Phylogenetic Analysis of Stenotrophomonas spp. Isolates Contributes to the Identification of Nosocomial and Community-Acquired Infections

Stenotrophomonas ssp. has a wide environmental distribution and is also found as an opportunistic pathogen, causing nosocomial or community-acquired infections. One species, S. maltophilia, presents multidrug resistance and has been associated with serious infections in pediatric and immunocompromised patients. Therefore, it is relevant to conduct resistance profile and phylogenetic studies in clinical isolates for identifying infection origins and isolates with augmented pathogenic potential. Here, multilocus sequence typing was performed for phylogenetic analysis of nosocomial isolates of Stenotrophomonas spp. and, environmental and clinical strains of S. maltophilia. Biochemical andmultidrug resistance profiles of nosocomial and clinical strains were determined. the inferred phylogenetic profile showed high clonal variability, what correlates with the adaptability process of Stenotrophomonas to different habitats. Two clinical isolates subgroups of S. maltophilia sharing high phylogenetic homogeneity presented intergroup recombination, thus indicating the high permittivity to horizontal gene transfer, a mechanism involved in the acquisition of antibiotic resistance and expression of virulence factors. for most of the clinical strains, phylogenetic inference was made using only partial ppsA gene sequence. Therefore, the sequencing of just one specific fragment of this gene would allow, in many cases, determining whether the infection with S. maltophilia was nosocomial or community-acquired.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ São Paulo, Fac Med, Dept Pediat, BR-05403900 São Paulo, BrazilHosp Israelita Albert Einstein, BR-05652900 São Paulo, BrazilInst Butantan, Bacteriol Lab, BR-05503900 São Paulo, BrazilFAPESP: 2010/04115-9Web of Scienc

Gene interaction networks and epigenetic control in health and disease transition

A utilização de técnicas de alto desempenho (high-throughput) – como microarranjos de DNA e sequenciamento de nova geração - para o estudo do genoma em doenças complexas (i.e. causadas pela interação de fatores ambientais e genéticos) tem levado à produção massiva de dados e exigido o desenvolvimento de abordagens sistemáticas para a investigação de fenômenos biológicos. Essas abordagens centram-se na integração de dados (expressão gênica global, sequenciamento de DNA, interações proteína-proteína), além da caracterização, modelagem e predição das propriedades emergentes dos sistemas biológicos, ou seja, dos processos que ocorrem em células e tecidos como resposta a mudanças ambientais e genéticas. Um exemplo dessa abordagem é o estudo de alterações transcricionais através da obtenção de redes de co-expressão gênica, ou GCNs (acrônimo para gene coexpression network) e a visualização gráfica dessas redes complexas. A análise das GCNs permite a determinação da topologia dessas redes e a avaliação de como as interações gene-gene são alteradas na transição saúde-doença. É também possível associar as propriedades topológicas dessas redes com a organização funcional do genoma. Mudanças em propriedades das GCNs, como entropia, modularidade, grau de conectividade dos nós e robustez, estão diretamente associadas à patofisiologia de determinadas doenças. As alterações em GCNs na transição saúde-doença e em resposta a fatores ambientais são mediadas por mecanismos epigenéticos – metilação do DNA, acetilação de histonas e RNAs não codificantes - que modulam a expressão dos genes. Assim, o estudo das GCNs e de seu controle por mecanismos epigenéticos têm permitido uma melhor compreensão das alterações dinâmicas envolvidas no estabelecimento e progressão das doenças complexas.The massive data generation derived from the study of complex diseases by high throughput techniques, such as DNA microarrays and new generation sequencing, has prompted the development of systematic approaches for the investigation of biological phenomena. These new approaches seek data integration, as well as the characterization, modeling and prediction of emergent properties in biological systems. Moreover, the analysis of transcriptional changes through gene coexpression networks (GCNs) has been utilized to capture the molecular mechanisms associated with complex diseases. Furthermore, the topological properties of GCNs have been associated with the functional organization of the genome. This approach has been used to identify different transition states associated with health and disease, where changes in the topological properties of GCNs, such as entropy, modularity, node centrality and network robustness are directly associated with the pathophysiological processes of specific diseases. On the other hand, epigenetic mechanisms - such as histone modifications and DNA methylation and non-coding RNAs - are the transforming forces behind GCNs alterations in response to environmental factors and in the health-disease transition. Therefore, the interplay between the genome, epigenetic mechanisms and environment represents the foundation for the study of dynamic changes that govern the development and progression of complex diseases

