Effect of human TGF-beta on the gene expression profile of Schistosoma mansoni adult worms

Schistosoma mansoni is responsible for schistosomiasis, a parasitic disease that affects 200 million people worldwide. Molecular mechanisms of host-parasite interaction are complex and involve a crosstalk between host signals and parasite receptors. TGF-beta signaling pathway has been shown to play an important role in S. mansoni development and embryogenesis. In particular human (h) TGF-beta has been shown to bind to a S. mansoni receptor, transduce a signal that regulates the expression of a schistosome target gene. Here we describe 381 parasite genes whose expression levels are affected by in vitro treatment with hTGF-beta. Among these differentially expressed genes we highlight genes related to morphology, development and cell cycle that could be players of cytokine effects on the parasite. We confirm by qPCR the expression changes detected with microarrays for 5 out of 7 selected genes. We also highlight a set of non-coding RNAs transcribed from the same loci of protein-coding genes that are differentially expressed upon hTCF-beta treatment. These datasets offer potential targets to be explored in order to understand the molecular mechanisms behind the possible role of hTGF-beta effects on parasite biology. (C) 2012 Elsevier B.V. All rights reserved.

Using gene-network landscape to dissect genotype effects of TCF7L2 genetic variant on diabetes and cardiovascular risk

Vaquero AR, Ferreira NE, Omae SV, Rodrigues MV, Teixeira SK, Krieger JE, Pereira AC. Using gene-network landscape to dissect genotype effects of TCF7L2 genetic variant on diabetes and cardiovascular risk. Physiol Genomics 44: 903-914, 2012. First published August 7, 2012; doi:10.1152/physiolgenomics.00030.2012.-The single nucleotide polymorphism (SNP) within the TCF7L2 gene, rs7903146, is, to date, the most significant genetic marker associated with Type 2 diabetes mellitus (T2DM) risk. Nonetheless, its functional role in disease pathology is poorly understood. The aim of the present study was to investigate, in vascular smooth muscle cells from 92 patients undergoing aortocoronary bypass surgery, the contribution of this SNP in T2DM using expression levels and expression correlation comparison approaches, which were visually represented as gene interaction networks. Initially, the expression levels of 41 genes (seven TCF7L2 splice forms and 40 other T2DM relevant genes) were compared between rs7903146 wild-type (CC) and T2DM-risk (CT + TT) genotype groups. Next, we compared the expression correlation patterns of these 41 genes between groups to observe if the relationships between genes were different. Five TCF7L2 splice forms and nine genes showed significant expression differences between groups. RXR alpha gene was pinpointed as showing the most different expression correlation pattern with other genes. Therefore, T2DM risk alleles appear to be influencing TCF7L2 splice form's expression in vascular smooth muscle cells, and RXR alpha gene is pointed out as a treatment target candidate for risk reduction in individuals with high risk of developing T2DM, especially individuals harboring TCF7L2 risk genotypes.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

Relating network rigidity, time scale hierarchies, and expression noise in gene networks

Fluctuation-dissipation theorems can be used to predict characteristics of noise from characteristics of the macroscopic response of a system. In the case of gene networks, feedback control determines the "network rigidity," defined as resistance to slow external changes. We propose an effective Fokker-Planck equation that relates gene expression noise to topology and to time scales of the gene network. We distinguish between two situations referred to as normal and inverted time hierarchies. The noise can be buffered by network feedback in the first situation, whereas it can be topology independent in the latter.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Gene by environment QTL mapping through multiple trait analyses in blood pressure salt-sensitivity: identification of a novel QTL in rat chromosome 5

Background The genetic mechanisms underlying interindividual blood pressure variation reflect the complex interplay of both genetic and environmental variables. The current standard statistical methods for detecting genes involved in the regulation mechanisms of complex traits are based on univariate analysis. Few studies have focused on the search for and understanding of quantitative trait loci responsible for gene × environmental interactions or multiple trait analysis. Composite interval mapping has been extended to multiple traits and may be an interesting approach to such a problem. Methods We used multiple-trait analysis for quantitative trait locus mapping of loci having different effects on systolic blood pressure with NaCl exposure. Animals studied were 188 rats, the progenies of an F2 rat intercross between the hypertensive and normotensive strain, genotyped in 179 polymorphic markers across the rat genome. To accommodate the correlational structure from measurements taken in the same animals, we applied univariate and multivariate strategies for analyzing the data. Results We detected a new quantitative train locus on a region close to marker R589 in chromosome 5 of the rat genome, not previously identified through serial analysis of individual traits. In addition, we were able to justify analytically the parametric restrictions in terms of regression coefficients responsible for the gain in precision with the adopted analytical approach. Conclusion Future work should focus on fine mapping and the identification of the causative variant responsible for this quantitative trait locus signal. The multivariable strategy might be valuable in the study of genetic determinants of interindividual variation of antihypertensive drug effectiveness.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Modularity and evolutionary constraints in a baculovirus gene regulatory network

