DeepReGraph co-clusters temporal gene expression and cis-regulatory elements through heterogeneous graph representation learning

BackgroundAxis patterning during development is accompanied by large-scale gene expression changes. These are brought about by changes in the histone modifications leading to dynamic alterations in chromatin architecture. The cis regulatory DNA elements also play an important role towards modulating gene expression in a context-dependent manner. Hydra belongs to the phylum Cnidaria where the first asymmetry in the body plan was observed and the oral-aboral axis originated. Wnt signaling has been shown to determine the head organizer function in the basal metazoan Hydra.ResultsTo gain insights into the evolution of cis regulatory elements and associated chromatin signatures, we ectopically activated the Wnt signaling pathway in Hydra and monitored the genome-wide alterations in key histone modifications. Motif analysis of putative intergenic enhancer elements from Hydra revealed the conservation of bilaterian cis regulatory elements that play critical roles in development. Differentially regulated enhancer elements were identified upon ectopic activation of Wnt signaling and found to regulate many head organizer specific genes. Enhancer activity of many of the identified cis regulatory elements was confirmed by luciferase reporter assay. Quantitative chromatin immunoprecipitation analysis upon activation of Wnt signaling further confirmed the enrichment of H3K27ac on the enhancer elements of Hv_Wnt5a, Hv_Wnt11 and head organizer genes Hv_Bra1, CnGsc and Hv_Pitx1. Additionally, perturbation of the putative H3K27me3 eraser activity using a specific inhibitor affected the ectopic activation of Wnt signaling indicating the importance of the dynamic changes in the H3K27 modifications towards regulation of the genes involved in the head organizer activity.ConclusionsThe activation-associated histone marks H3K4me3, H3K27ac and H3K9ac mark chromatin in a similar manner as seen in bilaterians. We identified intergenic cis regulatory elements which harbor sites for key transcription factors involved in developmental processes. Differentially regulated enhancers exhibited motifs for many zinc-finger, T-box and ETS related TFs whose homologs have a head specific expression in Hydra and could be a part of the pioneer TF network in the patterning of the head. The ability to differentially modify the H3K27 residue is critical for the patterning of Hydra axis revealing a dynamic acetylation/methylation switch to regulate gene expression and chromatin architecture.

In order to understand even the simplest cellular processes, we need to integrate proteomic, gene expression and other biomolecular data. To date, most computational approaches aimed at integrating proteomics and gene expression data use direct gene/protein correlation measures. However, due to post-transcriptional and translational regulations, the correspondence between the expression of a gene and its protein is complicated. We apply a multivariate statistical method, co-inertia analysis (CIA), to visualise gene and proteomic expression data stemming from the same biological samples. Principal components analysis or correspondence analysis can be used for data exploration on single datasets. CIA is then used to explore the relationships between two or more datasets. We further explore the data by projecting gene ontology (GO) information onto these plots to describe the cellular processes in action. We apply these techniques to gene expression and protein abundance data from studies of the human malarial parasite life cycle and the NCI-60 cancer cell lines. In each case, we visualise gene expression, protein abundance and GO classes in the same low dimensional projections and identify GO classes that are likely to be of biological importance.

<div>Abstract<p><b>Purpose:</b> Several prognostic gene expression profiles have been identified in breast cancer. In spite of this progress in prognostic classification, the underlying mechanisms that drive these gene expression patterns remain unknown. Specific genomic alterations, such as copy number alterations, are an important factor in tumor development and progression and are also associated with changes in gene expression.</p><p><b>Experimental Design:</b> We carried out array comparative genomic hybridization in 68 human breast carcinomas for which gene expression and clinical data were available. We used a two-class supervised algorithm, Supervised Identification of Regions of Aberration in aCGH data sets, for the identification of regions of chromosomal alterations that are associated with specific sample labeling. Using gene expression data from the same tumors, we identified genes in the altered regions for which the expression level is significantly correlated with the copy number and validated our results in public available data sets.</p><p><b>Results:</b> Specific chromosomal aberrations are related to clinicopathologic characteristics and prognostic gene expression signatures. The previously identified poor prognosis, 70-gene expression signature is associated with the gain of 3q26.33-27.1, 8q22.1-24.21, and 17q24.3-25.1; the 70-gene good prognosis profile is associated with the loss at 16q12.1-13 and 16q22.1-24.1; basal-like tumors are associated with the gain of 6p12.3-23, 8q24.21-22, and 10p12.33-14 and losses at 4p15.31, 5q12.3-13.1, 5q33.1, 10q23.33, 12q13.13-3, 15q15.1, and 15q21.1; HER2+ breast show amplification at 17q11.1-12 and 17q21.31-23.2 (including <i>HER2</i> gene).</p><p><b>Conclusions:</b> There is a strong correlation between the different gene expression signatures and underlying genomic changes. These findings help to establish a link between genomic changes and gene expression signatures, enabling a better understanding of the tumor biology that causes poor prognosis. Clin Cancer Res; 16(2); 651–63</p></div>

