Comparative toxicological assessment of cigarettes and new category products via an in vitro 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. Here, we evaluated the inflammatory and signaling responses of human lung epithelial cells to aqueous aerosol extracts (AqE) generated from the 1R6F reference cigarette, the glo™ THP, and the Vype ePen 3.0 e-cigarette using multiplex analysis of 37 inflammatory and phosphoprotein markers. Cellular exposure to the different RRPs and 1R6F AqEs resulted in distinct response profiles with 1R6F being the most biologically active followed by glo™ and ePen 3.0. 1R6F activated stress-related and pro-survival markers c-JUN, CREB1, p38 MAPK and MEK1 and led to the release of IL-1α. glo™ activated MEK1 and decreased IL-1ß levels, whilst ePen 3.0 affected IL-1ß levels but had no effect on the signaling activity compared to untreated cells. Our results demonstrated the reduced biological effect of RRPs and suggest that targeted analysis of inflammatory and cell signaling mediators is a valuable tool for the routine assessment of RRPs.

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. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.

Protein kinase D drives the secretion of invasion mediators in triple-negative breast cancer cell lines.

The protein kinase D (PKD) family members regulate the fission of cargo vesicles at the Golgi complex and play a pro-oncogenic role in triple-negative breast cancer (TNBC). Whether PKD facilitates the secretion of tumor-promoting factors in TNBC, however, is still unknown. Using the pharmacological inhibition of PKD activity and siRNA-mediated depletion of PKD2 and PKD3, we identified the PKD-dependent secretome of the TNBC cell lines MDA-MB-231 and MDA-MB-468. Mass spectrometry-based proteomics and antibody-based assays revealed a significant downregulation of extracellular matrix related proteins and pro-invasive factors such as LIF, MMP-1, MMP-13, IL-11, M-CSF and GM-CSF in PKD-perturbed cells. Notably, secretion of these proteins in MDA-MB-231 cells was predominantly controlled by PKD2 and enhanced spheroid invasion. Consistently, PKD-dependent secretion of pro-invasive factors was more pronounced in metastatic TNBC cell lines. Our study thus uncovers a novel role of PKD2 in releasing a pro-invasive secretome.

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. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre. RESULTS: We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts. CONCLUSION: Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

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. Our results show that TgRANKL and WT mice have similar levels of susceptibility to mammary carcinogenesis, while OPG-Fc treatment restored mammary ductal density, and prevented ductal branching and the formation of neoplastic foci in both genotypes. humTgRANKL mice also developed MPA/DMBA-induced tumors with similar incidence and burden to those of WT and TgRANKL mice. The prophylactic treatment of humTgRANKL mice with Denosumab significantly prevented the rate of appearance of mammary tumors from 86.7% to 15.4% and the early stages of carcinogenesis, whereas therapeutic treatment did not lead to any significant attenuation of tumor incidence or tumor burden compared to control mice, suggesting the importance of RANKL primarily in the initial stages of tumorigenesis. Overall, we provide unique genetic tools for investigating the involvement of RANKL in breast carcinogenesis, and allow the preclinical evaluation of novel therapeutics that target hormone-related breast cancers.

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. Additionally, our data indicate that the signaling output upon loss or downregulation of one RAF isoform can be compensated by overexpression of other RAF isoforms. We furthermore demonstrate that, while single RAF inhibitors cannot efficiently inhibit ERK reactivation caused by RAF overexpression, a combination of two structurally distinct RAF inhibitors synergizes to robustly suppress pathway reactivation.

