Biology subjects

Savino, A.

Publications and source records attributed to Savino, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Psychedelic Hormesis: LSD Activates Adaptive Stress Transcriptional Programs in the Prefrontal Cortex

Background Classic psychedelics are serotonergic agents increasingly recognized for their ability to produce rapid and long-lasting therapeutic effects in several neuropsychiatric conditions, yet the molecular mechanisms that translate acute serotonergic perturbation into long-term brain adaptation remain poorly understood. Psychedelic action is commonly attributed to enhanced neuronal plasticity, but emerging evidence suggests broader engagement of stress-responsive and homeostatic processes across neural and non-neural cell types. Methods To define the temporal structure of psychedelic-induced transcriptional responses, we profiled gene expression in the prefrontal cortex of mice at early and delayed time points following a single administration of lysergic acid diethylamide (LSD). Differentially expressed genes were then contextualized through pathway-level and cross-dataset comparisons with established models of adaptive and chronic stress. Results LSD induced a coherent, temporally organized transcriptional program extending beyond immediate neuronal activation. Early responses reflected adaptive metabolic and stress-related signaling, whereas later ones involved circadian and neuroendocrine regulation. A persistent transcriptional core spanning both time points indicated sustained regulation of metabolic, circadian, and stress-responsive pathways. Comparative analyses showed that, although LSD-induced transcriptional profile shares features with general stress responses, its preferentially aligns with adaptive, hormetic stress paradigms rather than maladaptive chronic stress. Conclusions These findings indicate that in the prefrontal cortex LSD activates adaptive stress transcriptional programs consistent with hormetic adaptation, providing a molecular framework to interpret the enduring effects of psychedelics beyond synaptic plasticity.

molecular biology↗

bioRxiv2026

Lysergic acid diethylamide reverses aging- and neurodegeneration-associated brain transcriptional programs

Psychedelic compounds such as lysergic acid diethylamide (LSD) are increasingly studied for their neuroplastic effects and potential relevance to brain aging and neurodegeneration. However, the molecular mechanisms linking psychedelic-induced plasticity to age-associated cognitive decline remain unclear. Brain aging and dementia are characterized by coordinated transcriptional programs that underlie synaptic dysfunction and altered neuron-glia interactions. If psychedelic-induced plasticity engages opposing molecular programs, it could counteract these conserved trajectories. In computational drug discovery, this concept has been formalized as the principle of transcriptional signature reversal, whereby compounds inducing gene expression states opposite to disease-associated programs may exert a therapeutic effect by counteracting disease-associated phenotypes. Here, we combine cross-species transcriptomic analyses with experimental validation to test whether LSD opposes conserved signatures of brain aging and dementia. By comparing transcriptional profiles induced by chronic LSD treatment in rodents with age- and dementia-associated gene expression changes in the human prefrontal cortex, we show that LSD induces gene expression patterns strongly anti-correlated with aging and neurodegeneration programs. This reversal is specific compared to other pharmacological perturbations and is reproducible across datasets and species. Moreover, LSD counteracts amyloid-{beta}-induced structural and molecular alterations in primary cortical neurons, linking transcriptomic opposition to functional rescue under neurodegenerative stress. Together, our findings suggest that LSD modulates molecular and cellular pathways associated with brain aging and neurodegeneration, linking systems-level gene expression changes to structural and functional resilience in neurodegeneration-relevant contexts.

neuroscience↗

bioRxiv2026

ATLAS: a scverse-compatible package for multi-omic single-cell trajectory inference integration

Single-cell trajectory inference is widely used to study cellular differentiation and fate decisions, yet most methods rely solely on transcriptomic data and therefore capture only part of the regulatory processes underlying cell-state transitions. Here we present ATLAS (Advanced Trajectory Learning from multi-omics At Single-cell resolution), a scverse-compatible Python package for trajectory inference from paired single-cell RNA-seq and ATAC-seq data. ATLAS integrates transcriptomic and chromatin accessibility information through Weighted Nearest Neighbor graphs, enabling both modalities to jointly inform pseudotime estimation, terminal-state identification, and fate probability inference within a unified multi-omic representation. Across synthetic and real datasets, ATLAS reconstructs coherent developmental trajectories, captures progressive fate commitment, and resolves biologically meaningful lineage structures, highlighting the value of multi-omic integration for characterizing cellular developmental dynamics. In addition, ATLAS enables joint analysis of transcription factor expression and accessibility-derived target-gene activity along pseudotime, providing insights into regulatory programs spanning transcriptomic and epigenomic layers that are not readily detectable from unimodal data. As a proof of concept, ATLAS recapitulates known hair follicle regulatory programs and reveals coherent multi-omic trajectories in which Lef1-associated regulatory patterns are linked to hair shaft differentiation. Overall, ATLAS provides an interoperable and biologically informative framework for studying cellular differentiation and regulatory dynamics in single-cell multi-omics experiments.

