Using genome-wide approaches, we have elucidated the regulatory circuitry governed by the XBP1 transcription factor, a key effector of the mammalian unfolded protein response (UPR), in skeletal muscle and secretory cells. We identified a core group of genes involved in constitutive maintenance of ER function in all cell types and tissue- and condition-specific targets. In addition, we identified a cadre of unexpected targets that link XBP1 to neurodegenerative and myodegenerative diseases, as well as to DNA damage and repair pathways. Remarkably, we found that XBP1 regulates functionally distinct targets through different sequence motifs. Further, we identified Mist1, a critical regulator of differentiation, as an important target of XBP1, providing an explanation for developmental defects associated with XBP1 loss of function. Our results provide a detailed picture of the regulatory roadmap governed by XBP1 in distinct cell types as well as insight into unexplored functions of XBP1.

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Batch effects are the systematic non-biological differences between batches (groups) of samples in microarray experiments due to various causes such as differences in sample preparation and hybridization protocols. Previous work focused mainly on the development of methods for effective batch effects removal. However, their impact on cross-batch prediction performance, which is one of the most important goals in microarray-based applications, has not been addressed. This paper uses a broad selection of data sets from the Microarray Quality Control Phase II (MAQC-II) effort, generated on three microarray platforms with different causes of batch effects to assess the efficacy of their removal. Two data sets from cross-tissue and cross-platform experiments are also included. Of the 120 cases studied using Support vector machines (SVM) and K nearest neighbors (KNN) as classifiers and Matthews correlation coefficient (MCC) as performance metric, we find that Ratio-G, Ratio-A, EJLR, mean-centering and standardization methods perform better or equivalent to no batch effect removal in 89, 85, 83, 79 and 75% of the cases, respectively, suggesting that the application of these methods is generally advisable and ratio-based methods are preferred.

Integrins are a family of transmembrane glycoprotein signaling receptors that can transmit bioinformation bidirectionally across the plasma membrane. Integrin αIIbβ3 is expressed at a high level in platelets and their progenitors, where it plays a central role in platelet functions, hemostasis, and arterial thrombosis. Integrin αIIbβ3 also participates in cancer progression, such as tumor cell proliferation and metastasis. In resting platelets, integrin αIIbβ3 adopts an inactive conformation. Upon agonist stimulation, the transduction of inside-out signals leads integrin αIIbβ3 to switch from a low-to high-affinity state for fibrinogen and other ligands. Ligand binding causes integrin clustering and subsequently promotes outside-in signaling, which initiates and amplifies a range of cellular events to drive essential platelet functions such as spreading, aggregation, clot retraction, and thrombus consolidation. Regulation of the bidirectional signaling of integrin αIIbβ3 requires the involvement of numerous interacting proteins, which associate with the cytoplasmic tails of αIIbβ3 in particular. Integrin αIIbβ3 and its signaling pathways are considered promising targets for antithrombotic therapy. This review describes the bidirectional signal transduction of integrin αIIbβ3 in platelets, as well as the proteins responsible for its regulation and therapeutic agents that target integrin αIIbβ3 and its signaling pathways.