An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation)

GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns

Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.

Time-/dose- series transcriptome data analysis and traditional Chinese medicine treatment of pneumoconiosis

Pneumoconiosis' pathogenesis is still unclear and specific drugs for its treatment are lacking. Analysis of series transcriptome data often uses a single comparison method, and there are few reports on using such data to predict the treatment of pneumoconiosis with traditional Chinese medicine (TCM). Here, we proposed a new method for analyzing series transcriptomic data, series difference analysis (SDA), and applied it to pneumoconiosis. By comparison with 5 gene sets including existing pneumoconiosis-related genes and gene set functional enrichment analysis, we demonstrated that the new method was not inferior to two existing traditional analysis methods. Furthermore, based on the TCM-drug target interaction network, we predicted the TCM corresponding to the common pneumoconiosis-related genes obtained by multiple methods, and combined them with the high-frequency TCM for its treatment obtained through literature mining to form a new TCM formula for it. After feeding it to pneumoconiosis modeling mice for two months, compared with the untreated group, the coat color, mental state and tissue sections of the mice in the treated group were markedly improved, indicating that the new TCM formula has a certain efficacy. Our study provides new insights into method development for series transcriptomic data analysis and treatment of pneumoconiosis.

Information sharing in high-dimensional gene expression data for improved parameter estimation in concentration-response modelling

In toxicological concentration-response studies, a frequent goal is the determination of an 'alert concentration', i.e. the lowest concentration where a notable change in the response in comparison to the control is observed. In high-throughput gene expression experiments, e.g. based on microarray or RNA-seq technology, concentration-response profiles can be measured for thousands of genes simultaneously. One approach for determining the alert concentration is given by fitting a parametric model to the data which allows interpolation between the tested concentrations. It is well known that the quality of a model fit improves with the number of measured data points. However, adding new replicates for existing concentrations or even several replicates for new concentrations is time-consuming and expensive. Here, we propose an empirical Bayes approach to information sharing across genes, where in essence a weighted mean of the individual estimate for one specific parameter of a fitted model and the mean of all estimates of the entire set of genes is calculated as a result. Results of a controlled plasmode simulation study show that for many genes a notable improvement in terms of the mean squared error (MSE) between estimate and true underlying value of the parameter can be observed. However, for some genes, the MSE increases, and this cannot be prevented by using a more sophisticated prior distribution in the Bayesian approach.

Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach

Quantitative proteomics has matured into an established tool and longitudinal proteomics experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, and has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a Robust longitudinal Differential Expression (RolDE) approach. The methods are evaluated using over 3000 semi-simulated spike-in proteomics datasets and three large experimental datasets. In the comparisons, RolDE performs overall best; it is most tolerant to missing values, displays good reproducibility and is the top method in ranking the results in a biologically meaningful way. Furthermore, RolDE is suitable for different types of data with typically unknown patterns in longitudinal expression and can be applied by non-experienced users.

Abrogation of Rb Tumor Suppression Initiates GBM in Differentiated Astrocytes by Driving a Progenitor Cell Program

Glioblastoma (GBM) remains lethal with no effective treatments. Despite the comprehensive identification of commonly perturbed molecular pathways, little is known about the disease’s etiology, particularly in early stages. Several studies indicate that GBM is initiated in neural progenitor and/or stem cells. Here, we report that differentiated astrocytes are susceptible to GBM development when initiated by perturbation of the RB pathway, which induces a progenitor phenotype. In vitro and in vivo inactivation of Rb tumor suppression (TS) induces cortical astrocytes to proliferate rapidly, express progenitor markers, repress differentiation markers, and form self-renewing neurospheres that are susceptible to multi-lineage differentiation. This phenotype is sufficient to cause grade II astrocytomas which stochastically progress to GBM. Together with previous findings, these results demonstrate that cell susceptibility to GBM depends on the initiating driver.

Long-Lasting Defence Priming by β-Aminobutyric Acid in Tomato Is Marked by Genome-Wide Changes in DNA Methylation

Exposure of plants to stress conditions or to certain chemical elicitors can establish a primed state, whereby responses to future stress encounters are enhanced. Stress priming can be long-lasting and likely involves epigenetic regulation of stress-responsive gene expression. However, the molecular events underlying priming are not well understood. Here, we characterise epigenetic changes in tomato plants primed for pathogen resistance by treatment with β-aminobutyric acid (BABA). We used whole genome bisulphite sequencing to construct tomato methylomes from control plants and plants treated with BABA at the seedling stage, and a parallel transcriptome analysis to identify genes primed for the response to inoculation by the fungal pathogen, Botrytis cinerea. Genomes of plants treated with BABA showed a significant reduction in global cytosine methylation, especially in CHH sequence contexts. Analysis of differentially methylated regions (DMRs) revealed that CHH DMRs were almost exclusively hypomethylated and were enriched in gene promoters and in DNA transposons located in the chromosome arms. Genes overlapping CHH DMRs were enriched for a small number of stress response-related gene ontology terms. In addition, there was significant enrichment of DMRs in the promoters of genes that are differentially expressed in response to infection with B. cinerea. However, the majority of genes that demonstrated priming did not contain DMRs, and nor was the overall distribution of methylated cytosines in primed genes altered by BABA treatment. Hence, we conclude that whilst BABA treatment of tomato seedlings results in characteristic changes in genome-wide DNA methylation, CHH hypomethylation appears only to target a minority of genes showing primed responses to pathogen infection. Instead, methylation may confer priming via in-trans regulation, acting at a distance from defence genes, and/or by targeting a smaller group of regulatory genes controlling stress responses.

Rewiring of the Liver Transcriptome across Multiple Time-Scales Is Associated with the Weight Loss-Independent Resolution of NAFLD Following RYGB

Roux-en-Y gastric bypass (RYGB) surgery potently improves obesity and a myriad of obesity-associated co-morbidities including type 2 diabetes and non-alcoholic fatty liver disease (NAFLD). Time-series omics data are increasingly being utilized to provide insight into the mechanistic underpinnings that correspond to metabolic adaptations in RYGB. However, the conventional computational biology methods used to interpret these temporal multi-dimensional datasets have been generally limited to pathway enrichment analysis (PEA) of isolated pair-wise comparisons based on either experimental condition or time point, neither of which adequately capture responses to perturbations that span multiple time scales. To address this, we have developed a novel graph network-based analysis workflow designed to identify modules enriched with biomolecules that share common dynamic profiles, where the network is constructed from all known biological interactions available through the Kyoto Encyclopedia of Genes and Genomes (KEGG) resource. This methodology was applied to time-series RNAseq transcriptomics data collected on rodent liver samples following RYGB, and those of sham-operated and weight-matched control groups, to elucidate the molecular pathways involved in the improvement of as NAFLD. We report several network modules exhibiting a statistically significant enrichment of genes whose expression trends capture acute-phase as well as long term physiological responses to RYGB in a single analysis. Of note, we found the HIF1 and P53 signaling cascades to be associated with the immediate and the long-term response to RYGB, respectively. The discovery of less intuitive network modules that may have gone overlooked with conventional PEA techniques provides a framework for identifying novel drug targets for NAFLD and other metabolic syndrome co-morbidities.

