Daniele Pepe

A genome-wide screening and SNPs-to-genes approach to identify novel genetic risk factors associated with frontotemporal dementia

Frontotemporal dementia (FTD) is the second most prevalent form of early onset dementia after Alzheimers disease (AD). We performed a case-control association study in an Italian FTD cohort (n = 530) followed by the novel single nucleotide polymorphisms (SNPs)-to-genes approach and functional annotation analysis. We identified 2 novel potential loci for FTD. Suggestive SNPs reached p-values ∼10−7 and odds ratio > 2.5 (2p16.3) and 1.5 (17q25.3). Suggestive alleles at 17q25.3 identified a disease-associated haplotype causing decreased expression of –cis genes such as RFNG and AATK involved in neuronal genesis and differentiation and axon outgrowth, respectively. We replicated this locus through the SNPs-to-genes approach. Our functional annotation analysis indicated significant enrichment for functions of the brain (neuronal genesis, differentiation, and maturation), the synapse (neurotransmission and synapse plasticity), and elements of the immune system, the latter supporting our recent international FTD–genome-wide association study. This is the largest genome-wide study in Italian FTD to date. Although our results are not conclusive, we set the basis for future replication studies and identification of susceptible molecular mechanisms involved in FTD pathogenesis.

Antithrombotic medications and the etiology of intracerebral hemorrhage MUCH-Italy

Objective: To test the hypothesis that the effect of antithrombotic medications on the risk of intracerebral hemorrhage (ICH) varies according to the location of the hematoma. Methods: Consecutive patients with ICH were enrolled as part of the Multicenter Study on Cerebral Hemorrhage in Italy (MUCH-Italy). Multivariable logistic regression models served to examine whether risk factors for ICH and location of the hematoma (deep vs lobar) predict treatment-specific ICH subgroups (antiplatelets-related ICH and oral anticoagulants [OACs]–related ICH). Results: A total of 870 (313 lobar ICH, 557 deep ICH) subjects were included. Of these, 223 (25.6%) were taking antiplatelets and 77 (8.8%) OACs at the time of stroke. The odds of antiplatelet-related ICH increased with aging (odds ratio [OR] 1.05; 95% confidence interval [CI] 1.03–1.07) and hypertension (OR 1.86; 95% CI 1.22–2.85) but had no relation with the anatomical location of ICH. Conversely, lobar location of the hematoma was associated with the subgroup of OAC-related ICH (OR 1.70; 95% CI 1.03–2.81) when compared to the subgroup of patients taking no antithrombotic medications. Within the subgroup of patients taking OACs, international normalized ratio (INR) values were higher in those with lobar ICH as compared to those with deep ICH (2.8 ± 1.1 vs 2.2 ± 0.8; p = 0.011). The proportion of patients with lobar hematoma increased with increasing intensity of anticoagulation, with a ∼2-fold increased odds of lobar compared to deep ICH (odds 2.17; p = 0.03) in those exposed to overanticoagulation (INR values >3.0). Conclusions: OACs, as opposed to antiplatelets, predispose to lobar location of brain hematomas according to a dose-response relationship.

Investigating perturbed pathway modules from gene expression data via structural equation models.

BackgroundIt is currently accepted that the perturbation of complex intracellular networks, rather than the dysregulation of a single gene, is the basis for phenotypical diversity. High-throughput gene expression data allow to investigate changes in gene expression profiles among different conditions. Recently, many efforts have been made to individuate which biological pathways are perturbed, given a list of differentially expressed genes (DEGs). In order to understand these mechanisms, it is necessary to unveil the variation of genes in relation to each other, considering the different phenotypes. In this paper, we illustrate a pipeline, based on Structural Equation Modeling (SEM) that allowed to investigate pathway modules, considering not only deregulated genes but also the connections between the perturbed ones.ResultsThe procedure was tested on microarray experiments relative to two neurological diseases: frontotemporal lobar degeneration with ubiquitinated inclusions (FTLD-U) and multiple sclerosis (MS). Starting from DEGs and dysregulated biological pathways, a model for each pathway was generated using databases information biological databases, in order to design how DEGs were connected in a causal structure. Successively, SEM analysis proved if pathways differ globally, between groups, and for specific path relationships. The results confirmed the importance of certain genes in the analyzed diseases, and unveiled which connections are modified among them.ConclusionsWe propose a framework to perform differential gene expression analysis on microarray data based on SEM, which is able to: 1) find relevant genes and perturbed biological pathways, investigating putative sub-pathway models based on the concept of disease module; 2) test and improve the generated models; 3) detect a differential expression level of one gene, and differential connection between two genes. This could shed light, not only on the mechanisms affecting variations in gene expression, but also on the causes of gene-gene relationship modifications in diseased phenotypes.

