Biology subjects

Di Filippo, M.

Publications and source records attributed to Di Filippo, M..

10 recordsLinked to original sources

bioRxiv2026

p63 and PITX1 sustain a pre-invasive malignant keratinocyte population in squamous cell carcinoma precursors

BackgroundCutaneous squamous cell carcinoma (cSCC) is among the most common human cancers, yet the cellular identity and molecular programs of its preinvasive precursor, actinic keratosis (AK), remains poorly defined. MethodsWe applied CITE-seq to patient-matched AK, UV-exposed normal skin, and non-UV-exposed normal skin (n=5 patients, 12 biopsies) and performed spatial whole-transcriptome profiling in an independent cohort (n=4) to map pre-invasive keratinocyte states at single-cell resolution. ResultsWe identify AK-specific keratinocytes (ASK), a discrete population localized to the dysplastic basal epidermis and characterized by UV-associated mutational signatures (SBS7b), high mutational burden, and recurrent copy number alterations including 9p loss and 8q gain. ASK occupies a basal-like undifferentiated state sustained by a {Delta}Np63/PITX1 regulatory module that attenuates Notch/HES1-driven differentiation and activates glycolytic metabolism. Comparison with published cSCC data reveals that ASK share core tumor-propagating gene networks with tumor-specific keratinocytes (TSK), including IGFBP6, IGFBP2, and ITGA6, but lack invasion effectors MMP1, MMP10, and PTHLH. Functional experiments identify IGFBP6 as a pro-proliferative factor in AK-derived keratinocytes. The AK microenvironment shows expansion of inflammatory basal keratinocytes, barrier disruption, and early immunosuppressive T cell remodeling. ConclusionsThese findings define the molecular identity of a pre-invasive malignant keratinocyte population governed by p63/PITX1 and distinguish early oncogenic programs shared with invasive cSCC from later-acquired invasion effectors, identifying candidate targets for prevention or treatment of squamous cell carcinoma.

cancer biology↗

bioRxiv2025

UVA irradiation promotes ROS-mediated formation of the common deletion in mitochondrial DNA

Ultraviolet (UV) radiation from the sun causes adverse skin changes such as premature aging. UVA radiation is the primary factor for photoaging due to its deep penetration into the dermis, and UV-induced mitochondrial DNA (mtDNA) alterations, including deletions, contribute to photoaging and cellular dysfunction. The most frequent mtDNA rearrangement is the common deletion (CD), characterized by the loss of nearly one-third of the genome, 4,977 base pairs. UV radiation exposure leads to the formation of the CD, however, a distinct characterization of UV-induced CD and the underlying molecular mechanisms driving its initiation remains unexplored. In this study, we showed that increasing doses of UV radiation led to an increase in the CD in human skin fibroblasts. We found that UVA induce the formation of the CD by increasing the cellular reactive oxygen species (ROS) and oxidized bases content in the mtDNA. Preconditioning cells with antioxidants prevented the accumulation of the UVA-induced CD, suggesting that this mutational mechanism is ROS-dependent. In stark contrast, UVB did not alter cellular ROS levels but increased the formation of cyclobutane pyrimidine dimers (CPD), leading to CD generation though a ROS-independent mechanism. We corroborated our findings by using a 3D human full-thickness skin equivalent model, where we detected UVA-dependent CD formation in both the epidermal and dermal layers of the skin. By analyzing bulk RNA from UVA-exposed human skin fibroblasts by RNA-Seq, we found that UVA led to the upregulation of genes encoding mitochondrial DNA replication proteins and to the downregulation of genes involved genes encoding mtDNA repair factors. Taken together, our findings provide insight into how UVA and UVB differ in their detrimental effects on mtDNA, with UVA impacting mtDNA maintenance and transcription via a ROS-dependent mechanism. Our findings also established the mtDNA CD as a novel potential biomarker for monitoring UVA-induced oxidative stress and photoaging in skin cells in vitro and in vivo.

genomics↗

bioRxiv2025

NLRP1 inflammasome activation in skin equivalents revealsmechanistic insights into the roles of keratinocytes in psoriasis

