Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity

Metagenomic next-generation sequencing for rapid detection of pulmonary infection in patients with acquired immunodeficiency syndrome

BACKGROUND

Acquired immunodeficiency syndrome (AIDS) is associated with a high rate of pulmonary infections (bacteria, fungi, and viruses). To overcome the low sensitivity and long turnaround time of traditional laboratory-based diagnostic strategies, we adopted metagenomic next-generation sequencing (mNGS) technology to identify and classify pathogens.

RESULTS

This study enrolled 75 patients with AIDS and suspected pulmonary infections who were admitted to Nanning Fourth People's Hospital. Specimens were collected for traditional microbiological testing and mNGS-based diagnosis. The diagnostic yields of the two methods were compared to evaluate the diagnostic value (detection rate and turn around time) of mNGS for infections with unknown causative agent. Accordingly, 22 cases (29.3%) had a positive culture and 70 (93.3%) had positive valve mNGS results (P value < 0.0001, Chi-square test). Meanwhile, 15 patients with AIDS showed concordant results between the culture and mNGS, whereas only one 1 patient showed concordant results between Giemsa-stained smear screening and mNGS. In addition, mNGS identified multiple microbial infections (at least three pathogens) in almost 60.0% of patients with AIDS. More importantly, mNGS was able to detect a large variety of pathogens from patient tissue displaying potential infection and colonization, while culture results remained negative. There were 18 members of pathogens which were consistently detected in patients with and without AIDS.

CONCLUSIONS

In conclusion, mNGS analysis provides fast and precise pathogen detection and identification, contributing substantially to the accurate diagnosis, real-time monitoring, and treatment appropriateness of pulmonary infection in patients with AIDS.

NanoViromics: long-read sequencing of dsRNA for plant virus and viroid rapid detection

There is a global need for identifying viral pathogens, as well as for providing certified clean plant materials, in order to limit the spread of viral diseases. A key component of management programs for viral-like diseases is having a diagnostic tool that is quick, reliable, inexpensive, and easy to use. We have developed and validated a dsRNA-based nanopore sequencing protocol as a reliable method for detecting viruses and viroids in grapevines. We compared our method, which we term direct-cDNA sequencing from dsRNA (dsRNAcD), to direct RNA sequencing from rRNA-depleted total RNA (rdTotalRNA), and found that it provided more viral reads from infected samples. Indeed, dsRNAcD was able to detect all of the viruses and viroids detected using Illumina MiSeq sequencing (dsRNA-MiSeq). Furthermore, dsRNAcD sequencing was also able to detect low-abundance viruses that rdTotalRNA sequencing failed to detect. Additionally, rdTotalRNA sequencing resulted in a false-positive viroid identification due to the misannotation of a host-driven read. Two taxonomic classification workflows, DIAMOND & MEGAN (DIA & MEG) and Centrifuge & Recentrifuge (Cent & Rec), were also evaluated for quick and accurate read classification. Although the results from both workflows were similar, we identified pros and cons for both workflows. Our study shows that dsRNAcD sequencing and the proposed data analysis workflows are suitable for consistent detection of viruses and viroids, particularly in grapevines where mixed viral infections are common.

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.

Revisiting fecal metatranscriptomics analyses of macaques with idiopathic chronic diarrhoea with a focus on trichomonad parasites

Trichomonads, anaerobic microbial eukaryotes members of the phylum Parabasalia, are common obligate extracellular symbionts that can lead to pathological or asymptomatic colonization of various mucosal surfaces in a wide range of animal hosts. Results from previous in vitro studies have suggested a number of intriguing mucosal colonization strategies by Trichomonads, notably highlighting the importance of interactions with bacteria. However, in vivo validation is currently lacking. A previous metatranscriptomics study into the cause of idiopathic chronic diarrhoea in macaques reported the presence of an unidentified protozoan parasite related to Trichomonas vaginalis. In this work, we performed a reanalysis of the published data in order to identify the parasite species present in the macaque gut. We also leveraged the information-rich metatranscriptomics data to investigate the parasite behaviour in vivo. Our results indicated the presence of at least 3 genera of Trichomonad parasite; Tetratrichomonas, Pentatrichomonas and Trichomitus, 2 of which had not been previously reported in the macaque gut. In addition, we identified common in vivo expression profiles shared amongst the Trichomonads. In agreement with previous findings for other Trichomonads, our results highlighted a relationship between Trichomonads and mucosal bacterial diversity which could be influential in health and disease.

Diagnostic efficacy of targeted high-throughput sequencing for lower respiratory infection in preterm infants

OBJECTIVE

To examine the pathogen diagnostic performance of targeted high-throughput next-gen sequencing (tNGS) in respiratory infectious diseases in preterm infants using dynamic follow-up.

