Grupo de Ingeniería Estadística Multivariante GIEM
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Differential expression in RNA-seq: A matter of depth
Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach-NOISeq-that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication. © 2011 by Cold Spring Harbor Laboratory Press.
Influence of ecological infrastructures on the increase of biodiversity and conservation of beneficial arthropods in citrus orchards
[EN] We performed a study in nineteen citrus plots representative of the agricultural landscape of the municipality of Altea (100 km south of Valencia, in eastern Spain) in order to determine the influence of ecological infrastructures on biodiversity and conservation of beneficial arthropods. The landscape was dominated by small citrus orchards mixed with low density urban areas, a consequence of touristic urban pressure. We have considered five factors: pest management system (zero residues vs. conventional), size of the plot, distance to nearest natural habitat, presence/absence of cover crop, and presence/absence of other non-citrus fruits in the plot. Four of the five factors showed a positive influence on biodiversification and conservation of beneficials: small plot size, short distance to natural habitat, presence of vegetation cover and presence of other fruits. These are the factors to promote in order to develop biological strategies alternative to traditional pesticide use in the management of citrus pests. Only the factor “pest management system” does not show a significant influence on biodiversity or on abundance of biological control agents.
RGmatch: matching genomic regions to proximal genes in omics data integration
[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.
acorde unravels functionally interpretable networks of isoform co-usage from single cell data
[EN] Alternative splicing (AS) is a highly-regulated post-transcriptional mechanism known to modulate isoform expression within genes and contribute to cell-type identity. However, the extent to which alternative isoforms establish co-expression networks that may be relevant in cellular function has not been explored yet. Here, we present acorde, a pipeline that successfully leverages bulk long reads and single-cell data to confidently detect alternative isoform co-expression relationships. To achieve this, we develop and validate percentile correlations, an innovative approach that overcomes data sparsity and yields accurate coexpression estimates from single-cell data. Next, acorde uses correlations to cluster coexpressed isoforms into a network, unraveling cell type-specific alternative isoform usage patterns. By selecting same-gene isoforms between these clusters, we subsequently detect and characterize genes with co-differential isoform usage (coDIU) across cell types. Finally, we predict functional elements from long read-defined isoforms and provide insight into biological processes, motifs, and domains potentially controlled by the coordination of post-transcriptional regulation. The code for acorde is available at https://github.com/ConesaLab/acorde.
Qualimap: evaluating next generation sequencing alignment data
Motivation: The sequence alignment/map (SAM) and the binary alignment/map (BAM) formats have become the standard method of representation of nucleotide sequence alignments for next-generation sequencing data. SAM/BAM ¿les usually contain information from tens to hundreds of millions of reads. Often, the sequencing technology, protocol, and/or the selected mapping algorithm introduce some unwanted biases in these data. The systematic detection of such biases is a non-trivial task that is crucial to to drive appropriate downstream analyses. Results: We have developed Qualimap, a Java application that supports user-friendly quality control of mapping data, by considering sequence features and their genomic properties. Qualimap takes sequence alignment data and provides graphical and statistical analyses for the evaluation of data. Such quality-control data are vital for highlighting problems in the sequencing and/or mapping processes, which must be addressed prior to further analyses
Association Between Sex Hormone Levels and Clinical Outcomes in Patients With COVID-19 Admitted to Hospital: An Observational, Retrospective, Cohort Study
[EN] Understanding the cause of sex disparities in COVID-19 outcomes is a major challenge. We investigate sex hormone levels and their association with outcomes in COVID-19 patients, stratified by sex and age. This observational, retrospective, cohort study included 138 patients aged 18 years or older with COVID-19, hospitalized in Italy between February 1 and May 30, 2020. The association between sex hormones (testosterone, estradiol, progesterone, dehydroepiandrosterone) and outcomes (ARDS, severe COVID-19, in-hospital mortality) was explored in 120 patients aged 50 years and over. STROBE checklist was followed. The median age was 73.5 years [IQR 61, 82]; 55.8% were male. In older males, testosterone was lower if ARDS and severe COVID-19 were reported than if not (3.6 vs. 5.3 nmol/L, p =0.0378 and 3.7 vs. 8.5 nmol/L, p =0.0011, respectively). Deceased males had lower testosterone (2.4 vs. 4.8 nmol/L, p =0.0536) and higher estradiol than survivors (40 vs. 24 pg/mL, p = 0.0006). Testosterone was negatively associated with ARDS (OR 0.849 [95% CI 0.734, 0.982]), severe COVID-19 (OR 0.691 [95% CI 0.546, 0.874]), and in-hospital mortality (OR 0.742 [95% CI 0.566, 0.972]), regardless of potential confounders, though confirmed only in the regression model on males. Higher estradiol was associated with a higher probability of death (OR 1.051 [95% CI 1.018, 1.084]), confirmed in both sex models. In males, higher testosterone seems to be protective against any considered outcome. Higher estradiol was associated with a higher probability of death in both sexes.