The neuroimaging signature of frontotemporal lobar degeneration associated with Granulin mutations: an effective connectivity study.

It has been suggested that monogenic frontotemporal lobar degeneration (FTLD) due to Granulin (GRN) mutations might present a specific pattern of atrophy, as compared with FTLD GRN-negative disease. Recent literature has suggested that the study of functional neural networks, rather than regional structural damage, might better elucidate the pathogenic mechanisms, showing complex relationships among structural alterations observed with conventional neuroimaging. The aim of this study was to evaluate effective brain connectivity in FTLD patients carrying GRN mutations (GRN+), compared with FTLD patients without pathogenetic GRN mutations (GRN−) and healthy controls (HCs). Methods: Twenty-six FTLD patients (13 GRN+ and 13 GRN− matched for age, sex, and phenotype) and 13 age- and sex-matched HCs underwent brain perfusion SPECT. Brain regions involved in FTLD (dorsolateral, anterior cingulate, orbitofrontal, posterior temporal, temporal pole, and parietal) were used as regions of interest to identify functionally interconnected areas. An effective connectivity (path) analysis was defined with a PC algorithm (named after its inventors Peter Spirtes and Clark Glymour) search procedure and structural equation fitting. Statistically significant differences among the 3 groups were determined. Results: The best-fitting model was obtained by the data-driven approach, and brain connectivity pathways resembling state-of-the-art anatomic knowledge were obtained. When GRN+ and GRN− groups were considered, the former presented a selective bilateral parietotemporal disconnection, compared with GRN− patients. Furthermore, in FTLD GRN+ patients an increased compensative connectivity of the temporal regions (temporal pole and posterior temporal cortices) was observed. Conclusion: The present work suggests that impairment of effective functional connectivity of the parietotemporal regions is the hallmark of GRN-related FTLD. However, compensative mechanisms—which should be further investigated—may occur.

Estimation of dysregulated pathway regions in MPP+ treated human neuroblastoma SH-EP cells with structural equation model

As it is widely accepted that the cellular system is modular and complex diseases are caused by combinations of genetic alternations affecting component of the cellular system, module-centric approaches are promising in the study of Parkinson’s disease (PD). To identify module/pathways associated with PD, the gene signaling pathway analysis of 1-methyl-4-phenylpyridinium (MPP+) treated cells of the PD model was performed with their genome-wide gene expression data. Significant pathway perturbation was observed at 14 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. To capture differentially regulated regions by MPP+ treatment within these 14 perturbed pathways, the shortest path models linking differentially expressed genes (DEGs) were constructed for each pathway. Each shortest path model was analyzed with structural equation model (SEM) and the significant regulation structure by MPP+ treatment was observed in three shortest pathway models including cell cycle, neurotrophin and phosphatidylinositol signaling pathways. In addition, the connection of neurotrophin and phosphatidylinositol signaling pathway via CALM1 was observed. Our results suggest that the dys-regulation of these three pathways might play important role in neuronal cell death by MPP+ treatment.

Determinants of premature familial arterial thrombosis in patients with juvenile ischaemic stroke. The Italian Project on Stroke in Young Adults (IPSYS).