Exosomal MicroRNAs as Potential Biomarkers of Hepatic Injury and Kidney Disease in Glycogen Storage Disease Type Ia Patients

Glycogen storage disease type Ia (GSDIa) is an inherited metabolic disorder caused by mutations in the enzyme glucose-6-phosphatase-α (G6Pase-α). Affected individuals develop renal and liver complications, including the development of hepatocellular adenoma/carcinoma and kidney failure. The purpose of this study was to identify potential biomarkers of the evolution of the disease in GSDIa patients. To this end, we analyzed the expression of exosomal microRNAs (Exo-miRs) in the plasma exosomes of 45 patients aged 6 to 63 years. Plasma from age-matched normal individuals were used as controls. We found that the altered expression of several Exo-miRs correlates with the pathologic state of the patients and might help to monitor the progression of the disease and the development of late GSDIa-associated complications.

Tissue-Specific Metabolic Reprogramming during Wound-Induced Organ Formation in Tomato Hypocotyl Explants

Plants have remarkable regenerative capacity, which allows them to survive tissue damage after exposure to biotic and abiotic stresses. Some of the key transcription factors and hormone crosstalk mechanisms involved in wound-induced organ regeneration have been extensively studied in the model plant Arabidopsis thaliana. However, little is known about the role of metabolism in wound-induced organ formation. Here, we performed detailed transcriptome analysis and used a targeted metabolomics approach to study de novo organ formation in tomato hypocotyl explants and found tissue-specific metabolic differences and divergent developmental pathways. Our results indicate that successful regeneration in the apical region of the hypocotyl depends on a specific metabolic switch involving the upregulation of photorespiratory pathway components and the differential regulation of photosynthesis-related gene expression and gluconeogenesis pathway activation. These findings provide a useful resource for further investigation of the molecular mechanisms involved in wound-induced organ formation in crop species such as tomato.

Virus systems biology: Proteomics profiling of dynamic protein networks during infection

The host cell proteome undergoes a variety of dynamic changes during viral infection, elicited by the virus itself or host cell defense mechanisms. Studying these changes on a global scale by integrating functional and physical interactions within protein networks during infection is an important tool to understand pathology. Indeed, proteomics studies dissecting protein signaling cascades and interaction networks upon infection showed how global information can significantly improve understanding of disease mechanisms of diverse viral infections. Here, we summarize and give examples of different experimental designs, proteomics approaches and bioinformatics analyses that allow profiling proteome changes and host-pathogen interactions to gain a molecular systems view of viral infection.

Circulating exosomal microRNAs as potential biomarkers of hepatic injury and inflammation in a murine model of glycogen storage disease type 1a

Most patients affected by glycogen storage disease type 1a (GSD1a), an inherited metabolic disorder caused by mutations in the enzyme glucose-6-phosphatase-α (G6Pase-α), develop renal and liver complications, including the development of hepatocellular adenoma/carcinoma. The purpose of this study was to identify potential biomarkers of the pathophysiology of the GSD1a-affected liver. To this end, we used the plasma exosomes of a murine model of GSD1a, the LS-G6pc ^{-/ -} mouse, to uncover the modulation in microRNA expression associated with the disease. The microRNAs differentially expressed between LS-G6pc ^-/- and wild-type mice, LS-G6pc ^-/- mice with hepatocellular adenoma and LS-G6pc ^-/- mice without adenoma, and LS-G6pc ^-/- mice with amyloidosis and LS-G6pc ^-/- mice without amyloidosis were identified. Pathway analysis demonstrated that the target genes of the differentially expressed microRNA were significantly enriched for the insulin signaling pathway, glucose and lipid metabolism, Wnt/β-catenin, telomere maintenance and hepatocellular carcinoma, and chemokine and immune regulation signaling pathways. Although some microRNAs were common to the different pathologic conditions, others were unique to the cancerous or inflammatory status of the animals. Therefore, the altered expression of several microRNAs is correlated with various pathologic liver states and might help to distinguish them during the progression of the disease and the development of late GSD1a-associated complications.

ZEITLUPE facilitates the rhythmic movements of Nicotiana attenuata flowers

Circadian organ movements are ubiquitous in plants. These rhythmic outputs are thought to be regulated by the circadian clock and auxin signalling, but the underlying mechanisms have not been clarified. Flowers of Nicotiana attenuata change their orientation during the daytime through a 140° arc to balance the need for pollinators and the protection of their reproductive organs. This rhythmic trait is under the control of the circadian clock and results from bending and re-straightening movements of the pedicel, stems that connect flowers to the inflorescence. Using an explant system that allowed pedicel growth and curvature responses to be characterized with high spatial and temporal resolution, we demonstrated that this movement is organ autonomous and mediated by auxin. Changes in the growth curvature of the pedicel are accompanied by an auxin gradient and dorsiventral asymmetry in auxin-dependent transcriptional responses; application of auxin transport inhibitors influenced the normal movements of this organ. Silencing the expression of the circadian clock component ZEITLUPE (ZTL) arrested changes in the growth curvature of the pedicel and altered auxin signalling and responses. IAA19-like, an Aux/IAA transcriptional repressor that is circadian regulated and differentially expressed between opposite tissues of the pedicel, and therefore possibly involved in the regulation of changes in organ curvature, physically interacted with ZTL. Together, these results are consistent with a direct link between the circadian clock and the auxin signalling pathway in the regulation of this rhythmic floral movement.

Likelihood contrasts: a machine learning algorithm for binary classification of longitudinal data

Machine learning methods have gained increased popularity in biomedical research during the recent years. However, very few of them support the analysis of longitudinal data, where several samples are collected from an individual over time. Additionally, most of the available longitudinal machine learning methods assume that the measurements are aligned in time, which is often not the case in real data. Here, we introduce a robust longitudinal machine learning method, named likelihood contrasts (LC), which supports study designs with unaligned time points. Our LC method is a binary classifier, which uses linear mixed models for modelling and log-likelihood for decision making. To demonstrate the benefits of our approach, we compared it with existing methods in four simulated and three real data sets. In each simulated data set, LC was the most accurate method, while the real data sets further supported the robust performance of the method. LC is also computationally efficient and easy to use.