BackgroundA complete understanding of the regulatory mechanisms of gene expression is the next important issue of genomics. Many bioinformaticians have developed methods and algorithms for predicting transcriptional regulatory mechanisms from sequence, gene expression, and binding data. However, most of these studies involved the use of yeast which has much simpler regulatory networks than human and has many genome wide binding data and gene expression data under diverse conditions. Studies of genome wide transcriptional networks of human genomes currently lag behind those of yeast.ResultsWe report herein a new method that combines gene expression data analysis with promoter analysis to infer transcriptional regulatory elements of human genes. The Z scores from the application of gene set analysis with gene sets of transcription factor binding sites (TFBSs) were successfully used to represent the activity of TFBSs in a given microarray data set. A significant correlation between the Z scores of gene sets of TFBSs and individual genes across multiple conditions permitted successful identification of many known human transcriptional regulatory elements of genes as well as the prediction of numerous putative TFBSs of many genes which will constitute a good starting point for further experiments. Using Z scores of gene sets of TFBSs produced better predictions than the use of mRNA levels of a transcription factor itself, suggesting that the Z scores of gene sets of TFBSs better represent diverse mechanisms for changing the activity of transcription factors in the cell. In addition, cis-regulatory modules, combinations of co-acting TFBSs, were readily identified by our analysis.ConclusionBy a strategic combination of gene set level analysis of gene expression data sets and promoter analysis, we were able to identify and predict many transcriptional regulatory elements of human genes. We conclude that this approach will aid in decoding some of the important transcriptional regulatory elements of human genes.

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

The Evolution of Gene Regulation Underlies a Morphological Difference between Two Drosophila Sister Species

Ablative fractional laser treatment is considered the gold standard for skin rejuvenation. In order to understand how fractional laser works to rejuvenate skin, we performed microarray profiling on skin biopsies to identify temporal and dose-response changes in gene expression following fractional laser treatment. The backs of 14 women were treated with ablative fractional laser (Fraxel®) and 4 mm punch biopsies were collected from an untreated site and at the treated sites 1, 3, 7, 14, 21 and 28 days after the single treatment. In addition, in order to understand the effect that multiple fractional laser treatments have on skin rejuvenation, several sites were treated sequentially with either 1, 2, 3, or 4 treatments (with 28 days between treatments) followed by the collection of 4 mm punch biopsies. RNA was extracted from the biopsies, analyzed using Affymetrix U219 chips and gene expression was compared between untreated and treated sites. We observed dramatic changes in gene expression as early as 1 day after fractional laser treatment with changes remaining elevated even after 1 month. Analysis of individual genes demonstrated significant and time related changes in inflammatory, epidermal, and dermal genes, with dermal genes linked to extracellular matrix formation changing at later time points following fractional laser treatment. When comparing the age-related changes in skin gene expression to those induced by fractional laser, it was observed that fractional laser treatment reverses many of the changes in the aging gene expression. Finally, multiple fractional laser treatments, which cover different regions of a treatment area, resulted in a sustained or increased dermal remodeling response, with many genes either differentially regulated or continuously upregulated, supporting previous observations that maximal skin rejuvenation requires multiple fractional laser treatments. In conclusion, fractional laser treatment of human skin activates a number of biological processes involved in wound healing and tissue regeneration.

