ORCID Profile
0000-0003-0687-7199
Current Organisations
University of Trento
,
The Capital Region of Denmark
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Publisher: Elsevier BV
Date: 04-2019
DOI: 10.1016/J.CYTO.2018.12.024
Abstract: Homozygous mutations in Triggering Receptor Expressed on Myeloid cells 2 gene (TREM2) are one of the major causes of Nasu Hakola Disease (NHD). We analysed Peripheral Blood Mononuclear Cells (PBMC) profile of 164 inflammatory factors in patients with NHD carrying the TREM2 Q33X mutation as compared with heterozygous and wild type in iduals. Several molecules related to bone formation and angiogenesis were altered in NHD compared to non-carriers: Bone Morphogenetic Protein (BMP)-1 mRNA levels were significantly increased in PBMC (2.32 fold-increase P = 0.01), as were Transforming Growth Factor Beta (TGFB)3 levels (1.51 fold-increase P = 0.02). Conversely, CXCL5 and Pro Platelet Basic Protein (PPBP) were strongly downregulated (-28.26, -9.85 fold-decrease over non-carriers, respectively, P = 0.01), as well as Platelet Factor 4 Variant 1 (PF4V1 -41.44, P = 0.03). Among other inflammatory factors evaluated, Interleukin (IL)-15 and Tumor Necrosis Factor Superfamily Member (TNFSF)4 mRNA levels were decreased in NHD as compared with non-carriers (-2.25 and -3.87 fold-decrease, P = 0.01 and 0.001, respectively). In heterozygous in iduals, no significant differences were observed, apart from IL-15 mRNA levels, that were decreased at the same extent as NHD (-2.05 fold-decrease over non-carriers, P = 0.002). We identified a signature in PBMC from patients with NHD consisting of strongly decreased mRNA levels of CXCL5, PPBP, PF4V1, mildly decreased IL-15 and TNFSF4 and mildly increased BMP-1 and TGFB3.
Publisher: Elsevier BV
Date: 2010
Publisher: Springer Science and Business Media LLC
Date: 13-05-2010
DOI: 10.1038/GENE.2010.18
Abstract: Progranulin (GRN) gene variability has been analyzed in a s le of 354 patients with multiple sclerosis (MS) compared with 343 controls. No significant differences were observed, but by stratifying according to MS subtypes, a significant increased frequency of the rs2879096 TT genotype was found in primary progressive MS (PPMS) patients versus controls (16.0 vs 3.5%, P=0.023, odds ratio (OR) 5.2, 95% confidence interval (CI) 1.2-21.4). In addition, in PPMS, an association with the C allele of rs4792938 was observed (55.3 vs 33.5%, P=0.011, OR 2.4, 95% CI 1.2-4.7). An independent population was studied as replication, failing to confirm results previously obtained. Stratifying according to gender, an association with rs4792938 C allele was found in male PPMS patients compared with controls (40.7 vs 26.9%, P=0.002, OR 1.87, 95% CI 1.2-2.8). An association with the rs2879096T allele was observed (29.2 in patients compared with 18.9% in controls, P=0.012, OR 1.77, 95% CI 1.1-2.8). Haplotype analysis showed that TC haplotype frequency is increased in PPMS male patients compared with male controls (25.7 vs 16.6% P=0.02, OR 1.69, 95% CI 1.1-2.7), whereas the respective GC haplotype seems to exert a protective effect, as its frequency is decreased in patients compared with controls (55.8% vs 70.9% P=0.001, OR 0.52, 95% CI 0.4-0.8). Therefore, GRN haplotypes likely influence the risk of developing PPMS in males.
Publisher: Springer Science and Business Media LLC
Date: 15-10-2018
DOI: 10.1038/S41467-018-05892-0
Abstract: The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype ( p = 7.18 × 10 −4 ) or temporal stage ( p = 3.96 × 10 −5 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.
Publisher: Elsevier BV
Date: 07-2022
Location: Italy
No related grants have been discovered for Giorgio Giulio Fumagalli.