Abstract Background The structure of regulatory networks remains an open question in our understanding of complex biological systems. Interactions during complete viral life cycles present unique opportunities to understand how host-parasite network take shape and behave. The Anticarsia gemmatalis multiple nucleopolyhedrovirus (AgMNPV) is a large double-stranded DNA virus, whose genome may encode for 152 open reading frames (ORFs). Here we present the analysis of the ordered cascade of the AgMNPV gene expression. Results We observed an earlier onset of the expression than previously reported for other baculoviruses, especially for genes involved in DNA replication. Most ORFs were expressed at higher levels in a more permissive host cell line. Genes with more than one copy in the genome had distinct expression profiles, which could indicate the acquisition of new functionalities. The transcription gene regulatory network (GRN) for 149 ORFs had a modular topology comprising five communities of highly interconnected nodes that separated key genes that are functionally related on different communities, possibly maximizing redundancy and GRN robustness by compartmentalization of important functions. Core conserved functions showed expression synchronicity, distinct GRN features and significantly less genetic diversity, consistent with evolutionary constraints imposed in key elements of biological systems. This reduced genetic diversity also had a positive correlation with the importance of the gene in our estimated GRN, supporting a relationship between phylogenetic data of baculovirus genes and network features inferred from expression data. We also observed that gene arrangement in overlapping transcripts was conserved among related baculoviruses, suggesting a principle of genome organization. Conclusions Albeit with a reduced number of nodes (149), the AgMNPV GRN had a topology and key characteristics similar to those observed in complex cellular organisms, which indicates that modularity may be a general feature of biological gene regulatory networks.

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Abiotic factors shape temporal variation in the structure of an ant-plant network

Despite recognition of key biotic processes in shaping the structure of biological communities, few empirical studies have explored the influences of abiotic factors on the structural properties of mutualistic networks. We tested whether temperature and precipitation contribute to temporal variation in the nestedness of mutualistic ant-plant networks. While maintaining their nested structure, nestedness increased with mean monthly precipitation and, particularly, with monthly temperature. Moreover, some species changed their role in network structure, shifting from peripheral to core species within the nested network. We could summarize that abiotic factors affect plant species in the vegetation (e.g., phenology), meaning presence/absence of food sources, consequently an increase/decrease of associations with ants, and finally, these variations to fluctuations in nestedness. While biotic factors are certainly important, greater attention needs to be given to abiotic factors as underlying determinants of the structures of ecological networks.

Allelic variations in the vitamin D receptor gene, insulin secretion and parents' heights are independently associated with height in obese children and adolescents

Polymorphisms in the VDR gene were reported to be associated with variations in intrauterine and postnatal growth and with adult height, but also with other traits that are strongly correlated such as the BMI, insulin sensitivity, insulin secretion and hyperglycemia. Here, we assessed the impact of VDR polymorphisms on body height and its interactions with obesity- and glucose tolerance-related traits in obese children and adolescents. We studied 173 prepubertal (Tanner's stage 1) and 146 pubertal (Tanner's stages 2-5) obese children who were referred for a weight-loss program. Three single nucleotide polymorphisms were genotyped: rs1544410 (BsmI), rs7975232 (ApaI) and rs731236 (TaqI). BsmI and TaqI genotypes were significantly associated with height in pubertal children, but the associations did not reach statistical significance in prepubertal children. In stepwise regression analyses, the lean body mass, insulin secretion, BsmI or TaqI genotypes and the father's and the mother's height were independently and positively associated with height in pubertal children. These covariables accounted for 46% of the trait variance. The height of homozygous carriers of the minor allele of BsmI was 0.65 z-scores (4 cm) higher than the height of homozygous carriers of the major allele (P=.0006). Haplotype analyses confirmed the associations of the minor alleles of BsmI and TaqI with increased height. In conclusion, VDR genotypes were significantly associated with height in pubertal obese children. The associations were independent from the effects of confounding traits, such as the body fat mass, insulin secretion, insulin sensitivity and glucose tolerance. (C) 2012 Elsevier Inc. All rights reserved.

Discriminating Different Classes of Biological Networks by Analyzing the Graphs Spectra Distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e. g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.

