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1.
PLoS One ; 16(8): e0244468, 2021.
Article in English | MEDLINE | ID: covidwho-1371999

ABSTRACT

The newly emerged and rapidly spreading SARS-CoV-2 causes coronavirus disease 2019 (COVID-19). To facilitate a deeper understanding of the viral biology we developed a capture sequencing methodology to generate SARS-CoV-2 genomic and transcriptome sequences from infected patients. We utilized an oligonucleotide probe-set representing the full-length genome to obtain both genomic and transcriptome (subgenomic open reading frames [ORFs]) sequences from 45 SARS-CoV-2 clinical samples with varying viral titers. For samples with higher viral loads (cycle threshold value under 33, based on the CDC qPCR assay) complete genomes were generated. Analysis of junction reads revealed regions of differential transcriptional activity among samples. Mixed allelic frequencies along the 20kb ORF1ab gene in one sample, suggested the presence of a defective viral RNA species subpopulation maintained in mixture with functional RNA in one sample. The associated workflow is straightforward, and hybridization-based capture offers an effective and scalable approach for sequencing SARS-CoV-2 from patient samples.


Subject(s)
COVID-19/pathology , SARS-CoV-2/genetics , Sequence Analysis, DNA/methods , COVID-19/virology , DNA, Complementary/chemistry , DNA, Complementary/metabolism , Gene Frequency , Genetic Variation , Genome, Viral , Humans , Open Reading Frames/genetics , RNA, Viral/genetics , RNA, Viral/metabolism , Real-Time Polymerase Chain Reaction , SARS-CoV-2/isolation & purification , Viral Load
2.
Microbiome ; 9(1): 2, 2021 01 04.
Article in English | MEDLINE | ID: covidwho-1067276

ABSTRACT

The inaugural "Microbiome for Mars" virtual workshop took place on July 13, 2020. This event assembled leaders in microbiome research and development to discuss their work and how it may relate to long-duration human space travel. The conference focused on surveying current microbiome research, future endeavors, and how this growing field could broadly impact human health and space exploration. This report summarizes each speaker's presentation in the order presented at the workshop.


Subject(s)
Astronauts , Delivery of Health Care/trends , Mars , Microbiota/physiology , Space Flight , Animals , Gastrointestinal Microbiome/genetics , Gastrointestinal Microbiome/physiology , Humans , Microbiota/genetics
3.
Pediatrics ; 146(4)2020 10.
Article in English | MEDLINE | ID: covidwho-914293

ABSTRACT

OBJECTIVES: Although the airway microbiota is a highly dynamic ecology, the role of longitudinal changes in airway microbiota during early childhood in asthma development is unclear. We aimed to investigate the association of longitudinal changes in early nasal microbiota with the risk of developing asthma. METHODS: In this prospective, population-based birth cohort study, we followed children from birth to age 7 years. The nasal microbiota was tested by using 16S ribosomal RNA gene sequencing at ages 2, 13, and 24 months. We applied an unsupervised machine learning approach to identify longitudinal nasal microbiota profiles during age 2 to 13 months (the primary exposure) and during age 2 to 24 months (the secondary exposure) and examined the association of these profiles with the risk of physician-diagnosed asthma at age 7 years. RESULTS: Of the analytic cohort of 704 children, 57 (8%) later developed asthma. We identified 4 distinct longitudinal nasal microbiota profiles during age 2 to 13 months. In the multivariable analysis, compared with the persistent Moraxella dominance profile during age 2 to 13 months, the persistent Moraxella sparsity profile was associated with a significantly higher risk of asthma (adjusted odds ratio, 2.74; 95% confidence interval, 1.20-6.27). Similar associations were observed between the longitudinal changes in nasal microbiota during age 2 to 24 months and risk of asthma. CONCLUSIONS: Children with an altered longitudinal pattern in the nasal microbiota during early childhood had a high risk of developing asthma. Our data guide the development of primary prevention strategies (eg, early identification of children at high risk and modification of microbiota) for childhood asthma. These observations present a new avenue for risk modification for asthma (eg, microbiota modification).


Subject(s)
Asthma/etiology , Microbiota , Nose/microbiology , Aerococcaceae/isolation & purification , Age Factors , Asthma/diagnosis , Asthma/microbiology , Child , Child, Preschool , Female , Finland , Follow-Up Studies , Gene Expression Profiling/methods , Haemophilus/isolation & purification , Humans , Incidence , Infant , Infant, Newborn , Machine Learning , Male , Microbiota/genetics , Moraxella/isolation & purification , Multivariate Analysis , Prospective Studies , RNA, Ribosomal, 16S/genetics , Respiratory Tract Infections/complications , Respiratory Tract Infections/epidemiology , Respiratory Tract Infections/microbiology , Risk , Streptococcus/isolation & purification
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