A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples

Entourage: all-in-one sequence analysis software for genome assembly, virus detection, virus discovery, and intrasample variation profiling

BACKGROUND

Pan-virus detection, and virome investigation in general, can be challenging, mainly due to the lack of universally conserved genetic elements in viruses. Metagenomic next-generation sequencing can offer a promising solution to this problem by providing an unbiased overview of the microbial community, enabling detection of any viruses without prior target selection. However, a major challenge in utilising metagenomic next-generation sequencing for virome investigation is that data analysis can be highly complex, involving numerous data processing steps.

RESULTS

Here, we present Entourage to address this challenge. Entourage enables short-read sequence assembly, viral sequence search with or without reference virus targets using contig-based approaches, and intrasample sequence variation quantification. Several workflows are implemented in Entourage to facilitate end-to-end virus sequence detection analysis through a single command line, from read cleaning, sequence assembly, to virus sequence searching. The results generated are comprehensive, allowing for thorough quality control, reliability assessment, and interpretation. We illustrate Entourage's utility as a streamlined workflow for virus detection by employing it to comprehensively search for target virus sequences and beyond in raw sequence read data generated from HeLa cell culture samples spiked with viruses. Furthermore, we showcase its flexibility and performance on a real-world dataset by analysing a preassembled Tara Oceans dataset. Overall, our results show that Entourage performs well even with low virus sequencing depth in single digits, and it can be used to discover novel viruses effectively. Additionally, by using sequence data generated from a patient with chronic SARS-CoV-2 infection, we demonstrate Entourage's capability to quantify virus intrasample genetic variations, and generate publication-quality figures illustrating the results.

CONCLUSIONS

Entourage is an all-in-one, versatile, and streamlined bioinformatics software for virome investigation, developed with a focus on ease of use. Entourage is available at https://codeberg.org/CENMIG/Entourage under the MIT license.

Clinical applications of metagenomics next-generation sequencing in infectious diseases

Infectious diseases are a great threat to human health. Rapid and accurate detection of pathogens is important in the diagnosis and treatment of infectious diseases. Metagenomics next-generation sequencing (mNGS) is an unbiased and comprehensive approach for detecting all RNA and DNA in a sample. With the development of sequencing and bioinformatics technologies, mNGS is moving from research to clinical application, which opens a new avenue for pathogen detection. Numerous studies have revealed good potential for the clinical application of mNGS in infectious diseases, especially in difficult-to-detect, rare, and novel pathogens. However, there are several hurdles in the clinical application of mNGS, such as: (1) lack of universal workflow validation and quality assurance; (2) insensitivity to high-host background and low-biomass samples; and (3) lack of standardized instructions for mass data analysis and report interpretation. Therefore, a complete understanding of this new technology will help promote the clinical application of mNGS to infectious diseases. This review briefly introduces the history of next-generation sequencing, mainstream sequencing platforms, and mNGS workflow, and discusses the clinical applications of mNGS to infectious diseases and its advantages and disadvantages.

Next-generation sequencing survey of acute febrile illness in Senegal (2020-2022)

Introduction

Acute febrile illnesses (AFI) in developing tropical and sub-tropical nations are challenging to diagnose due to the numerous causes and non-specific symptoms. The proliferation of rapid diagnostic testing and successful control campaigns against malaria have revealed that non-Plasmodium pathogens still contribute significantly to AFI burden. Thus, a more complete understanding of local trends and potential causes is important for selecting the correct treatment course, which in turn will reduce morbidity and mortality. Next-generation sequencing (NGS) in a laboratory setting can be used to identify known and novel pathogens in individuals with AFI.

Methods

In this study, plasma was collected from 228 febrile patients tested negative for malaria at clinics across Senegal from 2020-2022. Total nucleic acids were extracted and converted to metagenomic NGS libraries. To identify viral pathogens, especially those present at low concentration, an aliquot of each library was processed with a viral enrichment panel and sequenced. Corresponding metagenomic libraries were also sequenced to identify non-viral pathogens.

Results and Discussion

Sequencing reads for pathogens with a possible link to febrile illness were identified in 51/228 specimens, including (but not limited to): Borrelia crocidurae (N = 7), West Nile virus (N = 3), Rickettsia felis (N = 2), Bartonella quintana (N = 1), human herpesvirus 8 (N = 1), and Saffold virus (N = 1). Reads corresponding to Plasmodium falciparum were detected in 19 specimens, though their presence in the cohort was likely due to user error of rapid diagnostic testing or incorrect specimen segregation at the clinics. Mosquito-borne pathogens were typically detected just after the conclusion of the rainy season, while tick-borne pathogens were mostly detected before the rainy season. The three West Nile virus strains were phylogenetically characterized and shown to be related to both European and North American clades. Surveys such as this will increase the understanding of the potential causes of non-malarial AFI, which may help inform diagnostic and treatment options for clinicians who provide care to patients in Senegal.

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.

Communicating computational workflows in a regulatory environment

The volume of nucleic acid sequence data has exploded recently, amplifying the challenge of transforming data into meaningful information. Processing data can require an increasingly complex ecosystem of customized tools, which increases difficulty in communicating analyses in an understandable way yet is of sufficient detail to enable informed decisions or repeats. This can be of particular interest to institutions and companies communicating computations in a regulatory environment. BioCompute Objects (BCOs; an instance of pipeline documentation that conforms to the IEEE 2791-2020 standard) were developed as a standardized mechanism for analysis reporting. A suite of BCOs is presented, representing interconnected elements of a computation modeled after those that might be found in a regulatory submission but are shared publicly - in this case a pipeline designed to identify viral contaminants in biological manufacturing, such as for vaccines.

Application of VirCapSeq-VERT and BacCapSeq in the diagnosis of presumed and definitive neuroinfectious diseases

Unbiased high-throughput sequencing (HTS) has enabled new insights into the diversity of agents implicated in central nervous system (CNS) infections. The addition of positive selection capture methods to HTS has enhanced the sensitivity while reducing sequencing costs and the complexity of bioinformatic analysis. Here we report the use of virus capture-based sequencing for vertebrate viruses (VirCapSeq-VERT) and bacterial capture sequencing (BacCapSeq) in investigating CNS infections. Thirty-four samples were categorized: (1) patients with definitive CNS infection by routine testing; (2) patients meeting clinically the Brighton criteria (BC) for meningoencephalitis; (3) patients with presumptive infectious etiology highest on the differential. RNA extracts from cerebrospinal fluid (CSF) were used for VirCapSeq-VERT, and DNA extracts were used for BacCapSeq analysis. Among 8 samples from known CNS infections in group 1, VirCapSeq and BacCapSeq confirmed 3 expected diagnoses (42.8%), were negative in 2 (25%), yielded an alternative result in 1 (11.1%), and did not detect 2 expected negative pathogens. The confirmed cases identified HHV-6, HSV-2, and VZV while the negative samples included JCV and HSV-2. In groups 2 and 3, 11/26 samples (42%) were positive for at least one pathogen; however, 27% of the total samples (7/26) were positive for commensal organisms. No microbial nucleic acids were detected in negative control samples. HTS showed limited promise for pathogen identification in presumed CNS infectious diseases in our small sample. Before conducting larger-scale prospective studies to assess the clinical value of this novel technique, clinicians should understand the benefits and limitations of using this modality.

