Assessing gene significance from cDNA microarray expression data via mixed models

PAH-pollution effects on sensitive and resistant embryos: Integrating structure and function with gene expression

Polycyclic aromatic hydrocarbons (PAHs) are among the most widespread natural and anthropogenic pollutants, and some PAHs are proven developmental toxicants. We chemically characterized clean and heavily polluted sites and exposed fish embryos to PAH polluted sediment extracts during four critical developmental stages. Embryos were collected from Fundulus heteroclitus populations inhabiting the clean and heavily polluted Superfund estuary. Embryos of parents from the clean sites are sensitive to PAH pollutants while those of parents from the heavily polluted site are resistant. Chemical analysis of embryos suggests PAH accumulation and pollution-induced toxicity among sensitive embryos during development that ultimately kills all sensitive embryos before hatching, while remarkably, the resistant embryos develop normally. The adverse effects on sensitive embryos are manifested as developmental delays, reduced heart rates, and severe heart, liver, and kidney morphological abnormalities. Gene expression analysis of early somitogenesis, heartbeat initiation, late organogenesis, and pre-hatching developmental stages reveals genes whose expression significantly differs between sensitive and resistant embryo populations and helps to explain mechanisms of sensitivity and resistance to polluted environments during vertebrate animal development.

Genetic determinants of ammonia-induced acute lung injury in mice

In this study, a genetically diverse panel of 43 mouse strains was exposed to ammonia, and genome-wide association mapping was performed employing a single-nucleotide polymorphism (SNP) assembly. Transcriptomic analysis was used to help resolve the genetic determinants of ammonia-induced acute lung injury. The encoded proteins were prioritized based on molecular function, nonsynonymous SNP within a functional domain or SNP within the promoter region that altered expression. This integrative functional approach revealed 14 candidate genes that included Aatf, Avil, Cep162, Hrh4, Lama3, Plcb4, and Ube2cbp, which had significant SNP associations, and Aff1, Bcar3, Cntn4, Kcnq5, Prdm10, Ptcd3, and Snx19, which had suggestive SNP associations. Of these genes, Bcar3, Cep162, Hrh4, Kcnq5, and Lama3 are particularly noteworthy and had pathophysiological roles that could be associated with acute lung injury in several ways.

Integrative Analysis of Proteome and Transcriptome Dynamics during Bacillus subtilis Spore Revival

This study demonstrated the progress of macromolecular synthesis during Bacillus subtilis spore germination and outgrowth. The transcriptome analysis has additionally allowed us to trace gene expression during this transformation process. For the first time, the basic survival kit for spore-based life has been identified. In addition, in this analysis based on monitoring of protein levels in germinating and outgrowing spores, the transition from (ribo)nucleotide and amino acid biosynthesis to the restoration of all metabolic pathways can be clearly seen. The integrative multi-omics approach applied in this study thus has helped us to achieve a comprehensive overview of the molecular mechanisms at the basis of spore germination and outgrowth as well as to identify important knowledge gaps in need of further study.

Bacillus subtilis spores can reactivate their metabolism through germination upon contact with germinants and can develop into vegetative cells upon outgrowth. However, the mechanisms at the basis of the molecular machinery that triggers the spore germination and outgrowth processes are still largely unclear. To gain further insights into these processes, the transcriptome and proteome changes occurring during the conversion of spores to vegetative cells were analyzed in the present study. For each time point sampled, the changes in the spore proteome were quantitatively monitored relative to the proteome of metabolically ¹⁵N-labeled vegetative cells. Of the quantified proteins, 60% are shared by vegetative cells and spores, indicating that the spores have a minimal protein set, sufficient to resume metabolism upon completion of germination. These shared proteins thus represent the most basic “survival kit” for spore-based life. We observed no significant change in the proteome or the transcriptome until the spore’s completion of germination. Our analysis identified 34 abundant mRNA transcripts in the dormant spores, 31 of which are rapidly degraded after germination. In outgrowing spores, we identified 3,152 differentially expressed genes and have demonstrated the differential expression of 322 proteins with our mass spectrometry analyses. Our data also showed that 173 proteins from dormant spores, including both proteins unique to spores and proteins shared with vegetative cells, were lost after completion of germination. The observed diverse timings of synthesis of different protein sets in spore outgrowth revealed a putative core strategy underlying the revival of ‘life’ from the B. subtilis spore.

IMPORTANCE This study demonstrated the progress of macromolecular synthesis during Bacillus subtilis spore germination and outgrowth. The transcriptome analysis has additionally allowed us to trace gene expression during this transformation process. For the first time, the basic survival kit for spore-based life has been identified. In addition, in this analysis based on monitoring of protein levels in germinating and outgrowing spores, the transition from (ribo)nucleotide and amino acid biosynthesis to the restoration of all metabolic pathways can be clearly seen. The integrative multi-omics approach applied in this study thus has helped us to achieve a comprehensive overview of the molecular mechanisms at the basis of spore germination and outgrowth as well as to identify important knowledge gaps in need of further study.

A personalised approach for identifying disease-relevant pathways in heterogeneous diseases

Numerous time-course gene expression datasets have been generated for studying the biological dynamics that drive disease progression; and nearly as many methods have been proposed to analyse them. However, barely any method exists that can appropriately model time-course data while accounting for heterogeneity that entails many complex diseases. Most methods manage to fulfil either one of those qualities, but not both. The lack of appropriate methods hinders our capability of understanding the disease process and pursuing preventive treatments. We present a method that models time-course data in a personalised manner using Gaussian processes in order to identify differentially expressed genes (DEGs); and combines the DEG lists on a pathway-level using a permutation-based empirical hypothesis testing in order to overcome gene-level variability and inconsistencies prevalent to datasets from heterogenous diseases. Our method can be applied to study the time-course dynamics, as well as specific time-windows of heterogeneous diseases. We apply our personalised approach on three longitudinal type 1 diabetes (T1D) datasets, where the first two are used to determine perturbations taking place during early prognosis of the disease, as well as in time-windows before autoantibody positivity and T1D diagnosis; and the third is used to assess the generalisability of our method. By comparing to non-personalised methods, we demonstrate that our approach is biologically motivated and can reveal more insights into progression of heterogeneous diseases. With its robust capabilities of identifying disease-relevant pathways, our approach could be useful for predicting events in the progression of heterogeneous diseases and even for biomarker identification.

