Consistent Differential Expression Pattern (CDEP) on microarray to identify genes related to metastatic behavior

Mechanical Strain Induces Transcriptomic Reprogramming of Saphenous Vein Progenitors

Intimal hyperplasia is the leading cause of graft failure in aortocoronary bypass grafts performed using human saphenous vein (SV). The long-term consequences of the altered pulsatile stress on the cells that populate the vein wall remains elusive, particularly the effects on saphenous vein progenitors (SVPs), cells resident in the vein adventitia with a relatively wide differentiation capacity. In the present study, we performed global transcriptomic profiling of SVPs undergoing uniaxial cyclic strain in vitro. This type of mechanical stimulation is indeed involved in the pathology of the SV. Results showed a consistent stretch-dependent gene regulation in cyclically strained SVPs vs. controls, especially at 72 h. We also observed a robust mechanically related overexpression of Adhesion Molecule with Ig Like Domain 2 (AMIGO2), a cell surface type I transmembrane protein involved in cell adhesion. The overexpression of AMIGO2 in stretched SVPs was associated with the activation of the transforming growth factor β pathway and modulation of intercellular signaling, cell-cell, and cell-matrix interactions. Moreover, the increased number of cells expressing AMIGO2 detected in porcine SV adventitia using an in vivo arterialization model confirms the upregulation of AMIGO2 protein by the arterial-like environment. These results show that mechanical stress promotes SVPs' molecular phenotypic switching and increases their responsiveness to extracellular environment alterations, thus prompting the targeting of new molecular effectors to improve the outcome of bypass graft procedure.

Reporters to mark and eliminate basal or luminal epithelial cells in culture and in vivo

The contribution of basal and luminal cells to cancer progression and metastasis is poorly understood. We report generation of reporter systems driven by either keratin-14 (K14) or keratin-8 (K8) promoter that not only express a fluorescent protein but also an inducible suicide gene. Transgenic mice express the reporter genes in the right cell compartments of mammary gland epithelia and respond to treatment with toxins. In addition, we engineered the reporters into 4T1 metastatic mouse tumor cell line and demonstrate that K14+ cells, but not K14- or K8+, are both highly invasive in three-dimensional (3D) culture and metastatic in vivo. Treatment of cells in culture, or tumors in mice, with reporter-targeting toxin inhibited both invasive behavior and metastasis in vivo. RNA sequencing (RNA-seq), secretome, and epigenome analysis of K14+ and K14- cells led to the identification of amphoterin-induced protein 2 (Amigo2) as a new cell invasion driver whose expression correlated with decreased relapse-free survival in patients with TP53 wild-type (WT) breast cancer.

Knowledge-Based Compact Disease Models: A Rapid Path from High-Throughput Data to Understanding Causative Mechanisms for a Complex Disease

High-throughput profiling of human tissues typically yields the gene lists composed of a variety of more or less relevant molecular entities. These lists are riddle by false positive observations that often obstruct generation of mechanistic hypothesis that may explain complex phenotype. From general probabilistic considerations, the gene lists enriched by the mechanistically relevant targets can be far more useful for subsequent experimental design or data interpretation. Using Alzheimer's disease as example, the candidate gene lists were processed into different tiers of evidence consistency established by enrichment analysis across subdatasets collected within the same experiment and across different experiments and platforms. The cutoffs were established empirically through ontological and semantic enrichment; resultant shortened gene list was reexpanded by Ingenuity Pathway Assistant tool. The resulting subnetworks provided the basis for generating mechanistic hypotheses that were partially validated by mined experimental evidence. This approach differs from previous consistency-based studies in that the cutoff on the Receiver Operating Characteristic of the true-false separation process is optimized by flexible selection of the consistency building procedure. The resultant Compact Disease Models (CDM) composed of the gene list distilled by this analytic technique and its network-based representation allowed us to highlight possible role of the protein traffic vesicles in the pathogenesis of Alzheimer's. Considering the distances and complexity of protein trafficking in neurons, it is plausible to hypothesize that spontaneous protein misfolding along with a shortage of growth stimulation may provide a shortcut to neurodegeneration. Several potentially overlapping scenarios of early-stage Alzheimer pathogenesis are discussed, with an emphasis on the protective effects of Angiotensin receptor 1 (AT-1) mediated antihypertensive response on cytoskeleton remodeling, along with neuronal activation of oncogenes, luteinizing hormone signaling and insulin-related growth regulation, forming a pleiotropic model of its early stages. Compact Disease Model generation is a flexible approach for high-throughput data analysis that allows extraction of meaningful, mechanism-centered gene sets compatible with instant translation of the results into testable hypotheses.

Simple Analysis of Deposited Gene Expression Datasets for the Non-Bioinformatician: How to Use GEO for Fibrosis Research

In the past decade, high-throughput techniques have facilitated the "-omics" research. Transcriptomic study, for instance, has advanced our understanding on the expression landscape of different human diseases and cellular mechanisms. The National Center for Biotechnology Center (NCBI) initialized Genetic Expression Omnibus (GEO) to promote the sharing of transcriptomic data to facilitate biomedical research. In this chapter, we will illustrate how to use GEO to search and analyze the public available transcriptomic data, and we will provide easy to follow protocol for researchers to data mine the powerful resources in GEO to retrieve relevant information that can be valuable for fibrosis research.

A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments

Background

The rank product method is a powerful statistical technique for identifying differentially expressed molecules in replicated experiments. A critical issue in molecule selection is accurate calculation of the p-value of the rank product statistic to adequately address multiple testing. Both exact calculation and permutation and gamma approximations have been proposed to determine molecule-level significance. These current approaches have serious drawbacks as they are either computationally burdensome or provide inaccurate estimates in the tail of the p-value distribution.

