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1.
Sci Rep ; 12(1): 939, 2022 01 18.
Article in English | MEDLINE | ID: covidwho-1634211

ABSTRACT

With the advent of highly sensitive real-time PCR, multiple pathogens have been identified from nasopharyngeal swabs of patients with acute respiratory infections (ARIs). However, the detection of microorganisms in the upper respiratory tract does not necessarily indicate disease causation. We conducted a matched case-control study, nested within a broader fever aetiology project, to facilitate determination of the aetiology of ARIs in hospitalised patients in Northeastern Laos. Consenting febrile patients of any age admitted to Xiengkhuang Provincial Hospital were included if they met the inclusion criteria for ARI presentation (at least one of the following: cough, rhinorrhoea, nasal congestion, sore throat, difficulty breathing, and/or abnormal chest auscultation). One healthy control for each patient, matched by sex, age, and village of residence, was recruited for the study. Nasopharyngeal swabs were collected from participants and tested for 33 pathogens by probe-based multiplex real-time RT-PCR (FastTrack Diagnostics Respiratory pathogen 33 kit). Attributable fraction of illness for a given microorganism was calculated by comparing results between patients and controls (= 100 * [OR - 1]/OR) (OR = odds ratio). Between 24th June 2019 and 24th June 2020, 205 consenting ARI patients and 205 matching controls were recruited. After excluding eight pairs due to age mismatch, 197 pairs were included in the analysis. Males were predominant with sex ratio 1.2:1 and children < 5 years old accounted for 59% of participants. At least one potential pathogen was detected in 173 (88%) patients and 175 (89%) controls. ARI in admitted patients were attributed to influenza B virus, influenza A virus, human metapneumovirus (HMPV), and respiratory syncytial virus (RSV) in 17.8%, 17.2%, 7.5%, and 6.5% of participants, respectively. SARS-CoV-2 was not detected in any cases or controls. Determining ARI aetiology in individual patients remains challenging. Among hospitalised patients with ARI symptoms presenting to a provincial hospital in Northeastern Laos, half were determined to be caused by one of several respiratory viruses, in particular influenza A virus, influenza B virus, HMPV, and RSV.


Subject(s)
Hospitalization , RNA Virus Infections , RNA Viruses/genetics , Respiratory Tract Infections , Reverse Transcriptase Polymerase Chain Reaction , Acute Disease , Adolescent , Adult , Case-Control Studies , Child , Child, Preschool , Female , Humans , Infant , Laos/epidemiology , Male , RNA Virus Infections/diagnosis , RNA Virus Infections/epidemiology , RNA Virus Infections/genetics , Respiratory Tract Infections/diagnosis , Respiratory Tract Infections/epidemiology , Respiratory Tract Infections/genetics , Respiratory Tract Infections/virology , Sex Factors
2.
Nucleic Acids Res ; 49(17): e102, 2021 09 27.
Article in English | MEDLINE | ID: covidwho-1594917

ABSTRACT

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient's treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.


Subject(s)
Algorithms , Computational Biology/methods , Haplotypes , High-Throughput Nucleotide Sequencing/methods , RNA Virus Infections/diagnosis , RNA Viruses/genetics , COVID-19/diagnosis , COVID-19/virology , Gene Frequency , HIV Infections/diagnosis , HIV Infections/virology , HIV-1/genetics , Humans , Mutation , Polymorphism, Single Nucleotide , RNA Virus Infections/virology , Reproducibility of Results , SARS-CoV-2/genetics , Sensitivity and Specificity
3.
Sci Rep ; 11(1): 3209, 2021 02 05.
Article in English | MEDLINE | ID: covidwho-1065951

ABSTRACT

Viral co-infections occur in COVID-19 patients, potentially impacting disease progression and severity. However, there is currently no dedicated method to identify viral co-infections in patient RNA-seq data. We developed PACIFIC, a deep-learning algorithm that accurately detects SARS-CoV-2 and other common RNA respiratory viruses from RNA-seq data. Using in silico data, PACIFIC recovers the presence and relative concentrations of viruses with > 99% precision and recall. PACIFIC accurately detects SARS-CoV-2 and other viral infections in 63 independent in vitro cell culture and patient datasets. PACIFIC is an end-to-end tool that enables the systematic monitoring of viral infections in the current global pandemic.


Subject(s)
COVID-19/diagnosis , Coinfection/diagnosis , Deep Learning , RNA Virus Infections/diagnosis , RNA Viruses/isolation & purification , SARS-CoV-2/isolation & purification , COVID-19 Testing , Coinfection/virology , Coronaviridae/isolation & purification , Humans , Metapneumovirus/classification , Metapneumovirus/isolation & purification , Neural Networks, Computer , Orthomyxoviridae/classification , Orthomyxoviridae/isolation & purification , RNA Virus Infections/virology , RNA Viruses/classification , RNA-Seq , Rhinovirus/classification , Rhinovirus/isolation & purification , SARS-CoV-2/classification , Sensitivity and Specificity
4.
J Proteome Res ; 19(11): 4259-4274, 2020 11 06.
Article in English | MEDLINE | ID: covidwho-960274

ABSTRACT

Emerging and re-emerging infectious diseases due to RNA viruses cause major negative consequences for the quality of life, public health, and overall economic development. Most of the RNA viruses causing illnesses in humans are of zoonotic origin. Zoonotic viruses can directly be transferred from animals to humans through adaptation, followed by human-to-human transmission, such as in human immunodeficiency virus (HIV), severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and, more recently, SARS coronavirus 2 (SARS-CoV-2), or they can be transferred through insects or vectors, as in the case of Crimean-Congo hemorrhagic fever virus (CCHFV), Zika virus (ZIKV), and dengue virus (DENV). At the present, there are no vaccines or antiviral compounds against most of these viruses. Because proteins possess a vast array of functions in all known biological systems, proteomics-based strategies can provide important insights into the investigation of disease pathogenesis and the identification of promising antiviral drug targets during an epidemic or pandemic. Mass spectrometry technology has provided the capacity required for the precise identification and the sensitive and high-throughput analysis of proteins on a large scale and has contributed greatly to unravelling key protein-protein interactions, discovering signaling networks, and understanding disease mechanisms. In this Review, we present an account of quantitative proteomics and its application in some prominent recent examples of emerging and re-emerging RNA virus diseases like HIV-1, CCHFV, ZIKV, and DENV, with more detail with respect to coronaviruses (MERS-CoV and SARS-CoV) as well as the recent SARS-CoV-2 pandemic.


Subject(s)
Communicable Diseases, Emerging , Proteomics , RNA Virus Infections , Animals , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Communicable Diseases, Emerging/diagnosis , Communicable Diseases, Emerging/therapy , Communicable Diseases, Emerging/virology , Coronavirus Infections/diagnosis , Humans , Pandemics , Pneumonia, Viral , RNA Virus Infections/diagnosis , RNA Virus Infections/therapy , RNA Virus Infections/virology , RNA Viruses
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