Biology subjects

Moreno, P.

Publications and source records attributed to Moreno, P..

26 records · Page 2Linked to original sources

bioRxiv2020

Multiple introductions, regional spread and local differentiation during the first week of COVID-19 epidemic in Montevideo, Uruguay

BackgroundAfter its emergence in China in December 2019, the new coronavirus disease (COVID-19) caused by SARS-CoV-2, has rapidly spread infecting more than 3 million people worldwide. South America is among the last regions hit by COVID-19 pandemic. In Uruguay, first cases were detected on March 13 th 2020 presumably imported by travelers returning from Europe. MethodsWe performed whole-genome sequencing of 10 SARS-CoV-2 from patients diagnosed during the first week (March 16th to 19th) of COVID-19 outbreak in Uruguay. Then, we applied genomic epidemiology using a global dataset to reconstruct the local spatio-temporal dynamics of SARS-CoV-2. ResultsOur phylogeographic analysis showed three independent introductions of SARS-CoV-2 from different continents. Also, we evidenced regional circulation of viral strains originally detected in Spain. Introduction of SARS-CoV-2 in Uruguay could date back as early as Feb 20th. Identification of specific mutations showed rapid local genetic differentiation. ConclusionsWe evidenced early independent introductions of SARS-CoV-2 that likely occurred before first cases were detected. Our analysis set the bases for future genomic epidemiology studies to understand the dynamics of SARS-CoV-2 in Uruguay and the Latin America and the Caribbean region.

microbiology↗

bioRxiv2020

User-friendly, scalable tools and workflows for single-cell analysis

Single-cell RNA-Seq (scRNA-Seq) data analysis requires expertise in command-line tools, programming languages and scaling on compute infrastructure. As scRNA-Seq becomes widespread, computational pipelines need to be more accessible, simpler and scalable. We introduce an interactive analysis environment for scRNA-Seq, based on Galaxy, with ~70 functions from major single-cell analysis tools, which can be run on compute clusters, cloud providers or single machines, to bring compute to the data in scRNA-Seq.

bioinformatics↗

bioRxiv2020

Htr6 and Sstr3 ciliary targeting relies on both IC3 loops and C-terminal tails

G protein-coupled receptors (GPCRs) are the most common pharmacological target in clinical practice. To perform their signaling functions, many GPCRs must accumulate at primary cilia, microtubule-based plasma membrane protrusions that work as cellular antennae. Despite their great importance, the molecular mechanisms underlying GPCR ciliary targeting remain poorly understood. Serotonin receptor 6 (Htr6) and somatostatin receptor 3 (Sstr3) are two brain-enriched ciliary GPCRs controlling cognition and involved in multiple pathologies such as Alzheimers disease and cancer. We previously showed that the third intracellular loops (IC3s) of Htr6 and Sstr3 contain ciliary targeting sequences (CTSs) that are sufficient to confer ciliary localization to non-ciliary GPCRs. However, these CTSs are dispensable for the ciliary targeting of Htr6 and Sstr3 themselves, suggesting these GPCRs have additional CTSs. Herein, we show that the C-terminal tails of Htr6 and Sstr3 also contain CTSs, which act redundantly with those in the IC3s. Accordingly, simultaneous disruption of CTS1 (IC3) and CTS2 (C-terminal tail) abolishes ciliary targeting of both receptors. Mapping the individual residues required for Htr6 ciliary targeting reveals RKQ and LPG motifs critical for CTS1 and CTS2 function, respectively. In Sstr3, CTS1 function relies on the tandem AP[AS]CQ motifs and a subsequent arginine-rich stretch, whereas CTS2 operation requires the juxtamembrane residues. Furthermore, we shed light on the mechanisms of action of Htr6 CTSs by showing how they regulate binding to Tulp3 and Rabl2, two adapters needed for ciliary GPCR targeting.

cell biology↗

bioRxiv2019

Scalable data analysis in proteomics and metabolomics using BioContainers and workflows engines.

