Cross-species comparison of genome-wide expression patterns

Cross-species comparative analysis of single presynapses

Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition.

Of Mouse and Man: Cross-Species Characterization of Hypertensive Cardiac Remodeling

Hypertension is a major public health concern and poses a significant risk for sudden cardiac death (SCD). However, the characterisation of human tissues tends to be macroscopic, with little appreciation for the quantification of the pathological remodelling responsible for the advancement of the disease. While the components of hypertensive remodelling are well established, the timeline and comparative quantification of pathological changes in hypertension have not been shown before. Here, we sought to identify the phasing of cardiac remodelling with hypertension using post-mortem tissue from SCD patients with early and advanced hypertensive heart disease (HHD). In order to study and quantify the progression of phenotypic changes, human specimens were contrasted to a well-described angiotensin-II-mediated hypertensive mouse model. While cardiomyocyte hypertrophy is an early adaptive response in the mouse that stabilises in established hypertension and declines as the disease progresses, this finding did not translate to the human setting. In contrast, optimising fibrosis quantification methods and applying them to each setting identified perivascular fibrosis as the prevailing possible cause for overall disease progression. Indeed, assessing myocardial inflammation highlights CD45+ inflammatory cell infiltration that precedes fibrosis and is an early-phase event in response to elevated arterial pressures that may underscore perivascular remodelling. Along with aetiology insight, we highlight cross-species comparison for quantification of cardiac remodelling in human hypertension. As such, this platform could assist with the development of therapies specific to the disease phase rather than targeting global components of hypertension, such as blood pressure lowering.

Comparative transcriptomic analysis of rat versus mouse cerebral cortex after traumatic brain injury

The heterogeneity of traumatic brain injury (TBI)-induced secondary injury has greatly hampered the development of effective treatments for TBI patients. Targeting common processes across species may be an innovative strategy to combat debilitating TBI. In the present study, a cross-species transcriptome comparison was performed for the first time to determine the fundamental processes of secondary brain injury in Sprague-Dawley rat and C57/BL6 mouse models of TBI, caused by acute controlled cortical impact. The RNA sequencing data from the mouse model of TBI were downloaded from the Gene Expression Omnibus (ID: GSE79441) at the National Center for Biotechnology Information. For the rat data, peri-injury cerebral cortex samples were collected for transcriptomic analysis 24 hours after TBI. Differentially expressed gene-based functional analysis revealed that common features between the two species were mainly involved in the regulation and activation of the innate immune response, including complement cascades as well as Toll-like and nucleotide oligomerization domain-like receptor pathways. These findings were further corroborated by gene set enrichment analysis. Moreover, transcription factor analysis revealed that the families of signal transducers and activators of transcription (STAT), basic leucine zipper (BZIP), Rel homology domain (RHD), and interferon regulatory factor (IRF) transcription factors play vital regulatory roles in the pathophysiological processes of TBI, and are also largely associated with inflammation. These findings suggest that targeting the common innate immune response might be a promising therapeutic approach for TBI. The animal experimental procedures were approved by the Beijing Neurosurgical Institute Animal Care and Use Committee (approval No. 201802001) on June 6, 2018.

Development of a DNA-based biosensor for the fast and sensitive detection of ochratoxin A in urine

In this paper, a novel DNA-based biosensor is proposed, which is based on paramagnetic microbeads carrying an ochratoxin A (OTA) capture aptamer. A sandwich-like detection complex is linked to the capture aptamer and is able to trigger, in presence of OTA, an isothermal rolling circle amplification (RCA) reaction. This latter generated autocatalytic units with a peroxidase activity (DNAzyme) that, in presence of a proper substrate, gave a blue-coloured product visible by the naked eye. The capture aptamer, blocked onto magnetic beads, allowed the specific capture of OTA in liquid samples. The modified detection aptamer, annealed to a circularized probe, was then used to detect the toxin capture event. Indeed, in the presence of OTA and an isothermal enzyme, the circular DNA was amplified, producing a single-stranded and tandem repeated long homologous copy of its sequence. In the DNA strand, a self-catalytic structure was formed with hemin as the catalytic core, inducing the development of blue colour in the presence of ABTS and hydrogen peroxide. The results showed that the biosensor has high sensitivity and selectivity for the detection of OTA, as low as 1.09 × 10^-12 ng/mL. Moreover, the proposed biosensor was successfully used for the detection of OTA in naturally contaminated rat urine. Accuracy and repeatability data obtained in recovery experiments were satisfying, being recoveries >95% with relative standard deviations in the range 3.6-15%. For the first time, an aptasensor was successfully applied to detect OTA in biological fluids. It can be used for mycotoxin biomonitoring and assessment of individual exposure.

