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

Utilizing Next-Generation Sequencing: Advancements in the Diagnosis of Fungal Infections

Next-generation sequencing (NGS) has emerged as a promising tool for diagnosing fungal infections. It enables the identification of a wide range of fungal species and provides more accurate and rapid results than traditional diagnostic methods. NGS-based approaches involve the sequencing of DNA or RNA from clinical samples, which can be used to detect and identify fungal pathogens in complex clinical samples. The development of targeted gene panels and whole-genome sequencing has allowed for identifying genetic markers associated with antifungal drug resistance, enabling clinicians to tailor patient treatment options. NGS can also provide insights into the pathogenesis of fungal infections and aid in discovering novel drug targets. Although NGS has some limitations, such as cost and data analysis, it can potentially revolutionize the future diagnosis and treatment of fungal infections.

Molecular Diagnostics for Invasive Fungal Diseases: Current and Future Approaches

Invasive fungal diseases (IFDs) comprise a growing healthcare burden, especially given the expanding population of immunocompromised hosts. Early diagnosis of IFDs is required to optimise therapy with antifungals, especially in the setting of rising rates of antifungal resistance. Molecular techniques including nucleic acid amplification tests and whole genome sequencing have potential to offer utility in overcoming limitations with traditional phenotypic testing. However, standardisation of methodology and interpretations of these assays is an ongoing undertaking. The utility of targeted Aspergillus detection has been well-defined, with progress in investigations into the role of targeted assays for Candida, Pneumocystis, Cryptococcus, the Mucorales and endemic mycoses. Likewise, whilst broad-range polymerase chain reaction assays have been in use for some time, pathology stewardship and optimising diagnostic yield is a continuing exercise. As costs decrease, there is also now increased access and experience with whole genome sequencing, including metagenomic sequencing, which offers unparalleled resolution especially in the investigations of potential outbreaks. However, their role in routine diagnostic use remains uncommon and standardisation of techniques and workflow are required for wider implementation.

Direct prediction of antimicrobial resistance in Pseudomonas aeruginosa by metagenomic next-generation sequencing

Objective

Pseudomonas aeruginosa has strong drug resistance and can tolerate a variety of antibiotics, which is a major problem in the management of antibiotic-resistant infections. Direct prediction of multi-drug resistance (MDR) resistance phenotypes of P. aeruginosa isolates and clinical samples by genotype is helpful for timely antibiotic treatment.

Methods

In the study, whole genome sequencing (WGS) data of 494 P. aeruginosa isolates were used to screen key anti-microbial resistance (AMR)-associated genes related to imipenem (IPM), meropenem (MEM), piperacillin/tazobactam (TZP), and levofloxacin (LVFX) resistance in P. aeruginosa by comparing genes with copy number differences between resistance and sensitive strains. Subsequently, for the direct prediction of the resistance of P. aeruginosa to four antibiotics by the AMR-associated features screened, we collected 74 P. aeruginosa positive sputum samples to sequence by metagenomics next-generation sequencing (mNGS), of which 1 sample with low quality was eliminated. Then, we constructed the resistance prediction model.

Results

We identified 93, 88, 80, 140 AMR-associated features for IPM, MEM, TZP, and LVFX resistance in P. aeruginosa. The relative abundance of AMR-associated genes was obtained by matching mNGS and WGS data. The top 20 features with importance degree for IPM, MEM, TZP, and LVFX resistance were used to model, respectively. Then, we used the random forest algorithm to construct resistance prediction models of P. aeruginosa, in which the areas under the curves of the IPM, MEM, TZP, and LVFX resistance prediction models were all greater than 0.8, suggesting these resistance prediction models had good performance.

Conclusion

In summary, mNGS can predict the resistance of P. aeruginosa by directly detecting AMR-associated genes, which provides a reference for rapid clinical detection of drug resistance of pathogenic bacteria.

Improving Bacterial Metagenomic Research through Long-Read Sequencing

Metagenomic sequencing analysis is central to investigating microbial communities in clinical and environmental studies. Short-read sequencing remains the primary approach for metagenomic research; however, long-read sequencing may offer advantages of improved metagenomic assembly and resolved taxonomic identification. To compare the relative performance for metagenomic studies, we simulated short- and long-read datasets using increasingly complex metagenomes comprising 10, 20, and 50 microbial taxa. Additionally, we used an empirical dataset of paired short- and long-read data generated from mouse fecal pellets to assess real-world performance. We compared metagenomic assembly quality, taxonomic classification, and metagenome-assembled genome (MAG) recovery rates. We show that long-read sequencing data significantly improve taxonomic classification and assembly quality. Metagenomic assemblies using simulated long reads were more complete and more contiguous with higher rates of MAG recovery. This resulted in more precise taxonomic classifications. Principal component analysis of empirical data demonstrated that sequencing technology affects compositional results as samples clustered by sequence type, not sample type. Overall, we highlight strengths of long-read metagenomic sequencing for microbiome studies, including improving the accuracy of classification and relative abundance estimates. These results will aid researchers when considering which sequencing approaches to use for metagenomic projects.