Application of high-throughput sequencing technologies and analytical tools for pathogen detection in urban water systems: Progress and future perspectives

The ubiquitous presence of pathogenic microorganisms, such as viruses, bacteria, fungi, and protozoa, in urban water systems poses a significant risk to public health. The emergence of infectious waterborne diseases mediated by urban water systems has become one of the leading global causes of mortality. However, the detection and monitoring of these pathogenic microorganisms have been limited by the complexity and diversity in the environmental samples. Conventional methods were restricted by long assay time, high benchmarks of identification, and narrow application sceneries. Novel technologies, such as high-throughput sequencing technologies, enable potentially full-spectrum detection of trace pathogenic microorganisms in complex environmental matrices. This review discusses the current state of high-throughput sequencing technologies for identifying pathogenic microorganisms in urban water systems with a concise summary. Furthermore, future perspectives in pathogen research emphasize the need for detection methods with high accuracy and sensitivity, the establishment of precise detection standards and procedures, and the significance of bioinformatics software and platforms. We have compiled a list of pathogens analysis software/platforms/databases that boast robust engines and high accuracy for preference. We highlight the significance of analyses by combining targeted and non-targeted sequencing technologies, short and long reads technologies, sequencing technologies, and bioinformatic tools in pursuing upgraded biosafety in urban water systems.

Rapid diagnosis of Mycobacterium marinum infection using targeted nanopore sequencing: a case report

Mycobacterium marinum (M. marinum) is a non-tuberculous mycobacterium (NTM) that can cause infectious diseases in aquatic animals and humans. Culture-based pathogen detection is the gold standard for diagnosing NTM infection. However, this method is time-consuming and has low positivity rates for fastidious organisms. Oxford Nanopore MinION sequencing is an emerging third-generation sequencing technology that can sequence DNA or RNA directly in a culture-independent manner and offers rapid microbial identification. Further benefits include low cost, short turnaround time, long read lengths, and small equipment size. Nanopore sequencing plays a crucial role in assessing drug resistance, clinical identification of microbes, and monitoring infectious diseases. Some reports on Mycobacterium tuberculosis (MTB) using nanopore sequencing have been published, however, there are few reports on NTM, such as M. marinum. Here, we report the use of nanopore sequencing for the diagnosis of M. marinum.

Transmission of yellow fever vaccine virus through blood transfusion and organ transplantation in the USA in 2021: report of an investigation

BACKGROUND

In 2021, four patients who had received solid organ transplants in the USA developed encephalitis beginning 2-6 weeks after transplantation from a common organ donor. We describe an investigation into the cause of encephalitis in these patients.

METHODS

From Nov 7, 2021, to Feb 24, 2022, we conducted a public health investigation involving 15 agencies and medical centres in the USA. We tested various specimens (blood, cerebrospinal fluid, intraocular fluid, serum, and tissues) from the organ donor and recipients by serology, RT-PCR, immunohistochemistry, metagenomic next-generation sequencing, and host gene expression, and conducted a traceback of blood transfusions received by the organ donor.

FINDINGS

We identified one read from yellow fever virus in cerebrospinal fluid from the recipient of a kidney using metagenomic next-generation sequencing. Recent infection with yellow fever virus was confirmed in all four organ recipients by identification of yellow fever virus RNA consistent with the 17D vaccine strain in brain tissue from one recipient and seroconversion after transplantation in three recipients. Two patients recovered and two patients had no neurological recovery and died. 3 days before organ procurement, the organ donor received a blood transfusion from a donor who had received a yellow fever vaccine 6 days before blood donation.

INTERPRETATION

This investigation substantiates the use of metagenomic next-generation sequencing for the broad-based detection of rare or unexpected pathogens. Health-care workers providing vaccinations should inform patients of the need to defer blood donation for at least 2 weeks after receiving a yellow fever vaccine. Despite mitigation strategies and safety interventions, a low risk of transfusion-transmitted infections remains.

FUNDING

US Centers for Disease Control and Prevention (CDC), the Biomedical Advanced Research and Development Authority, and the CDC Epidemiology and Laboratory Capacity Cooperative Agreement for Infectious Diseases.

Application of VirCapSeq-VERT and BacCapSeq In the Diagnosis of Presumed and Definitive Neuroinfectious Diseases

Background

Unbiased high-throughput sequencing (HTS) has enabled new insights into the diversity of agents implicated in central nervous system (CNS) infections. The addition of positive selection capture methods to HTS has enhanced the sensitivity while reducing sequencing costs and complexity of bioinformatic analysis. Here we report the use of virus capture based sequencing for vertebrate viruses (VirCapSeq-VERT) and bacterial capture sequencing (BacCapSeq) in investigating CNS infections.

Design/Methods

Thirty-four samples were categorized: (1) Patients with definitive CNS infection by routine testing; (2) Patients meeting clinically Brighton Criteria (BC) for meningoencephalitis (3) Patients with presumptive infectious etiology highest on the differential. RNA extracts from cerebrospinal fluid (CSF) were used for VirCapSeq-VERT and DNA extracts were used for BacCapSeq analysis.

Results

Among 8 samples from known CNS infections in group 1, VirCapSeq and BacCapSeq confirmed 3 expected diagnoses (42.8%), were negative in 2 (25%), yielded an alternative result in 1 (11.1%), and did not detect 2 expected negative pathogens. The confirmed cases identified HHV-6, HSV-2, and VZV while the negative samples included JCV and HSV-2. In groups 2 and 3,11/26 samples (42%) were positive for at least one pathogen, however 27% of the total samples (7/26) were positive for commensal organisms. No microbial nucleic acids were detected in negative control samples.

Conclusions

HTS showed limited promise for pathogen identification in presumed CNS infectious diseases in our small sample. Before conducting larger-scale prospective studies to assess clinical value of this novel technique, clinicians should understand benefits and limitations of using this modality.

Diagnosis of Non-Tuberculous Mycobacterial Pulmonary Disease by Metagenomic Next-Generation Sequencing on Bronchoalveolar Lavage Fluid

Purpose

Metagenomic next-generation sequencing (mNGS) has been extensively used in the diagnosis of infectious diseases but has rarely been applied in non-tuberculous mycobacterial pulmonary disease (NTMPD). This study analyzed the diagnostic performance of mNGS in bronchoalveolar lavage fluid (BALF) samples to identify non-tuberculous mycobacteria (NTM).

Patients and Methods

A total of 231 patients with suspected NTMPD were recruited from the First Affiliated Hospital, School of Medicine, Zhejiang University, from March 2021 to October 2022. A total of 118 cases were ultimately included. Of these patients, 61 cases were enrolled in the NTMPD group, 23 cases were enrolled in the suspected-NTMPD group, and 34 cases were enrolled in the non-NTMPD group. The diagnostic performance of traditional culture, acid-fast staining (AFS), and mNGS for NTMPD was assessed.

