Biology subjects

Pace, E.

Publications and source records attributed to Pace, E..

4 recordsLinked to original sources

bioRxiv2025

Redirecting TCR specificity in regulatory T cells toward class I HLA antigens mediates tissue-specific homing

Type 1 diabetes (T1D) is marked by the overexpression of class I major histocompatibility complex (MHC) antigens in pancreatic islets, which are targeted by islet-specific CD8+ T cells. Here, we aimed to improve regulatory T cell (Treg) infiltration into pancreatic islets by redirecting their specificity toward class I-restricted islet antigens. We functionally validated two public islet specific HLA-A2 (*02:01) restricted TCRs, one specific for ZnT8186-194 (clone D222D), the second for IGRP265-273 (clone 32) by dual locus (TRAC/CD4) homology-directed editing. Clone D222D was peptide-specific and CD8{beta} dependent while clone 32 exhibited antigen promiscuity and showed CD8 dependency. Engineered CD4to8 TCR Tregs maintained stable phenotypes, suppressed significantly better than their polyclonal counterpart, and showed co-receptor-dependent migration in vivo. This approach demonstrates that TCR specificity, reflected by its functional activity, is crucial for tissue-specific trafficking, paving the way to improve the efficacy of Treg therapies for T1D.

immunology↗

bioRxiv2021

Mesoporous silica particles functionalized with newly extracted fish oil (Omeg@Silica) inhibit lung cancer cell growth

Omeg@Silica microparticles consisting of whole fish oil rich in omega-3 lipids, vitamin D3 and zeaxanthin extracted with biobased limonene from anchovy fillet leftovers (AnchoisOil) encapsulated within mesoporous silica particles are highly effective in modulating oxidative stress, mitochondrial damage or in promoting antitumor effects in lung cancer cells. A panel of three different human non-small cell lung cancer (NSCLC) cell lines (A549, Colo 699 and SKMES) was used. Cancer cells were treated with AnchoisOil dispersed in ethanol (10 and 15 g/ml) or encapsulated in silica, and cell cycle, reactive oxigen species (ROS) and mitochondrial stress (MitoSOX) assessed by flow cytometry. The effects on long-term proliferation (clonogenic assay) were also evaluated. The sub-micron Omeg@Silica microparticles were more effective than fish oil alone in increasing ROS and mitocondrial damage, in altering cell cycle as well as in reducing colony formation ability in the tested lung cancer cell lines. These results suggest that Omeg@Silica mesoporous silica functionalized with whole fish oil has antitumor effects in NSCLC cell lines and support its investigation in lung cancer therapy.

cancer biology↗

bioRxiv2019

Biophotons: low signal/noise ratio reveals crucial events

We study the emission of photons from germinating seeds using an experimental technique designed to detect photons of extremely small intensity when the signal/noise ratio is low. We analyze the dark count signal in the absence of germinating seeds as well as the photon emission during the germination process. The technique of analysis adopted here was originally designed to measure the temporal complexity of astrophysical, sociological and physiological processes. The foundation of this method, called Diffusion Entropy Analysis (DEA), rests on Kolmogorov complexity. The updated version of DEA used in this paper is designed to determine if the signal complexity is generated by either non-ergodic crucial events with a non-stationary correlation function or by the infinite memory of a stationary but non-integrable correlation function or by a mixture of both processes. We find that dark count yields the ordinary scaling, thereby showing that no complexity of either kinds may occur in the absence of any seeds in the chamber. In the presence of seeds in the chamber anomalous scaling emerges, reminiscent of that found in neuro-physiological processes. However, this is a mixture of both processes and with the progress of germination the non-ergodic component tends to vanish and complexity is dominated by the stationary infinite memory. We argue that this may be a sign of quantum coherence that according to some authors is the important ingredient of cognition.

biophysics↗

bioRxiv2017

Predicting Ligand-Dependent Tumors From Multi-Dimensional Signaling Features

Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using a novel approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo.\n\nSummaryPrediction of ligand-induced growth of cancer cell lines, which correlates with ligand-blocking antibody efficacy, could be significantly improved by learning from features of a mechanistic signaling model, and was applied to reveal a correlation between growth factor expression and predicted response in patient samples.

systems biology↗