bioinformatics↗

bioRxiv2026

CRISPR-enhanced assessment of variants of unknown significance nominates oncology therapeutic targets and drug repositioning opportunities

Interpreting infrequent somatic variants remains a challenge in cancer genomics. We developed CRISPR-VUS, a framework that uses public Cancer Dependency Map data to identify Dependency-Associated Mutations (DAMs) - variants linked to increased host-gene dependency - with resolution extending to singleton events. Analysis of 977 cell lines across 36 cancer types identified 2,376 DAMs in 1,383 genes, including 1,260 not established as cancer drivers. DAM-bearing genes converge on oncogenic networks, while recurrence in histology-matched tumours, functional-impact predictions, tractability and pharmacological associations enable systematic prioritisation. Prime editing showed that the prioritised NSCLC-specific RTN4IP1-A80T DAM conferred a significant competitive growth advantage in a lung epithelial model, nominating a candidate driver allele. Exploratory pharmacological testing showed a greater maximal response to istaroxime in ATP1B3-I189M-bearing than in ATP1B3-wild-type cells. CRISPR-VUS combines dependency-based rare-variant discovery with evidence-guided prioritisation to nominate candidate drivers, therapeutic targets and drug-repositioning hypotheses. Interactive results are available at https://vus-portal.fht.org/.

bioinformatics↗

bioRxiv2026

Integrative Comparison of GeneHancer and Single-Cell Co-accessibility Reveals Active Enhancer-Gene Interactions

Linking enhancers to their target genes remains challenging due to the context-independent nature of curated annotations and the noise inherent in data-driven predictions. GeneHancer provides a comprehensive catalogue of enhancer-gene associations, but many elements are inactive in specific biological settings. Conversely, co-accessibility inferred from single-cell chromatin accessibility data captures sample-specific regulatory structure but may reflect indirect or non-functional interactions. This work integrates these complementary perspectives by comparing GeneHancer annotations with co-accessibility networks derived from a human PBMC Multiome dataset. Using Circe to infer peak-peak co-accessibility and GRAIGH to map peaks onto GeneHancer elements, this approach identifies enhancer-gene associations supported both by prior evidence and by accessibility patterns in the dataset. Only a small subset of GeneHancer links is validated by co-accessibility, yet these conserved associations display substantially higher cell-type specificity and stronger accessibility-expression concordance than either the full or "Elite" GeneHancer sets. This refined subset isolates regulatory interactions that are both biologically plausible and active in the sample, reducing redundancy and improving interpretability. Our results show that integrating curated enhancer annotations with single-cell epigenomic evidence yields a focused, high-confidence regulatory map suited for analyzing transcriptional regulation and cell identity in a dataset-specific manner.

bioinformatics↗

bioRxiv2025

Spike Train Scalograms (STS): a Deep Learning Classification Pipeline for Neuronal Cell Types

Classifying neuronal cell types is crucial for understanding the intricate circuitry of the cerebral cortex, which comprises diverse specialized neurons essential for brain function. Traditional Machine Learning (ML) approaches rely on manually engineered electrophysiological (EP) features, often overlooking subtle and complex patterns within spike train data. This study introduces a novel Spike Train Scalograms (STS)-based Deep Learning (DL) pipeline that integrates Continuous Wavelet Transform (CWT) scalograms with pre-trained Convolutional Neural Network (CNN) architectures to classify neuronal cell types with high accuracy. Utilizing patch-clamp EP recordings from 5,590 murine cortical neurons, the pipeline transforms spike trains into time-frequency representations via CWT, capturing both transient and sustained signal characteristics. These scalograms are then processed by fine-tuned CNN architectures, including InceptionV3, which achieved a balanced accuracy and weighted F1-Score of 90.53% and 90.03%, respectively. The STS pipeline effectively distinguishes between major neuronal types such as Pvalb, Sst, Vip/Lamp5, and Excitatory neurons, even in the presence of class imbalances. Moreover, an explainability analysis using saliency maps and SHAP revealed high correspondence between the DL approach, the ML baseline and biological knowledge of these neuronal types. The results demonstrate that, by combining an advanced spectral analysis with DL techniques, neurons can be classified with high accuracy, employing only two raw sweeps rather then the full stimulation range required by shallow approaches.

