Identifying biological themes within lists of genes with EASE

Unfolded Protein Response Inhibition Reduces Middle East Respiratory Syndrome Coronavirus-Induced Acute Lung Injury

Tissue- and cell-specific expression patterns are highly variable within and across individuals, leading to altered host responses after acute virus infection. Unraveling key tissue-specific response patterns provides novel opportunities for defining fundamental mechanisms of virus-host interaction in disease and the identification of critical tissue-specific networks for disease intervention in the lung. Currently, there are no approved therapeutics for Middle East respiratory syndrome coronavirus (MERS-CoV) patients, and little is understood about how lung cell types contribute to disease outcomes. MERS-CoV replicates equivalently in primary human lung microvascular endothelial cells (MVE) and fibroblasts (FB) and to equivalent peak titers but with slower replication kinetics in human airway epithelial cell cultures (HAE). However, only infected MVE demonstrate observable virus-induced cytopathic effect. To explore mechanisms leading to reduced MVE viability, donor-matched human lung MVE, HAE, and FB were infected, and their transcriptomes, proteomes, and lipidomes were monitored over time. Validated functional enrichment analysis demonstrated that MERS-CoV-infected MVE were dying via an unfolded protein response (UPR)-mediated apoptosis. Pharmacologic manipulation of the UPR in MERS-CoV-infected primary lung cells reduced viral titers and in male mice improved respiratory function with accompanying reductions in weight loss, pathological signatures of acute lung injury, and times to recovery. Systems biology analysis and validation studies of global kinetic transcript, protein, and lipid data sets confirmed that inhibition of host stress pathways that are differentially regulated following MERS-CoV infection of different tissue types can alleviate symptom progression to end-stage lung disease commonly seen following emerging coronavirus outbreaks.

CPA: a web-based platform for consensus pathway analysis and interactive visualization

In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.

Disparate responses to salinity across species and organizational levels in anchialine shrimps

Environmentally induced plasticity in gene expression is one of the underlying mechanisms of adaptation to habitats with variable environments. For example, euryhaline crustaceans show predictable changes in the expression of ion-transporter genes during salinity transfers, although studies have typically been limited to specific genes, taxa and ecosystems of interest. Here, we investigated responses to salinity change at multiple organizational levels in five species of shrimp representing at least three independent invasions of the anchialine ecosystem, defined as habitats with marine and freshwater influences with spatial and temporal fluctuations in salinity. Although all five species were generally strong osmoregulators, salinity-induced changes in gill physiology and gene expression were highly species specific. While some species exhibited patterns similar to those of previously studied euryhaline crustaceans, instances of distinct and atypical patterns were recovered from closely related species. Species-specific patterns were found when examining: (1) numbers and identities of differentially expressed genes, (2) salinity-induced expression of genes predicted a priori to play a role in osmoregulation, and (3) salinity-induced expression of orthologs shared among all species. Notably, ion transport genes were unchanged in the atyid Halocaridina rubra while genes normally associated with vision and light perception were among those most highly upregulated. Potential reasons for species-specific patterns are discussed, including variation among anchialine habitats in salinity regimes and divergent evolution in anchialine taxa. Underexplored mechanisms of osmoregulation in crustaceans revealed here by the application of transcriptomic approaches to ecologically and taxonomically understudied systems are also explored.

Identifying significantly impacted pathways: a comprehensive review and assessment

BACKGROUND

Many high-throughput experiments compare two phenotypes such as disease vs. healthy, with the goal of understanding the underlying biological phenomena characterizing the given phenotype. Because of the importance of this type of analysis, more than 70 pathway analysis methods have been proposed so far. These can be categorized into two main categories: non-topology-based (non-TB) and topology-based (TB). Although some review papers discuss this topic from different aspects, there is no systematic, large-scale assessment of such methods. Furthermore, the majority of the pathway analysis approaches rely on the assumption of uniformity of p values under the null hypothesis, which is often not true.

RESULTS

This article presents the most comprehensive comparative study on pathway analysis methods available to date. We compare the actual performance of 13 widely used pathway analysis methods in over 1085 analyses. These comparisons were performed using 2601 samples from 75 human disease data sets and 121 samples from 11 knockout mouse data sets. In addition, we investigate the extent to which each method is biased under the null hypothesis. Together, these data and results constitute a reliable benchmark against which future pathway analysis methods could and should be tested.

CONCLUSION

Overall, the result shows that no method is perfect. In general, TB methods appear to perform better than non-TB methods. This is somewhat expected since the TB methods take into consideration the structure of the pathway which is meant to describe the underlying phenomena. We also discover that most, if not all, listed approaches are biased and can produce skewed results under the null.

Effects of rheumatoid arthritis associated transcriptional changes on osteoclast differentiation network in the synovium

Background

Osteoclast differentiation in the inflamed synovium of rheumatoid arthritis (RA) affected joints leads to the formation of bone lesions. Reconstruction and analysis of protein interaction networks underlying specific disease phenotypes are essential for designing therapeutic interventions. In this study, we have created a network that captures signal flow leading to osteoclast differentiation. Based on transcriptome analysis, we have indicated the potential mechanisms responsible for the phenotype in the RA affected synovium.

Method

We collected information on gene expression, pathways and protein interactions related to RA from literature and databases namely Gene Expression Omnibus, Kyoto Encyclopedia of Genes and Genomes pathway and STRING. Based on these information, we created a network for the differentiation of osteoclasts. We identified the differentially regulated network genes and reported the signaling that are responsible for the process in the RA affected synovium.

Result

Our network reveals the mechanisms underlying the activation of the neutrophil cytosolic factor complex in connection to osteoclastogenesis in RA. Additionally, the study reports the predominance of the canonical pathway of NF-κB activation in the diseased synovium. The network also confirms that the upregulation of T cell receptor signaling and downregulation of transforming growth factor beta signaling pathway favor osteoclastogenesis in RA. To the best of our knowledge, this is the first comprehensive protein–protein interaction network describing RA driven osteoclastogenesis in the synovium.

Discussion

This study provides information that can be used to build models of the signal flow involved in the process of osteoclast differentiation. The models can further be used to design therapies to ameliorate bone destruction in the RA affected joints.

In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass ( Micropterus salmoides)

In recent years, decreases in fish populations have been attributed, in part, to the effect of environmental chemicals on ovarian development. To understand the underlying molecular events we developed a dynamic model of ovary development linking gene transcription to key physiological end points, such as gonadosomatic index (GSI), plasma levels of estradiol (E2) and vitellogenin (VTG), in largemouth bass ( Micropterus salmoides). We were able to identify specific clusters of genes, which are affected at different stages of ovarian development. A subnetwork was identified that closely linked gene expression and physiological end points and by interrogating the Comparative Toxicogenomic Database (CTD), quercetin and tretinoin (ATRA) were identified as two potential candidates that may perturb this system. Predictions were validated by investigation of reproductive associated transcripts using qPCR in ovary and in the liver of both male and female largemouth bass treated after a single injection of quercetin and tretinoin (10 and 100 μg/kg). Both compounds were found to significantly alter the expression of some of these genes. Our findings support the use of omics and online repositories for identification of novel, yet untested, compounds. This is the first study of a dynamic model that links gene expression patterns across stages of ovarian development.

Biological networks in Parkinson's disease: an insight into the epigenetic mechanisms associated with this disease

BACKGROUND

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorders in the world. Studying PD from systems biology perspective involving genes and their regulators might provide deeper insights into the complex molecular interactions associated with this disease.

RESULT

We have studied gene co-expression network obtained from a PD-specific microarray data. The co-expression network identified 11 hub genes, of which eight genes are not previously known to be associated with PD. Further study on the functionality of these eight novel hub genes revealed that these genes play important roles in several neurodegenerative diseases. Furthermore, we have studied the tissue-specific expression and histone modification patterns of the novel hub genes. Most of these genes possess several histone modification sites those are already known to be associated with neurodegenerative diseases. Regulatory network namely mTF-miRNA-gene-gTF involves microRNA Transcription Factor (mTF), microRNA (miRNA), gene and gene Transcription Factor (gTF). Whereas long noncoding RNA (lncRNA) mediated regulatory network involves miRNA, gene, mTF and lncRNA. mTF-miRNA-gene-gTF regulatory network identified a novel feed-forward loop. lncRNA-mediated regulatory network identified novel lncRNAs of PD and revealed the two-way regulatory pattern of PD-specific miRNAs where miRNAs can be regulated by both the TFs and lncRNAs. SNP analysis of the most significant genes of the co-expression network identified 20 SNPs. These SNPs are present in the 3' UTR of known PD genes and are controlled by those miRNAs which are also involved in PD.