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. METHODS: In this study, 52 patients were divided into three groups depending on their ascending aorta diameter: 4.0-4.5 cm (N = 23), 4.6-5.0 cm (N = 20), and >5.0 cm (N = 9). A total of 30 controls were in-house populations ethnically matched to cases without known or visible ATAA-related symptoms and with no ATAA familial history. Before the debut of our study, all patients provided medical history and underwent physical examination. Diagnosis was confirmed by echocardiography and angio-computed tomography (CT) scans. Targeted-proteomic analysis was conducted to identify possible biomarkers for the diagnosis of ATAA. RESULTS: A Kruskal-Wallis test revealed that C-C motif chemokine ligand 5 (CCL5), defensin beta 1 (HBD1), intracellular adhesion molecule-1 (ICAM1), interleukin-8 (IL8), tumor necrosis factor alpha (TNFα) and transforming growth factor-beta 1 (TGFB1) expressions are significantly increased in ATAA patients in comparison to control subjects with physiological aorta diameter (p < 0.0001). The receiver-operating characteristic analysis showed that the area under the curve values for CCL5 (0.84), HBD1 (0.83) and ICAM1 (0.83) were superior to that of the other analyzed proteins. CONCLUSIONS: CCL5, HBD1 and ICAM1 are very promising biomarkers with satisfying sensitivity and specificity that could be helpful in stratifying risk for the development of ATAA. These biomarkers may assist in the diagnosis and follow-up of patients at risk of developing ATAA. This retrospective study is very encouraging; however, further in-depth studies may be worthwhile to investigate the role of these biomarkers in the pathogenesis of ATAA.

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. In this report, we have stimulated human proximal tubular epithelial cells (HKC-8) with LPA and 175 other possibly pathological stimuli, and simultaneously detected the levels of 27 intracellular phosphoproteins and 32 extracellular secreted molecules with multiplex ELISA. This quantification revealed a large amount of information concerning the signaling and the physiology of HKC-8 cells that can be extrapolated to other proximal tubular epithelial cells. LPA responses clustered with pro-inflammatory stimuli such as TNF and IL-1, promoting the phosphorylation of important inflammatory signaling hubs, including CREB1, ERK1, JUN, IκΒα, and MEK1, as well as the secretion of inflammatory factors of clinical relevance, including CCL2, CCL3, CXCL10, ICAM1, IL-6, and IL-8, most of them shown for the first time in proximal tubular epithelial cells. The identified LPA-induced signal-transduction pathways, which were pharmacologically validated, and the secretion of the inflammatory factors offer novel insights into the possible role of LPA in CKD pathogenesis.

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. However, during degeneration immune cells and inflammatory factors can infiltrate through defects in the cartilage endplate and annulus fibrosus fissures, further accelerating the catabolic environment. Remarkably, though, catabolic ECM disruption also occurs in the absence of immune cell infiltration, largely due to native disc cell production of catabolic enzymes and cytokines. An unbalanced metabolism could be induced by many different factors, including a harsh microenvironment, biomechanical cues, genetics, and infection. The complex, multifactorial nature of IDD brings the challenge of identifying key factors which initiate the degenerative cascade, eventually leading to back pain. These factors are often investigated through methods including animal models, 3D cell culture, bioreactors, and computational models. However, the crosstalk between the IVD, immune system, and shifted metabolism is frequently misconstrued, often with the assumption that the presence of cytokines and chemokines is synonymous to inflammation or an immune response, which is not true for the intact disc. Therefore, this review will tackle immunomodulatory and IVD cell roles in IDD, clarifying the differences between cellular involvements and implications for therapeutic development and assessing models used to explore inflammatory or catabolic IVD environments.

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. Our computational model arises from a combination of knowledge-based and data-driven approaches that includes in-depth analyses of evidence reported in the specialized literature and targeted network enrichment. We achieved a mechanistic network of molecular interactions that represent both biosynthetic, inflammatory and degradative chondrocyte activity. The network is calibrated against experimental data through a genetic algorithm, and 81% of the responses tested have a normalized root squared error lower than 0.15. The model captures chondrocyte-reported behaviors with 95% accuracy, and it correctly predicts the main outcomes of OA treatment based on blood-derived biologics. The proposed methodology allows us to model an optimal regulatory network that controls chondrocyte metabolism based on measurable soluble molecules. Further research should target the incorporation of mechanical signals.

