Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review.

Profile hidden Markov models (pHMMs) are able to achieve high sensitivity in remote homology search, making them popular choices for detecting novel or highly diverged viruses in metagenomic data. However, many existing pHMM databases have different design focuses, making it difficult for users to decide the proper one to use. In this review, we provide a thorough evaluation and comparison for multiple commonly used profile HMM databases for viral sequence discovery in metagenomic data. We characterized the databases by comparing their sizes, their taxonomic coverage, and the properties of their models using quantitative metrics. Subsequently, we assessed their performance in virus identification across multiple application scenarios, utilizing both simulated and real metagenomic data. We aim to offer researchers a thorough and critical assessment of the strengths and limitations of different databases. Furthermore, based on the experimental results obtained from the simulated and real metagenomic data, we provided practical suggestions for users to optimize their use of pHMM databases, thus enhancing the quality and reliability of their findings in the field of viral metagenomics.

Utilising biosensor-based approaches for identifying neurotropic viruses.

Neurotropic viruses, with their ability to invade the central nervous system, present a significant public health challenge, causing a spectrum of neurological diseases. Clinical manifestations of neurotropic viral infections vary widely, from mild to life-threatening conditions, such as HSV-induced encephalitis or poliovirus-induced poliomyelitis. Traditional diagnostic methods, including polymerase chain reaction, serological assays, and imaging techniques, though valuable, have limitations. To address these challenges, biosensor-based methods have emerged as a promising approach. These methods offer advantages such as rapid results, high sensitivity, specificity, and potential for point-of-care applications. By targeting specific biomarkers or genetic material, biosensors utilise technologies like surface plasmon resonance and microarrays, providing a direct and efficient means of diagnosing neurotropic infections. This review explores the evolving landscape of biosensor-based methods, highlighting their potential to enhance the diagnostic toolkit for neurotropic viruses.

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.

Correlation of SARS-CoV-2 in Wastewater and Individual Testing Results in a Jail, Atlanta, Georgia, USA.

Institution-level wastewater-based surveillance was implemented during the COVID-19 pandemic, including in carceral facilities. We examined the relationship between COVID-19 diagnostic test results of residents in a jail in Atlanta, Georgia, USA (average population ≈2,700), and quantitative reverse transcription PCR signal for SARS-CoV-2 in weekly wastewater samples collected during October 2021‒May 2022. The jail offered residents rapid antigen testing at entry and periodic mass screenings by reverse transcription PCR of self-collected nasal swab specimens. We aggregated individual test data, calculated the Spearman correlation coefficient, and performed logistic regression to examine the relationship between strength of SARS-CoV-2 PCR signal (cycle threshold value) in wastewater and percentage of jail population that tested positive for COVID-19. Of 13,745 nasal specimens collected, 3.9% were COVID-positive (range 0%-29.5% per week). We observed a strong inverse correlation between diagnostic test positivity and cycle threshold value (r = -0.67; p<0.01). Wastewater-based surveillance represents an effective strategy for jailwide surveillance of COVID-19.

Recent Advances in DNA Nanotechnology-Based Sensing Platforms for Rapid Virus Detection.

Several major viral pandemics in history have significantly impacted the public health of human beings. The COVID-19 pandemic has further underscored the critical need for early detection and screening of infected individuals. However, current detection techniques are confronted with deficiencies in sensitivity and accuracy, restricting the capability of detecting trace amounts of viruses in human bodies and in the environments. The advent of DNA nanotechnology has opened up a feasible solution for rapid and sensitive virus determination. By harnessing the designability and addressability of DNA nanostructures, a range of rapid virus sensing platforms have been proposed. This review overviewed the recent progress, application, and prospect of DNA nanotechnology-based rapid virus detection platforms. Furthermore, the challenges and developmental prospects in this field were discussed.

Point-of-care dengue detection: polydopamine-modified electrode for rapid NS1 protein testing for clinical samples.

