Probe design for simultaneous, targeted capture of diverse metagenomic targets

Clinical implement of Probe-Capture Metagenomics in sepsis patients: A multicentre and prospective study

BACKGROUND

Accurate pathogen identification is critical for managing sepsis. However, traditional microbiological methods are time-consuming and exhibit limited sensitivity, particularly with blood samples. Metagenomic sequencing of plasma or whole blood was highly affected by the proportion of host nucleic acid.

METHODS

We developed a Probe-Capture Metagenomic assay and established a multicentre prospective cohort to assess its clinical utility. In this study, 184 blood samples from patients suspected of sepsis were sent for blood culture and Probe-Capture Metagenomic sequencing before using antibiotics. The pathogen-positive rate and auxiliary abilities in diagnosis were compared among Probe-Capture Metagenomics, blood culture and real-time PCR (RT-PCR). Antibiotic therapy adjustments were based on the identification of pathogens, and changes in the Sequential Organ Failure Assessment (SOFA) score were monitored on days 0, 3 and 7 of admission.

RESULTS

A total of 184 sepsis patients were enrolled, with a mean age of 66 years (range 56-74). The Probe-Capture Metagenomics method, confirmed by RT-PCR, demonstrated a significantly higher pathogen detection rate than blood culture alone (51.6% vs. 17.4%, p < .001). When combining the results of blood culture and RT-PCR, Probe-Capture Metagenomics achieved a concordance rate of 91.8% (169/184), with a sensitivity of 100% and specificity of 87.1%. In terms of clinical impact, antibiotic therapy was adjusted for 64 patients (34.8%) based on the results from Probe-Capture Metagenomics, and 41 patients (22.3%) showed a > 2-point decrease in SOFA score following antibiotic adjustments.

CONCLUSION

Probe-Capture Metagenomics significantly enhances the ability of pathogen detection compared with traditional metagenomics. Compared to blood culture and RT-PCR in sepsis patients, it leads to improved antibiotic treatment and better patient outcomes. This study, for the first time, evaluates the clinical impact of metagenomic sequencing by integrating antibiotic adjustments and SOFA score changes, indicating that approximately one-fifth of sepsis patients benefit from this advanced diagnostic approach.

TRIAL REGISTRATION

This study has been registered in clinical trials (clinicaltrials.gov) on 30 November 2018, and the registration number is NCT03760315.

KEY POINTS

Probe-Capture Metagenome had a significantly higher positive rate than blood culture (51.6% vs. 17.4%, p < .001). Combining blood culture and RT-PCR results, Probe-Capture Metagenome achieved a consistency rate of 91.8%. Antibiotics were adjusted in 34.8% of patients based on Probe-Capture Metagenome results, and 22.3% of patients experienced a more than 2-point decrease in SOFA score.

OLTA: Optimizing bait seLection for TArgeted sequencing

MOTIVATION

Targeted enrichment via capture probes, also known as baits, is a promising complementary procedure for next-generation sequencing methods. This technique uses short biotinylated oligonucleotide probes that hybridize with complementary genetic material in a sample. Following hybridization, the target fragments can be easily isolated and processed with minimal contamination from irrelevant material. Designing an efficient set of baits for a set of target sequences, however, is an NP-hard problem.

RESULTS

We develop a novel heuristic algorithm that leverages the similarities between the characteristics of the Minimum Bait Cover and the Closest String problems to reduce the number of baits to cover a given target sequence. Our results on real and synthetic datasets demonstrate that our algorithm, OLTA produces fewest baits for nearly all experimental settings and datasets. On average, it produces 6% and 11% fewer baits than the next best state-of-the-art methods for two major real datasets, AIV and MEGARES. Also, its bait set has the highest utilization and the minimum redundancy.

AVAILABILITY AND IMPLEMENTATION

Our algorithm is available at github.com/FuelTheBurn/OLTA-Optimizing-bait-seLection-for-TArgeted-sequencing. Test data and other software are archived at doi.org/10.5281/zenodo.15086636.

