Significance analysis of microarray transcript levels in time series experiments

Role of the Extracytoplasmic Function Sigma Factor SigE in the Stringent Response of Mycobacterium tuberculosis

Bacteria respond to nutrient starvation implementing the stringent response, a stress signaling system resulting in metabolic remodeling leading to decreased growth rate and energy requirements. A well-characterized model of stringent response in Mycobacterium tuberculosis is the one induced by growth in low phosphate. The extracytoplasmic function (ECF) sigma factor SigE was previously suggested as having a key role in the activation of stringent response. In this study, we challenge this hypothesis by analyzing the temporal dynamics of the transcriptional response of a sigE mutant and its wild-type parental strain to low phosphate using RNA sequencing. We found that both strains responded to low phosphate with a typical stringent response trait, including the downregulation of genes encoding ribosomal proteins and RNA polymerase. We also observed transcriptional changes that support the occurring of an energetics imbalance, compensated by a reduced activity of the electron transport chain, decreased export of protons, and a remodeling of central metabolism. The most striking difference between the two strains was the induction in the sigE mutant of several stress-related genes, in particular, the genes encoding the ECF sigma factor SigH and the transcriptional regulator WhiB6. Since both proteins respond to redox unbalances, their induction suggests that the sigE mutant is not able to maintain redox homeostasis in response to the energetics imbalance induced by low phosphate. In conclusion, our data suggest that SigE is not directly involved in initiating stringent response but in protecting the cell from stress consequent to the low phosphate exposure and activation of stringent response. IMPORTANCE Mycobacterium tuberculosis can enter a dormant state enabling it to establish latent infections and to become tolerant to antibacterial drugs. Dormant bacteria's physiology and the mechanism(s) used by bacteria to enter dormancy during infection are still unknown due to the lack of reliable animal models. However, several in vitro models, mimicking conditions encountered during infection, can reproduce different aspects of dormancy (growth arrest, metabolic slowdown, drug tolerance). The stringent response, a stress response program enabling bacteria to cope with nutrient starvation, is one of them. In this study, we provide evidence suggesting that the sigma factor SigE is not directly involved in the activation of stringent response as previously hypothesized, but it is important to help the bacteria to handle the metabolic stress related to the adaptation to low phosphate and activation of stringent response, thus giving an important contribution to our understanding of the mechanism behind stringent response development.

Myogenin controls via AKAP6 non-centrosomal microtubule-organizing center formation at the nuclear envelope

Non-centrosomal microtubule-organizing centers (MTOCs) are pivotal for the function of multiple cell types, but the processes initiating their formation are unknown. Here, we find that the transcription factor myogenin is required in murine myoblasts for the localization of MTOC proteins to the nuclear envelope. Moreover, myogenin is sufficient in fibroblasts for nuclear envelope MTOC (NE-MTOC) formation and centrosome attenuation. Bioinformatics combined with loss- and gain-of-function experiments identified induction of AKAP6 expression as one central mechanism for myogenin-mediated NE-MTOC formation. Promoter studies indicate that myogenin preferentially induces the transcription of muscle- and NE-MTOC-specific isoforms of Akap6 and Syne1, which encodes nesprin-1α, the NE-MTOC anchor protein in muscle cells. Overexpression of AKAP6β and nesprin-1α was sufficient to recruit endogenous MTOC proteins to the nuclear envelope of myoblasts in the absence of myogenin. Taken together, our results illuminate how mammals transcriptionally control the switch from a centrosomal MTOC to an NE-MTOC and identify AKAP6 as a novel NE-MTOC component in muscle cells.