CONCLUSION

Our study identified eight novel hub genes which can be considered as possible candidates for future biomarker identification studies for PD. The two regulatory networks studied in our work provide a detailed overview of the cellular regulatory mechanisms where the non-coding RNAs namely miRNA and lncRNA, can act as epigenetic regulators of PD. SNPs identified in our study can be helpful for identifying PD at an earlier stage. Overall, this study may impart a better comprehension of the complex molecular interactions associated with PD from systems biology perspective.

Gene and microRNA transcriptional signatures of angiotensin II in endothelial cells

Growth of atherosclerotic plaque requires neovascularization (angiogenesis). To elucidate the involvement of angiotensin II (Ang II) in angiogenesis, we performed gene microarray and microRNA (miRNA) polymerase chain reaction array analyses on human coronary artery endothelial cells exposed to moderate concentration of Ang II for 2 and 12 hours. At 12, but not 2, hours, cultures treated with Ang II exhibited shifts in transcriptional activity involving 267 genes (>1.5-fold difference; P < 0.05). Resulting transcriptome was most significantly enriched for genes associated with blood vessel development, angiogenesis, and regulation of proliferation. Majority of upregulated genes implicated in angiogenesis shared a commonality of being either regulators (HES1, IL-18, and CXCR4) or targets (ADM, ANPEP, HES1, KIT, NOTCH4, PGF, and SOX18) of STAT3. In line with these findings, STAT3 inhibition attenuated Ang II-dependent stimulation of tube formation in Matrigel assay. Expression analysis of miRNAs transcripts revealed that the pattern of differential expression for miRNAs was largely consistent with proangiogenic response with a prominent theme of upregulation of miRs targeting PTEN (miR-19b-3p, miR-21-5p, 23b-3p, and 24-3p), many of which are directly or indirectly STAT3 dependent. We conclude that STAT3 signaling may be an intrinsic part of Ang II-mediated proangiogenic response in human endothelial cells.

Reconstitution of the mouse germ cell specification pathway in culture by pluripotent stem cells

The generation of properly functioning gametes in vitro requires reconstitution of the multistepped pathway of germ cell development. We demonstrate here the generation of primordial germ cell-like cells (PGCLCs) in mice with robust capacity for spermatogenesis. PGCLCs were generated from embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) through epiblast-like cells (EpiLCs), a cellular state highly similar to pregastrulating epiblasts but distinct from epiblast stem cells (EpiSCs). Reflecting epiblast development, EpiLC induction from ESCs/iPSCs is a progressive process, and EpiLCs highly competent for the PGC fate are a transient entity. The global transcription profiles, epigenetic reprogramming, and cellular dynamics during PGCLC induction from EpiLCs meticulously capture those associated with PGC specification from the epiblasts. Furthermore, we identify Integrin-β3 and SSEA1 as markers that allow the isolation of PGCLCs with spermatogenic capacity from tumorigenic undifferentiated cells. Our findings provide a paradigm for the first step of in vitro gametogenesis.

Functional conservation of DNA methylation in the pea aphid and the honeybee

DNA methylation is a fundamental epigenetic mark known to have wide-ranging effects on gene regulation in a variety of animal taxa. Comparative genomic analyses can help elucidate the function of DNA methylation by identifying conserved features of methylated genes and other genomic regions. In this study, we used computational approaches to distinguish genes marked by heavy methylation from those marked by little or no methylation in the pea aphid, Acyrthosiphon pisum. We investigated if these two classes had distinct evolutionary histories and functional roles by conducting comparative analysis with the honeybee, Apis (Ap.) mellifera. We found that highly methylated orthologs in A. pisum and Ap. mellifera exhibited greater conservation of methylation status, suggesting that highly methylated genes in ancestral species may remain highly methylated over time. We also found that methylated genes tended to show different rates of evolution than unmethylated genes. In addition, genes targeted by methylation were enriched for particular biological processes that differed from those in relatively unmethylated genes. Finally, methylated genes were preferentially ubiquitously expressed among alternate phenotypes in both species, whereas genes lacking signatures of methylation were preferentially associated with condition-specific gene regulation expression. Overall, our analyses support a conserved role for DNA methylation in insects with comparable methylation systems.

Transcriptional comparisons between equine articular repair tissue, neonatal cartilage, cultured chondrocytes and mesenchymal stromal cells

Human and equine cell transplant strategies for cartilage lesions usually result in scar tissue that is similar to what is produced naturally during the repair process. In this study, culture-expanded de-differentiated chondrocytes and primary bone marrow stromal cells at a pre-transplantation time-point were compared along with neonatal cartilage to repair tissue. Transcriptional profiling using a 9413-probeset equine-specific cDNA microarray and targeted real-time quantitative polymerase chain reaction validation were used to characterize relationships between these cell types and repair tissue both broadly and for individual cartilage biomarkers. The greatest divergence in expression was detected for transcripts encoding matrix proteins that typically define the differentiation status of normal articular cartilage and fibrocartilage repair tissue. Expression patterns and gene ontology analyses indicated that while the repair cells were more chondrogenic than bone marrow stromal cells and de-differentiated cultured chondrocytes, steady-state levels of transcripts encoding cartilage biomarkers were substantially lower than the amounts found in neonatal articular cartilage. By characterizing gene expression differences amongst these tissues, we present important targets to monitor when developing improvements to cartilage engineering therapies.

Genome-wide transcription factor binding site/promoter databases for the analysis of gene sets and co-occurrence of transcription factor binding motifs

Background

The use of global gene expression profiling is a well established approach to understand biological processes. One of the major goals of these investigations is to identify sets of genes with similar expression patterns. Such gene signatures may be very informative and reveal new aspects of particular biological processes. A logical and systematic next step is to reduce the identified gene signatures to the regulatory components that induce the relevant gene expression changes. A central issue in this context is to identify transcription factors, or transcription factor binding sites (TFBS), likely to be of importance for the expression of the gene signatures.

Results

We develop a strategy that efficiently produces TFBS/promoter databases based on user-defined criteria. The resulting databases constitute all genes in the Santa Cruz database and the positions for all TFBS provided by the user as position weight matrices. These databases are then used for two purposes, to identify significant TFBS in the promoters in sets of genes and to identify clusters of co-occurring TFBS. We use two criteria for significance, significantly enriched TFBS in terms of total number of binding sites for the promoters, and significantly present TFBS in terms of the fraction of promoters with binding sites. Significant TFBS are identified by a re-sampling procedure in which the query gene set is compared with typically 10⁵ gene lists of similar size randomly drawn from the TFBS/promoter database. We apply this strategy to a large number of published ChIP-Chip data sets and show that the proposed approach faithfully reproduces ChIP-Chip results. The strategy also identifies relevant TFBS when analyzing gene signatures obtained from the MSigDB database. In addition, we show that several TFBS are highly correlated and that co-occurring TFBS define functionally related sets of genes.

Conclusions

The presented approach of promoter analysis faithfully reproduces the results from several ChIP-Chip and MigDB derived gene sets and hence may prove to be an important method in the analysis of gene signatures obtained through ChIP-Chip or global gene expression experiments. We show that TFBS are organized in clusters of co-occurring TFBS that together define highly coherent sets of genes.

