Biology subjects

Alexopoulos, L. G.

Publications and source records attributed to Alexopoulos, L. G..

7 recordsLinked to original sources

bioRxiv2025

Understanding the physiological behaviour of disc cells in an in vitro imitation of the healthy and degenerated disc niche

INTRODUCTIONThe natural environment within the nucleus pulposus (NP) is hypoxic, acidic, low in nutrition and exerts a high osmotic-pressure. NP cells have adapted to this harsh environment, however, in vitro conditions often fail to recapitulate this environment. Hence, this study aimed to develop media to mimic the conditions of the native NP, with regards to pH, osmolality, glucose, combined with culture in physioxia (5% O2), and determine their effects upon human NP cells and tissue. METHODSAll media utilised low glucose DMEM/serum free conditions with supplements supporting matrix synthesis, based on media recommended for alginate culture (standard media). Healthy media modulated osmolarity (425mOsm/Kg), and pH7.2, degenerate media consisted of 325mOsm/kg and pH6.8. The latter was further supplemented with 100pg/ml IL-1{beta} (degenerate+IL-1{beta} media). NP cells in 3D alginate and NP tissue explants were cultured in these media for up to 2 weeks in physioxia (5% O2) to determine effects on viability, mitochondrial activity, protein expression and secretome. RESULTSMedia osmolarity and pH remained stable and cell viability was not altered by any media composition. Mitochondrial activity was increased during short term cultures, whilst a decrease was seen following 14 days in degenerate media. The secretome of NP cells was differentially affected in healthy or degenerate media, with most increases in catabolic cytokines observed following the addition of IL-1{beta}. Tissue explants showed stability of protein expression of matrix components in both healthy and degenerate+IL-1{beta} media, with limited effects seen on the secretome. DISCUSSIONThe media formulations developed here can provide more appropriate environmental conditions in vitro, mimicking more closely the in vivo conditions observed within healthy and degenerate IVDs. The application of which can provide more appropriate culture conditions to test potential therapeutic approaches and understand more fully the pathogenesis of disease using in vitro and ex vivo models.

cell biology↗

bioRxiv2024

ARID1A-induced transcriptional reprogramming rewires signalling responses to drug treatment in melanoma

Resistance to BRAF and MAPK inhibitors is a significant challenge in melanoma treatment, driven by adaptive and acquired mechanisms that allow tumour cells to evade therapy. Here, we examined early signalling responses to single and combined BRAF and MAPK inhibition in a BRAFV600E, drug-sensitive melanoma cell line and a drug-resistant ARID1A-knockout (KO) derivative. ARID1A, frequently mutated in melanoma, is associated with resistance and immune evasion. Using an innovative systems biology approach that integrates transcriptomics, proteomics, phosphoproteomics, and functional kinomics through matrix factorization and network analysis, we identified key signalling alterations and resistance mechanisms. We found that ARID1A-KO cells exhibited transcriptional rewiring, sustaining MAPK1/3 and JNK activity post-treatment, bypassing feedback sensitivity observed in parental cells. This rewiring suppressed PRKD1 activation, increased JUN activity--a central resistance network node--and disrupted PKC dynamics through elevated basal RTKs (e.g., EGFR, ROS1) and Ephrin receptor activity post-treatment. ARID1A mutations also reduced HLA-related protein expression and enriched extracellular matrix components, potentially limiting immune infiltration and reducing immunotherapy efficacy. Our graph-theoretical multi-omics approach uncovered novel resistance-associated signalling pathways, identifying PRKD1, JUN, and NCK1 as critical nodes. While receptor activation redundancies complicate single-target therapies, they also present opportunities for combination strategies. This study highlights ARID1As role in reshaping signalling and immune interactions, offering new insights into melanoma resistance mechanisms. By identifying actionable targets, including JUN and immune pathways, we provide a foundation for developing integrated therapeutic strategies to overcome resistance in BRAF/MAPK inhibitor-treated melanoma. One sentence summaryThis study reveals how ARID1A-mediated transcriptional rewiring drives resistance to MAPK inhibitors in melanoma by altering signalling pathways, immune interactions, and receptor dynamics, highlighting potential targets for combinatorial therapies.

systems biology↗

bioRxiv2021

DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification

MotivationThe analysis and comparison of compounds transcriptomic signatures can help elucidate a compounds Mechanism of Action (MoA) in a biological system. In order to take into account the complexity of the biological system, several computational methods have been developed that utilize prior knowledge of molecular interactions to create a signaling network representation that best explains the compounds effect. However, due to their complex structure, large scale datasets of compound-induced signaling networks and methods specifically tailored to their analysis and comparison are very limited. Our goal is to develop graph deep learning models that are optimized to transform compound-induced signaling networks into high-dimensional representations and investigate their relationship with their respective MoAs. ResultsWe created a new dataset of compound-induced signaling networks by applying the CARNIVAL network creation pipeline on the gene expression profiles of the CMap dataset. Furthermore, we developed a novel unsupervised graph deep learning pipeline, called deepSNEM, to encode the information in the compound-induced signaling networks in fixed-length high-dimensional representations. The core of deepSNEM is a graph transformer network, trained to maximize the mutual information between whole-graph and sub-graph representations that belong to similar perturbations. By clustering the deepSNEM embeddings, using the k-means algorithm, we were able to identify distinct clusters that are significantly enriched for mTOR, topoisomerase, HDAC and protein synthesis inhibitors respectively. Additionally, we developed a subgraph importance pipeline and identified important nodes and subgraphs that were found to be directly related to the most prevalent MoA of the assigned cluster. As a use case, deepSNEM was applied on compounds gene expression profiles from various experimental platforms (MicroArrays and RNA sequencing) and the results indicate that correct hypotheses can be generated regarding their MoA. Availability and ImplementationThe source code and pre-trained deepSNEM models are available at https://github.com/BioSysLab/deepSNEM. ContactEmail for correspondence: leo@mail.ntua.gr. Supplementary informationAccompanying supplementary material are available online.

