Biology subjects

Fiorentino, J.

Publications and source records attributed to Fiorentino, J..

12 recordsLinked to original sources

bioRxiv2026

PHANES: an integrated resource of protein features for the human proteome

Protein analysis and machine-learning workflows increasingly rely on large-scale numerical representations of proteins. While protein language models provide powerful embeddings, their limited interpretability and high computational cost hinder their use in many biological applications. We present PHANES, a human proteome database and web server providing a comprehensive, biologically interpretable set of features for 20335 proteins. PHANES integrates sequence-derived physicochemical descriptors, structure-based and graph-theoretical features computed from AlphaFold predictions, thermostability indices, and tissue expression specificity. In addition to precomputed features for the human proteome, an integrated web server enables on-demand feature calculation for user-provided protein structures. PHANES supports data export, visualization and comparative analysis at the level of individual proteins and protein groups, bridging the gap between complex numerical representations and biological interpretation while providing an interpretable foundation for exploratory analyses and downstream machine-learning applications. PHANES is freely available at https://tools.tartaglialab.com/phanes.

bioinformatics↗

bioRxiv2026

LBR nucleoplasmic domains regulate X-chromosome solubility and nuclear organization

The nuclear lamina plays a central role in genome organization, yet how specific lamina-associated proteins regulate chromosome architecture during development remains unclear. Here, we show that the nucleoplasmic domains of the Lamin B Receptor (LBR) are essential for X-chromosome localization at the nuclear periphery and chromatin architecture during neural differentiation. Using genetic dissection of LBR function, combined with genome-wide chromatin solubility profiling and transcriptional analyses, we demonstrate that loss of LBR N-terminal domains impairs proper cell differentiation and X chromosome inactivation (XCI), selectively disrupting chromatin structure in neural progenitors but not in pluripotent cells. Strikingly, these effects are disproportionately concentrated - but not limited to - on the inactive X chromosome, which undergoes a pronounced shift toward a more soluble chromatin state. Our findings establish the nucleoplasmic function of LBR as a key determinant of X-chromosome functionality and identify chromatin solubility and accessibility as a previously underappreciated layer of genome regulation by the nuclear lamina in XCI. Finally, our work provides definitive genetic evidence that LBRs nuclear architectural functions are molecularly separable from its metabolic sterol reductase activity, which is preserved in our model, and are critically necessary for XCI in differentiating mouse female XX ESCs models.

genetics↗

bioRxiv2026

HERCULES: an integrative deep-learning framework for predicting RNA-binding propensity and mutation effects at single-residue resolution

RNA-binding proteins (RBPs) regulate essential aspects of RNA metabolism, yet accurately identifying RNA-binding domains (RBDs) and quantifying the impact of sequence variation on RNA-binding ability remain challenging. Here, we present HERCULES (Hybrid framEwoRk for RNA-binding domain loCalization and mUtation anaLysis using physicochemical and languagE modelS), a unified sequence-based framework for simultaneous RBD localization, global RNA-binding propensity prediction and mutation effect assessment. HERCULES integrates a fine-tuned protein language model with an explicit residue-level physicochemical module, combining global contextual representations with local mutation-sensitive descriptors. On an independent test set, the HERCULES global score discriminates RBPs from non-RBPs with an AUROC of 0.86. At residue resolution, HERCULES outperforms state-of-the-art sequence-based predictors in identifying canonical, non-canonical and putative RBDs across Pfam-annotated proteins. Using a curated dataset of experimentally validated RNA-binding-disrupting mutations, HERCULES correctly classifies 87% of deleterious variants, including single-amino acid substitutions. Evaluation on experimentally resolved protein-RNA complexes further demonstrates robust residue-level performance and improved generalization when contact annotations are augmented with AlphaFold3-predicted complexes. By unifying domain localization and mutation sensitivity within a single sequence-only framework, HERCULES provides a mechanistically interpretable approach for studying RNA-protein interactions. HERCULES is freely available at https://tools.tartaglialab.com/hercules and as an open-source Python package at https://github.com/tartaglialabIIT/hercules.git.

biochemistry↗

bioRxiv2025

scRAPID-web: a web server for predicting protein-RNA interactions from single-cell transcriptomics

SummarySingle-cell RNA sequencing (scRNA-seq) enables high-resolution studies of gene regulation, capturing gene expression at the individual cell level. We previously developed scRAPID, a computational pipeline for predicting protein-RNA interactions and identifying hub RNA-binding proteins (RBP) and RNAs through the integration of gene regulatory network (GRNs) inference from scRNA-seq data and catRAPID predictions. To make this tool accessible to a broader audience, we introduce scRAPID-web, a user-friendly web server supporting analysis of scRNA-seq data across eight model organisms. scRAPID-web offers customizable options to preprocess the input gene expression matrix, such as gene selection and cell type filtering. Users can choose from three GRN inference algorithms and decide whether to focus the analysis on specific gene types. Precompiled libraries allow fast filtering and motif-based validation of the inferred interactions. Results include detailed tables of predicted protein-RNA pairs and hubs, along with an interactive network visualization of potential RBP complexes built based on the inferred shared targets. scRAPID-web democratizes access to GRN-based analyses, providing insights into protein-RNA interactions and regulatory complexes in diverse cellular contexts. Availability and implementationscRAPID-web can be accessed at: https://tools.tartaglialab.com/scrapid.

