Inferring gene regulatory networks of ALS from blood transcriptome profiles
Xena G. Pappalardo,
Giorgio Jansen,
Matteo Amaradio,
Jole Costanza,
Renato Umeton,
Francesca Guarino,
Vito De Pinto,
Stephen G. Oliver,
Angela Messina,
Giuseppe Nicosia
Affiliations
Xena G. Pappalardo
Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
Giorgio Jansen
Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy; Department of Biochemistry, University of Cambridge, Cambridge, UK
Matteo Amaradio
Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
Jole Costanza
The National Institute of Molecular Genetics “Romeo and Enrica Invernizzi”, Milano, Italy
Renato Umeton
Department of Informatics and Analytics, Dana-Farber Cancer Institute, Boston, MA, USA
Francesca Guarino
Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy; National Institute of Biostructures and Biosystems, Section of Catania, Catania, Italy
Vito De Pinto
Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy; National Institute of Biostructures and Biosystems, Section of Catania, Catania, Italy
Stephen G. Oliver
Department of Biochemistry, University of Cambridge, Cambridge, UK; Corresponding author.
Angela Messina
Department of Biological, Geological and Environmental Sciences, University of Catania, Catania, Italy; National Institute of Biostructures and Biosystems, Section of Catania, Catania, Italy; Corresponding author. Department of Biological, Geological and Environmental Sciences, University of Catania, Catania, Italy.
Giuseppe Nicosia
Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy; Department of Biochemistry, University of Cambridge, Cambridge, UK; Corresponding author. Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.
One of the most robust approaches to the prediction of causal driver genes of complex diseases is to apply reverse engineering methods to infer a gene regulatory network (GRN) from gene expression profiles (GEPs). In this work, we analysed 794 GEPs of 1117 human whole-blood samples from Amyotrophic Lateral Sclerosis (ALS) patients and healthy subjects reported in the GSE112681 dataset. GRNs for ALS and healthy individuals were reconstructed by ARACNe-AP (Algorithm for the Reconstruction of Accurate Cellular Networks - Adaptive Partitioning). In order to examine phenotypic differences in the ALS population surveyed, several datasets were built by arranging GEPs according to sex, spinal or bulbar onset, and survival time. The designed reverse engineering methodology identified a significant number of potential ALS-promoting mechanisms and putative transcriptional biomarkers that were previously unknown. In particular, the characterization of ALS phenotypic networks by pathway enrichment analysis has identified a gender-specific disease signature, namely network activation related to the radiation damage response, reported in the networks of bulbar and female ALS patients. Also, focusing on a smaller interaction network, we selected some hub genes to investigate their inferred pathological and healthy subnetworks. The inferred GRNs revealed the interconnection of the four selected hub genes (TP53, SOD1, ALS2, VDAC3) with p53-mediated pathways, suggesting the potential neurovascular response to ALS neuroinflammation. In addition to being well consistent with literature data, our results provide a novel integrated view of ALS transcriptional regulators, expanding information on the possible mechanisms underlying ALS and also offering important insights for diagnostic purposes and for developing possible therapies for a disease yet incurable.
