Biology subjects

Gallo, J.

Publications and source records attributed to Gallo, J..

6 recordsLinked to original sources

bioRxiv2026

3D neuroblastoma models expose divergent responses to magnetic hyperthermia and photothermal therapy

PurposeHyperthermia is emerging as an adjunct strategy in pediatric oncology, yet its translation is limited by poor understanding of how different modalities impact complex tumor microenvironments. Neuroblastoma (NB), the most common extracranial solid tumor in children, displays profound heterogeneity that hampers therapeutic predictability. Here, we performed the first systematic head-to-head comparison of photothermal therapy (PTT) and magnetic hyperthermia (MH) in tissue-engineered NB (TE-NB) models. MethodsTE-NB scaffolds incorporating five NB cell lines were loaded with magnetic nanoparticles (MNPs) and subjected to PTT (808-nm laser, 130 W/cm2, 10 min) or MH (285 kHz, 20 mT, 60 min). Constructs were analyzed at 24 h, 48 h, and 5 d post-treatment for DNA content, cell viability, proliferation (Ki67 immunohistochemistry), and apoptosis (caspase-3/7 staining). ResultsMH produced consistent MNP-dependent heating with minimal background, while PTT was dominated by nonspecific medium absorption. Both modalities modulated proliferation within 24 h, but effects varied sharply by cell line and scaffold region, reflecting microenvironmental heterogeneity. By 48 h, PTT often triggered paradoxical increases in proliferation, whereas MH disrupted scaffold integrity, reduced DNA content, and suppressed Ki67 expression. Notably, neither modality induced sustained caspase-3/7 activation, indicating that cytotoxicity proceeds via non-apoptotic pathways. ConclusionOur findings position MH as a superior modality for uniform heating and proliferation control in 3D NB models, but also highlight that hyperthermia should be considered a context-dependent modulator rather than a binary cytotoxic agent. By integrating patient-specific TE-NB platforms, "precision hyperthermia" could define individualized therapeutic windows, optimize combinations with pro-apoptotic or immunomodulatory agents, and accelerate translation of hyperthermia strategies for children with NB.

bioengineering↗

bioRxiv2026

Introduction to Single-cell Physiologically-Based Pharmacokinetic (scPBPK) Models

The current investigation introduces single-cell physiologically-based pharmacokinetic (scPBPK) models to gain insight into drug disposition at the cellular scale. The transition from standard PBPK (sPBPK) models to scPBPK models required depiction of expression-dependent (ED) processes, such as drug metabolism or membrane transport. ED processes utilize weighting functions - a defined or data-driven distribution -that yield heterogeneity in individual cell kinetics. Two scPBPK model examples are provided, one involving a drug (AZD1775) subject to 3 ED blood-brain barrier transport processes, and another drug (midazolam) with a single ED process of metabolism by hepatocytes. For both examples, the weighting function for each ED process was defined by a negative binomial distribution that is often used in scRNAseq analytics. The AZD1775 model simulations indicated a large degree of single cell drug concentration heterogeneity, whereas those for midazolam did not, due to high membrane transport relative to metabolism. scPBPK models offer a means to probe cellular pharmacokinetics compatible with modern omic technologies and may be extended to pharmacodynamic models. TeaserThe modeling framework to predict drug concentrations in single cells is presented.

pharmacology and toxicology↗

bioRxiv2023

The critical role of spatio-temporal control in combinatorial chemo- and magnetic hyperthermia thermo-therapy: 'the where', 'the how' and 'the when'

Combinatorial treatments hold the key to the future of cancer treatment as they enhance therapeutic indexes by inducing synergistic effects and reducing resistance processes, while often providing a safer option for patients with fewer off-target effects. However, combinatorial treatments bring extra problems to cancer management not only derived from the actual compatibility of the treatments, but also from their hands-on administration. Operational parameters such as administration order and dosing (dose, spacing) have to be optimized in order to positively impact patient prognosis. Here we present a systematic study on the optimization and the importance of these parameters within the framework of a combinatorial chemo-/thermo-therapy. Parameters like where, how and when are investigated in detail. Furthermore, we delve into the underlying biochemical mechanisms driving the observed effects through transcriptome analysis.