Comparative morphometric analysis of lungs of the semifossorial giant pouched rat (Cricetomys gambianus) and the subterranean Nigerian mole rat (Cryptomys foxi)

Lungs of the rodent species, the African giant pouched rat (Cricetomys gambianus) and the Nigerian mole rat (Cryptomys foxi) were investigated. Significant morphometric differences exist between the two species. The volume of the lung per unit body mass was 2.7 times larger; the respiratory surface area 3.4 times greater; the volume of the pulmonary capillary blood 2 times more; the harmonic mean thickness of the blood-gas (tissue) barrier (τht) ~29% thinner and; the total pulmonary morphometric diffusing capacity (DLo2) for O2 2.3 times more in C. foxi. C. gambianus occupies open burrows that are ventilated with air while C. foxi lives in closed burrows. The less morphometrically specialized lungs of C. gambianus may be attributed to its much larger body mass (~6 times more) and possibly lower metabolic rate and its semifossorial life whereas the 'superior' lungs of C. foxi may largely be ascribed to the subterranean hypoxic and hypercapnic environment it occupies. Compared to other rodents species that have been investigated hitherto, the τht was mostly smaller in the lungs of the subterranean species and C. foxi has the highest mass-specific DLo2. The fossorial- and the subterranean rodents have acquired various pulmonary structural specializations that relate to habitats occupied.

Domestication and Temperature Modulate Gene Expression Signatures and Growth in the Australasian Snapper Chrysophrys auratus

Identifying genes and pathways involved in domestication is critical to understand how species change in response to human-induced selection pressures, such as increased temperatures. Given the profound influence of temperature on fish metabolism and organismal performance, a comparison of how temperature affects wild and domestic strains of snapper is an important question to address. We experimentally manipulated temperature conditions for F₁-hatchery and wild Australasian snapper (Chrysophrys auratus) for 18 days to mimic seasonal extremes and measured differences in growth, white muscle RNA transcription and hematological parameters. Over 2.2 Gb paired-end reads were assembled de novo for a total set of 33,017 transcripts (N50 = 2,804). We found pronounced growth and gene expression differences between wild and domesticated individuals related to global developmental and immune pathways. Temperature-modulated growth responses were linked to major pathways affecting metabolism, cell regulation and signaling. This study is the first step toward gaining an understanding of the changes occurring in the early stages of domestication, and the mechanisms underlying thermal adaptation and associated growth in poikilothermic vertebrates. Our study further provides the first transcriptome resources for studying biological questions in this non-model fish species.

A Qualitative Modeling Approach for Whole Genome Prediction Using High-Throughput Toxicogenomics Data and Pathway-Based Validation

Efficient high-throughput transcriptomics (HTT) tools promise inexpensive, rapid assessment of possible biological consequences of human and environmental exposures to tens of thousands of chemicals in commerce. HTT systems have used relatively small sets of gene expression measurements coupled with mathematical prediction methods to estimate genome-wide gene expression and are often trained and validated using pharmaceutical compounds. It is unclear whether these training sets are suitable for general toxicity testing applications and the more diverse chemical space represented by commercial chemicals and environmental contaminants. In this work, we built predictive computational models that inferred whole genome transcriptional profiles from a smaller sample of surrogate genes. The model was trained and validated using a large scale toxicogenomics database with gene expression data from exposure to heterogeneous chemicals from a wide range of classes (the Open TG-GATEs data base). The method of predictor selection was designed to allow high fidelity gene prediction from any pre-existing gene expression data set, regardless of animal species or data measurement platform. Predictive qualitative models were developed with this TG-GATES data that contained gene expression data of human primary hepatocytes with over 941 samples covering 158 compounds. A sequential forward search-based greedy algorithm, combining different fitting approaches and machine learning techniques, was used to find an optimal set of surrogate genes that predicted differential expression changes of the remaining genome. We then used pathway enrichment of up-regulated and down-regulated genes to assess the ability of a limited gene set to determine relevant patterns of tissue response. In addition, we compared prediction performance using the surrogate genes found from our greedy algorithm (referred to as the SV2000) with the landmark genes provided by existing technologies such as L1000 (Genometry) and S1500 (Tox21), finding better predictive performance for the SV2000. The ability of these predictive algorithms to predict pathway level responses is a positive step toward incorporating mode of action (MOA) analysis into the high throughput prioritization and testing of the large number of chemicals in need of safety evaluation.

