Inflammation is part of the organism’s response to deleterious stimuli, such as pathogens, damaged cells, or irritants. Macrophages orchestrate the inflammatory response obtaining different activation phenotypes broadly defined as M1 (pro-inflammatory) or M2 (homeostatic) phenotypes, which contribute to pathogen elimination or disease pathogenesis. The type and magnitude of the response of macrophages are shaped by endogenous and exogenous factors and can be affected by nutrients or therapeutic agents. Multiple studies have shown that natural products possess immunomodulatory properties and that marine algae contain products with such action. We have previously shown that disulfides isolated from Dictyopteris membranacea suppress nitric oxide (NO) production from activated macrophages, suggesting potential anti-inflammatory actions. In this study, we investigated the anti-inflammatory mechanism of action of bis(5-methylthio-3-oxo-undecyl) disulfide (1), 5-methylthio-1-(3-oxo-undecyl) disulfanylundecan-3-one (2) and 3-hexyl-4,5-dithiocycloheptanone (3). Our results showed that all three compounds inhibited M1 activation of macrophages by down regulating the production of pro-inflammatory cytokines TNFα, IL-6 and IL-12, suppressed the expression of the NO converting enzyme iNOS, and enhanced expression of the M2 activation markers Arginase1 and MRC1. Moreover, disulfides 1 and 2 suppressed the expression of glucose transporters GLUT1 and GLUT3, suggesting that compounds 1 and 2 may affect cell metabolism. We showed that this was due to AKT/MAPK/ERK signaling pathway modulation and specifically by elevated AKT phosphorylation and MAPK/ERK signal transduction reduction. Hence, disulfides 1–3 can be considered as potent candidates for the development of novel anti-inflammatory molecules with homeostatic properties.

Malignant gliomas constitute a complex disease phenotype that demands optimum decision-making as they are highly heterogeneous. Such inter-individual variability also renders optimum patient stratification extremely difficult. microRNA (hsa-miR-20a, hsa-miR-21, hsa-miR-21) expression levels were determined by RT-qPCR, upon FFPE tissue sample collection of glioblastoma multiforme patients (n = 37). In silico validation was then performed through discriminant analysis. Immunohistochemistry images from biopsy material were utilized by a hybrid deep learning system to further cross validate the distinctive capability of patient risk groups. Our standard-of-care treated patient cohort demonstrates no age- or sex- dependence. The expression values of the 3-miRNA signature between the low- (OS > 12 months) and high-risk (OS < 12 months) groups yield a p-value of 12 months) vs. high-risk (OS < 12 months) glioblastoma multiforme patients. Our 3-microRNA signature (hsa-miR-20a, hsa-miR-21, hsa-miR-10a) may further empower glioblastoma multiforme prognostic evaluation in clinical practice and enrich drug repurposing pipelines.

The accumulation of cell biomass is associated with dramatically increased bioenergetic and biosynthetic demand. Metabolic reprogramming, once thought as an epiphenomenon, currently relates to disease progression, also in response to extracellular fate-decisive signals. Glioblastoma multiforme patients often suffer misdiagnosis, short survival time, low quality of life, and poor disease management options. Today, tumor genetic testing and histological analysis guide diagnosis and treatment. We and others appreciate that metabolites complement translational biomarkers and molecular signatures in disease profiling and phenotyping. Herein, we coupled a mixed-methods content analysis to a mass spectrometry-based untargeted metabolomic analysis on plasma samples from glioblastoma multiforme patients to delineate the role of metabolic remodeling in biological plasticity and, hence, disease severity. Following data processing and analysis, we established a bioenergetic profile coordinated by the mitochondrial function and redox state, lipids, and energy substrates. Our findings show that epigenetic modulators are key players in glioblastoma multiforme cell metabolism, in particular when microRNAs are considered. We propose that biological plasticity in glioblastoma multiforme is a mechanism of adaptation and resistance to treatment which is eloquently revealed by bioenergetics.

Background and Objective: Lung cancer, the second most prevalent cancer globally, poses significant challenges in early detection and prognostic assessment. Despite advancements in targeted therapies and immunotherapy, the timely identification of relapse remains elusive. Blood-based liquid biopsy biomarkers, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating-free RNAs (cfRNAs), and extracellular vesicles (EVs)/exosomes, offer promise for non-invasive monitoring. Methods: We employ a comprehensive approach integrating miRNA/lncRNA/metabolomic datasets, following a mixed-methods content analysis, to identify candidate biomarkers in NSCLC. NSCLC-associated miRNA/gene/lncRNA associations were linked to in silico-derived molecular pathways. Results: For data validation, mass spectrometry-based untargeted metabolomics of plasma EVs highlighted miRNA/lncRNA/metabotypes, linking “glycerophospholipid metabolism” to lncRNA H19 and “alanine, aspartate and glutamate metabolism” to miR-29a-3p. Prognostic significance was established for miR-29a-3p, showing lower expression in NSCLC patients with disease progression compared to stable disease (p = 0.004). Kaplan–Meier survival analysis indicated that patients with miR-29a-3p under-expression had significantly shorter overall survival (OS) (p = 0.038). Despite the expression of lncRNA H19 in plasma EVs being undetected, its expression in plasma cfRNAs correlated significantly with disease progression (p = 0.035). Conclusions: Herein, we showcase the potential of plasma EV-derived miR-29a-3p as a prognostic biomarker and underscore the intricate interplay of miRNAs, lncRNAs, and metabolites in NSCLC biology. Our findings offer new insights and avenues for further exploration, contributing to the ongoing quest for effective biomarkers in early-stage NSCLC.

Drug repurposing is a state-of-the-art pipeline that expands pharmaceutical research and development portfolia. Despite computational tools are rapidly advancing, they predominantly rely on the examination of textual (1D) data. Herein, we present Cloudscreen ^®, a "one-stop-shop" platform for drug repurposing, which seamlessly integrates 3D (structural data through predictive protein-ligand modeling) and 1D data alongside state-of-the-art machine learning algorithms. Our platform leverages a knowledge graph database, curated from diverse sources, including publicly available repositories and databases coupled to in-house computations. We are harnessing the power of artificial intelligence to predict and assess the efficacy and safety of repurposed biomolecules for novel therapeutic indications while interrogating the human variome. Moreover, Cloudscreen ^® expands such prediction capabilities based on AlphaFold models, ADMETox and pharmacogenomics (emphasis on missense variants). Cloudscreen ^® is a powerful tool that results from the synergy of wet- and dry-lab validated datasets and hence, provides “one-stop-shop” predictive insights into the uncharted therapeutic possibilities for repurposed drugs.