A novel network analysis approach reveals DNA damage, oxidative stress and calcium/cAMP homeostasis-associated biomarkers in frontotemporal dementia

Abstract

Frontotemporal Dementia (FTD) is the form of neurodegenerative dementia with the highest prevalence after Alzheimer's disease, equally distributed in men and women. It includes several variants, generally characterized by behavioural instability and language impairments. Although few mendelian genes (MAPT, GRN, and C9orf72) have been associated to the FTD phenotype, in most cases there is only evidence of multiple risk loci with relatively small effect size. To date, there are no comprehensive studies describing FTD at molecular level, highlighting possible genetic interactions and signalling pathways at the origin FTD-associated neurodegeneration. In this study, we designed a broad FTD genetic interaction map of the Italian population, through a novel network-based approach modelled on the concepts of disease-relevance and interaction perturbation, combining Steiner tree search and Structural Equation Model (SEM) analysis. Our results show a strong connection between Calcium/cAMP metabolism, oxidative stress-induced Serine/Threonine kinases activation, and postsynaptic membrane potentiation, suggesting a possible combination of neuronal damage and loss of neuroprotection, leading to cell death. In our model, Calcium/cAMP homeostasis and energetic metabolism impairments are primary causes of loss of neuroprotection and neural cell damage, respectively. Secondly, the altered postsynaptic membrane potentiation, due to the activation of stress-induced Serine/Threonine kinases, leads to neurodegeneration. Our study investigates the molecular underpinnings of these processes, evidencing key genes and gene interactions that may account for a significant fraction of unexplained FTD aetiology. We emphasized the key molecular actors in these processes, proposing them as novel FTD biomarkers that could be crucial for further epidemiological and molecular studies.

Fig 1. Data analysis workflow.

Our network-based data analysis method includes three inputs: (i) a reference interactome that is used as a gene-gene interaction space; (ii) a set of seed nodes representing terminals (sources and targets) of information spreading through the interactome; and (iii) quantitative data used to build network weights. Weights are then used to generate a Steiner tree connecting seed genes through paths maximizing edge perturbation, using a weighted heuristic shortest path algorithm. The resulting Steiner tree is then converted into a Structural Equation Model (SEM) and fitted, to assess its validity. During SEM-based procedure, covariance between pairs of leaf genes (i.e., ancestral bow-free nodes) are tested and fitted using a latent variable (LV) model. The group variable C = {0, 1} influences a LV, modelling the unobserved cause(s) acting on the two target genes. Significant covariances are retained in the extended network, representing the final disease-network.

Fig 2. Steiner tree.

Steiner tree obtained applying a Shortest Path Heuristic (SPH) algorithm. The tree has 167 nodes and 166 edges. The size of each node (i.e. gene) is proportional to its degree (i.e. the number of incoming and outgoing connections). Node colours indicate: perturbed seeds (green), non-perturbed seeds (red), perturbed connectors (blue), and non-perturbed connectors (yellow). Red edges correspond to perturbed interactions, while edge thickness is proportional to their weight (i.e. their perturbance level). A perturbed interaction has a weight w < 0.33 (i.e. p < 0.05 threshold over the nominal p-value). The entire network is characterized by a backbone CAMK2A-TCF7L2- CTNNB1-JUN-MAK8-PRKACG, where CAMK2A and MAPK8-PRKACG are the main perturbed hubs, CTNNB1 and JUN represent the sinks of the entire system, and TCF7L2 is the bottleneck connecting them.

Fig 3. Essential node sub-network.

Node essentiality is determined by considering nodes having both degree centrality and weighted betweenness centrality over the upper-quartile. Essential nodes are placed in non-redundant portions of the network and thus cannot be removed without a deep impact on network connectivity. These genes intercept the network backbone, represented by the axis TCF7L2-JUN-MAK8-PRKACG, carrying the top perturbation levels, especially in proximity of the sources. Nodes and edges are labelled according to the conventions followed in Fig 2.

Fig 4. ncWNT sub-network.

This sub-network focuses on the module characterized by a series of receptors and enzymes regulating Calcium/cAMP homeostasis and involved in the non-canonical Ca⁺²/WNT signaling pathway. Among them, the most perturbed are the EGFR receptor and its target phospholipase PLCB3, and the routes ITPR1-CAMK2A-CALM2-PPP3CC and ITPR1-CAMK2A-EP300-TCF7L2. JUN is a large sink between this module and the MAPK-JNK one. Nodes and edges are labelled according to the conventions followed in Fig 2.

Fig 5. MAPK-JNK sub-network.

This sub-network focuses on the module characterized by a series Serine/Threonine kinases involved in the MAPK-JNK signaling pathway. Among them, MAPK8 is the one having the highest outgoing connectivity of the entire network, and the most perturber incoming interaction carried by PRKACG, another FTD-network hub. Other deeply perturbed interactions include MAPK8-TNF, MAPK8-CRK. JUN is a large sink within this module and the non-canonical WNT pathway (ncWNT) one. Nodes and edges are labelled according to the conventions followed in Fig 2.