Temporal Transcriptome Analysis Reveals Dynamic Gene Expression Patterns Driving β-Cell Maturation

Newly differentiated pancreatic β cells lack proper insulin secretion profiles of mature functional β cells. The global gene expression differences between paired immature and mature β cells have been studied, but the dynamics of transcriptional events, correlating with temporal development of glucose-stimulated insulin secretion (GSIS), remain to be fully defined. This aspect is important to identify which genes and pathways are necessary for β-cell development or for maturation, as defective insulin secretion is linked with diseases such as diabetes. In this study, we assayed through RNA sequencing the global gene expression across six β-cell developmental stages in mice, spanning from β-cell progenitor to mature β cells. A computational pipeline then selected genes differentially expressed with respect to progenitors and clustered them into groups with distinct temporal patterns associated with biological functions and pathways. These patterns were finally correlated with experimental GSIS, calcium influx, and insulin granule formation data. Gene expression temporal profiling revealed the timing of important biological processes across β-cell maturation, such as the deregulation of β-cell developmental pathways and the activation of molecular machineries for vesicle biosynthesis and transport, signal transduction of transmembrane receptors, and glucose-induced Ca²⁺ influx, which were established over a week before β-cell maturation completes. In particular, β cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal β cells displayed high basal insulin secretion, which decreased to the low levels found in mature β cells only a week later. Different genes associated with calcium-mediated processes, whose alterations are linked with insulin resistance and deregulation of glucose homeostasis, showed increased expression across β-cell stages, in accordance with the temporal acquisition of proper GSIS. Our temporal gene expression pattern analysis provided a comprehensive database of the underlying molecular components and biological mechanisms driving β-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional β cells from human embryonic stem/induced pluripotent cell for transplantation-based type 1 diabetes therapy.

Elastic net-based prediction of IFN-β treatment response of patients with multiple sclerosis using time series microarray gene expression profiles

INF-β has been widely used to treat patients with multiple sclerosis (MS) in relapse. Accurate prediction of treatment response is important for effective personalization of treatment. Microarray data have been frequently used to discover new genes and to predict treatment responses. However, conventional analytical methods suffer from three difficulties: high-dimensionality of datasets; high degree of multi-collinearity; and achieving gene identification in time-course data. The use of Elastic net, a sparse modelling method, would decrease the first two issues; however, Elastic net is currently unable to solve these three issues simultaneously. Here, we improved Elastic net to accommodate time-course data analyses. Numerical experiments were conducted using two time-course microarray datasets derived from peripheral blood mononuclear cells collected from patients with MS. The proposed methods successfully identified genes showing a high predictive ability for INF-β treatment response. Bootstrap sampling resulted in an 81% and 78% accuracy for each dataset, which was significantly higher than the 71% and 73% accuracy obtained using conventional methods. Our methods selected genes showing consistent differentiation throughout all time-courses. These genes are expected to provide new predictive biomarkers that can influence INF-β treatment for MS patients.

Mycobacterium tuberculosis-induced miR-155 subverts autophagy by targeting ATG3 in human dendritic cells

Autophagy is a primordial eukaryotic pathway, which provides the immune system with multiple mechanisms for the elimination of invading pathogens including Mycobacterium tuberculosis (Mtb). As a consequence, Mtb has evolved different strategies to hijack the autophagy process. Given the crucial role of human primary dendritic cells (DC) in host immunity control, we characterized Mtb-DC interplay by studying the contribution of cellular microRNAs (miRNAs) in the post-transcriptional regulation of autophagy related genes. From the expression profile of de-regulated miRNAs obtained in Mtb-infected human DC, we identified 7 miRNAs whose expression was previously found to be altered in specimens of TB patients. Among them, gene ontology analysis showed that miR-155, miR-155* and miR-146a target mRNAs with a significant enrichment in biological processes linked to autophagy. Interestingly, miR-155 was significantly stimulated by live and virulent Mtb and enriched in polysome-associated RNA fraction, where actively translated mRNAs reside. The putative pair interaction among the E2 conjugating enzyme involved in LC3-lipidation and autophagosome formation-ATG3-and miR-155 arose by target prediction analysis, was confirmed by both luciferase reporter assay and Atg3 immunoblotting analysis of miR-155-transfected DC, which showed also a consistent Atg3 protein and LC3 lipidated form reduction. Late in infection, when miR-155 expression peaked, both the level of Atg3 and the number of LC3 puncta per cell (autophagosomes) decreased dramatically. In accordance, miR-155 silencing rescued autophagosome number in Mtb infected DC and enhanced autolysosome fusion, thereby supporting a previously unidentified role of the miR-155 as inhibitor of ATG3 expression. Taken together, our findings suggest how Mtb can manipulate cellular miRNA expression to regulate Atg3 for its own survival, and highlight the importance to develop novel therapeutic strategies against tuberculosis that would boost autophagy.

Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens in human history and remains the second leading cause of death from an infectious agent worldwide. The major reason of Mtb success relies on its ability to evade host immunity. Autophagy, a cellular mechanism involved in intracellular pathogen elimination, is one of the pathways hijacked by Mtb to elude the control of dendritic cells (DC), major cellular effectors of immune response. Recently, it has become clear that Mtb infection not only alters cellular gene expression, but also controls the level of small RNA molecules, namely microRNAs (miRNAs), which function as negative regulators of mRNA translation into protein. In the present study, we observed that the infection of human DC with Mtb leads to a strong induction of host miR-155, a critical regulator of host immune response. By mean of miR-155 induction, Mtb reduces Atg3 protein content, a crucial enzyme needed for the initial phase of the autophagic process. Interestingly, miR-155 silencing during Mtb infection restores Atg3 level and rescues autophagy. These findings contribute to better elucidate Mtb-triggered escape mechanisms and highlight the importance to develop host-directed therapies to combat tuberculosis based on autophagy boosting.

Comparative transcriptional analysis of hop responses to infection with Verticillium nonalfalfae

KEY MESSAGE

Dynamic transcriptome profiling revealed excessive, yet ineffective, immune response to V. nonalfalfae infection in susceptible hop, global gene downregulation in shoots of resistant hop and only a few infection-associated genes in roots. Hop (Humulus lupulus L.) production is hampered by Verticillium wilt, a disease predominantly caused by the soil-borne fungus Verticillium nonalfalfae. Only a few hop cultivars exhibit resistance towards it and mechanisms of this resistance have not been discovered. In this study, we compared global transcriptional responses in roots and shoots of resistant and susceptible hop plants infected by a lethal strain of V. nonalfalfae. Time-series differential gene expression profiles between infected and mock inoculated plants were determined and subjected to network-based analysis of functional enrichment. In the resistant hop cultivar, a remarkably low number of genes were differentially expressed in roots in response to V. nonalfalfae infection, while the majority of differentially expressed genes were down-regulated in shoots. The most significantly affected genes were related to cutin biosynthesis, cell wall biogenesis, lateral root development and terpenoid biosynthesis. On the other hand, susceptible hop exhibited a strong defence response in shoots and roots, including increased expression of genes associated with plant responses, such as innate immunity, wounding, jasmonic acid pathway and chitinase activity. Strong induction of defence-associated genes in susceptible hop and a low number of infection-responsive genes in the roots of resistant hop are consistent with previous findings, confirming the pattern of excessive response of the susceptible cultivar, which ultimately fails to protect the plant from V. nonalfalfae. This research offers a multifaceted overview of transcriptional responses of susceptible and resistant hop cultivars to V. nonalfalfae infection and represents a valuable resource in the study of this plant-pathogen interaction.

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.

FunPat: function-based pattern analysis on RNA-seq time series data

BACKGROUND

Dynamic expression data, nowadays obtained using high-throughput RNA sequencing, are essential to monitor transient gene expression changes and to study the dynamics of their transcriptional activity in the cell or response to stimuli. Several methods for data selection, clustering and functional analysis are available; however, these steps are usually performed independently, without exploiting and integrating the information derived from each step of the analysis.

METHODS

Here we present FunPat, an R package for time series RNA sequencing data that integrates gene selection, clustering and functional annotation into a single framework. FunPat exploits functional annotations by performing for each functional term, e.g. a Gene Ontology term, an integrated selection-clustering analysis to select differentially expressed genes that share, besides annotation, a common dynamic expression profile.

