Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

Genome-wide DNA methylation patterns reveal clinically relevant predictive and prognostic subtypes in human osteosarcoma

Aberrant methylation of genomic DNA has been reported in many cancers. Specific DNA methylation patterns have been shown to provide clinically useful prognostic information and define molecular disease subtypes with different response to therapy and long-term outcome. Osteosarcoma is an aggressive malignancy for which approximately half of tumors recur following standard combined surgical resection and chemotherapy. No accepted prognostic factor save tumor necrosis in response to adjuvant therapy currently exists, and traditional genomic studies have thus far failed to identify meaningful clinical associations. We studied the genome-wide methylation state of primary tumors and tested how they predict patient outcomes. We discovered relative genomic hypomethylation to be strongly predictive of response to standard chemotherapy. Recurrence and survival were also associated with genomic methylation, but through more site-specific patterns. Furthermore, the methylation patterns were reproducible in three small independent clinical datasets. Downstream transcriptional, in vitro, and pharmacogenomic analysis provides insight into the clinical translation of the methylation patterns. Our findings suggest the assessment of genomic methylation may represent a strategy for stratifying patients for the application of alternative therapies.

Identifying individualized risk subpathways reveals pan-cancer molecular classification based on multi-omics data

Cancer is a highly heterogeneous disease with different functional disorders among individuals. The initiation and progression of cancer is usually related to dysregulation of local regions within pathways. Identification of individualized risk pathways is crucial for revealing the mechanisms of tumorigenesis and heterogeneity. However, approach that focused on mining patient-specific risk subpathway regions is still lacking. Here, we developed an individualized cancer risk subpathway identification method that was referred as InCRiS by integrating multi-omics data. Then, the method was applied to nearly 3000 samples across 9 TCGA cancer types and its robustness and reliability were evaluated. Dissecting dysregulated subpathways in these tumor samples revealed several key regions which may play oncogenic roles in cancer. The construction of risk subpathway dysregulation profile of pan-cancers revealed 11 pan-cancer molecular classification (InCRiS subtypes) with significantly different clinical outcomes. Moreover, subpathway regions that tend to be disordered in individuals of specific subtypes were examined for understanding the pathogenesis of tumor and some key genes such as CTNNB1, EP300 and PRKDC were nominated in different subtypes. In summary, the proposed method and resulting data presented useful resources to study the mechanism of tumor heterogeneity and to discovery novel therapeutic targets for precise treatment of cancer.

Identification of genes associated with sudden cardiac death: a network- and pathway-based approach

Background

Sudden cardiac death (SCD) accounts for a large proportion of the total deaths across different age groups. Although numerous candidate genes related to SCD have been identified by genetic association studies and genome wide association studies (GWAS), the molecular mechanisms underlying SCD are still unclear, and the biological functions and interactions of these genes remain obscure. To clarify this issue, we performed a comprehensive and systematic analysis of SCD-related genes by a network and pathway-based approach.

Methods

By screening the publications deposited in the PubMed and Gene-Cloud Biotechnology Information (GCBI) databases, we collected the genes genetically associated with SCD, which were referred to as the SCD-related gene set (SCDgset). To analyze the biological processes and biochemical pathways of the SCD-related genes, functional analysis was performed. To explore interlinks and interactions of the enriched pathways, pathway crosstalk analysis was implemented. To construct SCD-specific molecular networks, Markov cluster algorithm and Steiner minimal tree algorithm were employed.

Results

We collected 257 genes that were reported to be associated with SCD and summarized them in the SCDgset. Most of the biological processes and biochemical pathways were related to heart diseases, while some of the biological functions may be noncardiac causes of SCD. The enriched pathways could be roughly grouped into two modules. One module was related to calcium signaling pathway and the other was related to MAPK pathway. Moreover, two different SCD-specific molecular networks were inferred, and 23 novel genes potentially associated with SCD were also identified.

Conclusions

In summary, by means of a network and pathway-based methodology, we explored the pathogenetic mechanism underlying SCD. Our results provide valuable information in understanding the pathogenesis of SCD and include novel biomarkers for diagnosing potential patients with heart diseases; these may help in reducing the corresponding risks and even aid in preventing SCD.

A Systems Biology Workflow for Drug and Vaccine Repurposing: Identifying Small-Molecule BCG Mimics to Reduce or Prevent COVID-19 Mortality

PURPOSE

Coronavirus disease 2019 (COVID-19) is expected to continue to cause worldwide fatalities until the World population develops 'herd immunity', or until a vaccine is developed and used as a prevention. Meanwhile, there is an urgent need to identify alternative means of antiviral defense. Bacillus Calmette-Guérin (BCG) vaccine that has been recognized for its off-target beneficial effects on the immune system can be exploited to boast immunity and protect from emerging novel viruses.

METHODS

We developed and employed a systems biology workflow capable of identifying small-molecule antiviral drugs and vaccines that can boast immunity and affect a wide variety of viral disease pathways to protect from the fatal consequences of emerging viruses.

RESULTS

Our analysis demonstrates that BCG vaccine affects the production and maturation of naïve T cells resulting in enhanced, long-lasting trained innate immune responses that can provide protection against novel viruses. We have identified small-molecule BCG mimics, including antiviral drugs such as raltegravir and lopinavir as high confidence hits. Strikingly, our top hits emetine and lopinavir were independently validated by recent experimental findings that these compounds inhibit the growth of SARS-CoV-2 in vitro.

CONCLUSIONS

Our results provide systems biology support for using BCG and small-molecule BCG mimics as putative vaccine and drug candidates against emergent viruses including SARS-CoV-2.