Psoriasis is a major inflammatory skin disease for which a causal therapy is still not available. The pro-inflammatory cytokines interleukin(IL)-1{beta} and IL-36{gamma} are key drivers of the disease phenotype, but the mechanisms underlying their regulation in psoriasis remain poorly understood. Generation of IL-1{beta} activity is regulated by protein complexes, termed inflammasomes. We activated the NLRP1 inflammasome in human keratinocytes cultivated in three-dimensional skin equivalents. NLRP1 activation induced histological and molecular features that are highly reminiscent of psoriasis. Mechanistically, the phenotype was dependent on IL-1, which triggered a pro-inflammatory epidermal-dermal crosstalk. This included induction of expression of IL-36{gamma}, which, together with IL-1{beta}, was released from keratinocytes through NLRP1-induced gasdermin D pores. The in vivo relevance of these findings is reflected by the expression of the NLRP1 sensor and signs of inflammasome activation in lesional skin of psoriatic patients. Finally, we discovered endogenous cytoplasmic double stranded (ds) RNA, recently associated with cellular perturbations in psoriasis, as a novel activator of the NLRP1 inflammasome in human keratinocytes. Our results identify a novel endogenous double-stranded RNA-mediated NLRP1-IL-1-IL-36{gamma} signaling axis relevant in psoriasis and suggest targeting of this pathway as a promising treatment strategy.

immunology↗

bioRxiv2025

Neuronal Hyperexcitability: A Key to Unravelling Hippocampal Synaptic Dysfunctions in Lafora Disease

Lafora disease (LD) is a rare progressive disorder caused by mutations in the EPM2A or EPM2B genes, characterized by the accumulation of Lafora bodies, drug-resistant epilepsy, and cognitive decline. To investigate the early molecular mechanisms of LD, we studied electrophysiological changes in the dentate gyrus (DG) of the Epm2aR240X knock-in mouse model at various ages. Electrophysiological recordings measured neuronal membrane properties, epileptic-like activity, epileptic thresholds, and synaptic plasticity in Epm2aR240X mice at 1, 3, and 12 months. We also employed PAS diastase staining, immunofluorescence, and Western blotting to detect Lafora bodies, amyloid beta deposition, and glutamate receptor subunit expression. Epileptic-like activity began at 1 month and intensified with age. Aberrant long-term potentiation (LTP) appeared at 3 months and worsened by 12 months. Notably, cannabidiol (CBD) treatment reduced excitability and restored LTP in older mice, suggesting its potential therapeutic value. These findings indicate that network hyperexcitability is an early event in LD, highlighting a therapeutic window for interventions like CBD.

neuroscience↗

bioRxiv2025

KILDA: identifying KIV-2 repeats from kmers

MotivationHigh concentration of lipoprotein(a), a lipoprotein with proatherogenic properties, is an important risk factor for cardiovascular disease. This concentration is mostly genetically determined by a complex interplay between the number of Kringle-IV type 2 repeats and Lipoprotein(a)-affecting variants. Besides lipoprotein(a) plasma concentration, there is an unmet need to identify individuals most at risk based on their LPA genotype. ResultsWe developed KILDA, a Nextflow pipeline, to identify the number of Kringle-IV type 2 repeats and Lp(a)-affecting variants directly from kmers generated from FASTQ files. The pipeline was tested on the 1000 Genomes Project (n=2459) and results were equivalent to DRAGEN-LPA (R2=0.93). In-silico datasets proved the robustness of KILDAs predictions under different scenarios of sequencing coverage and quality. ConclusionKILDA is an open-source and free-to-use pipeline to identify the number of Kringle-IV type 2 repeats and lipoprotein(a)-associated variants. Its results are equivalent to DRAGEN-LPA, offering a free and robust tool for determining the LPA kringle number even when inputting low coverage libraries. AvailabilityKILDA is publicly available at https://github.com/HCL-HUBL/KILDA along with a recipe to build an Apptainer image containing all the required dependencies. Contact: corentin.molitor@chu-lyon.fr Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. First, metabolic regulation controls metabolic fluxes (i.e., the rate of individual metabolic reactions) through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. A second regulatory layer sets the maximal theoretical level for each enzyme-controlled reaction by controlling the expression level of the catalyzing enzyme. In isolation, high-throughput data, such as metabolomics and transcriptomics data do not allow for accurate characterization of the hierarchical regulation of metabolism outlined above. Hence, they must be integrated in order to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we proposes INTEGRATE, a computational pipeline that integrates metabolomics (intracellular and optionally extracellular) and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomic data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients. Author summaryThe study of metabolism and its regulation finds increasing application in various fields, including biotransformations, wellness, and health. Metabolism can be studied using post-genomic technologies, notably transcriptomics and metabolomics, that provide snapshots of transcripts and metabolites in specific physio-pathological conditions. In the health field, the transcriptome and, more recently, the metabolome have been broadly profiled at the pre-clinical and clinical levels. The informative power of single omic technologies is inadequate since metabolism regulation involves a complex interplay of regulatory steps. While gene expression regulates metabolism by setting the upper level of metabolic enzymes, the interaction of metabolites with metabolic enzymes directly auto-regulates metabolism. Therefore there is a need for methods that integrate multiple data sources. We present INTEGRATE, a computational pipeline that captures dynamic features from the static snapshots provided by transcriptomic and metabolomic data. Through integration in a steady-state metabolic model, the pipeline predicts which reactions are controlled purely by metabolic control rather than by gene expression or a combination of the two. This knowledge is crucial in a clinical setting to develop personalized therapies in patients of multifactorial diseases, such as cancer. Besides cancer, INTEGRATE can be applied to different fields in which metabolism plays a driving role.

