Abstract Neuroblastoma (NB) is a solid tumor that accounts for 15% of all pediatric oncological deaths, and much is due to the low response to therapy in relapsed tumors. High-risk NB may present deletions in chromosome 11q, which may be associated with other chromosomal alterations and a poor response to therapy, but this association is still poorly understood. Using a systems biology network approach, we studied three patients with high-risk NB with deleted 11q stage 4 to highlight the connections between treatment resistance and copy number alterations in distinct cases. We built different protein-protein interaction networks for each patient based on protein-coding genes mapped at the cytobands pre- and post-chemotherapy from distinct copy number alterations data. In the post-chemotherapy networks, we identified five common regulatory nodes corresponding to the gained region located in ch17q:BIRC5, BRCA1, PRKCA, SUMO2, andGPS1. A crosslink between DNA damage and chromatin remodeling proteins was also found - a connection still poorly understood in NB. We identified a potential connection between XPB gain and chemoresistance of NB. The findings help elucidate the molecular profiles of high-risk NB with 11q deletion in pre- and post-chemotherapy tumor samples, which may reflect unique profiles in poor response to treatment. (NB 15 deaths tumors Highrisk High risk q approach highrisk high cases proteinprotein protein proteincoding coding pre postchemotherapy post chemotherapy data ch17qBIRC5, ch17qBIRC5 chqBIRC ch17q BIRC5, BIRC5 ch BIRC ch17q:BIRC5 BRCA1 BRCA PRKCA SUMO2 SUMO andGPS1 andGPS samples 1 qBIRC ch17qBIRC chq ch17q:BIRC

Abstract There are still numerous challenges to be overcome in microarray data analysis because advanced, state-of-the-art analyses are restricted to programming users. Here we present the Gene Expression Analysis Platform, a versatile, customizable, optimized, and portable software developed for microarray analysis. GEAP was developed in C# for the graphical user interface, data querying, storage, results filtering and dynamic plotting, and R for data processing, quality analysis, and differential expression. Through a new automated system that identifies microarray file formats, retrieves contents, detects file corruption, and solves dependencies, GEAP deals with datasets independently of platform. GEAP covers 32 statistical options, supports quality assessment, differential expression from single and dual-channel experiments, and gene ontology. Users can explore results by different plots and filtering options. Finally, the entire data can be saved and organized through storage features, optimized for memory and data retrieval, with faster performance than R. These features, along with other new options, are not yet present in any microarray analysis software. GEAP accomplishes data analysis in a faster, straightforward, and friendlier way than other similar software, while keeping the flexibility for sophisticated procedures. By developing optimizations, unique customizations and new features, GEAP is destined for both advanced and non-programming users.

Abstract Cryptococcus neoformans and Cryptococcus gattii are the etiological agents of cryptococcosis, a high mortality disease. The development of such disease depends on the interaction of fungal cells with macrophages, in which they can reside and replicate. In order to dissect the molecular mechanisms by which cryptococcal cells modulate the activity of macrophages, a genome-scale comparative analysis of transcriptional changes in macrophages exposed to Cryptococcus spp. was conducted. Altered expression of nearly 40 genes was detected in macrophages exposed to cryptococcal cells. The major processes were associated with the mTOR pathway, whose associated genes exhibited decreased expression in macrophages incubated with cryptococcal cells. Phosphorylation of p70S6K and GSK-3β was also decreased in macrophages incubated with fungal cells. In this way, Cryptococci presence could drive the modulation of mTOR pathway in macrophages possibly to increase the survival of the pathogen.

Abstract Cri-du-chat syndrome (CdCs) is one of the most common contiguous gene syndromes, with an incidence of 1:15,000 to 1:50,000 live births. To better understand the etiology of CdCs at the molecular level, we investigated theprotein–protein interaction (PPI) network within the critical chromosomal region 5p15.3–p15.2 associated with CdCs using systemsbiology. Data were extracted from cytogenomic findings from patients with CdCs. Based on clinical findings, molecular characterization of chromosomal rearrangements, and systems biology data, we explored possible genotype–phenotype correlations involving biological processes connected with CdCs candidate genes. We identified biological processes involving genes previously found to be associated with CdCs, such as TERT, SLC6A3, and CTDNND2, as well as novel candidate proteins with potential contributions to CdCs phenotypes, including CCT5, TPPP, MED10, ADCY2, MTRR, CEP72, NDUFS6, and MRPL36. Although further functional analyses of these proteins are required, we identified candidate proteins for the development of new multi-target genetic editing tools to study CdCs. Further research may confirm those that are directly involved in the development of CdCs phenotypes and improve our understanding of CdCs-associated molecular mechanisms.