Integrative Analysis of Gene Expression and Regulatory Network Interaction Data Reveals the Protein Kinase C Family of Serine/Threonine Receptors as a Significant Druggable Target for Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disease affecting the ventral midbrain dopaminergic neurons, resulting in motor defects mainly tremor, rigidity, and bradykinesia along with a wide array of non-motor symptoms. The current study is focused on determining the potential druggable targets of PD by consolidating gene expression profiling and network methodology. Initially, the differentially expressed genes were established from which the central network was constructed by assimilating the interacting partners. Investigating the topological parameters of the network, the genes SYT1, CXCR4, CDC42, KIT, RET, DRD2, NTN1, PRKACB, KDR, NR4A2, SLC18A2, CCK, TH, KCNJ6, and TAC1 were identified as the hub genes and can be explored as potential candidate genes for PD therapeutics. Gene ontology and cluster analysis of the hub genes has provided further insights about the pathophysiology of the disease. Among the hub genes, PRKACB is observed in relatively all the enriched pathways which are modulated by G protein-coupled receptors through protein kinases. Further, we noticed SYT1 as a novel biomarker for PD. Moreover, the regulatory network was constructed with the hub genes as seed nodes with associated transcription factors (TFs) and microRNA (miRNAs). In this analysis, we identified MYC as the major TF and the miRNAs miR-21, miR-155, miR-7, and miR26A1 have a significant role in modulating the hub genes. Briefly, these significant hub genes and their enriched pathways, TFs, and miRNAs have aided in the better understanding of molecular mechanisms underlying PD and its potential core target genes.

Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges

Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.

MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma

There is a lack of well validated prognostic biomarkers in osteosarcoma, a rare, recalcitrant disease for which treatment standards have not changed in over 20 years. We performed microRNA sequencing in 74 frozen osteosarcoma biopsy samples, constituting the largest single center translationally analyzed osteosarcoma cohort to date, and we separately analyzed a multi-omic dataset from a large NCI supported national cooperative group cohort. We validated the prognostic value of candidate microRNA signatures and contextualized them in relevant transcriptomic and epigenomic networks. Our results reveal the existence of molecularly defined phenotypes associated with outcome independent of clinicopathologic features. Through machine learning based integrative pharmacogenomic analysis, the microRNA biomarkers identify novel therapeutics for stratified application in osteosarcoma. The previously unrecognized osteosarcoma subtypes with distinct clinical courses and response to therapy could be translatable for discerning patients appropriate for more intensified, less intensified, or alternate therapeutic regimens.

NGSEA: Network-Based Gene Set Enrichment Analysis for Interpreting Gene Expression Phenotypes with Functional Gene Sets

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

A Two-Stage Whole-Genome Gene Expression Association Study of Young-Onset Hypertension in Han Chinese Population of Taiwan

Hypertension is an important public health problem in the world. Since the intermediate position of the gene expression between genotype and phenotype makes it suitable to link genotype to phenotype, we carried out a two-stage whole-genome gene expression association study to find differentially expressed genes and pathways for hypertension. In the first stage, 126 cases and 149 controls were used to find out the differentially expressed genes. In the second stage, an independent set of samples (127 cases and 150 controls) was used to validate the results. Additionally, we conducted a gene set enrichment analysis (GSEA) to search for differentially affected pathways. A total of nine genes were implicated in the first stage (Bonferroni-corrected p-value < 0.05). Among these genes, ZRANB1, FAM110A, PREP, ANKRD9 and LAMB2 were also differentially expressed in an existing database of hypertensive mouse model (GSE19817). A total of 16 pathways were identified by the GSEA. ZRANB1 and six pathways identified are related to TNF-α. Three pathways are related to interleukin, one to metabolic syndrome, and one to Hedgehog signaling. Identification of these genes and pathways suggest the importance of 1. inflammation, 2. visceral fat metabolism, and 3. adipocytes and osteocytes homeostasis in hypertension mechanisms and complications.

Genetically unmatched human iPSC and ESC exhibit equivalent gene expression and neuronal differentiation potential

The potential uniformity between differentiation and therapeutic potential of human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) remains debatable. We studied the gene expression profiles, pathways analysis and the ability to differentiated into neural progenitor cells (NPCs) and motor neurons (MNs) of genetically unmatched integration-free hiPSC versus hESC to highlight possible differences/similarities between them at the molecular level. We also provided the functional information of the neurons derived from the different hESCs and hiPSCs lines using the Neural Muscular Junction (NMJ) Assay. The hiPSC line was generated by transfecting human epidermal fibroblasts (HEF) with episomal DNAs expressing Oct4, Sox2, Klf4, Nanog, L-Myc and shRNA against p53. For the hESCs line, we used the NIH-approved H9 cell line. Using unsupervised clustering both hESCs and hiPSCs were clustered together implying homogeneous genetic states. The genetic profiles of hiPSCs and hESCs were clearly similar but not identical. Collectively, our data indicate close molecular similarities between genetically unmatched hESCs and hiPS in term of gene expression, and signaling pathways. Moreover, both cell types exhibited similar cholinergic motor neurons differentiation potential with marked ability of the differentiated hESCs and hiPSCs-derived MNs to induce contraction of myotubes after 4 days of co-culture.

Data Perturbation Independent Diagnosis and Validation of Breast Cancer Subtypes Using Clustering and Patterns

Molecular stratification of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifications can be shown to be stable against variations in sample source and data perturbation. Classifications inferred from one set of samples in one lab should be able to consistently stratify a different set of samples in another lab. We present a method for assessing such stability and apply it to the breast cancer (BCA) datasets of Sorlie et al. 2003 and Ma et al. 2003. We find that within the now commonly accepted BCA categories identified by Sorlie et al. Luminal A and Basal are robust, but Luminal B and ERBB2+ are not. In particular, 36% of the samples identified as Luminal B and 55% identified as ERBB2+ cannot be assigned an accurate category because the classification is sensitive to data perturbation. We identify a “core cluster” of samples for each category, and from these we determine “patterns” of gene expression that distinguish the core clusters from each other. We find that the best markers for Luminal A and Basal are (ESR1, LIV1, GATA-3) and (CCNE1, LAD1, KRT5), respectively. Pathways enriched in the patterns regulate apoptosis, tissue remodeling and the immune response. We use a different dataset (Ma et al. 2003) to test the accuracy with which samples can be allocated to the four disease subtypes. We find, as expected, that the classification of samples identified as Luminal A and Basal is robust but classification into the other two subtypes is not.

