Evidence map›Paper›PMID 35404985›Full record

ArticlePLoS computational biology2022

Construction of disease-specific cytokine profiles by associating disease genes with immune responses.

Tianyun Liu, Shiyin Wang, Michael Wornow, Russ B Altman

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 34% of its field
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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Genetic/Protein Association of Atopic Dermatitis and Tooth Agenesis.International journal of molecular sciences · 2023
    Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Tianyun LiuDepartment of Bioengineering, Stanford University, Stanford, California, United States of America.ORCID 0000-0003-4912-383X
Shiyin WangChinese Undergraduate Visiting Research Program, Stanford University, Stanford, California, United States of America.ORCID 0000-0003-1156-8681
Michael WornowDepartment of Computer Science, Stanford University, Stanford, California, United States of America.ORCID 0000-0003-2215-6527
Russ B AltmanDepartments of Bioengineering, Genetics & Medicine, Stanford University, Stanford, California, United States of America.ORCID 0000-0003-3859-2905
Stanford University · US

Funding

Undergraduate Summer Research Experiences Support for Combining systems biology and structural biology to find new therapeuticsR01GM102365 · NIGMS · STANFORD UNIVERSITY · PI ALTMAN, RUSS BIAGIO · 2012 to 2021
$3.0M
NIGMS NIH HHS R01 GM102365
6 · The paper itself

Abstract

The pathogenesis of many inflammatory diseases is a coordinated process involving metabolic dysfunctions and immune response-usually modulated by the production of cytokines and associated inflammatory molecules. In this work, we seek to understand how genes involved in pathogenesis which are often not associated with the immune system in an obvious way communicate with the immune system. We have embedded a network of human protein-protein interactions (PPI) from the STRING database with 14,707 human genes using feature learning that captures high confidence edges. We have found that our predicted Association Scores derived from the features extracted from STRING's high confidence edges are useful for predicting novel connections between genes, thus enabling the construction of a full map of predicted associations for all possible pairs between 14,707 human genes. In particular, we analyzed the pattern of associations for 126 cytokines and found that the six patterns of cytokine interaction with human genes are consistent with their functional classifications. To define the disease-specific roles of cytokines we have collected gene sets for 11,944 diseases from DisGeNET. We used these gene sets to predict disease-specific gene associations with cytokines by calculating the normalized average Association Scores between disease-associated gene sets and the 126 cytokines; this creates a unique profile of inflammatory genes (both known and predicted) for each disease. We validated our predicted cytokine associations by comparing them to known associations for 171 diseases. The predicted cytokine profiles correlate (p-value<0.0003) with the known ones in 95 diseases. We further characterized the profiles of each disease by calculating an "Inflammation Score" that summarizes different modes of immune responses. Finally, by analyzing subnetworks formed between disease-specific pathogenesis genes, hormones, receptors, and cytokines, we identified the key genes responsible for interactions between pathogenesis and inflammatory responses. These genes and the corresponding cytokines used by different immune disorders suggest unique targets for drug discovery.

Indexed as

CytokinesInflammationHumansImmunityCytokines

Identifiers

PMID35404985
PMCPMC9022887
OpenAlexW4225338660

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.