Investigating the inflammatory signaling characterizing lysosomal storage disorders (LSDs)

Systems Pharmacology
Data integration
Knowledge extraction
Pathway analysis


Develop a text-mining and systems biology framework to investigate the inflammatory signaling characterizing lysosomal storage disorders (LSDs).

What we did

First, we set up a text-mining analysis to identify the cytokines linked to three LSDs: Gaucher disease, Fabry disease and Acid Sphingomyelinase Deficiency (ASMD). We processed PubMed and PubMed Central (PMC) documents using Natural Language Processing (NLP) techniques and we identified sentences with a linguistic relationship between disease-related concepts and cytokines. We then applied a systems biology approach to investigate the gene regulatory network of the cytokines using publicly available transcriptomics data. Moreover, using ligand-receptor information, we built a cytokine-driven immune cell-communication



We found numerous transcription factors that are putative regulators of cytokine gene expression in a cell-specific context, such as the signaling axes controlled by STAT2, JUN, and NR4A2 as candidate regulators of the monocyte Gaucher disease cytokine network. Overall, our results suggest the presence of a complex inflammatory signaling in LSDs involving many cellular and molecular players that could be further investigated as putative targets of anti-inflammatory therapies.


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Parolo S, Tomasoni D, Bora P, Ramponi A, Kaddi C, Azer K, Domenici E, Neves-Zaph S, Lombardo R. Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis. Front Cell Dev Biol. 2021 Aug 20;9:703489.

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