biochemistry↗

bioRxiv2021

Drug specific transcriptomic signatures in healthy human subject iPSC derived cardiomyocytes

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focused on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced pluripotent stem cell-derived cardiomyocytes. Using bulk transcriptomics profiles, we applied singular value decomposition to identify drug-selective patterns in cell lines obtained from multiple healthy human subjects. Cellular pathways affected by highly cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Whole genome sequences of the cell lines, using outlier responses enabled us to correctly reidentify a genomic variant associated with anthracycline cardiotoxicity and predict genomic variants potentially associated with TKI cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale predictive understanding of cardiotoxicity for drug development and patient stratification. One sentence summaryGenes, pathways, and cell types of the human heart associated with antineoplastic drug cardiotoxicity.

systems biology↗

bioRxiv2020

Exploring G and C-quadruplex structures as potential targets against the severe acute respiratory syndrome coronavirus 2

In this paper we report the analysis of the 2019-nCoV genome and related viruses using an upgraded version of the open-source algorithm G4-iM Grinder. This version improves the functionality of the software, including an easy way to determine the potential biological features affected by the candidates found. The quadruplex definitions of the algorithm were optimized for 2019-nCoV. Using a lax quadruplex definition ruleset, which accepts amongst other parameters two residue G- and C-tracks, hundreds of potential quadruplex candidates were discovered. These sequences were evaluated by their in vitro formation probability, their position in the viral RNA, their uniqueness and their conservation rates (calculated in over three thousand different COVID-19 clinical cases and sequenced at different times and locations during the ongoing pandemic). These results were compared sequentially to other Coronaviridae members, other Group IV (+)ssRNA viruses and the entire realm. Sequences found in common with other species were further analyzed and characterized. Sequences with high scores unique to the 2019-nCoV were studied to investigate the variations amongst similar species. Quadruplex formation of the best candidates was then confirmed experimentally. Using NMR and CD spectroscopy, we found several highly stable RNA quadruplexes that may be suitable theranostic targets against the 2019-nCoV. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/257493v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1ab4843org.highwire.dtl.DTLVardef@152ebeorg.highwire.dtl.DTLVardef@afd7aforg.highwire.dtl.DTLVardef@793707_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2018

Systems therapeutics analyses identify genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer

The ability to predict responsiveness to drugs in individual patients is limited. We hypothesized that integrating molecular information from databases would yield predictions that could be experimentally tested to develop genomic signatures for sensitivity or resistance to specific drugs. We analyzed TCGA data for lung adenocarcinoma (LUAD) patients and identified a subset where xanthine dehydrogenase expression correlated with decreased survival. We tested allopurinol, a FDA approved drug that inhibits xanthine dehydrogenase on a library of human Non Small Cell Lung Cancer (NSCLC) cell lines from CCLE and identified sensitive and resistant cell lines. We utilized the gene expression profiles of these cell lines to identify six-gene signatures for allopurinol sensitive and resistant cell lines. Network building and analyses identified JAK2 as an additional target in allopurinol-resistant lines. Treatment of resistant cell lines with allopurinol and CEP-33779 (a JAK2 inhibitor) resulted in cell death. The effectiveness of allopurinol alone or allopurinol and CEP-33779 were verified in vivo using tumor formation in NCR-nude mice. We utilized the six-gene signatures to predict five additional allopurinolsensitive NSCLC lines, and four allopurinol-resistant lines susceptible to combination therapy. We found that drug treatment of all cell lines yielded responses as predicted by the genomic signatures. We searched the library of patient derived NSCLC tumors from Jackson Laboratory to identify tumors that would be predicted to be sensitive or resistant to allopurinol treatment. Both patient derived tumors predicted to be allopurinol sensitive showed the predicted sensitivity, and the predicted resistant tumors were sensitive to combination therapy. These data indicate that we can use integrated molecular information from cancer databases to predict drug responsiveness in individual patients and thus enable precision medicine.

systems biology↗