Novel implementation of conditional co-regulation by graph theory to derive co-expressed genes from microarray data

Predicting horizontal gene transfers with perfect transfer networks

BACKGROUND

Horizontal gene transfer inference approaches are usually based on gene sequences: parametric methods search for patterns that deviate from a particular genomic signature, while phylogenetic methods use sequences to reconstruct the gene and species trees. However, it is well-known that sequences have difficulty identifying ancient transfers since mutations have enough time to erase all evidence of such events. In this work, we ask whether character-based methods can predict gene transfers. Their advantage over sequences is that homologous genes can have low DNA similarity, but still have retained enough important common motifs that allow them to have common character traits, for instance the same functional or expression profile. A phylogeny that has two separate clades that acquired the same character independently might indicate the presence of a transfer even in the absence of sequence similarity.

OUR CONTRIBUTIONS

We introduce perfect transfer networks, which are phylogenetic networks that can explain the character diversity of a set of taxa under the assumption that characters have unique births, and that once a character is gained it is rarely lost. Examples of such traits include transposable elements, biochemical markers and emergence of organelles, just to name a few. We study the differences between our model and two similar models: perfect phylogenetic networks and ancestral recombination networks. Our goals are to initiate a study on the structural and algorithmic properties of perfect transfer networks. We then show that in polynomial time, one can decide whether a given network is a valid explanation for a set of taxa, and show how, for a given tree, one can add transfer edges to it so that it explains a set of taxa. We finally provide lower and upper bounds on the number of transfers required to explain a set of taxa, in the worst case.

Systems toxicology identifies mechanistic impacts of 2-amino-4,6-dinitrotoluene (2A-DNT) exposure in Northern Bobwhite

Background

A systems toxicology investigation comparing and integrating transcriptomic and proteomic results was conducted to develop holistic effects characterizations for the wildlife bird model, Northern bobwhite (Colinus virginianus) dosed with the explosives degradation product 2-amino-4,6-dinitrotoluene (2A-DNT). A subchronic 60d toxicology bioassay was leveraged where both sexes were dosed via daily gavage with 0, 3, 14, or 30 mg/kg-d 2A-DNT. Effects on global transcript expression were investigated in liver and kidney tissue using custom microarrays for C. virginianus in both sexes at all doses, while effects on proteome expression were investigated in liver for both sexes and kidney in males, at 30 mg/kg-d.

Results

As expected, transcript expression was not directly indicative of protein expression in response to 2A-DNT. However, a high degree of correspondence was observed among gene and protein expression when investigating higher-order functional responses including statistically enriched gene networks and canonical pathways, especially when connected to toxicological outcomes of 2A-DNT exposure. Analysis of networks statistically enriched for both transcripts and proteins demonstrated common responses including inhibition of programmed cell death and arrest of cell cycle in liver tissues at 2A-DNT doses that caused liver necrosis and death in females. Additionally, both transcript and protein expression in liver tissue was indicative of induced phase I and II xenobiotic metabolism potentially as a mechanism to detoxify and excrete 2A-DNT. Nuclear signaling assays, transcript expression and protein expression each implicated peroxisome proliferator-activated receptor (PPAR) nuclear signaling as a primary molecular target in the 2A-DNT exposure with significant downstream enrichment of PPAR-regulated pathways including lipid metabolic pathways and gluconeogenesis suggesting impaired bioenergetic potential.

Conclusion

Although the differential expression of transcripts and proteins was largely unique, the consensus of functional pathways and gene networks enriched among transcriptomic and proteomic datasets provided the identification of many critical metabolic functions underlying 2A-DNT toxicity as well as impaired PPAR signaling, a key molecular initiating event known to be affected in di- and trinitrotoluene exposures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1798-4) contains supplementary material, which is available to authorized users.

