Biology subjects

Morelli, M.

Publications and source records attributed to Morelli, M..

11 recordsLinked to original sources

bioRxiv2026

From Single-Cell Emergent Behaviors to Clinical Outcome: PTEN-driven Migratory Efficiency as a Potential New Vulnerability in Glioblastoma

BackgroundGlioblastoma (GB) is a highly aggressive brain tumor with a median survival of approximately 14 months, primarily due to its ability to infiltrate healthy brain tissue both as single cells and in collectives. A deeper understanding of GB cell motility, both individual and collective, is crucial for developing patient-specific therapies. We aimed to characterize migration in patient-derived GB cells using advanced modeling to identify stratification markers and therapeutic vulnerabilities. MethodsWe developed Single-Cell Behavior Live Imaging (ScBLI), an approach integrating live imaging with computational analysis, applied to 30 GB primary cell cultures. Trajectories and morphological features were tracked and analyzed. Diffusion Entropy Analysis (DEA) was applied to classify trajectories based on the Delta Scaling parameter ({delta} scaling). We evaluated functional responses correlating all findings with clinical outcomes and transcriptomic profiles. ResultsWe analyzed 4,279 cell trajectories. Based on {delta} scaling (range 0.28-0.837), we defined three distinct motility groups: Low (L, {delta} scaling [≤]0.5), Medium (M, 0.5 < {delta} scaling [≤] 0.7), and High (H, {delta} scaling >0.7). Functional assays demonstrated that Group H cells are more performant in both positive and negative chemotaxis. Clinically, the three groups showed a clear linear progression with patient survival: High {delta} scaling correlated with the shortest survival (poorer prognosis), while Low {delta} correlated with the longest survival, suggesting that structured motility drives invasiveness. Integrative multi-omic analysis, encompassing both exome and transcriptome profiling, demonstrated that these groups are defined by distinct molecular landscapes rather than poor behavioral traits. Moreover, exome data revealed that Group H is significantly enriched in PTEN alterations (75% vs. 8% in Group L), with PTEN gain-of-function (GoF) mutations exclusively restricted to this group (100% vs 0% in Group L). Notably, within our extended cohort (n=51) currently characterized by whole-exome sequencing, we observed that specific PTEN GoF mutations were associated with a significantly shorter survival compared to PTEN wild-type cases (median OS 6.4 vs 16.6 months; p=0.02), which typically harbor the canonical loss of chromosome 10q. A similar clinical trend was observed when comparing directly GoF carriers to patients with truncating (Ter) alterations (median OS 6.4 vs 14.3 months; p=0.09). Conversely, no survival difference was found between truncating (Ter) mutations and wild-type cases. ConclusionOur findings demonstrate for the first time that migratory efficiency, quantified through DEA, represents a powerful predictor of glioblastoma aggressiveness. Tumor cells adopting highly efficient exploration strategies are strongly associated with poor clinical outcomes and are characterized by distinct molecular signatures, notably PTEN gain-of-function alterations. Statement of significanceOur multi-scale computational framework elucidates emergent behavioral phenotypes as pivotal drivers of glioblastoma progression. By demonstrating a correlation between enhanced migratory efficiency, PTEN gain-of-function, and significantly reduced overall survival, we establish a foundational paradigm for deciphering the emergent complexity governing tumor invasiveness.

cancer biology↗

bioRxiv2025

Ultra-performance liquid chromatography-mass spectrometry analysis of post-mortem brain tissue reveals specific amino acid profile dysregulation in Parkinson's Disease and Alzheimer's Disease patients