Identification of putative ortholog gene blocks involved in gestant and lactating mammary gland development: a rodent cross-species microarray transcriptomics approach

The mammary gland (MG) undergoes functional and metabolic changes during the transition from pregnancy to lactation, possibly by regulation of conserved genes. The objective was to elucidate orthologous genes, chromosome clusters and putative conserved transcriptional modules during MG development. We analyzed expression of 22,000 transcripts using murine microarrays and RNA samples of MG from virgin, pregnant, and lactating rats by cross-species hybridization. We identified 521 transcripts differentially expressed; upregulated in early (78%) and midpregnancy (89%) and early lactation (64%), but downregulated in mid-lactation (61%). Putative orthologous genes were identified. We mapped the altered genes to orthologous chromosomal locations in human and mouse. Eighteen sets of conserved genes associated with key cellular functions were revealed and conserved transcription factor binding site search entailed possible coregulation among all eight block sets of genes. This study demonstrates that the use of heterologous array hybridization for screening of orthologous gene expression from rat revealed sets of conserved genes arranged in chromosomal order implicated in signaling pathways and functional ontology. Results demonstrate the utilization power of comparative genomics and prove the feasibility of using rodent microarrays to identification of putative coexpressed orthologous genes involved in the control of human mammary gland development.

Quantification of transcript levels with quantitative RT-PCR

Differential gene expression is a key factor driving phenotypic divergence. Determining when and where gene expression has diverged between organisms requires a quantitative method. While large-scale approaches such as microarrays or high-throughput mRNA sequencing can identify candidates, quantitative RT-PCR is the definitive method for confirming gene expression differences. Here, we describe the steps for performing qRT-PCR including extracting total RNA, reverse-transcribing it to make a pool of cDNA, and then quantifying relative expression of a few candidate genes using real-time or quantitative PCR.

Interspecies translation of disease networks increases robustness and predictive accuracy

Gene regulatory networks give important insights into the mechanisms underlying physiology and pathophysiology. The derivation of gene regulatory networks from high-throughput expression data via machine learning strategies is problematic as the reliability of these models is often compromised by limited and highly variable samples, heterogeneity in transcript isoforms, noise, and other artifacts. Here, we develop a novel algorithm, dubbed Dandelion, in which we construct and train intraspecies Bayesian networks that are translated and assessed on independent test sets from other species in a reiterative procedure. The interspecies disease networks are subjected to multi-layers of analysis and evaluation, leading to the identification of the most consistent relationships within the network structure. In this study, we demonstrate the performance of our algorithms on datasets from animal models of oculopharyngeal muscular dystrophy (OPMD) and patient materials. We show that the interspecies network of genes coding for the proteasome provide highly accurate predictions on gene expression levels and disease phenotype. Moreover, the cross-species translation increases the stability and robustness of these networks. Unlike existing modeling approaches, our algorithms do not require assumptions on notoriously difficult one-to-one mapping of protein orthologues or alternative transcripts and can deal with missing data. We show that the identified key components of the OPMD disease network can be confirmed in an unseen and independent disease model. This study presents a state-of-the-art strategy in constructing interspecies disease networks that provide crucial information on regulatory relationships among genes, leading to better understanding of the disease molecular mechanisms.

Immuno-hybridization chain reaction for enhancing detection of individual cytokine-secreting human peripheral mononuclear cells

We present here a new method to enhance the detection of secreted cytokines and chemokines from single human mononuclear cells. The technique uses a hybridization chain reaction (HCR) to amplify signals resulting from sandwich immunoassays. This immuno-HCR employs oligonucleotide-based initiators covalently linked to antibodies to propagate a chain reaction of hybridization events involving a pair of complementary hairpin oligomers bearing fluorescent labels. Integrating this strategy for signal amplification with microengraving (a soft lithographic method for printing arrays of secreted proteins from thousands of single cells) improves both the limits of detection and sensitivity for cytokines and chemokines captured from individual cells by an average of 200-fold relative to methods for direct detection by fluoresence. This approach should enhance the utility of microengraving for defining the immunological signatures of diseases and responses to interventional therapies based on multiplexed single-cell analysis.