Predicting novel human gene ontology annotations using semantic analysis

The correct interpretation of many molecular biology experiments depends in an essential way on the accuracy and consistency of the existing annotation databases. Such databases are meant to act as repositories for our biological knowledge as we acquire and refine it. Hence, by definition, they are incomplete at any given time. In this paper, we describe a technique that improves our previous method for predicting novel GO annotations by extracting implicit semantic relationships between genes and functions. In this work, we use a vector space model and a number of weighting schemes in addition to our previous latent semantic indexing approach. The technique described here is able to take into consideration the hierarchical structure of the Gene Ontology (GO) and can weight differently GO terms situated at different depths. The prediction abilities of 15 different weighting schemes are compared and evaluated. Nine such schemes were previously used in other problem domains, while six of them are introduced in this paper. The best weighting scheme was a novel scheme, n2tn. Out of the top 50 functional annotations predicted using this weighting scheme, we found support in the literature for 84 percent of them, while 6 percent of the predictions were contradicted by the existing literature. For the remaining 10 percent, we did not find any relevant publications to confirm or contradict the predictions. The n2tn weighting scheme also outperformed the simple binary scheme used in our previous approach.

Dissecting the dynamics of dysregulation of cellular processes in mouse mammary gland tumor

Background

Elucidating the sequence of molecular events underlying breast cancer formation is of enormous value for understanding this disease and for design of an effective treatment. Gene expression measurements have enabled the study of transcriptome-wide changes involved in tumorigenesis. This usually occurs through identification of differentially expressed genes or pathways.

Results

We propose a novel approach that is able to delineate new cancer-related cellular processes and the nature of their involvement in tumorigenesis. First, we define modules as densely interconnected and functionally enriched areas of a Protein Interaction Network. Second, 'differential expression' and 'differential co-expression' analyses are applied to the genes in these network modules, allowing for identification of processes that are up- or down-regulated, as well as processes disrupted (low co-expression) or invoked (high co-expression) in different tumor stages. Finally, we propose a strategy to identify regulatory miRNAs potentially responsible for the observed changes in module activities. We demonstrate the potential of this analysis on expression data from a mouse model of mammary gland tumor, monitored over three stages of tumorigenesis. Network modules enriched in adhesion and metabolic processes were found to be inactivated in tumor cells through the combination of dysregulation and down-regulation, whereas the activation of the integrin complex and immune system response modules is achieved through increased co-regulation and up-regulation. Additionally, we confirmed a known miRNA involved in mammary gland tumorigenesis, and present several new candidates for this function.

Conclusions

Understanding complex diseases requires studying them by integrative approaches that combine data sources and different analysis methods. The integration of methods and data sources proposed here yields a sensitive tool, able to pinpoint new processes with a role in cancer, dissect modulation of their activity and detect the varying assignments of genes to functional modules over the course of a disease.

Integrated molecular analysis suggests a three-class model for low-grade gliomas: a proof-of-concept study

INTRODUCTION

We used an integrated molecular analysis strategy to perform class discovery on a population of low-grade gliomas (astrocytomas, oligodendrogliomas, and mixed gliomas) to improve our understanding of the molecular relationships among these tumors and to reconcile genotypic relationships with current histologic and molecular strategies for tumor classification.

METHODS

Gene expression profiling was performed on a cross-section of World Health Organization (WHO) grades I-II gliomas. Unsupervised class discovery algorithms identified and validated tumor clusters with genotypic similarity, and these data were integrated with chromosomal copy number assays and RT-PCR data to define molecular tumor subclasses. Machine learning models allowed accurate, prospective classification of unknown tumors into these molecular subgroups. This molecular classification model was compared to current histologic (WHO) and molecular pathologic (chromosome 1p and 19q deletions, p53 alterations, and Ki-67 expression) methods for glioma classification.

RESULTS

Molecular class discovery suggested a three-class model for low-grade gliomas. One discrete cluster of gliomas identified the pilocytic astrocytomas, a second grouped the 1p/19q codeleted oligodendrogliomas, and the mixture of remaining 1p/19q intact gliomas, including astrocytomas, oligodendrogliomas, and oligoastrocytomas, formed a third cluster with a discrete pattern of expression.

CONCLUSIONS

Integration of genomic, transcriptomic, and morphologic data for class discovery suggests a three-class model for low-grade gliomas. Class I represents tumors with molecular similarity to pilocytic astrocytomas, class II tumors are similar to 1p/19q codeleted oligodendrogliomas, and class III represents infiltrative low-grade gliomas. This classification is similar to current clinical paradigms for low-grade gliomas; our work suggests a molecular basis for such models. This classification may supplement or may serve as the basis for a molecular pathologic alternative to current grading schemes for low-grade gliomas and may highlight potential targets for future biologically based treatments or strategies for future clinical trials.

Correlation between gene expression and clinical data through linear and nonlinear principal components analyses: muscular dystrophies as case studies

The large dimension of microarray data and the complex dependence structure among genes make data analysis extremely challenging. In the last decade several statistical techniques have been proposed to tackle genome-wide expression data; however, clinical and molecular data associated to pathologies have often been considered as separate dimensions of the same phenomenon, especially when clinical variables lie on a multidimensional space. A better comprehension of the relationships between clinical and molecular data can be obtained if both data types are combined and integrated. In this work we adopt a multidimensional correlation strategy together with linear and nonlinear principal component, to integrate genetic and clinical information obtained from two sets of dystrophic patients. With this approach we decompose different aspects of clinical manifestations and correlate these features with the correspondent patterns of differential gene expression.

Downregulation of oligodendrocyte transcripts is associated with impaired prefrontal cortex function in rats

Abnormalities of brain white matter and oligodendroglia are among the most consistent findings in schizophrenia (Sz) research. Various gene expression microarray studies of post-mortem Sz brains showed a downregulation of myelin transcripts, while imaging and microscopy studies demonstrated decreases in prefrontal cortical (PFC) white matter volume and oligodendroglia density. Currently, the extent to which reduced oligodendrocyte markers contribute to pathophysiological domains of Sz is unknown. We exposed adolescent rats to cuprizone (CPZ), a copper chelator known to cause demyelination in mice, and examined expression of oligodendrocyte mRNA transcripts and PFC-mediated behavior. Rats on the CPZ diet showed decreased expression of mRNA transcripts encoding oligodendroglial proteins within the medial PFC, but not in the hippocampus or the striatum. These rats also displayed a specific deficit in the ability to shift between perceptual dimensions in the attentional set-shifting task, a PFC-mediated behavioral paradigm modeled after the Wisconsin Card Sorting Test (WCST). The inability to shift strategies corresponds to the deficits exhibited by Sz patients in the WCST. The results demonstrate that a reduction in oligodendrocyte markers is associated with impaired PFC-mediated behaviors. Thus, CPZ exposure of rats can serve as a model to examine the contribution of oligodendrocyte perturbation to cognitive deficits observed in Sz.

MicroRNA classifiers for predicting prognosis of squamous cell lung cancer

Non-small cell lung cancer (NSCLC), which is comprised mainly of adenocarcinoma and squamous cell carcinoma (SCC), is the cause of 80% of all lung cancer deaths in the United States. NSCLC is also associated with a high rate of relapse after clinical treatment and, therefore, requires robust prognostic markers to better manage therapy options. The aim of this study was to identify microRNA (miRNA) expression profiles in SCC of the lung that would better predict prognosis. Total RNA from 61 SCC samples and 10 matched normal lung samples was processed for small RNA species and profiled on MirVana miRNA Bioarrays (version 2, Ambion). We identified 15 miRNAs that were differentially expressed between normal lung and SCC, including members of the miR-17-92 cluster and its paralogues. We also identified miRNAs, including miR-155 and let-7, which had previously been shown to have prognostic value in adenocarcinoma. Based on cross-fold validation analyses, miR-146b alone was found to have the strongest prediction accuracy for stratifying prognostic groups at approximately 78%. The miRNA signatures were superior in predicting overall survival than a previously described 50-gene prognostic signature. Whereas there was no overlap between the mRNAs targeted by the prognostic miRNAs and the 50-gene expression signature, there was a significant overlap in the corresponding biological pathways, including fibroblast growth factor and interleukin-6 signaling. Our data indicate that miRNAs may have greater clinical utility in predicting the prognosis of patients with squamous cell lung carcinomas than mRNA-based signatures.