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 in vitro 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 in silico approach screened 20'000 compounds, while complementary in vitro and proteomic assays were developed to test the efficacy of the 46 in silico predictions. This approach successfully identified six compounds, including the known anti-steatogenic drugs resveratrol and sirolimus. In short, gallamine triethiotide, diflorasone, fenoterol, and pralidoxime ameliorate steatosis similarly to resveratrol/sirolimus. The implementation holds great potential in reducing screening time in the early drug discovery stages and in delivering promising compounds for in vivo testing.

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. Inflammatory mediators were measured in EAT biopsies collected from a region of the Left Anterior Descending Artery (LAD) with severe stenosis (diseased segment) and without stenosis (non-diseased segment). RESULTS: Mean age was 64.3±11.1 years, and mean EAT thickness was 7.4±1.9 mm. Dyslipidemia was the most prevalent comorbidity (81% of the patients). Out of a total of 11 cytokines, resistin (p=0.039), matrix metallopeptidase 9 (MMP-9) (p=0.020), C-C motif chemokine ligand 5 (CCL-5) (p=0.021), and follistatin (p=0.038) were significantly increased in the diseased compared with the non-diseased EAT segments. Indexed tumor necrosis factor-alpha (TNF-α), defined as the diseased to non-diseased cytokine levels ratio, was significantly correlated with increased EAT thickness both in the whole cohort (p=0.043) and in a subpopulation of patients with dyslipidemia (p=0.009). Treatment with lipid-lowering agents significantly decreased indexed TNF-α levels (p=0.015). No significant alterations were observed in the circulating levels of these cytokines with respect to CAD-associated comorbidities. CONCLUSION: Perivascular EAT is a source of cytokine secretion in distinct areas surrounding the coronary arteries in patients with advanced CAD. Adipocyte-derived TNF-α is a prominent mediator of local inflammation.

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. Bioinformatics-driven pathway analysis indicated that immunogenic treatments instigated a NF-κB/AP-1-inflammatory stress response, which dissociated from both cell death and UPR. This stress-induced inflammation was specifically abolished by the IRE1α-kinase inhibitor KIRA6. Supernatants from immunogenic chemotherapy and KIRA6 co-treated cancer cells were deprived of proinflammatory/chemoattractant factors and failed to mobilize neutrophils and induce dendritic cell maturation. Furthermore, KIRA6 significantly reduced the in vivo vaccination potential of dying cancer cells responding to immunogenic chemotherapy. Mechanistically, we found that the anti-inflammatory effect of KIRA6 was still effective in IRE1α-deficient cells, indicating a hitherto unknown off-target effector of this IRE1α-kinase inhibitor. Generation of a KIRA6-clickable photoaffinity probe, mass spectrometry, and co-immunoprecipitation analysis identified cytosolic HSP60 as a KIRA6 off-target in the IKK-driven NF-κB pathway. In sum, our study unravels that HSP60 is a KIRA6-inhibitable upstream regulator of the NF-κB/AP-1-inflammatory stress responses evoked by immunogenic treatments. It also urges caution when interpreting the anti-inflammatory action of IRE1α chemical inhibitors.

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. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-ß, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a "healthy-like" status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies. RESULTS: Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-ß activated kinase 1 (TAK1) kinase, involved in Transforming growth factor ß-1 proprotein (TGF-ß), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. CONCLUSIONS: Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.

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. Plasma and urine biomarker levels were measured using independent prospective cohorts of CKD: the Boston Kidney Biopsy Cohort, a cohort of individuals with biopsy-confirmed semiquantitative assessment of kidney fibrosis, and the Seattle Kidney Study, a cohort of patients with common forms of CKD. Ordinal logistic regression and Cox proportional hazards regression models were used to test associations of biomarkers with interstitial fibrosis and tubular atrophy and progression to end-stage kidney disease and death, respectively. Sc/snRNA-seq data confirmed cell-specific expression of biomarker genes in fibroblasts. After multivariable adjustment, higher levels of plasma CDH11, SMOC2, and PEDF and urinary CDH11 and PEDF were significantly associated with increasing severity of interstitial fibrosis and tubular atrophy in the Boston Kidney Biopsy Cohort. In both cohorts, higher levels of plasma and urinary SMOC2 and urinary CDH11 were independently associated with progression to end-stage kidney disease. Higher levels of urinary PEDF associated with end-stage kidney disease in the Seattle Kidney Study, with a similar signal in the Boston Kidney Biopsy Cohort, although the latter narrowly missed statistical significance. Thus, we identified CDH11, SMOC2, and PEDF as promising non-invasive biomarkers of kidney fibrosis.