Transcriptome coexpression analysis using ATTED-II for integrated transcriptomic/metabolomic analysis

Transcriptome coexpression analysis is an excellent tool for predicting the physiological functions of genes. It is based on the "guilt-by-association" principle. Generally, genes involved in certain metabolic processes are coordinately regulated. In other words, coexpressed genes tend to be involved in common or closely related biological processes. Genes of which the metabolic functions have been identified are preselected as "guide" genes and are used to check the transcriptome coexpression fidelity to the pathway and to determine the threshold value of correlation coefficients to be used for subsequent analysis. The coexpression analysis provides a network of the relationships between "guide" and candidate genes that serves to create the criteria by which gene functions can be predicted. Here we describe a procedure to narrow down the number of candidate genes by means of the publicly available database, designated Arabidopsis thaliana trans-factor and cis-element prediction database (ATTED-II).

Paradigm of Time-sequence Development of the Intestine of Suckling Piglets with Microarray

The interaction of the genes involved in intestinal development is the molecular basis of the regulatory mechanisms of intestinal development. The objective of this study was to identify the significant pathways and key genes that regulate intestinal development in Landrace piglets, and elucidate their rules of operation. The differential expression of genes related to intestinal development during suckling time was investigated using a porcine genome array. Time sequence profiles were analyzed for the differentially expressed genes to obtain significant expression profiles. Subsequently, the most significant profiles were assayed using Gene Ontology categories, pathway analysis, network analysis, and analysis of gene co-expression to unveil the main biological processes, the significant pathways, and the effective genes, respectively. In addition, quantitative real-time PCR was carried out to verify the reliability of the results of the analysis of the array. The results showed that more than 8000 differential expression transcripts were identified using microarray technology. Among the 30 significant obtained model profiles, profiles 66 and 13 were the most significant. Analysis of profiles 66 and 13 indicated that they were mainly involved in immunity, metabolism, and cell division or proliferation. Among the most effective genes in these two profiles, CN161469, which is similar to methylcrotonoyl-Coenzyme A carboxylase 2 (beta), and U89949.1, which encodes a folate binding protein, had a crucial influence on the co-expression network.

Multiscale feature analysis of salivary gland branching morphogenesis

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues.

Co-expression of cell-wall related genes: new tools and insights

Global transcript analyses based on publicly available microarray dataset have revealed that genes with similar function tend to be transcriptionally coordinated. Indeed, many genes involved in the formation of cellulose, hemicelluloses, and lignin have been identified using co-expression approaches in Arabidopsis. To facilitate these transcript analyses, several web-based tools have been developed that allow researchers to investigate co-expression relationships of their gene(s) of interest. In addition, several tools now also provide the possibility of comparative transcriptional analyses across species, which potentially increases the predictive power. In this short review, we describe recent developments and updates of plant-related co-expression tools, and summarize studies that have successfully used expression profiling in cell wall research. Finally, we illustrate the value of comparative co-expression relationships across species using genes involved in lignin biosynthesis.

The irradiance dependent transcriptional regulation of AtCLPB3 expression

Transcript abundance analysis was applied to determine whether expression of genes coding for 50 principal constituents of chloroplast and mitochondria proteolytic machinery, i.e. isoforms of proteases and regulatory subunits of Clp and FtsH families as well as Deg group of chymotrypsin family are differentially expressed in response to acclimation to elevated irradiance. Of 50 genes analysed only a single one coding for ClpB3 regulatory subunit was found to be up-regulated and gene coding for Deg2 to be down-regulated significantly during acclimation to excessive irradiance conditions. Hierarchical clustering of transcript abundance data revealed that CLPB3 co-expressed tightly with genes coding for PAP1, GBF6 and bHLH family member transcription factors during the acclimation. It was found that CLPB3 contains cis-regulatory elements able to bind all three transcription factors. By performing analyses of publicly available transcriptomic data sets from a range of long-term abiotic stresses we suggest that PAP1 may mediate condition-dependent transcriptional response of CLPB3, induced by a group of long-term abiotic stresses.

