Genome-wide interaction study of brain beta-amyloid burden and cognitive impairment in Alzheimer’s disease

The lack of strong association between brain beta-amyloid deposition and cognitive impairment has been a challenge for the Alzheimer’s disease (AD) field. Although beta-amyloid is necessary for the pathologic diagnosis of AD, it is not sufficient to make the pathologic diagnosis or cause dementia. We sought to identify the genetic modifiers of the relation between cortical beta-amyloid burden (measured using [18F]Florbetapir-PET) and cognitive dysfunction (measured using ADAS-cog) by conducting a genome-wide interaction study on baseline data from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) phases GO/2 (n=678). Near genome-wide significant interaction effect was observed for rs73069071 within the IAPP (amylin) and SLCO1A2 genes (P=6.2×10−8). Congruent results were found using data from participants followed up from ADNI-1 (Pone-tailed=0.028, n=165). Meta-analysis across ADNI-GO/2 and ADNI-1 revealed a genome-wide significant interaction effect (P=1.1×10−8). Our results were further supported by similar interaction effects on temporal lobe cortical thickness (whole-brain voxelwise analysis: family-wise error corrected P=0.013) and longitudinal changes in ADAS-cog score and left middle temporal thickness and amygdalar volume (Pone-tailed=0.026, 0.019, and 0.003, respectively). Using postmortem beta-amyloid immunohistochemistry data from 243 AD participants in the Religious Orders Study and Memory and Aging Project, we also observed similar rs73069071-by-beta-amyloid deposition interaction effect on global cognitive function (Pone-tailed=0.005). Our findings provide insight into the complexity of the relationship between beta-amyloid burden and AD-related cognitive impairment. Although functional studies are required to elucidate the role of rs73069071 in AD pathophysiology, our results support the recently growing evidence on the role of amylin in AD

Neuropathological correlates and genetic architecture of microglial activation in elderly human brain

The consequences of microglial activation in the aging human brain remain unknown. This study quantified microglial morphology and density in the elderly human brain to show that cortical microglial activation strongly associates with AD pathogenesis and may be an upstream contributor to cognitive decline via the accumulation of tau pathology

Classification algorithms with multi-modal data fusion could accurately distinguish neuromyelitis optica from multiple sclerosis

International audienceNeuromyelitis optica (NMO) exhibits substantial similarities to multiple sclerosis (MS) in clinical manifestations and imaging results and has long been considered a variant of MS. With the advent of a specific biomarker in NMO, known as anti-aquaporin 4, this assumption has changed; however, the differential diagnosis remains challenging and it is still not clear whether a combination of neuroimaging and clinical data could be used to aid clinical decision-making. Computer-aided diagnosis is a rapidly evolving process that holds great promise to facilitate objective differential diagnoses of disorders that show similar presentations. In this study, we aimed to use a powerful method for multi-modal data fusion, known as a multi-kernel learning and performed automatic diagnosis of subjects. We included 30 patients with NMO, 25 patients with MS and 35 healthy volunteers and performed multi-modal imaging with T1-weighted high resolution scans, diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). In addition, subjects underwent clinical examinations and cogni-tive assessments. We included 18 a priori predictors from neuroimaging, clinical and cognitive measures in the initial model. We used 10-fold cross-validation to learn the importance of each modality, train and finally test the model performance. The mean accuracy in differentiating between MS and NMO was 88%, where visible white matter lesion load, normal appearing white matter (DTI) and functional connectivity had the most important contributions to the final classification. In a multi-class classification problem we distinguished between all of 3 groups (MS, NMO and healthy controls) with an average accuracy of 84%. In this classification, visible white matter lesion load, functional connectivity, and cognitive scores were the 3 most important modalities. Our work provides preliminary evidence that computational tools can be used to help make an objective differential diagnosis of NMO and MS

A pharmacogenetic study implicates NINJ2 in the response to Interferon-ß in multiple sclerosis

[Background] Multiple sclerosis (MS) is a disease in which biomarker identification is fundamental to predict response to treatments and to deliver the optimal drug to patients. We previously found an association between rs7298096, a polymorphism upstream to the NINJ2 gene, and the 4-year response to interferon-β (IFNβ) treatment in MS patients.[Objectives] To analyse the association between rs7298096 and time to first relapse (TTFR) during IFNβ therapy in MS patients and to better investigate its functional role. [Methods] Survival analysis was applied in three MS cohorts from different countries (n = 1004). We also studied the role of the polymorphism on gene expression using GTEx portal and a luciferase assay. We interrogated GEO datasets to explore the relationship between NINJ2 expression, IFNβ and TTFR. [Results] Rs7298096AA patients show a shorter TTFR than rs7298096G-carriers (Pmeta-analysis = 3 × 10−4, hazard ratio = 1.41). Moreover, rs7298096AA is associated with a higher NINJ2 expression in blood (p = 7.0 × 10−6), which was confirmed in vitro (p = 0.009). Finally, NINJ2 expression is downregulated by IFNβ treatment and related to TTFR. [Conclusions] Rs7298096 could influence MS disease activity during IFNβ treatment by modulating NINJ2 expression in blood. The gene encodes for an adhesion molecule involved in inflammation and endothelial cells activation, supporting its role in MS

A multi-step genomic approach prioritized TBKBP1 gene as relevant for multiple sclerosis susceptibility

Background Over 200 genetic loci have been associated with multiple sclerosis (MS) explaining similar to 50% of its heritability, suggesting that additional mechanisms may account for the "missing heritability" phenomenon.Objective To analyze a large cohort of Italian individuals to identify markers associated with MS with potential functional impact in the disease.Methods We studied 2571 MS and 3234 healthy controls (HC) of continental Italian origin. Discovery phase included a genome wide association study (1727 MS, 2258 HC), with SNPs selected according to their association in the Italian cohort only or in a meta-analysis of signals with a cohort of European ancestry (4088 MS, 7144 HC). Top associated loci were then tested in two Italian cohorts through array-based genotyping (903 MS, 884 HC) and pool-based target sequencing (588 MS, 408 HC). Finally, functional prioritization through conditional eQTL and mQTL has been performed.Results Top associated signals overlap with already known MS loci on chromosomes 3 and 17. Three SNPs (rs4267364, rs8070463, rs67919208), all involved in the regulation of TBKBP1, were prioritized to be functionally relevant.Conclusions No evidence of novel signal of association with MS specific for the Italian continental population has been found; nevertheless, two MS loci seems to play a relevant role, raising the interest to further investigations for TBKBP1 gene