BackgroundCombined metabolomic and HPLC-based analyses have identified significant metabolic alterations in serum and plasma amino acid levels of Parkinsons disease (PD) patients, underscoring their potential as biomarkers. However, it remains unclear whether these biochemical changes also manifest within the central nervous system or are confined to peripheral metabolism, reflecting systemic metabolic disturbances. MethodsTo address this issue, here we measured the levels of 44 different amino acids in post-mortem brain samples from MPTP-intoxicated, L-DOPA-treated monkeys and PD patients at different Braak Lewy body (LB) stages, compared to their respective controls, through targeted Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). ResultsIn MPTP-intoxicated monkeys, UPLC-MS revealed significant elevations in GABA, citrulline, threonine, isoleucine, phenylalanine, valine, glycine, and serine in the putamen, whereas we failed to detect alterations in the superior frontal gyrus (SFG). In PD patients, caudate-putamen (CPu) analysis demonstrated consistent serine upregulation across Braak LB stages 3-4 and 6, with stage 6 specifically showing additional proline increases and phosphoethanolamine decreases. Notably, serine was the sole amino acid significantly altered in both the putamen of MPTP-intoxicated monkeys and the CPu of PD patients. No significant amino acid alterations were observed in the SFG of PD patients, mirroring the findings in monkeys. In contrast, Alzheimers disease (AD) patient SFG samples showed significant increases in tryptophan, phenylalanine, threonine, tyrosine, and methionine relative to controls. ConclusionsThese findings demonstrate that cerebral amino acid alterations in PD are region-specific and primarily localized to brain areas receiving nigrostriatal dopaminergic innervation. Moreover, the cortical amino acid profile in AD differs substantially from that in PD, suggesting disease-specific metabolic signatures in distinct neurodegenerative conditions.

neuroscience↗

bioRxiv2025

Scalable Image-Based Quantification of Cell Permeability and Actin Remodeling: An Example in a Gut-on-Chip Platform

Evaluating cellular responses to toxic compounds is essential for assessing the safety and potential hazards of drugs, environmental pollutants, and food contaminants. Traditional in vitro methods often lack the precision and scalability required for comprehensive toxicological assessments. This protocol presents an efficient and scalable approach for quantifying cellular damage and toxicity using organ-on-chip technology, specifically the OrganoPlate(R) platform. By combining fluorescent probes--DRAQ7 for cell membrane integrity, ActinGreen for cytoskeletal changes, and NucBlue for nuclear counting--with high-throughput image analysis via CellProfiler, this method provides detailed and quantitative insights into cellular damage induced by toxic compounds. The protocol includes step-by-step instructions for staining, image acquisition, and data analysis, as well as troubleshooting guidance. CellProfilers open-source nature, flexibility, and automation capabilities enable reproducible, high-throughput workflows, offering significant advantages over traditional manual image analysis. Its ability to assess cytotoxicity in human tissue models makes this protocol a valuable tool for safety testing in drug discovery and environmental toxicology. Furthermore, the approach is highly adaptable, accommodating a variety of cell types and toxic compounds, and is well-suited for rapid screening and risk assessment across diverse research and industrial applications.

cell biology↗

bioRxiv2024

Mycotoxin candidalysin impairs epithelial barrier integrity and initiates inflammation in a gut-on-chip model

Candida albicans is an opportunistic pathogenic yeast commonly found in the gastrointestinal tract, vagina, and mouth of healthy humans. Under certain conditions, it can become invasive, causing mucosal or life-threatening systemic infections. One mechanism used by C.albicans to breach the epithelial barrier is the secretion of candidalysin, a cytolytic peptide toxin. Candidalysin damages epithelial membranes and activates the innate epithelial immune response, making it key to C.albicans pathogenicity and a promising therapeutic target. Although candidalysin mediates C. albicans translocation through intestinal layers, its impact on epithelial responses is not fully understood. This study aims to characterize this response and develop scalable, quantitative methodologies to assess candidalysins toxicological effects using gut-on-chip models. We used the OrganoPlate(R), a microfluidic platform to culture up to 64 perfused, membrane-free intestinal epithelial tubes. We exposed Caco-2 tubes to candidalysin and evaluated their response with trans-epithelial electrical resistance (TEER), protein detection, and immunostaining. We then validated our findings in a proof-of-concept experiment using human intestinal organoid tubules. Candidalysin impaired barrier integrity, as indicated by decreased TEER and increased permeability in a fluorescent dye assay. It also induced actin remodeling and DRAQ7 dye uptake, a marker of cell permeability. This disruption was associated with the release of LDH, cytokines, and the antimicrobial peptide LL37, suggesting cellular damage, inflammation, and antimicrobial activity. This study strengthens our understanding of candidalysins role in C. albicans pathogenesis and suggests new therapeutic strategies targeting this toxin. Moreover, the use of patient-derived organoids shows promise for capturing patient heterogeneity and developing personalized treatments. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/621017v4_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@dd8e35org.highwire.dtl.DTLVardef@1a33d18org.highwire.dtl.DTLVardef@113eorg.highwire.dtl.DTLVardef@6c2b9b_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2024