METHODS

Clinical samples of respiratory secretions were consecutively collected from 20 preterm infants weekly for 5 weeks, during which 10 developed bronchopulmonary dysplasia. Pathogen identification from these collected specimens was performed by both conventional cultivation and tNGS.

RESULTS

We found that targeted next-generation sequencing shared a 90.9% full or partial consistency for lower respiratory pathogen detection with the traditional culture-based approach, and increased the detection rate by 105.9%. Moreover, most of the pathogens identified by tNGS were diminished in patients after treatment.

CONCLUSION

This study reveals the high sensitivity and performance of targeted high-throughput sequencing for respiratory infectious disease diagnosis and pathogen identification. The trial registry number is NCT03850457, and the trial URL is https://clinicaltrials.gov/ct2/show/NCT03850457.

Detection of Klebsiella pneumoniae human gut carriage: a comparison of culture, qPCR, and whole metagenomic sequencing methods

Klebsiella pneumoniae is an important opportunistic healthcare-associated pathogen and major contributor to the global spread of antimicrobial resistance. Gastrointestinal colonization with K. pneumoniae is a major predisposing risk factor for infection and forms an important hub for the dispersal of resistance. Current culture-based detection methods are time consuming, give limited intra-sample abundance and strain diversity information, and have uncertain sensitivity. Here we investigated the presence and abundance of K. pneumoniae at the species and strain level within fecal samples from 103 community-based adults by qPCR and whole metagenomic sequencing (WMS) compared to culture-based detection. qPCR demonstrated the highest sensitivity, detecting K. pneumoniae in 61.2% and 75.8% of direct-fecal and culture-enriched sweep samples, respectively, including 52/52 culture-positive samples. WMS displayed lower sensitivity, detecting K. pneumoniae in 71.2% of culture-positive fecal samples at a 0.01% abundance cutoff, and was inclined to false positives in proportion to the relative abundance of other Enterobacterales present. qPCR accurately quantified K. pneumoniae to 16 genome copies/reaction while WMS could estimate relative abundance to at least 0.01%. Quantification by both methods correlated strongly with each other (Spearman's rho = 0.91). WMS also supported accurate intra-sample K. pneumoniae sequence type (ST)-level diversity detection from fecal microbiomes to 0.1% relative abundance, agreeing with the culture-based detected ST in 16/19 samples. Our results show that qPCR and WMS are sensitive and reliable tools for detection, quantification, and strain analysis of K. pneumoniae from fecal samples with potential to support infection control and enhance insights in K. pneumoniae gastrointestinal ecology.

Metagenomics of diabetic foot ulcer undergoing treatment with total contact casting: a case study

OBJECTIVE

Diabetic foot ulcers (DFUs) are characterised by the presence of many microbes, some of which may not be identified by traditional culture techniques. Total contact casting (TCC) remains the gold-standard for offloading, yet little is known about the microbiome of wounds that progress from hard-to-heal to closed within a TCC.

METHOD

A patient with a DFU underwent weekly treatment with TCC to closure. Samples for next-generation sequencing (NGS) and bioinformatics analysis of tissue samples were collected during each visit. Detection, identification, characterisation of the microbial community and abundance of microbes in each sample were compared.

RESULTS

Abundance of microbes, identified by species and strain, changed with each treatment visit. By the final week of treatment, species diversity of the wound microbiome had decreased significantly, highlighted by an observed decrease in the number of total microorganisms present. Resistance genes for tetracyclines were detected in the first sample, but not in subsequent samples.

CONCLUSION

The results of this study suggest dynamic microbiological changes associated with DFUs as they progress to healing within a TCC. As NGS becomes more readily available, further studies will be helpful to gain an improved understanding of the significance of the wound microbiome in patients with DFUs.

Encoding mu-opioid receptor biased agonism with interaction fingerprints

Opioids are potent painkillers, however, their therapeutic use requires close medical monitoring to diminish the risk of severe adverse effects. The G-protein biased agonists of the μ-opioid receptor (MOR) have shown safer therapeutic profiles than non-biased ligands. In this work, we performed extensive all-atom molecular dynamics simulations of two markedly biased ligands and a balanced reference molecule. From those simulations, we identified a protein-ligand interaction fingerprint that characterizes biased ligands. Then, we built and virtually screened a database containing 68,740 ligands with proven or potential GPCR agonistic activity. Exemplary molecules that fulfill the interacting pattern for biased agonism are showcased, illustrating the usefulness of this work for the search of biased MOR ligands and how this contributes to the understanding of MOR biased signaling.

