Predictive minimum description length principle approach to inferring gene regulatory networks

Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold that defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we propose a new inference algorithm that incorporates mutual information (MI), conditional mutual information (CMI), and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter. The performance of the proposed algorithm is evaluated using both synthetic time series data sets and a biological time series data set (Saccharomyces cerevisiae). The results show that the proposed algorithm produced fewer false edges and significantly improved the precision when compared to existing MDL algorithm.

Design, validation and annotation of transcriptome-wide oligonucleotide probes for the oligochaete annelid Eisenia fetida

High density oligonucleotide probe arrays have increasingly become an important tool in genomics studies. In organisms with incomplete genome sequence, one strategy for oligo probe design is to reduce the number of unique probes that target every non-redundant transcript through bioinformatic analysis and experimental testing. Here we adopted this strategy in making oligo probes for the earthworm Eisenia fetida, a species for which we have sequenced transcriptome-scale expressed sequence tags (ESTs). Our objectives were to identify unique transcripts as targets, to select an optimal and non-redundant oligo probe for each of these target ESTs, and to annotate the selected target sequences. We developed a streamlined and easy-to-follow approach to the design, validation and annotation of species-specific array probes. Four 244K-formatted oligo arrays were designed using eArray and were hybridized to a pooled E. fetida cRNA sample. We identified 63,541 probes with unsaturated signal intensities consistently above the background level. Target transcripts of these probes were annotated using several sequence alignment algorithms. Significant hits were obtained for 37,439 (59%) probed targets. We validated and made publicly available 63.5K oligo probes so the earthworm research community can use them to pursue ecological, toxicological, and other functional genomics questions. Our approach is efficient, cost-effective and robust because it (1) does not require a major genomics core facility; (2) allows new probes to be easily added and old probes modified or eliminated when new sequence information becomes available, (3) is not bioinformatics-intensive upfront but does provide opportunities for more in-depth annotation of biological functions for target genes; and (4) if desired, EST orthologs to the UniGene clusters of a reference genome can be identified and selected in order to improve the target gene specificity of designed probes. This approach is particularly applicable to organisms with a wealth of EST sequences but unfinished genome.

A new approach to construct pathway connected networks and its application in dose responsive gene expression profiles of rat liver regulated by 2,4DNT

Abstract

Background

Military and industrial activities have lead to reported release of 2,4-dinitrotoluene (2,4DNT) into soil, groundwater or surface water. It has been reported that 2,4DNT can induce toxic effects on humans and other organisms. However the mechanism of 2,4DNT induced toxicity is still unclear. Although a series of methods for gene network construction have been developed, few instances of applying such technology to generate pathway connected networks have been reported.

Results

Microarray analyses were conducted using liver tissue of rats collected 24h after exposure to a single oral gavage with one of five concentrations of 2,4DNT. We observed a strong dose response of differentially expressed genes after 2,4DNT treatment. The most affected pathways included: long term depression, breast cancer regulation by stathmin1, WNT Signaling; and PI3K signaling pathways. In addition, we propose a new approach to construct pathway connected networks regulated by 2,4DNT. We also observed clear dose response pathway networks regulated by 2,4DNT.

Conclusions

We developed a new method for constructing pathway connected networks. This new method was successfully applied to microarray data from liver tissue of 2,4DNT exposed animals and resulted in the identification of unique dose responsive biomarkers in regards to affected pathways.

In vitro gene regulatory networks predict in vivo function of liver

BACKGROUND

Evolution of toxicity testing is predicated upon using in vitro cell based systems to rapidly screen and predict how a chemical might cause toxicity to an organ in vivo. However, the degree to which we can extend in vitro results to in vivo activity and possible mechanisms of action remains to be fully addressed.