Understanding the Molecular Mechanisms of Asthma through Transcriptomics

The transcriptome represents the complete set of RNA transcripts that are produced by the genome under a specific circumstance or in a specific cell. High-throughput methods, including microarray and bulk RNA sequencing, as well as recent advances in biostatistics based on machine learning approaches provides a quick and effective way of identifying novel genes and pathways related to asthma, which is a heterogeneous disease with diverse pathophysiological mechanisms. In this manuscript, we briefly review how to analyze transcriptome data and then provide a summary of recent transcriptome studies focusing on asthma pathogenesis and asthma drug responses. Studies reviewed here are classified into 2 classes based on the tissues utilized: blood and airway cells.

Transcriptomic Analysis Reveals Involvement of the Macrophage Migration Inhibitory Factor Gene Network in Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a progressive hereditary muscular disease with X-linked recessive inheritance, that leads patients to premature death. The loss of dystrophin determines membrane instability, causing cell damage and inflammatory response. Macrophage migration inhibitory factor (MIF) is a cytokine that exerts pleiotropic properties and is implicated in the pathogenesis of a variety of diseases. Recently, converging data from independent studies have pointed to a possible role of MIF in dystrophic muscle disorders, including DMD. In the present study, we have investigated the modulation of MIF and MIF-related genes in degenerative muscle disorders, by making use of publicly available whole-genome expression datasets. We show here a significant enrichment of MIF and related genes in muscle samples from DMD patients, as well as from patients suffering from Becker’s disease and limb-girdle muscular dystrophy type 2B. On the other hand, transcriptomic analysis of in vitro differentiated myotubes from healthy controls and DMD patients revealed no significant alteration in the expression levels of MIF-related genes. Finally, by analyzing DMD samples as a time series, we show that the modulation of the genes belonging to the MIF network is an early event in the DMD muscle and does not change with the increasing age of the patients, Overall, our analysis suggests that MIF may play a role in vivo during muscle degeneration, likely promoting inflammation and local microenvironment reaction.

Investigating protein patterns in human leukemia cell line experiments: A Bayesian approach for extremely small sample sizes

Human cancer cell line experiments are valuable for investigating drug sensitivity biomarkers. The number of biomarkers measured in these experiments is typically on the order of several thousand, whereas the number of samples is often limited to one or at most three replicates for each experimental condition. We have developed an innovative Bayesian approach that efficiently identifies clusters of proteins that exhibit similar patterns of expression. Motivated by the availability of ion mobility mass spectrometry data on cell line experiments in myelodysplastic syndrome and acute myeloid leukemia, our methodology can identify proteins that follow biologically meaningful trends of expression. Extensive simulation studies demonstrate good performance of the proposed method even in the presence of relatively small effects and sample sizes.

A Longitudinal Model of Human Neuronal Differentiation for Functional Investigation of Schizophrenia Polygenic Risk

BACKGROUND

Common psychiatric disorders are characterized by complex disease architectures with many small genetic effects that contribute and complicate biological understanding of their etiology. There is therefore a pressing need for in vitro experimental systems that allow for interrogation of polygenic psychiatric disease risk to study the underlying biological mechanisms.

METHODS

We have developed an analytical framework that integrates genome-wide disease risk from genome-wide association studies with longitudinal in vitro gene expression profiles of human neuronal differentiation.

RESULTS

We demonstrate that the cumulative impact of risk loci of specific psychiatric disorders is significantly associated with genes that are differentially expressed and upregulated during differentiation. We find the strongest evidence for schizophrenia, a finding that we replicate in an independent dataset. A longitudinal gene cluster involved in synaptic function primarily drives the association with schizophrenia risk.

CONCLUSIONS

These findings reveal that in vitro human neuronal differentiation can be used to translate the polygenic architecture of schizophrenia to biologically relevant pathways that can be modeled in an experimental system. Overall, this work emphasizes the use of longitudinal in vitro transcriptomic signatures as a cellular readout and the application to the genetics of complex traits.

Immunohistochemical determination of Ang-1, Ang-2 and Tie-2 in placentas of sows at 30, 60 and 114 days of gestation and validation through a bioinformatic approach

Angiopoietins (Ang-1, Ang-2) participate in vascular development and placental growth, both bind to Tie-2. This study aimed to determine the localization of angiopoietins in placental development of sows by immunohistochemistry and to validate the gene expression during gestation through a bioinformatic approach. Samples were collected from fifteen maternal-fetal interface from approximately 30 (n = 5), 60 (n = 5) and 114 (n = 5) days of gestation for immunohistochemistry. A bioinformatic approach was performed by re-analysis of public datasets to determine the increase or decrease of genes involved in angiogenesis during pregnancy. There was no significant statistical difference of Ang-1 during gestation, although there was a tendency to increase from mid- to term-gestation (P = 0.7680). A notable decrease of Ang-2 was observed from early- to term-pregnancy (P ≤ 0.05), consistent with the gene expression determined through bioinformatics. Furthermore, there were greater abundances of Tie-2 at both early and at term periods, but lesser abundances at mid-gestation (P ≤ 0.05). The bioinformatics approach indicated that genes related to biological processes such as angiogenesis (i.e., development and morphogenesis of blood vessels) were expressed to a greater extent in early gestation as compared with later in gestation. The Ang-1 gene expression related to cell maturation, response to hypoxia and apoptosis, however, increased as gestation period advanced. In conclusion, angiopoietins may have an important role in the vascular development thus ensuring adequate placental growth in sows. The presence of angiopoietins in the trophoblast suggests a specific role for these pro-angiogenic factors in the tissue formation at the maternal-fetal interface.

Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance

BACKGROUND

Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients' treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors.

METHODS

To determine the dynamics of these molecular changes, we obtained high throughput omics data (RNA-sequencing and DNA methylation) weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. The CoGAPS unsupervised algorithm was used to determine the dynamics of the molecular changes associated with resistance during the time course of resistance development.

RESULTS

CoGAPS was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically.

CONCLUSIONS

Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize the resistant phenotype. These genes include FGFR1, which was associated with EGFR inhibitors resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. This understanding of the time course progression of molecular changes in acquired resistance is important for the development of alternative treatment strategies that would introduce appropriate selection of new drugs to treat cancer before the resistant phenotype develops.