Gene expression based survival prediction for cancer patients-A topic modeling approach

Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression data, many projects use some dimensionality reduction techniques to overcome this hurdle. We introduce a novel methodology, inspired by topic modeling from the natural language domain, to derive expressive features from the high-dimensional gene expression data. There, a document is represented as a mixture over a relatively small number of topics, where each topic corresponds to a distribution over the words; here, to accommodate the heterogeneity of a patient's cancer, we represent each patient (≈ document) as a mixture over cancer-topics, where each cancer-topic is a mixture over gene expression values (≈ words). This required some extensions to the standard LDA model-e.g., to accommodate the real-valued expression values-leading to our novel discretized Latent Dirichlet Allocation (dLDA) procedure. After using this dLDA to learn these cancer-topics, we can then express each patient as a distribution over a small number of cancer-topics, then use this low-dimensional "distribution vector" as input to a learning algorithm-here, we ran the recent survival prediction algorithm, MTLR, on this representation of the cancer dataset. We initially focus on the METABRIC dataset, which describes each of n = 1,981 breast cancer patients using the r = 49,576 gene expression values, from microarrays. Our results show that our approach (dLDA followed by MTLR) provides survival estimates that are more accurate than standard models, in terms of the standard Concordance measure. We then validate this "dLDA+MTLR" approach by running it on the n = 883 Pan-kidney (KIPAN) dataset, over r = 15,529 gene expression values-here using the mRNAseq modality-and find that it again achieves excellent results. In both cases, we also show that the resulting model is calibrated, using the recent "D-calibrated" measure. These successes, in two different cancer types and expression modalities, demonstrates the generality, and the effectiveness, of this approach. The dLDA+MTLR source code is available at https://github.com/nitsanluke/GE-LDA-Survival.

Genetic constraints on microevolutionary divergence of sex-biased gene expression

The evolution of sex-specific phenotypes is an important dimension of diversification and local adaptation. The sex-dependent regulation of gene expression is considered a key genomic mechanism facilitating sex-dependent adaptation. In many species, genes with male-biased expression evolve faster in DNA sequence and expression level than genes with female-biased or sexually monomorphic expression. While positive selection may be responsible for rapid DNA sequence evolution, why expression of male-biased genes also evolves rapidly remains unclear. Beyond sex differences in selection, some aspects of the genetic architecture of gene expression could contribute to the rapid evolution of male-biased gene expression. First, male-biased genes might simply have greater standing genetic variance than female-biased genes. Second, male-biased genes could be less constrained by pleiotropy, either within or between sexes. Here, we evaluate these alternative explanations on an intraspecific scale using a series of quantitative genetic experiments conducted on natural variation in male and female gene expression in the fly Drosophila serrata Male-biased genes had significantly higher genetic variance than female-biased genes and were generally more narrowly expressed across tissues, suggesting lower within-individual pleiotropy. However, consistent with stronger constraints due to between-sex pleiotropy, their between-sex genetic correlations, r_MF, were higher than for female-biased genes and more strongly negatively associated with sex bias. Using an extensive clinal dataset, we tested whether sex differences in gene expression divergence among populations have been shaped by pleiotropy. Here too, male-biased gene divergence was more strongly associated with between-sex pleiotropy than was female-biased gene divergence. Systematic differences in genetic variance and pleiotropy may be important factors influencing sex-specific adaptation arising through changes in gene expression.This article is part of the theme issue 'Linking local adaptation with the evolution of sex differences'.

Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data

Metabolite profiles from biological samples suffer from both technical variations and subject-specific variants. To improve the quality of metabolomics data, conventional data processing methods can be employed to remove technical variations. These methods do not consider sources of subject variation as separate factors from biological factors of interest. This can be a significant issue when performing quantitative metabolomics in clinical trials or screening for a potential biomarker in early-stage disease, because changes in metabolism or a desired-metabolite signal are small compared to the total metabolite signals. As a result, inter-individual variability can interfere subsequent statistical analyses. Here, we propose an additional data processing step using linear mixed-effects modelling to readjust an individual metabolite signal prior to multivariate analyses. Published clinical metabolomics data was used to demonstrate and evaluate the proposed method. We observed a substantial reduction in variation of each metabolite signal after model fitting. A comparison with other strategies showed that our proposed method contributed to improved classification accuracy, precision, sensitivity and specificity. Moreover, we highlight the importance of patient metadata as it contains rich information of subject characteristics, which can be used to model and normalize metabolite abundances. The proposed method is available as an R package lmm2met.

Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications

Inferring networks and dynamics of genes, proteins, cells and other biological entities from high-throughput biological omics data is a central and challenging issue in computational and systems biology. This is essential for understanding the complexity of human health, disease susceptibility and pathogenesis for Predictive, Preventive, Personalized and Participatory (P4) system and precision medicine. The delineation of the possible interactions of all genes/proteins in a genome/proteome is a task for which conventional experimental techniques are ill suited. Urgently needed are rapid and inexpensive computational and statistical methods that can identify interacting candidate disease genes or drug targets out of thousands that can be further investigated or validated by experimentations. Moreover, identifying biological dynamic systems, and simultaneously estimating the important kinetic structural and functional parameters, which may not be experimentally accessible could be important directions for drug-disease-gene network studies. In this article, we present an overview and comparison of recent developments of dynamic modeling and network approaches for time-course omics data, and their applications to various biological systems, health conditions and disease statuses. Moreover, various data reduction and analytical schemes ranging from mathematical to computational to statistical methods are compared including their merits, drawbacks and limitations. The most recent software, associated web resources and other potentials for the compared methods are also presented and discussed in detail.

Power in pairs: assessing the statistical value of paired samples in tests for differential expression

Background

When genomics researchers design a high-throughput study to test for differential expression, some biological systems and research questions provide opportunities to use paired samples from subjects, and researchers can plan for a certain proportion of subjects to have paired samples. We consider the effect of this paired samples proportion on the statistical power of the study, using characteristics of both count (RNA-Seq) and continuous (microarray) expression data from a colorectal cancer study.

Results

We demonstrate that a higher proportion of subjects with paired samples yields higher statistical power, for various total numbers of samples, and for various strengths of subject-level confounding factors. In the design scenarios considered, the statistical power in a fully-paired design is substantially (and in many cases several times) greater than in an unpaired design.

