Functional clustering of time series gene expression data by Granger causality

Background: A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results: In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions: This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.

Physical training prevents body weight gain but does not modify adipose tissue gene expression

The relationship of body weight (BW) with white adipose tissue (WAT) mass and WAT gene expression pattern was investigated in mice submitted to physical training (PT). Adult male C57BL/6 mice were submitted to two 1.5-h daily swimming sessions (T, N = 18), 5 days/week for 4 weeks or maintained sedentary (S, N = 15). Citrate synthase activity increased significantly in the T group (P < 0.05). S mice had a substantial weight gain compared to T mice (4.06 ± 0.43 vs 0.38 ± 0.28 g, P < 0.01). WAT mass, adipocyte size, and the weights of gastrocnemius and soleus muscles, lung, kidney, and adrenal gland were not different. Liver and heart were larger and the spleen was smaller in T compared to S mice (P < 0.05). Food intake was higher in T than S mice (4.7 ± 0.2 vs 4.0 ± 0.3 g/animal, P < 0.05) but oxygen consumption at rest did not differ between groups. T animals showed higher serum leptin concentration compared to S animals (6.37 ± 0.5 vs 3.11 ± 0.12 ng/mL). WAT gene expression pattern obtained by transcription factor adipocyte determination and differentiation-dependent factor 1, fatty acid synthase, malic enzyme, hormone-sensitive lipase, adipocyte lipid binding protein, leptin, and adiponectin did not differ significantly between groups. Collectively, our results showed that PT prevents BW gain and maintains WAT mass due to an increase in food intake and unchanged resting metabolic rate. These responses are closely related to unchanged WAT gene expression patterns.

Arginine induces GH gene expression by activating NOS/NO signaling in rat isolated hemi-pituitaries

The amino acid arginine (Arg) is a recognized secretagogue of growth hormone (GH), and has been shown to induce GH gene expression. Arg is the natural precursor of nitric oxide (NO), which is known to mediate many of the effects of Arg, such as GH secretion. Arg was also shown to increase calcium influx in pituitary cells, which might contribute to its effects on GH secretion. Although the mechanisms involved in the effects of Arg on GH secretion are well established, little is known about them regarding the control of GH gene expression. We investigated whether the NO pathway and/or calcium are involved in the effects of Arg on GH gene expression in rat isolated pituitaries. To this end, pituitaries from approximately 170 male Wistar rats (~250 g) were removed, divided into two halves, pooled (three hemi-pituitaries) and incubated or not with Arg, as well as with different pharmacological agents. Arg (71 mM), the NO donor sodium nitroprusside (SNP, 1 and 0.1 mM) and a cyclic guanosine monophosphate (cGMP) analogue (8-Br-cGMP, 1 mM) increased GH mRNA expression 60 min later. The NO acceptor hemoglobin (0.3 µM) blunted the effect of SNP, and the combined treatment with Arg and L-NAME (a NO synthase (NOS) inhibitor, 55 mM) abolished the stimulatory effect of Arg on GH gene expression. The calcium channel inhibitor nifedipine (3 µM) also abolished Arg-induced GH gene expression. The present study shows that Arg directly induces GH gene expression in hemi-pituitaries isolated from rats, excluding interference from somatostatinergic neurons, which are supposed to be inhibited by Arg. Moreover, the data demonstrate that the NOS/NO signaling pathway and calcium mediate the Arg effects on GH gene expression.

