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Ayrton Senna
Little Ayrton couldn't keep still and was happiest behind the wheel of a go-kart-- or his father's car. He practiced hard through rain or shine to become World Champion of Formula One. Voted as one of the best Formula One drivers of all time, Senna also gave back to those in his home country, Brazil. His legacy lives on every time the engines start on a racing track. This inspiring story of the racing legend's life features a fact and photo section at the back.
Book
POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis
Metabolomics and proteomics, like other omics domains, usually face a data mining challenge in providing an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the most difficult challenges and it is critical in the subsequent biological interpretation of the results. Because of this, combined with the computational programming skills needed for this type of analysis, several bioinformatic tools aimed at simplifying metabolomics and proteomics data analysis have emerged. However, sometimes the analysis is still limited to a few hidebound statistical methods and to data sets with limited flexibility. POMAShiny is a web-based tool that provides a structured, flexible and user-friendly workflow for the visualization, exploration and statistical analysis of metabolomics and proteomics data. This tool integrates several statistical methods, some of them widely used in other types of omics, and it is based on the POMA R/Bioconductor package, which increases the reproducibility and flexibility of analyses outside the web environment. POMAShiny and POMA are both freely available at https://github.com/nutrimetabolomics/POMAShiny and https://github.com/nutrimetabolomics/POMA, respectively.
Journal Article
Aberrant epigenome in iPSC‐derived dopaminergic neurons from Parkinson's disease patients
The epigenomic landscape of Parkinson's disease (PD) remains unknown. We performed a genomewide DNA methylation and a transcriptome studies in induced pluripotent stem cell (iPSC)‐derived dopaminergic neurons (DAn) generated by cell reprogramming of somatic skin cells from patients with monogenic LRRK2‐associated PD (L2PD) or sporadic PD (sPD), and healthy subjects. We observed extensive DNA methylation changes in PD DAn, and of RNA expression, which were common in L2PD and sPD. No significant methylation differences were present in parental skin cells, undifferentiated iPSCs nor iPSC‐derived neural cultures not‐enriched‐in‐DAn. These findings suggest the presence of molecular defects in PD somatic cells which manifest only upon differentiation into the DAn cells targeted in PD. The methylation profile from PD DAn, but not from controls, resembled that of neural cultures not‐enriched‐in‐DAn indicating a failure to fully acquire the epigenetic identity own to healthy DAn in PD. The PD‐associated hypermethylation was prominent in gene regulatory regions such as enhancers and was related to the RNA and/or protein downregulation of a network of transcription factors relevant to PD (FOXA1, NR3C1, HNF4A, and FOSL2). Using a patient‐specific iPSC‐based DAn model, our study provides the first evidence that epigenetic deregulation is associated with monogenic and sporadic PD.
Synopsis
This is the first proof‐of‐principle that induced pluripotent stem cell (iPSC)‐derived dopaminergic neurons (DAn) from sporadic and monogenetic Parkinson's disease (PD) patients show the same epigenomic changes as compared to healthy controls. For a video version of this synopsis, see:
http://embopress.org/video_EMM-2015-05439
.
Epigenomic changes are common in patients with sporadic PD and patients with a monogenic form of PD associated with mutations in the gene LRRK2.
PD‐associated methylation changes are latent in parental somatic cells or undifferentiated iPSCs and become uncovered upon differentiation into DAn (cells targeted in PD) but not into other neural types.
PD‐associated methylation changes correlate with gene expression, target functionally‐ active sequences (enhancers), and are related to the aberrant down‐regulation of a network of transcription factors relevant to PD.
Graphical Abstract
This is the first proof‐of‐principle that induced pluripotent stem cell (iPSC)‐derived dopaminergic neurons (DAn) from sporadic and monogenetic Parkinson's disease (PD) patients show the same epigenomic changes as compared to healthy controls.