Conclusions

For the many biological systems and research questions where paired samples are feasible and relevant, substantial statistical power gains can be achieved at the study design stage when genomics researchers plan on using paired samples from the largest possible proportion of subjects. Any cost savings in a study design with unpaired samples are likely accompanied by underpowered and possibly biased results.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5236-2) contains supplementary material, which is available to authorized users.

Investigation of Nrf2, AhR and ATF4 Activation in Toxicogenomic Databases

Toxicological responses to chemical insult are largely regulated by transcriptionally activated pathways that may be independent, correlated and partially or fully overlapping. Investigating the dynamics of the interactions between stress responsive transcription factors from toxicogenomic data and defining the signature of each of them is an additional step toward a system level understanding of perturbation driven mechanisms. To this end, we investigated the segregation of the genes belonging to the three following transcriptionally regulated pathways: the AhR pathway, the Nrf2 pathway and the ATF4 pathway. Toxicogenomic datasets from three projects (carcinoGENOMICS, Predict-IV and TG-GATEs) obtained in various experimental conditions (in human and rat in vitro liver and kidney models and rat in vivo, with bolus administration and with repeated doses) were combined and consolidated where overlaps between datasets existed. A bioinformatic analysis was performed to refine pathways' signatures and to create chemical activation capacity scores to classify chemicals by their potency and selectivity of activation of each pathway. With some refinement such an approach may improve chemical safety classification and allow biological read across on a pathway level.

The Effects of Age and Dose on Gene Expression and Segmental Bone Defect Repair After BMP-2 Delivery

Age is a well‐known influential factor in bone healing, with younger patients generally healing bone fractures more rapidly and suffering fewer complications compared with older patients. Yet the impact age has on the response to current bone healing treatments, such as delivery of bone morphogenetic protein 2 (BMP‐2), remains poorly characterized. It remains unclear how or if therapeutic dosing of BMP‐2 should be modified to account for age‐related differences in order to minimize potential adverse effects and consequently improve patient bone‐healing outcomes. For this study, we sought to address this issue by using a preclinical critically sized segmental bone defect model in rats to investigate age‐related differences in bone repair after delivery of BMP‐2 in a collagen sponge, the current clinical standard. Femoral defects were created in young (7‐week‐old) and adult (8‐month‐old) rats, and healing was assessed using gene expression analyses, longitudinal radiography, ex vivo micro‐computed tomography (µCT), as well as torsional testing. We found that young rats demonstrated elevated expression of genes related to osteogenesis, chondrogenesis, and matrix remodeling at the early 1‐week time point compared with adult rats. These early gene expression differences may have impacted long‐term healing as the regenerated bones of young rats exhibited higher bone mineral densities compared with those of adult rats after 12 weeks. Furthermore, the young rats demonstrated significantly more bone formation and increased mechanical strength when BMP‐2 dose was increased from 1 µg to 10 µg, a finding not observed in adult rats. Overall, these results indicate there are age‐related differences in BMP‐2‐mediated bone regeneration, including relative dose sensitivity, suggesting that age is an important consideration when implementing a BMP‐2 treatment strategy. © 2018 The Authors JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.

Mitochondrial-related consequences of heat stress exposure during bovine oocyte maturation persist in early embryo development

Hyperthermia during estrus has direct consequences on the maturing oocyte that carries over to the resultant embryo to compromise its ability to continue in development. Because early embryonic development is reliant upon maternal transcripts and other ooplasmic components, we examined impact of heat stress on bovine oocyte transcripts using microarray. Oocytes were matured at 38.5ºC for 24 h or 41.0ºC for the first 12 h of in vitro maturation; 38.5ºC thereafter. Transcriptome profile was performed on total (adenylated + deadenylated) RNA and polyadenylated mRNA populations. Heat stress exposure altered the abundance of several transcripts important for mitochondrial function. The extent to which transcript differences are coincident with functional changes was evaluated by examining reactive oxygen species, ATP content, and glutathione levels. Mitochondrial reactive oxygen species levels were increased by 6 h exposure to 41.0ºC while cytoplasmic levels were reduced compared to controls (P < 0.0001). Exposure to 41.0ºC for 12 h increased total and reduced glutathione levels in oocytes at 12 h but reduced them by 24 h (time × temperature P < 0.001). ATP content was higher in heat-stressed oocytes at 24 h (P < 0.0001). Heat-induced increases in ATP content of matured oocytes persisted in early cleavage-stage embryos (8- to 16-cell embryos; P < 0.05) but were no longer apparent in blastocysts (P > 0.05). Collectively, results indicate that direct exposure of maturing oocytes to heat stress may alter oocyte mitochondrial processes/function, which is inherited by the early embryo after fertilization.

Synthetic antimicrobial peptides delocalize membrane bound proteins thereby inducing a cell envelope stress response

BACKGROUND

Three amphipathic cationic antimicrobial peptides (AMPs) were characterized by determining their effect on Gram-positive bacteria using Bacillus subtilis strain 168 as a model organism. These peptides were TC19 and TC84, derivatives of thrombocidin-1 (TC-1), the major AMPs of human blood platelets, and Bactericidal Peptide 2 (BP2), a synthetic designer peptide based on human bactericidal permeability increasing protein (BPI).

METHODS

To elucidate the possible mode of action of the AMPs we performed a transcriptomic analysis using microarrays. Physiological analyses were performed using transmission electron microscopy (TEM), fluorescence microscopy and various B. subtilis mutants that produce essential membrane bound proteins fused to green fluorescent protein (GFP).

RESULTS

The transcriptome analysis showed that the AMPs induced a cell envelope stress response (cell membrane and cell wall). The cell membrane stress response was confirmed with the physiological observations that TC19, TC84 and BP2 perturb the membrane of B. subtilis. Using B. subtilis mutants, we established that the cell wall stress response is due to the delocalization of essential membrane bound proteins involved in cell wall synthesis. Other essential membrane proteins, involved in cell membrane synthesis and metabolism, were also delocalized due to alterations caused by the AMPs.

CONCLUSIONS

We showed that peptides TC19, TC84 and BP2 perturb the membrane causing essential proteins to delocalize, thus preventing the possible repair of the cell envelope after the initial interference with the membrane.

GENERAL SIGNIFICANCE

These AMPs show potential for eventual clinical application against Gram-positive bacterial cells and merit further application-oriented investigation.

MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform

Summary

Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments.

Availability and Implementation

MAPPI-DAT is developed in Python, using R for data analysis and MySQL as data management system. MAPPI-DAT is cross-platform and can be ran on Microsoft Windows, Linux and OS X/macOS. The source code and a Microsoft Windows executable are freely available under the permissive Apache2 open source license at https://github.com/compomics/MAPPI-DAT.