RESULTS

Here we use the nitroaromatic 2,4,6-trinitrotoluene (TNT) as a model chemical to compare and determine how we might extrapolate from in vitro data to in vivo effects. We found 341 transcripts differentially expressed in common among in vitro and in vivo assays in response to TNT. The major functional term corresponding to these transcripts was cell cycle. Similarly modulated common pathways were identified between in vitro and in vivo. Furthermore, we uncovered the conserved common transcriptional gene regulatory networks between in vitro and in vivo cellular liver systems that responded to TNT exposure, which mainly contain 2 subnetwork modules: PTTG1 and PIR centered networks. Interestingly, all 7 genes in the PTTG1 module were involved in cell cycle and downregulated by TNT both in vitro and in vivo.

CONCLUSIONS

The results of our investigation of TNT effects on gene expression in liver suggest that gene regulatory networks obtained from an in vitro system can predict in vivo function and mechanisms. Inhibiting PTTG1 and its targeted cell cycle related genes could be key mechanism for TNT induced liver toxicity.

Generation, analysis and functional annotation of expressed sequence tags from the sheepshead minnow (Cyprinodon variegatus)

BACKGROUND

Sheepshead minnow (Cyprinodon variegatus) are small fish capable of withstanding exposure to very low levels of dissolved oxygen, as well as extreme temperatures and salinities. It is an important model in understanding the impacts and biological response to hypoxia and co-occurring compounding stressors such as polycyclic aromatic hydrocarbons, endocrine disrupting chemicals, metals and herbicides. Here, we initiated a project to sequence and analyze over 10,000 ESTs generated from the Sheepshead minnow (Cyprinodon variegatus) as a resource for investigating stressor responses.

RESULTS

We sequenced 10,858 EST clones using a normalized cDNA library made from larval, embryonic and adult suppression subtractive hybridization-PCR (SSH) libraries. Post- sequencing processing led to 8,099 high quality sequences. Clustering analysis of these ESTs indentified 4,223 unique sequences containing 1,053 contigs and 3,170 singletons. BLASTX searches produced 1,394 significant (E-value < 10-5) hits and further Gene Ontology (GO) analysis annotated 388 of these genes. All the EST sequences were deposited by Expressed Sequence Tags database (dbEST) in GenBank (GenBank: GE329585 to GE337683). Gene discovery and annotations are presented and discussed. This set of ESTs represents a significant proportion of the Sheepshead minnow (Cyprinodon variegatus) transcriptome, and provides a material basis for the development of microarrays useful for further gene expression studies in association with stressors such as hypoxia, cadmium, chromium and pyrene.

In silico identification of conserved microRNAs and their target transcripts from expressed sequence tags of three earthworm species

MicroRNAs are a recently identified class of small regulatory RNAs that target more than 30% protein-coding genes. Elevating evidence shows that miRNAs play a critical role in many biological processes, including developmental timing, tissue differentiation, and response to chemical exposure. In this study, we applied a computational approach to analyze expressed sequence tags, and identified 32 miRNAs belonging to 22 miRNA families, in three earthworm species Eisenia fetida, Eisenia andrei, and Lumbricus rubellus. These newly identified earthworm miRNAs possess a difference of 2-4 nucleotides from their homologous counterparts in Caenorhabditis elegans. They also share similar features with other known animal miRNAs, for instance, the nucleotide U being dominant in both mature and pre-miRNA sequences, particularly in the first position of mature miRNA sequences at the 5' end. The newly identified earthworm miRNAs putatively regulate mRNA genes that are involved in many important biological processes and pathways related to development, growth, locomotion, and reproduction as well as response to stresses, particularly oxidative stress. Future efforts will focus on experimental validation of their presence and target mRNA genes to further elucidate their biological functions in earthworms.

Time lagged information theoretic approaches to the reverse engineering of gene regulatory networks

Background

A number of models and algorithms have been proposed in the past for gene regulatory network (GRN) inference; however, none of them address the effects of the size of time-series microarray expression data in terms of the number of time-points. In this paper, we study this problem by analyzing the behaviour of three algorithms based on information theory and dynamic Bayesian network (DBN) models. These algorithms were implemented on different sizes of data generated by synthetic networks. Experiments show that the inference accuracy of these algorithms reaches a saturation point after a specific data size brought about by a saturation in the pair-wise mutual information (MI) metric; hence there is a theoretical limit on the inference accuracy of information theory based schemes that depends on the number of time points of micro-array data used to infer GRNs. This illustrates the fact that MI might not be the best metric to use for GRN inference algorithms. To circumvent the limitations of the MI metric, we introduce a new method of computing time lags between any pair of genes and present the pair-wise time lagged Mutual Information (TLMI) and time lagged Conditional Mutual Information (TLCMI) metrics. Next we use these new metrics to propose novel GRN inference schemes which provides higher inference accuracy based on the precision and recall parameters.