Local recurrence after breast-conserving therapy in relation to gene expression patterns in a large series of patients

PURPOSE

The majority of patients with early-stage breast cancer are treated with breast-conserving therapy (BCT). Several clinical risk factors are associated with local recurrence (LR) after BCT but are unable to explain all instances of LR after BCT. Here, gene expression microarrays are used to identify novel risk factors for LR after BCT.

EXPERIMENTAL DESIGN

Gene expression profiles of 56 primary invasive breast carcinomas from patients who developed a LR after BCT were compared with profiles of 109 tumors from patients who did not develop a LR after BCT. Both unsupervised and supervised methods of classification were used to separate patients into groups corresponding to disease outcome. In addition, for 15 patients, the gene expression profile in the recurrence was compared with that of the primary tumor.

RESULTS

The two main clusters found by hierarchical cluster analysis of all 165 primary invasive breast carcinomas revealed no association with LR. Predefined gene sets (molecular subtypes and "chromosomal instability" signature) are associated with LR (P = 0.0002 and 0.003, respectively). Significant analysis of microarrays revealed an association between LR and cell proliferation, not captured by histologic grading. Class prediction analysis constructed a gene classifier, which was successfully validated, cross-platform, on an independent data set of 161 patients (log-rank P = 0.041). In multivariate analysis, young age was the only independent predictor of LR.

CONCLUSIONS

We have constructed and cross-platform validated a gene expression profile predictive for LR after BCT, which is characterized by genes involved in cell proliferation but not a surrogate for high histologic grade.

Earthworm genomes, genes and proteins: the (re)discovery of Darwin's worms

Small incremental biological change, winnowed by natural selection over geological time scales to produce large consequences, was Darwin's singular insight that revolutionized the life sciences. His publications after 1859, including the ‘earthworm book’, were all written to amplify and support the evolutionary theory presented in the Origin. Darwin was unable to provide a physical basis for the inheritance of favoured traits because of the absence of genetic knowledge that much later led to the ‘modern synthesis’. Mistaken though he was in advocating systemic ‘gemmules’ as agents of inheritance, Darwin was perceptive in seeking to underpin his core vision with concrete factors that both determine the nature of a trait in one generation and convey it to subsequent generations. This brief review evaluates the molecular genetic literature on earthworms published during the last decade, and casts light on the specific aspects of earthworm evolutionary biology that more or less engaged Darwin: (i) biogeography, (ii) species diversity, (iii) local adaptations and (iv) sensitivity. We predict that the current understanding will deepen with the announcement of a draft earthworm genome in Darwin's bicentenary year, 2009. Subsequently, the earthworm may be elevated from the status of a soil sentinel to that elusive entity, an ecologically relevant genetic model organism.

Genetic control of human brain transcript expression in Alzheimer disease

We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.

The human cytomegalovirus-encoded chemokine receptor US28 promotes angiogenesis and tumor formation via cyclooxygenase-2

The human cytomegalovirus (HCMV), potentially associated with the development of malignancies, encodes the constitutively active chemokine receptor US28. Previously, we have shown that US28 expression induces an oncogenic phenotype both in vitro and in vivo. Microarray analysis revealed differential expression of genes involved in oncogenic signaling in US28-expressing NIH-3T3 cells. In particular, the expression of cyclooxygenase-2 (COX-2), a key mediator of inflammatory diseases and major determinant in several forms of cancer, was highly up-regulated. US28 induced increases in COX-2 expression via activation of nuclear factor-kappaB, driving the production of vascular endothelial growth factor. Also, in HCMV-infected cells, US28 contributed to the viral induction of COX-2. Finally, the involvement of COX-2 in US28-mediated tumor formation was evaluated using the COX-2 selective inhibitor Celecoxib. Targeting COX-2 in vivo with Celecoxib led to a marked delay in the onset of tumor formation in nude mice injected with US28-transfected NIH-3T3 cells and a reduction of subsequent growth by repressing the US28-induced angiogenic activity. Hence, the development of HCMV-related proliferative diseases may partially be ascribed to the ability of US28 to activate COX-2.

Differential expression of toll-like receptor genes: sepsis compared with sterile inflammation 1 day before sepsis diagnosis

Toll-like receptors (TLRs) are critical components of innate immunity. This study was designed to evaluate differential expression of genes for TLR and associated signal transduction molecules in critically ill patients developing sepsis compared with those with sterile inflammation. Uninfected critically ill patients with systemic inflammatory response syndrome were prospectively followed daily for development of sepsis. They were divided into two groups and compared in a case-control manner: (a) preseptic patients (n = 45) who subsequently developed sepsis, and (b) uninfected systemic inflammatory response syndrome patients (n = 45) who remained uninfected. Whole blood RNA was collected (PAXGene tube) at study entry and 1, 2, and 3 days before clinical sepsis diagnosis (or time-matched uninfected control) and analyzed via Affymetrix Hg_U133 Plus 2.0 microarrays. Genes were considered differentially expressed if they met univariate significance controlled for multiple comparisons at P < 0.005. Differentially expressed probes were uploaded into the Database for Annotation, Visualization and Integrated Discovery. The TLR pathway (Kyoto Encyclopedia of Genes and Genomes-KEGG) significance was determined via Expression Analysis Systematic Explorer (EASE) scoring. A total of 2,974 Affymetrix probes representing 2,190 unique genes were differentially expressed 1 day before sepsis diagnosis. Thirty-six probes representing 25 genes were annotated to the TLR pathway (KEGG) via the Database for Annotation, Visualization and Integrated Discovery with an EASE score at P < 0.0004. Notable TLR genes demonstrating increased expression include TLR-4 (median, 1.43-fold change), TLR-5 (2.08-fold change), and MAPK14 (1.90-fold change). An additional 11 unique genes were manually annotated into the TLR pathway based on known relevance such as TLR-8 (1.54-fold change). The total 36 genes contained 28 showing increased expression and 8 showing decreased expression. Differential gene expression was noted for TLR receptors (eight genes), TLR intracellular signal transduction cascade molecules (27 genes), and TLR-related effector molecules (one gene). The TLR and downstream signaling genes are differentially expressed in critically ill patients developing sepsis compared with those with sterile inflammation. These expression differences occur before phenotypic-based diagnosis of clinical sepsis.

Chromosomal instability is associated with higher expression of genes implicated in epithelial-mesenchymal transition, cancer invasiveness, and metastasis and with lower expression of genes involved in cell cycle checkpoints, DNA repair, and chromatin maintenance

Chromosomal instability-a hallmark of epithelial cancers-is an ongoing process that results in aneuploidy and karyotypic heterogeneity of a cancer cell population. Previously, we stratified cancer cell lines in the NCI-60 drug discovery panel based on their karyotypic complexity and heterogeneity. Using this stratification in conjunction with drug response data for the cell lines allowed us to identify classes of chemical compounds whose growth-inhibitory activity correlates with karyotypic complexity and chromosomal instability. In this article, we asked the question: What are the biological processes, pathways, or genes associated with chromosomal instability of cancer cells? We found that increased instability of the chromosomal content in a cancer cell population, particularly, persistent gains and losses of chromosomes, is associated with elevated expression of genes involved with aggressive cellular behavior, including invasion- and metastasis-associated changes in cell communication, adhesion, motility, and migration. These same karyotypic features are negatively correlated with the expression of genes involved in cell cycle checkpoints, DNA repair, and chromatin maintenance.

A comparison of four clustering methods for brain expression microarray data

Background

DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed.

Results

k-means outperforms the other methods, with 100% gene coverage and GO enrichments only slightly exceeded by memISA and ISA. Those two methods produce greater GO enrichments on the datasets used, but at the cost of much lower gene coverage, fewer clusters produced, and speed. The clusters they find are largely different to those produced by k-means. Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone), without negatively impacting gene coverage. memISA can also find potentially disease-related clusters. In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both. Two of these clusters are enriched for genes of the MAP kinase pathway, suggesting a possible role for this pathway in the aetiology of schizophrenia.

Conclusion

Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets. However, memISA and ISA can add extra high-quality clusters to the set produced by k-means, so combining these three methods is the method of choice.