Supplementary information

Supplementary data are available at Bioinformatics online.

The identification of functional motifs in temporal gene expression analysis

The identification of transcription factor binding sites is essential to the understanding of the regulation of gene expression and the reconstruction of genetic regulatory networks. The in silico identification of cis-regulatory motifs is challenging due to sequence variability and lack of sufficient data to generate consensus motifs that are of quantitative or even qualitative predictive value. To determine functional motifs in gene expression, we propose a strategy to adopt false discovery rate (FDR) and estimate motif effects to evaluate combinatorial analysis of motif candidates and temporal gene expression data. The method decreases the number of predicted motifs, which can then be confirmed by genetic analysis. To assess the method we used simulated motif/expression data to evaluate parameters. We applied this approach to experimental data for a group of iron responsive genes in Salmonella typhimurium 14028S. The method identified known and potentially new ferric-uptake regulator (Fur) binding sites. In addition, we identified uncharacterized functional motif candidates that correlated with specific patterns of expression. A SAS code for the simulation and analysis gene expression data is available from the first author upon request.

Bayesian state space models for dynamic genetic network construction across multiple tissues

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Computational dynamic approaches for temporal omics data with applications to systems medicine

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

Caldicellulosiruptor saccharolyticus transcriptomes reveal consequences of chemical pretreatment and genetic modification of lignocellulose

Recalcitrance of plant biomass is a major barrier for commercially feasible cellulosic biofuel production. Chemical and enzymatic assays have been developed to measure recalcitrance and carbohydrate composition; however, none of these assays can directly report which polysaccharides a candidate microbe will sense during growth on these substrates. Here, we propose using the transcriptomic response of the plant biomass‐deconstructing microbe, Caldicellulosiruptor saccharolyticus, as a direct measure of how suitable a sample of plant biomass may be for fermentation based on the bioavailability of polysaccharides. Key genes were identified using the global gene response of the microbe to model plant polysaccharides and various types of unpretreated, chemically pretreated and genetically modified plant biomass. While the majority of C. saccharolyticus genes responding were similar between plant biomasses; subtle differences were discernable, most importantly between chemically pretreated or genetically modified biomass that both exhibit similar levels of solubilization by the microbe. Furthermore, the results here present a new paradigm for assessing plant–microbe interactions that can be deployed as a biological assay to report on the complexity and recalcitrance of plant biomass.

Proteome-scale Binary Interactomics in Human Cells

Because proteins are the main mediators of most cellular processes they are also prime therapeutic targets. Identifying physical links among proteins and between drugs and their protein targets is essential in order to understand the mechanisms through which both proteins themselves and the molecules they are targeted with act. Thus, there is a strong need for sensitive methods that enable mapping out these biomolecular interactions. Here we present a robust and sensitive approach to screen proteome-scale collections of proteins for binding to proteins or small molecules using the well validated MAPPIT (Mammalian Protein-Protein Interaction Trap) and MASPIT (Mammalian Small Molecule-Protein Interaction Trap) assays. Using high-density reverse transfected cell microarrays, a close to proteome-wide collection of human ORF clones can be screened for interactors at high throughput. The versatility of the platform is demonstrated through several examples. With MAPPIT, we screened a 15k ORF library for binding partners of RNF41, an E3 ubiquitin protein ligase implicated in receptor sorting, identifying known and novel interacting proteins. The potential related to the fact that MAPPIT operates in living human cells is illustrated in a screen where the protein collection is scanned for interactions with the glucocorticoid receptor (GR) in its unliganded versus dexamethasone-induced activated state. Several proteins were identified the interaction of which is modulated upon ligand binding to the GR, including a number of previously reported GR interactors. Finally, the screening technology also enables detecting small molecule target proteins, which in many drug discovery programs represents an important hurdle. We show the efficiency of MASPIT-based target profiling through screening with tamoxifen, a first-line breast cancer drug, and reversine, an investigational drug with interesting dedifferentiation and antitumor activity. In both cases, cell microarray screens yielded known and new potential drug targets highlighting the utility of the technology beyond fundamental biology.

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

Identification of molecular markers for oocyte competence in bovine cumulus cells

Cumulus cells (CCs) have an important role during oocyte growth, competence acquisition, maturation, ovulation and fertilization. In an attempt to isolate potential biomarkers for bovine in vitro fertilization, we identified genes differentially expressed in bovine CCs from oocytes with different competence statuses, through microarray analysis. The model of follicle size, in which competent cumulus-oocyte complexes (COCs) were recovered from bigger follicles (≥8.0 mm in diameter) and less competent ones from smaller follicles (1-3 mm), was used. We identified 4178 genes that were differentially expressed (P < 0.05) in the two categories of CCs. The list was further enriched, through the use of a 2.5-fold change in gene expression as a cutoff value, to include 143 up-regulated and 80 down-regulated genes in CCs of competent COCs compared to incompetent COCs. These genes were screened according to their cellular roles, most of which were related to cell cycle, DNA repair, energy metabolism, metabolism of amino acids, cell signaling, meiosis, ovulation and inflammation. Three candidate genes up-regulated (FGF11, IGFBP4, SPRY1) and three down-regulated (ARHGAP22, COL18A1 and GPC4) in CCs from COCs of big follicles (≥8.1 mm) were selected for qPCR analysis. The selected genes showed the same expression patterns by qPCR and microarray analysis. These genes may be potential genetic markers that predict oocyte competence in in vitro fertilization routines.

Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments

Data variability can be important in microarray data analysis. Thus, when clustering gene expression profiles, it could be judicious to make use of repeated data. In this paper, the problem of analysing repeated data in the model-based cluster analysis context is considered. Linear mixed models are chosen to take into account data variability and mixture of these models are considered. This leads to a large range of possible models depending on the assumptions made on both the covariance structure of the observations and the mixture model. The maximum likelihood estimation of this family of models through the EM algorithm is presented. The problem of selecting a particular mixture of linear mixed models is considered using penalized likelihood criteria. Illustrative Monte Carlo experiments are presented and an application to the clustering of gene expression profiles is detailed. All those experiments highlight the interest of linear mixed model mixtures to take into account data variability in a cluster analysis context.