DNA Methylation of Synaptic Genes in the Prefrontal Cortex Is Associated with Aging and Age-Related Cognitive Impairment

The current study investigates DNA methylation as a possible epigenetic regulator of transcription associated with aging and cognitive function. Young and aged male Fischer 344 rats were behaviorally characterized on a set shifting task, and whole genome bisulfite sequencing was employed to profile the DNA methylome of the medial prefrontal cortex (mPFC). DNA methylation was also compared to RNA expression in the mPFC from the same animals. Variability in methylation was mainly observed for CpG sites as opposed to CHG and CHH sites. Gene bodies, specifically introns, contain the highest levels of methylation. During aging, hypermethylation was observed for genes linked to synaptic function and GTPase activity. Furthermore, impaired cognitive flexibility during aging was associated with hypermethylation of genes linked to postsynaptic density, dendrites, the axon terminus, and Ca²⁺ channels. Finally, comparison with RNA expression confirmed that hypermethylation was correlated with decreased expression of synaptic genes. The results indicate that DNA methylation over the lifespan contributes to synaptic modification observed in brain aging and age-related cognitive impairment.

An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets

Background

A microRNA (miRNA) collection on the imprinted 14q32 MEG3 region has been associated with outcome in osteosarcoma. We assessed the clinical utility of this miRNA set and their association with methylation status.

Methods

We integrated coding and non-coding RNA data from three independent annotated clinical osteosarcoma cohorts (n = 65, n = 27, and n = 25) and miRNA and methylation data from one in vitro (19 cell lines) and one clinical (NCI Therapeutically Applicable Research to Generate Effective Treatments (TARGET) osteosarcoma dataset, n = 80) dataset. We used time-dependent receiver operating characteristic (tdROC) analysis to evaluate the clinical value of candidate miRNA profiles and machine learning approaches to compare the coding and non-coding transcriptional programs of high- and low-risk osteosarcoma tumors and high- versus low-aggressiveness cell lines. In the cell line and TARGET datasets, we also studied the methylation patterns of the MEG3 imprinting control region on 14q32 and their association with miRNA expression and tumor aggressiveness.

Results

In the tdROC analysis, miRNA sets on 14q32 showed strong discriminatory power for recurrence and survival in the three clinical datasets. High- or low-risk tumor classification was robust to using different microRNA sets or classification methods. Machine learning approaches showed that genome-wide miRNA profiles and miRNA regulatory networks were quite different between the two outcome groups and mRNA profiles categorized the samples in a manner concordant with the miRNAs, suggesting potential molecular subtypes. Further, miRNA expression patterns were reproducible in comparing high-aggressiveness versus low-aggressiveness cell lines. Methylation patterns in the MEG3 differentially methylated region (DMR) also distinguished high-aggressiveness from low-aggressiveness cell lines and were associated with expression of several 14q32 miRNAs in both the cell lines and the large TARGET clinical dataset. Within the limits of available CpG array coverage, we observed a potential methylation-sensitive regulation of the non-coding RNA cluster by CTCF, a known enhancer-blocking factor.

Conclusions

Loss of imprinting/methylation changes in the 14q32 non-coding region defines reproducible previously unrecognized osteosarcoma subtypes with distinct transcriptional programs and biologic and clinical behavior. Future studies will define the precise relationship between 14q32 imprinting, non-coding RNA expression, genomic enhancer binding, and tumor aggressiveness, with possible therapeutic implications for both early- and advanced-stage patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s13045-017-0465-4) contains supplementary material, which is available to authorized users.

Oxygen and tissue culture affect placental gene expression

INTRODUCTION

Placental explant culture is an important model for studying placental development and functions. We investigated the differences in placental gene expression in response to tissue culture, atmospheric and physiologic oxygen concentrations.

METHODS

Placental explants were collected from normal term (38-39 weeks of gestation) placentae with no previous uterine contractile activity. Placental transcriptomic expressions were evaluated with GeneChip^® Human Genome U133 Plus 2.0 arrays (Affymetrix).

RESULTS

We uncovered sub-sets of genes that regulate response to stress, induction of apoptosis programmed cell death, mis-regulation of cell growth, proliferation, cell morphogenesis, tissue viability, and protection from apoptosis in cultured placental explants. We also identified a sub-set of genes with highly unstable pattern of expression after exposure to tissue culture. Tissue culture irrespective of oxygen concentration induced dichotomous increase in significant gene expression and increased enrichment of significant pathways and transcription factor targets (TFTs) including HIF1A. The effect was exacerbated by culture at atmospheric oxygen concentration, where further up-regulation of TFTs including PPARA, CEBPD, HOXA9 and down-regulated TFTs such as JUND/FOS suggest intrinsic heightened key biological and metabolic mechanisms such as glucose use, lipid biosynthesis, protein metabolism; apoptosis, inflammatory responses; and diminished trophoblast proliferation, differentiation, invasion, regeneration, and viability.

DISCUSSION

These findings demonstrate that gene expression patterns differ between pre-culture and cultured explants, and the gene expression of explants cultured at atmospheric oxygen concentration favours stressed, pro-inflammatory and increased apoptotic transcriptomic response.

Gene set analysis controlling for length bias in RNA-seq experiments

Background

In gene set analysis, the researchers are interested in determining the gene sets that are significantly correlated with an outcome, e.g. disease status or treatment. With the rapid development of high throughput sequencing technologies, Ribonucleic acid sequencing (RNA-seq) has become an important alternative to traditional expression arrays in gene expression studies. Challenges exist in adopting the existent algorithms to RNA-seq data given the intrinsic difference of the technologies and data. In RNA-seq experiments, the measure of gene expression is correlated with gene length. This inherent correlation may cause bias in gene set analysis.

Results

We develop SeqGSA, a new method for gene set analysis with length bias adjustment for RNA-seq data. It extends from the R package GSA designed for microarrays. Our method compares the gene set maxmean statistic against permutations, while also taking into account of the statistics of the other gene sets. To adjust for the gene length bias, we implement a flexible weighted sampling scheme in the restandardization step of our algorithm. We show our method improves the power of identifying significant gene sets that are affected by the length bias. We also show that our method maintains the type I error comparing with another representative method for gene set enrichment test.

Conclusions

SeqGSA is a promising tool for testing significant gene pathways with RNA-seq data while adjusting for inherent gene length effect. It enhances the power to detect gene sets affected by the bias and maintains type I error under various situations.

A decision analysis model for KEGG pathway analysis

Background

The knowledge base-driven pathway analysis is becoming the first choice for many investigators, in that it not only can reduce the complexity of functional analysis by grouping thousands of genes into just several hundred pathways, but also can increase the explanatory power for the experiment by identifying active pathways in different conditions. However, current approaches are designed to analyze a biological system assuming that each pathway is independent of the other pathways.