Functional Magnetic Resonance Spectroscopy of Prolonged Motor Activation using Conventional and Spectral GLM Analyses

BackgroundFunctional MRS (fMRS) is a technique used to measure metabolic changes in response to increased neuronal activity, providing unique insights into neurotransmitter dynamics and neuroenergetics. In this study we investigate the response of lactate and glutamate levels in the motor cortex during a sustained motor task using conventional spectral fitting and explore the use of a novel analysis approach based on the application of linear modelling directly to the spectro-temporal fMRS data. MethodsfMRS data were acquired at a field strength of 3 Tesla from 23 healthy participants using a short echo-time (28ms) semi-LASER sequence. The functional task involved rhythmic hand clenching over a duration of 8 minutes and standard MRS preprocessing steps, including frequency and phase alignment, were employed. Both conventional spectral fitting and direct linear modelling were applied, and results from participant-averaged spectra and metabolite-averaged individual analyses were compared. ResultsWe observed a 20% increase in lactate in response to the motor task, consistent with findings at higher magnetic field strengths. However, statistical testing showed some variability between the two averaging schemes and fitting algorithms. While lactate changes were supported by the direct spectral modelling approach, smaller increases in glutamate (2%) were inconsistent. Exploratory spectral modelling identified a 4% decrease in aspartate, aligning with conventional fitting and observations from prolonged visual stimulation. ConclusionWe demonstrate that lactate dynamics in response to a prolonged motor task are observed using short-echo time semi-LASER at 3 Tesla, and that direct linear modelling of fMRS data is a useful complement to conventional analysis. Future work includes mitigating spectral confounds, such as scalp lipid contamination and lineshape drift, and further validation of our novel direct linear modelling approach through experimental and simulated datasets.

neuroscience↗

bioRxiv2024

Learning and actioning general principles of cancer cell drug sensitivity

High-throughput screening platforms for the profiling of drug sensitivity of hundreds of cancer cell lines (CCLs) have generated large datasets that hold the potential to unlock targeted, anti-tumor therapies. In this study, we leveraged these datasets to create predictive models of cancer cells drug sensitivity. To this aim we trained explainable machine learning algorithms by employing cell line transcriptomics to predict the growth inhibitory potential of drugs. We used large language models (LLMs) to expand descriptions of the mechanisms of action (MOA) for each drug starting from available annotations, which were matched to the semantically closest pathways from reference knowledge bases. By leveraging this AI-curated resource, and the interpretability of our model, we demonstrated that pathways enriched for genes crucial for prediction often matched known drug-MOAs and essential genes, suggesting that our models learned the molecular determinants of drug response. Furthermore, we demonstrated that by incorporating only LLM-curated genes associated with MOAs, we enhanced the predictive accuracy of our drug models. To enhance translatability to a clinical setting, we employed a pipeline to align bulk RNAseq from CCLs, used for training the models, to those from patient samples, used for inference. We proved the effectiveness of our approach on TCGA samples, where patients best scoring drugs matched those prescribed for their cancer type. We further showed its usefulness by predicting and experimentally validating effective drugs for the patients of two highly lethal solid tumors, i.e. pancreatic cancer and glioblastoma. In summary, our method facilitates the inference and interpretation of cancer cell line drug sensitivity and holds potential to effectively translate them into new cancer therapeutics. Highlights-Interpretable drug-response prediction models were trained on large scale pharmacogenomics data sets (i.e. GDSC and PRISM). -Large language models were used to enhance the curation of biological pathways associated to drugs MOA -Unbiased interpretation of the models demonstrated learning of drug MOAs and gene essentiality -Inference of TCGA cohort samples recovered mono- and combination cancer drug prescriptions and indicate potential repurposing candidates. -Drug candidates predicted from bulk RNAseq samples of pancreatic cancer and glioblastoma were experimentally validated.