Model-based or algorithm-based? Statistical evidence for diabetes and treatments using gene expression

Gene expression profiles obtained from samples of diabetic and normal rats with and without treatments can be used to identify genes that distinguish normal and diabetic individuals and also to evaluate the effectiveness of drug treatments. This study examines changes in global gene expression in rat muscle caused by streptozotocin-induced diabetes and vanadyl sulfate treatment. We explored model-based and algorithm-based methods with gene screening measures for microarray gene expression data to classify and predict individuals with high risk of diabetes. Results show that the mixed ANOVA model-based approach provides an efficient way to conduct an investigation of the inherent variability in gene expression data and to estimate the effects of experimental factors such as treatments and diseases and their interactions. The algorithm-based weighted voting and neural network classifiers show good classification performance for the diabetes and treatment groups. Although neural network performs better than weighted voting with higher classification rate, the interpretation of weighted voting is more straightforward. The study indicates that the choice of the gene selection procedure is at least as important as the choice of the classification procedure. We conclude that both mixed model-based and algorithm-based approaches provide the statistical evidence of the biological hypotheses that vanadyl sulfate treatment of diabetic animals restores gene expression patterns to normal. Although model-based and algorithm-based methods provide different strengths and perspective for the analysis of the same set of data, in general both can be considered and developed for analyzing factorial design experiments with multiple groups and factors. This study represents a major step towards the discovery of responsible genes related to diabetes and its treatment.

High-content imaging-based BAC-GFP toxicity pathway reporters to assess chemical adversity liabilities

Adaptive cellular stress responses are paramount in the healthy control of cell and tissue homeostasis and generally activated during toxicity in a chemical-specific manner. Here, we established a platform containing a panel of distinct adaptive stress response reporter cell lines based on BAC-transgenomics GFP tagging in HepG2 cells. Our current panel of eleven BAC-GFP HepG2 reporters together contains (1) upstream sensors, (2) downstream transcription factors and (3) their respective target genes, representing the oxidative stress response pathway (Keap1/Nrf2/Srxn1), the unfolded protein response in the endoplasmic reticulum (Xbp1/Atf4/BiP/Chop) and the DNA damage response (53bp1/p53/p21). Using automated confocal imaging and quantitative single-cell image analysis, we established that all reporters allowed the time-resolved, sensitive and mode-of-action-specific activation of the individual BAC-GFP reporter cell lines as defined by a panel of pathway-specific training compounds. Implementing the temporal pathway activity information increased the discrimination of training compounds. For a set of >30 hepatotoxicants, the induction of Srxn1, BiP, Chop and p21 BAC-GFP reporters correlated strongly with the transcriptional responses observed in cryopreserved primary human hepatocytes. Together, our data indicate that a phenotypic adaptive stress response profiling platform will allow a high throughput and time-resolved classification of chemical-induced stress responses, thus assisting in the future mechanism-based safety assessment of chemicals.

Transcriptional response of yellow perch to changes in ambient metal concentrations-A reciprocal field transplantation experiment

Recent local adaptation to pollution has been evidenced in several organisms inhabiting environments heavily contaminated by metals. Nevertheless, the molecular mechanisms underlying adaptation to high metal concentrations are poorly understood, especially in fishes. Yellow perch (Perca flavescens) populations from lakes in the mining area of Rouyn-Noranda (QC, Canada) have been faced with metal contamination for about 90 years. Here, we examine gene transcription patterns of fish reciprocally transplanted between a reference and a metal-contaminated lake and also fish caged in their native lake. After four weeks, 111 genes were differentially transcribed in metal-naïve fish transferred to the metal-contaminated lake, revealing a plastic response to metal exposure. Genes involved in the citric cycle and beta-oxidation pathways were under-transcribed, suggesting a potential strategy to mitigate the effects of metal stress by reducing energy turnover. However, metal-contaminated fish transplanted to the reference lake did not show any transcriptomic response, indicating a reduced plastic response capability to sudden reduction in metal concentrations. Moreover, the transcription of other genes, especially ones involved in energy metabolism, was affected by caging. Overall, our results highlight environmental stress response mechanisms in yellow perch at the transcriptomic level and support a rapid adaptive response to metal exposure through genetic assimilation.

The Detection of Metabolite-Mediated Gene Module Co-Expression Using Multivariate Linear Models

Investigating whether metabolites regulate the co-expression of a predefined gene module is one of the relevant questions posed in the integrative analysis of metabolomic and transcriptomic data. This article concerns the integrative analysis of the two high-dimensional datasets by means of multivariate models and statistical tests for the dependence between metabolites and the co-expression of a gene module. The general linear model (GLM) for correlated data that we propose models the dependence between adjusted gene expression values through a block-diagonal variance-covariance structure formed by metabolic-subset specific general variance-covariance blocks. Performance of statistical tests for the inference of conditional co-expression are evaluated through a simulation study. The proposed methodology is applied to the gene expression data of the previously characterized lipid-leukocyte module. Our results show that the GLM approach improves on a previous approach by being less prone to the detection of spurious conditional co-expression.

Confounding Factors in the Transcriptome Analysis of an In-Vivo Exposure Experiment

Confounding factors

In transcriptomics experimentation, confounding factors frequently exist alongside the intended experimental factors and can severely influence the outcome of a transcriptome analysis. Confounding factors are regularly discussed in methodological literature, but their actual, practical impact on the outcome and interpretation of transcriptomics experiments is, to our knowledge, not documented. For instance, in-vivo experimental factors; like Individual, Sample-Composition and Time-of-Day are potentially formidable confounding factors. To study these confounding factors, we designed an extensive in-vivo transcriptome experiment (n = 264) with UVR exposure of murine skin containing six consecutive samples from each individual mouse (n = 64).

Analysis Approach

Evaluation of the confounding factors: Sample-Composition, Time-of-Day, Handling-Stress, and Individual-Mouse resulted in the identification of many genes that were affected by them. These genes sometimes showed over 30-fold expression differences. The most prominent confounding factor was Sample-Composition caused by mouse-dependent skin composition differences, sampling variation and/or influx/efflux of mobile cells. Although we can only evaluate these effects for known cell type specifically expressed genes in our complex heterogeneous samples, it is clear that the observed variations also affect the cumulative expression levels of many other non-cell-type-specific genes.