Analysis of three Xanthomonas axonopodis pv. citri effector proteins in pathogenicity and their interactions with host plant proteins

Xanthomonas axonopodis pv. citri, the bacterium responsible for citrus canker, uses effector proteins secreted by a type III protein secretion system to colonize its hosts. Among the putative effector proteins identified for this bacterium, we focused on the analysis of the roles of AvrXacE1, AvrXacE2 and Xac3090 in pathogenicity and their interactions with host plant proteins. Bacterial deletion mutants in avrXacE1, avrXacE2 and xac3090 were constructed and evaluated in pathogenicity assays. The avrXacE1 and avrXacE2 mutants presented lesions with larger necrotic areas relative to the wild-type strain when infiltrated in citrus leaves. Yeast two-hybrid studies were used to identify several plant proteins likely to interact with AvrXacE1, AvrXacE2 and Xac3090. We also assessed the localization of these effector proteins fused to green fluorescent protein in the plant cell, and observed that they co-localized to the subcellular spaces in which the plant proteins with which they interacted were predicted to be confined. Our results suggest that, although AvrXacE1 localizes to the plant cell nucleus, where it interacts with transcription factors and DNA-binding proteins, AvrXacE2 appears to be involved in lesion-stimulating disease 1-mediated cell death, and Xac3090 is directed to the chloroplast where its function remains to be clarified.

Whole blood genome-wide gene expression profile in males after prolonged wakefulness and sleep recovery

Pellegrino R, Sunaga DY, Guindalini C, Martins RC, Mazzotti DR, Wei Z, Daye ZJ, Andersen ML, Tufik S. Whole blood genome-wide gene expression profile in males after prolonged wakefulness and sleep recovery. Physiol Genomics 44: 1003-1012, 2012. First published September 4, 2012; doi: 10.1152/physiolgenomics.00058.2012.-Although the specific functions of sleep have not been completely elucidated, the literature has suggested that sleep is essential for proper homeostasis. Sleep loss is associated with changes in behavioral, neurochemical, cellular, and metabolic function as well as impaired immune response. Using high-resolution microarrays we evaluated the gene expression profiles of healthy male volunteers who underwent 60 h of prolonged wakefulness (PW) followed by 12 h of sleep recovery (SR). Peripheral whole blood was collected at 8 am in the morning before the initiation of PW (Baseline), after the second night of PW, and one night after SR. We identified over 500 genes that were differentially expressed. Notably, these genes were related to DNA damage and repair and stress response, as well as diverse immune system responses, such as natural killer pathways including killer cell lectin-like receptors family, as well as granzymes and T-cell receptors, which play important roles in host defense. These results support the idea that sleep loss can lead to alterations in molecular processes that result in perturbation of cellular immunity, induction of inflammatory responses, and homeostatic imbalance. Moreover, expression of multiple genes was downregulated following PW and upregulated after SR compared with PW, suggesting an attempt of the body to re-establish internal homeostasis. In silico validation of alterations in the expression of CETN3, DNAJC, and CEACAM genes confirmed previous findings related to the molecular effects of sleep deprivation. Thus, the present findings confirm that the effects of sleep loss are not restricted to the brain and can occur intensely in peripheral tissues.

Mistletoes Play Different Roles in a Modular Host-Parasite Network

Antagonistic interactions between host plants and mistletoes often form complex networks of interacting species. Adequate characterization of network organization requires a combination of qualitative and quantitative data. Therefore, we assessed the distribution of interactions between mistletoes and hosts in the Brazilian Pantanal and characterized the network structure in relation to nestedness and modularity. Interactions were highly asymmetric, with mistletoes presenting low host specificity (i.e., weak dependence) and with hosts being highly susceptible to mistletoe-specific infections. We found a non-nested and modular pattern of interactions, wherein each mistletoe species interacted with a particular set of host species. Psittacanthus spp. infected more species and individuals and also caused a high number of infections per individual, whereas the other mistletoes showed a more specialized pattern of infection. For this reason, Psittacanthus spp. were regarded as module hubs while the other mistletoe species showed a peripheral role. We hypothesize that this pattern is primarily the result of different seed dispersal systems. Although all mistletoe species in our study are bird dispersed, the frugivorous assemblage of Psittacanthus spp. is composed of a larger suite of birds, whereas Phoradendron are mainly dispersed by Euphonia species. The larger assemblage of bird species dispersing Psittacanthus seeds may also increase the number of hosts colonized and, consequently, its dominance in the study area. Nevertheless, other restrictions on the interactions among species, such as the differential capacity of mistletoe infections, defense strategies of hosts and habitat types, can also generate or enhance the observed pattern.