Biology subjects

Garofalo, M.

Publications and source records attributed to Garofalo, M..

4 recordsLinked to original sources

bioRxiv2026

AI-Driven Computational Design of Peptide-Based WWP1 Inhibitors as Promising Therapeutic Agents Against Breast Cancer, Including Triple-Negative Subtype

Breast cancer (BC) is the second most common noncutaneous cancer and the second leading cause of cancer-related death in women. BC is classified into three primary subtypes, with triple-negative breast cancer (TNBC) having the poorest prognosis because it lacks specific targetable markers. Preclinical studies on TNBC indicated a common occurrence of diminished tumor-suppressor activity of PTEN, activating the PI3K/AKT/mTOR signaling pathway. Notably, published studies reveal that the WWP1 enzyme plays a pivotal role in driving PTEN degradation via ubiquitination, unveiling a promising therapeutic target for treating TNBC. In the search of new WWP1 inhibitors, we used artificial intelligence (AI)-driven computational strategies for de novo design of peptide-based WWP1 inhibitors and identified a hexapeptide, termed WI23-B, which demonstrated high nanomolar binding affinity to WWP1. In TR-FRET enzymatic assays, WI23-B inhibited WWP1 activity with an IC of approximately 11 {micro}M. In MCF7 and MDA-MB-231 breast cancer cell lines, WI23-B showed promising cytotoxic efficacy, particularly in combination with the PI3K inhibitor BYL719, also when it was loaded into nanocapsules. Collectively, these findings highlight WI23-B as a promising lead peptide with potent WWP1 inhibitory activity and synergistic antiproliferative effects when combined with PI3K inhibitors. While further structural optimization is required to enhance its potency and pharmacological properties, our results provide a strong foundation for the development of next-generation WWP1 inhibitors. Such agents have the potential to reshape therapeutic strategies for BC and TNBC by enabling more effective and less toxic treatment regimens, ultimately reducing the reliance on high-dose chemotherapy and minimizing adverse effects.

cancer biology↗

bioRxiv2020

Different RNA profiles in plasma derived small and large extracellular vesicles of Neurodegenerative diseases patients.

BackgroundIdentifying robust biomarkers is essential for early diagnosis of neurodegenerative diseases (NDs). Large (LEVs) and small extracellular vesicles (SEVs) are extracellular vesicles (EVs) of different sizes and biological functions transported in blood and they may be valid biomarkers for NDs. The aim of our study was to investigate common and different mRNA/miRNA signatures in plasma derived LEVs and SEVs of Alzheimers Disease (AD), Parkinsons disease (PD), Amyotrophic Lateral Sclerosis (ALS) and Fronto-Temporal Dementia (FTD) patients. MethodsLEVs and SEVs were isolated from plasma of patients and healthy volunteers (CTR) by filtration and ultracentrifugation and RNA was extracted. Whole transcriptome and miRNA libraries were carried out by Next Generation Sequencing (NGS). ResultsWe detected different deregulated RNAs in LEVs and SEVs from patients with the same disease. MiRNAs resulted to be the most interesting subpopulation of transcripts transported by plasma derived SEVs since they appeared to discriminate all NDs disease from CTRs and they can provide a signature for each NDs. Common enriched pathways for SEVs were mainly linked to ubiquitin mediated proteolysis and Toll-like receptor signaling pathways and for LEVs to neurotrophin signaling and Glycosphingolipid biosynthesis pathway. ConclusionLEVs and SEVs are involved in different pathways and this might give a specificity to their role in the spreading/protection of the disease. The study of common and different RNAs transported by LEVs and SEVs can be of great interest for biomarker discovery and for pathogenesis studies in neurodegeneration.

neuroscience↗

bioRxiv2019

Systematic development of peptide inhibitors targeting the CXCL12/HMGB1 interaction

During inflammatory reactions, the production and release of chemotactic factors guide the recruitment of selective leukocyte subpopulations. HMGB1 and the chemokine CXCL12, both released in the microenvironment, form a heterocomplex, which exclusively acts on the chemokine receptor CXCR4, enhancing cell migration and, in some pathological conditions such as Rheumatoid Arthritis, exacerbating the immune response. An excessive cell influx at the inflammatory site can be diminished by disrupting the heterocomplex. Here, we report the computationally driven identification of a novel peptide (HBP08), which binds HMGB1 with the highest affinity reported so far (Kd of 0.8 {+/-} 0.1 M), able to selectively inhibit the activity of the CXCL12/HMGB1 heterocomplex. The identification of this peptide represents an important step towards the development of innovative pharmacological tools for the treatment of severe chronic inflammatory conditions characterized by an uncontrolled immune response.

biochemistry↗

bioRxiv2019

Machine learning predicts immunoglobulin light chain toxicity through somatic mutations

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LCs) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage. However, delays in diagnosis are common, with a consequent poor patients prognosis, as symptoms usually appear only after strong organ involvement. Here, we present LICTOR, a machine learning approach predicting LC toxicity in AL, based on the distribution of somatic mutations acquired during clonal selection. LICTOR achieved a specificity and a sensitivity of 0.82 and 0.76, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.87. Tested on an independent set of 12 LCs sequences with known clinical phenotypes, LICTOR achieved a prediction accuracy of 83%. Furthermore, we were able to abolish the toxic phenotype of an LC by in silico reverting two germline-specific somatic mutations identified by LICTOR and by experimentally assessing the loss of in vivo toxicity in a Caenorhabditis elegans model. Therefore, LICTOR represents a promising strategy for AL diagnosis and reducing high mortality rates in AL.

bioinformatics↗