Ex Vivo and In Vitro Proteomic Approach to Elucidate the Relevance of IL-4 and IL-10 in Intervertebral Disc Pathophysiology.

Background: This study investigates the native presence and potential anabolic effects of interleukin (IL)-4 and IL-10 in the human intervertebral disc (IVD).

Methods: Human nucleus pulposus (NP) cells cultured in 3D from trauma and degenerate IVDs and NP explants were stimulated with 10 ng/mL IL-4, IL-10, or each in combination with 1 ng/mL IL-1β stimulation. The role of IL-4 and IL-10 in the IVD was evaluated using immunohistochemistry, gene expression, and Luminex multiplex immunoassay proteomics (73 secreted) and phosphoproteomics (21 phosphorylated proteins).

Comparative toxicological assessment of cigarettes and new category products via an multiplex proteomics platform.

Cigarette smoking is a risk factor for several diseases such as cancer, cardiovascular disease (CVD), and chronic obstructive pulmonary diseases (COPD), however, the underlying mechanisms are not fully understood. Alternative nicotine products with reduced risk potential (RRPs) including tobacco heating products (THPs), and e-cigarettes have recently emerged as viable alternatives to cigarettes that may contribute to the overall strategy of tobacco harm reduction due to the significantly lower levels of toxicants in these products' emissions as compared to cigarette smoke. Assessing the effects of RRPs on biological responses is important to demonstrate the potential value of RRPs towards tobacco harm reduction.

Multiscale networks in multiple sclerosis.

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls.

Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

Background: Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.

Methods: We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells.

A Combination of Conformation-Specific RAF Inhibitors Overcome Drug Resistance Brought about by RAF Overexpression.

Cancer cells often adapt to targeted therapies, yet the molecular mechanisms underlying adaptive resistance remain only partially understood. Here, we explore a mechanism of RAS/RAF/MEK/ERK (MAPK) pathway reactivation through the upregulation of RAF isoform (RAFs) abundance. Using computational modeling and in vitro experiments, we show that the upregulation of RAFs changes the concentration range of paradoxical pathway activation upon treatment with conformation-specific RAF inhibitors.

Α Humanized RANKL Transgenic Mouse Model of Progestin-Induced Mammary Carcinogenesis for Evaluation of Novel Therapeutics.

Receptor activator of nuclear factor-κB ligand (RANKL) is critically involved in mammary gland pathophysiology, while its pharmaceutical inhibition is being currently investigated in breast cancer. Herein, we investigated whether the overexpression of human RANKL in transgenic mice affects hormone-induced mammary carcinogenesis, and evaluated the efficacy of anti-RANKL treatments, such as OPG-Fc targeting both human and mouse RANKL or Denosumab against human RANKL. We established novel MPA/DMBA-driven mammary carcinogenesis models in TgRANKL mice that express both human and mouse RANKL, as well as in humanized humTgRANKL mice expressing only human RANKL, and compared them to MPA/DMBA-treated wild-type (WT) mice.

Targeted Proteomic Analysis of Patients with Ascending Thoracic Aortic Aneurysm.

Background: There is a need for clinical markers to aid in the detection of individuals at risk of harboring an ascending thoracic aneurysm (ATAA) or developing one in the future.

Objectives: To our knowledge, ATAA remains without a specific biomarker. This study aims to identify potential biomarkers for ATAA using targeted proteomic analysis.

Immuno-Modulatory Effects of Intervertebral Disc Cells.

Low back pain is a highly prevalent, chronic, and costly medical condition predominantly triggered by intervertebral disc degeneration (IDD). IDD is often caused by structural and biochemical changes in intervertebral discs (IVD) that prompt a pathologic shift from an anabolic to catabolic state, affecting extracellular matrix (ECM) production, enzyme generation, cytokine and chemokine production, neurotrophic and angiogenic factor production. The IVD is an immune-privileged organ.

Lysophosphatidic Acid Is a Proinflammatory Stimulus of Renal Tubular Epithelial Cells.

Chronic kidney disease (CKD) refers to a spectrum of diseases defined by renal fibrosis, permanent alterations in kidney structure, and low glomerular-filtration rate. Prolonged epithelial-tubular damage involves a series of changes that eventually lead to CKD, highlighting the importance of tubular epithelial cells in this process. Lysophosphatidic acid (LPA) is a bioactive lipid that signals mainly through its six cognate LPA receptors and is implicated in several chronic inflammatory pathological conditions.

Regulatory network-based model to simulate the biochemical regulation of chondrocytes in healthy and osteoarthritic environments.

In osteoarthritis (OA), chondrocyte metabolism dysregulation increases relative catabolic activity, which leads to cartilage degradation. To enable the semiquantitative interpretation of the intricate mechanisms of OA progression, we propose a network-based model at the chondrocyte level that incorporates the complex ways in which inflammatory factors affect structural protein and protease expression and nociceptive signals. Understanding such interactions will leverage the identification of new potential therapeutic targets that could improve current pharmacological treatments.

A network-based computational and experimental framework for repurposing compounds toward the treatment of non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is among the most common liver pathologies, however, none approved condition-specific therapy yet exists. The present study introduces a drug repositioning (DR) approach that combines steatosis models with a network-based computational platform, constructed upon genomic data from diseased liver biopsies and compound-treated cell lines, to propose effectively repositioned therapeutic compounds. The introduced approach screened 20'000 compounds, while complementary and proteomic assays were developed to test the efficacy of the 46 predictions.