Factors predicting family history (FH) of premature arterial thrombosis in young patients with ischaemic stroke (IS) have not been extensively investigated, and whether they might influence the risk of post-stroke recurrence is still unknown. In the present study we analysed 1,881 consecutive first-ever IS patients aged 18-45 years recruited from January 2000 to January 2012 as part of the Italian Project on Stroke in Young Adults (IPSYS). FH of premature arterial thrombosis was any thrombotic event [IS, myocardial infarction or other arterial events event] < 45 years in probands first-degree relatives. Compared with patients without FH of premature arterial thrombosis, those with FH (n = 85) were more often smokers (odds ratio [OR], 1.94; 95 % confidence interval [CI], 1.21-3.09) and carriers of procoagulant abnormalities (OR, 3.66; 95 % CI, 2.21-6.06). Smoking (OR, 2.48; 95 % CI, 1.20-5.15), the A1691 mutation in factor V gene (OR, 3.64; 95 % CI, 1.31-10.10), and the A20210 mutation in the prothrombin gene (OR, 8.40; 95 % CI 3.35-21.05) were associated with FH of premature stroke (n = 33), while circulating anti-phospholipids to FH of premature myocardial infarction (n = 45; OR, 3.48; 95 % CI, 1.61-7.51). Mean follow-up time was 46.6 ± 38.6 months. Recurrent events occurred more frequently in the subgroup of patients with FH of premature stroke [19.4 %); p = 0.051] compared to patients without such a FH. In conclusion, young IS patients with FH of premature arterial thrombosis exhibit a distinct risk-factor profile, an underlying procoagulant state and have worse vascular prognosis than those with no FH of juvenile thrombotic events.

Estimating the Inheritance of Frontotemporal Lobar Degeneration in the Italian Population

Frontotemporal dementia (FTD) has a strong genetic basis, with familial forms occurring in 30-50% of cases. Causative genes have been identified, with an autosomal dominant pattern of inheritance. Notwithstanding, in a number of cases with positive family history no pathogenetic mutation has been reported, and the role of genetics in sporadic cases is still unclear. In the present study, we aim to estimate the genetic contribution to FTD using concordance among parent-offspring pairs. Heritability of early-onset (EO, <65 years) and late-onset (LO, ≥65 years) FTD was estimated by examining the concordance between parents and offspring. Probands with at least one parent whose dementia status was known were recruited from 15 Italian centers, and the presence or absence of dementia was considered in siblings. Different prevalence estimates, as available by literature data, were tested. A total of 260 probands and 1619 family members were considered in this study. We found that parent-offspring concordance in FTD was 6.25%, resulting in hereditability of 98.5% (95% confidence interval (CI): 85.0%-100.0%). Equal heritability for both sexes regardless of parental gender was reported. EO-FTD showed hereditability of 86.3% (95% CI: 77.0%-95.0%) and LO-FTD of 75.7% (95% CI: 65.0%-86.0%). Estimating the contribution of genetics in FTD may help in driving future genetic studies to identify new pathogenetic determinants. We suggest that in most of the cases FTD is a genetic-based disease, even in the elderly. Different inheritance modality might be considered in future work, beyond autosomal dominant disease.

Sem Best Shortest Paths for the Characterization of Differentially Expressed Genes

In the last years, systems and computational biology focused their efforts in uncovering the causal relationships among the observable perturbations of gene regulatory networks and human diseases. This problem becomes even more challenging when network models and algorithms have to take into account slightly significant effects, caused by often peripheral or unknown genes that cooperatively cause the observed diseased phenotype. Many solutions, from community and pathway analysis to information flow simulation, have been proposed, with the aim of reproducing biological regulatory networks and cascades, directly from empirical data as gene expression microarray data. In this contribute, we propose a methodology to evaluate the most important shortest paths between differentially expressed genes in biological interaction networks, with absolutely no need of user-defined parameters or heuristic rules, enabling a free-of-bias discovery and overcoming common issues affecting the most recent network-based algorithms.

Pathway Composite Variables: A Useful Tool for the Interpretation of Biological Pathways in the Analysis of Gene Expression Data

Biological pathways represent a useful tool for the identification, in the intricate network of biomolecules, of subnetworks able to explain specific activities in an organism. The advent of high-throughput gene expression technologies allowed to analyze simultaneously the expression of thousands of genes. Pathway analysis is often used to give a meaning to the set of differentially expressed genes. However, classical analyses generate a list of pathways that are over-represented or perturbed (depending on the approach used), but they do not consider, in many cases, the role of the connections between the biomolecules (genes or proteins) in the explanation of the biological phenomena studied. In this note we propose a fine-tuned method, based on Structural Equation Modeling principles, to discover pathway modules eventually able to characterize, in a network perspective, the mechanisms of the pathogenesis of a disease. The procedure relies on the concepts of shortest path, to find the initial modules, and of pathway composite variable, to improve and facilitate the interpretation of the modules proposed. The method was tested on microarray data of frontotemporal lobar degeneration with ubiquitinated inclusions.