ArticleJournal of proteome research2020
Age-Dependent Glycomic Response to the 2009 Pandemic H1N1 Influenza Virus and Its Association with Disease Severity.
Article in Journal of proteome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- CRISPR screens and lectin microarrays identify high mannose N-glycan regulators.Nature communications · 2024Article
- Patient subtyping analysis of baseline multi-omic data reveals distinct pre-immune states associated with antibody response to seasonal influenza vaccination.Clinical immunology (Orlando, Fla.) · 2024Article
- Infection and the Glycome─New Insights into Host Response.ACS infectious diseases · 2024Review
- CRISPR screens and lectin microarrays identify novel high mannose N-glycan regulators.bioRxiv : the preprint server for biology · 2024Article
- Influenza Defective Interfering Virus Promotes Multiciliated Cell Differentiation and Reduces the Inflammatory Response in Mice.Journal of virology · 2023Article
- A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities.ACS chemical biology · 2022Article
- α2,6-Sialylation Is Upregulated in Severe COVID-19, Implicating the Complement Cascade.ACS infectious diseases · 2022Article
- Prevaccination Glycan Markers of Response to an Influenza Vaccine Implicate the Complement Pathway.Journal of proteome research · 2022Article
- α2,6-Sialylation is Upregulated in Severe COVID-19 Implicating the Complement Cascade.medRxiv : the preprint server for health sciences · 2022Article
- Review
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
Abstract
Influenza A viruses cause a spectrum of responses, from mild coldlike symptoms to severe respiratory illness and death. Intrinsic host factors, such as age, can influence disease severity. Glycosylation plays a critical role in influenza pathogenesis; however, the molecular drivers of influenza outcomes remain unknown. In this work, we characterized the host glycomic response to the H1N1 2009 pandemic influenza A virus (H1N1pdm09) as a function of age-dependent severity in a ferret model. Using our dual-color lectin microarray technology, we examined baseline glycosylation and glycomic response to infection in newly weaned and aged animals, models for young children and the elderly, respectively. Compared to adult uninfected ferrets, we observed higher levels of α-2,6-sialosides, the receptor for H1N1pdm09, in newly weaned and aged animals. We also observed age-dependent loss of O-linked α-2,3-sialosides. The loss of these highly charged groups may impact viral clearance by mucins, which corresponds to the lower clearance rates observed in aged animals. Upon infection, we observed dramatic changes in the glycomes of aged animals, a population severely impacted by the virus. In contrast, no significant alterations were observed in the newly weaned animals, which show mild to moderate responses to the H1N1pdm09. High mannose, a glycan recently identified as a marker of severity in adult animals, increased with severity in the aged population. However, the response was delayed, in line with the delayed development of pneumonia observed. Overall, our results may help explain the differential susceptibility to influenza A infection and severity observed as a function of age.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.