Journal Article
A Two-Step Algorithm for Handling Block-Wise Missing Data in Multi-Omics
High-throughput technologies produce large-scale omics datasets, and their integration facilitates biomarker discovery and predictive modeling. However, challenges such as data heterogeneity, high dimensionality, and block-wise missing data complicate the analysis. To address these issues, optimization techniques, including regularization and constraint-based approaches, have been already employed for regression and binary classification problems. Building on these methods, we extended this framework to support multi-class classification. Indeed, applied to a multi-class classification task for breast cancer subtypes, our model achieves accuracy between 73% and 81% under various block-wise missing data scenarios. Additionally, we assess its performance on a regression problem using the exposome dataset, integrating a larger number of omics datasets. Across different missing data scenarios, our model demonstrates a strong correlation (75%) between true and predicted responses. Furthermore, we have updated the bwm R package, which previously supported binary and continuous response types, to also include multi-class response types.
Journal Article
Methylation regulation of Antiviral host factors, Interferon Stimulated Genes (ISGs) and T-cell responses associated with natural HIV control
GWAS, immune analyses and biomarker screenings have identified host factors associated with in vivo HIV-1 control. However, there is a gap in the knowledge about the mechanisms that regulate the expression of such host factors. Here, we aimed to assess DNA methylation impact on host genome in natural HIV-1 control. To this end, whole DNA methylome in 70 untreated HIV-1 infected individuals with either high (>50,000 HIV-1-RNA copies/ml, n = 29) or low (<10,000 HIV-1-RNA copies/ml, n = 41) plasma viral load (pVL) levels were compared and identified 2,649 differentially methylated positions (DMPs). Of these, a classification random forest model selected 55 DMPs that correlated with virologic (pVL and proviral levels) and HIV-1 specific adaptive immunity parameters (IFNg-T cell responses and neutralizing antibodies capacity). Then, cluster and functional analyses identified two DMP clusters: cluster 1 contained hypo-methylated genes involved in antiviral and interferon response (e.g. PARP9, MX1, and USP18) in individuals with high viral loads while in cluster 2, genes related to T follicular helper cell (Tfh) commitment (e.g. CXCR5 and TCF7) were hyper-methylated in the same group of individuals with uncontrolled infection. For selected genes, mRNA levels negatively correlated with DNA methylation, confirming an epigenetic regulation of gene expression. Further, these gene expression signatures were also confirmed in early and chronic stages of infection, including untreated, cART treated and elite controllers HIV-1 infected individuals (n = 37). These data provide the first evidence that host genes critically involved in immune control of the virus are under methylation regulation in HIV-1 infection. These insights may offer new opportunities to identify novel mechanisms of in vivo virus control and may prove crucial for the development of future therapeutic interventions aimed at HIV-1 cure.
Journal Article
Fiscal policy on Peru's gross domestic product through fiscal multipliers, 2000 - 2022
This study examines the impact of fiscal policy on Peru's Gross Domestic Product (GDP) using fiscal multipliers and a structured vector autoregressive (SVAR) model to analyze the economic response to fiscal shocks. Fiscal multipliers were analyzed in the context of current government spending, capital spending, and taxes, considering quarterly data, to investigate their effects on Peru's real gross domestic product. Using an SVAR methodology and impulse response analysis (IRF), it was possible to obtain that, after an increase in current spending, GDP initially decreases; and as time passes, it fluctuates and recovers. Capital spending shows that an increase leads to an almost immediate increase in GDP, and in the long term, it maintains stability. On the other hand, in response to taxes, initial negative effects were found in the short term. However, they are offset and tend to stabilize, where the magnitude and duration of this effect depend on several economic and political factors.