Analysis of time-course microarray data: Comparison of common tools

High-throughput time-series data have a special value for studying the dynamism of biological systems. However, the interpretation of such complex data can be challenging. The aim of this study was to compare common algorithms recently developed for the detection of differentially expressed genes in time-course microarray data. Using different measures such as sensitivity, specificity, predictive values, and related signaling pathways, we found that limma, timecourse, and gprege have reasonably good performance for the analysis of datasets in which only test group is followed over time. However, limma has the additional advantage of being able to report significance cut off, making it a more practical tool. In addition, limma and TTCA can be satisfactorily used for datasets with time-series data for all experimental groups. These findings may assist investigators to select appropriate tools for the detection of differentially expressed genes as an initial step in the interpretation of time-course big data.

Wounding Triggers Callus Formation via Dynamic Hormonal and Transcriptional Changes

Wounding is a primary trigger of organ regeneration, but how wound stress reactivates cell proliferation and promotes cellular reprogramming remains elusive. In this study, we combined transcriptome analysis with quantitative hormonal analysis to investigate how wounding induces callus formation in Arabidopsis (Arabidopsis thaliana). Our time course RNA-seq analysis revealed that wounding induces dynamic transcriptional changes, starting from rapid stress responses followed by the activation of metabolic processes and protein synthesis and subsequent activation of cell cycle regulators. Gene ontology analyses further uncovered that wounding modifies the expression of hormone biosynthesis and response genes, and quantitative analysis of endogenous plant hormones revealed accumulation of cytokinin prior to callus formation. Mutants defective in cytokinin synthesis and signaling display reduced efficiency in callus formation, indicating that de novo synthesis of cytokinin is critical for wound-induced callus formation. We further demonstrate that type-B ARABIDOPSIS RESPONSE REGULATOR-mediated cytokinin signaling regulates the expression of CYCLIN D3;1 (CYCD3;1) and that mutations in CYCD3;1 and its homologs CYCD3;2 and 3 cause defects in callus formation. In addition to these hormone-mediated changes, our transcriptome data uncovered that wounding activates multiple developmental regulators, and we found novel roles of ETHYLENE RESPONSE FACTOR 115 and PLETHORA3 (PLT3), PLT5, and PLT7 in callus generation. All together, these results provide novel mechanistic insights into how wounding reactivates cell proliferation during callus formation.

Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation

Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico modelling of a reconstructed gene regulatory network (GRN) with in vitro validation of the predictions, we sought to determine the mechanisms of regulation of immune responses in human primary LCs. The key role of Interferon regulatory factors (IRFs) as controllers of the human Langerhans cell response to epidermal cytokines was revealed by whole transcriptome analysis. Applying Boolean logic we assembled a Petri net-based model of the IRF-GRN which provides molecular pathway predictions for the induction of different transcriptional programmes in LCs. In silico simulations performed after model parameterisation with transcription factor expression values predicted that human LC activation of antigen-specific CD8 T cells would be differentially regulated by epidermal cytokine induction of specific IRF-controlled pathways. This was confirmed by in vitro measurement of IFN-γ production by activated T cells. As a proof of concept, this approach shows that stochastic modelling of a specific immune networks renders transcriptome data valuable for the prediction of functional outcomes of immune responses.

TTCA: an R package for the identification of differentially expressed genes in time course microarray data

BACKGROUND

The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements.

RESULTS

The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF).

CONCLUSION

Here we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1.

Identification of temporal genes involved in the mechanisms of spinal cord injury

OBJECTIVES

As a high-cost neurological disability, spinal cord injury (SCI) can result in permanent paralysis and loss of sensation. To identify the temporal genes involved in the pathogenesis of SCI, we analysed the expression profile of GSE45006.

METHODS

GSE45006 was downloaded from Gene Expression Omnibus, including 20 SCI samples (samples at 1 day, 3 days, 1 week, 2 weeks and 8 weeks after injury, four repetitions for each time point) and 4 normal samples. The Bayesian Estimation of Temporal Regulation (BETR) and randomForest packages were used to screen the temporal genes and the top 100 temporal genes, respectively. Then, the gplots package and Pearson correlation analysis separately were used to perform hot map analysis and expression pattern clustering for the top 100 temporal genes. Using the clusterProfiler package and TargetMine tool, their potential functions were analysed by enrichment analyses. Moreover, interaction relationships between these temporal genes and pathways were investigated by pathway-gene crosslinking networks.

RESULTS

In total, 1907 temporal genes were identified. The top 100 temporal genes were obtained and divided into six clusters. Most of the gene functions were enriched in biological process categories. ARG1 and NOS3 in cluster 4 were enriched in biological process of arginine catabolic process. TGFβ2, TGFβ3, ALDH2 and ALDH3A2 were correlated with numerous pathways in the pathway-gene crosslinking network. Pathways related to TGFβ2 and TGFβ3 were connected to pathways related to ARG1 and NOS3 via ARG1.

CONCLUSION

Several temporal genes, including TGFβ2, TGFβ3, ALDH2, ALDH3A2, ARG1 and NOS3, might be involved in SCI.

DynOmics to identify delays and co-expression patterns across time course experiments

Dynamic changes in biological systems can be captured by measuring molecular expression from different levels (e.g., genes and proteins) across time. Integration of such data aims to identify molecules that show similar expression changes over time; such molecules may be co-regulated and thus involved in similar biological processes. Combining data sources presents a systematic approach to study molecular behaviour. It can compensate for missing data in one source, and can reduce false positives when multiple sources highlight the same pathways. However, integrative approaches must accommodate the challenges inherent in ‘omics’ data, including high-dimensionality, noise, and timing differences in expression. As current methods for identification of co-expression cannot cope with this level of complexity, we developed a novel algorithm called DynOmics. DynOmics is based on the fast Fourier transform, from which the difference in expression initiation between trajectories can be estimated. This delay can then be used to realign the trajectories and identify those which show a high degree of correlation. Through extensive simulations, we demonstrate that DynOmics is efficient and accurate compared to existing approaches. We consider two case studies highlighting its application, identifying regulatory relationships across ‘omics’ data within an organism and for comparative gene expression analysis across organisms.

