Background: Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course. Methods: We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data. Results: We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient's risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922). Conclusions: The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient's risk scores can be used in clinical settings.

Hypomorphic mutations in DNA-methyltransferase DNMT3B cause majority of the rare disorder Immunodeficiency, Centromere instability and Facial anomalies syndrome cases (ICF1). By unspecified mechanisms, mutant-DNMT3B interferes with lymphoid-specific pathways resulting in immune response defects. Interestingly, recent findings report that DNMT3B shapes intragenic CpG-methylation of highly-transcribed genes. However, how the DNMT3B-dependent epigenetic network modulates transcription and whether ICF1-specific mutations impair this process remains unknown. We performed a transcriptomic and epigenomic study in patient-derived B-cell lines to investigate the genome-scale effects of DNMT3B dysfunction. We highlighted that altered intragenic CpG-methylation impairs multiple aspects of transcriptional regulation, like alternative TSS usage, antisense transcription and exon splicing. These defects preferentially associate with changes of intragenic H3K4me3 and at lesser extent of H3K27me3 and H3K36me3. In addition, we highlighted a novel DNMT3B activity in modulating the self-regulatory circuit of sense-antisense pairs and the exon skipping during alternative splicing, through interacting with RNA molecules. Strikingly, altered transcription affects disease relevant genes, as for instance the memory-B cell marker CD27 and PTPRC genes, providing us with biological insights into the ICF1-syndrome pathogenesis. Our genome-scale approach sheds light on the mechanisms still poorly understood of the intragenic function of DNMT3B and DNA methylation in gene expression regulation.

The upcoming launch of the next generation of hyperspectral satellites (PRISMA, EnMap, HyspIRI, etc.) will meet the increasing demand for the availability/accessibility of hyperspectral information on agricultural land use from the agriculture community. To this purpose, algorithms for the classification of remotely sensed images are here considered for agricultural monitoring of cultivated area, exploiting remotely sensed high spectral resolution images. Classification is accomplished by procedures based on discriminant analysis tools that well suit hyperspectrality, circumventing what in statistics is called "the curse of dimensionality". As a byproduct of classification, a full assessment of the spectral bands of the sensor is obtained, ranking them with the purpose of understanding their role in segmentation and classification. The methodology has been validated on two independent image datasets gathered by the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) sensor for which ground validations were available. A comparison with the popular multiclass SVM (Support Vector Machines) classifier is also presented. Results show that a good classification (minimum global success rate 95% through all experiments) is achieved by using the 10 spectral bands selected as the most discriminant by the proposed procedure; moreover, it also appears that nonparametric techniques generally outperform parametric ones. The present study confirms that the new generation of hyperspectral satellite data like PRISMA can ripen an end-user application for agricultural land-use of cultivated area.