Enriching the future of public health microbiology with hybridization bait capture

SUMMARYPublic health microbiology focuses on microorganisms and infectious agents that impact human health. For years, this field has relied on culture or molecular methods to investigate complex samples of public health importance. However, with the increase in accuracy and decrease in sequencing cost over the last decade, there has been a transition to the use of next-generation sequencing in public health microbiology. Nevertheless, many available sequencing methods (e.g., shotgun metagenomics and amplicon sequencing) do not work well in complex sample types, require deep sequencing, or have inherent biases associated with them. Hybridization bait capture, also known as target enrichment, brings in solutions for such limitations. It is an increasingly popular technique to simultaneously characterize many thousands of genetic elements while reducing the amount of sequencing needed (thereby reducing the sequencing costs). Here, we summarize the concept of hybridization bait capture for public health, reviewing a total of 35 bait sets designed in six key topic areas for public health microbiology [i.e., antimicrobial resistance (AMR), bacteria, fungi, parasites, vectors, and viruses], and compare hybridization bait capture to previously relied upon methods. Furthermore, we provide an in-depth comparison of the three most popular bait sets designed for AMR by evaluating each of them against three major AMR databases: Comprehensive Antibiotic Resistance Database, Microbial Ecology Group Antimicrobial Resistance Database, and Pathogenicity Island Database. Thus, this article provides a review of hybridization bait capture for public health microbiologists.

Direct genome sequencing of respiratory viruses from low viral load clinical specimens using the target capture sequencing technology

The use of metagenomic next-generation sequencing technology to obtain complete viral genome sequences directly from clinical samples with low viral load remains challenging-especially in the case of respiratory viruses-due to the low copy number of viral versus host genomes. To overcome this limitation, target capture sequencing for the enrichment of specific genomes has been developed and applied for direct genome sequencing of viruses. However, as the efficiency of enrichment varies depending on the probes, the type of clinical sample, etc., validation is essential before target capture sequencing can be applied to clinical diagnostics. In this study, we evaluated the utility of target capture sequencing with a comprehensive viral probe panel for clinical respiratory specimens collected from patients diagnosed with SARS-CoV-2 or influenza type A. We focused on clinical specimens containing low copy numbers of viral genomes. Target capture sequencing yielded approximately 180- and 2,000-fold higher read counts of SARS-CoV-2 and influenza A virus, respectively, than metagenomic sequencing when the RNA extracted from specimens contained 59.3 copies/µL of SARS-CoV-2 or 625.1 copies/µL of influenza A virus. In addition, the target capture sequencing identified sequence reads in all SARS-CoV-2- or influenza type A-positive specimens with <26 RNA copies/µL, some of which also yielded >70% of the full-length genomes of SARS-CoV-2 or influenza A virus. Furthermore, the target capture sequencing using comprehensive probes identified co-infections with viruses other than SARS-CoV-2, suggesting that this approach will not only detect a wide range of viruses but also contribute to epidemiological studies.IMPORTANCETarget capture sequencing has been developed and applied for direct genome sequencing of viruses in clinical specimens to overcome the low detection sensitivity of metagenomic next-generation sequencing. In this study, we evaluated the utility of target capture sequencing with a comprehensive viral probe panel for clinical respiratory specimens collected from patients diagnosed with SARS-CoV-2 or influenza type A, focusing on clinical specimens containing low copy numbers of viral genomes. Our results showed that the target capture sequencing yielded dramatically higher read counts than metagenomic sequencing for both viruses. Furthermore, the target capture sequencing using comprehensive probes identified co-infections with other viruses, suggesting that this approach will not only detect a wide range of viruses but also contribute to epidemiological studies.

Considerations and Opportunities for Probe Capture Enrichment Sequencing of Emerging Viruses from Wastewater

Until recently, wastewater-based monitoring for pathogens of public health concern primarily used PCR-based quantification methods and targeted sequencing for specific pathogens (e.g., SARS-CoV-2). In the past three years, researchers have expanded sequencing to monitor a broad range of pathogens, applying probe capture enrichment to wastewater. The goals of those studies included (1) monitoring and expanding fundamental knowledge of disease dynamics for known pathogens and (2) evaluating the potential for early detection of emerging diseases resulting from zoonotic spillover or novel viral variants. Several studies using off-the-shelf probe panels designed for clinical and environmental surveillance reported that enrichment increased virus relative abundance but did not recover complete genomes for most nonenteric viruses. Based on our experience and recent results reported by others using these panels for wastewater, clinical, and synthetic samples, we discuss challenges and technical factors that affect the rates of false positive and false negative results. We identify trade-offs and opportunities throughout the workflow, including in wastewater sample processing, probe panel design, and bioinformatic analysis. We suggest tailored methods of virus concentration and background removal, carefully designed probe panels, and multithresholded bioinformatics analysis.

