has distinct advantages over approaches only regarding unique peptides or aggregating results to the lowest common ancestor, as demonstrated on examples of viral diagnostics and an acid mine drainage dataset. AVAILABILITY AND IMPLEMENTATION: Pipasic source code is freely available from https://sourceforge.net/projects/pipasic/. CONTACT: RenardB@rki.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GIIRA--RNA-Seq driven gene finding incorporating ambiguous reads.

MOTIVATION: The reliable identification of genes is a major challenge in genome research, as further analysis depends on the correctness of this initial step. With high-throughput RNA-Seq data reflecting currently expressed genes, a particularly meaningful source of information has become commonly available for gene finding. However, practical application in automated gene identification is still not the standard case. A particular challenge in including RNA-Seq data is the difficult handling of ambiguously mapped reads. RESULTS: We present GIIRA (Gene Identification Incorporating RNA-Seq data and Ambiguous reads), a novel prokaryotic and eukaryotic gene finder that is exclusively based on a RNA-Seq mapping and inherently includes ambiguously mapped reads. GIIRA extracts candidate regions supported by a sufficient number of mappings and reassigns ambiguous reads to their most likely origin using a maximum-flow approach. This avoids the exclusion of genes that are predominantly supported by ambiguous mappings. Evaluation on simulated and real data and comparison with existing methods incorporating RNA-Seq information highlight the accuracy of GIIRA in identifying the expressed genes. AVAILABILITY AND IMPLEMENTATION: GIIRA is implemented in Java and is available from https://sourceforge.net/projects/giira/.

Analyzing genome coverage profiles with applications to quality control in metagenomics.

MOTIVATION: Genome coverage, the number of sequencing reads mapped to a position in a genome, is an insightful indicator of irregularities within sequencing experiments. While the average genome coverage is frequently used within algorithms in computational genomics, the complete information available in coverage profiles (i.e. histograms over all coverages) is currently not exploited to its full extent. Thus, biases such as fragmented or erroneous reference genomes often remain unaccounted for. Making this information accessible can improve the quality of sequencing experiments and quantitative analyses. RESULTS: We introduce a framework for fitting mixtures of probability distributions to genome coverage profiles. Besides commonly used distributions, we introduce distributions tailored to account for common artifacts. The mixture models are iteratively fitted based on the Expectation-Maximization algorithm. We introduce use cases with focus on metagenomics and develop new analysis strategies to assess the validity of a reference genome with respect to (meta-) genomic read data. The framework is evaluated on simulated data as well as applied to a large-scale metagenomic study, for which we compute the validity of 75 microbial genomes. The results indicate that the choice and quality of reference genomes is vital for metagenomic analyses and that validation of coverage profiles is crucial to avoid incorrect conclusions. AVAILABILITY: The code is freely available and can be downloaded from http://sourceforge.net/projects/fitgcp/. CONTACT: RenardB@rki.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metagenomic abundance estimation and diagnostic testing on species level.

One goal of sequencing-based metagenomic community analysis is the quantitative taxonomic assessment of microbial community compositions. In particular, relative quantification of taxons is of high relevance for metagenomic diagnostics or microbial community comparison. However, the majority of existing approaches quantify at low resolution (e.g. at phylum level), rely on the existence of special genes (e.g. 16S), or have severe problems discerning species with highly similar genome sequences. Yet, problems as metagenomic diagnostics require accurate quantification on species level. We developed Genome Abundance Similarity Correction (GASiC), a method to estimate true genome abundances via read alignment by considering reference genome similarities in a non-negative LASSO approach. We demonstrate GASiC's superior performance over existing methods on simulated benchmark data as well as on real data. In addition, we present applications to datasets of both bacterial DNA and viral RNA source. We further discuss our approach as an alternative to PCR-based DNA quantification.

SIMA: simultaneous multiple alignment of LC/MS peak lists.

MOTIVATION: Alignment of multiple liquid chromatography/mass spectrometry (LC/MS) experiments is a necessity today, which arises from the need for biological and technical repeats. Due to limits in sampling frequency and poor reproducibility of retention times, current LC systems suffer from missing observations and non-linear distortions of the retention times across runs. Existing approaches for peak correspondence estimation focus almost exclusively on solving the pairwise alignment problem, yielding straightforward but suboptimal results for multiple alignment problems. RESULTS: We propose SIMA, a novel automated procedure for alignment of peak lists from multiple LC/MS runs. SIMA combines hierarchical pairwise correspondence estimation with simultaneous alignment and global retention time correction. It employs a tailored multidimensional kernel function and a procedure based on maximum likelihood estimation to find the retention time distortion function that best fits the observed data. SIMA does not require a dedicated reference spectrum, is robust with regard to outliers, needs only two intuitive parameters and naturally incorporates incomplete correspondence information. In a comparison with seven alternative methods on four different datasets, we show that SIMA yields competitive and superior performance on real-world data. AVAILABILITY: A C++ implementation of the SIMA algorithm is available from http://hci.iwr.uni-heidelberg.de/MIP/Software.