Evidence map›Paper›PMID 36289281›Full record

ArticleScientific reports2022

Mapping human disease-associated enzymes into Reactome allows characterization of disease groups and their interactions.

Castrense Savojardo, Davide Baldazzi, Giulia Babbi, Pier Luigi Martelli, Rita Casadio

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Resources and tools for rare disease variant interpretation.Frontiers in molecular biosciences · 2023
    Review
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

5 authors.

Castrense SavojardoBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Davide BaldazziCRO, Centro di Riferimento Oncologico, Aviano, Italy.
Giulia BabbiBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Pier Luigi MartelliBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy. pierluigi.martelli@unibo.it.
Rita CasadioBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy. rita.casadio@unibo.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

According to databases such as OMIM, Humsavar, Clinvar and Monarch, 1494 human enzymes are presently associated to 2539 genetic diseases, 75% of which are rare (with an Orphanet code). The Mondo ontology initiative allows a standardization of the disease name into specific codes, making it possible a computational association between genes, variants, diseases, and their effects on biological processes. Here, we tackle the problem of which biological processes enzymes can affect when the protein variant is disease-associated. We adopt Reactome to describe human biological processes, and by mapping disease-associated enzymes in the Reactome pathways, we establish a Reactome-disease association. This allows a novel categorization of human monogenic and polygenic diseases based on Reactome pathways and reactions. Our analysis aims at dissecting the complexity of the human genetic disease universe, highlighting all the possible links within diseases and Reactome pathways. The novel mapping helps understanding the biochemical/molecular biology of the disease and allows a direct glimpse on the present knowledge of other molecules involved. This is useful for a complete overview of the disease molecular mechanism/s and for planning future investigations. Data are collected in DAR, a database that is free for search and available at https://dar.biocomp.unibo.it .

Indexed as

Biological PhenomenaComputational BiologyDatabases, FactualHumans

Identifiers

PMID36289281
PMCPMC9605996

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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.