Identification of Potential Key lncRNAs and Genes Associated with Aging Based on Microarray Data of Adipocytes from Mice

Objective. This study aimed to screen potential crucial lncRNAs and genes involved in aging. Methods. The data of 9 peripheral white adipocytes, respectively, taken from male C57BL/6J mice (6 months, 14 months, and 18 months of age) in GSE25905 were used in this study. Differentially time series expressed lncRNA genes (DE-lncRNAs) and mRNA genes (DEGs) were identified. After cluster analysis of lncRNAs expression pattern, target genes of DE-lncRNAs were predicted from the DEGs, and functional analysis for target genes was conducted. Results. A total of 8301 time series-related DEGs and 43 time series-related DE-lncRNAs were identified. Among them, 41 DE-lncRNAs targeted 1880 DEGs. The DEGs positively regulated by DE-lncRNAs were mainly related to the development of blood vessel and the pathways of cholesterol biosynthesis and elastic fibre formation. Furthermore, the DEGs negatively regulated by DE-lncRNAs were correlated with protein metabolism. Conclusion. These DE-lncRNAs and DEGs are potentially involved in the process of aging.

Dynamics of the mouse brain cortical synaptic proteome during postnatal brain development

Development of the brain involves the formation and maturation of numerous synapses. This process requires prominent changes of the synaptic proteome and potentially involves thousands of different proteins at every synapse. To date the proteome analysis of synapse development has been studied sparsely. Here, we analyzed the cortical synaptic membrane proteome of juvenile postnatal days 9 (P9), P15, P21, P27, adolescent (P35) and different adult ages P70, P140 and P280 of C57Bl6/J mice. Using a quantitative proteomics workflow we quantified 1560 proteins of which 696 showed statistically significant differences over time. Synaptic proteins generally showed increased levels during maturation, whereas proteins involved in protein synthesis generally decreased in abundance. In several cases, proteins from a single functional molecular entity, e.g., subunits of the NMDA receptor, showed differences in their temporal regulation, which may reflect specific synaptic development features of connectivity, strength and plasticity. SNARE proteins, Snap 29/47 and Stx 7/8/12, showed higher expression in immature animals. Finally, we evaluated the function of Cxadr that showed high expression levels at P9 and a fast decline in expression during neuronal development. Knock down of the expression of Cxadr in cultured primary mouse neurons revealed a significant decrease in synapse density.

Time-course gene expression data on the transcriptional effects of Aminaphtone on ECV304 endothelial cells

We previously showed that Aminaphtone, a drug used in the treatment of chronic venous insufficiency, modulates several vasoactive factors, such as endothelin-1 and adhesion molecules. Here, we provide data of time-course experiments about the effects of Aminaphtone on gene expression at the genome-wide level in human endothelial cells undergoing cytokine stimulation in vitro. ECV-304 endothelial cells were incubated with interleukin-1β (IL-1β) in the presence or absence of Aminaphtone for 1, 3, and 6 h. Gene expression profiles were analyzed by microarray. This article contains complete data on the genes significantly modulated by the drug over time. The data are supplemental to our original research article reporting detailed analysis of the actions of Aminaphtone on IL-1β stimulated endothelial cells at the molecular level, "Gene expression profiling reveals novel protective effects of Aminaphtone on ECV304 endothelial cells" (Salazar et al., 2016) [1].

Natural Cubic Spline Regression Modeling Followed by Dynamic Network Reconstruction for the Identification of Radiation-Sensitivity Gene Association Networks from Time-Course Transcriptome Data

Gene expression time-course experiments allow to study the dynamics of transcriptomic changes in cells exposed to different stimuli. However, most approaches for the reconstruction of gene association networks (GANs) do not propose prior-selection approaches tailored to time-course transcriptome data. Here, we present a workflow for the identification of GANs from time-course data using prior selection of genes differentially expressed over time identified by natural cubic spline regression modeling (NCSRM). The workflow comprises three major steps: 1) the identification of differentially expressed genes from time-course expression data by employing NCSRM, 2) the use of regularized dynamic partial correlation as implemented in GeneNet to infer GANs from differentially expressed genes and 3) the identification and functional characterization of the key nodes in the reconstructed networks. The approach was applied on a time-resolved transcriptome data set of radiation-perturbed cell culture models of non-tumor cells with normal and increased radiation sensitivity. NCSRM detected significantly more genes than another commonly used method for time-course transcriptome analysis (BETR). While most genes detected with BETR were also detected with NCSRM the false-detection rate of NCSRM was low (3%). The GANs reconstructed from genes detected with NCSRM showed a better overlap with the interactome network Reactome compared to GANs derived from BETR detected genes. After exposure to 1 Gy the normal sensitive cells showed only sparse response compared to cells with increased sensitivity, which exhibited a strong response mainly of genes related to the senescence pathway. After exposure to 10 Gy the response of the normal sensitive cells was mainly associated with senescence and that of cells with increased sensitivity with apoptosis. We discuss these results in a clinical context and underline the impact of senescence-associated pathways in acute radiation response of normal cells. The workflow of this novel approach is implemented in the open-source Bioconductor R-package splineTimeR.

Gene expression profiling reveals novel protective effects of Aminaphtone on ECV304 endothelial cells

Aminaphtone, a drug used in the treatment of chronic venous insufficiency (CVI), showed a remarkable role in the modulation of several vasoactive factors, like endothelin-1 and adhesion molecules. We analysed in vitro the effects of Aminaphtone on whole-genome gene expression and production of different inflammatory proteins. ECV-304 endothelial cells were stimulated with IL-1β 100U/ml in the presence or absence of Aminaphtone 6μg/ml. Gene expression profiles were compared at 1, 3, and 6h after stimulation by microarray. Supernatants of ECV-304 cultures were analysed at 3, 6, 12, and 24h by multiplex ELISA for production of several cytokine and chemokines. Microarrays showed a significant down-regulation at all times of a wide range of inflammatory genes. Aminaphtone appeared also able to modulate the regulation of immune response process (down-regulating cytokine biosynthesis, transcripts involved in lymphocyte differentiation and cell proliferation, and cytokine-cytokine receptor interaction) and to regulate genes engaged in homeostasis, secretion, body fluid levels, response to hypoxia, cell division, and cell-to-cell communication and signalling. Results were confirmed and extended analysing the secretome, which showed significant reduction of the release of 14 cytokines and chemokines. These effects are predicted to be mediated by interaction with different transcription factors. Aminaphtone was able to modulate the expression of inflammatory molecules relevant to the pathogenesis of several conditions in which the endothelial dysfunction is the main player and early event, like scleroderma, lung fibrosis, or atherosclerosis.