Semantic integration of gene expression analysis tools and data sources using software connectors

Abstract Background The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

Hepatocyte nuclear factors 1α/4α and forkhead box A2 regulate the solute carrier 2A2 (Slc2a2) gene expression in the liver and kidney of diabetic rats

AIMS: Solute carrier 2a2 (Slc2a2) gene codifies the glucose transporter GLUT2, a key protein for glucose flux in hepatocytes and renal epithelial cells of proximal tubule. In diabetes mellitus, hepatic and tubular glucose output has been related to Slc2a2/GLUT2 overexpression; and controlling the expression of this gene may be an important adjuvant way to improve glycemic homeostasis. Thus, the present study investigated transcriptional mechanisms involved in the diabetes-induced overexpression of the Slc2a2 gene. MAIN METHODS: Hepatocyte nuclear factors 1α and 4α (HNF-1α and HNF-4α), forkhead box A2 (FOXA2), sterol regulatory element binding protein-1c (SREBP-1c) and the CCAAT-enhancer-binding protein (C/EBPβ) mRNA expression (RT-PCR) and binding activity into the Slc2a2 promoter (electrophoretic mobility assay) were analyzed in the liver and kidney of diabetic and 6-day insulin-treated diabetic rats. KEY FINDINGS: Slc2a2/GLUT2 expression increased by more than 50% (P<0.001) in the liver and kidney of diabetic rats, and 6-day insulin treatment restores these values to those observed in non-diabetic animals. Similarly, the mRNA expression and the binding activity of HNF-1α, HNF-4α and FOXA2 increased by 50 to 100% (P<0.05 to P<0.001), also returning to values of non-diabetic rats after insulin treatment. Neither the Srebf1 and Cebpb mRNA expression, nor the SREBP-1c and C/EBP-β binding activity was altered in diabetic rats. SIGNIFICANCE: HNF-1α, HNF-4α and FOXA2 transcriptional factors are involved in diabetes-induced overexpression of Slc2a2 gene in the liver and kidney. These data point out that these transcriptional factors are important targets to control GLUT2 expression in these tissues, which can contribute to glycemic homeostasis in diabetes.

Manipulation of the periovulatory sex steroidal milieu affects endometrial but not luteal gene expression in early diestrus Nelore cows

In beef cattle, the ability to conceive has been associated positively with size of the preovulatory follicle (POF). Proestrus estradiol and subsequent progesterone concentrations can regulate the endometrium to affect receptivity and fertility. The aim of the present study was to verify the effect of the size of the POF on luteal and endometrial gene expression during subsequent early diestrus in beef cattle. Eighty-three multiparous, nonlactating, presynchronized Nelore cows received a progesterone-releasing device and estradiol benzoate on Day–10 (D 10). Animals received cloprostenol (large follicle-large CL group; LF-LCL; N ¼ 42) or not (small follicle-small CL group; SF-SCL; N ¼ 41) on D 10. Progesterone devices were withdrawn and cloprostenol administered 42 to 60 hours (LF-LCL) or 30 to 36 hours (SF-SCL) before GnRH treatment (D0). Tissues were collected at slaughter on D7. The LF-LCL group had larger (P < 0.0001) POF (13.24 0.33 mm vs. 10.76 0.29 mm), greater (P < 0.0007) estradiol concentrations on D0 (2.94 0.28 pg/mL vs. 1.27 0.20 pg/mL), and greater (P < 0.01) progesterone concentrations on D7 (3.71 0.25 ng/mL vs. 2.62 0.26 ng/mL) compared with the SF-SCL group. Luteal gene expression of vascular endothelial growth factor A, kinase insert domain receptor, fms-related tyrosine kinase 1, steroidogenic acute regulatory protein, cytochrome P450, family 11, subfamily A, polypeptide 1, and hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid deltaisomerase 7 was similar between groups. Endometrial gene expression of oxytocin receptor and peptidase inhibitor 3, skin-derived was reduced, and estrogen receptor alpha 2, aldo-keto reductase family 1, member C4, and lipoprotein lipase expression was increased in LF-LCL versus SF-SCL. Results support the hypothesis that the size of the POF alters the periovulatory endocrine milieu (i.e., proestrus estradiol and diestrus progesterone concentrations) and acts on the uterus to alter endometrial gene expression. It is proposed that the uterine environment and receptivity might also be modulated. Additionally, it is suggested that increased progesterone secretion of cows ovulating larger follicles is likely due to increased CL size rather than increased luteal expression of steroidogenic genes.