Integrative transcriptional analysis between human and mouse cancer cells provides a common set of transformation associated genes

Mouse functional genomics is largely used to investigate relevant aspects of mammalian physiology and pathology. To which degree mouse models may offer accurate representations of molecular events underlining human diseases such as cancer is not yet fully established. Herein we compare gene expression signatures between a set of human cancer cell lines (NCI-60 cell collection) and a mouse cellular model of oncogenic K-ras dependent transformation in order to identify their closeness at the transcriptional level. The results of our integrative and comparative analysis show that in both species as compared to normal cells or tissues the transformation process involves the activation of a transcriptional response. Furthermore, the cellular mouse model of K-ras dependent transformation has a good degree of similarity with several human cancer cell lines and in particular with cell lines containing oncogenic Ras mutations. Moreover both species have similar genetic signatures that are associated to the same altered cellular pathways (e.g. Spliceosome and Proteasome) or to deregulation of the same genes (e.g. cyclin D1, AHSA1 and HNRNPD) detected in the comparison between cancer cells versus normal cells or tissues. In summary, we report one of the first in-depth analysis of global gene expression profiles of a K-ras dependent mouse cell model of transformation and a large collection of human cancer cells as compared to their normal counterparts. Taken together our findings show a strong correlation in the transcriptional and pathway alteration responses between the two species, therefore validating the use of the mouse model as an appropriate tool to investigate human cancer, and indicating that the comparative analysis, as described here, offers a useful approach to identify cancer-specific gene signatures.

The reconstruction of condition-specific transcriptional modules provides new insights in the evolution of yeast AP-1 proteins

AP-1 proteins are transcription factors (TFs) that belong to the basic leucine zipper family, one of the largest families of TFs in eukaryotic cells. Despite high homology between their DNA binding domains, these proteins are able to recognize diverse DNA motifs. In yeasts, these motifs are referred as YRE (Yap Response Element) and are either seven (YRE-Overlap) or eight (YRE-Adjacent) base pair long. It has been proposed that the AP-1 DNA binding motif preference relies on a single change in the amino acid sequence of the yeast AP-1 TFs (an arginine in the YRE-O binding factors being replaced by a lysine in the YRE-A binding Yaps). We developed a computational approach to infer condition-specific transcriptional modules associated to the orthologous AP-1 protein Yap1p, Cgap1p and Cap1p, in three yeast species: the model yeast Saccharomyces cerevisiae and two pathogenic species Candida glabrata and Candida albicans. Exploitation of these modules in terms of predictions of the protein/DNA regulatory interactions changed our vision of AP-1 protein evolution. Cis-regulatory motif analyses revealed the presence of a conserved adenine in 5' position of the canonical YRE sites. While Yap1p, Cgap1p and Cap1p shared a remarkably low number of target genes, an impressive conservation was observed in the YRE sequences identified by Yap1p and Cap1p. In Candida glabrata, we found that Cgap1p, unlike Yap1p and Cap1p, recognizes YRE-O and YRE-A motifs. These findings were supported by structural data available for the transcription factor Pap1p (Schizosaccharomyces pombe). Thus, whereas arginine and lysine substitutions in Cgap1p and Yap1p proteins were reported as responsible for a specific YRE-O or YRE-A preference, our analyses rather suggest that the ancestral yeast AP-1 protein could recognize both YRE-O and YRE-A motifs and that the arginine/lysine exchange is not the only determinant of the specialization of modern Yaps for one motif or another.

Evolving gene expression: from G to E to GxE

Analyses of gene expression data sets for multiple individuals and species promise to shed light on the mode of evolution of gene expression. However, complementary complexities challenge this goal. Characterization of the genetic variation underlying gene expression can easily be compromised by lack of environmental control. Conversely, the breadth of conclusions from studies of environmental effects has been limited by the use of single strains. Controlled studies have hinted at extensive genexenvironment interaction. Thus, both genetics and environment are key components in models of the evolution of gene expression. We review the literature on the evolution of gene expression in terms of genetics (G), environmental response (E) and GxE interactions to make this conceptual point.

A comprehensive analysis of gene expression evolution between humans and mice

Evolutionary changes in gene expression account for most phenotypic differences between species. Advances in microarray technology have made the systematic study of gene expression evolution possible. In this study, gene expression patterns were compared between human and mouse genomes using two published methods. Specifically, we studied how gene expression evolution was related to GO terms and tried to decode the relationship between promoter evolution and gene expression evolution. The results showed that (1) the significant enrichment of biological processes in orthologs of expression conservation reveals functional significance of gene expression conservation. The more conserved gene expression in some biological processes than is expected in a purely neutral model reveals negative selection on gene expression. However, fast evolving genes mainly support the neutrality of gene expression evolution, and (2) gene expression conservation is positively but only slightly correlated with promoter conservation based on a motif-count score of the promoter alignment. Our results suggest a neutral model with negative selection for gene expression evolution between humans and mice, and promoter evolution could have some effects on gene expression evolution.