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.

Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples

Direct-targeted next-generation sequencing (tNGS), with its undoubtedly superior diagnostic capacity over real-time PCR (RT-PCR), and direct-non-targeted NGS (ntNGS), with its higher capacity to identify and characterize multiple agents, are both likely to become diagnostic methods of choice in the future. tNGS is a rapid and sensitive method for precise characterization of suspected agents. ntNGS, also known as agnostic diagnosis, does not require a hypothesis and has been used to identify unsuspected infections in clinical samples. Implemented in the form of multiplexed total DNA metagenomics or as total RNA sequencing, the approach produces comprehensive and actionable reports that allow semi-quantitative identification of most of the agents present in respiratory, cloacal, and tissue samples. The diagnostic benefits of the use of direct tNGS and ntNGS are high specificity, compatibility with different types of clinical samples (fresh, frozen, FTA cards, and paraffin-embedded), production of nearly complete infection profiles (viruses, bacteria, fungus, and parasites), production of "semi-quantitative" information, direct agent genotyping, and infectious agent mutational information. The achievements of NGS in terms of diagnosing poultry problems are described here, along with future applications. Multiplexing, development of standard operating procedures, robotics, sequencing kits, automated bioinformatics, cloud computing, and artificial intelligence (AI) are disciplines converging toward the use of this technology for active surveillance in poultry farms. Other advances in human and veterinary NGS sequencing are likely to be adaptable to avian species in the future.

Direct prediction of carbapenem resistance in Pseudomonas aeruginosa by whole genome sequencing and metagenomic sequencing

Carbapenem resistance is a major concern in the management of antibiotic-resistant Pseudomonas aeruginosa infections. The direct prediction of carbapenem-resistant phenotype from genotype in P. aeruginosa isolates and clinical samples would promote timely antibiotic therapy. The complex carbapenem resistance mechanism and the high prevalence of variant-driven carbapenem resistance in P. aeruginosa make it challenging to predict the carbapenem-resistant phenotype through the genotype. In this study, using whole genome sequencing (WGS) data of 1,622 P. aeruginosa isolates followed by machine learning, we screened 16 and 31 key gene features associated with imipenem (IPM) and meropenem (MEM) resistance in P. aeruginosa, including oprD(HIGH), and constructed the resistance prediction models. The areas under the curves of the IPM and MEM resistance prediction models were 0.906 and 0.925, respectively. For the direct prediction of carbapenem resistance in P. aeruginosa from clinical samples by the key gene features selected and prediction models constructed, 72 P. aeruginosa-positive sputum samples were collected and sequenced by metagenomic sequencing (MGS) based on next-generation sequencing (NGS) or Oxford Nanopore Technology (ONT). The prediction applicability of MGS based on NGS outperformed that of MGS based on ONT. In 72 P. aeruginosa-positive sputum samples, 65.0% (26/40) of IPM-insensitive and 63.2% (24/38) of MEM-insensitive P. aeruginosa were directly predicted by NGS-based MGS with positive predictive values of 0.897 and 0.889, respectively. By the direct detection of the key gene features associated with carbapenem resistance of P. aeruginosa, the carbapenem resistance of P. aeruginosa could be directly predicted from cultured isolates by WGS or from clinical samples by NGS-based MGS, which could assist the timely treatment and surveillance of carbapenem-resistant P. aeruginosa.

Yeast metagenomics: analytical challenges in the analysis of the eukaryotic microbiome

Even if their impact is often underestimated, yeasts and yeast-like fungi represent the most prevalent eukaryotic members of microbial communities on Earth. They play numerous roles in natural ecosystems and in association with their hosts. They are involved in the food industry and pharmaceutical production, but they can also cause diseases in other organisms, making the understanding of their biology mandatory. The ongoing loss of biodiversity due to overexploitation of environmental resources is a growing concern in many countries. Therefore, it becomes crucial to understand the ecology and evolutionary history of these organisms to systematically classify them. To achieve this, it is essential that our knowledge of the mycobiota reaches a level similar to that of the bacterial communities. To overcome the existing challenges in the study of fungal communities, the first step should be the establishment of standardized techniques for the correct identification of species, even from complex matrices, both in wet lab practices and in bioinformatic tools.

