Biology subjects

Greco, D.

Publications and source records attributed to Greco, D..

16 recordsLinked to original sources

bioRxiv2026

3D-Printable and Cytocompatible Hydrogel from Acinetobacter baylyi ADP1 Extracellular Matrix

Tissue engineering has advanced significantly, yet multicomponent hydrogels inspired by the compositional complexity of natural extracellular matrices (ECMs) are still underexplored. Most current hydrogels are based on single-component formulations, which can limit their biochemical and mechanical versatility. Developing synthetic multicomponent hydrogels remains challenging because it requires the controlled integration of multiple functional groups within a single material platform. Here, a biologically driven strategy is introduced by leveraging Acinetobacter baylyi ADP1, a bacterium that naturally produces extracellular polymeric substances (EPS) composed of a multicomponent matrix of polysaccharides and proteins. Through three-day cultivation and a simple extraction method, a hydrogel is obtained that can be methacrylated and photocrosslinked using red or blue light. This hydrogel is porous, cytocompatible, 3D-bioprintable, injectable, and undergoes rapid gelation for in situ crosslinking. This work highlights the potential of using bacterial-derived multicomponent hydrogels for biofabrication.

bioengineering↗

bioRxiv2026

Integrating Semantic Retrieval, LLM-based Refinement, and Structured Expert Curation for Scalable AOP Gene Mapping

Toxicogenomics can support regulatory toxicology, but its use is limited by the difficulty of translating molecular responses into mechanistic, decision-relevant interpretations. Adverse Outcome Pathways (AOPs) provide a framework for this translation, yet omics applications require scalable mapping of Key Events (KEs) to molecular features. Here, we present an AI-assisted, multi-step workflow for KE-to-gene mapping that uses embedding-based semantic retrieval to identify candidate ontology/pathway terms, large language model-assisted refinement to filter these candidates, and double-independent expert group curation with rule-based consolidation to finalize mappings and derive confidence scores. Compared with earlier NLP-based approaches, the workflow improves KE-to-ontology/pathway mapping performance and generates candidate annotations that better align with expert judgment while substantially reducing the need for manual augmentation. Explicit gene and protein mentions in KE titles were additionally grounded to improve specificity, and each curated mapping was assigned curator reason codes to support transparent, traceable, and confidence-aware reuse. Applied across AOP-Wiki, the workflow produced a comprehensive KE-to-gene set resource covering 1,254 KEs across 523 AOPs and linking 15,833 human genes. Utility is demonstrated through CTD-based AOP fingerprinting of curated reference chemical groups, highlighting expanded coverage and confidence-informed interpretation of chemical-associated gene signatures in an AOP context. The workflow and resulting resource provide a practical bridge between toxicogenomics and AOP-based mechanistic interpretation and support routine updating and future extension to additional omics layers within OECD Omics2AOP.

bioinformatics↗

bioRxiv2025

OpTiles: An R Package for Adaptive Tiling and Methylation Variability Profiling

SummaryOpTiles is an R package that dynamically defines tiling windows based on the distribution of sequenced CpGs, addressing the limitations of traditional fixed-tiling approaches in targeted methylation datasets. By integrating CpG density with intra-region methylation variability, it provides a reliability metric and extended functionality for annotating, prioritizing, and interpreting complex methylation data. AvailabilityOpTiles is implemented in R and source code is freely available at https://github.com/fhaive/OpTiles. Data are available on Zenodo at https://doi.org/10.5281/zenodo.16961293 Contactdario.greco@tuni.fi

bioinformatics↗

bioRxiv2025

Curated and harmonised transcriptomics datasets of interstitial lung diseases

This study provides manually curated and homogenised transcriptomics data of interstitial lung disease (ILD) patients retrieved from the NCBI Gene Expression Omnibus and European Nucleotide Archive repositories. The compendium includes 30 transcriptomics datasets generated with DNA microarrays and RNA sequencing technologies for a total of 1,371 samples. All the datasets underwent metadata curation and harmonisation, data quality check, and preprocessing with standardised procedures. Furthermore, a robust data model was developed to standardise phenotypic data, thereby enhancing comparability across heterogeneous datasets. Gene expression data and lists of differentially expressed genes computed between ILD and healthy samples are provided. Among the ILDs included in this study, idiopathic pulmonary fibrosis (IPF) is the most represented worldwide. Co-expression networks of IPF and healthy samples were inferred, which are also included in this study. This work significantly improves the Findability, Accessibility, Interoperability, and Reusability (FAIR) of publicly available transcriptomics data of ILDs, providing a platform to implement and validate integrated systems biology and pharmacology approaches for novel interstitial lung disease diagnostics and therapeutics.

bioinformatics↗

bioRxiv2025

MUUMI: an R package for statistical and network-based meta-analysis for MUlti-omics data Integration

