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.

The Impact of Regulatory Heterogeneity on Agri-food Trade

We estimate the impact of regulatory heterogeneity on agri-food trade using a gravity analysis that relies on detailed data on non-tariff measures (NTMs) collected by the NTM-Impact project. The data cover a broad range of import requirements for agricultural and food products for the EU and nine of its major trade partners. We find that trade is significantly reduced when importing countries have stricter maximum residue limits (MRLs) for plant products than exporting countries. For most other measures, due to their qualitative nature, we were unable to infer whether the importer has stricter standards relative to the exporter, and we do not find a robust relationship between these measures and trade. Our findings suggest that, at least for some import standards, harmonising regulations will increase trade. We also conclude that tariff reductions remain an effective means to increase trade even when NTMs abound.

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.

Sympathetic overactivity precedes metabolic dysfunction in a fructose model of glucose intolerance in mice

De Angelis K, Senador DD, Mostarda C, Irigoyen MC, Morris M. Sympathetic overactivity precedes metabolic dysfunction in a fructose model of glucose intolerance in mice. Am J Physiol Regul Integr Comp Physiol 302: R950-R957, 2012. First published February 8, 2012; doi: 10.1152/ajpregu.00450.2011.-Consumption of high levels of fructose in humans and animals leads to metabolic and cardiovascular dysfunction. There are questions as to the role of the autonomic changes in the time course of fructose-induced dysfunction. C57/BL male mice were given tap water or fructose water (100 g/l) to drink for up to 2 mo. Groups were control (C), 15-day fructose (F15), and 60-day fructose (F60). Light-dark patterns of arterial pressure (AP) and heart rate (HR), and their respective variabilities were measured. Plasma glucose, lipids, insulin, leptin, resistin, adiponectin, and glucose tolerance were quantified. Fructose increased systolic AP (SAP) at 15 and 60 days during both light (F15: 123 +/- 2 and F60: 118 +/- 2 mmHg) and dark periods (F15: 136 +/- 4 and F60: 136 +/- 5 mmHg) compared with controls (light: 111 +/- 2 and dark: 117 +/- 2 mmHg). SAP variance (VAR) and the low-frequency component (LF) were increased in F15 (>60% and >80%) and F60 (>170% and >140%) compared with C. Cardiac sympatho-vagal balance was enhanced, while baroreflex function was attenuated in fructose groups. Metabolic parameters were unchanged in F15. However, F60 showed significant increases in plasma glucose (26%), cholesterol (44%), triglycerides (22%), insulin (95%), and leptin (63%), as well as glucose intolerance. LF of SAP was positively correlated with SAP. Plasma leptin was correlated with triglycerides, insulin, and glucose tolerance. Results show that increased sympathetic modulation of vessels and heart preceded metabolic dysfunction in fructose-consuming mice. Data suggest that changes in autonomic modulation may be an initiating mechanism underlying the cluster of symptoms associated with cardiometabolic disease.

Exogenous Administration of 15d-PGJ(2)-Loaded Nanocapsules Inhibits Bone Resorption in a Mouse Periodontitis Model

The 15-deoxy-(Delta 12,14)-PG J(2) (15d-PGJ(2)) has demonstrated excellent anti-inflammatory results in different experimental models. It can be used with a polymeric nanostructure system for modified drug release, which can change the therapeutic properties of the active principle, leading to increased stability and slower/prolonged release. The aim of the current study was to test a nano-technological formulation as a carrier for 15d-PGJ(2), and to investigate the immunomodulatory effects of this formulation in a mouse periodontitis model. Poly (D, L-lactide-coglycolide) nanocapsules (NC) were used to encapsulate 15d-PGJ(2). BALB/c mice were infected on days 0, 2, and 4 with Aggregatibacter actinomycetemcomitans and divided into groups (n = 5) that were treated daily during 15 d with 1, 3, or 10 mu g/kg 15d-PGJ(2)-NC. The animals were sacrificed, the submandibular lymph nodes were removed for FACS analysis, and the jaws were analyzed for bone resorption by morphometry. Immunoinflammatory markers in the gingival tissue were analyzed by reverse transcriptase-quantitative PCR, Western blotting, or ELISA. Infected animals treated with the 15d-PGJ(2)-NC presented lower bone resorption than infected animals without treatment (p < 0.05). Furthermore, infected animals treated with 10 mu g/kg 15d-PGJ(2)-NC had a reduction of CD4(+)CD25(+)FOXP3(+) cells and CD4/CD8 ratio in the submandibular lymph node (p < 0.05). Moreover, CD55 was upregulated, whereas RANKL was downregulated in the gingival tissue of the 10 mu g/kg treated group (p < 0.05). Several proinflammatory cytokines were decreased in the group treated with 10 mu g/kg 15d-PGJ(2)-NC, and high amounts of 15d-PGJ(2) were observed in the gingiva. In conclusion, the 15d-PGJ(2)-NC formulation presented immunomodulatory effects, decreasing bone resorption and inflammatory responses in a periodontitis mouse model. The Journal of Immunology, 2012, 189: 1043-1052.