Results

It was observed that beyond a certain number of time-points (i.e., a specific size) of micro-array data, the performance of the algorithms measured in terms of the recall-to-precision ratio saturated due to the saturation in the calculated pair-wise MI metric with increasing data size. The proposed algorithms were compared to existing approaches on four different biological networks. The resulting networks were evaluated based on the benchmark precision and recall metrics and the results favour our approach.

Conclusions

To alleviate the effects of data size on information theory based GRN inference algorithms, novel time lag based information theoretic approaches to infer gene regulatory networks have been proposed. The results show that the time lags of regulatory effects between any pair of genes play an important role in GRN inference schemes.

Quail Genomics: a knowledgebase for Northern bobwhite

BACKGROUND

The Quail Genomics knowledgebase (http://www.quailgenomics.info) has been initiated to share and develop functional genomic data for Northern bobwhite (Colinus virginianus). This web-based platform has been designed to allow researchers to perform analysis and curate genomic information for this non-model species that has little supporting information in GenBank.

DESCRIPTION

A multi-tissue, normalized cDNA library generated for Northern bobwhite was sequenced using 454 Life Sciences next generation sequencing. The Quail Genomics knowledgebase represents the 478,142 raw ESTs generated from the sequencing effort in addition to assembled nucleotide and protein sequences including 21,980 unigenes annotated with meta-data. A normalized MySQL relational database was established to provide comprehensive search parameters where meta-data can be retrieved using functional and structural information annotation such as gene name, pathways and protein domain. Additionally, blast hit cutoff levels and microarray expression data are available for batch searches. A Gene Ontology (GO) browser from Amigo is locally hosted providing 8,825 unigenes that are putative orthologs to chicken genes. In an effort to address over abundance of Northern bobwhite unigenes (71,384) caused by non-overlapping contigs and singletons, we have built a pipeline that generates scaffolds/supercontigs by aligning partial sequence fragments against the indexed protein database of chicken to build longer sequences that can be visualized in a web browser.

CONCLUSION

Our effort provides a central repository for storage and a platform for functional interrogation of the Northern bobwhite sequences providing comprehensive GO annotations, meta-data and a scaffold building pipeline. The Quail Genomics knowledgebase will be integrated with Japanese quail (Coturnix coturnix) data in future builds and incorporate a broader platform for these avian species.

Influence of ovarian stage on transcript profiles in fathead minnow (Pimephales promelas) ovary tissue

Interpretation of toxicogenomic experiments conducted with ovary tissue from asynchronous-spawning small fish species is complicated by background variation in the relative abundance and proportion of follicles at different stages within the ovary tissue sample. This study employed both real-time quantitative polymerase chain reaction and a 15,000 gene oligonucleotide microarray to examine variation in the fathead minnow (Pimephales promelas) ovarian transcriptional profile as a function of quantitative and qualitative differences in ovarian histology. The objectives were to provide data that could potentially aid interpretation of future toxicogenomics experiments, identify putative stage-related transcriptional markers, and generate insights into basic biological regulation of asynchronous oocyte development. Multiple lines of evidence from the present study indicate that variation in the transcriptional profile is primarily dependent on the relative abundance of previtellogenic versus vitellogenic follicles in the ovary. Due to the relatively small proportions of mature ovulated follicles or atretic follicles in the overall follicle population, few potential transcriptional markers of maturation, ovulation, or atresia could be identified. However, among the 460 differentially expressed genes identified in the present study, several targets, including HtrA serine peptidase 3 (htra3), tissue inhibitor of metalloproteinase 3 (timp3), aquaporin 8 (aqp8), transgelin 2 like (tagln2), Nedd4 family interacting protein 2 (ndfip2), chemokine ligand 12a (cxcl12a), midkine-related growth factor (mdka), and jagged 1b (jag 1b) exhibited responses and functional properties that support them as candidate molecular markers of significant shift in gross ovarian stage. Genes associated with a diversity of functions including cellular development, morphogenesis, coated vesicle transport, sexual reproduction, and neuron development, among others, were statistically enriched within the list of 460 genes differentially expressed among different ovarian classes. Overall, results of this study provide insights into background variation in ovary transcript profiles that should aid and enhance the interpretation of toxicogenomic data generated in experiments conducted with small, asynchronous-spawning fish species.

