Life expectancy has gradually grown over the last century. This has deeply affected healthcare costs, since the growth of an aging population is correlated to the increasing burden of chronic diseases. This represents the interesting challenge of how to manage patients with chronic diseases in order to improve health care budgets. Effective primary prevention could represent a promising route. To this end, precision, together with personalized medicine, are useful instruments in order to investigate pathological processes before the appearance of clinical symptoms and to guide physicians to choose a targeted therapy to manage the patient. Cardiovascular and neurodegenerative diseases represent suitable models for taking full advantage of precision medicine technologies applied to all stages of disease development. The availability of high technology incorporating artificial intelligence and advancement progress made in the field of biomedical research have been substantial to understand how genes, epigenetic modifications, aging, nutrition, drugs, microbiome and other environmental factors can impact health and chronic disorders. The aim of the present review is to address how precision and personalized medicine can bring greater clarity to the clinical and biological complexity of these types of disorders associated with high mortality, involving tremendous health care costs, by describing in detail the methods that can be applied. This might offer precious tools for preventive strategies and possible clues on the evolution of the disease and could help in predicting morbidity, mortality and detecting chronic disease indicators much earlier in the disease course. This, of course, will have a major effect on both improving the quality of care and quality of life of the patients and reducing time efforts and healthcare costs.

Melanoma aggressiveness determines its growth and metastatic potential. This study aimed at identifying new molecular pathways controlling melanoma cell malignancy. Ten metastatic melanoma cell lines were characterized by their proliferation, migration and invasion capabilities. The most representative cells were also characterized by spheroid formation assay, gene- and protein- expression profiling as well as cytokines secretion and the most relevant pathways identified through bioinformatic analysis were tested by in silico transcriptomic validation on datasets generated from biopsies specimens of melanoma patients. Further, matrix metalloproteases (MMPs) activity was tested by zymography assays and TNF-alpha role was validated by anti-TNF cell-treatment. An aggressiveness score (here named Melanoma AGgressiveness Score: MAGS) was calculated by measuring proliferation, migration, invasion and cell-doubling time in10human melanoma cell lines which were clustered in two distinct groups, according to the corresponding MAGS. SK-MEL-28 and A375 cell lines were selected as representative models for the less and the most aggressive phenotype, respectively. Gene-expression and protein expression data were collected for SK-MEL-28 and A375 cells by Illumina-, multiplex x-MAP-and mass-spectrometry technology. The collected data were subjected to an integrated Ingenuity Pathway Analysis, which highlighted that cytokine/chemokine secretion, as well as Cell-To-Cell Signaling and Interaction functions as well as matrix metalloproteases activity were significantly different in these two cell types. The key role of these pathways was then confirmed by functional validation. TNF role was confirmed by exposing cells to the anti-TNF Infliximab antibody. Upon such treatment melanoma cells aggressiveness was strongly reduced. Metalloproteases activity was assayed, and their role was confirmed by comparing transcriptomic data from cutaneous melanoma patients (n = 45) and benign nevi (n = 18). Inflammatory signals such as TNF and MMP-2 activity are key intrinsic players to determine melanoma cells aggressiveness suggesting new venue sin the identification of novel molecular targets with potential therapeutic relevance.

Pharmacological inhibition of DOT1L blocks estrogen receptor signaling in breast cancer.

Platelet activation triggers thrombus formation in physiological and pathological conditions, such as acute coronary syndromes. Current therapies still fail to prevent thrombotic events in numerous patients, indicating that the mechanisms modulating platelet response during activation need to be clarified. The evidence that platelets are capable of de novo protein synthesis in response to stimuli raised the issue of how megakaryocyte-derived mRNAs are regulated in these anucleate cell fragments. Proteogenomics was applied here to investigate this phenomeon in platelets activated in vitro with Collagen or Thrombin Receptor Activating Peptide. Combining proteomics and transcriptomics allowed in depth platelet proteome characterization, revealing a significant effect of either stimulus on proteome composition. In silico analysis revealed the presence of resident immature RNAs in resting platelets, characterized by retained introns, while unbiased proteogenomics correlated intron removal by RNA splicing with changes on proteome composition upon activation. This allowed identification of a set of transcripts undergoing maturation by intron removal during activation and resulting in accumulation of the corresponding peptides at exon-exon junctions. These results indicate that RNA splicing events occur in platelets during activation and that maturation of specific pre-mRNAs is part of the activation cascade, contributing to a dynamic fine-tuning of the transcriptome.

The RNA-binding protein Argonaute 2 (AGO2) is a key effector of RNA-silencing pathways It exerts a pivotal role in microRNA maturation and activity and can modulate chromatin remodeling, transcriptional gene regulation and RNA splicing. Estrogen receptor beta (ERβ) is endowed with oncosuppressive activities, antagonizing hormone-induced carcinogenesis and inhibiting growth and oncogenic functions in luminal-like breast cancers (BCs), where its expression correlates with a better prognosis of the disease. Applying interaction proteomics coupled to mass spectrometry to characterize nuclear factors cooperating with ERβ in gene regulation, we identify AGO2 as a novel partner of ERβ in human BC cells. ERβ–AGO2 association was confirmed in vitro and in vivo in both the nucleus and cytoplasm and is shown to be RNA-mediated. ChIP-Seq demonstrates AGO2 association with a large number of ERβ binding sites, and total and nascent RNA-Seq in ERβ + vs ERβ − cells, and before and after AGO2 knock-down in ERβ + cells, reveals a widespread involvement of this factor in ERβ-mediated regulation of gene transcription rate and RNA splicing. Moreover, isolation and sequencing by RIP-Seq of ERβ-associated long and small RNAs in the cytoplasm suggests involvement of the nuclear receptor in RISC loading, indicating that it may also be able to directly control mRNA translation efficiency and stability. These results demonstrate that AGO2 can act as a pleiotropic functional partner of ERβ, indicating that both factors are endowed with multiple roles in the control of key cellular functions.

Estrogen Receptor alpha (ERα) is a ligand-inducible transcription factor that mediates estrogen signaling in hormone-responsive cells, where it controls key cellular functions by assembling in gene-regulatory multiprotein complexes. For this reason, interaction proteomics has been shown to represent a useful tool to investigate the molecular mechanisms underlying ERα action in target cells. RNAs have emerged as bridging molecules, involved in both assembly and activity of transcription regulatory protein complexes. By applying Tandem Affinity Purification (TAP) coupled to mass spectrometry (MS) before and after RNase digestion in vitro, we generated a dataset of nuclear ERα molecular partners whose association with the receptor involves RNAs. These data provide a useful resource to elucidate the combined role of nuclear RNAs and the proteins identified here in ERα signaling to the genome in breast cancer and other cell types.