ANOVA

ANOVA analysis can only attempt to neutralize the effects of the well-defined confounding factors, such as Individual-Mouse, on the experimental factors UV-Dose and Recovery-Time. Also, by definition, ANOVA only yields reproducible gene-expression differences, but we found that these differences were very small compared to the fold changes induced by the confounding factors, questioning the biological relevance of these ANOVA-detected differences. Furthermore, it turned out that many of the differentially expressed genes found by ANOVA were also present in the gene clusters associated with the confounding factors.

Conclusion

Hence our overall conclusion is that confounding factors have a major impact on the outcome of in-vivo transcriptomics experiments. Thus the set-up, analysis, and interpretation of such experiments should be approached with the utmost prudence.

Arabidopsis AtbHLH112 regulates the expression of genes involved in abiotic stress tolerance by binding to their E-box and GCG-box motifs

Plant basic helix-loop-helix (bHLH) transcription factors play essential roles in abiotic stress tolerance. However, most bHLHs have not been functionally characterized. Here, we characterized the functional role of a bHLH transcription factor from Arabidopsis, AtbHLH112, in response to abiotic stress. AtbHLH112 is a nuclear-localized protein, and its nuclear localization is induced by salt, drought and abscisic acid (ABA). In addition, AtbHLH112 serves as a transcriptional activator, with the activation domain located at its N-terminus. In addition to binding to the E-box motifs of stress-responsive genes, AtbHLH112 binds to a novel motif with the sequence 'GG[GT]CC[GT][GA][TA]C' (GCG-box). Gain- and loss-of-function analyses showed that the transcript level of AtbHLH112 is positively correlated with salt and drought tolerance. AtbHLH112 mediates stress tolerance by increasing the expression of P5CS genes and reducing the expression of P5CDH and ProDH genes to increase proline levels. AtbHLH112 also increases the expression of POD and SOD genes to improve reactive oxygen species (ROS) scavenging ability. We present a model suggesting that AtbHLH112 is a transcriptional activator that regulates the expression of genes via binding to their GCG- or E-boxes to mediate physiological responses, including proline biosynthesis and ROS scavenging pathways, to enhance stress tolerance.

Alternative signaling network activation through different insulin receptor family members caused by pro-mitogenic antidiabetic insulin analogues in human mammary epithelial cells

Introduction

Insulin analogues are designed to have improved pharmacokinetic parameters compared to regular human insulin. This provides a sustained control of blood glucose levels in diabetic patients. All novel insulin analogues are tested for their mitogenic side effects, however these assays do not take into account the molecular mode of action of different insulin analogues. Insulin analogues can bind the insulin receptor and the insulin-like growth factor 1 receptor with different affinities and consequently will activate different downstream signaling pathways.

Methods

Here we used a panel of MCF7 human breast cancer cell lines that selectively express either one of the isoforms of the INSR or the IGF1R. We applied a transcriptomics approach to assess the differential transcriptional programs activated in these cells by either insulin, IGF1 or X10 treatment.

Results

Based on the differentially expressed genes between insulin versus IGF1 and X10 treatment, we retrieved a mitogenic classifier gene set. Validation by RT-qPCR confirmed the robustness of this gene set. The translational potential of these mitogenic classifier genes was examined in primary human mammary cells and in mammary gland tissue of mice in an in vivo model. The predictive power of the classifier genes was evaluated by testing all commercial insulin analogues in the in vitro model and defined X10 and glargine as the most potent mitogenic insulin analogues.

Conclusions

We propose that these mitogenic classifier genes can be used to test the mitogenic potential of novel insulin analogues as well as other alternative molecules with an anticipated affinity for the IGF1R.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-015-0600-5) contains supplementary material, which is available to authorized users.

Genome-wide transcriptomic analysis of the effects of sub-ambient atmospheric oxygen and elevated atmospheric carbon dioxide levels on gametophytes of the moss, Physcomitrella patens

It is widely accepted that atmospheric O2 has played a key role in the development of life on Earth, as evident from the coincidence between the rise of atmospheric O2 concentrations in the Precambrian and biological evolution. Additionally, it has also been suggested that low atmospheric O2 is one of the major drivers for at least two of the five mass-extinction events in the Phanerozoic. At the molecular level, our understanding of the responses of plants to sub-ambient O2 concentrations is largely confined to studies of the responses of underground organs, e.g. roots to hypoxic conditions. Oxygen deprivation often results in elevated CO2 levels, particularly under waterlogged conditions, due to slower gas diffusion in water compared to air. In this study, changes in the transcriptome of gametophytes of the moss Physcomitrella patens arising from exposure to sub-ambient O2 of 13% (oxygen deprivation) and elevated CO2 (1500 ppmV) were examined to further our understanding of the responses of lower plants to changes in atmospheric gaseous composition. Microarray analyses revealed that the expression of a large number of genes was affected under elevated CO2 (814 genes) and sub-ambient O2 conditions (576 genes). Intriguingly, the expression of comparatively fewer numbers of genes (411 genes) was affected under a combination of both sub-ambient O2 and elevated CO2 condition (low O2-high CO2). Overall, the results point towards the effects of atmospheric changes in CO2 and O2 on transcriptional reprogramming, photosynthetic regulation, carbon metabolism, and stress responses.

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.

Gene set analysis methods use prior biological knowledge to analyze gene expression data. This prior knowledge takes the form of predefined groups of genes, linked through their biological function. Gene set analysis methods have been successfully applied in transversal studies, their results being more sensitive and interpretable than those of methods investigating genomic data one gene at a time. The time-course gene set analysis (TcGSA) introduced here is an extension of such gene set analysis to longitudinal data. This method identifies a priori defined groups of genes whose expression is not stable over time, taking into account the potential heterogeneity between patients and between genes. When biological conditions are compared, it identifies the gene sets that have different expression dynamics according to these conditions. Data from 2 studies are analyzed: data from an HIV therapeutic vaccine trial, and data from a recent study on influenza and pneumococcal vaccines. In both cases, TcGSA provided new insights compared to standard approaches thanks to an increased sensitivity compared to other approaches. Those results highlight the benefits of the TcGSA method for analyzing gene expression dynamics.