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. In this study, we have examined the microenvironment of HLA-DR positive metastatic melanoma samples using a multi-omics approach. First, using spatial, single-cell mapping by multiplexed immunohistochemistry, we found that the microenvironment of HLA-DR positive melanoma regions was enriched by professional antigen presenting cells, including classical dendritic cells and macrophages, while a more general cytotoxic T cell exhaustion phenotype was present in these regions. In parallel, transcriptomic analysis on micro dissected tissue from HLA-DR positive and HLA-DR negative areas showed increased IFNγ signaling, enhanced leukocyte adhesion and mononuclear cell proliferation in HLA-DR positive areas. Finally, multiplexed cytokine profiling identified an increased expression of germinal center cytokines CXCL12, CXCL13 and CCL19 in HLA-DR positive metastatic lesions, which, together with IFNγ and IL4 could serve as biomarkers to discriminate tumor samples containing HLA-DR overexpressing tumor cells from HLA-DR negative samples. Overall, this suggests that HLA-DR positive areas in melanoma attract the anti-tumor immune cell infiltration by creating a dystrophic germinal center-like microenvironment where an enhanced antigen presentation leads to an exhausted microenvironment, nevertheless representing a fertile ground for a better efficacy of anti-PD-1 inhibitors due to simultaneous higher levels of PD-1 in the immune cells and PD-L1 in the HLA-DR positive melanoma cells.

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. Seroprevalence based on single-antigen readouts was strongly influenced by both the antigen type and cut-off value and ranged widely [0.8% (95% CI 0.4-1.5%)-7.5% (95% CI 6.0-8.9%)]. A multi-antigen approach requiring partial agreement between RBD and N or S1 readouts (RBD&N|S1 rule) was less affected by cut-off selection, resulting in robust seroprevalence estimation [0.6% (95% CI 0.3-1.1%)-1.2% (95% CI 0.7-2.0%)] and accurate identification of seroconverted individuals.

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. To understand the initiation and progression of this disease, it is crucial to identify key top-down and bottom-up regulations' processes, across the cell, tissue, and organ levels, in health and disease. Owing to unremitting investigation of experimental research, the comprehension of detailed cell signaling pathways and their effect on matrix turnover significantly rose. Likewise, in silico research substantially contributed to a holistic understanding of spatiotemporal effects and complex, multifactorial interactions within the IVD. Together with important achievements in the research of biomaterials, manifold promising approaches for regenerative treatment options were presented over the last years. This review provides an integrative analysis of the current knowledge about (1) the multiscale function and regulation of the IVD in health and disease, (2) the possible regenerative strategies, and (3) the in silico models that shall eventually support the development of advanced therapies.

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. Here, we introduce COMBSecretomics (https://github.com/EffieChantzi/COMBSecretomics.git), the first pragmatic methodological framework designed to search exhaustively for second- and higher-order mixtures of candidate treatments that can modify, or even reverse malfunctioning secretomic patterns of human cells. This framework comes with two novel model-free combination analysis methods; a tailor-made generalization of the highest single agent principle and a data mining approach based on top-down hierarchical clustering. Quality control procedures to eliminate outliers and non-parametric statistics to quantify uncertainty in the results obtained are also included. COMBSecretomics is based on a standardized reproducible format and could be employed with any experimental platform that provides the required protein release data. Its practical use and functionality are demonstrated by means of a proof-of-principle pharmacological study related to cartilage degradation. COMBSecretomics is the first methodological framework reported to enable secretome-related second- and higher-order drug combination analysis. It could be used in drug discovery and development projects, clinical practice, as well as basic biological understanding of the largely unexplored changes in cell-cell communication that occurs due to disease and/or associated pharmacological treatment conditions.