Results

Patients in the NTMPD group had a higher proportion of bronchiectasis (P=0.007). Among mNGS-positive samples in the NTMPD group, a significantly higher reads number of NTM was observed in AFS-positive patients [61.50 (22.00, 395.00) vs 15.50 (6.00, 36.25), P=0.008]. Meanwhile, mNGS demonstrated a sensitivity of 90.2%, which was far superior to AFS (42.0%) and culture (77.0%) (P<0.001). The specificity of mNGS in detecting NTM was 100%, which was the same as that of traditional culture. The area under the receiver operating characteristic curve of mNGS was 0.951 (95% CI 0.906-0.996), which was higher than that of culture (0.885 [95% CI 0.818-0.953]) and AFS (0.686 [95% CI 0.562-0.810]). In addition to NTM, other pulmonary pathogens were also found by mNGS.

Conclusion

mNGS using BALF samples is a rapid and effective diagnostic tool for NTMPD, and mNGS is recommended for patients with suspected NMTPD or NTM coinfected pneumonia.

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.

Adeno-associated virus type 2 in US children with acute severe hepatitis

As of August 2022, clusters of acute severe hepatitis of unknown aetiology in children have been reported from 35 countries, including the USA1,2. Previous studies have found human adenoviruses (HAdVs) in the blood from patients in Europe and the USA3-7, although it is unclear whether this virus is causative. Here we used PCR testing, viral enrichment-based sequencing and agnostic metagenomic sequencing to analyse samples from 16 HAdV-positive cases from 1 October 2021 to 22 May 2022, in parallel with 113 controls. In blood from 14 cases, adeno-associated virus type 2 (AAV2) sequences were detected in 93% (13 of 14), compared to 4 (3.5%) of 113 controls (P < 0.001) and to 0 of 30 patients with hepatitis of defined aetiology (P < 0.001). In controls, HAdV type 41 was detected in blood from 9 (39.1%) of the 23 patients with acute gastroenteritis (without hepatitis), including 8 of 9 patients with positive stool HAdV testing, but co-infection with AAV2 was observed in only 3 (13.0%) of these 23 patients versus 93% of cases (P < 0.001). Co-infections by Epstein-Barr virus, human herpesvirus 6 and/or enterovirus A71 were also detected in 12 (85.7%) of 14 cases, with higher herpesvirus detection in cases versus controls (P < 0.001). Our findings suggest that the severity of the disease is related to co-infections involving AAV2 and one or more helper viruses.

Metagenomic Detection of Divergent Insect- and Bat-Associated Viruses in Plasma from Two African Individuals Enrolled in Blood-Borne Surveillance

Metagenomic next-generation sequencing (mNGS) has enabled the high-throughput multiplexed identification of sequences from microbes of potential medical relevance. This approach has become indispensable for viral pathogen discovery and broad-based surveillance of emerging or re-emerging pathogens. From 2015 to 2019, plasma was collected from 9586 individuals in Cameroon and the Democratic Republic of the Congo enrolled in a combined hepatitis virus and retrovirus surveillance program. A subset (n = 726) of the patient specimens was analyzed by mNGS to identify viral co-infections. While co-infections from known blood-borne viruses were detected, divergent sequences from nine poorly characterized or previously uncharacterized viruses were also identified in two individuals. These were assigned to the following groups by genomic and phylogenetic analyses: densovirus, nodavirus, jingmenvirus, bastrovirus, dicistrovirus, picornavirus, and cyclovirus. Although of unclear pathogenicity, these viruses were found circulating at high enough concentrations in plasma for genomes to be assembled and were most closely related to those previously associated with bird or bat excrement. Phylogenetic analyses and in silico host predictions suggested that these are invertebrate viruses likely transmitted through feces containing consumed insects or through contaminated shellfish. This study highlights the power of metagenomics and in silico host prediction in characterizing novel viral infections in susceptible individuals, including those who are immunocompromised from hepatitis viruses and retroviruses, or potentially exposed to zoonotic viruses from animal reservoir species.

Unlocking the microbial studies through computational approaches: how far have we reached?

The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.

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.

A case for investment in clinical metagenomics in low-income and middle-income countries

Clinical metagenomics is the diagnostic approach with the broadest capacity to detect both known and novel pathogens. Clinical metagenomics is costly to run and requires infrastructure, but the use of next-generation sequencing for SARS-CoV-2 molecular epidemiology in low-income and middle-income countries (LMICs) offers an opportunity to direct this infrastructure to the establishment of clinical metagenomics programmes. Local implementation of clinical metagenomics is important to create relevant systems and evaluate cost-effective methodologies for its use, as well as to ensure that reference databases and result interpretation tools are appropriate to local epidemiology. Rational implementation, based on the needs of LMICs and the available resources, could ultimately improve individual patient care in instances in which available diagnostics are inadequate and supplement emerging infectious disease surveillance systems to ensure the next pandemic pathogen is quickly identified.

Clinical Characteristics and Follow-up of Cases of Streptococcus suis Meningitis in Patients of Liuzhou, China

We analyzed the clinical characteristics and outcomes of patients with Streptococcus suis meningitis in Liuzhou, China, to improve diagnostic accuracy and lower the chances of misdiagnosis. The major clinical manifestations, auxiliary examination results, treatment strategies, treatment efficacy, and follow-up results of 17 consecutively admitted patients with S. suis meningitis were evaluated. The most common clinical manifestations were fever (15/17), sensorineural hearing loss (13/17), headache (11/17), and altered mental status (8/17). In addition, 64.71% of the patients had residual symptoms of sensorineural hearing loss at discharge, and moderate disabilities occurred in 68.75% of the patients in the form of sensorineural deafness (11/17) and hemiparesis (1/17). The cerebrospinal fluid (CSF) of nine patients was used for metagenomic analysis with next-generation sequencing. The metagenomic analysis of CSF of four patients was positive, whereas blood and CSF cultures were negative. The average modified Rankin Scale (mRS) and Activities of Daily Living (ADL) scores improved significantly at the 6-month follow-up compared with those at admission (P < 0.05). There was no correlation between altered mRS and ADL scores and the CSF findings (P > 0.05). Early administration of antibiotics can prevent sensorineural hearing loss. Early CSF metagenomic analysis may be superior to blood and CSF culture.

Fast and Accurate Identification of Candida auris by High Resolution Mass Spectrometry

The emerging pathogen Candida auris has been associated with nosocomial outbreaks on six continents. Genetic analysis indicates simultaneous and independent emergence of separate clades of the species in different geographical locations. Both invasive infection and colonization have been observed, warranting attention due to variable antifungal resistance profiles and hospital transmission. MALDI-TOF based identification methods have become routine in hospitals and research institutes. However, identification of the newly emerging lineages of C. auris yet remains a diagnostic challenge. In this study an innovative liquid chromatography (LC)-high resolution Orbitrap^TM mass spectrometry method was used for identification of C. auris from axenic microbial cultures. A set of 102 strains from all five clades and different body locations were investigated. The results revealed correct identification of all C. auris strains within the sample cohort, with an identification accuracy of 99.6% from plate culture, in a time-efficient manner. Furthermore, application of the applied mass spectrometry technology provided the species identification down to clade level, thus potentially providing the possibility for epidemiological surveillance to track pathogen spread. Identification beyond species level is required specially to differentiate between nosocomial transmission and repeated introduction to a hospital.