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.

The goal of this study was to define the temporal changes in gene expression after retinal injury and to relate these changes to the inflammatory and reactive response. A specific emphasis was placed on the tetraspanin family of proteins and their relationship with markers of reactive gliosis. Retinal tears were induced in adult rats by scraping the retina with a needle. After different survival times (4 hours, and 1, 3, 7, and 30 days), the retinas were removed, and mRNA was isolated, prepared, and hybridized to the Affymatrix RG-U34A microarray (Santa Clara, CA). Microarray results were confirmed by using RT-PCR and correlation to protein levels was determined. Of the 8750 genes analyzed, approximately 393 (4.5%) were differentially expressed. Clustering analysis revealed three major profiles: (1) The early response was characterized by the upregulation of transcription factors; (2) the delayed response included a high percentage of genes related to cell cycle and cell death; and (3) the late, sustained profile clustered a significant number of genes involved in retinal gliosis. The late, sustained cluster also contained the upregulated crystallin genes. The tetraspanins Cd9, Cd81, and Cd82 were also associated with the late, sustained response. The use of microarray technology enables definition of complex genetic changes underlying distinct phases of the cellular response to retinal injury. The early response clusters genes associate with the transcriptional regulation of the wound-healing process and cell death. Most of the genes in the late, sustained response appear to be associated with reactive gliosis.

Temporal gene expression data contain ample information to characterize gene function and are now widely used in bio-medical research. A dense temporal gene expression usually shows various patterns in expression levels under different biological conditions. The existing literature investigates the gene trajectory using the mean function. However, temporal gene expression curves usually show a strong degree of heterogeneity under multiple conditions. As a result, rates of change for gene expressions may be different in non-central locations and a mean function model may not capture the non-central location of the gene expression distribution. Further, the mean regression model depends on the normality assumptions of the error terms of the model, which may be impractical when analyzing gene expression data. In this research, a linear quantile mixed model is used to find the trajectory of gene expression data. This method enables the changes in gene expression over time to be studied by estimating a family of quantile functions. A statistical test is proposed to test the similarity between two different gene expressions based on estimated parameters using a quantile model. Then, the performance of the proposed test statistic is examined using extensive simulation studies. Simulation studies demonstrate the good statistical performance of this proposed test statistic and show that this method is robust against normal error assumptions. As an illustration, the proposed method is applied to analyze a dataset of 18 genes in P. aeruginosa, expressed in 24 biological conditions. Furthermore, a minimum Mahalanobis distance is used to find the clustering tree for gene expressions.

An Approach to Virtual Karyotyping for Unbalanced Rearrangements Based on Gene Expression Data.

Although it is impossible to discuss the cis-acting DNA sequence elements without reference to the trans-acting factors that bind to these sequences, this chapter will primarily focus on defining the different types of positive and negative cis-acting transcriptional regulatory elements. The trans-acting factors will be described in considerable detail in Chapter 3. All the cis-acting elements that affect transcription are defined on the basis of functional criteria. Therefore, none of these elements can be definitively identified solely by the examination of nucleotide sequence data. In general cis-acting elements are DNA sequences containing binding sites for several different transcription factors that are required en bloc for the element to function fully. The first defining criterion is whether the putative element has a positive or negative effect on transcription. In general, positive cis-acting elements contain binding sites for positive trans-acting factors (transcriptional activators) and negative cis-acting elements contain binding sites for negative trans-acting factors (transcriptional repressors). Examples of positive cis-regulatory elements include promoters and enhancers, whereas silencers and transcription-arrest sites represent examples of negative regulatory elements.