Early diagnosis of dengue infection by detecting the dengue virus non-structural protein 1 (DENV-NS1) is important to the patients to initiate speedy treatment. Enzyme-linked immunosorbent assay (ELISA)-based NS1 detection and RT-PCR are time-consuming and too complex to be employed in remote areas of dengue-endemic countries. Meanwhile, those of NS1 rapid test by lateral flow assay suffer from low detection limit. Electrochemical-based biosensors using screen-printed gold electrodes (SPGEs) have become a reliable detection method to convey both ELISA's high sensitivity and rapid test portability. In this research, we developed an electrochemical biosensor for DENV-NS1 detection by employing polydopamine (PDA)-modified SPGE. The electrodeposition of PDA on the surface of SPGE serves as a bioconjugation avenue for anti-NS1 antibody through a simple and low-cost immobilization procedure. The biosensor performance was evaluated to detect DENV-NS1 protein in PBS and human serum through a differential pulse voltammetric (DPV) technique. The developed sensing platform displayed a low limit of detection (LOD) of 1.63 pg mL-1 and a wide linear range of 10 pg mL-1 to 1 ng mL-1 (R2 ∼ 0.969). The sensing platform also detected DEV-NS1 from four different serotypes in the clinical samples collected from dengue patients in India and Indonesia, with acceptable sensitivity, specificity, and accuracy values of 90.00%, 80.95%, and 87.65%, respectively. This result showcased the facile and versatile method of PDA coating onto the surface of screen-printed gold electrodes for a miniaturized point-of-care (PoC) detection device.

SARS-CoV-2 RNA Detection in Wastewater and Its Effective Correlation with Clinical Data during the Outbreak of COVID-19 in Salamanca.

Wastewater treatment plants (WWTPs) are the final stage of the anthropogenic water cycle where a wide range of chemical and biological markers of human activity can be found. In COVID-19 disease contexts, wastewater surveillance has been used to infer community trends based on viral abundance and SARS-CoV-2 RNA variant composition, which has served to anticipate and establish appropriate protocols to prevent potential viral outbreaks. Numerous studies worldwide have provided reliable and robust tools to detect and quantify SARS-CoV-2 RNA in wastewater, although due to the high dilution and degradation rate of the viral RNA in such samples, the detection limit of the pathogen has been a bottleneck for the proposed protocols so far. The current work provides a comprehensive and systematic study of the different parameters that may affect the detection of SARS-CoV-2 RNA in wastewater and hinder its quantification. The results obtained using synthetic viral RNA as a template allow us to consider that 10 genome copies per µL is the minimum RNA concentration that provides reliable and consistent values for the quantification of SARS-CoV-2 RNA. RT-qPCR analysis of wastewater samples collected at the WWTP in Salamanca (western Spain) and at six pumping stations in the city showed that below this threshold, positive results must be confirmed by sequencing to identify the specific viral sequence. This allowed us to find correlations between the SARS-CoV-2 RNA levels found in wastewater and the COVID-19 clinical data reported by health authorities. The close match between environmental and clinical data from the Salamanca case study has been confirmed by similar experimental approaches in four other cities in the same region. The present methodological approach reinforces the usefulness of wastewater-based epidemiology (WBE) studies in the face of future pandemic outbreaks.

Dual Gene Detection of H5N1 Avian Influenza Virus Based on Dual RT-RPA.

The H5N1 avian influenza virus seriously affects the health of poultry and humans. Once infected, the mortality rate is very high. Therefore, accurate and timely detection of the H5N1 avian influenza virus is beneficial for controlling its spread. This article establishes a dual gene detection method based on dual RPA for simultaneously detecting the HA and M2 genes of H5N1 avian influenza virus, for the detection of H5N1 avian influenza virus. Design specific primers for the conserved regions of the HA and M2 genes. The sensitivity of the dual RT-RPA detection method for HA and M2 genes is 1 × 10-7 ng/µL. The optimal primer ratio is 1:1, the optimal reaction temperature is 40 °C, and the optimal reaction time is 20 min. Dual RT-RPA was used to detect 72 samples, and compared with RT-qPCR detection, the Kappa value was 1 (p value < 0.05), and the clinical sample detection sensitivity and specificity were both 100%. The dual RT-RPA method is used for the first time to simultaneously detect two genes of the H5N1 avian influenza virus. As an accurate and convenient diagnostic tool, it can be used to diagnose the H5N1 avian influenza virus.

An Entomological Investigation during a Recent Rift Valley Fever Epizootic/Epidemic Reveals New Aspects of the Vectorial Transmission of the Virus in Madagascar.