Transfusion-Transmitted Cache Valley Virus Infection in a Kidney Transplant Recipient With Meningoencephalitis

BACKGROUND

Cache Valley virus (CVV) is a mosquito-borne virus that is a rare cause of disease in humans. In the fall of 2020, a patient developed encephalitis 6 weeks following kidney transplantation and receipt of multiple blood transfusions.

METHODS

After ruling out more common etiologies, metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) was performed. We reviewed the medical histories of the index kidney recipient, organ donor, and recipients of other organs from the same donor and conducted a blood traceback investigation to evaluate blood transfusion as a possible source of infection in the kidney recipient. We tested patient specimens using reverse-transcription polymerase chain reaction (RT-PCR), the plaque reduction neutralization test, cell culture, and whole-genome sequencing.

RESULTS

CVV was detected in CSF from the index patient by mNGS, and this result was confirmed by RT-PCR, viral culture, and additional whole-genome sequencing. The organ donor and other organ recipients had no evidence of infection with CVV by molecular or serologic testing. Neutralizing antibodies against CVV were detected in serum from a donor of red blood cells received by the index patient immediately prior to transplant. CVV neutralizing antibodies were also detected in serum from a patient who received the co-component plasma from the same blood donation.

CONCLUSIONS

Our investigation demonstrates probable CVV transmission through blood transfusion. Clinicians should consider arboviral infections in unexplained meningoencephalitis after blood transfusion or organ transplantation. The use of mNGS might facilitate detection of rare, unexpected infections, particularly in immunocompromised patients.

Longitudinal comparison of the developing gut virome in infants and their mothers

The human gut virome and its early life development are poorly understood. Prior studies have captured single-point assessments with the evolution of the infant virome remaining largely unexplored. We performed viral metagenomic sequencing on stool samples collected longitudinally from a cohort of 53 infants from age 2 weeks to 3 years (80.7 billion reads), and from their mothers (9.8 billion reads) to examine and compare viromes. The asymptomatic infant virome consisted of bacteriophages, nonhuman dietary/environmental viruses, and human-host viruses, predominantly picornaviruses. In contrast, human-host viruses were largely absent from the maternal virome. Previously undescribed, sequence-divergent vertebrate viruses were detected in the maternal but not infant virome. As infants aged, the phage component evolved to resemble the maternal virome, but by age 3, the human-host component remained dissimilar from the maternal virome. Thus, early life virome development is determined predominantly by dietary, infectious, and environmental factors rather than direct maternal acquisition.

Enhanced Viral Metagenomics with Lazypipe 2

Viruses are the main agents causing emerging and re-emerging infectious diseases. It is therefore important to screen for and detect them and uncover the evolutionary processes that support their ability to jump species boundaries and establish themselves in new hosts. Metagenomic next-generation sequencing (mNGS) is a high-throughput, impartial technology that has enabled virologists to detect either known or novel, divergent viruses from clinical, animal, wildlife and environmental samples, with little a priori assumptions. mNGS is heavily dependent on bioinformatic analysis, with an emerging demand for integrated bioinformatic workflows. Here, we present Lazypipe 2, an updated mNGS pipeline with, as compared to Lazypipe1, significant improvements in code stability and transparency, with added functionality and support for new software components. We also present extensive benchmarking results, including evaluation of a novel canine simulated metagenome, precision and recall of virus detection at varying sequencing depth, and a low to extremely low proportion of viral genetic material. Additionally, we report accuracy of virus detection with two strategies: homology searches using nucleotide or amino acid sequences. We show that Lazypipe 2 with nucleotide-based annotation approaches near perfect detection for eukaryotic viruses and, in terms of accuracy, outperforms the compared pipelines. We also discuss the importance of homology searches with amino acid sequences for the detection of highly divergent novel viruses.

Evaluation of tools for taxonomic classification of viruses

Viruses are the most abundant infectious agents on earth, and they infect living organisms such as bacteria, plants and animals, among others. They play an important role in the balance of different ecosystems by modulating microbial populations. In humans, they are responsible for some common diseases and may cause severe illnesses. Viral metagenomic studies have become essential and offer the possibility to understand and extend the knowledge of virus diversity and functionality. For these approaches, an essential step is the classification of viral sequences. In this work, 11 taxonomic classification tools were compared by analysing their performances, in terms of sensitivity and precision, to classify reads at the species and family levels using the same (viral and nonviral) datasets and evaluation metrics, as well as their processing times and memory requirements. The results showed that factors such as richness (numbers of viral species in samples), taxonomic level in the classification and read length influence tool performance. High values of viral richness in samples decreased the performances of most tools. Additionally, the classifications were better at higher taxonomic levels, such as families, compared to lower taxonomic levels, such as species, and were more evident in short reads. The results also indicated that BLAST and Kraken2 were the best tools for classifying all types of reads, while FastViromeExplorer and VirusFinder were only good when used for long reads and Centrifuge, DIAMOND, and One Codex when used for short reads. Regarding nonviral datasets (human and bacterial), all tools correctly classified them as nonviral.

The electronic tree of life (eToL): a net of long probes to characterize the microbiome from RNA-seq data

BACKGROUND

Microbiome analysis generally requires PCR-based or metagenomic shotgun sequencing, sophisticated programs, and large volumes of data. Alternative approaches based on widely available RNA-seq data are constrained because of sequence similarities between the transcriptomes of microbes/viruses and those of the host, compounded by the extreme abundance of host sequences in such libraries. Current approaches are also limited to specific microbial groups. There is a need for alternative methods of microbiome analysis that encompass the entire tree of life.

RESULTS

We report a method to specifically retrieve non-human sequences in human tissue RNA-seq data. For cellular microbes we used a bioinformatic 'net', based on filtered 64-mer sequences designed from small subunit ribosomal RNA (rRNA) sequences across the Tree of Life (the 'electronic tree of life', eToL), to comprehensively (98%) entrap all non-human rRNA sequences present in the target tissue. Using brain as a model, retrieval of matching reads, re-exclusion of human-related sequences, followed by contig building and species identification, is followed by confirmation of the abundance and identity of the corresponding species groups. We provide methods to automate this analysis. The method reduces the computation time versus metagenomics by a factor of >1000. A variant approach is necessary for viruses. Again, because of significant matches between viral and human sequences, a 'stripping' approach is essential. Contamination during workup is a potential problem, and we discuss strategies to circumvent this issue. To illustrate the versatility of the method we report the use of the eToL methodology to unambiguously identify exogenous microbial and viral sequences in human tissue RNA-seq data across the entire tree of life including Archaea, Bacteria, Chloroplastida, basal Eukaryota, Fungi, and Holozoa/Metazoa, and discuss the technical and bioinformatic challenges involved.

