Biology subjects

Locatelli, M.

Publications and source records attributed to Locatelli, M..

8 recordsLinked to original sources

bioRxiv2026

Mechanotransduction tuned by intermediate filaments promotes DNA repair in stiff environments

High mammographic density increases breast cancer risk. Dense tissues are rich in fibrillar collagen, forming a rigid extracellular matrix around the epithelium. Although cancer initiation and progression are closely tied to DNA damage and to the DNA damage response, if and how tissue stiffness affects genome maintenance is not fully elucidated. We found that repair factor recruitment is more efficient, and that DNA breaks are repaired faster by cells on rigid substrates. Mechanistically, hemidesmosomal integrins, intermediate filaments, the LINC complex, and YAP are key mediators of this mechanogenomic response. Rigid tissue contexts favor survival of cells with DNA damage, which may lead to the accumulation of somatic mutations, as identified here with RNAseq analyses of normal breast tissue. Our findings provide new insights into how tissue rigidity influences cancer risk.

cell biology↗

bioRxiv2026

Protein response to deleterious episodes of GC-biased gene conversion in mammals of different Ne

GC-biased gene conversion (gBGC) is a widespread recombination-associated process that favours the fixation of GC alleles independently of their fitness. In coding regions, it can oppose purifying selection and favour the accumulation of deleterious AT to GC mutations. In mammals where recombination hotspots are predominantly determined by PRDM9, the deleterious effect of gBGC is expected to be episodic due to the rapid turnover of hotspots. The deleterious impact of gBGC episodes on proteins has been well documented, particularly in rodents and primates. However, the evolutionary response of the affected proteins after the episodes end has been scarcely investigated. Theory predicts that a decline in gBGC intensity should trigger a compensatory response that enables previous damage to be repaired. Recently, we reported evidence for pervasive compensatory evolution following gBGC episodes in Hydromyini rodents. Here, we aimed to investigate whether this post- gBGC compensation is widespread among mammals in which gBGC is episodic. Using a phylogenetic approach, we analysed substitution patterns in coding exons from five mammalian taxa spanning a range of effective population size (Ne): Murini, Rattini, Cetacea, Cercopithecidae and Hominoidae. We detected thousands of gBGC episodes, characterised by an increased rate of non-synonymous AT to GC substitutions. We found evidence for compensatory evolution in non-primate taxa, and a lack of signal in Primates. Although the influence of Ne remains unclear, our study highlights both the evolutionary significance of post-gBGC compensation and the methodological challenges in detecting it.

genomics↗

bioRxiv2024

Integration of diverse bioactivity data into the Chemical Checker compound universe

Chemical signatures encode the physicochemical and structural properties of small molecules into numerical descriptors, forming the basis for chemical comparisons and search algorithms. The increasing availability of bioactivity data has improved compound representations to include biological effects, although bioactivity descriptors are often limited to a few well-documented molecules. To address this issue, we implemented a collection of deep neural networks able to leverage the experimentally determined bioactivity data associated to small molecules and infer the missing bioactivity signatures for any compound of interest. However, unlike static chemical descriptors, these bioactivity signatures dynamically evolve with new data and processing strategies. Here, we present a computational protocol to modify or generate novel bioactivity spaces and signatures, describing the main steps needed to leverage diverse bioactivity data with the current knowledge, as catalogued in the Chemical Checker (CC), using the predefined data curation pipeline. We illustrate the functioning of the protocol through four specific examples, including the incorporation of new compounds to an already existing bioactivity space, a change in the data pre-processing without altering the underlying experimental data, and the creation of two novel bioactivity spaces from scratch, which are completed in under 9 hours using GPU computing. Overall, this protocol offers a guideline for installing, testing and running the CC data integration approach on user-provided data, with the aim of extending the annotation presented for a limited number of small molecules to a larger chemical landscape. Key pointsO_LIThe Chemical Checker is a large collection of processed, harmonized and integrated bioactivity signatures for over 1 million small molecules. Data are organized into 5 levels of increasing biological complexity and curation degrees: from chemical properties to clinical outcomes and from raw data representing explicit knowledge to embedded signatures inferred from observed bioactivity patterns. C_LIO_LIThe Chemical Checker package provides a predefined and versatile framework to integrate user-provided data to the Chemical Checker universe of small molecules and generate novel customized bioactivity signatures. C_LI Key referencesDuran-Frigola et al. "Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker." Nature Biotechnology 38.9 (2020): 1087-1096. Bertoni et al. "Bioactivity descriptors for uncharacterized chemical compounds." Nature Communications 12.1 (2021): 3932.

bioinformatics↗

bioRxiv2024

Helicases DDX5 and DDX17 promote Hepatitis B Virus transcription termination heterogeneity in infected human hepatocytes