Disentangling physiopathological mechanisms of biological systems through high-level integration of omics data has become a standard procedure in life sciences. However, platform heterogeneity, batch effects, and the lack of unified methods for single- and multi-omics analyses represent relevant drawbacks that hinder the extrapolation of a meaningful biological interpretation. Statistical meta-analysis is widely used in order to integrate several omics datasets of the same type, leading to the extrapolation of robust molecular signatures within the investigated system. Conversely, statistical meta-analysis does not allow the simultaneous investigation of different molecular layers, and, therefore, the integration of multi-modal data deriving from multi-omics experiments. Although in the last few years a number of valid tools designed for multi-omics data integration have emerged, they have never been combined with statistical meta-analysis tools in a unique analytical solution in order to support meaningful biological interpretation. Network science is at the forefront of systems biology, where the inference of molecular interactomes allowed the investigation of perturbed biological systems, by shedding light on the disrupted relationships that keep the homeostasis of complex systems. Here, we present MUUMI, an R package that unifies network-based data integration and statistical meta-analysis within a single analytical framework. MUUMI allows the identification of robust molecular signatures through multiple meta-analytic methods, inference and analysis of molecular interactomes and the integration of multiple omics layers through similarity network fusion. We demonstrate the functionalities of MUUMI by presenting two case studies in which we analysed 1) 17 transcriptomic datasets on idiopathic pulmonary fibrosis (IPF) from both microarray and RNA-Seq platforms and 2) multi-omics data of THP-1 macrophages exposed to different polarising stimuli. In both examples, MUUMI revealed biologically coherent signatures, underscoring its value in elucidating complex biological processes. Availability and implementationMUUMI is freely available at https://github.com/fhaive/muumi.

bioinformatics↗

bioRxiv2025

Endothelial Sensitivity to Pro-Fibrotic Signals Links Systemic exposure to Pulmonary Fibrosis

Pulmonary fibrosis (PF) is a life-threatening condition characterised by excessive extracellular matrix deposition and tissue scarring. While much of PF research has focused on alveolar epithelial cells and fibroblasts, endothelial cells have emerged as active contributors to the disease initiation, especially in the context of systemic exposure to pro-fibrotic substances. Here, we investigate early transcriptomic and secretory responses of human umbilical vein endothelial cells (HUVEC) to subtoxic doses of bleomycin, a known pro-fibrotic agent, and TGF-beta, a key cytokine in fibrosis. Bleomycin exposure induced a rapid and extensive shift in the endothelial transcriptional programme, including signatures of endothelial to mesenchymal transition, cellular senescence, and immune cell recruitment. These findings suggest endothelial cells as early initiators of pro-fibrotic signals, independent of contributions from other cell types. In contrast, TGF-beta effects were limited and transient, indicating its pro-fibrotic action may require another initial stimulus and interplay with other cells like fibroblasts. This study highlights the sensitivity of endothelial cells to systemic pro-fibrotic exposure and provides a blueprint of early pro-fibrotic mechanisms, emphasising their pivotal role in PF pathogenesis.

molecular biology↗

bioRxiv2024

Toxicogenomic Assessment of In Vitro Macrophages Exposed to Profibrotic Challenge.

Immune signalling is a crucial component in the progression of fibrosis. However, approaches for the safety assessment of potentially profibrotic substances, providing information on mechanistic immune responses, are underdeveloped. This study utilises a comprehensive analysis of RNA sequencing data from macrophages exposed in vitro to multiple sublethal concentrations of the profibrotic agent bleomycin, over multiple timepoints. Using a toxicogenomic framework, we performed dose-dependent analysis to filter genes truly altered by bleomycin exposure from noise and identified a subset of immune genes with a sustained dose-dependent and differential expression response to profibrotic challenge. We performed an immunoassay and revealed cytokines and proteinases responding to bleomycin exposure that closely correlate to transcriptomic alterations, underscoring the integration between transcriptional immune response and external immune signalling activity. This study not only increases our understanding of the immunological mechanisms of fibrosis, but also offers an innovative framework for the toxicological evaluation of substances with potential fibrogenic effects on macrophage signalling. Our work brings a new immunotoxicogenomic direction for hazard assessment of fibrotic compounds, through implementation of a time and resource efficient in vitro methodology.

pharmacology and toxicology↗

bioRxiv2024

Methylation and Transcriptomic Profiling Reveals Short Term and Long Term Regulatory Responses in Polarized Macrophages

