The suburban question: grassroots politics and place making in Spanish suburbs

Manuel Castells spoke of the urban as a unit of collective consumption, yet much of the politics of collective consumption he documented was evident in the suburbs. The tendency for suburbs of most complexions to lack services and amenities has been and continues to be a focus of politics in Europe. In Spain, as elsewhere in Europe, a grassroots politics surrounding the making good of these deficits in basic services and amenities has broadened and formalised somewhat to become part of a competitive local representative politics concerned with shaping a sense of place. Here we consider this legacy of grassroots politics as it has played out more recently in a politics of place making in Getafe and Badalona in metropolitan Madrid and Barcelona, respectively. In conclusion, we suggest that this enduring suburban question—of making the suburban urban—places them at the centre of contemporary metropolitan governance and politics. However, it also raises further issues for study—notably, the scalar politics in which suburban place making is empowered or constrained, the role of political parties and individual politicians on the place-making process, and the point at which grassroots politics of collective consumption becomes urban entrepreneurialism

Undisclosed, unmet and neglected challenges in multi-omics studies

[EN] Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies.This work has been funded by the Spanish Ministry of Science and Innovation with grant number BES-2016-076994 to A.A.-L.Tarazona, S.; Arzalluz-Luque, Á.; Conesa, A. (2021). Undisclosed, unmet and neglected challenges in multi-omics studies. Nature Computational Science. 1(6):395-402. https://doi.org/10.1038/s43588-021-00086-z3954021

A multi-omic study for uncovering molecular mechanisms associated with hyperammonemia-induced cerebellar function impairment in rats

[EN] Patients with liver cirrhosis may develop covert or minimal hepatic encephalopathy (MHE). Hyperammonemia (HA) and peripheral inflammation play synergistic roles in inducing the cognitive and motor alterations in MHE. The cerebellum is one of the main cerebral regions affected in MHE. Rats with chronic HA show some motor and cognitive alterations reproducing neurological impairment in cirrhotic patients with MHE. Neuroinflammation and altered neurotransmission and signal transduction in the cerebellum from hyperammonemic (HA) rats are associated with motor and cognitive dysfunction, but underlying mechanisms are not completely known. The aim of this work was to use a multi-omic approach to study molecular alterations in the cerebellum from hyperammonemic rats to uncover new molecular mechanisms associated with hyperammonemia-induced cerebellar function impairment. We analyzed metabolomic, transcriptomic, and proteomic data from the same cerebellums from control and HA rats and performed a multi-omic integrative analysis of signaling pathway enrichment with the PaintOmics tool. The histaminergic system, corticotropin-releasing hormone, cyclic GMP-protein kinase G pathway, and intercellular communication in the cerebellar immune system were some of the most relevant enriched pathways in HA rats. In summary, this is a good approach to find altered pathways, which helps to describe the molecular mechanisms involved in the alteration of brain function in rats with chronic HA and to propose possible therapeutic targets to improve MHE symptoms.This work was supported by the Ministerio de Ciencia e Innovación of Spain (SAF2017-82917-R) and Consellería Educación Generalitat Valenciana (PROMETEOII/2014/033), co-funded with European Regional Development Funds (ERDF).Tarazona, S.; Carmona, H.; Conesa, A.; Llansola, M.; Felipo, V. (2021). A multi-omic study for uncovering molecular mechanisms associated with hyperammonemia-induced cerebellar function impairment in rats. Cell Biology and Toxicology. 37(1):129-149. https://doi.org/10.1007/s10565-020-09572-y12914937

spongeScan: A web for detecting microRNA binding elements in lncRNA sequences.

Non-coding RNA transcripts such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are important genetic regulators. However, the functions of many of these transcripts are still not clearly understood. Recently, it has become apparent that there is significant crosstalk between miRNAs and lncRNAs and that this creates competition for binding between the miRNA, a lncRNA and other regulatory targets. Indeed, various competitive endogenous RNAs (ceRNAs) have already been identified where a lncRNA acts by sequestering miRNAs. This implies the down-regulation in the interaction of the miRNAs with their mRNA targets, what has been called a sponge effect. Multiple approaches exist for the prediction of miRNA targets in mRNAs. However, few methods exist for the prediction of miRNA response elements (MREs) in lncRNAs acting as ceRNAs (sponges). Here, we present spongeScan (http://spongescan.rc.ufl.edu), a graphical web tool to compute and visualize putative MREs in lncRNAs, along with different measures to assess their likely behavior as ceRNAs