A Rift Valley fever (RVF) outbreak occurred in at least five regions of Madagascar in 2021. The aim of this study was to provide an overview of the richness, abundance, ecology, and trophic preferences of mosquitoes in the Mananjary district and to investigate the distribution of mosquitoes that were RT-PCR-positive for RVFV. Three localities were prospected from 26 April to 4 May 2021, using light traps, BG-Sentinel traps baited with an artificial human odor, Muirhead-Thomson pit traps, and indoor pyrethroid spray catches. A total of 2806 mosquitoes belonging to at least 26 species were collected. Of 512 monospecific pools of mosquitoes tested with real-time RT-PCR, RVFV was detected in 37 pools representing 10 mosquito species. The RVFV-positive species were as follows: Aedes albopictus, Ae. argenteopunctatus, Anopheles coustani, An. gambiae s.l., An. mascarensis, An. squamosus/cydippis, Culex antennatus, Cx. decens, Cx. Tritaeniorhynchus, and Uranotaenia spp. Of the 450 tested engorged females, 78.7% had taken a blood meal on humans, 92.9% on cattle, and 71.6% had taken mixed (human-cattle) blood meals. This investigation suggests the potential role of mosquitoes in RVFV transmission within this epizootic/epidemic context and that the human populations at the three study sites were highly exposed to mosquitoes. Therefore, the use of impregnated mosquito nets as an appropriate prevention method is recommended.

Development of a multiplex real-time PCR assay for the simultaneous detection of mpox virus and orthopoxvirus infections.

Since May 2022, the multi-country outbreak of monkeypox (mpox) has raised a great concern worldwide. Early detection of mpox virus infection is recognized as an efficient way to prevent mpox transmission. Mpox specific detection methods reported up to now are based on the SNPs among mpox virus and other orthopoxviruses. We have therefore developed a real-time PCR based mpox detection method targeting mpox virus specific sequences (N3R and B18Rplus). We have also optimized an orthopoxvirus detection system which targets the highly conserved E9L and D6R genes. The mpox and orthopoxvirus real-time PCR assays have a high sensitivity (1 copy/reaction) and specificity. Mpox viral DNA and clinical samples from mpox patients are detected with the mpox detection system. Furthermore, we have established a multiplex real-time PCR detection system allowing simultaneous and efficient detection of mpox and orthopoxvirus infections.

Isolation of Double-Stranded RNAs by Lithium Chloride Fractionation.

This procedure provides a comprehensive method for isolating double-stranded RNA (dsRNA) that relies on the different solubility of various nucleic acids in lithium chloride (LiC1). The approach offers several notable advantages including simplicity, avoidance of enzymatic treatments, and the ability to obtain relatively high yields of undegraded dsRNA over other conventional techniques. Moreover, it allows for the separation of different groups of cellular and viral nucleic acids from a single tissue sample. This method was further improved to increase the purity of dsRNA using plant tissues infected by RNA viruses.

Signal-Amplified Nanobiosensors for Virus Detection Using Advanced Nanomaterials.

Rapid diagnosis and treatment of infectious illnesses are crucial for clinical outcomes and public health. Biosensing developments enhance diagnostics at the point of care. This is superior to traditional procedures, which need centralized lab facilities, specialized personnel, and large equipment. The emerging coronavirus epidemic threatens global health and economic security. Increasing viral surveillance and regulatory actions against disease transmission necessitate rapid, sensitive testing tools for viruses. Due to their sensitivity and specificity, biosensors offer a possible reliable and quantifiable viral detection method. Current advances in genetic engineering, such as genetic alteration and material engineering, have provided several opportunities to enhance biosensors' sensitivity, selectivity, and recognition efficiency. This chapter explains biosensing techniques, biosensor varieties, and signal amplification technologies. Challenges and potential developments for viral microorganisms based on biosensors and signal amplification were also investigated.

Microsphere-Enabled Micropillar Array for Whispering Gallery Mode Virus Detection.