RESULTS

FunPat performance was assessed on both simulated and real data. With respect to a stand-alone selection step, the integration of the clustering step is able to improve the recall without altering the false discovery rate. FunPat also shows high precision and recall in detecting the correct temporal expression patterns; in particular, the recall is significantly higher than hierarchical, k-means and a model-based clustering approach specifically designed for RNA sequencing data. Moreover, when biological replicates are missing, FunPat is able to provide reproducible lists of significant genes. The application to real time series expression data shows the ability of FunPat to select differentially expressed genes with high reproducibility, indirectly confirming high precision and recall in gene selection. Moreover, the expression patterns obtained as output allow an easy interpretation of the results.

CONCLUSIONS

A novel analysis pipeline was developed to search the main temporal patterns in classes of genes similarly annotated, improving the sensitivity of gene selection by integrating the statistical evidence of differential expression with the information on temporal profiles and the functional annotations. Significant genes are associated to both the most informative functional terms, avoiding redundancy of information, and the most representative temporal patterns, thus improving the readability of the results. FunPat package is provided in R/Bioconductor at link: http://sysbiobig.dei.unipd.it/?q=node/79.

Modulation of primary cell function of host Pseudomonas bacteria by the conjugative plasmid pCAR1

The impacts of plasmid carriage on the host cell were comprehensively analysed using the conjugative plasmid pCAR1 in three different Pseudomonas hosts, P. putida KT2440, P. aeruginosa PAO1 and P. fluorescens Pf0-1. Plasmid carriage reduced host fitness, swimming motility, and resistance to osmotic or pH stress. Plasmid carriage brought about alterations in primary metabolic capacities in the TCA cycle of the hosts. Differentially transcribed genes in the three hosts associated with plasmid carriage were identified by growth phase-dependent transcriptome analyses. Plasmid carriage commonly showed a greater effect on the host transcriptome at the transition and early stationary phases. The transcriptome alterations were similar between KT2440 and PAO1. Transcriptions of numbers of genes encoding ribosomal proteins, F-type ATPase, and RNAP core in both strains were not suppressed enough in the early stationary phase by plasmid carriage. These responses may have been responsible for the reduction in host fitness, motility and stress resistances. Host-specific responses to plasmid carriage were transcriptional changes of genes on putative prophage or foreign DNA regions. The extents of the impacts on host phenotypes and transcriptomes were similarly greatest in KT2440 and lowest in Pf0-1. These findings suggest that host cell function was actively regulated by plasmid carriage.

Leucine modulates dynamic phosphorylation events in insulin signaling pathway and enhances insulin-dependent glycogen synthesis in human skeletal muscle cells

BACKGROUND

Branched-chain amino acids, especially leucine, are known to interact with insulin signaling pathway and glucose metabolism. However, the mechanism by which this is exerted, remain to be clearly defined. In order to examine the effect of leucine on muscle insulin signaling, a set of experiments was carried out to quantitate phosphorylation events along the insulin signaling pathway in human skeletal muscle cell cultures. Cells were exposed to insulin, leucine or both, and phosphorylation events of key insulin signaling molecules were tracked over time so as to monitor time-related responses that characterize the signaling events and could be missed by a single sampling strategy limited to pre/post stimulus events.

RESULTS

Leucine is shown to increase the magnitude of insulin-dependent phosphorylation of protein kinase B (AKT) at Ser473 and glycogen synthase kinase (GSK3β) at Ser21-9. Glycogen synthesis follows the same pattern of GSK3β, with a significant increase at 100 μM leucine plus insulin stimulus. Moreover, data do not show any statistically significant increase of pGSK3β and glycogen synthesis at higher leucine concentrations. Leucine is also shown to increase the magnitude of insulin-mediated extracellularly regulated kinase (ERK) phosphorylation; however, differently from AKT and GSK3β, ERK shows a transient behavior, with an early peak response, followed by a return to the baseline condition.