Differential gene expression associated with postnatal equine articular cartilage maturation

Background

Articular cartilage undergoes an important maturation process from neonate to adult that is reflected by alterations in matrix protein organization and increased heterogeneity of chondrocyte morphology. In the horse, these changes are influenced by exercise during the first five months of postnatal life. Transcriptional profiling was used to evaluate changes in articular chondrocyte gene expression during postnatal growth and development.

Methods

Total RNA was isolated from the articular cartilage of neonatal (0–10 days) and adult (4–5 years) horses, subjected to one round of linear RNA amplification, and then applied to a 9,367-element equine-specific cDNA microarray. Comparisons were made with a dye-swap experimental design. Microarray results for selected genes (COL2A1, COMP, P4HA1, TGFB1, TGFBR3, TNC) were validated by quantitative polymerase chain reaction (qPCR).

Results

Fifty-six probe sets, which represent 45 gene products, were up-regulated (p < 0.01) in chondrocytes of neonatal articular cartilage relative to chondrocytes of adult articular cartilage. Conversely, 586 probe sets, which represent 499 gene products, were up-regulated (p < 0.01) in chondrocytes of adult articular cartilage relative to chondrocytes of neonatal articular cartilage. Collagens, matrix-modifying enzymes, and provisional matrix non-collagenous proteins were expressed at higher levels in the articular cartilage of newborn foals. Those genes with increased mRNA abundance in adult chondrocytes included leucine-rich small proteoglycans, matrix assembly, and cartilage maintenance proteins.

Conclusion

Differential expression of genes encoding matrix proteins and matrix-modifying enzymes between neonates and adults reflect a cellular maturation process in articular chondrocytes. Up-regulated transcripts in neonatal cartilage are consistent with growth and expansion of the articular surface. Expression patterns in mature articular cartilage indicate a transition from growth to homeostasis, and tissue function related to withstanding shear and weight-bearing stresses.

Identification of tumor promotion marker genes for predicting tumor promoting potential of chemicals in BALB/c 3T3 cells

Tumor promoters can cause development of tumors in initiated cells and the majority of them are non-genotoxic carcinogens. The detection of tumor promoters is important for the prevention of cancer. The in vitro two-stage transformation assay, using BALB/c 3T3 cells, is a useful system, and benefits from a convenient protocol and high predictability of mammalian carcinogenicity. But these assays are time-consuming and often require expertise for microscopic observation. To construct an in vitro tumor promoting activity test system, we performed large-scale gene expression analyses, using DNA microarrays, of BALB/c 3T3 cells following treatment with nine chemicals that are known to induce tumor promotion: TPA, zinc chloride, sodium orthovanadate, okadaic acid, insulin, lithocolic acid, phenobarbital sodium, sodium saccharide, sodium arsenite. As a result of DNA microarray and real time PCR analyses, 22 marker genes were identified. These consisted of genes related to cell cycle, regulation of transcription, anti-apoptosis, and positive regulation of cell proliferation. There was a correlation between these 22 marker genes and the cell transformation assay results in BALB/c 3T3 cells. These results suggest that this tumor promoting activity test system, based on 22 marker genes, can become a valuable tool for screening potential tumor promoters.

UNBS5162, a novel naphthalimide that decreases CXCL chemokine expression in experimental prostate cancers

Several naphthalimides have been evaluated clinically as potential anticancer agents. UNBS3157, a naphthalimide that belongs to the same class as amonafide, was designed to avoid the specific activating metabolism that induces amonafide's hematotoxicity. The current study shows that UNBS3157 rapidly and irreversibly hydrolyzes to UNBS5162 without generating amonafide. In vivo UNBS5162 after repeat administration significantly increased survival in orthotopic human prostate cancer models. Results obtained by the National Cancer Institute (NCI) using UNBS3157 and UNBS5162 against the NCI 60 cell line panel did not show a correlation with any other compound present in the NCI database, including amonafide, thereby suggesting a unique mechanism of action for these two novel naphthalimides. Affymetrix genome-wide microarray analysis and enzyme-linked immunosorbent assay revealed that in vitro exposure of PC-3 cells to UNBS5162 (1 microM for 5 successive days) dramatically decreased the expression of the proangiogenic CXCL chemokines. Histopathology additionally revealed antiangiogenic properties in vivo for UNBS5162 in the orthotopic PC-3 model. In conclusion, the present study reveals UNBS5162 to be a pan-antagonist of CXCL chemokine expression, with the compound displaying antitumor effects in experimental models of human refractory prostate cancer when administered alone and found to enhance the activity of taxol when coadministered with the taxoid.

Gene Vector Analysis (Geneva): a unified method to detect differentially-regulated gene sets and similar microarray experiments

Background

Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map.

Results

We describe an analytical method (Geneva: Gene Vector Analysis) to relate genes to biological properties and to other similar experiments in a uniform way. This new method works on both gene sets and on gene lists/vectors as input queries, and can effectively query databases consisting of sets of biologically related sets, or of results from other microarray experiments. We also present an improvement to the null model estimate by using the empirical background distribution drawn from previous experiments. We validated Geneva by rediscovering a number of previous findings, and by finding significant relationships within microarrays in the GEO repository.

Conclusion

Provided a reasonable corpus of previous experiments is available, this method is more accurate than the class label permutation model, especially for data sets with limited number of replicates. Geneva is, moreover, computationally faster because the background distributions can be precomputed. We also provide a standard evaluation data set based on 5 pairs of related experiments that should share similar functional relationships and 28 pairs of unrelated experiments from GEO. Discovering relationships amongst GEO data sets has implications for drug repositioning, and understanding relationships between diseases and drugs.

Expression profiles of the mouse lung identify a molecular signature of time-to-birth

A greater understanding of the regulatory processes contributing to lung development could help ameliorate morbidity and mortality in premature infants and identify individuals at risk for congenital and/or chronic lung diseases. Genomics technologies have provided rich gene expression datasets for the developing lung that enable systems biology approaches for identifying large-scale molecular signatures within this complex phenomenon. Here, we applied unsupervised principal component analysis on two developing lung datasets and identified common dominant transcriptomic signatures. Of particular interest, we identify an overlying biological program we term "time-to-birth," which describes the distance in age from the day of birth. We identify groups of genes contributing to the time-to-birth molecular signature. Statistically overrepresented are genes involved in oxygen and gas transport activity, as expected for a transition to air breathing, as well as host defense function. In addition, we identify genes with expression patterns associated with the initiation of alveolar formation. Finally, we present validation of gene expression patterns across the two datasets, and independent validation of select genes by qPCR and immunohistochemistry. These data contribute to our understanding of genetic components contributing to large-scale biological processes and may be useful, particularly in animal models of abnormal lung development, to predict the state of organ development or preparation for birth.

Alveolar epithelial cell injury with Epstein-Barr virus upregulates TGFbeta1 expression

Idiopathic pulmonary fibrosis (IPF) is a refractory and lethal interstitial lung disease characterized by alveolar epithelial cells apoptosis, fibroblast proliferation, and ECM protein deposition. Epstein-Barr virus (EBV) has previously been localized to alveolar epithelial cells of IPF patients and is associated with a poor prognosis. In this study, we utilized a microarray-based differential gene expression analysis strategy to identify molecular drivers of EBV-associated lung fibrosis. Two cell lines, primary human alveolar epithelial cells type 2 and A549 cells, were infected with EBV. EBV lytic phase induction increased active and total transforming growth factor-beta1 (TGFbeta1) transcript expression in association with reduced cell proliferation and increased caspase 3/7 activity. Exposing EBV-infected cells to ganciclovir resulted in TGFbeta1 deregulation and reduced expression of EBV early response genes, BRLF1 and BZLF1. We targeted the BRLF1 and BZLF1 gene products, Rta and Zta, by silencing RNA, and this resulted in the normalization of TGFbeta1 transcript and cell proliferation levels. Our study using a viral cell line model complements existing human and animal model data and further provides evidence to suggest that viral epithelial cell injury may play a role in IPF.