Macrophage plasticity allows the adoption of distinct functional states in response to environmental cues. While unique transcriptomic profiles define these states, focusing solely on transcription neglects potential long-term effects. The investigation of epigenetic changes can be used to understand how temporary stimuli can result in lasting effects. Moreover, epigenetic alterations play an important role in the pathophysiology of macrophages, including phenomena related to the trained innate immunity, which allow faster and more efficient inflammatory responses upon subsequent encounters with the same pathogen. In this study, we used a multi-omics approach to elucidate the interplay between gene expression and DNA-methylation, unravelling the long-term effects of diverse polarizing environments on macrophage activity. We identified a common core set of genes that are differentially methylated regardless of exposure suggesting a potential mechanism for rapid adaptation to various stimuli. These conserved epigenetic modifications might represent a fundamental state that allows for flexible responses to various environmental cues. Functional analysis revealed that processes requiring rapid responses displayed transcriptomic regulation, whereas functions critical for long-term adaptations exhibited co-regulation at both transcriptomic and epigenetic levels. Our study unveils a novel set of genes critically linked to the long-term effects of macrophage polarization. This discovery underscores the potential of epigenetics in elucidating how macrophages establish long-term memory and influence health outcomes. Highlights- Environmental signals trigger gene changes in macrophages, leaving a long-lasting epigenetic reprogramming - Epigenetic changes and metabolic shifts in polarized macrophages suggest training mechanisms - Common gene set epigenetically altered across different cues, suggest common adaptation to various stimuli Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/599278v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@1006cacorg.highwire.dtl.DTLVardef@deb49eorg.highwire.dtl.DTLVardef@12428e9org.highwire.dtl.DTLVardef@f9e1c9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering is used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes and Alzheimer disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.

bioinformatics↗

bioRxiv2024

A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasises how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, we developed a network-based approach to characterise toxicological responses in the context of a biological system, inferring biological system specific networks. We directly linked molecular alterations to the adverse outcome pathway (AOP) framework, establishing connections with toxicologically relevant phenotypic events. We applied this framework on a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrated how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve our understanding of toxicological mechanisms from a systems biology perspective, guiding the hazard assessment of nanomaterials and other advanced materials.

pharmacology and toxicology↗

bioRxiv2024

Beyond Basic Characterization and Omics: Immunomodulatory Roles of Platelet-Derived Extracellular Vesicles Unveiled by Functional Testing

Renowned for their role in hemostasis and thrombosis, platelets are also increasingly recognized for their contribution in innate immunity, immunothrombosis and inflammatory diseases. Platelets express a wide range of receptors, which allows them to reach a variety of activation endpoints and grants them immunomodulatory functions. Activated platelets release extracellular vesicles (PEVs), whose formation and molecular cargo has been shown to depend on receptor-mediated activation and environmental cues. This study compares the immunomodulatory profiles of PEVs generated via activation of platelets by different receptors, glycoprotein VI, C-type lectin-like receptor 2, and combining all thrombin-collagen receptors. Functional assays in vivo in zebrafish and in vitro in human macrophages respectively highlighted distinct homing and secretory responses triggered by the PEVs. In contrast, omics analyses of protein and miRNA cargo combined with physicochemical particle characterization found only subtle differences between the PEV types, which were insufficient to explain their different functional immunomodulatory profiles. Constitutively released PEVs, formed in the absence of an exogenous activator, displayed a disparate activation profile from the receptor induced PEVs. Our findings underscore that PEVs are tunable through receptor-mediated activation. To truly comprehend their role(s) in mediating platelet functions among immune cells, conducting functional assays is imperative. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/570750v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@75a9dborg.highwire.dtl.DTLVardef@e2787corg.highwire.dtl.DTLVardef@543525org.highwire.dtl.DTLVardef@6d1362_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2022

Molecular annotation of AOPs guides the development of the next generation mechanistic chemical safety assessment and new approach methods

Mechanistic toxicology has emerged as a powerful framework to inform on the safety of chemicals and guide the development of new safe-by-design compounds. Although toxicogenomics provides support towards mechanistic evaluation of chemical exposures, the implementation of toxicogenomics-based evidence in the regulatory setting is still hindered by uncertainties related to the analysis and interpretation of such data. Adverse Outcome Pathways (AOPs) are multi-scale models that link chemical exposures to adverse outcomes through causal cascades of key events (KEs). The use of mechanistic evidence through the AOP framework is actively promoted for the development of new approach methods (NAMs) and to reduce animal experimentation. However, in order to unleash the full potential of AOPs and build confidence into toxicogenomics, robust and unified associations between KEs and patterns of molecular alteration need to be established. Here, we hypothesised that systematic curation of molecular events associated with KEs would enable the modelling of AOPs through gene-level data, creating the much-needed link between toxicogenomics and the systemic mechanisms depicted by the AOPs. This, in turn, introduces novel ways of benefitting from the AOP concept, including predictive models, read-across, and targeted assays, while also reducing the need for multiple testing strategies. Hence, we developed a multi-step strategy to annotate the AOPs relevant to human health risk assessment. We show that our framework successfully highlights relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, we defined and experimentally validated a panel of robust AOP-derived in vitro biomarkers for pulmonary fibrosis.