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.

Fathead minnow steroidogenesis: in silico analyses reveals tradeoffs between nominal target efficacy and robustness to cross-talk

Background

Interpreting proteomic and genomic data is a major challenge in predictive ecotoxicology that can be addressed by a systems biology approach. Mathematical modeling provides an organizational platform to consolidate protein dynamics with possible genomic regulation. Here, a model of ovarian steroidogenesis in the fathead minnow, Pimephales promelas, (FHM) is developed to evaluate possible transcriptional regulation of steroid production observed in microarray studies.

Results

The model was developed from literature sources, integrating key signaling components (G-protein and PKA activation) with their ensuing effect on steroid production. The model properly predicted trajectory behavior of estradiol and testosterone when fish were exposed to fadrozole, a specific aromatase inhibitor, but failed to predict the steroid hormone behavior occurring one week post-exposure as well as the increase in steroid levels when the stressor was removed. In vivo microarray data implicated three modes of regulation which may account for over-production of steroids during a depuration phase (when the stressor is removed): P450 enzyme up-regulation, inhibin down-regulation, and luteinizing hormone receptor up-regulation. Simulation studies and sensitivity analysis were used to evaluate each case as possible source of compensation to endocrine stress.

Conclusions

Simulation studies of the testosterone and estradiol response to regulation observed in microarray data supported the hypothesis that the FHM steroidogenesis network compensated for endocrine stress by modulating the sensitivity of the ovarian network to global cues coming from the hypothalamus and pituitary. Model predictions of luteinizing hormone receptor regulation were consistent with depuration and in vitro data. These results challenge the traditional approach to network elucidation in systems biology. Generally, the most sensitive interactions in a network are targeted for further elucidation but microarray evidence shows that homeostatic regulation of the steroidogenic network is likely maintained by a mildly sensitive interaction. We hypothesize that effective network elucidation must consider both the sensitivity of the target as well as the target's robustness to biological noise (in this case, to cross-talk) when identifying possible points of regulation.

A novel gene network inference algorithm using predictive minimum description length approach

BACKGROUND

Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold which defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we proposed a new inference algorithm which incorporated mutual information (MI), conditional mutual information (CMI) and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter.

RESULTS

The performance of the proposed algorithm was evaluated using both synthetic time series data sets and a biological time series data set for the yeast Saccharomyces cerevisiae. The benchmark quantities precision and recall were used as performance measures. The results show that the proposed algorithm produced less false edges and significantly improved the precision, as compared to the existing algorithm. For further analysis the performance of the algorithms was observed over different sizes of data.

CONCLUSIONS

We have proposed a new algorithm that implements the PMDL principle for inferring gene regulatory networks from time series DNA microarray data that eliminates the need of a fine tuning parameter. The evaluation results obtained from both synthetic and actual biological data sets show that the PMDL principle is effective in determining the MI threshold and the developed algorithm improves precision of gene regulatory network inference. Based on the sensitivity analysis of all tested cases, an optimal CMI threshold value has been identified. Finally it was observed that the performance of the algorithms saturates at a certain threshold of data size.