Results

We derive strict lower and upper bounds to the exact p-value along with an accurate approximation that can be used to assess the significance of the rank product statistic in a computationally fast manner. The bounds and the proposed approximation are shown to provide far better accuracy over existing approximate methods in determining tail probabilities, with the slightly conservative upper bound protecting against false positives. We illustrate the proposed method in the context of a recently published analysis on transcriptomic profiling performed in blood.

Conclusions

We provide a method to determine upper bounds and accurate approximate p-values of the rank product statistic. The proposed algorithm provides an order of magnitude increase in throughput as compared with current approaches and offers the opportunity to explore new application domains with even larger multiple testing issue. The R code is published in one of the Additional files and is available at http://www.ru.nl/publish/pages/726696/rankprodbounds.zip.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0367-1) contains supplementary material, which is available to authorized users.

Knowledge-based compact disease models identify new molecular players contributing to early-stage Alzheimer's disease

BACKGROUND

High-throughput profiling of human tissues typically yield as results the gene lists comprised of a mix of relevant molecular entities with multiple false positives that obstruct the translation of such results into mechanistic hypotheses. From general probabilistic considerations, gene lists distilled for the mechanistically relevant components can be far more useful for subsequent experimental design or data interpretation.

RESULTS

The input candidate gene lists were processed into different tiers of evidence consistency established by enrichment analysis across subsets of the same experiments and across different experiments and platforms. The cut-offs were established empirically through ontological and semantic enrichment; resultant shortened gene list was re-expanded by Ingenuity Pathway Assistant tool. The resulting sub-networks provided the basis for generating mechanistic hypotheses that were partially validated by literature search. This approach differs from previous consistency-based studies in that the cut-off on the Receiver Operating Characteristic of the true-false separation process is optimized by flexible selection of the consistency building procedure. The gene list distilled by this analytic technique and its network representation were termed Compact Disease Model (CDM). Here we present the CDM signature for the study of early-stage Alzheimer's disease. The integrated analysis of this gene signature allowed us to identify the protein traffic vesicles as prominent players in the pathogenesis of Alzheimer's. Considering the distances and complexity of protein trafficking in neurons, it is plausible that spontaneous protein misfolding along with a shortage of growth stimulation result in neurodegeneration. Several potentially overlapping scenarios of early-stage Alzheimer pathogenesis have been discussed, with an emphasis on the protective effects of AT-1 mediated antihypertensive response on cytoskeleton remodeling, along with neuronal activation of oncogenes, luteinizing hormone signaling and insulin-related growth regulation, forming a pleiotropic model of its early stages. Alignment with emerging literature confirmed many predictions derived from early-stage Alzheimer's disease' CDM.

CONCLUSIONS

A flexible approach for high-throughput data analysis, the Compact Disease Model generation, allows extraction of meaningful, mechanism-centered gene sets compatible with instant translation of the results into testable hypotheses.

Molecular profiling of multiple human cancers defines an inflammatory cancer-associated molecular pattern and uncovers KPNA2 as a uniform poor prognostic cancer marker

BACKGROUND

Immune evasion is one of the recognized hallmarks of cancer. Inflammatory responses to cancer can also contribute directly to oncogenesis. Since the immune system is hardwired to protect the host, there is a possibility that cancers, regardless of their histological origins, endow themselves with a common and shared inflammatory cancer-associated molecular pattern (iCAMP) to promote oncoinflammation. However, the definition of iCAMP has not been conceptually and experimentally investigated.

METHODS AND FINDINGS

Genome-wide cDNA expression data was analyzed for 221 normal and 324 cancer specimens from 7 cancer types: breast, prostate, lung, colon, gastric, oral and pancreatic. A total of 96 inflammatory genes with consistent dysregulation were identified, including 44 up-regulated and 52 down-regulated genes. Protein expression was confirmed by immunohistochemistry for some of these genes. The iCAMP contains proteins whose roles in cancer have been implicated and others which are yet to be appreciated. The clinical significance of many iCAMP genes was confirmed in multiple independent cohorts of colon and ovarian cancer patients. In both cases, better prognosis correlated strongly with high CXCL13 and low level of GREM1, LOX, TNFAIP6, CD36, and EDNRA. An "Inflammatory Gene Integrated Score" was further developed from the combination of 18 iCAMP genes in ovarian cancer, which predicted overall survival. Noticeably, as a selective nuclear import protein whose immuno-regulatory function just begins to emerge, karyopherin alpha 2 (KPNA2) is uniformly up-regulated across cancer types. For the first time, the cancer-specific up-regulation of KPNA2 and its clinical significance were verified by tissue microarray analysis in colon and head-neck cancers.

CONCLUSION

This work defines an inflammatory signature shared by seven epithelial cancer types and KPNA2 as a consistently up-regulated protein in cancer. Identification of iCAMP may not only serve as a novel biomarker for prognostication and individualized treatment of cancer, but also have significant biological implications.

Reuse of public genome-wide gene expression data

Our understanding of gene expression has changed dramatically over the past decade, largely catalysed by technological developments. High-throughput experiments - microarrays and next-generation sequencing - have generated large amounts of genome-wide gene expression data that are collected in public archives. Added-value databases process, analyse and annotate these data further to make them accessible to every biologist. In this Review, we discuss the utility of the gene expression data that are in the public domain and how researchers are making use of these data. Reuse of public data can be very powerful, but there are many obstacles in data preparation and analysis and in the interpretation of the results. We will discuss these challenges and provide recommendations that we believe can improve the utility of such data.