Identifying Conserved and Divergent Transcriptional Modules by Cross-species Matrix Decomposition on Microarray Data

Cross-species comparison of gene expression profiles allows deciphering fundamental and species-specific transcriptional programs of cells and offers insight into organization and evolution of the genome and genetic network. Here, we propose an algorithm for comparing microarray data from different species to unravel transcriptional modules that are conserved or divergent through evolution. The proposed algorithm is based on cross-species matrix decomposition that includes a nonlinear independent component analysis followed a generalized probabilistic sparse matrix factorization on microarray data from different species. The proposed algorithm captures transcriptional modularity that might result from highly nonlinear interactions among genes, and partitions genes into mutually non-exclusive transcriptional modules. The conserved transcriptional modules are identified by the latent variables that are associated with predominant biological prototypes shared across species. We illustrated the application of the proposed algorithm by an analysis of human and mouse embryonic stem cell (ESC) data. The analysis uncovered conserved and divergent transcriptional modules in the ESC transcriptomes, shedding light on the understanding of fundamental and species-specific regulatory mechanisms controlling ESC development.

Consistent deregulation of gene expression between human and murine MLL rearrangement leukemias

Important biological and pathologic properties are often conserved across species. Although several mouse leukemia models have been well established, the genes deregulated in both human and murine leukemia cells have not been studied systematically. We performed a serial analysis of gene expression in both human and murine MLL-ELL or MLL-ENL leukemia cells and identified 88 genes that seemed to be significantly deregulated in both types of leukemia cells, including 57 genes not reported previously as being deregulated in MLL-associated leukemias. These changes were validated by quantitative PCR. The most up-regulated genes include several HOX genes (e.g., HOX A5, HOXA9, and HOXA10) and MEIS1, which are the typical hallmark of MLL rearrangement leukemia. The most down-regulated genes include LTF, LCN2, MMP9, S100A8, S100A9, PADI4, TGFBI, and CYBB. Notably, the up-regulated genes are enriched in gene ontology terms, such as gene expression and transcription, whereas the down-regulated genes are enriched in signal transduction and apoptosis. We showed that the CpG islands of the down-regulated genes are hypermethylated. We also showed that seven individual microRNAs (miRNA) from the mir-17-92 cluster, which are overexpressed in human MLL rearrangement leukemias, are also consistently overexpressed in mouse MLL rearrangement leukemia cells. Nineteen possible targets of these miRNAs were identified, and two of them (i.e., APP and RASSF2) were confirmed further by luciferase reporter and mutagenesis assays. The identification and validation of consistent changes of gene expression in human and murine MLL rearrangement leukemias provide important insights into the genetic base for MLL-associated leukemogenesis.

Genome adaptation to chemical stress: clues from comparative transcriptomics in Saccharomyces cerevisiae and Candida glabrata

Comparative transcriptomics of Saccharomyces cerevisiae and Candida glabrata revealed a remarkable conservation of response to drug-induced stress, despite underlying differences in the regulatory networks.

Background

Recent technical and methodological advances have placed microbial models at the forefront of evolutionary and environmental genomics. To better understand the logic of genetic network evolution, we combined comparative transcriptomics, a differential clustering algorithm and promoter analyses in a study of the evolution of transcriptional networks responding to an antifungal agent in two yeast species: the free-living model organism Saccharomyces cerevisiae and the human pathogen Candida glabrata.

Results

We found that although the gene expression patterns characterizing the response to drugs were remarkably conserved between the two species, part of the underlying regulatory networks differed. In particular, the roles of the oxidative stress response transcription factors ScYap1p (in S. cerevisiae) and Cgap1p (in C. glabrata) had diverged. The sets of genes whose benomyl response depends on these factors are significantly different. Also, the DNA motifs targeted by ScYap1p and Cgap1p are differently represented in the promoters of these genes, suggesting that the DNA binding properties of the two proteins are slightly different. Experimental assays of ScYap1p and Cgap1p activities in vivo were in accordance with this last observation.

Conclusions

Based on these results and recently published data, we suggest that the robustness of environmental stress responses among related species contrasts with the rapid evolution of regulatory sequences, and depends on both the coevolution of transcription factor binding properties and the versatility of regulatory associations within transcriptional networks.

Simplified ontologies allowing comparison of developmental mammalian gene expression

The Developmental eVOC ontologies presented are simplified orthogonal ontologies describing the temporal and spatial distribution of developmental human and mouse anatomy.