Oral leucine supplementation is sensed by the brain but neither reduces food intake nor induces an anorectic pattern of gene expression in the hypothalamus

Leucine activates the intracellular mammalian target of the rapamycin (mTOR) pathway, and hypothalamic mTOR signaling regulates food intake. Although central infusion of leucine reduces food intake, it is still uncertain whether oral leucine supplementation is able to affect the hypothalamic circuits that control energy balance. We observed increased phosphorylation of p70s6k in the mouse hypothalamus after an acute oral gavage of leucine. We then assessed whether acute oral gavage of leucine induces the activation of neurons in several hypothalamic nuclei and in the brainstem. Leucine did not induce the expression of Fos in hypothalamic nuclei, but it increased the number of Fos-immunoreactive neurons in the area postrema. In addition, oral gavage of leucine acutely increased the 24 h food intake of mice. Nonetheless, chronic leucine supplementation in the drinking water did not change the food intake and the weight gain of ob/ob mice and of wild-type mice consuming a low- or a high-fat diet. We assessed the hypothalamic gene expression and observed that leucine supplementation increased the expression of enzymes (BCAT1, BCAT2 and BCKDK) that metabolize branched-chain amino acids. Despite these effects, leucine supplementation did not induce an anorectic pattern of gene expression in the hypothalamus. In conclusion, our data show that the brain is able to sense oral leucine intake. However, the food intake is not modified by chronic oral leucine supplementation. These results question the possible efficacy of leucine supplementation as an appetite suppressant to treat obesity

Multimodality and flexibility of stochastic gene expression

We consider a general class of mathematical models for stochastic gene expression where the transcription rate is allowed to depend on a promoter state variable that can take an arbitrary (finite) number of values. We provide the solution of the master equations in the stationary limit, based on a factorization of the stochastic transition matrix that separates timescales and relative interaction strengths, and we express its entries in terms of parameters that have a natural physical and/or biological interpretation. The solution illustrates the capacity of multiple states promoters to generate multimodal distributions of gene products, without the need for feedback. Furthermore, using the example of a three states promoter operating at low, high, and intermediate expression levels, we show that using multiple states operons will typically lead to a significant reduction of noise in the system. The underlying mechanism is that a three-states promoter can change its level of expression from low to high by passing through an intermediate state with a much smaller increase of fluctuations than by means of a direct transition.

Constitutive gene expression profile segregates toxicity in locally advanced breast cancer patients treated with high-dose hyperfractionated radical radiotherapy

[EN] Breast cancer patients show a wide variation in normal tissue reactions after radiotherapy. The individual sensitivity to x-rays limits the efficiency of the therapy. Prediction of individual sensitivity to radiotherapy could help to select the radiation protocol and to improve treatment results. The aim of this study was to assess the relationship between gene expression profiles of ex vivo un-irradiated and irradiated lymphocytes and the development of toxicity due to high-dose hyperfractionated radiotherapy in patients with locally advanced breast cancer. Raw data from microarray experiments were uploaded to the Gene Expression Omnibus Database http://www.ncbi.nlm.nih.gov/geo/ (GEO accession GSE15341). We obtained a small group of 81 genes significantly regulated by radiotherapy, lumped in 50 relevant pathways. Using ANOVA and t-test statistical tools we found 20 and 26 constitutive genes (0 Gy) that segregate patients with and without acute and late toxicity, respectively. Non-supervised hierarchical clustering was used for the visualization of results. Six and 9 pathways were significantly regulated respectively. Concerning to irradiated lymphocytes (2 Gy), we founded 29 genes that separate patients with acute toxicity and without it. Those genes were gathered in 4 significant pathways. We could not identify a set of genes that segregates patients with and without late toxicity. In conclusion, we have found an association between the constitutive gene expression profile of peripheral blood lymphocytes and the development of acute and late toxicity in consecutive, unselected patients. These observations suggest the possibility of predicting normal tissue response to irradiation in high-dose non-conventional radiation therapy regimens. Prospective studies with higher number of patients are needed to validate these preliminary results.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.