Evidence map›Paper›PMID 38328256›Full record

ArticlebioRxiv : the preprint server for biology2024

Patient Subtyping Analysis of Baseline Multi-omic Data Reveals Distinct Pre-immune States Predictive of Vaccination Responses.

Cigdem Sevim Bayrak, Christian Forst, Drew R Jones, David Gresham, Smruti Pushalkar, Shaohuan Wu, Christine Vogel, Lara Mahal, Elodie Ghedin, Ted Ross and 2 more

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 5 institutions in 2 countries.

Cigdem Sevim BayrakORCID 0000-0002-3883-5535
Christian Forst
Drew R Jones
David Gresham
Smruti Pushalkar
Shaohuan Wu
Christine Vogel
Lara Mahal
Ted Ross
Adolfo García-SastreORCID 0000-0002-6551-1827
Bin Zhang
Icahn School of Medicine at Mount Sinai · USNew York University · USNYU Langone Health · USUniversity of Alberta · CAUniversity of Georgia · US

Funding

Next generation gene expression analysisR35GM127089 · NIGMS · NEW YORK UNIVERSITY · PI Christine Vogel · 2018 to 2026
$4.0M
NIGMS NIH HHS R35 GM127089
6 · The paper itself

Abstract

Understanding the molecular mechanisms that underpin diverse vaccination responses is a critical step toward developing efficient vaccines. Molecular subtyping approaches can offer valuable insights into the heterogeneous nature of responses and aid in the design of more effective vaccines. In order to explore the molecular signatures associated with the vaccine response, we analyzed baseline transcriptomics data from paired samples of whole blood, proteomics and glycomics data from serum, and metabolomics data from urine, obtained from influenza vaccine recipients (2019-2020 season) prior to vaccination. After integrating the data using a network-based model, we performed a subtyping analysis. The integration of multiple data modalities from 62 samples resulted in five baseline molecular subtypes with distinct molecular signatures. These baseline subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation across subtypes in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these significant differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining vaccine response and efficacy. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.

Identifiers

PMID38328256
PMCPMC10849502
OpenAlexW4391098461

What OpenQuestion holds

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Registered trials

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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.