Model organisms represent an important resource for understanding the fundamental aspects of mammalian biology. Mapping of biological phenomena between model organisms is complex and if it is to be meaningful, a simplified representation can be a powerful means for comparison. The Developmental eVOC ontologies presented here are simplified orthogonal ontologies describing the temporal and spatial distribution of developmental human and mouse anatomy. We demonstrate the ontologies by identifying genes showing a bias for developmental brain expression in human and mouse.

Evidence-based annotation of the malaria parasite's genome using comparative expression profiling

A fundamental problem in systems biology and whole genome sequence analysis is how to infer functions for the many uncharacterized proteins that are identified, whether they are conserved across organisms of different phyla or are phylum-specific. This problem is especially acute in pathogens, such as malaria parasites, where genetic and biochemical investigations are likely to be more difficult. Here we perform comparative expression analysis on Plasmodium parasite life cycle data derived from P. falciparum blood, sporozoite, zygote and ookinete stages, and P. yoelii mosquito oocyst and salivary gland sporozoites, blood and liver stages and show that type II fatty acid biosynthesis genes are upregulated in liver and insect stages relative to asexual blood stages. We also show that some universally uncharacterized genes with orthologs in Plasmodium species, Saccharomyces cerevisiae and humans show coordinated transcription patterns in large collections of human and yeast expression data and that the function of the uncharacterized genes can sometimes be predicted based on the expression patterns across these diverse organisms. We also use a comprehensive and unbiased literature mining method to predict which uncharacterized parasite-specific genes are likely to have roles in processes such as gliding motility, host-cell interactions, sporozoite stage, or rhoptry function. These analyses, together with protein-protein interaction data, provide probabilistic models that predict the function of 926 uncharacterized malaria genes and also suggest that malaria parasites may provide a simple model system for the study of some human processes. These data also provide a foundation for further studies of transcriptional regulation in malaria parasites.

Biomolecular network querying: a promising approach in systems biology

The rapid accumulation of various network-related data from multiple species and conditions (e.g. disease versus normal) provides unprecedented opportunities to study the function and evolution of biological systems. Comparison of biomolecular networks between species or conditions is a promising approach to understanding the essential mechanisms used by living organisms. Computationally, the basic goal of this network comparison or 'querying' is to uncover identical or similar subnetworks by mapping the queried network (e.g. a pathway or functional module) to another network or network database. Such comparative analysis may reveal biologically or clinically important pathways or regulatory networks. In particular, we argue that user-friendly tools for network querying will greatly enhance our ability to study the fundamental properties of biomolecular networks at a system-wide level.

The Generation Challenge Programme comparative plant stress-responsive gene catalogue

The Generation Challenge Programme (GCP; www.generationcp.org) has developed an online resource documenting stress-responsive genes comparatively across plant species. This public resource is a compendium of protein families, phylogenetic trees, multiple sequence alignments (MSA) and associated experimental evidence. The central objective of this resource is to elucidate orthologous and paralogous relationships between plant genes that may be involved in response to environmental stress, mainly abiotic stresses such as water deficit (‘drought’). The web-based graphical user interface (GUI) of the resource includes query and visualization tools that allow diverse searches and browsing of the underlying project database. The web interface can be accessed at http://dayhoff.generationcp.org.

Deciphering modular and dynamic behaviors of transcriptional networks

The coordinated and dynamic modulation or interaction of genes or proteins acts as an important mechanism used by a cell in functional regulation. Recent studies have shown that many transcriptional networks exhibit a scale-free topology and hierarchical modular architecture. It has also been shown that transcriptional networks or pathways are dynamic and behave only in certain ways and controlled manners in response to disease development, changing cellular conditions, and different environmental factors. Moreover, evolutionarily conserved and divergent transcriptional modules underline fundamental and species-specific molecular mechanisms controlling disease development or cellular phenotypes. Various computational algorithms have been developed to explore transcriptional networks and modules from gene expression data. In silico studies have also been made to mimic the dynamic behavior of regulatory networks, analyzing how disease or cellular phenotypes arise from the connectivity or networks of genes and their products. Here, we review the recent development in computational biology research on deciphering modular and dynamic behaviors of transcriptional networks, highlighting important findings. We also demonstrate how these computational algorithms can be applied in systems biology studies as on disease, stem cells, and drug discovery.