CONCLUSIONS

These experiments demonstrate a complementary effect of leucine on insulin signaling in a human skeletal muscle cell culture, promoting insulin-activated GSK3β phosphorylation and glycogen synthesis.

Reducing bias in RNA sequencing data: a novel approach to compute counts

Background

In the last decade, Next-Generation Sequencing technologies have been extensively applied to quantitative transcriptomics, making RNA sequencing a valuable alternative to microarrays for measuring and comparing gene transcription levels. Although several methods have been proposed to provide an unbiased estimate of transcript abundances through data normalization, all of them are based on an initial count of the total number of reads mapping on each transcript. This procedure, in principle robust to random noise, is actually error-prone if reads are not uniformly distributed along sequences, as happens indeed due to sequencing errors and ambiguity in read mapping.

Here we propose a new approach, called maxcounts, to quantify the expression assigned to an exon as the maximum of its per-base counts, and we assess its performance in comparison with the standard approach described above, which considers the total number of reads aligned to an exon. The two measures are compared using multiple data sets and considering several evaluation criteria: independence from gene-specific covariates, such as exon length and GC-content, accuracy and precision in the quantification of true concentrations and robustness of measurements to variations of alignments quality.

Results

Both measures show high accuracy and low dependency on GC-content. However, maxcounts expression quantification is less biased towards long exons with respect to the standard approach. Moreover, it shows lower technical variability at low expressions and is more robust to variations in the quality of alignments.

Conclusions

In summary, we confirm that counts computed with the standard approach depend on the length of the feature they are summarized on, and are sensitive to the non-uniform distribution of reads along transcripts. On the opposite, maxcounts are robust to biases due to the non-uniformity distribution of reads and are characterized by a lower technical variability. Hence, we propose maxcounts as an alternative approach for quantitative RNA-sequencing applications.

TGFβ1-induced Baf60c regulates both smooth muscle cell commitment and quiescence

Smooth muscle cells (SMCs) play critical roles in a number of diseases; however, the molecular mechanism underlying their development is unclear. Although the role of TGFβ1 signaling in SMC development is well established, the downstream molecular signals are not fully understood. We used several rat multipotent adult progenitor cell ((r)MAPC) lines that express levels of Oct4 mRNA similar to hypoblast stem cells (HypoSC), and can differentiate robustly to mesodermal and endodermal cell types. TGFβ1 alone, or with PDGF-BB, induces differentiation of rMAPCs to SMCs, which expressed structural SMC proteins, including α-smooth muscle actin (αSMA), and contribute to the SMC coat of blood vessels in vivo. A genome-wide time-course transcriptome analysis revealed that transcripts of Baf60c, part of the SWI/SNF actin binding chromatin remodeling complex D-3 (SMARCD3/BAF60c), were significantly induced during MAPC-SMC differentiation. We demonstrated that BAF60c is a necessary co-regulator of TGFβ1 mediated induction of SMC genes. Knock-down of Baf60c decreased SMC gene expression in rMAPCs whereas ectopic expression of Baf60c was sufficient to commit rMAPCs to SMCs in the absence of exogenous cytokines. TGFβ1 activates Baf60c via the direct binding of SMAD2/3 complexes to the Baf60c promoter region. Chromatin- and co-immunoprecipitation studies demonstrated that regulation of SMC genes by BAF60c is mediated via interaction with SRF binding CArG box-containing promoter elements in SMC genes. We noted that compared with TGFβ1, Baf60c overexpression in rMAPC yielded SMC with a more immature phenotype. Similarly, Baf60c induced an immature phenotype in rat aortic SMCs marked by increased cell proliferation and decreased contractile marker expression. Thus, Baf60c is important for TGFβ-mediated commitment of primitive stem cells (rMAPCs) to SMCs and is associated with induction of a proliferative state of quiescent SMCs. The MAPC-SMC differentiation system may be useful for identification of additional critical (co-)regulators of SMC development.