Mutant PRPF8 Causes Widespread Splicing Changes in Spliceosome Components in Retinitis Pigmentosa Patient iPSC-Derived RPE Cells
[EN] Retinitis pigmentosa (RP) is a rare, progressive disease that affects photoreceptors and retinal pigment epithelial (RPE) cells with blindness as a final outcome. Despite high medical and social impact, there is currently no therapeutic options to slow down the progression of or cure the disease. The development of effective therapies was largely hindered by high genetic heterogeneity, inaccessible disease tissue, and unfaithful model organisms. The fact that components of ubiquitously expressed splicing factors lead to the retina-specific disease is an additional intriguing question. Herein, we sought to correlate the retinal cell-type-specific disease phenotype with the splicing profile shown by a patient with autosomal recessive RP, caused by a mutation in pre-mRNA splicing factor 8 (PRPF8). In order to get insight into the role of PRPF8 in homeostasis and disease, we capitalize on the ability to generate patient-specific RPE cells and reveal differentially expressed genes unique to RPE cells. We found that spliceosomal complex and ribosomal functions are crucial in determining cell-type specificity through differential expression and alternative splicing (AS) and that PRPF8 mutation causes global changes in splice site selection and exon inclusion that particularly affect genes involved in these cellular functions. This finding corroborates the hypothesis that retinal tissue identity is conferred by a specific splicing program and identifies retinal AS events as a framework toward the design of novel therapeutic opportunities.
Transcriptome modulation during host shift is driven by secondary metabolites in desert Drosophila
[EN] High-throughput transcriptome studies are breaking new ground to investigate the responses that organisms deploy in alternative environments. Nevertheless, much remains to be understood about the genetic basis of host plant adaptation. Here, we investigate genome-wide expression in the fly Drosophila buzzatii raised in different conditions. This species uses decaying tissues of cactus of the genus Opuntia as primary rearing substrate and secondarily, the necrotic tissues of the columnar cactus Trichocereus terscheckii. The latter constitutes a harmful host, rich in mescaline and other related phenylethylamine alkaloids. We assessed the transcriptomic responses of larvae reared in Opuntia sulphurea and T. terscheckii, with and without the addition of alkaloids extracted from the latter. Whole-genome expression profiles were massively modulated by the rearing environment, mainly by the presence of T. terscheckii alkaloids. Differentially expressed genes were mainly related to detoxification, oxidation–reduction and stress response; however, we also found genes involved in development and neurobiological processes. In conclusion, our study contributes new data onto the role of transcriptional plasticity in response to alternative rearing environments.
Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients
[EN] The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers¿ performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.
Extracellular vesicles from dental pulp mesenchymal stem cells modulate macrophage phenotype during acute and chronic cardiac inflammation in athymic nude rats with myocardial infarction
[EN] Background/aims Extracellular vesicles (EVs) derived from dental pulp mesenchymal stem cells (DP-MSCs) are a promising therapeutic option for the treatment of myocardial ischemia. The aim of this study is to determine whether MSC-EVs could promote a pro-resolving environment in the heart by modulating macrophage populations.Methods EVs derived from three independent biopsies of DP-MSCs (MSC-EVs) were isolated by tangential flow-filtration and size exclusion chromatography and were characterized by omics analyses. Biological processes associated with these molecules were analyzed using String and GeneCodis platforms. The immunomodulatory capacity of MSC-EVs to polarize macrophages towards a pro-resolving or M2-like phenotype was assessed by evaluating surface markers, cytokine production, and efferocytosis. The therapeutic potential of MSC-EVs was evaluated in an acute myocardial infarction (AMI) model in nude rats. Infarct size and the distribution of macrophage populations in the infarct area were evaluated 7 and 21 days after intramyocardial injection of MSC-EVs.Results Lipidomic, proteomic, and miRNA-seq analysis of MSC-EVs revealed their association with biological processes involved in tissue regeneration and regulation of the immune system, among others. MSC-EVs promoted the differentiation of pro-inflammatory macrophages towards a pro-resolving phenotype, as evidenced by increased expression of M2 markers and decreased secretion of pro-inflammatory cytokines. Administration of MSC-EVs in rats with AMI limited the extent of the infarcted area at 7 and 21 days post-infarction. MSC-EV treatment also reduced the number of pro-inflammatory macrophages within the infarct area, promoting the resolution of inflammation.Conclusion EVs derived from DP-MSCs exhibited similar characteristics at the omics level irrespective of the biopsy from which they were derived. All MSC-EVs exerted effective pro-resolving responses in a rat model of AMI, indicating their potential as therapeutic agents for the treatment of inflammation associated with AMI.