CONCLUSIONS

This generic methodology is likely to find wide application in microbiome analysis including diagnostics.

Oropouche virus as an emerging cause of acute febrile illness in Colombia

Arbovirus infections are frequent causes of acute febrile illness (AFI) in tropical countries. We conducted health facility-based AFI surveillance at four sites in Colombia (Cucuta, Cali, Villavicencio, Leticia) during 2019-2022. Demographic, clinical and risk factor data were collected from persons with AFI that consented to participate in the study (n = 2,967). Serologic specimens were obtained and tested for multiple pathogens by RT-PCR and rapid test (Antigen/IgM), with 20.7% identified as dengue positive from combined testing. Oropouche virus (OROV) was initially detected in serum by metagenomic next-generation sequencing (mNGS) and virus target capture in a patient from Cúcuta. Three additional infections from Leticia were confirmed by conventional PCR, sequenced, and isolated in tissue culture. Phylogenetic analysis determined there have been at least two independent OROV introductions into Colombia. To assess OROV spread, a RT-qPCR dual-target assay was developed which identified 87/791 (10.9%) viremic cases in AFI specimens from Cali (3/53), Cucuta (3/19), Villavicencio (38/566), and Leticia (43/153). In parallel, an automated anti-nucleocapsid antibody assay detected IgM in 27/503 (5.4%) and IgG in 92/568 (16.2%) patients screened, for which 24/68 (35.3%) of PCR positives had antibodies. Dengue was found primarily in people aged <18 years and linked to several clinical manifestations (weakness, skin rash and petechiae), whereas Oropouche cases were associated with the location, climate phase, and odynophagia symptom. Our results confirm OROV as an emerging pathogen and recommend increased surveillance to determine its burden as a cause of AFI in Colombia.

The Role of Infection and Inflammation in the Pathogenesis of Pediatric Arterial Ischemic Stroke

Infections play an important role in the pathogenesis of acute ischemic stroke (AIS) in neonates and children. In neonates, chorioamnionitis or intrauterine inflammation has been implicated as a common risk factor for AIS. In infants and children, recent investigations demonstrated that even minor childhood infections are associated with subsequent increased risk for AIS. Post-infectious inflammatory mechanisms following infections with herpesviruses may lead to focal cerebral arteriopathy (FCA), one of the most common causes of AIS in a previously healthy child. Other agents such as parvovirus B19, dengue virus, and SARS-CoV-2 have recently been implicated as other potential triggers. Infections are compelling treatable stroke risk factors, with available therapies for both pathogens and downstream inflammatory effects. However, infections are common in childhood, while stroke is uncommon. The ongoing VIPS II (Vascular effects of Infection in Pediatric Stroke) study aims to identify the array of pathogens that may lead to childhood AIS and whether either unusual strains or unusual combinations of pathogens explain this paradox. Immune modulation with corticosteroids for FCA is another active area of research, with European and U.S. trials launching soon. The results of these new pediatric stroke studies combined with findings emerging from the larger field of immune-mediated post-infectious diseases will likely lead to new approaches to the prevention and treatment of pediatric stroke. This review highlights recent developments from both clinical and animal model research enhancing our understanding of this relationship between infection, inflammation, and stroke in neonates and children.

Next Generation Sequencing Approaches to Characterize the Respiratory Tract Virome

The COVID-19 pandemic and heightened perception of the risk of emerging viral infections have boosted the efforts to better understand the virome or complete repertoire of viruses in health and disease, with a focus on infectious respiratory diseases. Next-generation sequencing (NGS) is widely used to study microorganisms, allowing the elucidation of bacteria and viruses inhabiting different body systems and identifying new pathogens. However, NGS studies suffer from a lack of standardization, in particular, due to various methodological approaches and no single format for processing the results. Here, we review the main methodological approaches and key stages for studies of the human virome, with an emphasis on virome changes during acute respiratory viral infection, with applications for clinical diagnostics and epidemiologic analyses.

HAYSTAC: A Bayesian framework for robust and rapid species identification in high-throughput sequencing data

Identification of specific species in metagenomic samples is critical for several key applications, yet many tools available require large computational power and are often prone to false positive identifications. Here we describe High-AccuracY and Scalable Taxonomic Assignment of MetagenomiC data (HAYSTAC), which can estimate the probability that a specific taxon is present in a metagenome. HAYSTAC provides a user-friendly tool to construct databases, based on publicly available genomes, that are used for competitive read mapping. It then uses a novel Bayesian framework to infer the abundance and statistical support for each species identification and provide per-read species classification. Unlike other methods, HAYSTAC is specifically designed to efficiently handle both ancient and modern DNA data, as well as incomplete reference databases, making it possible to run highly accurate hypothesis-driven analyses (i.e., assessing the presence of a specific species) on variably sized reference databases while dramatically improving processing speeds. We tested the performance and accuracy of HAYSTAC using simulated Illumina libraries, both with and without ancient DNA damage, and compared the results to other currently available methods (i.e., Kraken2/Bracken, KrakenUniq, MALT/HOPS, and Sigma). HAYSTAC identified fewer false positives than both Kraken2/Bracken, KrakenUniq and MALT in all simulations, and fewer than Sigma in simulations of ancient data. It uses less memory than Kraken2/Bracken, KrakenUniq as well as MALT both during database construction and sample analysis. Lastly, we used HAYSTAC to search for specific pathogens in two published ancient metagenomic datasets, demonstrating how it can be applied to empirical datasets. HAYSTAC is available from https://github.com/antonisdim/HAYSTAC.

The emerging field of paleo-metagenomics (i.e., metagenomics from ancient DNA) holds great promise for novel discoveries in fields as diverse as pathogen evolution and paleoenvironmental reconstruction. However, there is presently a lack of computational methods for species identification from microbial communities in both degraded and nondegraded DNA material. Here, we present “HAYSTAC”, a user-friendly software package that implements a novel probabilistic model for species identification in metagenomic data obtained from both degraded and non-degraded DNA material. Through extensive benchmarking, we show that HAYSTAC can be used for accurately profiling the community composition, as well as for direct hypothesis testing for the presence of extremely low-abundance taxa, in complex metagenomic samples. After analysing simulated and publicly available datasets, HAYSTAC consistently produced the lowest number of false positive identifications during taxonomic profiling, produced robust results when databases of restricted size were used, and showed increased sensitivity for pathogen detection compared to other specialist methods. The newly proposed probabilistic model and software employed by HAYSTAC can have a substantial impact on the robust and rapid pathogen discovery in degraded/shallow sequenced metagenomic samples while optimising the use of computational resources.

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

BACKGROUND

Emerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant data remain comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone.

RESULTS

We present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that it can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and visualize the landscape of fungal pathogenicity. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats.

CONCLUSIONS

The neural networks trained using our data collection enable accurate detection of novel fungal pathogens. A curated set of over 1400 genomes with host and pathogenicity metadata supports training of machine-learning models and sequence comparison, not limited to the pathogen detection task.