II: Effects of a dopamine receptor antagonist on fathead minnow dominance behavior and ovarian gene expression in the fathead minnow and zebrafish

Neurotransmitters such as dopamine play an important role in reproductive behaviors and signaling. Neuroendocrine-active chemicals in the environment have potential to interfere with and/or alter these processes. A companion study with the dopamine 2 receptor antagonist, haloperidol, found no evidence of a direct effect of the chemical on fish reproduction. This study considered haloperidol's potential effects on behavior and ovarian gene expression. Male fathead minnows exposed to 50 microg haloperidol/L for 96 h were found to be significantly more dominant than control males. In terms of molecular signaling, investigated using oligonucleotide microarrays, there was little similarity in the identity and functions of genes differentially expressed in the ovaries of fathead minnows (Pimephales promelas) versus zebrafish (Danio rerio) exposed under the same conditions. Results suggest that non-lethal concentrations of haloperidol do not induce ovarian molecular responses that could serve as biomarkers of exposure to D2R antagonists, but may impact behavior.

I. Effects of a dopamine receptor antagonist on fathead minnow, Pimephales promelas, reproduction

Neurotransmitters such as dopamine play an important role in regulating fish reproduction. However, the potential for neuroendocrine active chemicals to disrupt fish reproduction has not been well studied, despite emerging evidence of their discharge into aquatic environments. This study is the first to apply the fathead minnow 21 d reproduction assay developed for the US Endocrine Disruptor Screening Program to evaluate the reproductive toxicity of a model neuroendocrine active chemical, the dopamine 2 receptor antagonist, haloperidol. Continuous exposure to up to 20 imcrog haloperidol/L had no significant effects on fathead minnow fecundity, secondary sex characteristics, gonad histology, or plasma steroid and vitellogenin concentrations. The only significant effect observed was an increase in gonadotropin-releasing hormone (cGnRH) transcripts in the male brain. Results suggest that non-lethal concentrations of haloperidol do not directly impair fish reproduction. Potential effects of haloperidol on reproductive behaviors and gene expression were examined in a companion study.

Neurochemical and electrophysiological diagnosis of reversible neurotoxicity in earthworms exposed to sublethal concentrations of CL-20

BACKGROUND, AIM, AND SCOPE

Hexanitrohexaazaisowurtzitane (CL-20) is a relatively new energetic compound sharing some degree of structural similarity with hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), a known neurotoxic compound. Previously, we demonstrated using a noninvasive electrophysiological technique that CL-20 was a more potent neurotoxicant than RDX to the earthworm Eisenia fetida. In the present study, we investigated the effect of CL-20 exposure and subsequent recovery on muscarinic acetylcholine receptors (mAChRs) to further define the mechanism of reversible neurotoxicity of CL-20 in E. fetida.

MATERIALS AND METHODS

We used a noninvasive electrophysiological technique to evaluate neurotoxicity in CL-20-treated worms, and then measured how such exposures altered levels of whole-body mAChR in the same animals.

RESULTS AND DISCUSSION

A good correlation exists between these two types of endpoints. Effect on mAChR levels was most prominent at day 6 of exposure. After 7 days of recovery, both conduction velocity and mAChR were significantly restored. Our results show that sublethal concentrations of CL-20 significantly reduced mAChR levels in a concentration- and duration-dependent manner, which was accompanied with significant decreases in the conduction velocity of the medial and lateral giant nerve fibers. After 7-day post exposure recovery, worms restored both neurochemical (mAChR) and neurophysiological (conduction velocity) endpoints that were reduced during 6-day exposures to CL-20 concentrations from 0.02 to 0.22 microg/cm(2).

CONCLUSIONS AND PERSPECTIVES

Our findings support the idea that CL-20 induced neurotoxic effects are reversible, and suggest that CL-20 neurotoxicity may be mediated through the cholinergic system. Future studies will investigate other neurotransmission systems such as GABA, glutamate, and monoamine. Ion channels in the nerve membrane should be examined to further define the precise mechanisms underlying CL-20 neurotoxicity.

