RESUMO
During the 2015-2016 Zika Virus (ZIKV) epidemic in Brazil, the geographical distributions of ZIKV infection and microcephaly outbreaks did not align. This raised doubts about the virus as the single cause of the microcephaly outbreak and led to research hypotheses of alternative explanatory factors, such as environmental variables and factors, agrochemical use, or immunizations. We investigated context and the intermediate and structural determinants of health inequalities, as well as social environment factors, to determine their interaction with ZIKV-positive- and ZIKV-negative-related microcephaly. The results revealed the identification of 382 associations among 382 nonredundant variables of Zika surveillance, including multiple determinants of environmental public health factors and variables obtained from 5565 municipalities in Brazil. This study compared those factors and variables directly associated with microcephaly incidence positive to ZIKV and those associated with microcephaly incidence negative to ZIKV, respectively, and mapped them in case and control subnetworks. The subnetworks of factors and variables associated with low birth weight and birthweight where birth incidence served as an additional control were also mapped. Non-significant differences in factors and variables were observed, as were weights of associations between microcephaly incidence, both positive and negative to ZIKV, which revealed diagnostic inaccuracies that translated to the underestimation of the scope of the ZIKV outbreak. A detailed analysis of the patterns of association does not support a finding that vaccinations contributed to microcephaly, but it does raise concerns about the use of agrochemicals as a potential factor in the observed neurotoxicity arising from the presence of heavy metals in the environment and microcephaly not associated with ZIKV. Summary: A comparative network inferential analysis of the patterns of variables and factors associated with Zika virus infections in Brazil during 2015-2016 coinciding with a microcephaly epidemic identified multiple contributing determinants. This study advances our understanding of the cumulative interactive effects of exposures to chemical and non-chemical stressors in the built, natural, physical, and social environments on adverse pregnancy and health outcomes in vulnerable populations.
Assuntos
Microcefalia , Infecção por Zika virus , Zika virus , Big Data , Brasil/epidemiologia , Feminino , Humanos , Incidência , Microcefalia/etiologia , Gravidez , Infecção por Zika virus/complicações , Infecção por Zika virus/diagnóstico , Infecção por Zika virus/epidemiologiaRESUMO
There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships. The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene interaction networks. GeNNet is the first platform to integrate the analytical process of transcriptome data with graph databases. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers can add new functionality to components of GeNNet. The derived data allows for testing previous hypotheses about an experiment and exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms. GeNNet is available as an open source platform at https://github.com/raquele/GeNNet and can be retrieved as a software container with the command docker pull quelopes/gennet.