Publications by authors named "Francesc Fernandez-Albert"

10 Publications

  • Page 1 of 1

A dual druggable genome-wide siRNA and compound library screening approach identifies modulators of parkin recruitment to mitochondria.

J Biol Chem 2020 03 7;295(10):3285-3300. Epub 2020 Jan 7.

Bristol Medical School, University of Bristol, Bristol BS8 1TD, United Kingdom. Electronic address:

Genetic and biochemical evidence points to an association between mitochondrial dysfunction and Parkinson's disease (PD). PD-associated mutations in several genes have been identified and include those encoding PTEN-induced putative kinase 1 (PINK1) and parkin. To identify genes, pathways, and pharmacological targets that modulate the clearance of damaged or old mitochondria (mitophagy), here we developed a high-content imaging-based assay of parkin recruitment to mitochondria and screened both a druggable genome-wide siRNA library and a small neuroactive compound library. We used a multiparameter principal component analysis and an unbiased parameter-agnostic machine-learning approach to analyze the siRNA-based screening data. The hits identified in this analysis included specific genes of the ubiquitin proteasome system, and inhibition of ubiquitin-conjugating enzyme 2 N (UBE2N) with a specific antagonist, Bay 11-7082, indicated that UBE2N modulates parkin recruitment and downstream events in the mitophagy pathway. Screening of the compound library identified kenpaullone, an inhibitor of cyclin-dependent kinases and glycogen synthase kinase 3, as a modulator of parkin recruitment. Validation studies revealed that kenpaullone augments the mitochondrial network and protects against the complex I inhibitor MPP+. Finally, we used a microfluidics platform to assess the timing of parkin recruitment to depolarized mitochondria and its modulation by kenpaullone in real time and with single-cell resolution. We demonstrate that the high-content imaging-based assay presented here is suitable for both genetic and pharmacological screening approaches, and we also provide evidence that pharmacological compounds modulate PINK1-dependent parkin recruitment.
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http://dx.doi.org/10.1074/jbc.RA119.009699DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062187PMC
March 2020

DMSO cryopreservation is the method of choice to preserve cells for droplet-based single-cell RNA sequencing.

Sci Rep 2019 07 23;9(1):10699. Epub 2019 Jul 23.

Translational Medicine & Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, 88397, Biberach, Germany.

Combining single-cell RNA sequencing (scRNA-seq) with upstream cell preservation procedures such as cryopreservation or methanol fixation has recently become more common. By separating cell handling and preparation, from downstream library generation, scRNA-seq workflows are more flexible and manageable. However, the inherent transcriptomic changes associated with cell preservation and how they may bias further downstream analysis remain unknown. Here, we present a side-by-side droplet-based scRNA-seq analysis, comparing the gold standard - fresh cells - to three different cell preservation workflows: dimethyl sulfoxide based cryopreservation, methanol fixation and CellCover reagent. Cryopreservation proved to be the most robust protocol, maximizing both cell integrity and low background ambient RNA. Importantly, gene expression profiles from fresh cells correlated most with those of cryopreserved cells. Such similarities were consistently observed across the tested cell lines (R ≥ 0.97), monocyte-derived macrophages (R = 0.97) and immune cells (R = 0.99). In contrast, both methanol fixation and CellCover preservation showed an increased ambient RNA background and an overall lower gene expression correlation to fresh cells. Thus, our results demonstrate the superiority of cryopreservation over other cell preservation methods. We expect our comparative study to provide single-cell omics researchers invaluable support when integrating cell preservation into their scRNA-seq studies.
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http://dx.doi.org/10.1038/s41598-019-46932-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6650608PMC
July 2019

FELLA: an R package to enrich metabolomics data.

BMC Bioinformatics 2018 Dec 22;19(1):538. Epub 2018 Dec 22.

B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, 08028, Spain.

Background: Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects.

Results: We introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets -three from Homo sapiens, two from Danio rerio and one from Mus musculus- and has reproduced findings from the original studies and from independent literature.

Conclusions: The R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.
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http://dx.doi.org/10.1186/s12859-018-2487-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6303911PMC
December 2018

BATLAS: Deconvoluting Brown Adipose Tissue.

Cell Rep 2018 10;25(3):784-797.e4

Institute of Food, Nutrition and Health, ETH Zurich, Schwerzenbach, Switzerland. Electronic address:

Recruitment and activation of thermogenic adipocytes have received increasing attention as a strategy to improve systemic metabolic control. The analysis of brown and brite adipocytes is complicated by the complexity of adipose tissue biopsies. Here, we provide an in-depth analysis of pure brown, brite, and white adipocyte transcriptomes. By combining mouse and human transcriptome data, we identify a gene signature that can classify brown and white adipocytes in mice and men. Using a machine-learning-based cell deconvolution approach, we develop an algorithm proficient in calculating the brown adipocyte content in complex human and mouse biopsies. Applying this algorithm, we can show in a human weight loss study that brown adipose tissue (BAT) content is associated with energy expenditure and the propensity to lose weight. This online available tool can be used for in-depth characterization of complex adipose tissue samples and may support the development of therapeutic strategies to increase energy expenditure in humans.
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http://dx.doi.org/10.1016/j.celrep.2018.09.044DOI Listing
October 2018

Null diffusion-based enrichment for metabolomics data.

PLoS One 2017 6;12(12):e0189012. Epub 2017 Dec 6.

Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain.