Strong Correlation of Genome-Wide Expression after Traumatic Brain Injury In Vitro and In Vivo Implicates a Role for SORLA

The utility of in vitro models of traumatic brain injury (TBI) depends on their ability to recapitulate the in vivo TBI cascade. In this study, we used a genome-wide approach to compare changes in gene expression at several time points post-injury in both an in vitro model and an in vivo model of TBI. We found a total of 2073 differentially expressed genes in our in vitro model and 877 differentially expressed genes in our in vivo model when compared to noninjured controls. We found a strong correlation in gene expression changes between the two models (r = 0.69), providing confidence that the in vitro model represented at least part of the in vivo injury cascade. From these data, we searched for genes with significant changes in expression over time (analysis of covariance) and identified sorting protein-related receptor with A-type repeats (SORLA). SORLA directs amyloid precursor protein to the recycling pathway by direct binding and away from amyloid-beta producing enzymes. Mutations of SORLA have been linked to Alzheimer's disease (AD). We confirmed downregulation of SORLA expression in organotypic hippocampal slice cultures by immunohistochemistry and Western blotting and present preliminary data from human tissue that is consistent with these experimental results. Together, these data suggest that the in vitro model of TBI used in this study strongly recapitulates the in vivo TBI pathobiology and is well suited for future mechanistic or therapeutic studies. The data also suggest the possible involvement of SORLA in the post-traumatic cascade linking TBI to AD.

A Predictive Coexpression Network Identifies Novel Genes Controlling the Seed-to-Seedling Phase Transition in Arabidopsis thaliana

The transition from a quiescent dry seed to an actively growing photoautotrophic seedling is a complex and crucial trait for plant propagation. This study provides a detailed description of global gene expression in seven successive developmental stages of seedling establishment in Arabidopsis (Arabidopsis thaliana). Using the transcriptome signature from these developmental stages, we obtained a coexpression gene network that highlights interactions between known regulators of the seed-to-seedling transition and predicts the functions of uncharacterized genes in seedling establishment. The coexpressed gene data sets together with the transcriptional module indicate biological functions related to seedling establishment. Characterization of the homeodomain leucine zipper I transcription factor AtHB13, which is expressed during the seed-to-seedling transition, demonstrated that this gene regulates some of the network nodes and affects late seedling establishment. Knockout mutants for athb13 showed increased primary root length as compared with wild-type (Columbia-0) seedlings, suggesting that this transcription factor is a negative regulator of early root growth, possibly repressing cell division and/or cell elongation or the length of time that cells elongate. The signal transduction pathways present during the early phases of the seed-to-seedling transition anticipate the control of important events for a vigorous seedling, such as root growth. This study demonstrates that a gene coexpression network together with transcriptional modules can provide insights that are not derived from comparative transcript profiling alone.

Bioinformatics Analysis of microRNA Time-Course Expression in Brown Rat (Rattus norvegicus): Spinal Cord Injury Self-Repair

STUDY DESIGN

This is a bioinformatic study designed to investigate the time-course expression changes of microRNAs (miRNAs) after spinal cord injury (SCI).

OBJECTIVE

To investigate the mechanism of SCI self-repair at miRNAs level and target genes level.

SUMMARY OF BACKGROUND DATA

SCI results in loss of sensory and locomotor function, and SCI self-repair might provide clinical therapies; however, the mechanism of SCI self-repair remains unclear.

METHODS

The miRNA expression profile (GSE19890) of adult female Wistar brown rats (Rattus norvegicus) in SCI (laminectony and contusion), sham (laminectony but no contusion), and control (untreated) groups was downloaded from Gene Expression Omnibus. Totally, 35 chips were available, including five controls, five SCI-1-day, five SCI-3-day, five SCI-7-day, five sham-1-day, five sham-3-day, and five sham-7-day. Betr and limma package were used to screen time-course differentially expressed miRNAs (DEmiRNAs), followed by Bayesian hierarchical clustering (BHC), synergetic and functional enrichment analysis through BHC and cluster Profiler packages, respectively. Furthermore, STRING database and Cytoscape software were used to construct interaction networks between time-course DEmiRNAs, and GenCLip2.0 software was applied to pathway enrichment for key genes associated with nervous system.

RESULTS

Totally, 68 time-course DEmiRNAs were identified and divided into 15 BHC clusters. Then, 100 time-course DEmiRNA pairs with synergetic function were identified, and time-course DEmiRNAs and target genes interaction networks were constructed, in which 10 genes (AKT1, VEGFA, CTNNB1, IGF1, APP, PTEN, CDC42, BDNF, SOD2, and IFNG) with highest degrees were found. Furthermore, key genes were significantly enriched in neurotrophin signaling pathway.

CONCLUSION

After SCI, miRNAs might collectively regulate target genes, facilitating or inhibiting self-repair. Modulation of these miRNAs might provide novel therapies for SCI treatment.

LEVEL OF EVIDENCE

N/A.

Library preparation methodology can influence genomic and functional predictions in human microbiome research

Observations from human microbiome studies are often conflicting or inconclusive. Many factors likely contribute to these issues including small cohort sizes, sample collection, and handling and processing differences. The field of microbiome research is moving from 16S rDNA gene sequencing to a more comprehensive genomic and functional representation through whole-genome sequencing (WGS) of complete communities. Here we performed quantitative and qualitative analyses comparing WGS metagenomic data from human stool specimens using the Illumina Nextera XT and Illumina TruSeq DNA PCR-free kits, and the KAPA Biosystems Hyper Prep PCR and PCR-free systems. Significant differences in taxonomy are observed among the four different next-generation sequencing library preparations using a DNA mock community and a cell control of known concentration. We also revealed biases in error profiles, duplication rates, and loss of reads representing organisms that have a high %G+C content that can significantly impact results. As with all methods, the use of benchmarking controls has revealed critical differences among methods that impact sequencing results and later would impact study interpretation. We recommend that the community adopt PCR-free-based approaches to reduce PCR bias that affects calculations of abundance and to improve assemblies for accurate taxonomic assignment. Furthermore, the inclusion of a known-input cell spike-in control provides accurate quantitation of organisms in clinical samples.