MultiBaC: an R package to remove batch effects in multi-omic experiments

Motivation: Batch effects in omics datasets are usually a source of technical noise that masks the biological signal and hampers data analysis. Batch effect removal has been widely addressed for individual omics technologies. However, multi-omic datasets may combine data obtained in different batches where omics type and batch are often confounded. Moreover, systematic biases may be introduced without notice during data acquisition, which creates a hidden batch effect. Current methods fail to address batch effect correction in these cases. Results: In this article, we introduce the MultiBaC R package, a tool for batch effect removal in multi-omics and hidden batch effect scenarios. The package includes a diversity of graphical outputs for model validation and assessment of the batch effect correction. Availability and implementation: MultiBaC package is available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/MultiBaC.html) and GitHub (https://github.com/ConesaLab/MultiBaC.git). The data underlying this article are available in Gene Expression Omnibus repository (accession numbers GSE11521, GSE1002, GSE56622 and GSE43747).This work was funded by the Generalitat Valenciana through PROMETEO grants program for excellence research groups [PROMETEO 2016/093] and by the Spanish MICINN [PID2020-119537RB-I00]. Funding for open access charge: Universitat Politècnica de València

RGmatch: matching genomic regions to proximal genes in omics data integration

© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.[EN] Background: The integrative analysis of multiple genomics data often requires that genome coordinates-based signals have to be associated with proximal genes. The relative location of a genomic region with respect to the gene (gene area) is important for functional data interpretation; hence algorithms that match regions to genes should be able to deliver insight into this information. Results: In this work we review the tools that are publicly available for making region-to-gene associations. We also present a novel method, RGmatch, a flexible and easy-to-use Python tool that computes associations either at the gene, transcript, or exon level, applying a set of rules to annotate each region-gene association with the region location within the gene. RGmatch can be applied to any organism as long as genome annotation is available. Furthermore, we qualitatively and quantitatively compare RGmatch to other tools. Conclusions: RGmatch simplifies the association of a genomic region with its closest gene. At the same time, it is a powerful tool because the rules used to annotate these associations are very easy to modify according to the researcher’s specific interests. Some important differences between RGmatch and other similar tools already in existence are RGmatch’s flexibility, its wide range of user options, compatibility with any annotatable organism, and its comprehensive and user-friendly output.The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under the grant agreement 306000 and the MINECO (Economy and Competitiveness Ministry) BIO2012-40244 grant.Furió Tarí, P.; Conesa, A.; Tarazona Campos, S. (2016). RGmatch: matching genomic regions to proximal genes in omics data integration. BMC Bioinformatics. 17((Suppl 15)). https://doi.org/10.1186/s12859-016-1293-1S129317(Suppl 15)Shu W, Chen H, Bo X, Wang S. Genome-wide analysis of the relationships between DNaseI HS, histone modifications and gene expression reveals distinct modes of chromatin domains. Nucleic Acids Res. 2011;39:7428–43.Song L, Zhang Z, Grasfeder LL, Boyle AP, Giresi PG, Lee B-K, et al. Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res. 2011;21:1757–67.He HH, Meyer CA, Chen MW, Jordan VC, Brown M, Liu XS. Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics. Genome Res. 2012;22:1015–25.Natarajan A, Yardimci GG, Sheffield NC, Crawford GE, Ohler U. Predicting cell-type-specific gene expression from regions of open chromatin. Genome Res. 2012;22:1711–22.Wang Y-M, Zhou P, Wang L-Y, Li Z-H, Zhang Y-N, Zhang Y-X. Correlation between DNase I hypersensitive site distribution and gene expression in HeLa S3 cells. PLoS One. 2012;7:e42414.Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38:576–89.Mclean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB, et al. GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol. 2010;28:495–501.Ji H, Jiang H, Ma W, Johnson DS, Myers RM, Wong WH, Ji H, Jiang H, Ma W, Johnson DS, Myers RM, Wong WH. An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat Biotechnol. 2008;26:1293–300.Wang B, Cunningham JM, Yang X. Seq2pathway: an R/Bioconductor package for pathway analysis of next-generation sequencing data. Bioinformatics. 2015;31:3043.Yu G, Wang L-G, He Q-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics. 2015;31:2382–3

Functional assessment of time course microarray data

<p>Abstract</p> <p>Motivation</p> <p>Time-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated.</p> <p>Methods</p> <p>We present three novel methodologies for the functional assessment of time-course microarray data. i) maSigFun derives from the maSigPro method, a regression-based strategy to model time-dependent expression patterns and identify genes with differences across series. maSigFun fits a regression model for groups of genes labeled by a functional class and selects those categories which have a significant model. ii) PCA-maSigFun fits a PCA model of each functional class-defined expression matrix to extract orthogonal patterns of expression change, which are then assessed for their fit to a time-dependent regression model. iii) ASCA-functional uses the ASCA model to rank genes according to their correlation to principal time expression patterns and assess functional enrichment on a GSA fashion. We used simulated and experimental datasets to study these novel approaches. Results were compared to alternative methodologies.</p> <p>Results</p> <p>Synthetic and experimental data showed that the different methods are able to capture different aspects of the relationship between genes, functions and co-expression that are biologically meaningful. The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.</p