Phylogenetic Analysis of Stenotrophomonas spp. Isolates Contributes to the Identification of Nosocomial and Community-Acquired Infections

Stenotrophomonas ssp. has a wide environmental distribution and is also found as an opportunistic pathogen, causing nosocomial or community-acquired infections. One species, S. maltophilia, presents multidrug resistance and has been associated with serious infections in pediatric and immunocompromised patients. Therefore, it is relevant to conduct resistance profile and phylogenetic studies in clinical isolates for identifying infection origins and isolates with augmented pathogenic potential. Here, multilocus sequence typing was performed for phylogenetic analysis of nosocomial isolates of Stenotrophomonas spp. and, environmental and clinical strains of S. maltophilia. Biochemical and multidrug resistance profiles of nosocomial and clinical strains were determined. The inferred phylogenetic profile showed high clonal variability, what correlates with the adaptability process of Stenotrophomonas to different habitats. Two clinical isolates subgroups of S. maltophilia sharing high phylogenetic homogeneity presented intergroup recombination, thus indicating the high permittivity to horizontal gene transfer, a mechanism involved in the acquisition of antibiotic resistance and expression of virulence factors. For most of the clinical strains, phylogenetic inference was made using only partial ppsA gene sequence. Therefore, the sequencing of just one specific fragment of this gene would allow, in many cases, determining whether the infection with S. maltophilia was nosocomial or community-acquired

Complex network analysis of CA3 transcriptome reveals pathogenic and compensatory pathways in refractory temporal lobe epilepsy

We previously described - studying transcriptional signatures of hippocampal CA3 explants - that febrile (FS) and afebrile (NFS) forms of refractory mesial temporal lobe epilepsy constitute two distinct genomic phenotypes. That network analysis was based on a limited number (hundreds) of differentially expressed genes (DE networks) among a large set of valid transcripts (close to two tens of thousands). Here we developed a methodology for complex network visualization (3D) and analysis that allows the categorization of network nodes according to distinct hierarchical levels of gene-gene connections (node degree) and of interconnection between node neighbors (concentric node degree). Hubs are highly connected nodes, VIPs have low node degree but connect only with hubs, and high-hubs have VIP status and high overall number of connections. Studying the whole set of CA3 valid transcripts we: i) obtained complete transcriptional networks (CO) for FS and NFS phenotypic groups; ii) examined how CO and DE networks are related; iii) characterized genomic and molecular mechanisms underlying FS and NFS phenotypes, identifying potential novel targets for therapeutic interventions. We found that: i) DE hubs and VIPs are evenly distributed inside the CO networks; ii) most DE hubs and VIPs are related to synaptic transmission and neuronal excitability whereas most CO hubs, VIPs and high hubs are related to neuronal differentiation, homeostasis and neuroprotection, indicating compensatory mechanisms. Complex network visualization and analysis is a useful tool for systems biology approaches to multifactorial diseases. Network centrality observed for hubs, VIPs and high hubs of CO networks, is consistent with the network disease model, where a group of nodes whose perturbation leads to a disease phenotype occupies a central position in the network.Conceivably, the chance for exerting therapeutic effects through the modulation of particular genes will be higher if these genes are highly interconnected in transcriptional networks.FAPESP (09/53443-1, 05/56446-0, 05/00587-5, 11/50761-2)CNPq (305635/2009-3, 301303/06-1, 573583/2008-0

Transcriptional network analysis reveals that AT1 and AT2 angiotensin II receptors are both involved in the regulation of genes essential for glioma progression.