bioinformatics↗

bioRxiv2024

Start&Stop - a PhysiCell and PhysiBoSS 2.0 add-on for interactive simulation control

In computational biology, in silico simulators are vital for exploring and understanding the behavior of complex biological systems. Hybrid multi-level simulators, such as PhysiCell and PhysiBoSS 2.0, integrate multiple layers of biological complexity, providing deeper insights into emergent patterns. However, one key limitation of these simulators is the inability to adjust simulation parameters once the simulation has started, which hinders the interactive exploration and adaptation of dynamic protocols ranging from biofabrication to in vitro pharmacological testing. To address this challenge, we introduce the Start & Stop add-on for PhysiCell and PhysiBoSS 2.0. This add-on offers multi-level state preservation and multi-modal stop control, triggered by simulation time or cell conditions, enabling users to pause a simulation, adjust parameters, and then resume from the exact halted state. We validate Start & Stop using two well-established PhysiBoSS 2.0 use cases, a tumor spheroid 3T3 mouse fibroblasts use case under tumor necrosis factor (TNF) stimulation, and a lung cancer cell line invasion simulation, demonstrating that it preserves the simulators original behavior while enabling interactive configuration changes that facilitate the exploration of diverse and adaptive treatment strategies. By enhancing flexibility and user interaction, Start & Stop makes PhysiCell and PhysiBoSS 2.0 more akin to real in vitro scenarios, thus expanding the range of potential simulations and advancing more effective protocol development in a variety of applications.

bioinformatics↗

bioRxiv2024

Fast and Accurate LSTM Meta-modeling of TNF-induced Tumor Resistance In Vitro

Multi-level, hybrid models and simulations, among other methods, are essential to enable predictions and hypothesis generation in systems biology research. However, the computational complexity of these models poses a bottleneck, limiting the applicability of methodologies relying on large number of simulations, such as the Optimization via Simulation (OvS) of complex biological processes. Meta-models based on approximate surrogate models simplify multi-level simulations, maintaining accuracy while reducing computational costs. Among Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks are well suited to handle sequential data, which often characterizes biological simulations. This paper presents an LSTM-based surrogate modeling approach for multi-level simulations of complex biological processes. Validation relies on the simulation of Tumor Necrosis Factor (TNF) administration to a 3T3 mouse fibroblasts tumor spheroid based on PhysiBoSS 2.0, a hybrid agent-based multi-level modeling framework. Results show that the proposed LSTM meta-model is accurate and fast compared with the simulator. In fact, it infers simulated behavior with an average relative error of 7.5%. Moreover, it is at least five orders of magnitude faster. Even considering the cost of training, this approach provides a faster, more accurate, and reusable surrogate of multi-scale simulations in computationally complex tasks, such as model-based OvS of biological processes.

bioinformatics↗

bioRxiv2024

Cross-omic Transcription Factors meta-analysis: an insight on TFs accessibility and expression correlation

It is well-known how sequencing technologies propelled cellular biology research in recent years, giving an incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with Single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to perform the mentioned sequencing modalities simultaneously on the same cells. Yet, there still needs to be a clear and dedicated way to analyze this multi-modal data. One of the current methods is to calculate the Gene Activity Matrix (GAM), which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well-defined, and it is unclear how various accessible regions impact the expression of the genes. Moreover, the transcription process is highly regulated by the Transcription Factors that binds to the different DNA regions. Therefore, this work presents a continuation of the meta-analysis of Genomic-Annotated Gene Activity Matrix (GAGAM) contributions, aiming to investigate the correlation between the TFs expression and motif information in the different functional genomic regions to understand the different Transcription Factors (TFs) dynamics involved in different cell types.