bioinformatics↗

bioRxiv2023

The Minimal Dataset for Cancer of the 1+Million Genomes Initiative

For a real impact on healthcare, precision cancer medicine requires accessibility and interoperability of clinical and genomic data across centres and countries. Due to the heterogeneous digitization in Europe and worldwide, the definition of models for standardised data collection and usability becomes mandatory if countries want to work together on this mission. The European Union 1+Million Genomes (1+MG) initiative, supported by the Horizon 2020 Beyond 1 Million Genomes project, aims at outlining data models, guidance, best practices, and technical infrastructures for transnational access to sequenced genomes, including cancer genomes. Within the framework of the cancer-focused Working Group 9, we developed the 1+MG-Minimal Dataset for Cancer (1+MG-MDC)-a data model encompassing 140 items and organized in eight conceptual domains for the collection of cancer-related clinical information and genomics metadata. The 1+MG-MDC, which results from a multidisciplinary effort, leverages pre-existing models and emphasizes the annotation and traceability of multiple aspects relevant to the complex longitudinal path of the cancer disease and its treatment. We strived to make the 1+MG-MDC easy to adopt, yet comprehensive, addressing the needs of both clinicians and researchers. We will periodically revise and update it to ensure it remains fit for purpose. We propose the 1+MG-MDC as a model to create homogeneous databases, which would, in turn, guide discussions on clinical and genomic features with prognostic or therapeutic value and foster real-world data research.

cancer biology↗

bioRxiv2023

BIRC3: A Prognostic Predictor and Novel Therapeutic Target in TMZ-Resistant Glioblastoma Tumors