Results

A decision analysis model is developed in this article that accounts for dependence among pathways in time-course experiments and multiple treatments experiments. This model introduces a decision coefficient—a designed index, to identify the most relevant pathways in a given experiment by taking into account not only the direct determination factor of each Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway itself, but also the indirect determination factors from its related pathways. Meanwhile, the direct and indirect determination factors of each pathway are employed to demonstrate the regulation mechanisms among KEGG pathways, and the sign of decision coefficient can be used to preliminarily estimate the impact direction of each KEGG pathway. The simulation study of decision analysis demonstrated the application of decision analysis model for KEGG pathway analysis.

Conclusions

A microarray dataset from bovine mammary tissue over entire lactation cycle was used to further illustrate our strategy. The results showed that the decision analysis model can provide the promising and more biologically meaningful results. Therefore, the decision analysis model is an initial attempt of optimizing pathway analysis methodology.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1285-1) contains supplementary material, which is available to authorized users.

Improving information retrieval in functional analysis

Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities.

Signalling pathway impact analysis based on the strength of interaction between genes

Signalling pathway analysis is a popular approach that is used to identify significant cancer‐related pathways based on differentially expressed genes (DEGs) from biological experiments. The main advantage of signalling pathway analysis lies in the fact that it assesses both the number of DEGs and the propagation of signal perturbation in signalling pathways. However, this method simplifies the interactions between genes by categorising them only as activation (+1) and suppression (−1), which does not encompass the range of interactions in real pathways, where interaction strength between genes may vary. In this study, the authors used newly developed signalling pathway impact analysis (SPIA) methods, SPIA based on Pearson correlation coefficient (PSPIA), and mutual information (MSPIA), to measure the interaction strength between pairs of genes. In analyses of a colorectal cancer dataset, a lung cancer dataset, and a pancreatic cancer dataset, PSPIA and MSPIA identified more candidate cancer‐related pathways than were identified by SPIA. Generally, MSPIA performed better than PSPIA.

Epstein-Barr Virus Infection of Mammary Epithelial Cells Promotes Malignant Transformation

Whether the human tumor virus, Epstein-Barr Virus (EBV), promotes breast cancer remains controversial and a potential mechanism has remained elusive. Here we show that EBV can infect primary mammary epithelial cells (MECs) that express the receptor CD21. EBV infection leads to the expansion of early MEC progenitor cells with a stem cell phenotype, activates MET signaling and enforces a differentiation block. When MECs were implanted as xenografts, EBV infection cooperated with activated Ras and accelerated the formation of breast cancer. Infection in EBV-related tumors was of a latency type II pattern, similar to nasopharyngeal carcinoma (NPC). A human gene expression signature for MECs infected with EBV, termed EBVness, was associated with high grade, estrogen-receptor-negative status, p53 mutation and poor survival. In 11/33 EBVness-positive tumors, EBV-DNA was detected by fluorescent in situ hybridization for the viral LMP1 and BXLF2 genes. In an analysis of the TCGA breast cancer data EBVness correlated with the presence of the APOBEC mutational signature. We conclude that a contribution of EBV to breast cancer etiology is plausible, through a mechanism in which EBV infection predisposes mammary epithelial cells to malignant transformation, but is no longer required once malignant transformation has occurred.

Comorbidities of Psoriasis - Exploring the Links by Network Approach

Increasing epidemiological studies in patients with psoriasis report the frequent occurrence of one or more associated disorders. Psoriasis is associated with multiple comorbidities including autoimmune disease, neurological disorders, cardiometabolic diseases and inflammatory-bowel disease. An integrated system biology approach is utilized to decipher the molecular alliance of psoriasis with its comorbidities. An unbiased integrative network medicine methodology is adopted for the investigation of diseasome, biological process and pathways of five most common psoriasis associated comorbidities. A significant overlap was observed between genes acting in similar direction in psoriasis and its comorbidities proving the mandatory occurrence of either one of its comorbidities. The biological processes involved in inflammatory response and cell signaling formed a common basis between psoriasis and its associated comorbidities. The pathway analysis revealed the presence of few common pathways such as angiogenesis and few uncommon pathways which includes CCKR signaling map and gonadotrophin-realising hormone receptor pathway overlapping in all the comorbidities. The work shed light on few common genes and pathways that were previously overlooked. These fruitful targets may serve as a starting point for diagnosis and/or treatment of psoriasis comorbidities. The current research provides an evidence for the existence of shared component hypothesis between psoriasis and its comorbidities.

Omega-3 fatty acids partially revert the metabolic gene expression profile induced by long-term calorie restriction

Calorie restriction (CR) consistently extends longevity and delays age-related diseases across several animal models. We have previously shown that different dietary fat sources can modulate life span and mitochondrial ultrastructure, function and membrane fatty acid composition in mice maintained on a 40% CR. In particular, animals consuming lard as the main fat source (CR-Lard) lived longer than CR mice consuming diets with soybean oil (CR-Soy) or fish oil (CR-Fish) as the predominant lipid source. In the present work, a transcriptomic analysis in the liver and skeletal muscle was performed in order to elucidate possible mechanisms underlying the changes in energy metabolism and longevity induced by dietary fat in CR mice. After 8 months of CR, transcription downstream of several mediators of inflammation was inhibited in liver. In contrast, proinflammatory signaling was increased in the CR-Fish versus other CR groups. Dietary fish oil induced a gene expression pattern consistent with increased transcriptional regulation by several cytokines (TNF, GM-CSF, TGF-β) and sex hormones when compared to the other CR groups. The CR-Fish also had lower expression of genes involved in fatty acid biosynthesis and increased expression of mitochondrial and peroxisomal fatty acid β-oxidation genes than the other CR diet groups. Our data suggest that a diet high in n-3 PUFA, partially reverts CR-related changes in gene expression of key processes, such as inflammation and steroid hormone signaling, and this may mitigate life span extension with CR in mice consuming diets high in fish oil.