This study provides critical insights into the effects of fiscal policy on Peru's economic growth by analyzing fiscal multipliers through a structured vector autoregressive (SVAR) model. By evaluating the impact of government spending and tax policies on GDP from 2000 to 2022, this research uncovers the dynamic responses of the economy to fiscal shocks. Key findings include the contrasting short- and long-term effects of current and capital expenditures and the stabilization tendencies following tax-induced GDP declines. These results emphasize the importance of targeted capital investments and adaptive fiscal strategies to maximize economic growth while ensuring fiscal sustainability. The study offers valuable guidance for policymakers to craft more effective fiscal policies in Peru and comparable economies.
Journal Article
Circulating SOD2 Is a Candidate Response Biomarker for Neoadjuvant Therapy in Breast Cancer
There is a great need for non-invasive tools that inform of an early molecular response to cancer therapeutic treatment. Here, we tested the hypothesis that proteolytically resistant proteins could be candidate circulating tumor biomarkers for cancer therapy. Proteins resistant to proteolysis are drastically under-sampled by current proteomic workflows. These proteins could be reliable sensors for the response to therapy since they are likely to stay longer in circulation. We selected manganese superoxide dismutase (SOD2), a mitochondrial redox enzyme, from a screening of proteolytic resistant proteins in breast cancer (BC). First, we confirmed the robustness of SOD2 and determined that its proteolytic resistance is mediated by its quaternary protein structure. We also proved that the release of SOD2 upon chemotherapy treatment correlates with cell death in BC cells. Then, after confirming that SOD2 is very stable in human serum, we sought to measure its circulating levels in a cohort of BC patients undergoing neoadjuvant therapy. The results showed that circulating levels of SOD2 increased when patients responded to the treatment according to the tumor shrinkage during neoadjuvant chemotherapy. Therefore, the measurement of SOD2 levels in plasma could improve the non-invasive monitoring of the therapeutic treatment in breast cancer patients. The identification of circulating biomarkers linked to the tumor cell death induced by treatment could be useful for monitoring the action of the large number of cancer drugs currently used in clinics. We envision that our approach could help uncover candidate tumor biomarkers to measure a tumor’s response to cancer therapy in real time by sampling the tumor throughout the course of treatment.
Journal Article
Government strategies for the prevention of human trafficking for sexual and labor exploitation in the Amazonas region
Human trafficking is a crime that has been affecting thousands of victims of both sexes and all ages around the world. That is why the study analyzes government strategies in the prevention of human trafficking for sexual and labor exploitation in the Amazonas region, Peru. Identifying that, in the Amazonas region, the problem of human trafficking manifests itself in remote and border areas, mainly in the province of Condorcanqui, where sexual exploitation of women and forced labor predominates, especially of boys, girls, and teenagers. The most common is \"sponsorship\", where people close to the victim deceive them by offering them work or study opportunities and then exploit them. This study suggests that there is more work to do in monitoring sex tourism, work modalities, boys and girls, and adolescents in vulnerable areas, and conditions of poverty, are some strategies to pay attention to prevent human trafficking.
Journal Article
Economic Contribution, Characterization, and Motivations of Tourists: The Raymi Llaqta in Peru
This study assesses the economic contribution and motivations of tourists attending the Raymi Llaqta festival in Chachapoyas, Peru. This study used an econometric analysis based on the application of two types of regression models: non-zero truncated Poisson regression and zero-truncated negative binomial regression. Data were collected through face-to-face structured interviews with domestic and foreign tourists who visited Chachapoyas during the festival. Results indicate significant spending on accommodation, food, transportation, and activities. Tourist satisfaction averaged 3.7, with notable appreciation for the festival’s variety, authenticity, local hospitality, and safety. While both foreign and domestic tourists expressed positive views on the cultural representation and organization of the event, they suggested improvements in hygiene and promotional efforts. Despite using a structured questionnaire, response bias could affect the accuracy of self-reported experiences. This research provides valuable insights into the festival’s direct economic impact on the local economy and highlights the importance of high tourist satisfaction for effective marketing strategies.
Journal Article
Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data
Background: Bioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined. Results: An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites. Conclusions: We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome.
Journal Article