BackgroundGlioblastoma (GB) is an incurable malignant tumor of the central nervous system, with a poor prognosis. Robust molecular biomarkers associated with therapeutic response or survival are still lacking in GB. Previously, using NADH-fluorescence lifetime imaging (NADH-FLIM), as a new drug screening precision medicine ex-vivo approach, we categorized patient-derived vital tumors into TMZ responder (Resp) and non-responder (Non-Resp) groups, revealing differentially expressed genes. MethodsExpanding on our previous study, we assessed TMZ response in a larger cohort of primary and recurrent ex-vivo live GB tumors (n=33) using NADH-FLIM. Transcriptome analysis was performed to characterize TMZ Resp and Non-Resp cases, and in-silico and functional cellular investigations were conducted to explore the efficacy of potential biomarkers. ResultsGenes dysregulated in the previous study showed consistent expression patterns. BIRC3, a potent apoptosis inhibitor, was significantly upregulated in TMZ-resistant samples. BIRC3 expression complemented MGMT status as a prognostic factor in multiple TCGA cohorts. BIRC3 functioned as a prognostic factor of survival also in separate European private glioblastoma cohorts. The BIRC3 antagonist, AZD5582, in combination with TMZ, effectively reversed TMZ resistance by restoring apoptosis in glioblastoma cell lines and patient-derived organoids. ConclusionsBIRC3 holds promise as a prognostic biomarker and predictor of TMZ response in GB. Assessing BIRC3 expression could aid in stratifying patients for combined TMZ and AZD5582 therapy. Our study highlights the potential of functional precision medicine and BIRC3 assessment as a standard tool in glioblastoma clinical oncology, improving outcomes. KEYPOINTSO_LIBIRC3, previously overlooked, identified through dynamic precision medicine using TMZ perturbation of glioblastoma tissue as a robust prognostic factor. C_LIO_LIThe gene BIRC3 is an independent prognostic factor associated with shorter survival and TMZ resistance, rigorously validated across various case studies and datasets, including two expansive European case studies. C_LIO_LIProposal of anti-BIRC3 drug, AZD5582, shows promise as a novel therapeutic option to overcome TMZ resistance in GB tumors, providing hope for improved outcomes and personalized treatment strategies for patients with limited treatment options C_LI IMPORTANCE OF THE STUDYGlioblastoma (GB), an aggressive cancer type with a bleak prognosis, lacks dependable biomarkers for treatment prediction. Few markers like MGMT promoter methylation, IDH1 mutation, TERT gene mutations, and EGFR amplification are known, but their predictive consistency varies. Temozolomide (TMZ) resistance, seen in over 50% of GB patients, complicates matters. BIRC3, an apoptosis-inhibiting gene, displays heightened expression in TMZ-resistant tumors. Our study examined BIRC3 in GB patient samples, finding it an independent prognostic factor linked to shorter survival and TMZ resistance. Our research builds upon Wang et al.s 2016 and 2017 findings, delving deeper through TCGA data and European case studies. BIRC3s consistent prominence suggests its significance, with functional experiments confirming its role. We assessed AZD5582, targeting BIRC3, which, when combined with TMZ, curtailed cell growth and induced apoptosis. Notably, AZD5582 countered TMZ resistance in patient-derived GB-EPXs, except for low BIRC3 cases. Our precision medicine approach enhances personalized therapies and outcomes, highlighting BIRC3s potential as a prognostic marker and AZD5582 as a new therapy for TMZ-resistant GB.

cancer biology↗

bioRxiv2022

Metabolic-imaging of human glioblastoma explants: a new precision-medicine model to predict tumor treatment response early

BackgroundGlioblastoma (GB) is the most severe form of brain cancer, with a 12-15 month median survival. Surgical resection, temozolomide (TMZ) treatment, and radiotherapy (RT) remain the primary therapeutic options for GB, and no new therapies have been introduced in recent years. This therapeutic standstill is primarily due to preclinical approaches that do not fully respect the complexity of GB cell biology and fail to test efficiently anti-cancer treatments. Therefore, better treatment screening approaches are needed. In this study, we have developed a novel functional precision medicine approach to test the response to anticancer treatments in organoids derived from the resected tumors of glioblastoma patients. MethodsGB organoids were grown for a short period of time to prevent any genetic and morphological evolution and divergence from the tumor of origin. We chose metabolic imaging by NAD(P)H fluorescence lifetime imaging microscopy (FLIM) to predict early and non-invasively ex-vivo anti-cancer treatment responses of GB organoids. TMZ was used as the benchmark drug to validate the approach. Whole-transcriptome and whole-exome analyses were then performed to characterize tumor cases stratification. ResultsOur functional precision medicine approach was completed within one week after surgery and two groups of TMZ Responder and Non Responder tumors were identified. FLIM-based metabolic tumor stratification was well-reflected at the molecular level, confirming the validity of our approach, highlighting also new target genes associated with TMZ treatment and identifying a new 17 gene molecular signature associated with survival. The number of promoter methylated tumors for the MGMT gene was higher in the responsive group, as expected, however, some non-methylated tumor cases turned out to be nevertheless responsive to TMZ, suggesting that our procedure could be synergistic with the classical MGMT methylation biomarker. ConclusionsFor the first time, FLIM-based metabolic imaging was used on ex-vivo live glioblastoma organoids. Unlike other approaches, ex-vivo patient-tailored drug response is performed at an early stage of tumor culturing with no animal involvement and with minimal tampering with the original tumor cytoarchitecture. This functional precision medicine approach can be exploited in a range of clinical and laboratory settings to improve the clinical management of GB patients and implemented on other cancers as well.