Supporting regenerative medicine by integrative dimensionality reduction

OBJECTIVE

The assessment of the developmental potential of stem cells is a crucial step towards their clinical application in regenerative medicine. It has been demonstrated that genome-wide expression profiles can predict the cellular differentiation stage by means of dimensionality reduction methods. Here we show that these techniques can be further strengthened to support decision making with i) a novel strategy for gene selection; ii) methods for combining the evidence from multiple data sets.

METHODS

We propose to exploit dimensionality reduction methods for the selection of genes specifically activated in different stages of differentiation. To obtain an integrated predictive model, the expression values of the selected genes from multiple data sets are combined. We investigated distinct approaches that either aggregate data sets or use learning ensembles.

RESULTS

We analyzed the performance of the proposed methods on six publicly available data sets. The selection procedure identified a reduced subset of genes whose expression values gave rise to an accurate stage prediction. The assessment of predictive accuracy demonstrated a high quality of predictions for most of the data integration methods presented.

CONCLUSION

The experimental results highlighted the main potentials of proposed approaches. These include the ability to predict the true staging by combining multiple training data sets when this could not be inferred from a single data source, and to focus the analysis on a reduced list of genes of similar predictive performance.

Function-based discovery of significant transcriptional temporal patterns in insulin stimulated muscle cells

Background

Insulin action on protein synthesis (translation of transcripts) and post-translational modifications, especially of those involving the reversible modifications such as phosphorylation of various signaling proteins, are extensively studied but insulin effect on transcription of genes, especially of transcriptional temporal patterns remains to be fully defined.

Methodology/Principal Findings

To identify significant transcriptional temporal patterns we utilized primary differentiated rat skeletal muscle myotubes which were treated with insulin and samples were collected every 20 min for 8 hours. Pooled samples at every hour were analyzed by gene array approach to measure transcript levels. The patterns of transcript levels were analyzed based on a novel method that integrates selection, clustering, and functional annotation to find the main temporal patterns associated to functional groups of differentially expressed genes. 326 genes were found to be differentially expressed in response to in vitro insulin administration in skeletal muscle myotubes. Approximately 20% of the genes that were differentially expressed were identified as belonging to the insulin signaling pathway. Characteristic transcriptional temporal patterns include: (a) a slow and gradual decrease in gene expression, (b) a gradual increase in gene expression reaching a peak at about 5 hours and then reaching a plateau or an initial decrease and other different variable pattern of increase in gene expression over time.

Conclusion/Significance

The new method allows identifying characteristic dynamic responses to insulin stimulus, common to a number of genes and associated to the same functional group. The results demonstrate that insulin treatment elicited different clusters of gene transcript profile supporting a temporal regulation of gene expression by insulin in skeletal muscle cells.

Stage prediction of embryonic stem cell differentiation from genome-wide expression data

MOTIVATION

The developmental stage of a cell can be determined by cellular morphology or various other observable indicators. Such classical markers could be complemented with modern surrogates, like whole-genome transcription profiles, that can encode the state of the entire organism and provide increased quantitative resolution. Recent findings suggest that such profiles provide sufficient information to reliably predict the cell's developmental stage.

RESULTS

We use whole-genome transcription data and several data projection methods to infer differentiation stage prediction models for embryonic cells. Given a transcription profile of an uncharacterized cell, these models can then predict its developmental stage. In a series of experiments comprising 14 datasets from the Gene Expression Omnibus, we demonstrate that the approach is robust and has excellent prediction ability both within a specific cell line and across different cell lines.

AVAILABILITY

Model inference and computational evaluation procedures in the form of Python scripts and accompanying datasets are available at http://www.biolab.si/supp/stagerank.