bioinformatics↗

bioRxiv2019

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.\n\nTherefore, a computational method was developed considering the diverse mechanisms and the multi-tissue nature of OA in order to suggest pharmaceutical compounds. Specifically, weighted gene co-expression network analysis (WGCNA) was utilized to identify gene modules that were preserved across four joint tissues. The driver genes of these modules were selected as an input for a network-based drug discovery approach.\n\nWGCNA identified two preserved modules that described functions related to extracellular matrix physiology and immune system responses. Compounds that affected various anti-inflammatory pathways and drugs targeted at coagulation pathways were suggested. 9 out of the top 10 compounds had a proven association with OA and significantly outperformed randomized approaches not including WGCNA. The method presented herein is a viable strategy to identify overlapping molecular mechanisms in multi-tissue diseases such as OA and employ this information for drug discovery and compound prioritization.

systems biology↗

bioRxiv2019

An ex vivo tissue model of cartilage degradation suggests that cartilage state can be determined from secreted key protein patterns

The pathophysiology of osteoarthritis (OA) involves dysregulation of anabolic and catabolic processes associated with a broad panel of cytokines and other secreted proteins and ultimately lead to cartilage degradation. An increased understanding about the interactions of these proteins by means of systematic in vitro analyses may give new ideas regarding pharmaceutical candidates for treatment of OA and related cartilage degradation. Therefore, first an ex vivo tissue model of cartilage degradation was established by culturing full thickness tissue explants with bacterial collagenase II. Then responses of healthy and degrading cartilage were analyzed by measuring protein abundance in tissue supernatant with a 26-multiplex protein profiling assay, after exposing them to a panel of 55 protein stimulations present in synovial joints of OA patients. Multivariate data analysis including exhaustive pairwise variable subset selection was used to identify the most outstanding changes in the measured protein secretions. This revealed that the MMP9 response is outstandingly low in degraded compared to healthy cartilage and that there are several protein pairs like IFNG and MMP9 that can be used for successful discrimination between degraded and healthy samples. Taken together, the results show that the characteristic changes in protein responses discovered seem promising for accurate detection/diagnosis of degrading cartilage in general and OA in particular. More generally the employed ex vivo tissue model seems promising for drug discovery and development projects related to cartilage degradation, for example when trying to uncover the unknown interactions between secreted proteins in healthy and degraded tissues.

bioengineering↗

bioRxiv2018

Qualitative modeling of signaling networks in replicative senescence by selecting optimal node and arc sets

Signaling networks are an important tool of modern systems biology and drug development. Here, we present a new methodology to qualitatively model signaling networks by combining experimental data and prior knowledge about protein connectivity. Unlike other methods, our approach does not focus solely on selecting which reactions are involved but also on whether a protein is present. This allows the user to model more complicated experiments and incorporate more knowledge into the model. To demonstrate the capabilities of our method we compared the signaling networks of young and replicative senescent human primary HFL-1 fibroblasts, whose differences are expected to be due mainly to changes in the expression of the proteins rather than the reactions involved. The resulting networks indicate that, compared to young cells, aged cells are not as responsive to insulin stimulation and activate pathways that establish and maintain senescence.\n\nAuthor summaryCells have developed a complex network of biochemical reactions to monitor their environment and react to changes. Although multiple pathways, tuned to identify specific stimuli, have been discovered, it is generally understood that the signaling process typically involves multiple pathways and is context depended. Consequently, reconstructing the signaling network utilized by cells at any given moment is not a trivial task. In this article, we report on a novel logic-based method for identifying signaling network by combining experimental data with prior knowledge about the connectivity of the involved proteins. Unlike other methods proposed so far, our method uses data to evaluate the presence or absence of reactions and proteins alike. We reconstructed and compared the signaling network of human primary HFL-1 fibroblasts as they undergo replicative senescence in the presence of 6 different stimuli. The resulting networks indicate that, compared to young cells, senescent cells are not responsive to insulin stimulation and activate pathways that are known to establish and maintain senescence.

systems biology↗

bioRxiv2018

A functional landscape of chronic kidney disease entities from public transcriptomic data

To develop efficient 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 CKD origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for nine kidney disease entities that account for a majority of CKD worldwide. We included data from five distinct studies and compared glomerular gene expression profiles to that of non-tumor parts of kidney cancer nephrectomy tissues. A major challenge was the integration of the data from different sources, platforms and conditions, that we mitigated with a bespoke stringent procedure. This allowed us to perform a global transcriptome-based delineation of different kidney disease entities, obtaining a landscape of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and nephrectomy tissue. Furthermore, we derived functional insights by inferring activity of signaling pathways and transcription factors from the collected gene expression data, and identified potential drug candidates based on expression signature matching. We validated representative findings by immunostaining in human kidney biopsies indicating e.g. that the transcription factor FOXM1 is significantly and specifically expressed in parietal epithelial cells in RPGN whereas not expressed in control kidney tissue. These results provide a foundation to comprehend the specific molecular mechanisms underlying different kidney disease entities, that can pave the way to identify biomarkers and potential therapeutic targets. To facilitate this, we provide our results as a free interactive web application: https://saezlab.shinyapps.io/ckd_landscape/. Translational StatementChronic kidney disease is a combination of entities with different etiologies. We integrate and analyse transcriptomics analysis of glomerular from different entities to dissect their different pathophysiology, what might help to identify novel entity-specific therapeutic targets.

pathology↗