Evidence map›Paper›PMID 39554028›Full record

ArticlebioRxiv : the preprint server for biology2024

Markov Field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques.

Shu Wang, Amy J Myers, Edward B Irvine, Chuangqi Wang, Pauline Maiello, Mark A Rodgers, Jaime Tomko, Kara Kracinovsky, H Jacob Borish, Michael C Chao and 10 more

Abstract readPreprint
PubMed Full text at PMC Publisher
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors.

Shu WangDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
Amy J MyersDepartment of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Edward B IrvineRagon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA 02139, USA.
Chuangqi WangDepartment of Immunology and Microbiology, University of Colorado, Anschuntz Medical Campus, Aurora, CO 80045, USA.
Pauline MaielloDepartment of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Mark A RodgersDepartment of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Jaime TomkoDepartment of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Kara KracinovskyDepartment of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
H Jacob BorishDepartment of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Michael C ChaoDepartment of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Douaa MugahidDepartment of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Patricia A DarrahVaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD 20814, USA.
Robert A SederVaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD 20814, USA.
Mario RoedererVaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD 20814, USA.
Charles A ScangaDepartment of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Philana Ling LinDepartment of Pediatrics, University of Pittsburgh School of Medicine, UPMC Children's Hospital of Pittsburgh, and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15620, USA.
Galit AlterRagon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA 02139, USA.
Sarah M FortuneRagon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA 02139, USA.
JoAnne L FlynnDepartment of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Douglas A LauffenburgerDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.ORCID 0000-0002-0050-989X

Funding

IMMUNE MECHANISMS OF PROTECTION AGAINST MYCOBACTERIUM TUBERCULOSIS CENTER (IMPAC-TB)75N93019C00071 · NIAID · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI FORTUNE, SARAH · 2019 to 2025
$57.3M
Research Project 2 The pregnancy AdaptOMEU19AI167899 · NIAID · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI DOUGLAS A LAUFFENBURGER · 2022 to 2026
$14.7M
Exploiting Fc-engineering to dissect mechanisms of anti-microbial action of M. tuberculosis-specific antibodiesF31AI150171 · NIAID · HARVARD SCHOOL OF PUBLIC HEALTH · PI IRVINE, EDWARD BUFORD · 2020 to 2021
$64k
NIAID NIH HHS 75N93019C00071NIAID NIH HHS F31 AI150171NIAID NIH HHS U19 AI167899
6 · The paper itself

Abstract

Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems, but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that BCG vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, cytometry) of vaccinated macaques, we applied Markov Fields (MF), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.e. macaques) relative to multivariate features. Furthermore, we find that integrating multiple data modes with MFs helps to remove spurious connections. Finally, we used the MF to predict outcomes of perturbations at various immune nodes, including a B-cell depletion that induced network-wide shifts without reducing vaccine protection, which we validated experimentally.

Identifiers

PMID39554028
PMCPMC11565837

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.