Rapid detection of pathogens and analytes at the point of care offers an opportunity for prompt patient management and public health control. This paper reports an open microfluidic platform coupled with active whispering gallery mode (WGM) microsphere resonators for the rapid detection of influenza viruses. The WGM microsphere resonators, precoated with influenza A polyclonal antibodies, are mechanically trapped in the open micropillar array, where the evaporation-driven flow continuously transports a small volume (∼µL) of sample to the resonators without auxiliaries. Selective chemical modification of the pillar array changes surface wettability and flow pattern, which enhances the detection sensitivity of the WGM resonator-based virus sensor. The optofluidic sensing platform is able to specifically detect influenza A viruses within 15 min using a few microliters of sample and displays a linear response to different virus concentrations.

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.

Food and Environmental Virology: Use of Passive Sampling to Characterize the Presence of SARS-CoV-2 and Other Viruses in Wastewater.

Fecal shedding of SARS-CoV-2 leads to a renaissance of wastewater-based epidemiology (WBE) as additional tool to follow epidemiological trends in the catchment of treatment plants. As alternative to the most commonly used composite samples in surveillance programs, passive sampling is increasingly studied. However, the many sorbent materials in different reports hamper the comparison of results and a standardization of the approach is necessary. Here, we compared different cost-effective sorption materials (cheesecloths, gauze swabs, electronegative filters, glass wool, and tampons) in torpedo-style housings with composite samples. Despite a remarkable variability of the concentration of SARS-CoV-2-specific gene copies, analysis of parallel-deposited passive samplers in the sewer demonstrated highest rate of positive samples and highest number of copies by using cheesecloths. Using this sorption material, monitoring of wastewater of three small catchments in the City of Dresden resulted in a rate of positive samples of 50% in comparison with composite samples (98%). During the investigation period, incidence of reported cases of SARS-CoV-2 in the catchments ranged between 16 and 170 per 100,000 persons and showed no correlation with the measured concentrations of E gene in wastewater. In contrast, constantly higher numbers of gene copies in passive vs. composite samples were found for human adenovirus and crAssphage indicating strong differences of efficacy of methods concerning the species investigated. Influenza virus A and B were sporadically detected allowing no comparison of results. The study contributes to the further understanding of possibilities and limits of passive sampling approaches in WBE.

Recent Advances in Lateral Flow Assays for Viral Protein Detection with Nanomaterial-Based Optical Sensors.

Controlling the progression of contagious diseases is crucial for public health management, emphasizing the importance of early viral infection diagnosis. In response, lateral flow assays (LFAs) have been successfully utilized in point-of-care (POC) testing, emerging as a viable alternative to more traditional diagnostic methods. Recent advancements in virus detection have primarily leveraged methods such as reverse transcription-polymerase chain reaction (RT-PCR), reverse transcription-loop-mediated isothermal amplification (RT-LAMP), and the enzyme-linked immunosorbent assay (ELISA). Despite their proven effectiveness, these conventional techniques are often expensive, require specialized expertise, and consume a significant amount of time. In contrast, LFAs utilize nanomaterial-based optical sensing technologies, including colorimetric, fluorescence, and surface-enhanced Raman scattering (SERS), offering quick, straightforward analyses with minimal training and infrastructure requirements for detecting viral proteins in biological samples. This review describes the composition and mechanism of and recent advancements in LFAs for viral protein detection, categorizing them into colorimetric, fluorescent, and SERS-based techniques. Despite significant progress, developing a simple, stable, highly sensitive, and selective LFA system remains a formidable challenge. Nevertheless, an advanced LFA system promises not only to enhance clinical diagnostics but also to extend its utility to environmental monitoring and beyond, demonstrating its potential to revolutionize both healthcare and environmental safety.

An oleanic acid decorated gold nanorod for highly efficient inhibition of hemagglutinin and visible rapid detection of the influenza virus.

Accurate diagnosis and effective antiviral treatments are urgently needed for the prevention and control of flu caused by influenza viruses. In this study, a novel oleanic acid (OA) functionalized gold nanorod OA-AuNP was prepared through a convenient ligand-exchange reaction. As hemagglutinin (HA) on the viral surface binds strongly to the multiple OA molecules on the surface of the nanoparticle, the prepared OA-AuNP was found to exhibit potent antiviral activity against a wide range of influenza A virus strains. Furthermore, the change in color resulting from the specific binding between HA and OA and the resultant aggregation of the OA-AuNP can be visually observed or measured by UV-vis spectra with a detection limit of 2 and 0.18 hemagglutination units (HAU), respectively, which is comparable to the commercially available influenza colloid gold rapid diagnostic kits. These findings demonstrate the potential of the OA-AuNP for the development of novel multivalent antiviral conjugates and the diagnosis of influenza virus.

