Nasopharyngeal Microbiota Profiling of SARS-CoV-2 Infected Patients

Maio, Flavio De; Posteraro, Brunella; Ponziani, Francesca Romana; Cattani, Paola; Gasbarrini, Antonio; Sanguinetti, Maurizio

doi:10.1186/s12575-020-00131-7

Cited by 92 publications

(115 citation statements)

References 23 publications

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“…1J ) compared with those infected with another respiratory virus (90%) or without a respiratory virus identified (86%). These findings are consistent with a study evaluating the microbiome using NP swabs in patients with mild SARS-CoV-2 infections ( 26 ).…”

Section: Resultssupporting

confidence: 92%

A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood

Granados

Santos

et al. 2021

Sci. Adv.

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease-19 (COVID-19), has emerged as the cause of a global pandemic. We used RNA sequencing to analyze 286 nasopharyngeal (NP) swab and 53 whole-blood (WB) samples from 333 patients with COVID-19 and controls. Overall, a muted immune response was observed in COVID-19 relative to other infections (influenza, other seasonal coronaviruses, and bacterial sepsis), with paradoxical down-regulation of several key differentially expressed genes. Hospitalized patients and outpatients exhibited up-regulation of interferon-associated pathways, although heightened and more robust inflammatory responses were observed in hospitalized patients with more clinically severe illness. Two-layer machine learning–based host classifiers consisting of complete (>1000 genes), medium (<100), and small (<20) gene biomarker panels identified COVID-19 disease with 85.1–86.5% accuracy when benchmarked using an independent test set. SARS-CoV-2 infection has a distinct biosignature that differs between NP swabs and WB and can be leveraged for COVID-19 diagnosis.

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Section: Resultssupporting

confidence: 92%

A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood

Granados

Santos

et al. 2021

Sci. Adv.

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“…We observed diverse commensal, opportunistic pathogens across the samples analyzed ( Supplementary Figure S4A available online at https://academic.oup.com/bib ). We performed a correlation of abundance of the background level bacterial pathogens with the SARS-CoV-2 quantification, but no significant correlation was found in the analyzed dataset consistent with a recent report [ 71 ]. Since we analyzed SARS-CoV-2 –positive samples from different sample sources (nasal/pharyngeal) and also of different sequencing/library types, we performed statistical tests to assess differential burden of background infectious bacteria.…”

Section: Resultssupporting

confidence: 80%

An integrated approach to determine the abundance, mutation rate and phylogeny of the SARS-CoV-2 genome

Desai

Rashmi

Rane

et al. 2021

Briefings in Bioinformatics

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The analysis of the SARS-CoV-2 genome datasets has significantly advanced our understanding of the biology and genomic adaptability of the virus. However, the plurality of advanced sequencing datasets—such as short and long reads—presents a formidable computational challenge to uniformly perform quantitative, variant or phylogenetic analysis, thus limiting its application in public health laboratories engaged in studying epidemic outbreaks. We present a computational tool, Infectious Pathogen Detector (IPD), to perform integrated analysis of diverse genomic datasets, with a customized analytical module for the SARS-CoV-2 virus. The IPD pipeline quantitates individual occurrences of 1060 pathogens and performs mutation and phylogenetic analysis from heterogeneous sequencing datasets. Using IPD, we demonstrate a varying burden (5.055–999655.7 fragments per million) of SARS-CoV-2 transcripts across 1500 short- and long-read sequencing SARS-CoV-2 datasets and identify 4634 SARS-CoV-2 variants (~3.05 variants per sample), including 449 novel variants, across the genome with distinct hotspot mutations in the ORF1ab and S genes along with their phylogenetic relationships establishing the utility of IPD in tracing the genome isolates from the genomic data (as accessed on 11 June 2020). The IPD predicts the occurrence and dynamics of variability among infectious pathogens—with a potential for direct utility in the COVID-19 pandemic and beyond to help automate the sequencing-based pathogen analysis and in responding to public health threats, efficaciously. A graphical user interface (GUI)-enabled desktop application is freely available for download for the academic users at http://www.actrec.gov.in/pi-webpages/AmitDutt/IPD/IPD.html and for web-based processing at http://ipd.actrec.gov.in/ipdweb/ to generate an automated report without any prior computational know-how.

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“…154 155 The association between viral load and COVID-19 severity provides some support for this hypothesis. [156][157][158] While one small study did not find significant differences in the nasopharyngeal microbiota between patients infected with SARS-CoV-2 and healthy controls, 159 other studies have reported significant differences in the oropharyngeal, lung and gastrointestinal microbiota between these groups. [160][161][162][163][164] In relation to the gastrointestinal microbiota, patients infected with SARS-CoV-2 have reduced bacterial diversity with a lower relative abundance of certain bacterial phyla including Faecalibacterium, and a higher relative abundance of others including Bacteroides.…”

Section: Microbiotamentioning

confidence: 98%

Why is COVID-19 less severe in children? A review of the proposed mechanisms underlying the age-related difference in severity of SARS-CoV-2 infections

2020

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In contrast to other respiratory viruses, children have less severe symptoms when infected with the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this review, we discuss proposed hypotheses for the age-related difference in severity of coronavirus disease 2019 (COVID-19).Factors proposed to explain the difference in severity of COVID-19 in children and adults include those that put adults at higher risk and those that protect children. The former include: (1) age-related increase in endothelial damage and changes in clotting function; (2) higher density, increased affinity and different distribution of angiotensin converting enzyme 2 receptors and transmembrane serine protease 2; (3) pre-existing coronavirus antibodies (including antibody-dependent enhancement) and T cells; (4) immunosenescence and inflammaging, including the effects of chronic cytomegalovirus infection; (5) a higher prevalence of comorbidities associated with severe COVID-19 and (6) lower levels of vitamin D. Factors that might protect children include: (1) differences in innate and adaptive immunity; (2) more frequent recurrent and concurrent infections; (3) pre-existing immunity to coronaviruses; (4) differences in microbiota; (5) higher levels of melatonin; (6) protective off-target effects of live vaccines and (7) lower intensity of exposure to SARS-CoV-2.

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Nasopharyngeal Microbiota Profiling of SARS-CoV-2 Infected Patients

Cited by 92 publications

References 23 publications

A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood

A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood

An integrated approach to determine the abundance, mutation rate and phylogeny of the SARS-CoV-2 genome

Why is COVID-19 less severe in children? A review of the proposed mechanisms underlying the age-related difference in severity of SARS-CoV-2 infections

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