AVAILABILITY AND IMPLEMENTATION

The data, models and code are hosted at https://zenodo.org/record/5846345, https://zenodo.org/record/5711877 and https://gitlab.com/dacs-hpi/deepac.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Nutrient levels and prokaryotes affect viral communities in plateau lakes

Viruses are the most abundant organisms in aquatic environments. Recent advances of viral metagenomic have greatly expanded our understanding of aquatic viral communities. However, little is known about the difference of viral communities and driving factors in freshwater lake. This study seeks to understand the spatio-temporal variation, differences, and driving factors of viral communities in two plateau lakes (Dianchi and Fuxian Lakes) with significant nutritional differences. The viral communities exhibited apparent seasonal variation in Dianchi Lake, while seasonal influences on the viral communities were greater than location-based influences. Two-thirds of all detected viral taxa were shared in two lakes, but there was variation in the composition of viral communities. Correlations between prokaryotic communities, environmental factors and viral communities were analyzed. The nutrients, chlorophyll a were primarily environmental parameters affecting viral communities, and the prokaryotic community was significantly correlated with the viral community. In addition, several viruses infecting humans were identified in two lakes, with the most abundant being Herpesviridae and Poxviridae. Overall, these findings provide information on the dynamics, composition, and differences of viral and prokaryotic communities in plateau lakes with different nutrient levels. These results suggest that nutritional levels and prokaryotic communities could play an important role in shaping viral communities in freshwater lakes.

Microseek: A Protein-Based Metagenomic Pipeline for Virus Diagnostic and Discovery

We present Microseek, a pipeline for virus identification and discovery based on RVDB-prot, a comprehensive, curated and regularly updated database of viral proteins. Microseek analyzes metagenomic Next Generation Sequencing (mNGS) raw data by performing quality steps, de novo assembly, and by scoring the Lowest Common Ancestor (LCA) from translated reads and contigs. Microseek runs on a local computer. The outcome of the pipeline is displayed through a user-friendly and dynamic graphical interface. Based on two representative mNGS datasets derived from human tissue and plasma specimens, we illustrate how Microseek works, and we report its performances. In silico spikes of known viral sequences, but also spikes of fake Neopneumovirus viral sequences generated with variable evolutionary distances from known members of the Pneumoviridae family, were used. Results were compared to Chan Zuckerberg ID (CZ ID), a reference cloud-based mNGS pipeline. We show that Microseek reliably identifies known viral sequences and performs well for the detection of distant pseudoviral sequences, especially in complex samples such as in human plasma, while minimizing non-relevant hits.

Effective Identification of Bacterial Genomes From Short and Long Read Sequencing Data

With the development of sequencing technology, microbiological genome sequencing analysis has attracted extensive attention. For inexperienced users without sufficient bioinformatics skills, making sense of sequencing data for microbial identification, especially for bacterial identification, through reads analysis is still challenging. In order to address the challenge of effectively analyzing genomic information, in this paper, we develop an effective approach and automatic bioinformatics pipeline called PBGI for bacterial genome identification, performing automatedly and customized bioinformatics analysis using short-reads or long-reads sequencing data produced by multiple platforms such as Illumina, PacBio and Oxford Nanopore. An evaluation of the proposed approach on the practical data set is presented, showing that PBGI provides a user-friendly way to perform bacterial identification through short or long reads analysis, and could provide accurate analyzing results. The source code of the PBGI is freely available at https://github.com/lyotvincent/PBGI.

Next Generation and Other Sequencing Technologies in Diagnostic Microbiology and Infectious Diseases

Next-generation sequencing (NGS) technologies have become increasingly available for use in the clinical microbiology diagnostic environment. There are three main applications of these technologies in the clinical microbiology laboratory: whole genome sequencing (WGS), targeted metagenomics sequencing and shotgun metagenomics sequencing. These applications are being utilized for initial identification of pathogenic organisms, the detection of antimicrobial resistance mechanisms and for epidemiologic tracking of organisms within and outside hospital systems. In this review, we analyze these three applications and provide a comprehensive summary of how these applications are currently being used in public health, basic research, and clinical microbiology laboratory environments. In the public health arena, WGS is being used to identify and epidemiologically track food borne outbreaks and disease surveillance. In clinical hospital systems, WGS is used to identify multi-drug-resistant nosocomial infections and track the transmission of these organisms. In addition, we examine how metagenomics sequencing approaches (targeted and shotgun) are being used to circumvent the traditional and biased microbiology culture methods to identify potential pathogens directly from specimens. We also expand on the important factors to consider when implementing these technologies, and what is possible for these technologies in infectious disease diagnosis in the next 5 years.

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.

The application of next generation sequencing technology in medical diagnostics: a perspective

Rapid isolation, characterization, and identification are prerequisites of any successful medical intervention to infectious disease treatment. This is a real challenge to the scientific as well as a medical community to find out a proper and robust method of pathogen detection. Classical cultural, as well as biochemical test-based identification, has its own limitations to their time-consuming and ineffectiveness for closely related pathovars. Molecular diagnostics became a popular alternative to classical techniques for the past couple of decades but it required some prior information to detect the pathogen successfully. Recently, with the advent of next-generation sequencing (NGS) technology identification, and characterization of almost all the pathogenic bacteria become possible without any information a priori. Metagenomic next generation sequencing is another specialized type of NGS that is profoundly utilized in medical biotechnology and diagnostics now a days. Therefore, the present review is focused on a brief introduction to NGS technology, its application in medical microbiology, and possible future aspects for the development of medical sciences.

Case report: A rare case of pulmonary mucormycosis caused by Lichtheimia ramosa in pediatric acute lymphoblastic leukemia and review of Lichtheimia infections in leukemia

Mucormycosis caused by Lichtheimia ramosa is an emerging and uncommon opportunistic infection in patients with hematological malignancies, with high mortality rates. Herein, we first report a case of pulmonary mucormycosis with Lichtheimia ramosa in a 3-year-old girl recently diagnosed with B-cell acute lymphoblastic leukemia. The diagnosis was made using computerized tomography of the lung, metagenomic next-generation sequencing (mNGS) of blood and sputum specimens, and microscopic examination to detect the development of Lichtheimia ramosa on the surgical specimen. She was effectively treated after receiving prompt treatment with amphotericin B and posaconazole, followed by aggressive surgical debridement. In our case, the fungal isolates were identified as Lichtheimia ramosa using mNGS, which assisted clinicians in quickly and accurately diagnosing and initiating early intensive treatment. This case also indicated the importance of strong clinical suspicion, as well as aggressive antifungal therapy combined with surgical debridement of affected tissues.

Why Cell-Free DNA Can Be a “Game Changer” for Lung Allograft Monitoring for Rejection and Infection

Purpose of Review

Although there has been improvement in short-term clinical outcomes for patients following lung transplant (LT), advances have not translated into longer-term allograft survival. Furthermore, invasive biopsies are still standard of practice for monitoring LT recipients for allograft injury. We review the relevant literature supporting the role of using plasma donor-derived cell-free DNA (dd-cfDNA) as a non-invasive biomarker for LT allograft injury surveillance and discuss future research directions.