Background & AimsTranscription termination fine tunes gene expression and contributes to specify the function of RNAs in eukaryotic cells. Transcription termination of hepatitis B virus (HBV) is subjected to the recognition of the canonical polyadenylation signal (cPAS) common to all viral transcripts. The regulation of the usage of this cPAS and its impact on viral gene expression and replication is currently unknown. Approach & ResultsTo unravel the regulation of HBV transcript termination, we implemented a 3 RACE-PCR assay coupled to single molecule sequencing both in in vitro infected hepatocytes and in chronically infected patients. The detection of a previously unidentified transcriptional readthrough indicated that the cPAS was not systematically recognized during HBV replication in vitro and in vivo. Gene expression downregulation experiments demonstrated a role for the RNA helicases DDX5 and DDX17 in promoting viral transcriptional readthrough, which was, in turn, associated to HBV RNA destabilization and decreased HBx protein expression. RNA and chromatin immunoprecipitation, together with mutation of cPAS sequence suggested a direct role of DDX5 and DDX17 in functionally linking cPAS recognition to transcriptional readthrough, HBV RNA stability and replication. ConclusionsOur findings identify DDX5 and DDX17 as crucial determinants for HBV transcriptional fidelity and as host restriction factors for HBV replication.

molecular biology↗

bioRxiv2023

BQsupports: systematic assessment of the support and novelty of new biomedical associations

Living a Big Data era in Biomedicine, there is an unmet need to systematically assess experimental observations in the context of available information. This assessment would offer a means for an unbiased validation of the results and provide an initial estimate of the potential novelty of the findings. Here we present BQsupports, a web-based tool built upon the Bioteque biomedical descriptors that systematically analyzes and quantifies the current support to a given set of observations. The tool relies on over 1,000 distinct types of biomedical descriptors, covering over 11 different biological and chemical entities, including genes, cell lines, diseases and small molecules. By exploring hundreds of descriptors, BQsupports provide support scores for each observation across a wide variety of biomedical contexts. These scores are then aggregated to summarize the biomedical support of the assessed dataset as a whole. Finally, the BQsupports also suggests predictive features of the given dataset, which can be exploited in downstream machine learning applications. Availability and implementationThe web application and underlying data are available online (https://bqsuppports.irbbarcelona.org). Contactpatrick.aloy@irbbarcelona.org

bioinformatics↗

bioRxiv2022

Integrating and formatting biomedical data in the Bioteque, a comprehensive repository of pre-calculated knowledge graph embeddings

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., drug treats disease, gene interacts with gene). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.

bioinformatics↗

bioRxiv2020

Evaluating urban greening scenarios for urban heat mitigation: a spatially-explicit approach

Urban green infrastructure, especially trees, are widely regarded as one of the most effective ways to reducing urban temperatures in extreme heat events, and alleviate its adverse impacts on human health and well-being. Nevertheless, urban planners and decision-makers are still lacking methods and tools to spatially evaluate the cooling effects of urban green spaces and exploit them to assess greening strategies at the urban agglomeration scale. This article introduces a novel spatially-explicit approach to simulate urban greening scenarios by increasing the tree canopy cover in the existing urban fabric, and evaluating their heat mitigation potential. The latter is achieved by applying the InVEST urban cooling model to the synthetic land use/land cover maps generated for the greening scenarios. A case study in the urban agglomeration of Lausanne, Switzerland, illustrates the development of tree canopy scenarios following distinct spatial distribution strategies. The spatial pattern of the tree canopy strongly influences the human exposure to the highest temperatures, and small increases in the abundance of tree canopy cover with the appropriate spatial configuration can have major impacts on human health and well-being. The proposed approach supports urban planning and the design of nature-based solutions to enhance climate resilience.

ecology↗

bioRxiv2020

Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences

Image-based particle tracking is an essential tool to answer research questions in cell biology and beyond. A major challenge of particle tracking in living systems is that low light exposure is required to avoid phototoxicity and photobleaching. In addition, high-speed imaging used to fully capture particle motion dictates fast image acquisition rates. Short exposure times come at the expense of tracking accuracy. This is generally true for quantitative microscopy approaches and particularly relevant to single molecule tracking where the number of photons emitted from a single chromophore is limited. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure datasets. However, it is not clear whether images generated by these methods yield accurate quantitative measurements such as diffusion parameters in (single) particle tracking experiments. Here, we evaluate the performance of two popular deep learning denoising software packages for particle tracking, using synthetic datasets and movies of diffusing chromatin as biological examples. With synthetic data, both supervised and unsupervised deep learning restored particle motions with high accuracy in two-dimensional datasets, whereas artifacts were introduced by the denoisers in 3D datasets. Experimentally, we found that, while both supervised and unsupervised approaches improved the number of trackable particles and tracking accuracy, supervised learning generally outperformed the unsupervised approach, as expected. We also highlight that with extremely noisy image sequences, deep learning algorithms produce deceiving artifacts, which underscores the need to carefully evaluate the results. Finally, we address the challenge of selecting hyper-parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optional particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of the approaches presented here to critically evaluate artificial intelligence solutions for quantitative microscopy.

biophysics↗