Evidence map›Paper›PMID 26969675›Full record

ArticleBMC genomics2016

Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method.

Angela Grassi, Barbara Di Camillo, Francesco Ciccarese, Valentina Agnusdei, Paola Zanovello, Alberto Amadori, Lorenzo Finesso, Stefano Indraccolo, Gianna Maria Toffolo

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 2016. 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 37% 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, 3 citations in OpenAlex.

  1. Review
  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

9 authors at 4 institutions in 1 country.

Angela GrassiDepartment of Surgery, Oncology and Gastroenterology, University of Padova, via Gattamelata 64, 35128, Padova, Italy. angela.grassi@unipd.it.
Barbara Di CamilloDepartment of Information Engineering, University of Padova, via Gradenigo 6/B, 35131, Padova, Italy.
Francesco CiccareseIstituto Oncologico Veneto - IRCCS, via Gattamelata 64, 35128, Padova, Italy.
Valentina AgnusdeiIstituto Oncologico Veneto - IRCCS, via Gattamelata 64, 35128, Padova, Italy.
Paola ZanovelloDepartment of Surgery, Oncology and Gastroenterology, University of Padova, via Gattamelata 64, 35128, Padova, Italy.
Alberto AmadoriDepartment of Surgery, Oncology and Gastroenterology, University of Padova, via Gattamelata 64, 35128, Padova, Italy.
Lorenzo FinessoInstitute of Electronics, Computer and Telecommunication Engineering, CNR, via Gradenigo 6/B, 35131, Padova, Italy.
Stefano IndraccoloIstituto Oncologico Veneto - IRCCS, via Gattamelata 64, 35128, Padova, Italy.
Gianna Maria ToffoloDepartment of Information Engineering, University of Padova, via Gradenigo 6/B, 35131, Padova, Italy.
Istituto Oncologico Veneto · ITUniversity of Padua · ITIstituti di Ricovero e Cura a Carattere Scientifico · ITInstitute of Electronics, Computer and Telecommunication Engineering · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs. A challenging task for many researchers working in the field of systems biology is to build up an experiment with a limited budget and produce a dataset suitable to reconstruct putative regulatory modules worth of biological validation.

resultsHere, we focus on small-scale gene expression screens and we introduce a novel experimental set-up and a customized method of analysis to make inference on regulatory modules starting from genetic perturbation data, e.g. knockdown and overexpression data. To illustrate the utility of our strategy, it was applied to produce and analyze a dataset of quantitative real-time RT-PCR data, in which interferon-α (IFN-α) transcriptional response in endothelial cells is investigated by RNA silencing of two candidate IFN-α modulators, STAT1 and IFIH1. A putative regulatory module was reconstructed by our method, revealing an intriguing feed-forward loop, in which STAT1 regulates IFIH1 and they both negatively regulate IFNAR1. STAT1 regulation on IFNAR1 was object of experimental validation at the protein level.

conclusionsDetailed description of the experimental set-up and of the analysis procedure is reported, with the intent to be of inspiration for other scientists who want to realize similar experiments to reconstruct gene regulatory modules starting from perturbations of possible regulators. Application of our approach to the study of IFN-α transcriptional response modulators in endothelial cells has led to many interesting novel findings and new biological hypotheses worth of validation.

Indexed as

Gene Regulatory NetworksRNA InterferenceDEAD-box RNA HelicasesGene Expression RegulationGene Knockdown TechniquesHumansHuman Umbilical Vein Endothelial CellsInterferon-alphaInterferon-Induced Helicase, IFIH1Models, GeneticReceptor, Interferon alpha-betaSTAT1 Transcription FactorDEAD-box RNA HelicasesIFIH1 protein, humanIFNAR1 protein, humanInterferon-alphaInterferon-Induced Helicase, IFIH1Receptor, Interferon alpha-betaSTAT1 protein, humanSTAT1 Transcription FactorExperimental set-upGene regulatory modulesIFN-α modulatorsRNA silencingSmall-scale gene expression screens

Identifiers

PMID26969675
PMCPMC4788926
OpenAlexW2293922191

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.