Response of Prochlorococcus to varying CO2:O2 ratios

Carbon fixation has a central role in determining cellular redox poise, increasingly understood to be a key parameter in cyanobacterial physiology. In the cyanobacterium Prochlorococcus-the most abundant phototroph in the oligotrophic oceans-the carbon-concentrating mechanism is reduced to the bare essentials. Given the ability of Prochlorococcus populations to grow under a wide range of oxygen concentrations in the ocean, we wondered how carbon and oxygen physiology intersect in this minimal phototroph. Thus, we examined how CO2:O2 gas balance influenced growth and chlorophyll fluorescence in Prochlorococcus strain MED4. Under O2 limitation, per-cell chlorophyll fluorescence fell at all CO2 levels, but still permitted substantial growth at moderate and high CO2. Under CO2 limitation, we observed little growth at any O2 level, although per-cell chlorophyll fluorescence fell less sharply when O2 was available. We explored this pattern further by monitoring genome-wide transcription in cells shocked with acute limitation of CO2, O2 or both. O2 limitation produced much smaller transcriptional changes than the broad suppression seen under CO2 limitation and CO2/O2 co-limitation. Strikingly, both CO2 limitation conditions initially evoked a transcriptional response that resembled the pattern previously seen in high-light stress, but at later timepoints we observed O2-dependent recovery of photosynthesis-related transcripts. These results suggest that oxygen has a protective role in Prochlorococcus when carbon fixation is not a sufficient sink for light energy.

A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data

Time course ‘omics’ experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. ‘Omics’ technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course ‘omics’ experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course ‘omics’ data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course ‘omics’ studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.

Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis

Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.

Inference of Low and High-Grade Glioma Gene Regulatory Networks Delineates the Role of Rnd3 in Establishing Multiple Hallmarks of Cancer

Gliomas are a highly heterogeneous group of brain tumours that are refractory to treatment, highly invasive and pro-angiogenic. Glioblastoma patients have an average survival time of less than 15 months. Understanding the molecular basis of different grades of glioma, from well differentiated, low-grade tumours to high-grade tumours, is a key step in defining new therapeutic targets. Here we use a data-driven approach to learn the structure of gene regulatory networks from observational data and use the resulting models to formulate hypothesis on the molecular determinants of glioma stage. Remarkably, integration of available knowledge with functional genomics datasets representing clinical and pre-clinical studies reveals important properties within the regulatory circuits controlling low and high-grade glioma. Our analyses first show that low and high-grade gliomas are characterised by a switch in activity of two subsets of Rho GTPases. The first one is involved in maintaining normal glial cell function, while the second is linked to the establishment of multiple hallmarks of cancer. Next, the development and application of a novel data integration methodology reveals novel functions of RND3 in controlling glioma cell migration, invasion, proliferation, angiogenesis and clinical outcome.

Gliomas are aggressive brain tumours that are invasive, heterogeneous, refractory to treatment and show poor survival rates. Surgical resection and chemotherapy can increase patient survival but ultimately the disease is fatal. Multiple grades of glioma exist, with lower grades associated to better prognosis. While the majority of high-grade gliomas occur de novo, it is common that low-grade gliomas progress to the more aggressive form known as glioblastoma. In this article, we have shown that by combining advanced network biology approaches with the right experimental models, we are able to reveal novel regulatory circuits controlling multiple hallmarks of glioma. Through analysis of multiple network models representing protein-protein interaction or gene co-expression data we have revealed a switch in the role of regulatory Rho GTPases between low and high-grade gliomas. Amongst these, we show that RND3 is up-regulated in glioblastomas and is a key regulator of tumour proliferation, migration and invasion. We confirm that expression and genomic copy number of RND3 are predictive of clinical outcome, suggesting that changes in the activity of this particular Rho GTPase could be an early event associated to transformation and tumour expansion.

Patterns of gene expression in the sheep heart during the perinatal period revealed by transcriptomic modeling

Septa from sheep hearts at 130 days gestation, term, and 14-day-old lambs were used to model the changes in gene expression patterns during the perinatal period using Agilent 15k ovine microarrays. We used Bioconductor for R to model five major patterns of coexpressed genes. Gene ontology and transcription factor analyses using Webgestalt modeled the biological significances and transcription factors of the gene expression patterns. Modeling indicated a decreased expression of genes associated with anatomical development and differentiation during this period, whereas those associated with increased protein synthesis and growth associated with maturation of the endoplasmic reticulum rose to term but did not further increase from the near term expression. Expression of genes associated with cell responsiveness, for example, immune responses, decreased at term but expression returned by postnatal day 14. Changes in genes related to metabolism showed differential substrate-associated patterns: those related to carbohydrate metabolism rose to term and remained stable thereafter, whereas those associated with fatty acid oxidation facility rose throughout the period. The timing of many of these maturational processes was earlier in relation to birth than in the rodent. The importance of the transcription factors, estrogen-related receptors, and v-myc avian myelocytomatosis viral oncogene homolog was also highlighted in the pattern of gene expression during development of the perinatal sheep heart.

Genome-wide expression analyses of the stationary phase model of ageing in yeast

Ageing processes involved in replicative lifespan (RLS) and chronological lifespan (CLS) have been found to be conserved among many organisms, including in unicellular Eukarya such as yeast Saccharomyces cerevisiae. Here we performed an integrated approach of genome wide expression profiles of yeast at different time points, during growth and starvation. The aim of the study was to identify transcriptional changes in those conditions by using several different computational analyses in order to propose transcription factors, biological networks and metabolic pathways that seem to be relevant during the process of chronological ageing in yeast. Specifically, we performed differential gene expression analysis, gene-set enrichment analysis and network-based analysis, and we identified pathways affected in the stationary phase and specific transcription factors driving transcriptional adaptations. The results indicate signal propagation from G protein-coupled receptors through signaling pathway components and other stress and nutrient-induced transcription factors resulting in adaptation of yeast cells to the lack of nutrients by activating metabolism associated with aerobic metabolism of carbon sources such as ethanol, glycerol and fatty acids. In addition, we found STE12, XBP1 and TOS8 as highly connected nodes in the subnetworks of ageing yeast.

Genome-wide expression analysis of soybean NF-Y genes reveals potential function in development and drought response

Nuclear factor-Y (NF-Y), a heterotrimeric transcription factor, is composed of NF-YA, NF-YB and NF-YC proteins. In plants, there are usually more than 10 genes for each family and their members have been identified to be key regulators in many developmental and physiological processes controlling gametogenesis, embryogenesis, nodule development, seed development, abscisic acid (ABA) signaling, flowering time, primary root elongation, blue light responses, endoplasmic reticulum (ER) stress response and drought tolerance. Taking the advantages of the recent soybean genome draft and information on functional characterizations of nuclear factor Y (NF-Y) transcription factor family in plants, we identified 21 GmNF-YA, 32 GmNF-YB, and 15 GmNF-YC genes in the soybean (Glycine max) genome. Phylogenetic analyses show that soybean's proteins share strong homology to Arabidopsis and many of them are closely related to functionally characterized NF-Y in plants. Expression analysis in various tissues of flower, leaf, root, seeds of different developmental stages, root hairs under rhizobium inoculation, and drought-treated roots and leaves revealed that certain groups of soybean NF-Y are likely involved in specific developmental and stress responses. This study provides extensive evaluation of the soybean NF-Y family and is particularly useful for further functional characterization of GmNF-Y proteins in seed development, nodulation and drought adaptation of soybean.