Non-negative matrix factorization for the analysis of complex gene expression data: identification of clinically relevant tumor subtypes

Non-negative matrix factorization (NMF) is a relatively new approach to analyze gene expression data that models data by additive combinations of non-negative basis vectors (metagenes). The non-negativity constraint makes sense biologically as genes may either be expressed or not, but never show negative expression. We applied NMF to five different microarray data sets. We estimated the appropriate number metagens by comparing the residual error of NMF reconstruction of data to that of NMF reconstruction of permutated data, thus finding when a given solution contained more information than noise. This analysis also revealed that NMF could not factorize one of the data sets in a meaningful way. We used GO categories and pre defined gene sets to evaluate the biological significance of the obtained metagenes. By analyses of metagenes specific for the same GO-categories we could show that individual metagenes activated different aspects of the same biological processes. Several of the obtained metagenes correlated with tumor subtypes and tumors with characteristic chromosomal translocations, indicating that metagenes may correspond to specific disease entities. Hence, NMF extracts biological relevant structures of microarray expression data and may thus contribute to a deeper understanding of tumor behavior.

Liver-specific hepatocyte nuclear factor-4alpha deficiency: greater impact on gene expression in male than in female mouse liver

Hepatocyte nuclear factor (HNF)-4alpha is a liver-enriched transcription factor that regulates numerous liver-expressed genes including several sex-specific cytochrome P450 genes. Presently, a liver-specific HNF4alpha-deficient mouse model was used to characterize the impact of liver HNF4alpha deficiency on a global scale using 41,174 feature microarrays. A total of 4994 HNF4alpha-dependent genes were identified, of which about 1000 fewer genes responded to the loss of HNF4alpha in female liver as compared with male liver. Sex differences in the impact of liver HNF4alpha deficiency were even more dramatic when genes showing sex-specific expression were examined. Thus, 372 of the 646 sex-specific genes characterized by a dependence on HNF4alpha responded to the loss of HNF4alpha in males only, as compared with only 61 genes that responded in females only. Moreover, in male liver, 78% of 508 male-specific genes were down-regulated and 42% of 356 female-specific genes were up-regulated in response to the loss of HNF4alpha, with sex specificity lost for 90% of sex-specific genes. This response to HNF4alpha deficiency is similar to the response of male mice deficient in the GH-activated transcription factor signal transducer and activator of transcription 5b (STAT5b), where 90% of male-specific genes were down-regulated and 61% of female-specific genes were up-regulated, suggesting these two factors cooperatively regulate liver sex specificity by mechanisms that are primarily active in males. Finally, 203 of 648 genes previously shown to bind HNF4alpha near the transcription start site in mouse hepatocytes were affected by HNF4alpha deficiency in mouse liver, with the HNF4alpha-bound gene set showing a 5-fold enrichment for genes positively regulated by HNF4alpha. Thus, a substantial fraction of the HNF4alpha-dependent genes reported here are likely to be direct targets of HNF4alpha.

Computational methods for analysis of cellular functions and pathways collectively targeted by differentially expressed microRNA

This report presents computational methods of analysis of cellular processes, functions, and pathways affected by differentially expressed microRNA, a statistical basis of the gene enrichment analysis method, a modification of enrichment analysis method accounting for combinatorial targeting of Gene Ontology categories by multiple miRNAs and examples of the global functional profiling of predicted targets of differentially expressed miRNAs in cancer. We have also summarized an application of Ingenuity Pathway Analysis tools for in depth analysis of microRNA target sets that may be useful for the biological interpretation of microRNA profiling data. To illustrate the utility of these methods, we report the main results of our recent computational analysis of five published datasets of aberrantly expressed microRNAs in five human cancers (pancreatic cancer, breast cancer, colon cancer, lung cancer, and lymphoma). Using a combinatorial target prediction algorithm and statistical enrichment analysis, we have determined Gene Ontology categories as well as biological functions, disease categories, toxicological categories, and signaling pathways that are: targeted by multiple microRNAs; statistically significantly enriched with target genes; and known to be affected in specific cancers. Our recent computational analysis of predicted targets of co-expressed miRNAs in five human cancers suggests that co-expressed miRNAs provide systemic compensatory response to the abnormal phenotypic changes in cancer cells by targeting a broad range of functional categories and signaling pathways reportedly affected in a particular cancer.

Genomic expression patterns distinguish long-term from short-term glioblastoma survivors: a preliminary feasibility study

We used microarray analysis to investigate associations between genotypic expression profiles and survival phenotypes in patients with primary glioblastoma (GBM). Tumor samples from 7 long-term glioblastoma survivors (>24 months) and 13 short-term survivors (<9 months) were analyzed to detect differential patterns of gene expression between these groups and to identify genotypic subclasses of glioblastomas that correlate with survival phenotypes. Five unsupervised and three supervised clustering algorithms consistently and accurately grouped the tumors into genotypic subgroups corresponding to the two clinical survival phenotypes. Three unique prospective mathematical classification algorithms were subsequently trained to use expression data to stratify unknown glioblastomas between survival groups and performed this task with 100% accuracy in validation studies. A set of 1478 genes with significant differential expression (p<0.01) between long-term and short-term survivors was identified, and additional mathematical filtering was used to isolate a 43-gene "fingerprint" that distinguished survival phenotypes. Differential regulation of a subset of these genes was confirmed using RT-PCR. Gene ontology analysis of the fingerprint demonstrated pathophysiologic functions for the gene products that are consistent with current models of tumor biology, suggesting that differential expression of these genes may contribute etiologically to the observed differences in survival. These results demonstrate that unique expression profiles characterize genotypic subsets of primary GBMs associated with differential survival phenotypes, and these profiles can be used in a prospective fashion to assign unknown tumors to survival groups. Future efforts will focus on building more robust classifiers and identifying additional subclasses of gliomas with phenotypic significance.

Aging impacts transcriptomes but not genomes of hormone-dependent breast cancers

Introduction

Age is one of the most important risk factors for human malignancies, including breast cancer; in addition, age at diagnosis has been shown to be an independent indicator of breast cancer prognosis. Except for inherited forms of breast cancer, however, there is little genetic or epigenetic understanding of the biological basis linking aging with sporadic breast cancer incidence and its clinical behavior.

Methods

DNA and RNA samples from matched estrogen receptor (ER)-positive sporadic breast cancers diagnosed in either younger (age ≤ 45 years) or older (age ≥ 70 years) Caucasian women were analyzed by array comparative genomic hybridization and by expression microarrays. Array comparative genomic hybridization data were analyzed using hierarchical clustering and supervised age cohort comparisons. Expression microarray data were analyzed using hierarchical clustering and gene set enrichment analysis; differential gene expression was also determined by conditional permutation, and an age signature was derived using prediction analysis of microarrays.

Results

Hierarchical clustering of genome-wide copy-number changes in 71 ER-positive DNA samples (27 younger women, 44 older women) demonstrated two age-independent genotypes; one with few genomic changes other than 1q gain/16q loss, and another with amplifications and low-level gains/losses. Age cohort comparisons showed no significant differences in total or site-specific genomic breaks and amplicon frequencies. Hierarchical clustering of 5.1 K genes variably expressed in 101 ER-positive RNA samples (53 younger women, 48 older women) identified six transcriptome subtypes with an apparent age bias (P < 0.05). Samples with higher expression of a poor outcome-associated proliferation signature were predominantly (65%) younger cases. Supervised analysis identified cancer-associated genes differentially expressed between the cohorts; with younger cases expressing more cell cycle genes and more than threefold higher levels of the growth factor amphiregulin (AREG), and with older cases expressing higher levels of four different homeobox (HOX) genes in addition to ER (ESR1). An age signature validated against two other independent breast cancer datasets proved to have >80% accuracy in discerning younger from older ER-positive breast cancer cases with characteristic differences in AREG and ESR1 expression.

Conclusion

These findings suggest that epigenetic transcriptome changes, more than genotypic variation, account for age-associated differences in sporadic breast cancer incidence and prognosis.

