Biology subjects

Ferrero, E.

Publications and source records attributed to Ferrero, E..

7 recordsLinked to original sources

bioRxiv2025

GeomeTRe: accurate calculation of geometrical descriptors of tandem repeat proteins

Structured tandem repeat proteins (STRPs) are characterized by preserved structural motifs arranged in a modular way. The structural and functional diversity of STRPs makes them particularly important for studying evolution, novel structure and function relationships, and ultimately for designing new synthetic proteins with specific functions. One crucial aspect of their classification is the estimation of geometrical parameters, which can provide better insight into their properties and the relationship between the spatial arrangement of repeated units and protein function. Calculating geometric descriptors for STRPs is challenging because naturally occurring repeats are not perfect and often contain insertions and deletions. Existing tools for predicting structural symmetry work well on simple cases but often fail for most natural proteins. Here we present GeomeTRe, an algorithm that calculates geometrical descriptors such as curvature (yaw), twist (roll), and pitch for a protein structure with known repeat unit positions. The algorithm simulates the movement of consecutive units, identifies rotational axes, and calculates the corresponding Tait-Bryan angles. The parameters of GeomeTRe can enhance STRP annotation and classification by identifying variations in geometric arrangements among different functional groups. The package is fast and suitable for processing large protein structure datasets when repeat region information (e.g., from RepeatsDB) is available. GeomeTRe is available as a Python package; source code and documentation can be found at https://github.com/BioComputingUP/GeomeTRe

bioinformatics↗

bioRxiv2024

Benchmarking tools for transcription factor prioritization

Spatiotemporal regulation of gene expression is controlled by transcription factor (TF) binding to regulatory elements, resulting in a plethora of cell types and cell states from the same genetic information. Due to the importance of regulatory elements, various sequencing methods have been developed to localise them in genomes, for example using ChIP-seq profiling of the histone mark H3K27ac that marks active regulatory regions. Moreover, multiple tools have been developed to predict TF binding to these regulatory elements based on DNA sequence. As altered gene expression is a hallmark of disease phenotypes, identifying TFs driving such gene expression programs is critical for the identification of novel drug targets. In this study, we curated 84 chromatin profiling experiments (H3K27ac ChIP-seq) where TFs were perturbed through e.g., genetic knockout or overexpression. We ran nine published tools to prioritize TFs using these real-world data sets and evaluated the performance of the methods in identifying the perturbed TFs. This allowed the nomination of three frontrunner tools, namely RcisTarget, MEIRLOP and monaLisa. Our analyses revealed opportunities and commonalities of tools that will help to guide further improvements and developments in the field.

bioinformatics↗

bioRxiv2019

Latent factor modelling of scRNA-seq data uncovers novel pathways dysregulated in cell subsets of autoimmune disease patients

Latent factor modelling applied to single-cell RNA-sequencing (scRNA-seq) data is a useful approach to discover gene signatures associated with cell states. However, it is often unclear what method is best suited for specific tasks and how latent factors should be interpreted from a biological perspective. Here, we compare four state-of-the-art methods and explore their stability, predictive power and coverage of known biology. We then propose an approach that leverages the derived latent factors to directly assign pathway activities to specific cell subsets. By applying this framework to scRNA-seq datasets from biopsies of rheumatoid arthritis and systemic lupus erythematosus patients, we discover both known and novel disease-relevant gene signatures in specific cellular subsets in a fully unsupervised way. Focusing on rheumatoid arthritis, we identify an inflammatory Oncostatin M receptor signalling signature active in a subset of synovial fibroblasts and dysregulation of the GAS6 - MERTK axis in a subset of synovial monocytes with efferocytic function. Overall, we provide insights into strengths and weaknesses of latent factors models for the analysis of scRNA-seq data, we develop a framework to identify cell subtypes in a function- or phenotype-driven way and use it to identify novel pathways dysregulated in rheumatoid arthritis.

bioinformatics↗

bioRxiv2019

The midbody interactome reveals new unexpected roles for PP1 phosphatases in cytokinesis