Development and evaluation of RT-qPCR assays for two neglected orthobunyaviruses: Oya virus and Ebinur Lake virus.

OBJECTIVES: Oya virus (OYAV) and Ebinur lake virus (EBIV) belong to the genus Orthobunyavirus within the Peribunyaviridae family, and both are recognized as the novel virus with potential threat to the animal or public health. Given their potential to cause outbreaks and their detection in diverse samples across different regions, the need for a reliable and efficient molecular detection method for OYAV and EBIV becomes imperative. METHODS: The S-segment of OYAV and EBIV was used for designing specific primer and probe sets, which were employed in a real-time reverse transcription quantitative PCR (RT-qPCR) assay. The analytical performance of these assays, encompassing specificity, sensitivity, reproducibility, and fitness for purpose, was thoroughly evaluated across various sample matrices. RESULTS: The developed RT-qPCR assays were very specific to their respective targets. Both assays were highly reproducible (%CV<3) and sensitive with the 95% limit of detection (LOD) of 0.80 PFU/mL for OYAV primer probe set and 0.37 PFU/mL for EBIV primer probe set. Furthermore, the assays fitness for purpose was good as it could detect the specific viruses in virus-spiked serum samples, virus-inoculated mosquito samples, field caught mosquitoes and biting midge samples. CONCLUSIONS: Our study has successfully developed specific, sensitive, and reliable RT-qPCR assays for the detection of OYAV and EBIV. These assays hold great promise for their potential application in clinical and field samples in the future.

High-throughput characterization and phenotyping of resistance and tolerance to virus infection in sweetpotato.

Breeders have made important efforts to develop genotypes able to resist virus attacks in sweetpotato, a major crop providing food security and poverty alleviation to smallholder farmers in many regions of Sub-Saharan Africa, Asia and Latin America. However, a lack of accurate objective quantitative methods for this selection target in sweetpotato prevents a consistent and extensive assessment of large breeding populations. In this study, an approach to characterize and classify resistance in sweetpotato was established by assessing total yield loss and virus load after the infection of the three most common viruses (SPFMV, SPCSV, SPLCV). Twelve sweetpotato genotypes with contrasting reactions to virus infection were grown in the field under three different treatments: pre-infected by the three viruses, un-infected and protected from re-infection, and un-infected but exposed to natural infection. Virus loads were assessed using ELISA, (RT-)qPCR, and loop-mediated isothermal amplification (LAMP) methods, and also through multispectral reflectance and canopy temperature collected using an unmanned aerial vehicle. Total yield reduction compared to control and the arithmetic sum of (RT-)qPCR relative expression ratios were used to classify genotypes into four categories: resistant, tolerant, susceptible, and sensitives. Using 14 remote sensing predictors, machine learning algorithms were trained to classify all plots under the said categories. The study found that remotely sensed predictors were effective in discriminating the different virus response categories. The results suggest that using machine learning and remotely sensed data, further complemented by fast and sensitive LAMP assays to confirm results of predicted classifications could be used as a high throughput approach to support virus resistance phenotyping in sweetpotato breeding.

Investigation and characterization of rice dwarfing epidemic caused by southern rice black-streaked dwarf virus in Jiangsu in 2023.

During the field surveys in Jiangsu Province, China, contiguous patches of rice plants with varying degrees of dwarfing, wax-white or dark brown enations at the base of stems, and abnormal heading symptoms were observed in the fields located in Jiangning District in Nanjing City, Jurong County in Zhenjiang City, and Zhangjiagang County in Suzhou City. Through molecular analyses, the presence of southern rice black-streaked dwarf virus was confirmed in symptomatic rice plants. The infections of other rice viruses that cause dwarfing were also ruled out. Additionally, Koch's postulates were fulfilled, further validating SRBSDV as the causal agent for the observed dwarfing disease epidemic. Furthermore, the phylogenetic analyses revealed that the SRBSDV prevalent in Jiangsu in 2023 may originate from multiple regions in Vietnam. Our study has documented the emergence of an SRBSDV epidemic in Jiangsu in 2023, marking the first incidence of southern rice black-streaked dwarf disease in this region.