CONTACT

blaz.zupan@fri.uni-lj.si

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Decreased secretion and unfolded protein response upregulation

Recombinant antibody fragments, for example, the classic monovalent single-chain antibody (scFv), are emerging as credible alternatives to monoclonal antibody (mAb) products. scFv fragments maintain a diverse range of potential applications in biotechnology and can be implemented as powerful therapeutic and diagnostic agents. As such, a variety of hosts have been used to produce antibody fragments resulting in varying degrees of success. Yeast, Saccharomyces cerevisiae, is an attractive host due to quality control mechanisms of the secretory pathway that ensure secreted proteins are properly folded. However, the expression of a recombinant protein in yeast is not trivial; neither are the quality control mechanisms the cell initiates to respond to overwhelming stress, such as an increased protein load, simplistic. The endoplasmic reticulum (ER) is a dynamic organelle, capable of sensing and adjusting its folding capacity in response to increased demand. When protein abundance or terminally misfolded proteins overwhelm the ER's capacity, the unfolded protein response (UPR) is activated. In the guidelines presented here, we discuss varying aspects of quality control, its modulation, and ways to design appropriate constructs for yeast recombinant protein expression. Furthermore, we have provided protocols and methods to monitor intracellular protein expression and trafficking as well as evaluation of the UPR, with essential controls. The latter part of this chapter will review considerations for the experimental design of microarray and quantitative polymerase chain reaction (q-PCR) techniques while suggesting appropriate means of data analysis.

A platform for processing expression of short time series (PESTS)

Background

Time course microarray profiles examine the expression of genes over a time domain. They are necessary in order to determine the complete set of genes that are dynamically expressed under given conditions, and to determine the interaction between these genes. Because of cost and resource issues, most time series datasets contain less than 9 points and there are few tools available geared towards the analysis of this type of data.

Results

To this end, we introduce a platform for Processing Expression of Short Time Series (PESTS). It was designed with a focus on usability and interpretability of analyses for the researcher. As such, it implements several standard techniques for comparability as well as visualization functions. However, it is designed specifically for the unique methods we have developed for significance analysis, multiple test correction and clustering of short time series data. The central tenet of these methods is the use of biologically relevant features for analysis. Features summarize short gene expression profiles, inherently incorporate dependence across time, and allow for both full description of the examined curve and missing data points.

Conclusions

PESTS is fully generalizable to other types of time series analyses. PESTS implements novel methods as well as several standard techniques for comparability and visualization functions. These features and functionality make PESTS a valuable resource for a researcher's toolkit. PESTS is available to download for free to academic and non-profit users at http://www.mailman.columbia.edu/academic-departments/biostatistics/research-service/software-development.

The transcriptional response in human umbilical vein endothelial cells exposed to insulin: a dynamic gene expression approach

Background

In diabetes chronic hyperinsulinemia contributes to the instability of the atherosclerotic plaque and stimulates cellular proliferation through the activation of the MAP kinases, which in turn regulate cellular proliferation. However, it is not known whether insulin itself could increase the transcription of specific genes for cellular proliferation in the endothelium. Hence, the characterization of transcriptional modifications in endothelium is an important step for a better understanding of the mechanism of insulin action and the relationship between endothelial cell dysfunction and insulin resistance.

Methodology and principal findings

The transcriptional response of endothelial cells in the 440 minutes following insulin stimulation was monitored using microarrays and compared to a control condition. About 1700 genes were selected as differentially expressed based on their treated minus control profile, thus allowing the detection of even small but systematic changes in gene expression. Genes were clustered in 7 groups according to their time expression profile and classified into 15 functional categories that can support the biological effects of insulin, based on Gene Ontology enrichment analysis. In terms of endothelial function, the most prominent processes affected were NADH dehydrogenase activity, N-terminal myristoylation domain binding, nitric-oxide synthase regulator activity and growth factor binding. Pathway-based enrichment analysis revealed “Electron Transport Chain” significantly enriched.

Results were validated on genes belonging to “Electron Transport Chain” pathway, using quantitative RT-PCR.