From raw materials to validated system: the construction of a genomic library and microarray to interpret systemic perturbations in Northern bobwhite

The limited availability of genomic tools and data for nonmodel species impedes computational and systems biology approaches in nonmodel organisms. Here we describe the development, functional annotation, and utilization of genomic tools for the avian wildlife species Northern bobwhite (Colinus virginianus) to determine the molecular impacts of exposure to 2,6-dinitrotoluene (2,6-DNT), a field contaminant of military concern. Massively parallel pyrosequencing of a normalized multitissue library of Northern bobwhite cDNAs yielded 71,384 unique transcripts that were annotated with gene ontology (GO), pathway information, and protein domain analysis. Comparative genome analyses with model organisms revealed functional homologies in 8,825 unique Northern bobwhite genes that are orthologous to 48% of Gallus gallus protein-coding genes. Pathway analysis and GO enrichment of genes differentially expressed in livers of birds exposed for 60 days (d) to 10 and 60 mg/kg/d 2,6-DNT revealed several impacts validated by RT-qPCR including: prostaglandin pathway-mediated inflammation, increased expression of a heme synthesis pathway in response to anemia, and a shift in energy metabolism toward protein catabolism via inhibition of control points for glucose and lipid metabolic pathways, PCK1 and PPARGC1, respectively. This research effort provides the first comprehensive annotated gene library for Northern bobwhite. Transcript expression analysis provided insights into the metabolic perturbations underlying several observed toxicological phenotypes in a 2,6-DNT exposure case study. Furthermore, the systemic impact of dinitrotoluenes on liver function appears conserved across species as PPAR signaling is similarly affected in fathead minnow liver tissue after exposure to 2,4-DNT.

Toward genome-wide metabolotyping and elucidation of metabolic system: metabolic profiling of large-scale bioresources

An improvement in plant production is increasingly important for a sustainable human society. For this purpose, understanding the mechanism of plant production, that is, the plant metabolic system, is an immediate necessity. After the sequencing of the Arabidopsis genome, it has become possible to obtain a bird's eye view of its metabolism by means of omics such as transcriptomics and proteomics. Availability of thousands of transcriptome data points in the public domain has resulted in great advances in the methodology of functional genomics. Metabolome data can be a "gold mine" of biological findings. However, as the total throughput of metabolomics is far lower than that of transcriptomics due to technical difficulties, there is currently no publicly available large-scale metabolome dataset that is comparable in size to the transcriptome dataset. Recently, we established a novel methodology, termed widely targeted metabolomics, which can generate thousands of metabolome data points in a high-throughput manner. We previously conducted a targeted metabolite analysis of large-scale Arabidopsis bioresources, namely transposon-tagged mutants and accessions, to make a smaller dataset of metabolite accumulation. In this paper, we release approximately 3,000 metabolic profiles obtained by targeted analysis for 36 metabolites and discuss the possible regulation of amino acid accumulation.

In silico evaluation of predicted regulatory interactions in Arabidopsis thaliana

Background

Prediction of transcriptional regulatory mechanisms in Arabidopsis has become increasingly critical with the explosion of genomic data now available for both gene expression and gene sequence composition. We have shown in previous work [1], that a combination of correlation measurements and cis-regulatory element (CRE) detection methods are effective in predicting targets for candidate transcription factors for specific case studies which were validated. However, to date there has been no quantitative assessment as to which correlation measures or CRE detection methods used alone or in combination are most effective in predicting TF→target relationships on a genome-wide scale.

Results

We tested several widely used methods, based on correlation (Pearson and Spearman Rank correlation) and cis-regulatory element (CRE) detection (≥1 CRE or CRE over-representation), to determine which of these methods individually or in combination is the most effective by various measures for making regulatory predictions. To predict the regulatory targets of a transcription factor (TF) of interest, we applied these methods to microarray expression data for genes that were regulated over treatment and control conditions in wild type (WT) plants. Because the chosen data sets included identical experimental conditions used on TF over-expressor or T-DNA knockout plants, we were able to test the TF→target predictions made using microarray data from WT plants, with microarray data from mutant/transgenic plants. For each method, or combination of methods, we computed sensitivity, specificity, positive and negative predictive value and the F-measure of balance between sensitivity and positive predictive value (precision). This analysis revealed that the ≥1 CRE and Spearman correlation (used alone or in combination) were the most balanced CRE detection and correlation methods, respectively with regard to their power to accurately predict regulatory-target interactions.