systems biology↗

bioRxiv2021

GPRuler: metabolic Gene-Protein-Reaction rules automatic reconstruction

BackgroundMetabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. ResultsBy mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. ConclusionsBy complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.

systems biology↗

bioRxiv2018

Genome-scale metabolic construction of the stress-tolerant hybrid yeast Zygosaccharomyces parabailii

Genome-scale metabolic models are powerful tools to understand and engineer cellular systems facilitating their use as cell factories. This is especially true for microorganisms with known genome sequences from which nearly complete sets of enzymes and metabolic pathways are determined, or can be inferred. Yeasts are highly diverse eukaryotes whose metabolic traits have long been exploited in industry, and although many of their genome sequences are available, few genome-scale metabolic models have so far been produced. For the first time, we reconstructed the genome-scale metabolic model of the hybrid yeast Zygosaccharomyces parabailii, which is a member of the Z. bailii sensu lato clade notorious for stress-tolerance and therefore relevant to industry. The model comprises 3096 reactions, 2091 metabolites, and 2413 genes. Our own laboratory data were then used to establish a biomass synthesis reaction, and constrain the extracellular environment. Through constraint-based modeling, our model reproduces the co-consumption and catabolism of acetate and glucose posing it as a promising platform for understanding and exploiting the metabolic potential of Z. parabailii.

systems biology↗

bioRxiv2018

Integration of single-cell RNA-seq data into metabolic models to characterize tumour cell populations

MotivationMetabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. Computational models hold the promise to bridge this gap, by estimating fluxes across metabolic pathways. Yet they currently portray the average behavior of intermixed subpopulations, masking their inherent heterogeneity known to hinder cancer diagnosis and treatment. If complemented with the information on single-cell transcriptome, now enabled by RNA sequencing (scRNA-seq), metabolic models of cancer populations are expected to empower the characterization of the mechanisms behind metabolic heterogeneity. To this aim, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate sc-transcriptomes into single-cell fluxomes.\n\nResultsWe show that the integration of scRNA-seq profiles of cells derived from lung ade-nocarcinoma and breast cancer patients, into a multi-scale stoichiometric model of cancer population: 1) significantly reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites.\n\nAvailabilityThe scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.\n\nContactchiara.damiani@unimib.it

systems biology↗

bioRxiv2018

MaREA: Metabolic feature extraction, enrichment and visualization of RNAseq data

The characterization of the metabolic deregulations that distinguish cancer phenotypes, and which might be effectively targeted by ad-hoc strategies, is a key open challenge. To this end, we here introduce MaREA (Metabolic Reaction Enrichment Analysis), a computational pipeline that processes cross-sectional RNAseq data to identify the metabolic reactions that are significantly up-/ down-regulated in different sample subgroups. MaREA relies on the definition of a Reaction Activity Score, computed as a function of the expression level of genes encoding for reaction enzymes, which can also be used as an effective metrics to cluster samples into distinct metabolic subgroups. MaREA finally allows to visualize the results in a graphical form directly on metabolic maps. We apply MaREA to distinct cancer datasets and we show that it can produce useful information and new experimental hypotheses on metabolic deregulation of cancer cells, also allowing to stratify patients in metabolic clusters with significantly different survival expectancy.

bioinformatics↗