The midbody is an organelle assembled at the intercellular bridge that connects the two daughter cells at the end of mitosis. It is composed of a multitude of proteins, organized in a precise and stereotyped pattern, that control the final separation of the daughter cells and prevent incorrect genome segregation. Furthermore, recent evidence indicates that the midbody is involved in many other important processes, including cell fate, pluripotency, apical-basal polarity, tissue organization, and cilium and lumen formation. Understanding the regulation and interactions of midbody proteins is therefore essential to unravel how this organelle executes its multiple functions. Here, we report the first experimentally-based characterization of the intricate midbody protein-protein interaction network (interactome), which identifies a plethora of novel interactions and provides an extremely valuable resource for dissecting the multiple roles of the midbody. Initial analysis of this interactome already revealed that PP1{beta}-MYPT1 phosphatase regulates microtubule dynamics in late cytokinesis and de-phosphorylates the kinesin component MKLP1/KIF23 of the centralspindlin complex, a key cytokinesis regulator. This de-phosphorylation antagonizes Aurora B kinase in order to modify the functions of centralspindlin and its interactions in late cytokinesis. Our findings expand the repertoire of PP1 functions during mitosis and indicate that spatiotemporal changes in the distribution of kinases and counteracting phosphatases finely tune the activity of cytokinesis proteins.

cell biology↗

bioRxiv2019

Defining tissue- and disease-associated macrophages using a transcriptome-based classification model

Macrophages are heterogeneous multifunctional leukocytes which are regulated in a tissue-and disease-specific context. Many different studies have been published using in vitro macrophage models to study disease. Here, we aggregated public expression data to define consensus expression profiles for eight commonly-used in vitro macrophage models. Altogether, we observed well-known but also novel markers for different macrophage subtypes. Using these data we subsequently built the classifier macIDR, capable of distinguishing macrophage subsets with high accuracy (>0.95). This classifier was subsequently applied to transcriptional profiles of tissue-isolated and disease-associated macrophages to specifically define macrophage characteristics in vivo. Classification of these in vivo macrophages showed that alveolar macrophages displayed high resemblance to interleukin-10 activated macrophages, whereas macrophages from patients with chronic obstructive pulmonary disease patients displayed a drop in interferon-{gamma} signature. Adipose tissue-derived macrophages were classified as unstimulated macrophages, but resembled LPS-activated macrophages more in diabetic-obese patients. Finally, rheumatoid arthritic synovial macrophages showed characteristics of both interleukin-10 or interferon-{gamma} signatures. Altogether, our results suggest that macIDR is capable of identifying macrophage-specific changes as a result of tissue-and disease-specific stimuli and thereby can be used to better define and model populations of macrophages that contribute to disease.

immunology↗

bioRxiv2018

Mice harboring a SCA28 patient mutation in AFG3L2 develop late-onset ataxia associated with enhanced mitochondrial proteotoxicity.

Spinocerebellar ataxia 28 is an autosomal dominant neurodegenerative disorder caused by missense mutations affecting the proteolytic domain of AFG3L2, a major component of the mitochondrial m-AAA protease. However, little is known of the underlying pathogenetic mechanisms or how to treat patients with SCA28. Currently available Afg3l2 mutant mice harbour deletions that lead to severe, early-onset neurological phenotypes that do not faithfully reproduce the late-onset and slowly progressing SCA28 phenotype. Here we describe production and detailed analysis of a new knock-in murine model harbouring an Afg3l2 allele carrying the p.Met665Arg patient-derived mutation. Heterozygous mutant mice developed normally but signs of ataxia were detectable by beam test at 18 months. Cerebellar pathology was negative; electrophysiological analysis showed increased spontaneous firing in Purkinje cells from heterozygous mutants with respect to wild-type controls, although not statistically significant. As homozygous mutants died perinatally with evidence of cardiac atrophy, for each genotype we generated mouse embryonic fibroblasts (MEFs) to investigate mitochondrial function. MEFs from mutant mice showed altered mitochondrial bioenergetics, with decreased basal oxygen consumption rate, ATP synthesis and mitochondrial membrane potential. Mitochondrial network formation and morphology was also altered, in line with greatly reduced expression of Opa1 fusogenic protein L-isoforms. The mitochondrial alterations observed in MEFs were also detected in cerebella of 18-month-old heterozygous mutants, suggesting they may be a hallmark of disease. Pharmacological inhibition of de novo mitochondrial protein translation with chloramphenicol caused reversal of mitochondrial morphology in homozygous mutant MEFs, supporting the relevance of mitochondrial proteotoxicity for SCA28 pathogenesis and therapy development.

genetics↗

bioRxiv2017

Opportunities And Obstacles For Deep Learning In Biology And Medicine

Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We examine applications of deep learning to a variety of biomedical problems--patient classification, fundamental biological processes, and treatment of patients--and discuss whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges. We find that deep learning has yet to revolutionize or definitively resolve any of these problems, but promising advances have been made on the prior state of the art. Even when improvement over a previous baseline has been modest, we have seen signs that deep learning methods may speed or aid human investigation. More work is needed to address concerns related to interpretability and how to best model each problem. Furthermore, the limited amount of labeled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning powering changes at both bench and bedside with the potential to transform several areas of biology and medicine.

bioinformatics↗