pharmacology and toxicology↗

bioRxiv2021

The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis

In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we collected, curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated transcriptomics analysis of public datasets shed light on a number of genes which are frequently deregulated in the psoriatic lesion compared with the unaffected skin in a large number of studies. In particular, CRABP2, LCN2, S100A12 and PDZK1IP1 were found to be deregulated in all of the datasets analyzed. Furthermore, the analysis of co-expression networks highlights genes showing aberrant patterns of connectivity in the lesional network as compared to the network inferred from unaffected skin samples. For instance, we identified co-expression patterns of SERPINB4, KYNU and S100A12 as being the most affected by the disease. Network analysis allowed us to identify YPEL1 and HUS1 as plausible, previously unknown, actors in the expression of the psoriasis phenotype. In addition, by exploiting topological properties of the network models, we highlighted a set of 250 non-deregulated genes, 223 of which have never been associated with the disease before, including CACNA1A, HADH, ATP5MC1 and CBARP among others. Finally, we characterized specific communities of co-expressed genes sustaining relevant molecular functions and specific immune cell types expression signatures playing a role in the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

systems biology↗

bioRxiv2021

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

New affordable therapeutic protocols for COVID-19 are urgently needed despite the increasing number of effective vaccines and monoclonal antibodies. To this end, there is increasing attention towards computational methods for drug repositioning and de novo drug design. Here, we systematically integrated multiple data-driven computational approaches to perform virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the set of prioritized drugs, we selected a subset of representative candidates to test in human cells. Two compounds, 7-hydroxystaurosporine and bafetinib, showed synergistic antiviral effects in our in vitro experiments, and strongly inhibited viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, we extracted and prioritized the chemical substructures of the identified drugs, providing a chemical vocabulary that may help to design new effective drugs.

pharmacology and toxicology↗

bioRxiv2018

Clustering based approach for population level identification of condition-associated T-cell receptor β-chain CDR3 sequences

MotivationDeep immune receptor sequencing, Repseq, provides unprecedented opportunities to identify condition-associated T-cell clones, represented by T-cell receptor (TCR) CDR3 sequences. TCR profiling has potential value for increasing immunopathological understanding of various diseases, and holds considerable clinical relevance. However, due to the immense diversity of the immune repertoire, identification of condition relevant TCR CDR3s from total repertoires has so far been limited either to mostly \"public\" CDR3 sequences, which are shared across unrelated individuals, or to comparisons of CDR3 frequencies from multiple samples from the same individual. A methodology for the identification of condition-associated TCR CDR3s by population level comparison of groups of Repseq samples is currently lacking.\n\nResultsWe implemented a computational pipeline that allows population level comparison of Repseq sample groups at the level of the immune repertoire sub-units that are shared across individuals. These sub-units (or sub-repertoires) represent shared immuno-genomic features across individuals that potentially encode common signatures in the immune response to antigens. The method first performs unsupervised clustering of CDR3 sequences within each sample based on their similarity in nucleotide or amino acid subsequence frequency. Next, it finds matching clusters across samples, the immune sub-repertoires, and performs statistical differential abundance testing at the level of the identified sub-repertoires. We applied the method on total TCR CDR3{beta} Repseq datasets of celiac disease patients in gluten exposed and unexposed conditions, as well as on public dataset of yellow fever vaccination volunteers before and after immunization. The method successfully identified condition-associated CDR3{beta} sequences, as evidenced by considerable agreement of TRBV-gene and positional amino acid usage patterns in the detected CDR3{beta} sequences with previously known CDR3{beta} species relevant to celiac disease. The method also recovered significantly high numbers of previously known CDR3{beta} sequences, relevant to each condition than would be expected by chance. We conclude that immune sub-repertoires of similar immuno-genomic features, shared across unrelated individuals, encode common immunological information. Moreover, they can serve as viable units of population level immune repertoire comparison, serving as proxy for identification of condition-associated CDR3 sequences.

bioinformatics↗

bioRxiv2018

eUTOPIA: Solution for omics data preprocessing and analysis

Application of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R that have become standard for high-throughput analytics. However, there is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results, which is computational skill neutral. We propose an integrated software solution, eUTOPIA, that implements a set of essential processing steps as a guided workflow presented to the user as an R Shiny application. eUTOPIA allows researchers to perform preprocessing and analysis of microarray data via a simple and intuitive graphical interface while using state of the art methods. eUTOPIA is free for academic use and can be obtained from the GitHub repository https://github.com/Greco-Lab/eUTOPIA.

bioinformatics↗