Epicardial Adipocyte-derived TNF-α Modulates Local Inflammation in Patients with Advanced Coronary Artery Disease.

Background: Epicardial Adipose Tissue (EAT) surrounds the epicardium and can mediate harmful effects related to Coronary Artery Disease (CAD).

Objective: We explored the regional differences between adipose stores surrounding diseased and non-diseased segments of coronary arteries in patients with advanced CAD.

Methods: We enrolled 32 patients with known CAD who underwent coronary artery bypass graft (CABG) surgery.

Stress-induced inflammation evoked by immunogenic cell death is blunted by the IRE1α kinase inhibitor KIRA6 through HSP60 targeting.

Mounting evidence indicates that immunogenic therapies engaging the unfolded protein response (UPR) following endoplasmic reticulum (ER) stress favor proficient cancer cell-immune interactions, by stimulating the release of immunomodulatory/proinflammatory factors by stressed or dying cancer cells. UPR-driven transcription of proinflammatory cytokines/chemokines exert beneficial or detrimental effects on tumor growth and antitumor immunity, but the cell-autonomous machinery governing the cancer cell inflammatory output in response to immunogenic therapies remains poorly defined. Here, we profiled the transcriptome of cancer cells responding to immunogenic or weakly immunogenic treatments.

Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis.

Background: Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects.

Methods: Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments.

Cadherin-11, Sparc-related modular calcium binding protein-2, and Pigment epithelium-derived factor are promising non-invasive biomarkers of kidney fibrosis.

Kidney fibrosis constitutes the shared final pathway of nearly all chronic nephropathies, but biomarkers for the non-invasive assessment of kidney fibrosis are currently not available. To address this, we characterize five candidate biomarkers of kidney fibrosis: Cadherin-11 (CDH11), Sparc-related modular calcium binding protein-2 (SMOC2), Pigment epithelium-derived factor (PEDF), Matrix-Gla protein, and Thrombospondin-2. Gene expression profiles in single-cell and single-nucleus RNA-sequencing (sc/snRNA-seq) datasets from rodent models of fibrosis and human chronic kidney disease (CKD) were explored, and Luminex-based assays for each biomarker were developed.

A Multi-Omics Analysis of Metastatic Melanoma Identifies a Germinal Center-Like Tumor Microenvironment in HLA-DR-Positive Tumor Areas.

The emergence of immune checkpoint inhibitors has dramatically changed the therapeutic landscape for patients with advanced melanoma. However, relatively low response rates and a high incidence of severe immune-related adverse events have prompted the search for predictive biomarkers. A positive predictive value has been attributed to the aberrant expression of Human Leukocyte Antigen-DR (HLA-DR) by melanoma cells, but it remains unknown why this is the case.

Accurate SARS-CoV-2 seroprevalence surveys require robust multi-antigen assays.

There is a plethora of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) serological tests based either on nucleocapsid phosphoprotein (N), S1-subunit of spike glycoprotein (S1) or receptor binding domain (RBD). Although these single-antigen based tests demonstrate high clinical performance, there is growing evidence regarding their limitations in epidemiological serosurveys. To address this, we developed a Luminex-based multiplex immunoassay that detects total antibodies (IgG/IgM/IgA) against the N, S1 and RBD antigens and used it to compare antibody responses in 1225 blood donors across Greece.

Multiscale Regulation of the Intervertebral Disc: Achievements in Experimental, In Silico, and Regenerative Research.

Intervertebral disc (IVD) degeneration is a major risk factor of low back pain. It is defined by a progressive loss of the IVD structure and functionality, leading to severe impairments with restricted treatment options due to the highly demanding mechanical exposure of the IVD. Degenerative changes in the IVD usually increase with age but at an accelerated rate in some individuals.

A Novel Multiplex Based Platform for Osteoarthritis Drug Candidate Evaluation.

Osteoarthritis (OA) is characterized by irreversible cartilage degradation with very limited therapeutic interventions. Drug candidates targeted at prototypic players had limited success until now and systems based approaches might be necessary. Consequently, drug evaluation platforms should consider the biological complexity looking beyond well-known contributors of OA.

COMBSecretomics: A pragmatic methodological framework for higher-order drug combination analysis using secretomics.

Multi drug treatments are increasingly used in the clinic to combat complex and co-occurring diseases. However, most drug combination discovery efforts today are mainly focused on anticancer therapy and rarely examine the potential of using more than two drugs simultaneously. Moreover, there is currently no reported methodology for performing second- and higher-order drug combination analysis of secretomic patterns, meaning protein concentration profiles released by the cells.

A Functional Landscape of CKD Entities From Public Transcriptomic Data.

Introduction: To develop effective therapies and identify novel early biomarkers for chronic kidney disease, an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in chronic kidney disease (CKD) origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for 9 kidney disease entities that account for most of CKD worldwide.

A Deep Learning Framework for Predicting Response to Therapy in Cancer.

A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks.

Multi-tissue network analysis for drug prioritization in knee osteoarthritis.

Knee osteoarthritis (OA) is a joint disease that affects several tissues: cartilage, synovium, meniscus and subchondral bone. The pathophysiology of this complex disease is still not completely understood and existing pharmaceutical strategies are limited to pain relief treatments. Therefore, a computational method was developed considering the diverse mechanisms and the multi-tissue nature of OA in order to suggest pharmaceutical compounds.