Gene expression responses in male fathead minnows exposed to binary mixtures of an estrogen and antiestrogen

Background

Aquatic organisms are continuously exposed to complex mixtures of chemicals, many of which can interfere with their endocrine system, resulting in impaired reproduction, development or survival, among others. In order to analyze the effects and mechanisms of action of estrogen/anti-estrogen mixtures, we exposed male fathead minnows (Pimephales promelas) for 48 hours via the water to 2, 5, 10, and 50 ng 17α-ethinylestradiol (EE₂)/L, 100 ng ZM 189,154/L (a potent antiestrogen known to block activity of estrogen receptors) or mixtures of 5 or 50 ng EE₂/L with 100 ng ZM 189,154/L. We analyzed gene expression changes in the gonad, as well as hormone and vitellogenin plasma levels.

Results

Steroidogenesis was down-regulated by EE₂ as reflected by the reduced plasma levels of testosterone in the exposed fish and down-regulation of genes in the steroidogenic pathway. Microarray analysis of testis of fathead minnows treated with 5 ng EE₂/L or with the mixture of 5 ng EE₂/L and 100 ng ZM 189,154/L indicated that some of the genes whose expression was changed by EE₂ were blocked by ZM 189,154, while others were either not blocked or enhanced by the mixture, generating two distinct expression patterns. Gene ontology and pathway analysis programs were used to determine categories of genes for each expression pattern.

Conclusion

Our results suggest that response to estrogens occurs via multiple mechanisms, including canonical binding to soluble estrogen receptors, membrane estrogen receptors, and other mechanisms that are not blocked by pure antiestrogens.

An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data

BACKGROUND

One of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its expression, and how a set of transcription factors coordinate to accomplish temporal and spatial specific regulations.

RESULTS

Here we propose a supervised machine learning approach to address these questions. We focus our study on the gene transcriptional regulation of the cell cycle in the budding yeast, thanks to the large amount of data available and relatively well-understood biology, although the main ideas of our method can be applied to other data as well. Our method starts with building an ensemble of decision trees for each microarray data to capture the association between the expression levels of yeast genes and the binding of transcription factors to gene promoter regions, as determined by chromatin immunoprecipitation microarray (ChIP-chip) experiment. Cross-validation experiments show that the method is more accurate and reliable than the naive decision tree algorithm and several other ensemble learning methods. From the decision tree ensembles, we extract logical rules that explain how a set of transcription factors act in concert to regulate the expression of their targets. We further compute a profile for each rule to show its regulation strengths at different time points. We also propose a spline interpolation method to integrate the rule profiles learned from several time series expression data sets that measure the same biological process. We then combine these rule profiles to build a transcriptional regulatory network for the yeast cell cycle. Compared to the results in the literature, our method correctly identifies all major known yeast cell cycle transcription factors, and assigns them into appropriate cell cycle phases. Our method also identifies many interesting synergetic relationships among these transcription factors, most of which are well known, while many of the rest can also be supported by other evidences.

CONCLUSION

The high accuracy of our method indicates that our method is valid and robust. As more gene expression and transcription factor binding data become available, we believe that our method is useful for reconstructing large-scale transcriptional regulatory networks in other species as well.

Sublethal effects of subacute exposure to RDX (1,3,5-trinitro-1,3,5-triazine) in the northern bobwhite (Colinus virginianus)

Northern bobwhite (Colinus virginianus) were orally exposed via gavage to 0, 0.5, 3, 8, 12, or 17 mg/kg of RDX (1,3,5-trinitro-1,3,5-triazine) in corn oil daily for 14 d to evaluate sublethal effects of this explosive in birds. Mortality occurred at a rates of 100, 67, and 25% for the 17, 12, and 8 mg/kg/d dose groups, respectively. Death was preceded by clonic and tonic convulsions and weight loss caused by gastrointestinal effects. Increases in serum globulin and total leukocytes were observed in the two highest-dose groups. Degeneration of testicular and splenic tissue also was observed. The no-observed-adverse-effects and lowest-observed-adverse-effects levels were determined as 3.0 and 8.0 mg/kg/d, respectively.