bioinformatics↗

bioRxiv2024

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

BackgroundThe Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. ResultsBiSDLs effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. ConclusionsBiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

synthetic biology↗

bioRxiv2023

Optimization of synthetic oscillatory biological networks through Reinforcement Learning

In the expanding realm of computational biology, Reinforcement Learning (RL) emerges as a novel and promising approach, especially for designing and optimizing complex synthetic biological circuits. This study explores the application of RL in controlling Hopf bifurcations within ODE-based systems, particularly under the influence of molecular noise. Through two case studies, we demonstrate RLs capabilities in navigating biological systems inherent non-linearity and high dimensionality. Our findings reveal that RL effectively identifies the onset of Hopf bifurcations and preserves biological plausibility within the optimized networks. However, challenges were encountered in achieving persistent oscillations and matching traditional algorithms computational speed. Despite these limitations, the study highlights RLs significant potential as an instrumental tool in computational biology, offering a novel perspective for exploring and optimizing oscillatory dynamics within complex biological systems. Our research establishes RL as a promising strategy for manipulating and designing intricate behaviors in biological networks.

bioengineering↗

bioRxiv2023

GRAIGH: Gene Regulation accessibility integrating GeneHancer database

Single-cell assays for transposase-accessible chromatin sequencing data are one of the most powerful tools for studying the epigenetic heterogeneity of cell populations. However, the chromatin accessibility landscape is not well understood and lacks a proper way to interpret it. This work proposes Gene Regulation Accessibility Integrating GeneHancer (GRAIGH), a novel approach to the interpretation of genome accessibility through the integration of the GeneHancer database information, which describes genome-wide enhancer-to-gene associations. Firstly, this paper presents the methods for integrating GeneHancer with scATAC-seq data, creating a new matrix where the features are the GeneHancer elements IDs instead of the accessibility peaks. Secondly, it investigates its capability to analyze the data and detect cellular heterogeneity. In particular, this work shows that the GeneHancer elements are selectively accessible for distinct cell types, and more importantly, their connected genes are precisely known marker genes. Moreover, it investigates the specificity of GeneHancer elements accessibility, demonstrating their high selectivity against the gene activity.

bioinformatics↗

bioRxiv2023

Neuronal Spike Shapes (NSS): a simple approach to study electrophysiological data for heterogeneity investigation.

The mammalian brain exhibits a remarkable diversity of neurons, contributing to its intricate architecture and functional complexity. The analysis of multimodal single-cell datasets enables the investigation of cell types and states heterogeneity. In this study, we introduce the Neuronal Spike Shapes (NSS), a straightforward approach for the exploration of excitability states of neurons based on their Action Potential (AP) waveforms. The NSS method describes the AP waveform based on a triangular representation complemented by a set of derived electrophysiological (EP) features. To support this hypothesis, we validate the proposed approach on two datasets of murine cortical neurons, focusing it on GABAergic neurons. The validation process involves a combination of NSS-based clustering analysis, features exploration, Differential Expression (DE), and Gene Ontology (GO) enrichment analysis. Results show that the NSS-based analysis captures neuronal excitability states that possess biological relevance independently of cell subtype. In particular, Neuronal Spike Shapes (NSS) captures, among others, a well-characterized fast-spiking excitability state, supported by both electrophysiological and transcriptomic validation. Gene Ontology Enrichment Analysis reveals voltage-gated potassium (K+) channels as specific markers of the identified NSS partitions. This finding strongly corroborates the biological relevance of NSS partitions as excitability states, as the expression of voltage-gated K+ channels regulates the hyperpolarization phase of the AP, being directly implicated in the regulation of neuronal excitability.

bioengineering↗

bioRxiv2023

A deep reinforcement learning approach to the optimization of epithelial sheets biofabrication

