amda 2 13 a major update for automated cross platform microarray data analysis

Microarray platforms require analytical pipelines with modules for data pre-processing including data normalization, statistical analysis for identification of differentially expressed genes, cluster analysis, and functional annotation. We previously developed the Automated Microarray Data Analysis (AMDA, version 2.3.5) pipeline to process Affymetrix 3′ IVT GeneChips. The availability of newer technologies that demand open-source tools for microarray data analysis has impelled us to develop an updated multi-platform version, AMDA 2.13. It includes additional quality control metrics, annotation-driven (annotation grade of Affymetrix NetAffx) and signal-driven (Inter-Quartile Range) gene filtering, and approaches to experimental design. To enhance understanding of biological data, differentially expressed genes have been mapped into KEGG pathways. Finally, a more stable and user-friendly interface was designed to integrate the requirements for different platforms. AMDA 2.13 allows the analysis of Affymetrix..

Contribution of Rare and Low-Frequency Variants to Multiple Sclerosis Susceptibility in the Italian Continental Population

Genome-wide association studies identified over 200 risk loci for multiple sclerosis (MS) focusing on common variants, which account for about 50% of disease heritability. The goal of this study was to investigate whether low-frequency and rare functional variants, located in MS-established associated loci, may contribute to disease risk in a relatively homogeneous population, testing their cumulative effect (burden) with gene-wise tests. We sequenced 98 genes in 588 Italian patients with MS and 408 matched healthy controls (HCs). Variants were selected using different filtering criteria based on allelic frequency and in silico functional impacts. Genes showing a significant burden (n = 17) were sequenced in an independent cohort of 504 MS and 504 HC. The highest signal in both cohorts was observed for the disruptive variants (stop-gain, stop-loss, or splicing variants) located in EFCAB13, a gene coding for a protein of an unknown function (p < 10(-4)). Among these variants, the minor allele of a stop-gain variant showed a significantly higher frequency in MS versus HC in both sequenced cohorts (p = 0.0093 and p = 0.025), confirmed by a meta-analysis on a third independent cohort of 1298 MS and 1430 HC (p = 0.001) assayed with an SNP array. Real-time PCR on 14 heterozygous individuals for this variant did not evidence the presence of the stop-gain allele, suggesting a transcript degradation by non-sense mediated decay, supported by the evidence that the carriers of the stop-gain variant had a lower expression of this gene (p = 0.0184). In conclusion, we identified a novel low-frequency functional variant associated with MS susceptibility, suggesting the possible role of rare/low-frequency variants in MS as reported for other complex diseases

Response to interferon-beta treatment in multiple sclerosis patients: a genome-wide association study

Up to 50% of multiple sclerosis (MS) patients do not respond to interferon-beta (IFN-β) treatment and determination of response requires lengthy clinical follow-up of up to 2 years. Response predictive genetic markers would significantly improve disease management. We aimed to identify IFN-β treatment response genetic marker(s) by performing a two-stage genome-wide association study (GWAS). The GWAS was carried out using data from 151 Australian MS patients from the ANZgene/WTCCC2 MS susceptibility GWAS (responder (R)=51, intermediate responders=24 and non-responders (NR)=76). Of the single-nucleotide polymorphisms (SNP) that were validated in an independent group of 479 IFN-β-treated MS patients from Australia, Spain and Italy (R=273 and NR=206), eight showed evidence of association with treatment response. Among the replicated associations, the strongest was observed for FHIT (Fragile Histidine Triad; combined P-value 6.74 × 10−6) and followed by variants in GAPVD1 (GTPase activating protein and VPS9 domains 1; combined P-value 5.83 × 10−5) and near ZNF697 (combined P-value 8.15 × 10−5)

Involvement of NINJ2 Protein in Inflammation and Blood–Brain Barrier Transmigration of Monocytes in Multiple Sclerosis

Multiple sclerosis (MS) is an inflammatory neurodegenerative disorder of the central nervous system (CNS). The migration of immune cells into the CNS is essential for its development, and plasma membrane molecules play an important role in triggering and maintaining the inflammation. We previously identified ninjurin2, a plasma membrane protein encoded by NINJ2 gene, as involved in the occurrence of relapse under Interferon-β treatment in MS patients. The aim of the present study was to investigate the involvement of NINJ2 in inflammatory conditions and in the migration of monocytes through the blood–brain barrier (BBB). We observed that NINJ2 is downregulated in monocytes and in THP-1 cells after stimulation with the pro-inflammatory cytokine LPS, while in hCMEC/D3 cells, which represent a surrogate of the BBB, LPS stimulation increases its expression. We set up a transmigration assay using an hCMEC/D3 transwell-based model, finding a higher transmigration rate of monocytes from MS subjects compared to healthy controls (HCs) in the case of an activated hCMEC/D3 monolayer. Moreover, a positive correlation between NINJ2 expression in monocytes and monocyte migration rate was observed. Overall, our results suggest that ninjurin2 could be involved in the transmigration of immune cells into the CNS in pro-inflammatory conditions. Further experiments are needed to elucidate the exact molecular mechanisms

Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach

A personalized approach is strongly advocated for treatment selection in Multiple Sclerosis patients due to the high number of available drugs. Machine learning methods proved to be valuable tools in the context of precision medicine. In the present work, we applied machine learning methods to identify a combined clinical and genetic signature of response to fingolimod that could support the prediction of drug response. Two cohorts of fingolimod-treated patients from Italy and France were enrolled and divided into training, validation, and test set. Random forest training and robust feature selection were performed in the first two sets respectively, and the independent test set was used to evaluate model performance. A genetic-only model and a combined clinical–genetic model were obtained. Overall, 381 patients were classified according to the NEDA-3 criterion at 2 years; we identified a genetic model, including 123 SNPs, that was able to predict fingolimod response with an AUROC= 0.65 in the independent test set. When combining clinical data, the model accuracy increased to an AUROC= 0.71. Integrating clinical and genetic data by means of machine learning methods can help in the prediction of response to fingolimod, even though further studies are required to definitely extend this approach to clinical application

Inverse correlation of genetic risk score with age at onset in bout-onset and progressive-onset multiple sclerosis

We correlated the weighted genetic risk score measured using 107 established susceptibility variants for multiple sclerosis (MS) with the age at onset in bout-onset (BOMS, n=906) and progressive-onset MS Italian patients (PrMS) (n=544). We observed an opposite relationship in the two disease courses: a higher weighted genetic risk score was associated with an earlier age at onset in BOMS (rho= -0.1; p=5 7 10(-3)) and a later age at onset in PrMS cases (rho=0.07; p=0.15) (p of difference of regression=1.4 7 10(-2)). These findings suggest that established MS risk variants anticipate the onset of the inflammatory phase, while they have no impact on, or even delay, the onset of the progressive phase

Clinical and pathological findings in neurolymphomatosis: Preliminary association with gene expression profiles in sural nerves

Although inflammation appears to play a role in neurolymphomatosis (NL), the mechanisms leading to degeneration in the peripheral nervous system are poorly understood. The purpose of this exploratory study was to identify molecular pathways underlying NL pathogenesis, combining clinical and neuropathological investigation with gene expression (GE) studies. We characterized the clinical and pathological features of eight patients with NL. We further analysed GE changes in sural nerve biopsies obtained from a subgroup of NL patients (n=3) and thirteen patients with inflammatory neuropathies as neuropathic controls. Based on the neuropathic symptoms and signs, NL patients were classified into three forms of neuropathy: chronic symmetrical sensorimotor polyneuropathy (SMPN, n=3), multiple mononeuropathy (MN, n=4) and acute motor-sensory axonal neuropathy (AMSAN, n=1). Predominantly diffuse malignant cells infiltration of epineurium was present in chronic SMPN, whereas endoneurial perivascular cells invasion was observed in MN. In contrast, diffuse endoneurium malignant cells localization occurred in AMSAN. We identified alterations in the expression of 1266 genes, with 115 up-regulated and 1151 down-regulated genes, which were mainly associated with ribosomal proteins (RP) and olfactory receptors (OR) signaling pathways, respectively. Among the top up-regulated genes were actin alpha 1 skeletal muscle (ACTA1) and desmin (DES). Similarly, in NL nerves ACTA1, DES and several RPs were highly expressed, associated with endothelial cells and pericytes abnormalities. Peripheral nerve involvement may be due to conversion towards a more aggressive phenotype, potentially explaining the poor prognosis. The candidate genes reported in this study may be a source of clinical biomarkers for NL

MOESM1 of Laser capture microdissection for transcriptomic profiles in human skin biopsies

Additional file 1: Table S1. Summary of characteristics of subjects and skin components. Quality parameters on extracted RNA (RIN and DV200), RNA input amount used for library preparation, RNA concentration of pre-pooled samples, amount of hybridized cDNA, final concentration of libraries and million of reads sequenced for sample are shown. Subjects are indicated by the sample ID. Samples from number 1 to 8 were patients and from number 9 to 14 were healthy controls. Samples pooled together are indicated by the same capital letter (from A to O) in the pool row. The samples that did not reach the requested amount for hybridization were excluded from the experiment (marked as “X”)