Gene set enrichment and topological analyses based on interaction networks in pediatric acute lymphoblastic leukemia

Pediatric acute lymphoblastic leukemia (ALL) accounts for over one-quarter of all pediatric cancers. Interacting genes and proteins within the larger human gene interaction network of the human genome are rarely investigated by studies investigating pediatric ALL. In the present study, interaction networks were constructed using the empirical Bayesian approach and the Search Tool for the Retrieval of Interacting Genes/proteins database, based on the differentially-expressed (DE) genes in pediatric ALL, which were identified using the RankProd package. Enrichment analysis of the interaction network was performed using the network-based methods EnrichNet and PathExpand, which were compared with the traditional expression analysis systematic explored (EASE) method. In total, 398 DE genes were identified in pediatric ALL, and LIF was the most significantly DE gene. The co-expression network consisted of 272 nodes, which indicated genes and proteins, and 602 edges, which indicated the number of interactions adjacent to the node. Comparison between EASE and PathExpand revealed that PathExpand detected more pathways or processes that were closely associated with pediatric ALL compared with the EASE method. There were 294 nodes and 1,588 edges in the protein-protein interaction network, with the processes of hematopoietic cell lineage and porphyrin metabolism demonstrating a close association with pediatric ALL. Network enrichment analysis based on the PathExpand algorithm was revealed to be more powerful for the analysis of interaction networks in pediatric ALL compared with the EASE method. LIF and MLLT11 were identified as the most significantly DE genes in pediatric ALL. The process of hematopoietic cell lineage was the pathway most significantly associated with pediatric ALL.

Pathway Analysis: State of the Art

Pathway analysis is a set of widely used tools for research in life sciences intended to give meaning to high-throughput biological data. The methodology of these tools settles in the gathering and usage of knowledge that comprise biomolecular functioning, coupled with statistical testing and other algorithms. Despite their wide employment, pathway analysis foundations and overall background may not be fully understood, leading to misinterpretation of analysis results. This review attempts to comprise the fundamental knowledge to take into consideration when using pathway analysis as a hypothesis generation tool. We discuss the key elements that are part of these methodologies, their capabilities and current deficiencies. We also present an overview of current and all-time popular methods, highlighting different classes across them. In doing so, we show the exploding diversity of methods that pathway analysis encompasses, point out commonly overlooked caveats, and direct attention to a potential new class of methods that attempt to zoom the analysis scope to the sample scale.

The mycotoxin aflatoxin B1 stimulates Epstein-Barr virus-induced B-cell transformation in in vitro and in vivo experimental models

Although Epstein-Barr virus (EBV) infection is widely distributed, certain EBV-driven malignancies are geographically restricted. EBV-associated Burkitt's lymphoma (eBL) is endemic in children living in sub-Saharan Africa. This population is heavily exposed to food contaminated with the mycotoxin aflatoxin B1 (AFB1). Here, we show that exposure to AFB1 in in vitro and in vivo models induces activation of the EBV lytic cycle and increases EBV load, two events that are associated with an increased risk of eBL in vivo. AFB1 treatment leads to the alteration of cellular gene expression, with consequent activations of signaling pathways, e.g. PI3K, that in turn mediate reactivation of the EBV life cycle. Finally, we show that AFB1 triggers EBV-driven cellular transformation both in primary human B cells and in a humanized animal model. In summary, our data provide evidence for a role of AFB1 as a cofactor in EBV-mediated carcinogenesis.

Using Gene Ontology to describe the role of the neurexin-neuroligin-SHANK complex in human, mouse and rat and its relevance to autism

Background

People with an autistic spectrum disorder (ASD) display a variety of characteristic behavioral traits, including impaired social interaction, communication difficulties and repetitive behavior. This complex neurodevelopment disorder is known to be associated with a combination of genetic and environmental factors. Neurexins and neuroligins play a key role in synaptogenesis and neurexin-neuroligin adhesion is one of several processes that have been implicated in autism spectrum disorders.

Results

In this report we describe the manual annotation of a selection of gene products known to be associated with autism and/or the neurexin-neuroligin-SHANK complex and demonstrate how a focused annotation approach leads to the creation of more descriptive Gene Ontology (GO) terms, as well as an increase in both the number of gene product annotations and their granularity, thus improving the data available in the GO database.

Conclusions

The manual annotations we describe will impact on the functional analysis of a variety of future autism-relevant datasets. Comprehensive gene annotation is an essential aspect of genomic and proteomic studies, as the quality of gene annotations incorporated into statistical analysis tools affects the effective interpretation of data obtained through genome wide association studies, next generation sequencing, proteomic and transcriptomic datasets.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0622-0) contains supplementary material, which is available to authorized users.

Gene set analysis methods: statistical models and methodological differences

Many methods of gene set analysis developed in recent years have been compared empirically in a number of comprehensive review articles. Although it is recognized that different methods tend to identify different gene sets as significant, no consensus has been worked out as to which method is preferable, as the recommendations are often contradictory. In this article, we want to group and compare different methods in terms of the methodological assumptions pertaining to definition of a sample and formulation of the actual null hypothesis. We discuss four models of statistical experiment explicitly or implicitly assumed by most if not all currently available methods of gene set analysis. We analyse validity of the models in the context of the actual biological experiment. Based on this, we recommend a group of methods that provide biologically interpretable results in statistically sound way. Finally, we demonstrate how correlated or low signal-to-noise data affects performance of different methods, observed in terms of the false-positive rate and power.

Targeting DDX3 with a small molecule inhibitor for lung cancer therapy

Lung cancer is the most common malignancy worldwide and is a focus for developing targeted therapies due to its refractory nature to current treatment. We identified a RNA helicase, DDX3, which is overexpressed in many cancer types including lung cancer and is associated with lower survival in lung cancer patients. We designed a first-in-class small molecule inhibitor, RK-33, which binds to DDX3 and abrogates its activity. Inhibition of DDX3 by RK-33 caused G1 cell cycle arrest, induced apoptosis, and promoted radiation sensitization in DDX3-overexpressing cells. Importantly, RK-33 in combination with radiation induced tumor regression in multiple mouse models of lung cancer. Mechanistically, loss of DDX3 function either by shRNA or by RK-33 impaired Wnt signaling through disruption of the DDX3-β-catenin axis and inhibited non-homologous end joining-the major DNA repair pathway in mammalian somatic cells. Overall, inhibition of DDX3 by RK-33 promotes tumor regression, thus providing a compelling argument to develop DDX3 inhibitors for lung cancer therapy.