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, the bioinformatics analysis is becoming an increasingly complex and convoluted process involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are targeted and design for single desktop application limiting the scalability and reproducibility of the data analysis. In this paper we overview the key steps of metabolomic and proteomics data processing including main tools and software use to perform the data analysis. We discuss the combination of software containers with workflows environments for large scale metabolomics and proteomics analysis. Finally, we introduced to the proteomics and metabolomics communities a new approach for reproducible and large-scale data analysis based on BioContainers and two of the most popular workflows environments: Galaxy and Nextflow.

bioinformatics↗

bioRxiv2019

Galectin-3 inhibits Paracoccidioides brasiliensis growth and impacts paracoccidioidomycosis through multiple mechanisms

The thermodimorphic pathogenic fungi Paracoccidioides brasiliensis and Paracoccioidioides lutzii are the etiologic causes of paracoccidioidomycosis (PCM), the most prevalent systemic mycosis in Latin America. Galectin-3 (Gal-3), an animal {beta}-galactoside-binding protein, modulates important roles during microbial infections, such as triggering a Th2-polarized immune response in PCM. Herein, we demonstrate that Gal-3 also plays other important roles in P. brasiliensis infection. We verified Gal-3 levels are upregulated in human and mice infections and establish that Gal-3 inhibits P. brasiliensis growth by inhibiting budding. Furthermore, Gal-3 affects disruption and internalization of extracellular vesicles (EV) from P. brasiliensis by macrophages. Our results suggest important roles for Gal-3 in P. brasiliensis infection, indicating that increased Gal-3 production during P. brasiliensis infection may account for affecting the fungal growth and EV stability, promoting a benefic course of experimental PCM. IMPORTANCEParacoccidiodomycosis (PCM) is the most prevalent systemic mycosis in Latin America. Although the immune mechanisms to control PCM are still not fully understood, several events of the host innate and adaptive immunity are crucial to determine the progress of the infection. Mammalian {beta}-galactoside-binding protein Galectin-3 (Gal-3) plays significant roles during microbial infections, and has been studied for its immunomodulatory roles but it can also have direct antimicrobial effects. We asked whether this protein plays a role in P. brasiliensis. We report herein that Gal-3 indeed has direct effects on fungal pathogen, inhibiting fungal growth and reducing extracellular vesicles stability. Our results suggest a direct role for Gal-3 in P. brasiliensis infection, with beneficial effects for the mammalian host.

microbiology↗

bioRxiv2018

Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud

Making reproducible, auditable and scalable data-processing analysis workflows is an important challenge in the field of bioinformatics. Recently, software containers and cloud computing introduced a novel solution to address these challenges. They simplify software installation, management and reproducibility by packaging tools and their dependencies. In this work we implemented a cloud provider agnostic and scalable container orchestration setup for the popular Galaxy workflow environment. This solution enables Galaxy to run on and offload jobs to most cloud providers (e.g. Amazon Web Services, Google Cloud or OpenStack, among others) through the Kubernetes container orchestrator. AvailabilityAll code has been contributed to the Galaxy Project and is available (since Galaxy 17.05) at https://github.com/galaxyproject/ in the galaxy and galaxy-kubernetes repositories. https://public.phenomenal-h2020.eu/ is an example deployment. Suppl. InformationSupplementary Files are available online. Contactpmoreno@ebi.ac.uk, European Molecular Biology Laboratory, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK, Tel: +44-1223-494267, Fax: +44-1223-484696.

bioinformatics↗

bioRxiv2018

PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

BackgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organisms metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent - and sometimes incompatible - analysis methods that are difficult to connect into a useful and complete data analysis solution.\n\nFindingsThe PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the projects continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm.\n\nConclusionsPhenoMeNal constitutes a keystone solution in cloud infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and omics research domains.

bioinformatics↗

bioRxiv2017

Interoperable and scalable metabolomics data analysis with microservices

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed in parallel using the Kubernetes container orchestrator. The access point is a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.

bioinformatics↗