Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology

Morris, Dylan H.; Gostic, Katelyn M.; Pompei, Simone; Bedford, Trevor; Łuksza, Marta; Neher, Richard A.; Grenfell, Bryan T.; Lässig, Michael; McCauley, John W.

doi:10.1016/j.tim.2017.09.004

Cited by 112 publications

(121 citation statements)

References 107 publications

(148 reference statements)

Supporting

Mentioning

112

Contrasting

Unclassified

Order By: Relevance

“…The burden of disease disproportionately affects low/middle 56 income settings [10]. Influenza diagnostics and surveillance are fundamental to identify the 57 emergence of novel strains, to improve prediction of potential epidemics and pandemics [4,11], 58 and to inform vaccine strategy [12]. Diagnostic data facilitate real-time surveillance, can 59 underpin infection control interventions [13,14] and inform the prescription of Neuraminidase 60…”

Section: Abstract: (Max 250 Words) 26mentioning

confidence: 99%

Metagenomic Nanopore sequencing of influenza virus direct from clinical respiratory samples

Lewandowski

Pullan

et al. 2019

Preprint

View full text Add to dashboard Cite

max 250 words) 26Influenza is a major global public health threat as a result of its highly pathogenic variants, large 27 zoonotic reservoir, and pandemic potential. Metagenomic viral sequencing offers the potential of 28 a diagnostic test for influenza which also provides insights on transmission, evolution and drug 29 resistance, and simultaneously detects other viruses. We therefore set out to apply Oxford 30 Nanopore Technology to metagenomic sequencing of respiratory samples. We generated 31 influenza reads down to a limit of detection of 10 2 -10 3 genome copies/ml in pooled samples, 32 observing a strong relationship between the viral titre and the proportion of influenza reads (p = 33 4.7x10 -5 ). Applying our methods to clinical throat swabs, we generated influenza reads for 27/27 34 samples with high-to-mid viral titres (Cycle threshold (Ct) values <30) and 6/13 samples with low 35 viral titres (Ct values 30-40). No false positive reads were generated from 10 influenza-negative 36 samples. Thus Nanopore sequencing operated with 83% sensitivity (95% CI 67-93%) and 100% 37 specificity (95% CI 69-100%) compared to the current diagnostic standard. Coverage of full 38 length virus was dependent on sample composition, being negatively influenced by increased 39 host and bacterial reads. However, at high influenza titres, we were able to reconstruct >99% 40 complete sequence for all eight gene segments. We also detected Human Coronavirus and 41 generated a near complete Human Metapneumovirus genome from clinical samples. While 42 further optimisation is required to improve sensitivity, this approach shows promise for the 43 Nanopore platform to be used in the diagnosis and genetic analysis of influenza and other 44 respiratory viruses. 45 46

show abstract

Section: Abstract: (Max 250 Words) 26mentioning

confidence: 99%

Metagenomic Nanopore sequencing of influenza virus direct from clinical respiratory samples

Lewandowski

Pullan

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The burden of disease disproportionately affects low-/middle-income settings (10). Influenza virus diagnostics and surveillance are fundamental to identify the emergence of novel strains, to improve the prediction of potential epidemics and pandemics (4,8), and to inform vaccine strategy (11). Diagnostic data facilitate real-time surveillance, can underpin infection control interventions (12,13), and can inform the prescription of neuraminidase inhibitors (NAI) (9).…”

mentioning

confidence: 99%

Metagenomic Nanopore Sequencing of Influenza Virus Direct from Clinical Respiratory Samples

et al. 2019

View full text Add to dashboard Cite

Influenza is a major global public health threat as a result of its highly pathogenic variants, large zoonotic reservoir, and pandemic potential. Metagenomic viral sequencing offers the potential for a diagnostic test for influenza virus which also provides insights on transmission, evolution, and drug resistance and simultaneously detects other viruses. We therefore set out to apply the Oxford Nanopore Technologies sequencing method to metagenomic sequencing of respiratory samples. We generated influenza virus reads down to a limit of detection of 10 2 to 10 3 genome copies/ml in pooled samples, observing a strong relationship between the viral titer and the proportion of influenza virus reads (P ϭ 4.7 ϫ 10 Ϫ5 ). Applying our methods to clinical throat swabs, we generated influenza virus reads for 27/27 samples with mid-to-high viral titers (cycle threshold [C T ] values, Ͻ30) and 6/13 samples with low viral titers (C T values, 30 to 40). No false-positive reads were generated from 10 influenza virus-negative samples. Thus, Nanopore sequencing operated with 83% sensitivity (95% confidence interval [CI], 67 to 93%) and 100% specificity (95% CI, 69 to 100%) compared to the current diagnostic standard. Coverage of full-length virus was dependent on sample composition, being negatively influenced by increased host and bacterial reads. However, at high influenza virus titers, we were able to reconstruct Ͼ99% complete sequences for all eight gene segments. We also detected a human coronavirus coinfection in one clinical sample. While further optimization is required to improve sensitivity, this approach shows promise for the Nanopore platform to be used in the diagnosis and genetic analysis of influenza virus and other respiratory viruses.

show abstract

“…While antigenic changes can often be explained in terms of amino acid changes, identifying “genetic signatures” which can predict changes in antigenicity from the location of amino acid changes on the structure of the HA is still difficult . Anticipation of the future fitness trajectories of particular genetic variants using predictive modelling is being assessed for its usefulness in supporting candidate vaccine virus selection, especially as the increasing volume of sequence data affords better trajectories, helping to prioritise the most likely emergent variant groups for detailed antigenic characterisation and the production of candidate vaccine viruses (CVVs). While it may be possible to anticipate the emergence of some future antigenic variants, it is not yet evident whether adopting a futuristic vaccine strain would or would not be advantageous for vaccine effectiveness.…”

Section: Virus Characterisation and Vaccine Virus Selectionmentioning

confidence: 99%

The WHO global influenza surveillance and response system (GISRS)—A future perspective

Hay

McCauley

2018

Influenza Resp Viruses

105

101

View full text Add to dashboard Cite

In the centenary year of the devastating 1918‐19 pandemic, it seems opportune to reflect on the success of the WHO Global Influenza Surveillance and Response System (GISRS) initiated 70 years ago to provide early warning of changes in influenza viruses circulating in the global population to help mitigate the consequences of such a pandemic and maintain the efficacy of seasonal influenza vaccines. Three pandemics later and in the face of pandemic threats from highly pathogenic zoonotic infections by different influenza A subtypes, it continues to represent a model platform for global collaboration and timely sharing of viruses, reagents and information to forestall and respond to public health emergencies.

show abstract

Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology

Cited by 112 publications

References 107 publications

Metagenomic Nanopore sequencing of influenza virus direct from clinical respiratory samples

Metagenomic Nanopore sequencing of influenza virus direct from clinical respiratory samples

Metagenomic Nanopore Sequencing of Influenza Virus Direct from Clinical Respiratory Samples

The WHO global influenza surveillance and response system (GISRS)—A future perspective

Contact Info

Product

Resources

About