RNAseq analysis of Aspergillus fumigatus in blood reveals a just wait and see resting stage behavior

Background: Invasive aspergillosis is started after germination of Aspergillus fumigatus conidia that are inhaled by susceptible individuals. Fungal hyphae can grow in the lung through the epithelial tissue and disseminate hematogenously to invade into other organs. Low fungaemia indicates that fungal elements do not reside in the bloodstream for long. Results: We analyzed whether blood represents a hostile environment to which the physiology of A. fumigatus has to adapt. An in vitro model of A. fumigatus infection was established by incubating mycelium in blood. Our model allowed to discern the changes of the gene expression profile of A. fumigatus at various stages of the infection. The majority of described virulence factors that are connected to pulmonary infections appeared not to be activated during the blood phase. Three active processes were identified that presumably help the fungus to survive the blood environment in an advanced phase of the infection: iron homeostasis, secondary metabolism, and the formation of detoxifying enzymes. Conclusions: We propose that A. fumigatus is hardly able to propagate in blood. After an early stage of sensing the environment, virtually all uptake mechanisms and energy-consuming metabolic pathways are shut-down. The fungus appears to adapt by trans-differentiation into a resting mycelial stage. This might reflect the harsh conditions in blood where A. fumigatus cannot take up sufficient nutrients to establish self-defense mechanisms combined with significant growth

Impact of Age on Inflammation-Based Scores among Patients Diagnosed with Stage III Non-Small Cell Lung Cancer

[EN] Background: Inflammatory and nutritional indexes are prognostic factors in non-small cell lung cancer (NSCLC). Furthermore, a low grade of chronic inflammation has been described in the older population (inflammaging). We aimed to evaluate the neutrophil-to-lymphocyte ratio (NLR), the Prognostic Nutritional Index (PNI), the advanced lung cancer inflammation index (ALI), the platelet-to-lymphocyte ratio (PLR), and the Glasgow Prognostic Score (GPS) in young and older patients diagnosed with locally advanced NSCLC to determine if significant differences between these groups exist.Methods:We conducted a retrospective study analyzing the impact of age on the NLR, PNI, ALI, PLR, and GPS among patients diagnosed with stage III NSCLC at Hospital Universitario Doctor Peset between 2010 and 2015.Results:We included 124 patients (84 young, 40 older patients). The median hemoglobin level and leukocyte count were lower in the older patients (p= 0.0158 andp= 0.001, respectively). A higher median C-reactive protein level was also found in this group (p= 0.0095). Regarding specific inflammatory indexes, the PNI, comprising inflammatory and nutritional parameters, was lower among the older patients (p= 0.0463). The median NLR, ALI, and PLR were similar in both age groups. Moreover, no differences between the age groups were found in the percentage of patients showing high versus low NLR (cutoff point, 5) or ALI (cutoff point, 18) or in the different GPS groups.Conclusions:The baseline PNI, hemoglobin level, and lymphocyte count were lower among the older patients; furthermore, CRP was higher, possibly, because of a more prominent inflammatory status in older patients with lung cancer. No other immunological or nutritional analytical variables were different between the age groups.Palomar-Abril, V.; Soria-Comes, T.; Tarazona Campos, S.; Martín Ureste, M.; Giner-Bosch, V.; Maestu-Maiques, IC. (2020). Impact of Age on Inflammation-Based Scores among Patients Diagnosed with Stage III Non-Small Cell Lung Cancer. Oncology. 98(8):528-533. https://doi.org/10.1159/000506204S52853398

MultiBaC: A strategy to remove batch effects between different omic data types

[EN] Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform-i.e. gene expression- is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is part of a research project that is totally funded by Conselleria d'Educacio, Cultura i Esport (Generalitat Valenciana) through PROMETEO grants program for excellence research groups.Ugidos, M.; Tarazona Campos, S.; Prats-Montalbán, JM.; Ferrer, A.; Conesa, A. (2020). MultiBaC: A strategy to remove batch effects between different omic data types. Statistical Methods in Medical Research. 29(10):2851-2864. https://doi.org/10.1177/0962280220907365S285128642910Kupfer, P., Guthke, R., Pohlers, D., Huber, R., Koczan, D., & Kinne, R. W. (2012). Batch correction of microarray data substantially improves the identification of genes differentially expressed in Rheumatoid Arthritis and Osteoarthritis. 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