Immune Regulatory Properties of Allogeneic Adipose-Derived Mesenchymal Stem Cells in the Treatment of Experimental Autoimmune Diabetes

Adipose-derived mesenchymal stem cells (ADMSCs) display immunosuppressive properties, suggesting a promising therapeutic application in several autoimmune diseases, but their role in type 1 diabetes (T1D) remains largely unexplored. The aim of this study was to investigate the immune regulatory properties of allogeneic ADMSC therapy in T cell-mediated autoimmune diabetes in NOD mice. ADMSC treatment reversed the hyperglycemia of early-onset diabetes in 78% of diabetic NOD mice, and this effect was associated with higher serum insulin, amylin, and glucagon-like peptide 1 levels compared with untreated controls. This improved outcome was associated with downregulation of the CD4(+) Th1-biased immune response and expansion of regulatory T cells (Tregs) in the pancreatic lymph nodes. Within the pancreas, inflammatory cell infiltration and interferon-gamma levels were reduced, while insulin, pancreatic duodenal homeobox-1, and active transforming growth factor-beta 1 expression were increased. In vitro, ADMSCs induced the expansion/proliferation of Tregs in a cell contact-dependent manner mediated by programmed death ligand 1. In summary, ADMSC therapy efficiently ameliorates autoimmune diabetes pathogenesis in diabetic NOD mice by attenuating the Th1 immune response concomitant with the expansion/proliferation of Tregs, thereby contributing to the maintenance of functional beta-cells. Thus, this study may provide a new perspective for the development of ADMSC-based cellular therapies for T1D. Diabetes 61:2534-2545, 2012

Genes involved in thoracic exoskeleton formation during the pupal-to-adult molt in a social insect model, Apis mellifera

Background: The insect exoskeleton provides shape, waterproofing, and locomotion via attached somatic muscles. The exoskeleton is renewed during molting, a process regulated by ecdysteroid hormones. The holometabolous pupa transforms into an adult during the imaginal molt, when the epidermis synthe3sizes the definitive exoskeleton that then differentiates progressively. An important issue in insect development concerns how the exoskeletal regions are constructed to provide their morphological, physiological and mechanical functions. We used whole-genome oligonucleotide microarrays to screen for genes involved in exoskeletal formation in the honeybee thoracic dorsum. Our analysis included three sampling times during the pupal-to-adult molt, i.e., before, during and after the ecdysteroid-induced apolysis that triggers synthesis of the adult exoskeleton. Results: Gene ontology annotation based on orthologous relationships with Drosophila melanogaster genes placed the honeybee differentially expressed genes (DEGs) into distinct categories of Biological Process and Molecular Function, depending on developmental time, revealing the functional elements required for adult exoskeleton formation. Of the 1,253 unique DEGs, 547 were upregulated in the thoracic dorsum after apolysis, suggesting induction by the ecdysteroid pulse. The upregulated gene set included 20 of the 47 cuticular protein (CP) genes that were previously identified in the honeybee genome, and three novel putative CP genes that do not belong to a known CP family. In situ hybridization showed that two of the novel genes were abundantly expressed in the epidermis during adult exoskeleton formation, strongly implicating them as genuine CP genes. Conserved sequence motifs identified the CP genes as members of the CPR, Tweedle, Apidermin, CPF, CPLCP1 and Analogous-to-Peritrophins families. Furthermore, 28 of the 36 muscle-related DEGs were upregulated during the de novo formation of striated fibers attached to the exoskeleton. A search for cis-regulatory motifs in the 5′-untranslated region of the DEGs revealed potential binding sites for known transcription factors. Construction of a regulatory network showed that various upregulated CP- and muscle-related genes (15 and 21 genes, respectively) share common elements, suggesting co-regulation during thoracic exoskeleton formation. Conclusions: These findings help reveal molecular aspects of rigid thoracic exoskeleton formation during the ecdysteroid-coordinated pupal-to-adult molt in the honeybee.

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.