bioinformatics↗

bioRxiv2024

Accurate Predictions of Phase Separating Proteins at Single Amino Acid Resolution

Liquid-liquid phase separation (LLPS) is a molecular mechanism that leads to the formation of membraneless organelles inside the cell. Despite recent advances in the experimental probing and computational prediction of proteins involved in this process, the identification of the protein regions driving LLPS and the prediction of the effect of mutations on LLPS are lagging behind. Here, we introduce catGRANULE 2.0 ROBOT (R - Ribonucleoprotein, O - Organization, in B - Biocondensates, O - Organelle, T - Types), an advanced algorithm for predicting protein LLPS at single amino acid resolution. Integrating physico-chemical properties of the proteins and structural features derived from AlphaFold models, catGRANULE 2.0 ROBOT significantly surpasses traditional sequence-based and state-of-the-art structure-based methods in performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.76 or higher. We present a comprehensive evaluation of the algorithm across multiple organisms and cellular components, demonstrating its effectiveness in predicting LLPS propensities at the single amino acid level and the impacts of mutations on LLPS. Our results are robustly supported by experimental validations, including immunofluorescence microscopy images from the Human Protein Atlas. catGRANULE 2.0 ROBOTs potential in protein design and mutation control can improve our understanding of proteins propensity to form subcellular compartments and help develop strategies to influence biological processes through LLPS. catGRANULE 2.0 ROBOT is freely available at https://tools.tartaglialab. com/catgranule2.

biochemistry↗

bioRxiv2024

Somatic Cell Identity Affects Specific Cell Differentiation Programs Upon Reprogramming In Vivo

Nuclear reprogramming can change cellular fates. Yet, reprogramming efficiency is low and the resulting cell types are often not functional. Here, we used nuclear transfer to eggs to follow single cells during reprogramming in vivo. We show that the differentiation success of reprogrammed cells varies across cell types and depends on the expression of genes specific to the previous cellular identity. We find subsets of reprogramming resistant cells that fail to form functional cell types, undergo cell death, or disrupt normal body patterning. Reducing expression levels of genes specific to the cell-type of origin leads to better reprogramming and improved differentiation trajectories. Thus, our work demonstrates that failing to reprogram in vivo is cell-type specific and emphasises the necessity of minimising aberrant transcripts of the previous somatic identity for improving reprogramming.

developmental biology↗

bioRxiv2023

Prediction of protein-RNA interactions from single-cell transcriptomic data

RNA-binding proteins play a crucial role in regulating RNA processing, yet our understanding of their interactions with coding and non-coding RNAs remains limited. Experimental studies are typically restricted to a small number of cell lines and a limited set of RNA-binding proteins (RBPs). Although computational methods based on sequence and structure can predict protein-RNA interactions accurately, they often lack the ability to consider cell-type-specific gene expression and the broader context of gene regulatory networks (GRNs). In the present study, we assess the performance of several GRN inference algorithms in predicting protein-RNA interactions from single-cell transcriptomic data, and we propose a pipeline, called scRAPID (single-cell transcriptomic-based RnA Protein Interaction Detection), that integrates these methods with the catRAPID algorithm, which can identify direct physical interactions between RBPs and RNA molecules. Our approach demonstrates that RBP-RNA interactions can be inferred from single-cell transcriptomic data, with performances comparable or superior to those achieved for the well-established task of inferring transcription factor-target interactions. Notably, the incorporation of catRAPID significantly enhances the accuracy of identifying interactions, particularly with long non-coding RNAs, and enables the identification of hub RBPs and hub RNAs. Additionally, we show that interactions between RBPs can be detected based on their inferred RNA targets. We have made the software freely available at https://github.com/tartaglialabIIT/scRAPID.

bioinformatics↗

bioRxiv2022

The PRALINE database: Protein and Rna humAn singLe nucleotIde variaNts in condEnsates

SummaryBiological condensates are membraneless organelles with different material properties. Proteins and RNAs are the main components, but most of their interactions are still unknown. Here we introduce PRALINE, a database for the interrogation of proteins and RNAs contained in stress-granules, processing bodies, and other assemblies including droplets and amyloids. PRALINE provides information about the predicted and experimentally validated protein-protein, protein-RNA and RNA-RNA interactions. For proteins, it reports the liquid-liquid phase separation and liquid-solid phase separation propensities. For RNAs, it provides information on predicted secondary structure content. PRALINE shows detailed information on human single-nucleotide variants, their clinical significance and presence in protein and RNA binding sites, and how they can affect condensates physical properties. AvailabilityPRALINE is freely accessible on the web at http://alvinlee.bio.uniroma1.it/praline. Supplementary informationGeneral information is at http://alvinlee.bio.uniroma1.it/praline/about, where we provide a detailed description of the datasets and the tools employed in the database. Data provided in PRALINE are available at http://alvinlee.bio.uniroma1.it/praline/downloads. The tutorial is at http://alvinlee.bio.uniroma1.it/praline/tutorial.

bioinformatics↗

bioRxiv2022

STREAMLINE: Structural and TopologicalPerformance Analysis of Algorithms for the Inference of Gene Regulatory Networks from Single-Cell Transcriptomic Data

In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms ability to capture structural properties of networks, which are fundamental, for example, for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs. To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four algorithms and provide guidance on which algorithm should be used depending on the global network property of interest.

bioinformatics↗

bioRxiv2022

Multi-omics analysis reveals a crucial role for Retinoic Acid in promoting epigenetic and transcriptional competence of an in vitro model of human Pharyngeal Endoderm.