Hybrid-Capture Target Enrichment in Human Pathogens: Identification, Evolution, Biosurveillance, and Genomic Epidemiology

High-throughput sequencing (HTS) has revolutionised the field of pathogen genomics, enabling the direct recovery of pathogen genomes from clinical and environmental samples. However, pathogen nucleic acids are often overwhelmed by those of the host, requiring deep metagenomic sequencing to recover sufficient sequences for downstream analyses (e.g., identification and genome characterisation). To circumvent this, hybrid-capture target enrichment (HC) is able to enrich pathogen nucleic acids across multiple scales of divergences and taxa, depending on the panel used. In this review, we outline the applications of HC in human pathogens-bacteria, fungi, parasites and viruses-including identification, genomic epidemiology, antimicrobial resistance genotyping, and evolution. Importantly, we explored the applicability of HC to clinical metagenomics, which ultimately requires more work before it is a reliable and accurate tool for clinical diagnosis. Relatedly, the utility of HC was exemplified by COVID-19, which was used as a case study to illustrate the maturity of HC for recovering pathogen sequences. As we unravel the origins of COVID-19, zoonoses remain more relevant than ever. Therefore, the role of HC in biosurveillance studies is also highlighted in this review, which is critical in preparing us for the next pandemic. We also found that while HC is a popular tool to study viruses, it remains underutilised in parasites and fungi and, to a lesser extent, bacteria. Finally, weevaluated the future of HC with respect to bait design in the eukaryotic groups and the prospect of combining HC with long-read HTS.

Molecular profiling in cholangiocarcinoma: A practical guide to next-generation sequencing

Cholangiocarcinomas (CCA) are a heterogeneous group of tumors that are classified as intrahepatic, perihilar, or distal according to the anatomic location within the biliary tract. Each CCA subtype is associated with distinct genomic alterations, including single nucleotide variants, copy number variants, and chromosomal rearrangements or gene fusions, each of which can influence disease prognosis and/or treatment outcomes. Molecular profiling using next-generation sequencing (NGS) is a powerful technique for identifying unique gene variants carried by an individual tumor, which can facilitate their accurate diagnosis as well as promote the optimal selection of gene variant-matched targeted treatments. NGS is particularly useful in patients with CCA because between one-third and one-half of these patients have genomic alterations that can be targeted by drugs that are either approved or in clinical development. NGS can also provide information about disease evolution and secondary resistance alterations that can develop during targeted therapy, and thus facilitate assessment of prognosis and choice of alternative targeted treatments. Pathologists play a critical role in assessing the viability of biopsy samples for NGS, and advising treating clinicians whether NGS can be performed and which of the available platforms should be used to optimize testing outcomes. This review aims to provide clinical pathologists and other healthcare professionals with practical step-by-step guidance on the use of NGS for molecular profiling of patients with CCA, with respect to tumor biopsy techniques, pre-analytic sample preparation, selecting the appropriate NGS panel, and understanding and interpreting results of the NGS test.

AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data

BACKGROUND

Low-complexity data analysis is the area that addresses the search and quantification of regions in sequences of elements that contain low-complexity or repetitive elements. For example, these can be tandem repeats, inverted repeats, homopolymer tails, GC-biased regions, similar genes, and hairpins, among many others. Identifying these regions is crucial because of their association with regulatory and structural characteristics. Moreover, their identification provides positional and quantity information where standard assembly methodologies face significant difficulties because of substantial higher depth coverage (mountains), ambiguous read mapping, or where sequencing or reconstruction defects may occur. However, the capability to distinguish low-complexity regions (LCRs) in genomic and proteomic sequences is a challenge that depends on the model's ability to find them automatically. Low-complexity patterns can be implicit through specific or combined sources, such as algorithmic or probabilistic, and recurring to different spatial distances-namely, local, medium, or distant associations.

FINDINGS

This article addresses the challenge of automatically modeling and distinguishing LCRs, providing a new method and tool (AlcoR) for efficient and accurate segmentation and visualization of these regions in genomic and proteomic sequences. The method enables the use of models with different memories, providing the ability to distinguish local from distant low-complexity patterns. The method is reference and alignment free, providing additional methodologies for testing, including a highly flexible simulation method for generating biological sequences (DNA or protein) with different complexity levels, sequence masking, and a visualization tool for automatic computation of the LCR maps into an ideogram style. We provide illustrative demonstrations using synthetic, nearly synthetic, and natural sequences showing the high efficiency and accuracy of AlcoR. As large-scale results, we use AlcoR to unprecedentedly provide a whole-chromosome low-complexity map of a recent complete human genome and the haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar.

CONCLUSIONS

The AlcoR method provides the ability of fast sequence characterization through data complexity analysis, ideally for scenarios entangling the presence of new or unknown sequences. AlcoR is implemented in C language using multithreading to increase the computational speed, is flexible for multiple applications, and does not contain external dependencies. The tool accepts any sequence in FASTA format. The source code is freely provided at https://github.com/cobilab/alcor.