Conclusions

As far as we know, this is the first systematic study in the literature monitoring transcriptional response to insulin in endothelial cells, in a time series microarray experiment. Since chronic hyperinsulinemia contributes to the instability of the atherosclerotic plaque and stimulates cellular proliferation, some of the genes identified in the present work are potential novel candidates in diabetes complications related to endothelial dysfunction.

A Boolean approach to linear prediction for signaling network modeling

The task of the DREAM4 (Dialogue for Reverse Engineering Assessments and Methods) “Predictive signaling network modeling” challenge was to develop a method that, from single-stimulus/inhibitor data, reconstructs a cause-effect network to be used to predict the protein activity level in multi-stimulus/inhibitor experimental conditions. The method presented in this paper, one of the best performing in this challenge, consists of 3 steps: 1. Boolean tables are inferred from single-stimulus/inhibitor data to classify whether a particular combination of stimulus and inhibitor is affecting the protein. 2. A cause-effect network is reconstructed starting from these tables. 3. Training data are linearly combined according to rules inferred from the reconstructed network. This method, although simple, permits one to achieve a good performance providing reasonable predictions based on a reconstructed network compatible with knowledge from the literature. It can be potentially used to predict how signaling pathways are affected by different ligands and how this response is altered by diseases.

The KM-Algorithm Identifies Regulated Genes in Time Series Expression Data

We present a statistical method to rank observed genes in gene expression time series experiments according to their degree of regulation in a biological process. The ranking may be used to focus on specific genes or to select meaningful subsets of genes from which gene regulatory networks can be built. Our approach is based on a state space model that incorporates hidden regulators of gene expression. Kalman (K) smoothing and maximum (M) likelihood estimation techniques are used to derive optimal estimates of the model parameters upon which a proposed regulation criterion is based. The statistical power of the proposed algorithm is investigated, and a real data set is analyzed for the purpose of identifying regulated genes in time dependent gene expression data. This statistical approach supports the concept that meaningful biological conclusions can be drawn from gene expression time series experiments by focusing on strong regulation rather than large expression values.

A permutation-based multiple testing method for time-course microarray experiments

Background

Time-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey et al. (2005) developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression trajectories change over time within a single biological group, or those that follow different time trajectories among multiple groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course) and alternative (time-course) hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational ease and their intuitive interpretation.

Results

In this paper, we propose a permutation-based multiple testing procedure based on the test statistic used by Storey et al. (2005). We also propose an efficient computation algorithm. Extensive simulations are conducted to investigate the performance of the permutation-based multiple testing procedure. The application of the proposed method is illustrated using the Caenorhabditis elegans dauer developmental data.

Conclusion

Our method is computationally efficient and applicable for identifying genes whose expression levels are time-dependent in a single biological group and for identifying the genes for which the time-profile depends on the group in a multi-group setting.

Gene expression in early and progression phases of autosomal dominant polycystic kidney disease

Background

Little is known about the genes involved in the initial cyst formation and disease progression in autosomal dominant polycystic kidney disease (ADPKD); however, such knowledge is necessary to explore therapeutic avenues for this common inherited kidney disease.

Findings

To uncover the genetic determinants and molecular mechanisms of ADPKD, we analyzed 4-point time-series DNA microarrays from Pkd1^L3/L3 mice to generate high resolution gene expression profiles at different stages of disease progression. We found different characteristic gene expression signatures in the kidneys of Pkd1^L3/L3 mice compared to age-matched controls during the initial phase of the disease. By postnatal week 1, the Pkd1^L3/L3 kidney already had a distinctive gene expression pattern different from the corresponding normal controls.

Conclusion

The genes differentially expressed, either induced or repressed, in ADPKD are important in immune defense, cell structure and motility, cellular proliferation, apoptosis and metabolic processes, and include members of three pathways (Wnt, Notch, and BMP) involved in morphogenetic signaling. Further analysis of the gene expression profiles from the early stage of cystogenesis to end stage disease identified a possible gene network involved in the pathogenesis of ADPKD.