Biology subjects

Milano, M.

Publications and source records attributed to Milano, M..

3 recordsLinked to original sources

bioRxiv2025

The ganglioside GD3 and its Synthase (ST8SIA1) as novel senescence markers associated with osteoarthritis

Osteoarthritis (OA), is the most common age-induced degenerative joint disease. It is associated with synovial inflammation, subchondral bone remodeling and cartilage degradation. One of the significant emerging causes of OA progression is senescent cell accumulation within the joint compartment during lifespan. Currently, there are no therapeutic approaches nor stratification tools that rely on the senescence burden in OA. In this study, we identified the b-series ganglioside 3 (GD3) as new senescent cell surface marker associated with OA. Joint RNA sequencing analysis revealed an increase expression of the GD3 synthase, ST8SIA1 in cartilage, synovial tissue, and subchondral bone marrow from OA patients compared to healthy donors. Moreover, we revealed a strong correlative association between the expression of ST8SIA1 and GD3 production with senescence hallmarks in an in vitro-induced 3D organotypic OA cartilage model but also with cartilage histological grading scores in human and preclinical murine OA joints. Anti-GD3 cell sorting showed that GD3-positive human OA chondrocytes or human OA synoviocytes are enriched in senescence and SASP markers compared to GD3-negative counterparts confirming that GD3 is a cell surface marker linked to the senescence stage. Intra-articular anti-GD3 antibody delivery in experimental OA model, reduced local expression of senescence and OA markers in association with a protection against OA-induced subchondral bone remodeling. Our research demonstrates a compelling linkage between ST8SIA1 gene, GD3 and senescence in OA pathology, revealing knowledge and perspectives for a better understanding and anti-senescence treatment of OA pathogenesis.

cell biology↗

bioRxiv2024

Non Parametric Differential Network Analysis: A Tool for Unveiling Specific Molecular Signatures.

The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential Network Analysis (DNA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DNA algorithms scrutinize alterations in interaction patterns derived from experimental data. Introducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes. Applying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions. Author summaryThis paper explores a novel technique for Differential Network Analysis (DNA) that considers sex-based variations. DNA compares biological networks under different conditions, like healthy vs. diseased states. Our method tackles the limitations of traditional DNA approaches, which often assume specific data distributions. We propose a non-parametric DNA methodology that integrates sex differences and identifies differential edges between networks. This approach utilizes data on gene expression levels and sex to construct a more accurate picture of the molecular mechanisms underlying diseases, particularly those exhibiting sex-dependent variations. Our method paves the way for a deeper understanding of how sex and age influence disease processes at the molecular level.

bioinformatics↗

bioRxiv2019

Cell type prioritization in single-cell data

We present a machine-learning method to prioritize the cell types most responsive to biological perturbations within high-dimensional single-cell data. We validate our method, Augur (https://github.com/neurorestore/Augur), on a compendium of single-cell RNA-seq, chromatin accessibility, and imaging transcriptomics datasets. We apply Augur to expose the neural circuits that enable walking after paralysis in response to spinal cord neurostimulation.

genomics↗