Metabolomics experiments identify metabolites whose abundance varies as the conditions under study change. Pathway enrichment tools help in the identification of key metabolic processes and in building a plausible biological explanation for these variations. Although several methods are available for pathway enrichment using experimental evidence, metabolomics does not yet have a comprehensive overview in a network layout at multiple molecular levels. We propose a novel pathway enrichment procedure for analysing summary metabolomics data based on sub-network analysis in a graph representation of a reference database. Relevant entries are extracted from the database according to statistical measures over a null diffusive process that accounts for network topology and pathway crosstalk. Entries are reported as a sub-pathway network, including not only pathways, but also modules, enzymes, reactions and possibly other compound candidates for further analyses. This provides a richer biological context, suitable for generating new study hypotheses and potential enzymatic targets. Using this method, we report results from cells depleted for an uncharacterised mitochondrial gene using GC and LC-MS data and employing KEGG as a knowledge base. Partial validation is provided with NMR-based tracking of 13C glucose labelling of these cells.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189012PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718512PMC
December 2017

Intensity drift removal in LC/MS metabolomics by common variance compensation.

Bioinformatics 2014 Oct 2;30(20):2899-905. Epub 2014 Jul 2.

Department d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain.

Unlabelled: Liquid chromatography coupled to mass spectrometry (LC/MS) has become widely used in Metabolomics. Several artefacts have been identified during the acquisition step in large LC/MS metabolomics experiments, including ion suppression, carryover or changes in the sensitivity and intensity. Several sources have been pointed out as responsible for these effects. In this context, the drift effects of the peak intensity is one of the most frequent and may even constitute the main source of variance in the data, resulting in misleading statistical results when the samples are analysed. In this article, we propose the introduction of a methodology based on a common variance analysis before the data normalization to address this issue. This methodology was tested and compared with four other methods by calculating the Dunn and Silhouette indices of the quality control classes. The results showed that our proposed methodology performed better than any of the other four methods. As far as we know, this is the first time that this kind of approach has been applied in the metabolomics context.

Availability And Implementation: The source code of the methods is available as the R package intCor at http://b2slab.upc.edu/software-and-downloads/intensity-drift-correction/.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btu423DOI Listing
October 2014

An R package to analyse LC/MS metabolomic data: MAIT (Metabolite Automatic Identification Toolkit).

Bioinformatics 2014 Jul 17;30(13):1937-9. Epub 2014 Mar 17.

B2SLab., Department d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Pau Gargallo, 5, 08028 Barcelona, Biomarkers & Nutrimetabolomic Lab., Department of Nutrition and Food Science-XaRTA, INSA, Faculty of Pharmacy, Food and Nutrition Torribera Campus, University of Barcelona, Av. Prat de la Riba 171, 08921, Sta Coloma de Gramenet, and INGENIO-CONSOLIDER Program, FUN-C-Food CSD2007-063, Av Joan XXIII s/n 08028, Barcelona, Spain.

Unlabelled: Current tools for liquid chromatography and mass spectrometry for metabolomic data cover a limited number of processing steps, whereas online tools are hard to use in a programmable fashion. This article introduces the Metabolite Automatic Identification Toolkit (MAIT) package, which makes it possible for users to perform metabolomic end-to-end liquid chromatography and mass spectrometry data analysis. MAIT is focused on improving the peak annotation stage and provides essential tools to validate statistical analysis results. MAIT generates output files with the statistical results, peak annotation and metabolite identification.

Availability And Implementation: http://b2slab.upc.edu/software-and-downloads/metabolite-automatic-identification-toolkit/.
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http://dx.doi.org/10.1093/bioinformatics/btu136DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4071204PMC
July 2014

Peak aggregation as an innovative strategy for improving the predictive power of LC-MS metabolomic profiles.

Anal Chem 2014 Mar 14;86(5):2320-5. Epub 2014 Feb 14.

B2SLab, ESAII Department, Polytechnic University of Catalonia, Barcelona , Barcelona, Spain.

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomic datasets consist of different features including (de)protonated molecules, fragments, adducts, and isotopes that may show high correlation values related to a high level of collinearity. There have been described several sources of these high correlation patterns regarding metabolomic datasets. Among these sources, it should be highlighted the high level of correlation computed between features coming from the same metabolite. It is well-known that soft ionization methods (such as electrospray) produce several mass features from a particular compound (i.e., metabolite spectrum). Typically, the statistical methods used in metabolomics consider spectral peaks as variables. However, it has been reported that a high collinearity between variables might be the responsible for high uncertainty values in the predictors of a regression. In this context, this technical note proposes a new strategy based on the application of the so-called peak aggregation methods (NMF Reduction, PCA Decomposition, Maximum Peak, and Spectrum Mean) to take advantage of the variable collinearity and solve the issue of high variable collinearity. A set of real samples obtained after human nutritional intervention with placebo or polyphenol-rich beverages was used to test this methodology. The results showed that applying any peak aggregation method (especially NMF and PCA) improves the statistical prediction power of class pertinence independently of the nature of the classifier (linear PLS-DA or nonlinear SVM). Overall, the introduction of this new approach resulted in a reduction of the dimensionality of the data and, in addition, in a significant increase in the overall predictive power of the data.
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http://dx.doi.org/10.1021/ac403702pDOI Listing
March 2014

Network-based enrichment analysis of gene expression through protein-protein interaction data.

Annu Int Conf IEEE Eng Med Biol Soc 2012 ;2012:6317-20

Dept. of Sistems Engineering, Automatics and Industrial Informatics, Technical University of Catalonia (UPC), Pau Gargallo 5, 08028, Barcelona, Spain.

High-throughput analysis of gene expression data is subject to technological and statistical issues that confuse the underlying expression-condition associations. In this contribution a network-based candidate gene prioritization strategy was applied to the enrichment of a publicly available gene expression dataset, focused on the study of the mechanosensitivity of genes exposed to altered pulmonary matrix stiffness. Results suggested that some genes which had not been taken into account in the original study could have an important role in the processes causing, or affected by, pulmonary fibrosis.
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http://dx.doi.org/10.1109/EMBC.2012.6347438DOI Listing
July 2013