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.

Regulatory variation in a TBX5 enhancer leads to isolated congenital heart disease

Recent studies have identified the genetic underpinnings of a growing number of diseases through targeted exome sequencing. However, this strategy ignores the large component of the genome that does not code for proteins, but is nonetheless biologically functional. To address the possible involvement of regulatory variation in congenital heart diseases (CHDs), we searched for regulatory mutations impacting the activity of TBX5, a dosage-dependent transcription factor with well-defined roles in the heart and limb development that has been associated with the HoltOram syndrome (hearthand syndrome), a condition that affects 1/100 000 newborns. Using a combination of genomics, bioinformatics and mouse genetic engineering, we scanned approximate to 700 kb of the TBX5 locus in search of cis-regulatory elements. We uncovered three enhancers that collectively recapitulate the endogenous expression pattern of TBX5 in the developing heart. We re-sequenced these enhancer elements in a cohort of non-syndromic patients with isolated atrial and/or ventricular septal defects, the predominant cardiac defects of the HoltOram syndrome, and identified a patient with a homozygous mutation in an enhancer approximate to 90 kb downstream of TBX5. Notably, we demonstrate that this single-base-pair mutation abrogates the ability of the enhancer to drive expression within the heart in vivo using both mouse and zebrafish transgenic models. Given the population-wide frequency of this variant, we estimate that 1/100 000 individuals would be homozygous for this variant, highlighting that a significant number of CHD associated with TBX5 dysfunction might arise from non-coding mutations in TBX5 heart enhancers, effectively decoupling the heart and hand phenotypes of the HoltOram syndrome.

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.

Ovariectomized OVA-sensitized mice display increased frequency of CD4+Foxp3+ T regulatory cells in the periphery

It is well established that female sex hormones have a pivotal role in inflammation. For instance, our group has previously reported that estradiol has proinflammatory actions during allergic lung response in animal models. Based on these findings, we have decided to further investigate whether T regulatory cells are affected by female sex hormones absence after ovariectomy. We evaluated by flow cytometry the frequencies of CD4+Foxp3+ T regulatory cells (Tregs) in central and peripheral lymphoid organs, such as the thymus, spleen and lymph nodes. Moreover, we have also used the murine model of allergic lung inflammation a to evaluate how female sex hormones would affect the immune response in vivo. To address that, ovariectomized or sham operated female Balb/c mice were sensitized or not with ovalbumin 7 and 14 days later and subsequently challenged twice by aerosolized ovalbumin on day 21. Besides the frequency of CD4+Foxp3+ T regulatory cells, we also measured the cytokines IL-4, IL-5, IL-10, IL-13 and IL-17 in the bronchoalveolar lavage from lungs of ovalbumine challenged groups. Our results demonstrate that the absence of female sex hormones after ovariectomy is able to increase the frequency of Tregs in the periphery. As we did not observe differences in the thymus-derived natural occurring Tregs, our data may indicate expansion or conversion of peripheral adaptive Tregs. In accordance with Treg suppressive activity, ovariectomized and ovalbumine-sensitized and challenged animals had significantly reduced lung inflammation. This was observed after cytokine analysis of lung explants showing significant reduction of pro-inflammatory cytokines, such as IL-4, IL-5, IL-13 and IL-17, associated to increased amount of IL-10. In summary, our data clearly demonstrates that OVA sensitization 7 days after ovariectomy culminates in reduced lung inflammation, which may be directly correlated with the expansion of Tregs in the periphery and further higher IL-10 secretion in the lungs.