Chronic inflammation is one of the major causes of cartilage destruction in osteoarthritis. Here, we systematically analyzed the changes in gene expression associated with the progression of cartilage destruction in monoiodoacetate-induced arthritis (MIA) of the rat knee. Sprague Dawley female rats were given intra-articular injection of monoiodoacetate in the knee. The progression of MIA was monitored macroscopically, microscopically and by micro-computed tomography. Grade 1 damage was observed by day 5 post-monoiodoacetate injection, progressively increasing to Grade 2 by day 9, and to Grade 3–3.5 by day 21. Affymetrix GeneChip was utilized to analyze the transcriptome-wide changes in gene expression, and the expression of salient genes was confirmed by real-time-PCR. Functional networks generated by Ingenuity Pathways Analysis (IPA) from the microarray data correlated the macroscopic/histologic findings with molecular interactions of genes/gene products. Temporal changes in gene expression during the progression of MIA were categorized into five major gene clusters. IPA revealed that Grade 1 damage was associated with upregulation of acute/innate inflammatory responsive genes (Cluster I) and suppression of genes associated with musculoskeletal development and function (Cluster IV). Grade 2 damage was associated with upregulation of chronic inflammatory and immune trafficking genes (Cluster II) and downregulation of genes associated with musculoskeletal disorders (Cluster IV). The Grade 3 to 3.5 cartilage damage was associated with chronic inflammatory and immune adaptation genes (Cluster III). These findings suggest that temporal regulation of discrete gene clusters involving inflammatory mediators, receptors, and proteases may control the progression of cartilage destruction. In this process, IL-1β, TNF-α, IL-15, IL-12, chemokines, and NF-κB act as central nodes of the inflammatory networks, regulating catabolic processes. Simultaneously, upregulation of asporin, and downregulation of TGF-β complex, SOX-9, IGF and CTGF may be central to suppress matrix synthesis and chondrocytic anabolic activities, collectively contributing to the progression of cartilage destruction in MIA.

BackgroundRhodospirillum centenum is a photosynthetic member of the Gram-negative Azospirillum clade members of which exhibit a complex developmental life-cycle featuring morphologically distinct cell types. Under periods of nutrient deprivation, replicative vegetative cells differentiate into metabolically dormant cysts that survive harsh environmental stresses such as desiccation. Encystment involves a multi-stage developmental process that includes the rounding of cells, production of large intracellular storage granules of poly-hydroxybutyrate (PHB) and the excretion of a protective exopolysaccharide coating that envelops dormant cysts.ResultsTo study the process of cyst development, we performed RNA-seq studies on cells that were induced to undergo cyst development. To assay for temporal changes in gene expression, RNA was extracted at 4, 24, 48, 72, 96 hours during development and subjected to deep sequence analysis. These results show that 812 genes exhibit log2 ≥ 1.5-fold changes in expression over a 96 hour cyst induction period demonstrating large global changes in gene expression during cyst development.ConclusionsNotable changes in expression occurred in numerous genes involved in cell wall and lipid biosynthesis, metabolic enzymes, and numerous regulatory genes such as histidine kinases and transcription factors. Many genes involved in protein synthesis and DNA replication were also significantly reduced during late stages of cyst development. Genes previously identified by genetic screens as being critical for cyst development also exhibited changes of expression during cyst induction. This study provides the first transcriptome profile of global changes in gene expression that occur during development of cysts in a Gram-negative species.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1250-9) contains supplementary material, which is available to authorized users.

Estrogen induction of uterine wet weight provides an excellent model to investigate relationships between changes in global gene expression and well-characterized physiological responses. In this study, time course microarray GeneChip data were analyzed using a novel approach to identify temporal changes in uterine gene expression following treatment of immature ovariectomized C57BL/6 mice with 0.1 mg/kg 17alpha-ethynylestradiol. Functional gene annotation information from public databases facilitated the association of changes in gene expression with physiological outcomes, which allowed detailed mechanistic inferences to be drawn regarding cell cycle control and proliferation, transcription and translation, structural tissue remodeling, and immunologic responses. These systematic approaches confirm previously established responses, identify novel estrogen-regulated transcriptional effects, and disclose the coordinated activation of multiple modes of action that support the uterotrophic response elicited by estrogen. In particular, it was possible to elucidate the physiological significance of the dramatic induction of arginase, a classic estrogenic response, by elucidating its mechanistic relevance and delineating the role of arginine and ornithine utilization in the estrogen-stimulated induction of uterine wet weight.