Nutrition-induced catch-up growth increases hypoxia inducible factor 1alpha RNA levels in the growth plate

Although catch-up growth is a well-known phenomenon, the local pathways at the epiphyseal growth plate that govern this process remain poorly understood. To study the mechanisms governing catch-up growth in the growth plate, we subjected prepubertal rats to 10 days of 40% food restriction, followed by a renewal of the regular food supply to induce catch-up growth. The animals were weighed daily, and their humeral length was measured at sacrifice. The proximal tibial epiphyseal growth plates (EGPs) were studied, and findings were compared with EGPs from animals fed ad libitum and animals under food restriction. The gene expression profile in the growth plates was examined using DNA microarrays, and the expression levels of selected genes were validated by real-time polymerase chain reaction. To localize gene expression in different growth plate zones, microdissection was used. Protein levels and localization were examined using immunohistochemistry. We showed that the expression level of 550 genes decreased during food restriction and increased during catch-up growth, starting already one day after refeeding. HIF-1alpha, as well as several of its downstream targets, was found among these genes. Immunohistochemistry showed a similar pattern for HIF-1alpha protein abundance. Additionally, HIF-1alpha mRNA and protein levels were higher in the proliferating than in the hypertrophic zone, and this distribution was unaffected by nutritional status. These findings indicate that nutrition has a profound effect on gene expression level during growth plate growth, and suggest an important role for HIF-1alpha in the growth plate and its response to nutritional manipulation.

Molecular alterations in prostate carcinomas that associate with in vivo exposure to chemotherapy: identification of a cytoprotective mechanism involving growth differentiation factor 15

PURPOSE

To identify molecular alterations associating with in vivo exposure of prostate carcinoma to chemotherapy and assess functional roles modulating tumor response and resistance.

EXPERIMENTAL DESIGN

Patients with high-risk localized prostate cancer (tumor-node-metastasis >or= T(2b) or prostate-specific antigen >or= 15 ng/mL or Gleason glade >or= 4+3) were enrolled into a phase II clinical trial of neoadjuvant chemotherapy with docetaxel and mitoxantrone followed by prostatectomy. Pretreatment prostate tissue was acquired by needle biopsy and posttreatment tissue was acquired by prostatectomy. Prostate epithelium was captured by microdissection, and transcript levels were quantitated by cDNA microarray hybridization. Gene expression changes associated with chemotherapy were determined by a random variance t test. Several were verified by quantitative reverse transcription PCR. In vitro analyses determining the influence of growth differentiation factor 15 (GDF15) on chemotherapy resistance were done.

RESULTS

Gene expression changes after chemotherapy were measured in 31 patients who completed four cycles of neoadjuvant chemotherapy. After excluding genes shown previously to be influenced by the radical prostatectomy procedure, we identified 51 genes with significant transcript level alterations following chemotherapy. This group included several cytokines, including GDF15, chemokine (C-X-C motif) ligand 10, and interleukin receptor 1beta. Overexpression of GDF15 or exposure of prostate cancer cell lines to exogenous recombinant GDF15 conferred resistance to docetaxel and mitoxantrone.

CONCLUSIONS

Consistent molecular alterations were identified in prostate cancer cells exposed to docetaxel and mitoxantrone chemotherapy. These alterations include transcripts encoding cytokines known to be regulated through the nuclear factor-kappaB pathway. Chemotherapy-induced cytokines and growth factors, such as GDF15, contribute to tumor cell therapy resistance and may serve as targets to improve responses.

Comparison of the effects of Leishmania major or Leishmania donovani infection on macrophage gene expression

The intracellular parasite Leishmania causes a wide spectrum of human disease, ranging from self-resolving cutaneous lesions to fatal visceral disease, depending on the species of Leishmania involved. The mechanisms by which different Leishmania species cause different pathologies are largely unknown. We have addressed this question by comparing the gene expression profiles of bone marrow-derived macrophages infected with either Leishmania donovani or L. major promastigotes. We found that the two species had very similar effects on macrophage gene expression. Both species caused a small (<2.5-fold) but statistically significant repression of several hundred genes. In addition, both species strongly induced and repressed about 60 genes. Comparing the effects of the two species showed that only 26 genes were regulated differently by L. major as opposed to L. donovani, including those for metallothioneins 1 and 2, HSP70 and -72, CCL4, Gadd45beta, Dsp1, matrix metalloprotease 13, T-cell death-associated gene 51 (Tdag51), RhoB, spermine/spermidine N1-acyl transferase 1 (SSAT), and Cox2. L. donovani-infected macrophages were also found to express higher levels of Cox2 protein and prostaglandin E synthase mRNA than L. major-infected macrophages. While both species have previously been shown to trigger prostaglandin E synthesis by bystander cells, this study suggests that infected macrophages themselves express prostaglandin E-synthesizing genes only in response to L. donovani.

Gene expression analysis of angioimmunoblastic lymphoma indicates derivation from T follicular helper cells and vascular endothelial growth factor deregulation

Angioimmunoblastic lymphoma (AILT) is the second most common subtype of peripheral T-cell lymphoma (PTCL) and is characterized by dismal prognosis. Thus far, only a few studies have dealt with its molecular pathogenesis. We performed gene expression profile (GEP) analysis of six AILT, six anaplastic large cell lymphomas (ALCL), 28 PTCL-unspecified (PTCL/U), and 20 samples of normal T lymphocytes (including CD4(+), CD8(+), and activated and resting subpopulations), aiming to (a) assess the relationship of AILT with other PTCLs, (b) establish the relationship between AILT and normal T-cell subsets, and (c) recognize the cellular programs deregulated in AILT possibly looking for novel potential therapeutic targets. First, we found that AILT and other PTCLs have rather similar GEP, possibly sharing common oncogenic pathways. Second, we found that AILTs are closer to activated CD4(+), rather than to resting or CD8(+) lymphocytes. Furthermore, we found that the molecular signature of follicular T helper cells was significantly overexpressed in AILT, reinforcing the idea that AILT may arise from such cellular counterpart. Finally, we identified several genes deregulated in AILT, including PDGFRA, REL, and VEGF. The expression of several molecules was then studied by immunohistochemistry on tissue microarrays containing 45 independent AILT cases. Notably, we found that the vascular endothelial growth factor (VEGF) was expressed not only by reactive cells, but also by neoplastic cells, and that nuclear factor-kappaB (NF-kappaB) activation is uncommon in AILT, as suggested by frequent exclusively cytoplasmic c-REL localization. Our study provides new relevant information on AILT biology and new candidates for possible therapeutic targets such as PDGFRA (platelet-derived growth factor alpha) and VEGF.

Breast cancer molecular signatures as determined by SAGE: correlation with lymph node status

Global gene expression measured by DNA microarray platforms have been extensively used to classify breast carcinomas correlating with clinical characteristics, including outcome. We generated a breast cancer Serial Analysis of Gene Expression (SAGE) high-resolution database of approximately 2.7 million tags to perform unsupervised statistical analyses to obtain the molecular classification of breast-invasive ductal carcinomas in correlation with clinicopathologic features. Unsupervised statistical analysis by means of a random forest approach identified two main clusters of breast carcinomas, which differed in their lymph node status (P=0.01); this suggested that lymph node status leads to globally distinct expression profiles. A total of 245 (55 up-modulated and 190 down-modulated) transcripts were differentially expressed between lymph node (+) and lymph node (-) primary breast tumors (fold change, >or=2; P<0.05). Various lymph node (+) up-modulated transcripts were validated in independent sets of human breast tumors by means of real-time reverse transcription-PCR (RT-PCR). We validated significant overexpression of transcripts for HOXC10 (P=0.001), TPD52L1 (P=0.007), ZFP36L1 (P=0.011), PLINP1 (P=0.013), DCTN3 (P=0.025), DEK (P=0.031), and CSNK1D (P=0.04) in lymph node (+) breast carcinomas. Moreover, the DCTN3 (P=0.022) and RHBDD2 (P=0.002) transcripts were confirmed to be overexpressed in tumors that recurred within 6 years of follow-up by real-time RT-PCR. In addition, meta-analysis was used to compare SAGE data associated with lymph node (+) status with publicly available breast cancer DNA microarray data sets. We have generated evidence indicating that the pattern of gene expression in primary breast cancers at the time of surgical removal could discriminate those tumors with lymph node metastatic involvement using SAGE to identify specific transcripts that behave as predictors of recurrence as well.