The knottin-like Blufensin family regulates genes involved in nuclear import and the secretory pathway in barley-powdery mildew interactions

Plants have evolved complex regulatory mechanisms to control a multi-layered defense response to microbial attack. Both temporal and spatial gene expression are tightly regulated in response to pathogen ingress, modulating both positive and negative control of defense. BLUFENSINs, small knottin-like peptides in barley, wheat, and rice, are highly induced by attack from fungal pathogens, in particular, the obligate biotrophic fungus, Blumeria graminis f. sp. hordei (Bgh), causal agent of barley powdery mildew. Previous research indicated that Blufensin1 (Bln1) functions as a negative regulator of basal defense mechanisms. In the current report, we show that BLN1 and BLN2 can both be secreted to the apoplast and Barley stripe mosaic virus (BSMV)-mediated overexpression of Bln2 increases susceptibility of barley to Bgh. Bimolecular fluorescence complementation (BiFC) assays signify that BLN1 and BLN2 can interact with each other, and with calmodulin. We then used BSMV-induced gene silencing to knock down Bln1, followed by Barley1 GeneChip transcriptome analysis, to identify additional host genes influenced by Bln1. Analysis of differential expression revealed a gene set enriched for those encoding proteins annotated to nuclear import and the secretory pathway, particularly Importin α1-b and Sec61 γ subunits. Further functional analysis of these two affected genes showed that when silenced, they also reduced susceptibility to Bgh. Taken together, we postulate that Bln1 is co-opted by Bgh to facilitate transport of disease-related host proteins or effectors, influencing the establishment of Bgh compatibility on its barley host.

Activation of the Nrf2 response by intrinsic hepatotoxic drugs correlates with suppression of NF-κB activation and sensitizes toward TNFα-induced cytotoxicity

Drug-induced liver injury (DILI) is an important problem both in the clinic and in the development of new safer medicines. Two pivotal adaptation and survival responses to adverse drug reactions are oxidative stress and cytokine signaling based on the activation of the transcription factors Nrf2 and NF-κB, respectively. Here, we systematically investigated Nrf2 and NF-κB signaling upon DILI-related drug exposure. Transcriptomics analyses of 90 DILI compounds in primary human hepatocytes revealed that a strong Nrf2 activation is associated with a suppression of endogenous NF-κB activity. These responses were translated into quantitative high-content live-cell imaging of induction of a selective Nrf2 target, GFP-tagged Srxn1, and the altered nuclear translocation dynamics of a subunit of NF-κB, GFP-tagged p65, upon TNFR signaling induced by TNFα using HepG2 cells. Strong activation of GFP-Srxn1 expression by DILI compounds typically correlated with suppression of NF-κB nuclear translocation, yet reversely, activation of NF-κB by TNFα did not affect the Nrf2 response. DILI compounds that provided strong Nrf2 activation, including diclofenac, carbamazepine and ketoconazole, sensitized toward TNFα-mediated cytotoxicity. This was related to an adaptive primary protective response of Nrf2, since loss of Nrf2 enhanced this cytotoxic synergy with TNFα, while KEAP1 downregulation was cytoprotective. These data indicate that both Nrf2 and NF-κB signaling may be pivotal in the regulation of DILI. We propose that the NF-κB-inhibiting effects that coincide with a strong Nrf2 stress response likely sensitize liver cells to pro-apoptotic signaling cascades induced by intrinsic cytotoxic pro-inflammatory cytokines.

The regulatory network of ThbZIP1 in response to abscisic acid treatment

Previously, a bZIP transcription factor from Tamarix hispida, ThbZIP1, was characterized: plants overexpressing ThbZIP1 displayed improved salt stress tolerance but were sensitive to abscisic acid (ABA). In the current study, we further characterized the regulatory network of ThbZIP1 and the mechanism of ABA sensitivity mediated by ThbZIP1. An ABF transcription factor from T. hispida, ThABF1, directly regulates the expression of ThbZIP1. Microarray analysis identified 1662 and 1609 genes that were respectively significantly upregulated or downregulated by ThbZIP1 when exposed to ABA. Gene ontology (GO) analysis showed that the processes including "response to stimulus," "catalytic activity," "binding function," and "metabolic process" were highly altered in ThbZIP1 expressing plants exposed to ABA. The gene expression in ThbZIP1 transformed plants were compared between exposed to ABA and salt on the genome scale. Genes differentially regulated by both salt and ABA treatment only accounted for 9.75% of total differentially regulated genes. GO analysis showed that structural molecule activity, organelle part, membrane-enclosed lumen, reproduction, and reproductive process are enhanced by ABA but inhibited by salt stress. Conversely, immune system and multi-organism process were improved by salt but inhibited by ABA. Transcription regulator activity, enzyme regulator activity, and developmental process were significantly altered by ABA but were not affected by salt stress. Our study provides insights into how ThbZIP1 mediates ABA and salt stress response at the molecular level.

Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution?

Molecular ecology has moved beyond the use of a relatively small number of markers, often noncoding, and it is now possible to use whole-genome measures of gene expression with microarrays and RNAseq (i.e. transcriptomics) to capture molecular response to environmental challenges. While transcriptome studies are shedding light on the mechanistic basis of traits as complex as personality or physiological response to catastrophic events, these approaches are still challenging because of the required technical expertise, difficulties with analysis and cost. Still, we found that in the last 10 years, 575 studies used microarrays or RNAseq in ecology. These studies broadly address three questions that reflect the progression of the field: (i) How much variation in gene expression is there and how is it structured? (ii) How do environmental stimuli affect gene expression? (iii) How does gene expression affect phenotype? We discuss technical aspects of RNAseq and microarray technology, and a framework that leverages the advantages of both. Further, we highlight future directions of research, particularly related to moving beyond correlation and the development of additional annotation resources. Measuring gene expression across an array of taxa in ecological settings promises to enrich our understanding of ecology and genome function.

A case study of normalization, missing data and variable selection methods in lipidomics

Lipidomics is an emerging field of science that holds the potential to provide a readout of biomarkers for an early detection of a disease. Our objective was to identify an efficient statistical methodology for lipidomics-especially in finding interpretable and predictive biomarkers useful for clinical practice. In two case studies, we address the need for data preprocessing for regression modeling of a binary response. These are based on a normalization step, in order to remove experimental variability, and on a multiple imputation step, to make the full use of the incompletely observed data with potentially informative missingness. Finally, by cross-validation, we compare stepwise variable selection to penalized regression models on stacked multiple imputed data sets and propose the use of a permutation test as a global test of association. Our results show that, depending on the design of the study, these data preprocessing methods modestly improve the precision of classification, and no clear winner among the variable selection methods is found. Lipidomics profiles are found to be highly important predictors in both of the two case studies.

Pathway and network approaches for identification of cancer signature markers from omics data

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.