Exploiting the wild crucifer Thlaspi arvense to identify conserved and novel genes expressed during a plant's response to cold stress

Thlaspi arvense, a wild species from the Brassicaceae family, was shown to have a higher level of freezing tolerance than either of its close relatives, the model plant Arabidopsis thaliana or the crop Brassica napus (canola). Over 600 clones were sequenced from a subtractive cDNA library generated from cold treated T. arvense tissue, establishing that T. arvense shared significant sequence identity with both A. thaliana and B. napus (90-92%). In light of the strong sequence similarity between T. arvense and A. thaliana and to exploit the available genomics resources for Arabidopsis, the efficacy of using long 70 mer oligonucleotide whole genome Arabidopsis microarrays was tested for T. arvense. Gene expression in T. arvense leaf tissue during the very early stages of cold acclimation (or cold stress) was assayed at three time points and compared to an untreated control. This analysis highlights some of the difficulties and benefits of using cross-species microarray analysis. The data suggested that T. arvense responds in a similar fashion to cold stress as the model plant A. thaliana. However, for a number of genes quantitative differences in the level and timing of expression were identified. One of the most notable differences suggested that sulphur assimilation leading to the increased production of the methyl donor S-adenosyl-methionine was playing a role in the response of T. arvense to cold stress.

Comparative toxicogenomic analysis of the hepatotoxic effects of TCDD in Sprague Dawley rats and C57BL/6 mice

In an effort to further characterize conserved and species-specific mechanisms of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-mediated toxicity, comparative temporal and dose-response microarray analyses were performed on hepatic tissue from immature, ovariectomized Sprague Dawley rats and C57BL/6 mice. For temporal studies, rats and mice were gavaged with 10 or 30 microg/kg of TCDD, respectively, and sacrificed after 2, 4, 8, 12, 18, 24, 72, or 168 h while dose-response studies were performed at 24 h. Hepatic gene expression profiles were monitored using custom cDNA microarrays containing 8567 (rat) or 13,361 (mouse) cDNA clones. Affymetrix data from male rats treated with 40 microg/kg TCDD were also included to expand the species comparison. In total, 3087 orthologous genes were represented in the cross-species comparison. Comparative analysis identified 33 orthologous genes that were commonly regulated by TCDD as well as 185 rat-specific and 225 mouse-specific responses. Functional annotation using Gene Ontology identified conserved gene responses associated with xenobiotic/chemical stress and amino acid and lipid metabolism. Rat-specific gene expression responses were associated with cellular growth and lipid metabolism while mouse-specific responses were associated with lipid uptake/metabolism and immune responses. The common and species-specific gene expression responses were also consistent with complementary histopathology, clinical chemistry, hepatic lipid analyses, and reports in the literature. These data expand our understanding of TCDD-mediated gene expression responses and indicate that species-specific toxicity may be mediated by differences in gene expression which may help explain the wide range of species sensitivities and will have important implications in risk assessment strategies.

GO-Diff: mining functional differentiation between EST-based transcriptomes

Background

Large-scale sequencing efforts produced millions of Expressed Sequence Tags (ESTs) collectively representing differentiated biochemical and functional states. Analysis of these EST libraries reveals differential gene expressions, and therefore EST data sets constitute valuable resources for comparative transcriptomics. To translate differentially expressed genes into a better understanding of the underlying biological phenomena, existing microarray analysis approaches usually involve the integration of gene expression with Gene Ontology (GO) databases to derive comparable functional profiles. However, methods are not available yet to process EST-derived transcription maps to enable GO-based global functional profiling for comparative transcriptomics in a high throughput manner.

Results

Here we present GO-Diff, a GO-based functional profiling approach towards high throughput EST-based gene expression analysis and comparative transcriptomics. Utilizing holistic gene expression information, the software converts EST frequencies into EST Coverage Ratios of GO Terms. The ratios are then tested for statistical significances to uncover differentially represented GO terms between the compared transcriptomes, and functional differences are thus inferred. We demonstrated the validity and the utility of this software by identifying differentially represented GO terms in three application cases: intra-species comparison; meta-analysis to test a specific hypothesis; inter-species comparison. GO-Diff findings were consistent with previous knowledge and provided new clues for further discoveries. A comprehensive test on the GO-Diff results using series of comparisons between EST libraries of human and mouse tissues showed acceptable levels of consistency: 61% for human-human; 69% for mouse-mouse; 47% for human-mouse.

Conclusion

GO-Diff is the first software integrating EST profiles with GO knowledge databases to mine functional differentiation between biological systems, e.g. tissues of the same species or the same tissue cross species. With rapid accumulation of EST resources in the public domain and expanding sequencing effort in individual laboratories, GO-Diff is useful as a screening tool before undertaking serious expression studies.