Recent Findings

Accumulating data has demonstrated that dd-cfDNA is associated with molecular and cellular injury due to acute (cellular and antibody-mediated) rejection, chronic lung allograft dysfunction, and relevant infectious pathogens. Strong performance in distinguishing rejection and allograft injury from stable patients has set the stage for clinical trials to assess dd-cfDNA utility for surveillance of LT patients. Research investigating the potential role of dd-cfDNA methylation signatures to map injured tissue and cell-free DNA in detecting allograft injury-related pathogens is ongoing.

Summary

There is an amassed breadth of clinical data to support a role for dd-cfDNA in monitoring rejection and other forms of allograft injury. Rigorously designed, robust clinical trials that encompass the diversity in patient demographics are paramount to furthering our understanding and adoption of plasma dd-cfDNA for surveillance of lung allograft health.

SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning

The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen .

Identification of Antibiotic Resistance Proteins via MiCId's Augmented Workflow. A Mass Spectrometry-Based Proteomics Approach

Fast and accurate identifications of pathogenic bacteria along with their associated antibiotic resistance proteins are of paramount importance for patient treatments and public health. To meet this goal from the mass spectrometry aspect, we have augmented the previously published Microorganism Classification and Identification (MiCId) workflow for this capability. To evaluate the performance of this augmented workflow, we have used MS/MS datafiles from samples of 10 antibiotic resistance bacterial strains belonging to three different species: Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The evaluation shows that MiCId's workflow has a sensitivity value around 85% (with a lower bound at about 72%) and a precision greater than 95% in identifying antibiotic resistance proteins. In addition to having high sensitivity and precision, MiCId's workflow is fast and portable, making it a valuable tool for rapid identifications of bacteria as well as detection of their antibiotic resistance proteins. It performs microorganismal identifications, protein identifications, sample biomass estimates, and antibiotic resistance protein identifications in 6-17 min per MS/MS sample using computing resources that are available in most desktop and laptop computers. We have also demonstrated other use of MiCId's workflow. Using MS/MS data sets from samples of two bacterial clonal isolates, one being antibiotic-sensitive while the other being multidrug-resistant, we applied MiCId's workflow to investigate possible mechanisms of antibiotic resistance in these pathogenic bacteria; the results showed that MiCId's conclusions agree with the published study. The new version of MiCId (v.07.01.2021) is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.

Dynamic patterns of microRNA expression during acute myeloid leukemia state-transition

MicroRNAs (miRNAs) have been shown to hold prognostic value in acute myeloid leukemia (AML); however, the temporal dynamics of miRNA expression in AML are poorly understood. Using serial samples from a mouse model of AML to generate time-series miRNA sequencing data, we are the first to show that the miRNA transcriptome undergoes state-transition during AML initiation and progression. We modeled AML state-transition as a particle undergoing Brownian motion in a quasi-potential and validated the AML state-space and state-transition model to accurately predict time to AML in an independent cohort of mice. The critical points of the model provided a framework to align samples from mice that developed AML at different rates. Our mathematical approach allowed discovery of dynamic processes involved during AML development and, if translated to humans, has the potential to predict an individual's disease trajectory.

The Chronic Wound Phageome: Phage Diversity and Associations with Wounds and Healing Outcomes

Two leading impediments to chronic wound healing are polymicrobial infection and biofilm formation. Recent studies have characterized the bacterial fraction of these microbiomes and have begun to elucidate compositional correlations to healing outcomes. However, the factors that drive compositional shifts are still being uncovered. The virome may play an important role in shaping bacterial community structure and function. Previous work on the skin virome determined that it was dominated by bacteriophages, viruses that infect bacteria. To characterize the virome, we enrolled 20 chronic wound patients presenting at an outpatient wound care clinic in a microbiome survey, collecting swab samples from healthy skin and chronic wounds (diabetic, venous, arterial, or pressure) before and after a single, sharp debridement procedure. We investigated the virome using a virus-like particle enrichment procedure, shotgun metagenomic sequencing, and a k-mer-based, reference-dependent taxonomic classification method. Taxonomic composition, diversity, and associations with covariates are presented. We find that the wound virome is highly diverse, with many phages targeting known pathogens, and may influence bacterial community composition and functionality in ways that impact healing outcomes.

IMPORTANCE Chronic wounds are an increasing medical burden. These wounds are known to be rich in microbial content, including both bacteria and bacterial viruses (phages). The viruses may play an important role in shaping bacterial community structure and function. We analyzed the virome and bacterial composition of 20 patients with chronic wounds. The viruses found in wounds are highly diverse compared to normal skin, unlike the bacterial composition, where diversity is decreased. These data represent an initial look at this relatively understudied component of the chronic wound microbiome and may help inform future phage-based interventions.

Metagenomics-enabled microbial surveillance

Lessons learnt from the COVID-19 pandemic include increased awareness of the potential for zoonoses and emerging infectious diseases that can adversely affect human health. Although emergent viruses are currently in the spotlight, we must not forget the ongoing toll of morbidity and mortality owing to antimicrobial resistance in bacterial pathogens and to vector-borne, foodborne and waterborne diseases. Population growth, planetary change, international travel and medical tourism all contribute to the increasing frequency of infectious disease outbreaks. Surveillance is therefore of crucial importance, but the diversity of microbial pathogens, coupled with resource-intensive methods, compromises our ability to scale-up such efforts. Innovative technologies that are both easy to use and able to simultaneously identify diverse microorganisms (viral, bacterial or fungal) with precision are necessary to enable informed public health decisions. Metagenomics-enabled surveillance methods offer the opportunity to improve detection of both known and yet-to-emerge pathogens.

VPipe: an Automated Bioinformatics Platform for Assembly and Management of Viral Next-Generation Sequencing Data

Next-generation sequencing (NGS) is a powerful tool for detecting and investigating viral pathogens; however, analysis and management of the enormous amounts of data generated from these technologies remains a challenge. Here, we present VPipe (the Viral NGS Analysis Pipeline and Data Management System), an automated bioinformatics pipeline optimized for whole-genome assembly of viral sequences and identification of diverse species. VPipe automates the data quality control, assembly, and contig identification steps typically performed when analyzing NGS data. Users access the pipeline through a secure web-based portal, which provides an easy-to-use interface with advanced search capabilities for reviewing results. In addition, VPipe provides a centralized system for storing and analyzing NGS data, eliminating common bottlenecks in bioinformatics analyses for public health laboratories with limited on-site computational infrastructure. The performance of VPipe was validated through the analysis of publicly available NGS data sets for viral pathogens, generating high-quality assemblies for 12 data sets. VPipe also generated assemblies with greater contiguity than similar pipelines for 41 human respiratory syncytial virus isolates and 23 SARS-CoV-2 specimens. IMPORTANCE Computational infrastructure and bioinformatics analysis are bottlenecks in the application of NGS to viral pathogens. As of September 2021, VPipe has been used by the U.S. Centers for Disease Control and Prevention (CDC) and 12 state public health laboratories to characterize >17,500 and 1,500 clinical specimens and isolates, respectively. VPipe automates genome assembly for a wide range of viruses, including high-consequence pathogens such as SARS-CoV-2. Such automated functionality expedites public health responses to viral outbreaks and pathogen surveillance.