Insights into protein interaction networks reveal non-receptor kinases as significant druggable targets for psoriasis

Psoriasis is a chronic disease of the skin characterized by hyper proliferation and inflammation of the epidermis and dermal components of the skin. T-cell-dependent inflammatory process in skin governs the pathogenesis of psoriasis. An in-silico search strategy was utilized to identify psoriatic therapeutic drug targets. The gene expression profiling of psoriatic skin identified a total of 427 differentially expressed genes (DEGs). Gene ontology investigation of DEGs identified genes involved in calcium binding, apoptosis, keratinisation, lipid transportation and homeostasis apart from immune mediated processes. The protein interaction networks identified proteins involved in various signaling mechanisms with high degree of interconnections. The gene modules derived from the main network were enriched with rich kinome. These sub-networks were dominated by the presence of non-receptor kinase family members which are major signal transmitters in immune response. The computational approach has aided in the identification of non-receptor kinases as potential targets for psoriasis drug development.

Comprehensive evaluation of composite gene features in cancer outcome prediction

Owing to the heterogeneous and continuously evolving nature of cancers, classifiers based on the expression of individual genes usually do not result in robust prediction of cancer outcome. As an alternative, composite gene features that combine functionally related genes have been proposed. It is expected that such features can be more robust and reproducible since they can capture the alterations in relevant biological processes as a whole and may be less sensitive to fluctuations in the expression of individual genes. Various algorithms have been developed for the identification of composite features and inference of composite gene feature activity, which all claim to improve the prediction accuracy. However, because of the limitations of test datasets incorporated by each individual study and inconsistent test procedures, the results of these studies are sometimes conflicting and unproducible. For this reason, it is difficult to have a comprehensive understanding of the prediction performance of composite gene features, particularly across different cancers, cancer subtypes, and cohorts. In this study, we implement various algorithms for the identification of composite gene features and their utilization in cancer outcome prediction, and perform extensive comparison and evaluation using seven microarray datasets covering two cancer types and three different phenotypes. Our results show that, while some algorithms outperform others for certain classification tasks, no single algorithm consistently outperforms other algorithms and individual gene features.

Reduced gene expression levels of Munc13-1 and additional components of the presynaptic exocytosis machinery upon conditional targeting of Vglut2 in the adolescent mouse

Presynaptic proteins orchestrate an intricate interplay of dynamic interactions in order to regulate quantal exocytosis of transmitter-filled vesicles, and their dysregulation might cause neurological and neuropsychiatric dysfunction. Mice carrying a spatiotemporal restriction in the expression of the Vesicular glutamate transporter 2 (Vglut2; aka Slc17a6) in the cortex, amygdala and hippocampal subiculum from the third postnatal week show a strong anxiolytic phenotype and certain behavioral correlates of schizophrenia. To further understand the molecular consequences of this targeted deletion of Vglut2, we performed an unbiased microarray analysis comparing gene expression levels in the subiculum of these conditional Vglut2 knockout mice (Vglut2^f/f;CamKII cKO) to those in control littermates. Expression of Unc13C (Munc13-3), a member of the Unc/Munc family, previously shown to be important for glutamatergic transmission, was identified to be significantly down-regulated. Subsequent analysis by quantitative RT-PCR revealed a 50% down-regulation of Munc 13-1, the gene encoding the Unc/Munc subtype described as an essential component in the majority of glutamtergic synapses in the hippocampus. Genes encoding additional components of the presynaptic machinery were also found regulated, including Rab3A, RIM1α, as well as Syntaxin1 and Synaptobrevin. Altered expression levels of these genes were further found in the amygdala and in the retrosplenial group of the cortex, additional regions in which Vglut2 was conditionally targeted. These findings suggest that expression levels of Vglut2 might be important for the maintenance of gene expression in the presynaptic machinery in the adult mouse brain. Synapse 68:624-633, 2014. © 2014 Wiley Periodicals, Inc.

Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model

In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method.

The characteristic direction: a geometrical approach to identify differentially expressed genes

Background

Identifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays, and differential gene expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling.

Results

Here we present a new geometrical multivariate approach to identify DEG called the Characteristic Direction. We demonstrate that the Characteristic Direction method is significantly more sensitive than existing methods for identifying DEG in the context of transcription factor (TF) and drug perturbation responses over a large number of microarray experiments. We also benchmarked the Characteristic Direction method using synthetic data, as well as RNA-Seq data. A large collection of microarray expression data from TF perturbations (73 experiments) and drug perturbations (130 experiments) extracted from the Gene Expression Omnibus (GEO), as well as an RNA-Seq study that profiled genome-wide gene expression and STAT3 DNA binding in two subtypes of diffuse large B-cell Lymphoma, were used for benchmarking the method using real data. ChIP-Seq data identifying DNA binding sites of the perturbed TFs, as well as known drug targets of the perturbing drugs, were used as prior knowledge silver-standard for validation. In all cases the Characteristic Direction DEG calling method outperformed other methods. We find that when drugs are applied to cells in various contexts, the proteins that interact with the drug-targets are differentially expressed and more of the corresponding genes are discovered by the Characteristic Direction method. In addition, we show that the Characteristic Direction conceptualization can be used to perform improved gene set enrichment analyses when compared with the gene-set enrichment analysis (GSEA) and the hypergeometric test.

Conclusions

The application of the Characteristic Direction method may shed new light on relevant biological mechanisms that would have remained undiscovered by the current state-of-the-art DEG methods. The method is freely accessible via various open source code implementations using four popular programming languages: R, Python, MATLAB and Mathematica, all available at: http://www.maayanlab.net/CD.

Introduction to sequencing the brain transcriptome

High-throughput next-generation sequencing is now entering its second decade. However, it was not until 2008 that the first report of sequencing the brain transcriptome appeared (Mortazavi, Williams, Mccue, Schaeffer, & Wold, 2008). These authors compared short-read RNA-Seq data for mouse whole brain with microarray results for the same sample and noted both the advantages and disadvantages of the RNA-Seq approach. While RNA-Seq provided exon level resolution, the majority of the reads were provided by a small proportion of highly expressed genes and the data analysis was exceedingly complex. Over the past 6 years, there have been substantial improvements in both RNA-Seq technology and data analysis. This volume contains 11 chapters that detail various aspects of sequencing the brain transcriptome. Some of the chapters are very methods driven, while others focus on the use of RNA-Seq to study such diverse areas as development, schizophrenia, and drug abuse. This chapter briefly reviews the transition from microarrays to RNA-Seq as the preferred method for analyzing the brain transcriptome. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, and can be used to extract genotype information, e.g., nonsynonymous coding single nucleotide polymorphisms. RNA-Seq embraces the complexity of the brain transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior-disease relationships is substantial.

