Biology subjects

Napolitano, F.

Publications and source records attributed to Napolitano, F..

11 recordsLinked to original sources

bioRxiv2025

Thermo-Dynamic Flux Balance Analysis, a novel approach to simulate circadian metabolism

Circadian metabolism arises from complex, time-dependent interactions across organs, yet experimental characterization in humans remains limited. Current whole-body metabolic models are either too large for dynamic simulation or insufficiently detailed to capture temporal physiology. We developed a multi-tissue human metabolic reconstruction, HGEM1.19+, an extensively curated model for dynamic simulation. We introduced Thermo-Dynamic Flux Balance Analysis (tdFBA), a novel formulation that integrates thermodynamic constraints, organ-specific enzyme capacities, solubility limits, osmotic balance and pH buffering. Murine circadian transcriptomics and metabolomics were integrated with human datasets to parameterize temporal dynamics. A genetic algorithm optimized internal parameters under CHOW diet conditions and model performance was evaluated using circadian metabolomics and gene-sage patterns. The framework recapitulated key metabolic oscillations under CHOW diet and partially reconstructed perturbed states including HFD, BMAL1 knockout, and tissue-specific re-entrainment. tdFBA enables physiologically grounded, multi-organ dynamic simulations and provides a foundation for exploring human circadian metabolism and perturbation responses.

systems biology↗

bioRxiv2025

Non-clinical safety of GRAd vector-based COVID-19 and HIV vaccines supports a platform regulatory approach

The rapid development of safe and efficacious vaccines is often hindered by extensive, mandated non-clinical safety evaluations in animals. Here we present the complete non-clinical studies for two investigational vaccines based on the GRAd platform, a gorilla-derived group C adenoviral vector. When administered intramuscularly, GRAd-COV2 and GRAdHIVNE1 were well tolerated. Studies in rats and rabbits showed localized distribution and transient, non-adverse inflammatory responses, while successfully inducing expected immune responses to their respective antigens. Notably, both vaccines demonstrated a consistent safety profile despite transgene and backbone differences, comparable to other replication-defective adenoviral vectors. The established non-clinical safety profile of the GRAd platform provides a robust foundation for a more efficient and streamlined regulatory pathway. By leveraging this prior knowledge, future GRAd-based vaccines can achieve accelerated clinical development while fully adhering to the ethical principles of replacement, reduction, and refinement of animal use in research.

pharmacology and toxicology↗

bioRxiv2024

Computational identification of small molecules for increased gene expression by synthetic circuits in mammalian cells

Engineering mammalian cells with synthetic circuits is leading the charge in next generation biotherapeutics and industrial biotech innovation. However, applications often depend on the cells productive capacity, which is limited by the finite cellular resources available. We have previously shown that cells engineered with incoherent feedforward loops (iFFL-cells) operate at higher capacity than those engineered with the open loop (OL). Here, we performed RNA-sequencing on cells expressing the iFFL and utilized DECCODE, an unbiased computational method, to match our data with thousands of drug-induced transcriptional profiles. DECCODE identified compounds that consistently enhance expression of both transiently and stably expressed genetic payloads across various experimental scenarios and cell lines, while also reducing external perturbations on integrated genes. Further, we show that drug treatment enhances the rate of AAV and lentivirus transduction, facilitating the prototyping of genetic devices for gene and cell therapies. Altogether, despite limiting intracellular resources is a pervasive, and strongly cell-dependent problem, we provide a versatile tool for a wide range of biomedical and industrial applications that demand enhanced productivity from engineered cells.

synthetic biology↗

bioRxiv2024

Multi-task deep latent spaces for cancer survival and treatment response prediction

MotivationCancer is a very heterogeneous disease that can be difficult to treat without addressing the specific mechanisms driving tumour progression in a given patient. High-throughput screening and sequencing data from cancer cell-lines has driven many developments in drug development, however, there are important aspects crucial to precision medicine that are often overlooked, namely the inherent differences between tumours in patients and the cell-lines used to model them in vitro. Recent developments in transfer learning methods for patient and cell-line data have shown progress in translating results from cell-lines to individual patients in silico. However, transfer learning can be forceful and there is a risk that clinically relevant patterns in the omics profiles of patients are lost in the process. ResultsWe present MODAE, a novel deep learning algorithm to integrate omics profiles from cell-lines and patients for the purposes of exploring precision medicine opportunities. MODAE implements patient survival prediction as an additional task in a drug-sensitivity transfer learning schema and aims to balance autoencoding, domain adaptation, drugsensitivity prediction, and survival prediction objectives in order to better preserve the heterogeneity between patients that is relevant to survival. While burdened with these additional tasks, MODAE performed on par with baseline survival models, but struggled in the drug-sensitivity prediction task. Nevertheless, these preliminary results were promising and show that MODAE provides a novel AI-based method for prioritizing drug treatments for high-risk patients. Availabilityhttps://github.com/UEFBiomedicalInformaticsLab/MODAE

bioinformatics↗

bioRxiv2023

Mapping Cell-to-cell Interactions from Spatially Resolved Transcriptomics Data

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently, through the introduction of spatially resolved transcriptomics technologies (SRTs), especially those that achieve single cell resolution. However, significant challenges remain to analyze such highly complex data properly. Here, we introduce a Bayesian multi-instance learning framework, spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight spacias power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates, and most importantly the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of spacia for all three commercialized single cell resolution ST technologies: MERSCOPE/Vizgen, CosMx/Nanostring, and Xenium/10X. Spacia unveiled how endothelial cells, fibroblasts and B cells in the tumor microenvironment contribute to Epithelial-Mesenchymal Transition and lineage plasticity in prostate cancer cells. We deployed spacia in a set of pan-cancer datasets and showed that B cells also participate in PDL1/PD1 signaling in tumors. We demonstrated that a CD8+ T cell/PDL1 effectiveness signature derived from spacia analyses is associated with patient survival and response to immune checkpoint inhibitor treatments in 3,354 patients. We revealed differential spatial interaction patterns between {gamma}{delta} T cells and liver hepatocytes in healthy and cancerous contexts. Overall, spacia represents a notable step in advancing quantitative theories of cellular communications.