The B.1.427/1.429 (epsilon) SARS-CoV-2 variants are more virulent than ancestral B.1 (614G) in Syrian hamsters

As novel SARS-CoV-2 variants continue to emerge, it is critical that their potential to cause severe disease and evade vaccine-induced immunity is rapidly assessed in humans and studied in animal models. In early January 2021, a novel SARS-CoV-2 variant designated B.1.429 comprising 2 lineages, B.1.427 and B.1.429, was originally detected in California (CA) and it was shown to have enhanced infectivity in vitro and decreased antibody neutralization by plasma from convalescent patients and vaccine recipients. Here we examine the virulence, transmissibility, and susceptibility to pre-existing immunity for B 1.427 and B 1.429 in the Syrian hamster model. We find that both variants exhibit enhanced virulence as measured by increased body weight loss compared to hamsters infected with ancestral B.1 (614G), with B.1.429 causing the most marked body weight loss among the 3 variants. Faster dissemination from airways to parenchyma and more severe lung pathology at both early and late stages were also observed with B.1.429 infections relative to B.1. (614G) and B.1.427 infections. In addition, subgenomic viral RNA (sgRNA) levels were highest in oral swabs of hamsters infected with B.1.429, however sgRNA levels in lungs were similar in all three variants. This demonstrates that B.1.429 replicates to higher levels than ancestral B.1 (614G) or B.1.427 in the oropharynx but not in the lungs. In multi-virus in-vivo competition experiments, we found that B.1. (614G), epsilon (B.1.427/B.1.429) and gamma (P.1) dramatically outcompete alpha (B.1.1.7), beta (B.1.351) and zeta (P.2) in the lungs. In the nasal cavity, B.1. (614G), gamma, and epsilon dominate, but the highly infectious alpha variant also maintains a moderate size niche. We did not observe significant differences in airborne transmission efficiency among the B.1.427, B.1.429 and ancestral B.1 (614G) and WA-1 variants in hamsters. These results demonstrate enhanced virulence and high relative oropharyngeal replication of the epsilon (B.1.427/B.1.429) variant in Syrian hamsters compared to an ancestral B.1 (614G) variant.

In 2020 and 2021, new variants of SARS-CoV-2 were detected in the UK, South Africa, Brazil, India, California and beyond. New SARS-CoV-2 variants will continue to emerge for the foreseeable future in the human population and the potential for these new variants to produce severe disease and evade vaccines needs to be understood. In this study, we used the hamster model to determine the epsilon (B.1.427/429) SARS-CoV-2 variants that emerged in California in late 2020 cause more severe disease and infected hamsters have higher viral RNA levels in oral swabs compared to the prior B.1 (614G) variant. These findings are consistent with human clinical data and help explain the emergence and rapid spread of this variant in early 2021.

Efficacy of an inactivated Zika vaccine against virus infection during pregnancy in mice and marmosets

Zika virus (ZIKV) is a mosquito-borne arbovirus that can cause severe congenital birth defects. The utmost goal of ZIKV vaccines is to prevent both maternal-fetal infection and congenital Zika syndrome. A Zika purified inactivated virus (ZPIV) was previously shown to be protective in non-pregnant mice and rhesus macaques. In this study, we further examined the efficacy of ZPIV against ZIKV infection during pregnancy in immunocompetent C57BL6 mice and common marmoset monkeys (Callithrix jacchus). We showed that, in C57BL/6 mice, ZPIV significantly reduced ZIKV-induced fetal malformations. Protection of fetuses was positively correlated with virus-neutralizing antibody levels. In marmosets, the vaccine prevented vertical transmission of ZIKV and elicited neutralizing antibodies that remained above a previously determined threshold of protection for up to 18 months. These proof-of-concept studies demonstrate ZPIV's protective efficacy is both potent and durable and has the potential to prevent the harmful consequence of ZIKV infection during pregnancy.

MCRL: using a reference library to compress a metagenome into a non-redundant list of sequences, considering viruses as a case study

MOTIVATION

Metagenomes offer a glimpse into the total genomic diversity contained within a sample. Currently, however, there is no straightforward way to obtain a non-redundant list of all putative homologs of a set of reference sequences present in a metagenome.

RESULTS

To address this problem, we developed a novel clustering approach called 'metagenomic clustering by reference library' (MCRL), where a reference library containing a set of reference genes is clustered with respect to an assembled metagenome. According to our proposed approach, reference genes homologous to similar sets of metagenomic sequences, termed 'signatures', are iteratively clustered in a greedy fashion, retaining at each step the reference genes yielding the lowest E values, and terminating when signatures of remaining reference genes have a minimal overlap. The outcome of this computation is a non-redundant list of reference genes homologous to minimally overlapping sets of contigs, representing potential candidates for gene families present in the metagenome. Unlike metagenomic clustering methods, there is no need for contigs to overlap to be associated with a cluster, enabling MCRL to draw on more information encoded in the metagenome when computing tentative gene families. We demonstrate how MCRL can be used to extract candidate viral gene families from an oral metagenome and an oral virome that otherwise could not be determined using standard approaches. We evaluate the sensitivity, accuracy and robustness of our proposed method for the viral case study and compare it with existing analysis approaches.

AVAILABILITY AND IMPLEMENTATION

https://github.com/a-tadmor/MCRL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Efficacy of needle aspiration in patients with oral-maxillofacial abscesses: A retrospective study of 15 consecutive patients

The aim of this study was to determine the adequacy and safety of needle aspiration (NA) as an alternative to open surgical drainage for oral-maxillofacial abscesses. Fifteen consecutive patients who were diagnosed with oral-maxillofacial abscesses via contrast-enhanced CT from January 2020 to December 2020 were included. All patients were on antibiotics and treated with NA under local anaesthesia using a 20 mL syringe. Data collection included patient characteristics, signs and symptoms, physical examinations, laboratory tests, imaging findings, and outcomes. Next-generation sequencing (NGS) was used to identify the infectious microorganisms from the abscess samples. The study included 15 patients with oral-maxillofacial abscesses. None of our 15 patients required surgical incision and drainage, although repeat aspiration was required. However, after the first NA, the pain was reportedly extremely relieved for all patients. The average duration of antibiotic treatment was 9.20 ± 5.15 days (range 4-23 days). The abscess-affected spaces mainly included the masseter space and submandibular space. Odontogenic infection was the most common aetiology in 15 patients (10/15). The average volume of the abscesses on CT was 5866.26 ± 3627.18 mm³. The main pathogens identified in this study were Prevotella oris (5/15), Peptostreptococcus stomatis (4/15) and Porphyromonas endodontalis (2/15). According to the results of our study, the data support the use of NA as an effective, minimally invasive treatment modality for oral-maxillofacial abscesses. Surgeons should familiarise themselves with this technique, as it can easily be performed in the clinic using local anaesthesia, culture samples may be obtained, and airway obstruction and pain may be relieved.