Over-expression of hNGF in adult human olfactory bulb neural stem cells promotes cell growth and oligodendrocytic differentiation

The adult human olfactory bulb neural stem/progenitor cells (OBNC/PC) are promising candidate for cell-based therapy for traumatic and neurodegenerative insults. Exogenous application of NGF was suggested as a promising therapeutic strategy for traumatic and neurodegenerative diseases, however effective delivery of NGF into the CNS parenchyma is still challenging due mainly to its limited ability to cross the blood-brain barrier, and intolerable side effects if administered into the brain ventricular system. An effective method to ensure delivery of NGF into the parenchyma of CNS is the genetic modification of NSC to overexpress NGF gene. Overexpression of NGF in adult human OBNSC is expected to alter their proliferation and differentiation nature, and thus might enhance their therapeutic potential. In this study, we genetically modified adult human OBNS/PC to overexpress human NGF (hNGF) and green fluorescent protein (GFP) genes to provide insight about the effects of hNGF and GFP genes overexpression in adult human OBNS/PC on their in vitro multipotentiality using DNA microarray, immunophenotyping, and Western blot (WB) protocols. Our analysis revealed that OBNS/PC-GFP and OBNS/PC-GFP-hNGF differentiation is a multifaceted process involving changes in major biological processes as reflected in alteration of the gene expression levels of crucial markers such as cell cycle and survival markers, stemness markers, and differentiation markers. The differentiation of both cell classes was also associated with modulations of key signaling pathways such MAPK signaling pathway, ErbB signaling pathway, and neuroactive ligand-receptor interaction pathway for OBNS/PC-GFP, and axon guidance, calcium channel, voltage-dependent, gamma subunit 7 for OBNS/PC-GFP-hNGF as revealed by GO and KEGG. Differentiated OBNS/PC-GFP-hNGF displayed extensively branched cytoplasmic processes, a significant faster growth rate and up modulated the expression of oligodendroglia precursor cells markers (PDGFRα, NG2 and CNPase) respect to OBNS/PC-GFP counterparts. These findings suggest an enhanced proliferation and oligodendrocytic differentiation potential for OBNS/PC-GFP-hNGF as compared to OBNS/PC-GFP.

Identification of significant features in DNA microarray data

DNA microarrays are a relatively new technology that can simultaneously measure the expression level of thousands of genes. They have become an important tool for a wide variety of biological experiments. One of the most common goals of DNA microarray experiments is to identify genes associated with biological processes of interest. Conventional statistical tests often produce poor results when applied to microarray data owing to small sample sizes, noisy data, and correlation among the expression levels of the genes. Thus, novel statistical methods are needed to identify significant genes in DNA microarray experiments. This article discusses the challenges inherent in DNA microarray analysis and describes a series of statistical techniques that can be used to overcome these challenges. The problem of multiple hypothesis testing and its relation to microarray studies are also considered, along with several possible solutions.

Double-stranded RNA induces molecular and inflammatory signatures that are directly relevant to COPD

Polyinosinic:polycytidylic acid (poly I:C) is a synthetic analogue of double-stranded (ds)RNA, a molecular pattern associated with viral infections, that is used to exacerbate inflammation in lung injury models. Despite its frequent use, there are no detailed studies of the responses elicited by a single topical administration of poly I:C to the lungs of mice. Our data provides the first demonstration that the molecular responses in the airways induced by poly I:C correlate to those observed in the lungs of chronic obstructive pulmonary disease (COPD) patients. These expression data also revealed three distinct phases of response to poly I:C, consistent with the changing inflammatory cell infiltrate in the airways. Poly I:C induced increased numbers of neutrophils and natural killer cells in the airways, which were blocked by CXCR2 and CCR5 antagonists, respectively. Using gene set variation analysis on representative clinical data sets, gene sets defined by poly I:C-induced differentially expressed genes were enriched in the molecular profiles of COPD but not idiopathic pulmonary fibrosis patients. Collectively, these data represent a new approach for validating the clinical relevance of preclinical animal models and demonstrate that a dual CXCR2/CCR5 antagonist may be an effective treatment for COPD patients.

Modules in Biological Networks

One of the most prominent properties of networks representing complex systems is modularity. Network-based module identification has captured the attention of a diverse group of scientists from various domains and a variety of methods have been developed. The ability to decompose complex biological systems into modules allows the use of modules rather than individual genes as units in biological studies. A modular view is shaping research methods in biology. Module-based approaches have found broad applications in protein complex identification, protein function prediction, protein expression prediction, as well as disease studies. Compared to single gene-level analyses, module-level analyses offer higher robustness and sensitivity. More importantly, module-level analyses can lead to a better understanding of the design and organization of complex biological systems.

Recreational Music-Making alters gene expression pathways in patients with coronary heart disease

BACKGROUND

Psychosocial stress profoundly impacts long-term cardiovascular health through adverse effects on sympathetic nervous system activity, endothelial dysfunction, and atherosclerotic development. Recreational Music Making (RMM) is a unique stress amelioration strategy encompassing group music-based activities that has great therapeutic potential for treating patients with stress-related cardiovascular disease.

MATERIAL/METHODS

Participants (n=34) with a history of ischemic heart disease were subjected to an acute time-limited stressor, then randomized to RMM or quiet reading for one hour. Peripheral blood gene expression using GeneChip® Human Genome U133A 2.0 arrays was assessed at baseline, following stress, and after the relaxation session.

RESULTS

Full gene set enrichment analysis identified 16 molecular pathways differentially regulated (P<0.005) during stress that function in immune response, cell mobility, and transcription. During relaxation, two pathways showed a significant change in expression in the control group, while 12 pathways governing immune function and gene expression were modulated among RMM participants. Only 13% (2/16) of pathways showed differential expression during stress and relaxation.

CONCLUSIONS

Human stress and relaxation responses may be controlled by different molecular pathways. Relaxation through active engagement in Recreational Music Making may be more effective than quiet reading at altering gene expression and thus more clinically useful for stress amelioration.