RNA-Seq versus oligonucleotide array assessment of dose-dependent TCDD-elicited hepatic gene expression in mice

BACKGROUND

Dose-dependent differential gene expression provides critical information required for regulatory decision-making. The lower costs associated with RNA-Seq have made it the preferred technology for transcriptomic analysis. However, concordance between RNA-Seq and microarray analyses in dose response studies has not been adequately vetted.

RESULTS

We compared the hepatic transcriptome of C57BL/6 mice following gavage with sesame oil vehicle, 0.01, 0.03, 0.1, 0.3, 1, 3, 10, or 30 μg/kg TCDD every 4 days for 28 days using Illumina HiSeq RNA-Sequencing (RNA-Seq) and Agilent 4 × 44 K microarrays using the same normalization and analysis approach. RNA-Seq and microarray analysis identified a total of 18,063 and 16,403 genes, respectively, that were expressed in the liver. RNA-Seq analysis for differentially expressed genes (DEGs) varied dramatically depending on the P1(t) cut-off while microarray results varied more based on the fold change criteria, although responses strongly correlated. Verification by WaferGen SmartChip QRTPCR revealed that RNA-Seq had a false discovery rate of 24% compared to 54% for microarray analysis. Dose-response modeling of RNA-Seq and microarray data demonstrated similar point of departure (POD) and ED50 estimates for common DEGs.

CONCLUSIONS

There was a strong correspondence between RNA-Seq and Agilent array transcriptome profiling when using the same samples and analysis strategy. However, RNA-Seq provided superior quantitative data, identifying more genes and DEGs, as well as qualitative information regarding identity and annotation for dose response modeling in support of regulatory decision-making.

CDK6 levels regulate quiescence exit in human hematopoietic stem cells

Regulated blood production is achieved through the hierarchical organization of dormant hematopoietic stem cell (HSC) subsets that differ in self-renewal potential and division frequency, with long-term (LT)-HSCs dividing the least. The molecular mechanisms underlying this variability in HSC division kinetics are unknown. We report here that quiescence exit kinetics are differentially regulated within human HSC subsets through the expression level of CDK6. LT-HSCs lack CDK6 protein. Short-term (ST)-HSCs are also quiescent but contain high CDK6 protein levels that permit rapid cell cycle entry upon mitogenic stimulation. Enforced CDK6 expression in LT-HSCs shortens quiescence exit and confers competitive advantage without impacting function. Computational modeling suggests that this independent control of quiescence exit kinetics inherently limits LT-HSC divisions and preserves the HSC pool to ensure lifelong hematopoiesis. Thus, differential expression of CDK6 underlies heterogeneity in stem cell quiescence states that functionally regulates this highly regenerative system.

Transcriptomics of the late gestation ovine fetal brain: modeling the co-expression of immune marker genes

Background

Major changes in gene expression occur in the fetal brain to modulate the function of this organ postnatally. Thus, factors can alter the genomics of the fetal brain, predisposing to neurological disorders later in life. We hypothesized that the physiological dynamics of the immune system transcriptome of the fetal brain during the last stage of gestation will reveal patterns of immune function and development in the developing brain. In this study we applied weighted gene co-expression analysis of microarrays performed on ovine fetal brain samples, to model the changes in gene expression throughout the second half of gestation.

Results

Clusters of co-expressed genes that strongly increase in expression toward the first day of extra-uterine life are related to the hematopoietic lineage, while activation of immune pathways is induced after birth. Moreover, the pattern of gene expression suggests induction of tolerance mechanisms, probably necessary to protect highly produced proteins –such as myelin basic protein- from an autoimmune attack.

Conclusions

This study provides insight into the dramatic changes in gene expression that take place in the brain during the fetal life, especially during the last stage of gestation, and suggests that the immune system may have an important role in maturation of the fetal brain, which if disrupted or altered, could have negative consequences in postnatal life.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1001) contains supplementary material, which is available to authorized users.

miRNA Temporal Analyzer (mirnaTA): a bioinformatics tool for identifying differentially expressed microRNAs in temporal studies using normal quantile transformation

Background

Understanding the biological roles of microRNAs (miRNAs) is a an active area of research that has produced a surge of publications in PubMed, particularly in cancer research. Along with this increasing interest, many open-source bioinformatics tools to identify existing and/or discover novel miRNAs in next-generation sequencing (NGS) reads become available. While miRNA identification and discovery tools are significantly improved, the development of miRNA differential expression analysis tools, especially in temporal studies, remains substantially challenging. Further, the installation of currently available software is non-trivial and steps of testing with example datasets, trying with one’s own dataset, and interpreting the results require notable expertise and time. Subsequently, there is a strong need for a tool that allows scientists to normalize raw data, perform statistical analyses, and provide intuitive results without having to invest significant efforts.

Findings

We have developed miRNA Temporal Analyzer (mirnaTA), a bioinformatics package to identify differentially expressed miRNAs in temporal studies. mirnaTA is written in Perl and R (Version 2.13.0 or later) and can be run across multiple platforms, such as Linux, Mac and Windows. In the current version, mirnaTA requires users to provide a simple, tab-delimited, matrix file containing miRNA name and count data from a minimum of two to a maximum of 20 time points and three replicates. To recalibrate data and remove technical variability, raw data is normalized using Normal Quantile Transformation (NQT), and linear regression model is used to locate any miRNAs which are differentially expressed in a linear pattern. Subsequently, remaining miRNAs which do not fit a linear model are further analyzed in two different non-linear methods 1) cumulative distribution function (CDF) or 2) analysis of variances (ANOVA). After both linear and non-linear analyses are completed, statistically significant miRNAs (P < 0.05) are plotted as heat maps using hierarchical cluster analysis and Euclidean distance matrix computation methods.

Conclusions

mirnaTA is an open-source, bioinformatics tool to aid scientists in identifying differentially expressed miRNAs which could be further mined for biological significance. It is expected to provide researchers with a means of interpreting raw data to statistical summaries in a fast and intuitive manner.