molecular biology↗

bioRxiv2022

Single-cell molecular characterization to partition the human glioblastoma tumor microenvironment (TME) genetic background

Glioblastoma (GB) is a devastating primary brain malignancy. Recurrence of GB is inevitable despite the standard treatment of surgery, chemotherapy, and radiation, and the median survival is limited to around 15 months. Barriers to treatment include the complex interactions among the different cellular components inhabitant of the tumor microenvironment. These challenges are further compounded by extensive inter- and intra-tumor heterogeneity and by the dynamic plasticity of GB cells. The complex heterogeneous nature of GB cells is helped by the local inflammatory tumor microenvironment, which mostly induces tumor aggressiveness and drug resistance. More effective therapy development heavily depends on higher resolution molecular subtype signatures. All cellular components of a GB tumor are subjected to a continuous pressure inducing proliferation that might favor the accumulation of genetic mutations. Understanding the genetic background of several cellular component s belonging to a GB microenvironment could give insights into tumor behavior and progression. In the present study by using fluorescent multiple labeling and DEPArray cell separator, we recovered several single cells or groups of single cells from populations of different origins from IDH-WT GB samples. From each GB sample, we collected astrocytes (GFAP+), microglia (IBA+), stem cells (CD133+), and endothelial cells (CD105+) and performed Copy Number Aberration (CNA) analysis with a low sequencing depth. The same tumors were subjected to a bulk CNA analysis. The tumor partition in its single components allowed single-cell molecular subtyping which revealed new aspects of GB altered genetic background. Nowadays, single-cell approaches are leading to a new understanding of GB physiology and disease. Moreover, single-cell CNAs resource will permit new insights into genome heterogeneity, mutational processes, and clonal evolution in malignant tissues.

cancer biology↗

bioRxiv2019

Interleukin (IL)-17/IL-36 axis participates to the crosstalk between endothelial cells and keratinocytes during inflammatory skin responses

In inflammatory skin conditions, such as psoriasis, vascular enlargement is associated with endothelial cell proliferation, release of cytokines and adhesion molecule expression. Interleukin (IL)-17A is a pro-inflammatory cytokine mainly secreted by T helper-17 cells that is critically involved in psoriasis pathogenesis. IL-36, IL-36{beta} and IL-36{gamma} are also inflammatory cytokines up-regulated in psoriasis and induced by various stimuli, including IL-17A. In this study, we found that human keratinocytes are the main source of IL-36, in particular of IL-36{gamma}. This cytokine was strongly induced by IL-17A and efficiently activated human dermal microvascular endothelial cells (HDMECs), which expressed both IL-17 and IL-36 receptors, by inducing a molecular signaling, such as phosphorylation of ERK1/2 and NF-{kappa}B P65 subunit. We highlighted the intense IL-17A- and IL-36{gamma}-dependent interplay between keratinocytes and HDMECs, likely active in the psoriatic lesions and leading to the establishment of a cytokine network responsible for the development and maintenance of the inflamed state. On HDMECs, IL-17A or IL-36{gamma} showed a synergic activity with TNF-, potently inducing inflammatory cytokine/chemokine release and ICAM-1 expression. We also investigated the involvement of IL-36{gamma} and VEGF-A, substantially reduced in lesional skin of psoriatic patients pharmacologically treated with the anti-IL-17A antibody Secukinumab. Importantly, keratinocyte-derived IL-36{gamma} represented an additional pro-angiogenic mediator of IL-17A. We observed that keratinocyte-derived VEGF-A influenced proliferation but not reduced inflammatory responses of HDMECs. On the other hand, inhibition of IL-36{gamma} released by IL-17A-treated keratinocytes impaired ICAM-1 expression in HDMECs. Taken together, our data demonstrated that IL-17A and IL-36{gamma} are highly involved in endothelial cells/keratinocytes crosstalk in inflammatory skin conditions.

cell biology↗