In vitro differentiation of human Pluripotent Stem Cells (hPSCs) into different cell types has enabled the study of developmental processes that are impossible to dissect in vivo. This innovation has allowed for the derivation of therapeutically relevant cell types that can be used for downstream applications and studies. The Pharyngeal Endoderm (PE) is considered an extremely relevant developmental tissue since it acts as a precursor to a plethora of organ systems such as Esophagus, Parathyroids, Thyroids, Lung, and Thymus. While several studies have highlighted the importance of these cells, an in vitro platform to generate human PE cells is still missing. Here we fill this knowledge gap, by providing a novel in vitro protocol for the derivation of bona fide PE cells from hPSCs. We demonstrated that our PE cells robustly express Pharyngeal Endoderm markers, they are transcriptionally similar to PE cells isolated from in vivo mouse development and represent a transcriptionally homogeneous population. Importantly, we elucidated the contribution of Retinoic Acid in promoting a transcriptional and epigenetic rewiring of PE cells. In addition, we defined the epigenetic landscape of PE cells by combining ATAC-Seq and ChIP-Seq of histone modifications. The integration of these data led to the identification of new putative regulatory regions and to the generation of a gene regulatory network orchestrating the development of PE cells. By combining hPSCs differentiation with computational genomics, our work reveals the epigenetic dynamics that occur during human PE differentiation, providing a solid resource and foundation for research focused on the development of PE derivatives and modeling of their developmental defects in genetic syndromes.

developmental biology↗

bioRxiv2021

The role of cell geometry and cell-cell communication in gradient sensing

Cells can measure shallow gradients of external signals to initiate and accomplish a migration or a morphogenetic process. Recently, starting from mathematical models like the local-excitation global-inhibition (LEGI) model and with the support of empirical evidence, it has been proposed that cellular communication improves the measurement of an external gradient. However, the mathematical models that have been used have over-simplified geometries (e.g., they are uni-dimensional) or assumptions about cellular communication, which limit the possibility to analyze the gradient sensing ability of more complex cellular systems. Here, we generalize the existing models to study the effects on gradient sensing of cell number, geometry and of long-versus short-range cellular communication in 2D systems representing epithelial tissues. We find that increasing the cell number can be detrimental for gradient sensing when the communication is weak and limited to nearest neighbour cells, while it is beneficial when there is long-range communication. We also find that, with long-range communication, the gradient sensing ability improves for tissues with more disordered geometries; on the other hand, an ordered structure with mostly hexagonal cells is advantageous with nearest neighbour communication. Our results considerably extend the current models of gradient sensing by epithelial tissues, making a step further toward predicting the mechanism of communication and its putative mediator in many biological processes. Author summaryGroups of cells collectively migrate in many biological processes, ranging from development to cancer metastasis. The migration is often driven by the gradient of a signaling molecule that can be shallow and noisy, raising the question of how cells can measure it reliably. Cellular communication has recently been suggested to play a key role in gradient sensing, and mathematical models with simplified cellular geometries have been developed to help interpret and design experiments. In this work, we generalize the existing mathematical models to investigate how short- and long-range cellular communication can increase gradient sensing in two-dimensional models of epithelial tissues. We analyze various cellular geometries and tissue size, and we identify the optimal setting that corresponds to different types of communication. Our findings will help pinpoint the communication mechanisms at work in a given tissue and the properties of the molecules that mediate the communication.

biophysics↗

bioRxiv2021

Characterisation of the transcriptional dynamics underpinning the function, fate, and migration of the mouse Anterior Visceral Endoderm

During early post-implantation development of the mouse embryo, the Anterior Visceral Endoderm (AVE) differs from surrounding visceral endoderm (VE) in its migratory behaviour and ability to restrict primitive streak formation to the opposite side of the egg cylinder. In order to characterise the molecular basis for the unique properties of the AVE, we combined single-cell RNA-sequencing of the VE prior to and during AVE migration, with high-resolution imaging, short-term lineage labelling, phosphoproteomics and pharmacological intervention. This revealed the transient nature of the AVE, the emergence of heterogeneities in AVE transcriptional states relative to position of cells, and its prominence in establishing gene expression asymmetries within the spatial constraints of the embryo. We identified a previously unknown requirement of Ephrin- and Semaphorin-signalling for AVE migration. These findings point to a tight coupling of transcriptional state and position in the AVE and reveal molecular heterogeneities underpinning its migratory behaviour and function.

developmental biology↗