Comparison of Gene Sets for Expression Profiling: Prediction of Metastasis from Low-Malignant Breast Cancer

PURPOSE

In the low-risk group of breast cancer patients, a subgroup experiences metastatic recurrence of the disease. The aim of this study was to examine the performance of gene sets, developed mainly from high-risk tumors, in a group of low-malignant tumors.

EXPERIMENTAL DESIGN

Twenty-six tumors from low-risk patients and 34 low-malignant T2 tumors from patients with slightly higher risk have been examined by genome-wide gene expression analysis. Nine prognostic gene sets were tested in this data set.

RESULTS

A 32-gene profile (HUMAC32) that accurately predicts metastasis has previously been developed from this data set. In the present study, six of the eight other gene sets have prognostic power in the low-malignant patient group, whereas two have no prognostic value. Despite a relatively small overlap between gene sets, there is high concordance of classification of samples. This, together with analysis of functional gene groups, indicates that the same pathways may be represented by several of the gene sets. However, the results suggest that low-risk patients may be classified more accurately with gene signatures developed especially for this patient group.

CONCLUSION

Several gene sets, mainly developed in high-risk cancers, predict metastasis from low-malignant cancer.

Identification of Pax5 as a target of MTA1 in B-cell lymphomas

Previously, we have shown that metastasis-associated protein 1 (MTA1) overexpression in transgenic mice was accompanied by high incidence of spontaneous B-cell lymphomas including diffuse large B-cell lymphomas (DLBCL). To understand the molecular basis of lymphoma in MTA1-transgenic (MTA1-TG) mice, we wished to identify a putative MTA1 target with a causal role in B-cell lymphogenesis. Using chromatin immunoprecipitation assays, we identified paired box gene 5 (Pax5), a molecule previously implicated in B-cell lymphogenesis, as a potential downstream effector of MTA1. Lymphomas from MTA1-TG mice also showed up-regulation of Pax5. We also found that MTA1 acetylated on Lys(626) interacted with p300 histone acetyltransferase, and that acetylated MTA1 was recruited to the Pax5 promoter to stimulate Pax5 transcription. Global gene profiling identified down-regulation of a set of genes, including those downstream of Pax5 and directly implicated in the B-cell lymphogenesis. Significance of these murine studies was established by evidence showing a widespread up-regulation of both MTA1 and Pax5 in DLBCL from humans. These observations provide in vivo genetic evidence for a role of MTA1 in lymphomagenesis.

Macromolecule biosynthesis: a key function of sleep

The function(s) of sleep remains a major unanswered question in biology. We assessed changes in gene expression in the mouse cerebral cortex and hypothalamus following different durations of sleep and periods of sleep deprivation. There were significant differences in gene expression between behavioral states; we identified 3,988 genes in the cerebral cortex and 823 genes in the hypothalamus with altered expression patterns between sleep and sleep deprivation. Changes in the steady-state level of transcripts for various genes are remarkably common during sleep, as 2,090 genes in the cerebral cortex and 409 genes in the hypothalamus were defined as sleep specific and changed (increased or decreased) their expression during sleep. The largest categories of overrepresented genes increasing expression with sleep were those involved in biosynthesis and transport. In both the cerebral cortex and hypothalamus, during sleep there was upregulation of multiple genes encoding various enzymes involved in cholesterol synthesis, as well as proteins for lipid transport. There was also upregulation during sleep of genes involved in synthesis of proteins, heme, and maintenance of vesicle pools, as well as antioxidant enzymes and genes encoding proteins of energy-regulating pathways. We postulate that during sleep there is a rebuilding of multiple key cellular components in preparation for subsequent wakefulness.

Coagulation and complement protein differences between septic and uninfected systemic inflammatory response syndrome patients

BACKGROUND

Systemic inflammatory response syndrome (SIRS) represents a host response to various insults. Recent advances have demonstrated an interconnection between inflammation, complement, and coagulation. This experiment was designed to evaluate differences in plasma protein profiles between clinically identical patients: septic versus uninfected SIRS patients, prior to clinical diagnosis of infection.

METHODS

Patients admitted to an intensive care unit of a major university, meeting two of four SIRS criteria were followed prospectively for development of sepsis. Plasma samples were collected daily and divided into two groups: a preseptic group that subsequently developed sepsis and a SIRS group that remained uninfected. Protein profiling was accomplished by three-dimensional liquid chromatography fractionation with electrospray ion trap mass spectrometry after immunodepletion of abundant proteins and a trypsin digest. Spectra peaks were identified using Agilent Technologies Spectrum Mill Workbench software. Relevance to biologic pathways was analyzed and statistical significance determined with DAVID 2.1 available at the National Institutes of Health.

RESULTS

A total of 134 unique proteins were significantly different between groups. Thirty-two of these (23.5%) mapped to the complement and coagulation cascade (KEGG), 10 (7.5%) mapped to classic complement pathway; 11 (8.2%) mapped to complement pathway, and 8 (6.0%) mapped to lectin binding complement pathway (Biocarta). These pathways were all significantly (p<0.0001) over-represented in sepsis patients compared to SIRS-only patients.

CONCLUSION

Using novel mass spectrometry methodology, we were able to demonstrate differential protein profiles in septic versus uninfected SIRS patients prior to clinical diagnosis of sepsis.

Linked gene ontology categories are novel and differ from associated gene ontology categories for the bipolar disorders

Family, and twin genetic studies strongly indicate gene variants as predisposing to the bipolar disorders. Now, about 3000 genes are genetically linked and about 44 genes are genetically associated. Rank differences, however, exist between the linked gene Genetic ontology categories and the associated gene Genetic ontology categories. For the linked gene Genetic ontology categories, the activation of NF-kappaB-inducing kinase category is over-represented; in contrast, the associated genes show the Synaptic transmission category as over-represented. Association studies report selecting positional candidate genes from previous linkage studies, or, selecting genes on the basis of pathophysiologic hypotheses. Only a few of the pathophysiologic hypotheses genes, however, had been previously linked to the bipolar disorders. In particular, only a couple of the Synaptic transmission genes had been previously linked to bipolar disorders.

Reprogramming of genetic networks during initiation of the Fetal Alcohol Syndrome

Fetal Alcohol Spectrum Disorders (FASD) are birth defects that result from maternal alcohol use. We used a non a priori approach to prioritize candidate pathways during alcohol-induced teratogenicity in early mouse embryos. Two C57BL/6 substrains (B6J, B6N) served as the basis for study. Dosing pregnant dams with alcohol (2x 2.9 g/kg ethanol spaced 4 hr on day 8) induced FASD in B6J at a higher incidence than B6N embryos. Counter-exposure to PK11195 (4 mg/kg) significantly protected B6J embryos but slightly promoted FASD in B6N embryos. Microarray transcript profiling was performed on the embryonic headfold 3 hr after the first maternal alcohol injection (GEO data series accession GSE1074). This analysis revealed metabolic and cellular reprogramming that was substrain-specific and/or PK11195-dependent. Mapping ethanol-responsive KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways revealed down-regulation of ribosomal proteins and proteasome, and up-regulation of glycolysis and pentose phosphate pathway in B6N embryos; and significant up-regulation of tight junction, focal adhesion, adherens junction, and regulation of the actin cytoskeleton (and near-significant up-regulation of Wnt signaling and apoptosis) pathways in both substrains. Expression networks constructed computationally from these altered genes identified entry points for EtOH at several hubs (MAPK1, ALDH3A2, CD14, PFKM, TNFRSF1A, RPS6, IGF1, EGFR, PTEN) and for PK11195 at AKT1. Our findings are consistent with the growing view that developmental exposure to alcohol alters common signaling pathways linking receptor activation to cytoskeletal reorganization. The programmatic shift in cell motility and metabolic capacity further implies cell signals and responses that are integrated by the mitochondrial recognition site for PK11195.