bioinformatics↗

bioRxiv2023

AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data

Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties, and represent a potentially effective therapeutic target towards long-term remission by means of differentiation induction. By leveraging an Artificial Intelligence (AI) approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the LINCS database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of 5 out of 6 tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron, and A-443654 offer a potential avenue for targeted therapies against breast CSCs.

bioinformatics↗

bioRxiv2023

Identification of treatment-responsive genes in spatial transcriptomics data by leveraging injection site information

Spatial Transcriptomics assays allow to study gene expression as a function of the spatial position of cells across a tissue sample. Although several methods have been proposed to identify spatially variable genes, they do not take into account the position of the injection site in the case of treated samples. In this study, we developed a method to identify treatment-responsive genes based on the assumption that the distance of the cells from the injection site across the tissue would affect the corresponding transcriptional response. In particular, we tested our approach using a publicly available ST dataset obtained after injection of heme into the striatum nucleus of a murine brain. We observed that several biologically relevant genes were detected by our method as showing a distance-dependent expression trend. We finally compared the results against a ground-truth gene set and a state-of art pattern-based method.

bioinformatics↗

bioRxiv2020

Automatic identification of small molecules that promote cell conversion and reprogramming

Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although numerous transcription factors have been discovered that are able to promote cell reprogramming and trans-differentiation, methods based on their up-regulation tend to show low efficiency. The identification of small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here we present DECCODE, an unbiased computational method for the identification of such molecules solely based on transcriptional data. DECCODE matches the largest available collection of drug-induced profiles (the LINCS database) for drug treatments against the largest publicly available dataset of primary cell transcriptional profiles (FANTOM5), to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive in silico and in vitro validation of DECCODE in the context of human induced pluripotent stem cells (hIPSCs) generation shows that the method is able to prioritize drugs enhancing cell reprogramming. We also generated predictions for cell conversion with single drugs and drug combinations for 145 different cell types and made them available for further studies.

bioinformatics↗

bioRxiv2017

gene2drug: a Computational Tool for Pathway-based Rational Drug Repositioning

MotivationDrug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects.\n\nResultsWe introduce a novel computational methodology for rational drug repositioning, which exploits the transcriptional responses following treatment with small molecule. Specifically, given a therapeutic target gene, a prioritisation of potential effective drugs is obtained by assessing their impact on the transcription of genes in the pathway(s) including the target. We performed in silico validation and comparison with a state-of-art technique based on similar principles. We next performed experimental validation in two different real-case drug repositioning scenarios: (i) upregulation of the glutamate-pyruvate transaminase, which has been shown to induce reduction of oxalate levels in a mouse model of primary hyperoxaluria, and (ii) activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy, whose modulation may be beneficial in neurodegenerative disorders.\n\nAvailabilityfree at http://gene2drug.tigem.it\n\nContactdibernardo@tigem.it

bioinformatics↗

bioRxiv2017

Serotonin Buffers Hippocampal Neuroplasticity And Emotional Behavior

Adaptation to environmental insults is an evolutionary mechanism essential for survival. The hippocampus participates in controlling adaptive responses to stress and emotional state through the modulation of neuroplasticity events, which are dysregulated in stress-related neuropsychiatric disorders. The neurotransmitter serotonin (5-HT) has been proposed as a pivotal player in hippocampal neuroplasticity in both normal and neuropsychiatric conditions though its role remains still poorly understood. Here, we investigated the impact of 5-HT deficiency on hippocampal activity combining RNA-seq, in vivo neuroimaging, neuroanatomical, biochemical and behavioral experiments on 5-HT depleted mice. We unveil that serotonin is required for appropriate activation of neuroplasticity adaptive mechanisms to environmental insults. Bidirectional deregulation of these programs in serotonin depleted mice is associated with opposite behavioral traits that model core symptoms of bipolar disorder. These findings delineate a previously unreported buffering role of 5-HT in instructing hippocampal activity and emotional responses to environmental demands.

neuroscience↗

bioRxiv2017

Integrated Structure-Transcription analysis of small molecules reveals widespread noise in drug-induced transcriptional responses and a transcriptional signature for drug-induced phospholipidosis

We performed an integrated analysis of drug chemical structures and drug-induced transcriptional responses. We demonstrated that a network representing 3D structural similarities among 5,452 compounds can be used to automatically group together drugs with similar scaffolds and mode-of-action. We then compared the structural network to a network representing transcriptional similarities among a subset of 1,309 drugs for which transcriptional response were available in the Connectivity Map dataset. Analysis of structurally similar, but transcriptionally different, drugs sharing the same mode of action (MOA) enabled us to detect and remove weak and noisy transcriptional responses, greatly enhancing the reliability and usefulness of transcription-based approaches to drug discovery and drug repositioning. Analysis of transcriptionally similar, but structurally different drugs with unrelated MOA, led us to the identification of a \"toxic\" transcriptional signature indicative of lysosomal stress (lysosomotropism) and lipid accumulation (phospholipidosis) partially masking the target-specific transcriptional effects of these drugs. We further demonstrated by High Content Screening that this transcriptional signature is caused by the activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy. Our results show that chemical structures and transcriptional profiles provide complementary information and that combined analysis can lead to new insights on on- and off-target effects of small molecules.

bioinformatics↗