The effector repertoire of Fusarium oxysporum determines the tomato xylem proteome composition following infection

Plant pathogens secrete small proteins, of which some are effectors that promote infection. During colonization of the tomato xylem vessels the fungus Fusarium oxysporum f.sp. lycopersici (Fol) secretes small proteins that are referred to as SIX (Secreted In Xylem) proteins. Of these, Six1 (Avr3), Six3 (Avr2), Six5, and Six6 are required for full virulence, denoting them as effectors. To investigate their activities in the plant, the xylem sap proteome of plants inoculated with Fol wild-type or either AVR2, AVR3, SIX2, SIX5, or SIX6 knockout strains was analyzed with nano-Liquid Chromatography-Mass Spectrometry (nLC-MSMS). Compared to mock-inoculated sap 12 additional plant proteins appeared while 45 proteins were no longer detectable in the xylem sap of Fol-infected plants. Of the 285 proteins found in both uninfected and infected plants the abundance of 258 proteins changed significantly following infection. The xylem sap proteome of plants infected with four Fol effector knockout strains differed significantly from plants infected with wild-type Fol, while that of the SIX2-knockout inoculated plants remained unchanged. Besides an altered abundance of a core set of 24 differentially accumulated proteins (DAPs), each of the four effector knockout strains affected specifically the abundance of a subset of DAPs. Hence, Fol effectors have both unique and shared effects on the composition of the tomato xylem sap proteome.

A multi-level biological approach to evaluate impacts of a major municipal effluent in wild St. Lawrence River yellow perch (Perca flavescens)

The development of integrated ecotoxicological approaches is of great interest in the investigation of global concerns such as impacts of municipal wastewater effluents on aquatic ecosystems. The objective of this study was to investigate the effects of a major wastewater municipal effluent on fish using a multi-level biological approach, from gene transcription and enzyme activities to histological changes. Yellow perch (Perca flavescens) were selected based on their wide distribution, their commercial and recreational importance, and the availability of a customized microarray. Yellow perch were sampled upstream of a major municipal wastewater treatment plant (WWTP) and 4 km and 10 km downstream from its point of discharge in the St. Lawrence River (Quebec, Canada). Concentrations of perfluoroalkyl substances (PFASs), polybrominated diphenyl ethers (PBDEs) and metals/trace elements in whole body homogenates were comparable to those from other industrialized regions of the world. Genomic results indicated that the transcription level of 177 genes was significantly different (p<0.024) between exposed and non-exposed fish. Among these genes, 38 were found to be differentially transcribed at both downstream sites. Impacted genes were associated with biological processes and molecular functions such as immunity, detoxification, lipid metabolism/energy homeostasis (e.g., peroxisome proliferation), and retinol metabolism suggesting impact of WWTP on these systems. Moreover, antioxidant enzyme activities were more elevated in perch collected at the 4 km site. Biomarkers of lipid metabolism, biosynthetic activity, and aerobic capacities were significantly lower (p<0.05) in fish residing near the outfall of the effluent. Histological examination of the liver indicated no differences between sites. Correlations between PFAS, PBDE, and metal/trace element tissue concentrations and markers of peroxisomal proliferation, oxidative stress, and retinoid metabolism were found at the gene and cellular levels. Present results suggest that relating transcriptomic analyses to phenotypic responses is important to better understand impacts of environmental contamination on wild fish populations.

Network-based identification of biomarkers coexpressed with multiple pathways

Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson’s correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson’s correlation networks when evaluated with MSigDB database.

Comparative transcriptomics and metabolomics in a rhesus macaque drug administration study

We describe a multi-omic approach to understanding the effects that the anti-malarial drug pyrimethamine has on immune physiology in rhesus macaques (Macaca mulatta). Whole blood and bone marrow (BM) RNA-Seq and plasma metabolome profiles (each with over 15,000 features) have been generated for five naïve individuals at up to seven timepoints before, during and after three rounds of drug administration. Linear modeling and Bayesian network analyses are both considered, alongside investigations of the impact of statistical modeling strategies on biological inference. Individual macaques were found to be a major source of variance for both omic data types, and factoring individuals into subsequent modeling increases power to detect temporal effects. A major component of the whole blood transcriptome follows the BM with a time-delay, while other components of variation are unique to each compartment. We demonstrate that pyrimethamine administration does impact both compartments throughout the experiment, but very limited perturbation of transcript or metabolite abundance was observed following each round of drug exposure. New insights into the mode of action of the drug are presented in the context of pyrimethamine's predicted effect on suppression of cell division and metabolism in the immune system.

Species and gene divergence in Littorina snails detected by array comparative genomic hybridization

Background

Array comparative genomic hybridization (aCGH) is commonly used to screen different types of genetic variation in humans and model species. Here, we performed aCGH using an oligonucleotide gene-expression array for a non-model species, the intertidal snail Littorina saxatilis. First, we tested what types of genetic variation can be detected by this method using direct re-sequencing and comparison to the Littorina genome draft. Secondly, we performed a genome-wide comparison of four closely related Littorina species: L. fabalis, L. compressa, L. arcana and L. saxatilis and of populations of L. saxatilis found in Spain, Britain and Sweden. Finally, we tested whether we could identify genetic variation underlying “Crab” and “Wave” ecotypes of L. saxatilis.

Results

We could reliably detect copy number variations, deletions and high sequence divergence (i.e. above 3%), but not single nucleotide polymorphisms. The overall hybridization pattern and number of significantly diverged genes were in close agreement with earlier phylogenetic reconstructions based on single genes. The trichotomy of L. arcana, L. compressa and L. saxatilis could not be resolved and we argue that these divergence events have occurred recently and very close in time. We found evidence for high levels of segmental duplication in the Littorina genome (10% of the transcripts represented on the array and up to 23% of the analyzed genomic fragments); duplicated genes and regions were mostly the same in all analyzed species. Finally, this method discriminated geographically distant populations of L. saxatilis, but we did not detect any significant genome divergence associated with ecotypes of L. saxatilis.

Conclusions

The present study provides new information on the sensitivity and the potential use of oligonucleotide arrays for genotyping of non-model organisms. Applying this method to Littorina species yields insights into genome evolution following the recent species radiation and supports earlier single-gene based phylogenies. Genetic differentiation of L. saxatilis ecotypes was not detected in this study, despite pronounced innate phenotypic differences. The reason may be that these differences are due to single-nucleotide polymorphisms.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-687) contains supplementary material, which is available to authorized users.