Network constrained clustering for gene microarray data

Many bioinformatics problems can be tackled from a fresh angle offered by the network perspective. Directly inspired by metabolic network structural studies, we propose an improved gene clustering approach for inferring gene signaling pathways from gene microarray data. Based on the construction of co-expression networks that consists of both significantly linear and non-linear gene associations together with controlled biological and statistical significance, our approach tends to group functionally related genes into tight clusters despite their expression dissimilarities. We illustrate our approach and compare it to the traditional clustering approaches on a yeast galactose metabolism dataset and a retinal gene expression dataset. Our approach greatly outperforms the traditional approach in rediscovering the relatively well known galactose metabolism pathway in yeast and in clustering genes of the photoreceptor differentiation pathway.

AVAILABILITY

The clustering method has been implemented in an R package "GeneNT" that is freely available from: http://www.cran.org.

Gene expression profiling of gilthead sea bream during early development and detection of stress-related genes by the application of cDNA microarray technology

Large-scale gene expression studies were performed for one of the main European aquaculture species, the gilthead sea bream Sparus auratus L. For this purpose, a cDNA microarray containing 10,176 clones from a cDNA library of mixed embryonic and larval stages was constructed. In addition to its importance for aquaculture, the taxonomic position and the relatively small genome size of sea bream makes it a prospective model for evolutionary biology and comparative genomics. However, so far, no large-scale analysis of gene expression exists for this species. In the present study, gene expression was analyzed in gilthead sea bream during early development, a significant period in the determination of quantitative traits and therefore of considerable interest for aquaculture. Synexpression groups expressed primarily early and late in development were determined and were composed of both known and novel genes. Furthermore, it was possible to identify stress response genes induced by cortisol injections using the cDNA microarray generated. The creation of gene expression profiles for sea bream by microarray hybridization will accelerate identification of candidate genes involved in multifactorial traits and certain regulatory pathways and will also contribute to a better understanding of the genetic background of fish physiology, which may help to improve aquaculture practices.

Bioinformatics approaches for cross-species liver cancer analysis based on microarray gene expression profiling

Background

The completion of the sequencing of human, mouse and rat genomes and knowledge of cross-species gene homologies enables studies of differential gene expression in animal models. These types of studies have the potential to greatly enhance our understanding of diseases such as liver cancer in humans. Genes co-expressed across multiple species are most likely to have conserved functions. We have used various bioinformatics approaches to examine microarray expression profiles from liver neoplasms that arise in albumin-SV40 transgenic rats to elucidate genes, chromosome aberrations and pathways that might be associated with human liver cancer.

Results

In this study, we first identified 2223 differentially expressed genes by comparing gene expression profiles for two control, two adenoma and two carcinoma samples using an F-test. These genes were subsequently mapped to the rat chromosomes using a novel visualization tool, the Chromosome Plot. Using the same plot, we further mapped the significant genes to orthologous chromosomal locations in human and mouse. Many genes expressed in rat 1q that are amplified in rat liver cancer map to the human chromosomes 10, 11 and 19 and to the mouse chromosomes 7, 17 and 19, which have been implicated in studies of human and mouse liver cancer. Using Comparative Genomics Microarray Analysis (CGMA), we identified regions of potential aberrations in human. Lastly, a pathway analysis was conducted to predict altered human pathways based on statistical analysis and extrapolation from the rat data. All of the identified pathways have been known to be important in the etiology of human liver cancer, including cell cycle control, cell growth and differentiation, apoptosis, transcriptional regulation, and protein metabolism.

Conclusion

The study demonstrates that the hepatic gene expression profiles from the albumin-SV40 transgenic rat model revealed genes, pathways and chromosome alterations consistent with experimental and clinical research in human liver cancer. The bioinformatics tools presented in this paper are essential for cross species extrapolation and mapping of microarray data, its analysis and interpretation.

Genomes, phylogeny, and evolutionary systems biology

With the completion of the human genome and the growing number of diverse genomes being sequenced, a new age of evolutionary research is currently taking shape. The myriad of technological breakthroughs in biology that are leading to the unification of broad scientific fields such as molecular biology, biochemistry, physics, mathematics, and computer science are now known as systems biology. Here, I present an overview, with an emphasis on eukaryotes, of how the postgenomics era is adopting comparative approaches that go beyond comparisons among model organisms to shape the nascent field of evolutionary systems biology.