Evidence map›Paper›PMID 34531863›Full record

ReviewFrontiers in immunology2021

Application of Genetic Studies to Flow Cytometry Data and Its Impact on Therapeutic Intervention for Autoimmune Disease.

Valeria Orrù, Maristella Steri, Francesco Cucca, Edoardo Fiorillo

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 49% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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 2 institutions in 1 country.

Valeria OrrùInstitute for Genetic and Biomedical Research, National Research Council (CNR), Sardinia, Italy.
Maristella SteriInstitute for Genetic and Biomedical Research, National Research Council (CNR), Sardinia, Italy.
Francesco CuccaInstitute for Genetic and Biomedical Research, National Research Council (CNR), Sardinia, Italy.
Edoardo FiorilloInstitute for Genetic and Biomedical Research, National Research Council (CNR), Sardinia, Italy.
National Research Council · ITInstitute of Genetic and Biomedical Research · IT

Funding

NIDA NIH HHS HHSN271201600005C
6 · The paper itself

Abstract

In recent years, systematic genome-wide association studies of quantitative immune cell traits, represented by circulating levels of cell subtypes established by flow cytometry, have revealed numerous association signals, a large fraction of which overlap perfectly with genetic signals associated with autoimmune diseases. By identifying further overlaps with association signals influencing gene expression and cell surface protein levels, it has also been possible, in several cases, to identify causal genes and infer candidate proteins affecting immune cell traits linked to autoimmune disease risk. Overall, these results provide a more detailed picture of how genetic variation affects the human immune system and autoimmune disease risk. They also highlight druggable proteins in the pathogenesis of autoimmune diseases; predict the efficacy and side effects of existing therapies; provide new indications for use for some of them; and optimize the research and development of new, more effective and safer treatments for autoimmune diseases. Here we review the genetic-driven approach that couples systematic multi-parametric flow cytometry with high-resolution genetics and transcriptomics to identify endophenotypes of autoimmune diseases for the development of new therapies.

Indexed as

Autoimmune DiseasesBiomarkersDisease ManagementDisease SusceptibilityFlow CytometryGenetic Predisposition to DiseaseGenetic TestingHumansImmunophenotypingPrecision MedicineResearchBiomarkersautoimmune diseasesdrug developmentflow cytometryGWASimmune profiling

Identifiers

PMID34531863
PMCPMC8438121
OpenAlexW3197181311

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.