Conclusion

These findings provide an approach and guidance for researchers interested in predicting transcriptional regulatory mechanisms using microarray data that they generate (or microarray data that is publically available) combined with CRE detection in promoter sequence data.

Co-expression tools for plant biology: opportunities for hypothesis generation and caveats

Gene co-expression analysis has emerged in the past 5 years as a powerful tool for gene function prediction. In essence, co-expression analysis asks the question 'what are the genes that are co-expressed, that is, those that show similar expression profiles across many experiments, with my gene of interest?'. Genes that are highly co-expressed may be involved in the biological process or processes of the query gene. This review describes the tools that are available for performing such analyses, how each of these perform, and also discusses statistical issues including how normalization of gene expression data can influence co-expression results, calculation of co-expression scores and P values, and the influence of data sets used for co-expression analysis. Finally, examples from the literature will be presented, wherein co-expression has been used to corroborate and discover various aspects of plant biology.

Quantification of spatial parameters in 3D cellular constructs using graph theory

Multispectral three-dimensional (3D) imaging provides spatial information for biological structures that cannot be measured by traditional methods. This work presents a method of tracking 3D biological structures to quantify changes over time using graph theory. Cell-graphs were generated based on the pairwise distances, in 3D-Euclidean space, between nuclei during collagen I gel compaction. From these graphs quantitative features are extracted that measure both the global topography and the frequently occurring local structures of the “tissue constructs.” The feature trends can be controlled by manipulating compaction through cell density and are significant when compared to random graphs. This work presents a novel methodology to track a simple 3D biological event and quantitatively analyze the underlying structural change. Further application of this method will allow for the study of complex biological problems that require the quantification of temporal-spatial information in 3D and establish a new paradigm in understanding structure-function relationships.

Transcriptional wiring of cell wall-related genes in Arabidopsis

Transcriptional coordination, or co-expression, of genes may signify functional relatedness of the corresponding proteins. For example, several genes involved in secondary cell wall cellulose biosynthesis are co-expressed with genes engaged in the synthesis of xylan, which is a major component of the secondary cell wall. To extend these types of analyses, we investigated the co-expression relationships of all Carbohydrate-Active enZYmes (CAZy)-related genes for Arabidopsis thaliana. Thus, the intention was to transcriptionally link different cell wall-related processes to each other, and also to other biological functions. To facilitate easy manual inspection, we have displayed these interactions as networks and matrices, and created a web-based interface (http://aranet.mpimp-golm.mpg.de/corecarb) containing downloadable files for all the transcriptional associations.

RiceArrayNet: a database for correlating gene expression from transcriptome profiling, and its application to the analysis of coexpressed genes in rice

Microarray data can be used to derive understanding of the relationships between the genes involved in various biological systems of an organism, given the availability of databases of gene expression measurements from the complete spectrum of experimental conditions and materials. However, there have been no reports, to date, of such a database being constructed for rice (Oryza sativa). Here, we describe the construction of such a database, called RiceArrayNet (RAN; http://www.ggbio.com/arraynet/), which provides information on coexpression between genes in terms of correlation coefficients (r values). The average number of coexpressed genes is 214, with sd of 440 at r >or= 0.5. Given the correlation between genes in a gene pair, the degrees of closeness between genes can be visualized in a relational tree and a relational network. The distribution of correlated genes according to degree of stringency shows how each gene is related to other genes. As an application of RAN, the 16-member L7Ae ribosomal protein family was explored for coexpressed genes and gene expression values within and between rice and Arabidopsis (Arabidopsis thaliana), and common and unique features in coexpression partners and expression patterns were observed for these family members. We observed a correlation pattern between Os01g0968800, a drought-responsive element-binding transcription factor, Os02g0790500, a trehalose-6-phosphate synthase, and Os06g0219500, a small heat shock factor, reflecting the fact that genes responding to the same biological stresses are regulated together. The RAN database can be used as a tool to gain insight into a particular gene by examining its coexpression partners.