Gliomas are aggressive primary brain tumors with high infiltrative potential. The expression of Angiotensin II (Ang II) receptors has been associated with poor prognosis in human astrocytomas, the most common type of glioma. In this study, we investigated the role of Angiotensin II in glioma malignancy through transcriptional profiling and network analysis of cultured C6 rat glioma cells exposed to Ang II and to inhibitors of its membrane receptor subtypes. C6 cells were treated with Ang II and specific antagonists of AT1 and AT2 receptors. Total RNA was isolated after three and six hours of Ang II treatment and analyzed by oligonucleotide microarray technology. Gene expression data was evaluated through transcriptional network modeling to identify how differentially expressed (DE) genes are connected to each other. Moreover, other genes co-expressing with the DE genes were considered in these analyses in order to support the identification of enriched functions and pathways. A hub-based network analysis showed that the most connected nodes in Ang II-related networks exert functions associated with cell proliferation, migration and invasion, key aspects for glioma progression. The subsequent functional enrichment analysis of these central genes highlighted their participation in signaling pathways that are frequently deregulated in gliomas such as ErbB, MAPK and p53. Noteworthy, either AT1 or AT2 inhibitions were able to down-regulate different sets of hub genes involved in protumoral functions, suggesting that both Ang II receptors could be therapeutic targets for intervention in glioma. Taken together, our results point out multiple actions of Ang II in glioma pathogenesis and reveal the participation of both Ang II receptors in the regulation of genes relevant for glioma progression. This study is the first one to provide systems-level molecular data for better understanding the protumoral effects of Ang II in the proliferative and infiltrative behavior of gliomas

Quantitative PCR (qPCR) experiments for selected genes.

<p>A) Gene expression levels of Angiotensin II receptors (Agtr1 and Agtr2) were evaluated by qPCR. B) Technical validation of oligonucleotide microarray data by qPCR of Ang II-regulated genes. The expression of the genes Agtrap, Map2k4, Bcar1, Lamb1, Prkca, Hbegf, Aqp2, Vegfa, Ctgf and Nlrp3 was assessed to confirm gene expression changes identified by oligonucleotide microarray analysis. The gene Gapdh was used as an internal control. The comparison is made between average log2 expression values derived from microarray experiments and arbitrary units obtained from qPCR assays.</p

Transcriptional network enrichment analysis of hub genes found at Ang II +PD123319 x Ang II 3 h comparison.

<p>(A) Scatter plot of betweenness centrality versus degree for nodes obtained in the transcriptional network analysis. Differentially expressed (DE) genes are represented as red (up-regulated) or green (down-regulated) diamonds in the graphic. DE-related genes are represented as purple diamonds. (B) Transcriptional interaction subnetwork containing the 25 DE genes and 15 DE-related genes with the highest centrality values in each network. DE genes are represented as red (up-regulated) or green (down-regulated) squares in the networks. DE-related genes are represented as purple diamonds. Genes previously associated with the keywords “Angiotensin II” and “glioma” display yellow border colors. Genes previously associated with the keywords “glioma”, “migration” or “invasion” display sea green border colors. (C) KEGG categories showing enrichment in functions for the hub genes.</p

Venn diagrams showing the number of common DE genes and the common enriched transcription factors across the experimental comparisons.

<p>(A) Number of common and exclusively regulated genes at 3 and 6 hours intervals for Ang x Control, Ang II+Los x Ang II and Ang II+PD123319 x Ang II comparisons. (B) Number and enriched transcription factors observed when each time interval was analyzed separately. (C) Number and enriched transcription factors observed for the same comparisons at both 3 and 6 hours time intervals.</p