HIHISIV: a database of gene expression in HIV and SIV host immune response.

In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus (HIV) both because they are evolutionarily linked and because they share physiological and anatomical similarities that are largely explored to understand the disease progression. The HIHISIV database was developed to support researchers to integrate and evaluate the large number of transcriptional data associated with the presence/absence of the pathogen (SIV or HIV) and the host response (NHP and human). The datasets are composed of microarray and RNA-Seq gene expression data that were selected, curated, analyzed, enriched, and stored in a relational database. Six query templates comprise the main data analysis functions and the resulting information can be downloaded. The HIHISIV database, available at  https://hihisiv.github.io , provides accurate resources for browsing and visualizing results and for more robust analyses of pre-existing data in transcriptome repositories.

Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer.

The human papillomavirus (HPV) is present in a significant fraction of head-and-neck squamous cell cancer (HNSCC). The main goal of this study was to identify distinct co-expression patterns between HPV+ and HPV- HNSCC and to provide insights into potential regulatory mechanisms/effects within the analyzed networks. We selected cases deposited in The Cancer Genome Atlas database comprising data of gene expression, methylation profiles and mutational patterns, in addition to clinical information. The intersection among differentially expressed and differentially methylated genes showed the negative correlations between the levels of methylation and expression, suggesting that these genes have their expression levels regulated by methylation alteration patterns in their promoter. Weighted correlation network analysis was used to identify co-expression modules and a systematic approach was applied to refine them and identify key regulatory elements integrating results from the other omics. Three distinct co-expression modules were associated with HPV status and molecular signatures. Validation using independent studies reporting biological experimental data converged for the most significant genes in all modules. This study provides insights into complex genetic and epigenetic particularities in the development and progression of HNSCC according to HPV status, and contribute to unveiling specific genes/pathways as novel therapeutic targets in HNSCC.

Analysis of the cervical microbiome and potential biomarkers from postpartum HIV-positive women displaying cervical intraepithelial lesions.

The cervical microbiota composition and diversity of HIV-positive women in the postpartum period is unknown. Using a high-throughput bacterial 16S rRNA gene sequencing, we identified four community state types (CSTs). CST III (Lactobacillusdominant) and CST IV (IV-A, IV-B.1, IV-B.2; high-diversity) were found in 41% and 59% of samples, respectively. We did not find association of any CST to postpartum period (six or twelve months), HPV infection or cytology (normal or lesion). However, five bacterial genera were associated with cervical lesions (Gardnerella, Aerococcus, Schlegelella, Moryella and Bifidobacterium), with significant odds ratio (OR) of 40 (2.28-706) for the presence of Moryella and 3.5 (1.36-8.9) for Schlegelella. Longitudinal analysis of samples at postpartum that regressed (lesion to normal), progressed (normal to lesion) and maintained the cytology (lesion or normal) evidenced Gardnerella with a significantly higher abundance in regressing lesions. In the current study, we report the first data on the cervical microbiota of HIV-positive women in the postpartum period. Consistent with previous studies of HIV-negative cohorts, HIV-positive women present a stable cervical microbiota of high-diversity in the postpartum period. Our results highlight that specific microbiota species may serve as sensors for changes in the cervical microenvironment associated with cervical lesions.

GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis.

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.

Comparative evolutionary epidemiology of dengue virus serotypes.

Evolutionary studies on dengue virus have frequently focused on intra-serotype diversity or on specific epidemics. In this study, we compiled a comprehensive data set of the envelope gene of dengue virus serotypes and conducted an extensive comparative study of evolutionary molecular epidemiology. We found that substitution rates are homogeneous among dengue serotypes, although their population dynamics have differed over the past few years as inferred by Bayesian coalescent methods. On a global scale, DENV-2 is the serotype with the highest effective population size. The genealogies also showed geographical structure within the serotypes. Finally, we also explored the causes of dengue virus serotype diversification by investigating the plausibility that it was driven by adaptive changes. Our results suggest that the envelope gene is under significant purifying selection and the hypothesis that dengue virus serotype diversification was the result of stochastic events cannot be ruled out.