Shotgun-metagenomics based prediction of antibiotic resistance and virulence determinants in Staphylococcus aureus from periprosthetic tissue on blood culture bottles

Shotgun-metagenomics may give valuable clinical information beyond the detection of potential pathogen(s). Identification of antimicrobial resistance (AMR), virulence genes and typing directly from clinical samples has been limited due to challenges arising from incomplete genome coverage. We assessed the performance of shotgun-metagenomics on positive blood culture bottles (n = 19) with periprosthetic tissue for typing and prediction of AMR and virulence profiles in Staphylococcus aureus. We used different approaches to determine if sequence data from reads provides more information than from assembled contigs. Only 0.18% of total reads was derived from human DNA. Shotgun-metagenomics results and conventional method results were consistent in detecting S. aureus in all samples. AMR and known periprosthetic joint infection virulence genes were predicted from S. aureus. Mean coverage depth, when predicting AMR genes was 209 ×. Resistance phenotypes could be explained by genes predicted in the sample in most of the cases. The choice of bioinformatic data analysis approach clearly influenced the results, i.e. read-based analysis was more accurate for pathogen identification, while contigs seemed better for AMR profiling. Our study demonstrates high genome coverage and potential for typing and prediction of AMR and virulence profiles in S. aureus from shotgun-metagenomics data.

Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis, Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology

This review discusses the current testing methodologies for COVID-19 diagnosis and explores next-generation sequencing (NGS) technology for the detection of SARS-CoV-2 and monitoring phylogenetic evolution in the current COVID-19 pandemic. The review addresses the development, fundamentals, assay quality control and bioinformatics processing of the NGS data. This article provides a comprehensive review of the obstacles and opportunities facing the application of NGS technologies for the diagnosis, surveillance, and study of SARS-CoV-2 and other infectious diseases. Further, we have contemplated the opportunities and challenges inherent in the adoption of NGS technology as a diagnostic test with real-world examples of its utility in the fight against COVID-19.

k-mer-Based Metagenomics Tools Provide a Fast and Sensitive Approach for the Detection of Viral Contaminants in Biopharmaceutical and Vaccine Manufacturing Applications Using Next-Generation Sequencing

Adventitious agent detection during the production of vaccines and biotechnology-based medicines is of critical importance to ensure the final product is free from any possible viral contamination. Increasing the speed and accuracy of viral detection is beneficial as a means to accelerate development timelines and to ensure patient safety. Here, several rapid viral metagenomics approaches were tested on simulated next-generation sequencing (NGS) data sets and existing data sets from virus spike-in studies done in CHO-K1 and HeLa cell lines. It was observed that these rapid methods had comparable sensitivity to full-read alignment methods used for NGS viral detection for these data sets, but their specificity could be improved. A method that first filters host reads using KrakenUniq and then selects the virus classification tool based on the number of remaining reads is suggested as the preferred approach among those tested to detect nonlatent and nonendogenous viruses. Such an approach shows reasonable sensitivity and specificity for the data sets examined and requires less time and memory as full-read alignment methods.

IMPORTANCE Next-generation sequencing (NGS) has been proposed as a complementary method to detect adventitious viruses in the production of biotherapeutics and vaccines to current in vivo and in vitro methods. Before NGS can be established in industry as a main viral detection technology, further investigation into the various aspects of bioinformatics analyses required to identify and classify viral NGS reads is needed. In this study, the ability of rapid metagenomics tools to detect viruses in biopharmaceutical relevant samples is tested and compared to recommend an efficient approach. The results showed that KrakenUniq can quickly and accurately filter host sequences and classify viral reads and had comparable sensitivity and specificity to slower full read alignment approaches, such as BLASTn, for the data sets examined.

The Role of Oxidative Stress and Antioxidants in Diabetic Wound Healing

Foot ulcers are one of the most common and severe complication of diabetes mellitus with significant resultant morbidity and mortality. Multiple factors impair wound healing include skin injury, diabetic neuropathy, ischemia, infection, inadequate glycemic control, poor nutritional status, and severe morbidity. It is currently believed that oxidative stress plays a vital role in diabetic wound healing. An imbalance of free radicals and antioxidants in the body results in overproduction of reactive oxygen species which lead to cell, tissue damage, and delayed wound healing. Therefore, decreasing ROS levels through antioxidative systems may reduce oxidative stress-induced damage to improve healing. In this context, we provide an update on the role of oxidative stress and antioxidants in diabetic wound healing through following four perspectives. We then discuss several therapeutic strategies especially dietary bioactive compounds by targeting oxidative stress to improve wounds healing.

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

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