Genes, behavior and next-generation RNA sequencing

Advances in next-generation sequencing suggest that RNA-Seq is poised to supplant microarray-based approaches for transcriptome analysis. This article briefly reviews the use of microarrays in the brain-behavior context and then illustrates why RNA-Seq is a superior strategy. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, detects allele specific expression and can be used to extract genotype information, e.g. nonsynonymous coding single nucleotide polymorphisms. Examples of where RNA-Seq has been used to assess brain gene expression are provided. Despite the advantages of RNA-Seq, some disadvantages remain. These include the high cost of RNA-Seq and the computational complexities associated with data analysis. RNA-Seq embraces the complexity of the transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior relationship is substantial.

Pathway analysis of the human brain transcriptome in disease

Pathway analysis is a powerful method for discerning differentially regulated genes and elucidating their biological importance. It allows for the identification of perturbed or aberrantly expressed genes within a biological context from extensive data sets and offers a simplistic approach for interrogating such data sets. With the growing use of microarrays and RNA-Seq, data for genome-wide studies are growing at an alarming rate, and the use of deep sequencing is revealing elements of the genome previously uncharacterised. Through the employment of pathway analysis, mechanisms in complex diseases may be explored and novel causatives found primarily through differentially regulated genes. Further, with the implementation of next generation sequencing, a deeper resolution may be attained, particularly in identification of isoform diversity and SNPs. Here, we look at a broad overview of pathway analysis in the human brain transcriptome and its relevance in teasing out underlying causes of complex diseases. We will outline processes in data gathering and analysis of particular diseases in which these approaches have been successful.

Genome-wide analysis of gene and protein expression of dysplastic naevus cells

Cutaneous melanoma, a type of skin tumor originating from melanocytes, often develops from premalignant naevoid lesions via a gradual transformation process driven by an accumulation of (epi)genetic lesions. These dysplastic naevi display altered morphology and often proliferation of melanocytes. Additionally, melanocytes in dysplastic naevi show structural mitochondrial and melanosomal alterations and have elevated reactive oxygen species (ROS) levels. For this study we performed genome-wide expression and proteomic analysis of melanocytes from dysplastic naevus (DNMC) and adjacent normal skin (MC) from 18 patients. Whole genome expression profiles of the DNMC and MC of each individual patient subjected to GO-based comparative statistical analysis yielded significantly differentially expressed GO classes including “organellar ribosome,” “mitochondrial ribosome,” “hydrogen ion transporter activity,” and “prefoldin complex.” Validation of 5 genes from these top GO classes revealed a heterogeneous differential expression pattern. Proteomic analysis demonstrated differentially expressed proteins in DNMC that are involved in cellular metabolism, detoxification, and cytoskeletal organization processes, such as GTP-binding Rho-like protein CDC42, glutathione-S-transferase omega-1 and prolyl 4-hydroxylase. Collectively these results point to deregulation of cellular processes, such as metabolism and protein synthesis, consistent with the observed elevated oxidative stress levels in DNMC potentially resulting in oxidative DNA damage in these cells.

A Steiner tree-based method for biomarker discovery and classification in breast cancer metastasis

Background

Metastatic breast cancer is a leading cause of cancer-related deaths in women worldwide. DNA microarray has become an important tool to help identify biomarker genes for improving the prognosis of breast cancer. Recently, it was shown that pathway-level relationships between genes can be incorporated to build more robust classification models and to obtain more useful biological insight from such models. Due to the unavailability of complete pathways, protein-protein interaction (PPI) network is becoming more popular to researcher and opens a new way to investigate the developmental process of breast cancer.

Methods

In this study, a network-based method is proposed to combine microarray gene expression profiles and PPI network for biomarker discovery for breast cancer metastasis. The key idea in our approach is to identify a small number of genes to connect differentially expressed genes into a single component in a PPI network; these intermediate genes contain important information about the pathways involved in metastasis and have a high probability of being biomarkers.

Results

We applied this approach on two breast cancer microarray datasets, and for both cases we identified significant numbers of well-known biomarker genes for breast cancer metastasis. Those selected genes are significantly enriched with biological processes and pathways related to cancer carcinogenic process, and, importantly, have much higher stability across different datasets than in previous studies. Furthermore, our selected genes significantly increased cross-data classification accuracy of breast cancer metastasis.

Conclusions

The randomized Steiner tree based approach described in this study is a new way to discover biomarker genes for breast cancer, and improves the prediction accuracy of metastasis. Though the analysis is limited here only to breast cancer, it can be easily applied to other diseases.

Transcription factors expressed in embryonic and adult olfactory bulb neural stem cells reveal distinct proliferation, differentiation and epigenetic control

TF genomic markers associated with neurogenesis, proliferation, differentiation, and epigenetic control in human embryonic neural stem cells (hENSC(, and adult human olfactory bulb neural stem cells (OBNSC) were studied by immunohistochemistry (IHC) and DNA microarray. The biological impact of TF gene changes in the examined cell types was estimated using DAVID to specify a different GO class and signaling pathway based on KEGG database. Eleven, and twenty eight TF genes were up-regulated (fold change≤2-39) in OBNSC, and hENSC respectively. KEGG pathway analysis for the up-regulated TF genes revealed significant enrichments for the basal transcription factor pathway, and Notch signaling pathway in OBNSCs, and hENSCs, respectively. Immunofluorescence analysis revealed a significantly greater number of β-tubulin III (TUBB3), MAP, glial fibrillary acidic protein (GFAP), and O4 in hENSC when compared to those in OBNSC. Furthermore, the expression of epigenetic-related TF-genes SMARCC1, TAF12, and UHRF1 increased significantly in OBNSC when compared with hENSC.

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network

BACKGROUND

Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets.

RESULTS

The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network. The semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed (i.e. false negatives) and addresses the false positive rates found with the use of simple pathway-based sets.

CONCLUSIONS

The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis. As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods.

Down-weighting overlapping genes improves gene set analysis

Background

The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set.

Results

In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method Pathway Analysis with Down-weighting of Overlapping Genes (PADOG). Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results.

Conclusions

PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG was implemented as an R package available at: http://bioinformaticsprb.med.wayne.edu/PADOG/or http://www.bioconductor.org.