Biomarker discovery and transcriptomic responses in Daphnia magna exposed to munitions constituents

Ecotoxicogenomic approaches are emerging as alternative methods in environmental monitoring because they allow insight into pollutant modes of action and help assess the causal agents and potential toxicity beyond the traditional end points of death, growth, and reproduction. Gene expression analysis has shown particular promise for identifying gene expression biomarkers of chemical exposure that can be further used to monitor specific chemical exposures in the environment. We focused on the development of gene expression markers to detect and discriminate between chemical exposures. Using a custom cDNA microarray for Daphnia magna, we identified distinct expression fingerprints in response to exposure at sublethal concentrations of Cu, Zn, Pb, and munitions constituents. Using the results obtained from microarray analysis, we chose a suite of potential biomarkers for each of the specific exposures. The selected potential biomarkers were tested in independent chemical exposures for specificity using quantitative reverse transcription polymerase chain reaction. Six genes were confirmed as differentially regulated bythe selected chemical exposures. Furthermore, each exposure was identified by response of a unique combination (suite) of individual gene expression biomarkers. These results demonstrate the potential for discovery and validation of novel biomarkers of chemical exposures using gene expression analysis, which could have broad applicability in environmental monitoring.

Endocrine disrupting chemicals in fish: developing exposure indicators and predictive models of effects based on mechanism of action

Knowledge of possible toxic mechanisms (or modes) of action (MOA) of chemicals can provide valuable insights as to appropriate methods for assessing exposure and effects, thereby reducing uncertainties related to extrapolation across species, endpoints and chemical structure. However, MOA-based testing seldom has been used for assessing the ecological risk of chemicals. This is in part because past regulatory mandates have focused more on adverse effects of chemicals (reductions in survival, growth or reproduction) than the pathways through which these effects are elicited. A recent departure from this involves endocrine-disrupting chemicals (EDCs), where there is a need to understand both MOA and adverse outcomes. To achieve this understanding, advances in predictive approaches are required whereby mechanistic changes caused by chemicals at the molecular level can be translated into apical responses meaningful to ecological risk assessment. In this paper we provide an overview and illustrative results from a large, integrated project that assesses the effects of EDCs on two small fish models, the fathead minnow (Pimephales promelas) and zebrafish (Danio rerio). For this work a systems-based approach is being used to delineate toxicity pathways for 12 model EDCs with different known or hypothesized toxic MOA. The studies employ a combination of state-of-the-art genomic (transcriptomic, proteomic, metabolomic), bioinformatic and modeling approaches, in conjunction with whole animal testing, to develop response linkages across biological levels of organization. This understanding forms the basis for predictive approaches for species, endpoint and chemical extrapolation. Although our project is focused specifically on EDCs in fish, we believe that the basic conceptual approach has utility for systematically assessing exposure and effects of chemicals with other MOA across a variety of biological systems.

Batch Blast Extractor: an automated blastx parser application

Motivation

BLAST programs are very efficient in finding similarities for sequences. However for large datasets such as ESTs, manual extraction of the information from the batch BLAST output is needed. This can be time consuming, insufficient, and inaccurate. Therefore implementation of a parser application would be extremely useful in extracting information from BLAST outputs.

Results

We have developed a java application, Batch Blast Extractor, with a user friendly graphical interface to extract information from BLAST output. The application generates a tab delimited text file that can be easily imported into any statistical package such as Excel or SPSS for further analysis. For each BLAST hit, the program obtains and saves the essential features from the BLAST output file that would allow further analysis. The program was written in Java and therefore is OS independent. It works on both Windows and Linux OS with java 1.4 and higher. It is freely available from:

ILOOP--a web application for two-channel microarray interwoven loop design

Microarray technology is widely applied to address complex scientific questions. However, there remain fundamental issues on how to design experiments to ensure that the resulting data enables robust statistical analysis. Interwoven loop design has several advantages over other designs. However it suffers in the complexity of design. We have implemented an online web application which allows users to find optimal loop designs for two-color microarray experiments. Given a number of conditions (such as treatments or time points) and replicates, the application will find the best possible design of the experiment and output experimental parameters. It is freely available from .