Tissue Engineering (TE) and Regenerative Medicine (RM) aim to replicate and replace tissues for curing disease. However, full tissue integration and homeostasis are still far from reach. Biofabrication is an emerging field that identifies the processes required for generating biologically functional products with the desired structural organization and functionality and can potentially revolutionize the regenerative medicine domain, which aims to use patients cells to restore the structure and function of damaged tissues and organs. However, biofabrication still has limitations in the quality of processes and products. Biofabrication processes are often improved empirically, but this is slow, costly, and provides partial results. Computational approaches can tap into biofabrication underused potential, supporting analysis, modeling, design, and optimization of biofabrication processes, speeding up their improvement towards a higher quality of products and subsequent higher clinical relevance. This work proposes a reinforcement learning-based computational design space exploration methodology to generate optimal in-silico protocols for the simulated fabrication of epithelial sheets. The optimization strategy relies on a Deep Reinforcement Learning (DRL) algorithm, the Advantage-Actor Critic, which relies on a neural network model for learning. In contrast, simulations rely on the PalaCell2D simulation framework. Validation demonstrates the proposed approach on two protocol generation targets: maximizing the final number of obtained cells and optimizing the spatial organization of the cell aggregate.

bioinformatics↗

bioRxiv2023

Meta-analysis of gene activity (MAGA) contributions and correlation with gene expression, through GAGAM.

It is well-known how sequencing technologies propelled cellular biology research in the latest years, giving an incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with Single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to perform the mentioned sequencing modalities simultaneously on the same cells. Yet, there still needs to be a clear and dedicated way to analyze this multi-modal data. One of the current methods is to calculate the Gene Activity Matrix, which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well-defined, and it is unclear how various accessible regions impact the expression of the genes. Therefore, this work presents a meta-analysis of the Gene Activity matrix based on the Genomic-Annotated Gene Activity Matrix model, aiming to investigate the different influences of its contributions on the activity and their correlation with the expression. This allows having a better grasp on how the different functional regions of the genome affect not only the activity but also the expression of the genes.

bioinformatics↗

bioRxiv2022

Benchmark data and software for assessing genome-wide CRISPR-Cas9 screening pipelines

Genome-wide recessive genetic screens using lentiviral CRISPR-guide RNA libraries are widely performed in mammalian cells to functionally characterise individual genes and for the discovery of new anti-cancer therapeutic targets. As the effectiveness of such powerful and precise tools for cancer pharmacogenomic is emerging, reference datasets for their quality assessment and the validation of the underlying experimental pipelines are becoming increasingly necessary. Here, we provide a dataset, an R package, and metrics for the assessment of novel experimental pipelines upon the execution of a single calibration viability screen of the HT-29 human colon cancer cell line, employing a commercially available genome-wide library of single guide RNAs: the Human Improved Genome-wide Knockout CRISPR (Sanger) Library. This dataset contains results from screening the HT-29 in multiple batches with the Sanger library, and outcomes from several levels of quality control tests on the resulting data. Data and accompanying R package can be used as a toolkit for benchmarking newly established experimental pipelines for CRISPR-Cas9 recessive screens, via the generation of a final quality-control report.

bioinformatics↗

bioRxiv2022

Contrast Subgraphs Allow Comparing Homogeneous and Heterogeneous Networks Derived from Omics Data

Biological networks are often used to describe the relationships between relevant entities, in particular genes and proteins, and are a powerful tool for functional genomics. Many important biological problems can be investigated by comparing biological networks between different conditions, or networks obtained with different techniques. We show that contrast subgraphs, a recently introduced technique to identify the most important structural differences between two networks, provide a versatile tool for comparing gene and protein networks of diverse origin. We show in three concrete examples how contrast subgraphs can provide new insight in functional genomics by extracting the gene/protein modules whose connectivity is most altered between two conditions or experimental techniques.

bioinformatics↗

bioRxiv2022

RAGE engagement by SARS-CoV-2 enables monocyte infection and underlies COVID-19 severity

The spread of SARS-CoV-2 has fueled the COVID-19 pandemic with its enduring medical and socioeconomic challenges due to subsequent waves and long-term consequences of great concern. Here we charted the molecular basis of COVID-19 pathogenesis, by analysing patients immune response at single-cell resolution across disease course and severity. This approach uncovered cell subpopulation-specific dysregulation in COVID-19 across disease course and severity and identified a severity-associated activation of the receptor for advanced glycation endproduct (RAGE) pathway in monocytes. In vitro experiments confirmed that monocytes bind the SARS-CoV-2 S1-RBD via RAGE and that RAGE-Spike interactions drive monocyte infection. Our results demonstrate that RAGE is a novel functional receptor of SARS-CoV-2 contributing to COVID-19 severity. One-Sentence SummaryMonocyte SARS-CoV-2 infection via the receptor for advanced glycation endproduct triggers severe COVID-19.

immunology↗