Mucormycosis in 2023: an update on pathogenesis and management

Mucormycosis (MCR) is an emerging and frequently lethal fungal infection caused by the Mucorales family, with Rhizopus, Mucor, and Lichtheimia, accounting for > 90% of all cases. MCR is seen in patients with severe immunosuppression such as those with hematologic malignancy or transplantation, Diabetes Mellitus (DM) and diabetic ketoacidosis (DKA) and immunocompetent patients with severe wounds. The recent SARS COV2 epidemy in India has resulted in a tremendous increase in MCR cases, typically seen in the setting of uncontrolled DM and corticosteroid use. In addition to the diversity of affected hosts, MCR has pleiotropic clinical presentations, with rhino-orbital/rhino-cerebral, sino-pulmonary and necrotizing cutaneous forms being the predominant manifestations. Major insights in MCR pathogenesis have brought into focus the host receptors (GRP78) and signaling pathways (EGFR activation cascade) as well as the adhesins used by Mucorales for invasion. Furthermore, studies have expanded on the importance of iron availability and the complex regulation of iron homeostasis, as well as the pivotal role of mycotoxins as key factors for tissue invasion. The molecular toolbox to study Mucorales pathogenesis remains underdeveloped, but promise is brought by RNAi and CRISPR/Cas9 approaches. Important recent advancements have been made in early, culture-independent molecular diagnosis of MCR. However, development of new potent antifungals against Mucorales remains an unmet need. Therapy of MCR is multidisciplinary and requires a high index of suspicion for initiation of early Mucorales-active antifungals. Reversal of underlying immunosuppression, if feasible, rapid DKA correction and in selected patients, surgical debulking are crucial for improved outcomes.

Amplicon-Based Microbiome Profiling: From Second- to Third-Generation Sequencing for Higher Taxonomic Resolution

The 16S rRNA amplicon-based sequencing approach represents the most common and cost-effective strategy with great potential for microbiome profiling. The use of second-generation sequencing (NGS) technologies has led to protocols based on the amplification of one or a few hypervariable regions, impacting the outcome of the analysis. Nowadays, comparative studies are necessary to assess different amplicon-based approaches, including the full-locus sequencing currently feasible thanks to third-generation sequencing (TGS) technologies. This study compared three different methods to achieve the deepest microbiome taxonomic characterization: (a) the single-region approach, (b) the multiplex approach, covering several regions of the target gene/region, both based on NGS short reads, and (c) the full-length approach, which analyzes the whole length of the target gene thanks to TGS long reads. Analyses carried out on benchmark microbiome samples, with a known taxonomic composition, highlighted a different classification performance, strongly associated with the type of hypervariable regions and the coverage of the target gene. Indeed, the full-length approach showed the greatest discriminating power, up to species level, also on complex real samples. This study supports the transition from NGS to TGS for the study of the microbiome, even if experimental and bioinformatic improvements are still necessary.

Interlaboratory Performance of a Real-Time PCR Method for Detection of Ceratocystisplatani, the Agent of Canker Stain of Platanus spp

Ceratocystis platani (CP), an ascomycetous fungus, is the agent of canker stain, a lethal vascular disease of Platanus species. Ceratocystis platani has been listed as a quarantine pest (EPPO A2 list) due to extensive damage caused in Southern Europe and the Mediterranean region. As traditional diagnostic assays are ineffective, a Real-Time PCR detection method based on EvaGreen, SYBR Green, and Taqman assays was previously developed, validated in-house, and included in the official EPPO standard PM7/14 (2). Here, we describe the results of a test performance study performed by nine European laboratories for the purpose of an interlaboratory validation. Verification of the DNA extracted from biological samples guaranteed the high quality of preparations, and the stability and the homogeneity of the aliquots intended for the laboratories. All of the laboratories reproduced nearly identical standard curves with efficiencies close to 100%. Testing of blind-coded DNA extracted from wood samples revealed that all performance parameters—diagnostic sensitivity, diagnostic specificity, accuracy and reproducibility—were best fit in most cases both at the laboratory and at the assay level. The previously established limit of detection, 3 fg per PCR reaction, was also validated with similar excellent results. The high interlaboratory performance of this Real-Time PCR method confirms its value as a primary tool to safeguard C. platani-free countries by way of an accurate monitoring, and to investigate the resistance level of potentially canker stain-resistant Platanus genotypes.