Graphite Web: Web tool for gene set analysis exploiting pathway topology

Differential effects of follicle-stimulating hormone glycoforms on the transcriptome profile of cultured rat granulosa cells as disclosed by RNA-seq

It has been documented that variations in glycosylation on glycoprotein hormones, confer distinctly different biological features to the corresponding glycoforms when multiple in vitro biochemical readings are analyzed. We here applied next generation RNA sequencing to explore changes in the transcriptome of rat granulosa cells exposed for 0, 6, and 12 h to 100 ng/ml of four highly purified follicle-stimulating hormone (FSH) glycoforms, each exhibiting different glycosylation patterns: a. human pituitary FSH18/21 (hypo-glycosylated); b. human pituitary FSH24 (fully glycosylated); c. Equine FSH (eqFSH) (hypo-glycosylated); and d. Chinese-hamster ovary cell-derived human recombinant FSH (recFSH) (fully-glycosylated). Total RNA from triplicate incubations was prepared from FSH glycoform-exposed cultured granulosa cells obtained from DES-pretreated immature female rats, and RNA libraries were sequenced in a HighSeq 2500 sequencer (2 x 125 bp paired-end format, 10-15 x 106 reads/sample). The computational workflow focused on investigating differences among the four FSH glycoforms at three levels: gene expression, enriched biological processes, and perturbed pathways. Among the top 200 differentially expressed genes, only 4 (0.6%) were shared by all 4 glycoforms at 6 h, whereas 118 genes (40%) were shared at 12 h. Follicle-stimulating hormone glycocoforms stimulated different patterns of exclusive and associated up regulated biological processes in a glycoform and time-dependent fashion with more shared biological processes after 12 h of exposure and fewer treatment-specific ones, except for recFSH, which exhibited stronger responses with more specifically associated processes at this time. Similar results were found for down-regulated processes, with a greater number of processes at 6 h or 12 h, depending on the particular glycoform. In general, there were fewer downregulated than upregulated processes at both 6 h and 12 h, with FSH18/21 exhibiting the largest number of down-regulated associated processes at 6 h while eqFSH exhibited the greatest number at 12 h. Signaling cascades, largely linked to cAMP-PKA, MAPK, and PI3/AKT pathways were detected as differentially activated by the glycoforms, with each glycoform exhibiting its own molecular signature. These data extend previous observations demonstrating glycosylation-dependent distinctly different regulation of gene expression and intracellular signaling pathways triggered by FSH in granulosa cells. The results also suggest the importance of individual FSH glycoform glycosylation for the conformation of the ligand-receptor complex and induced signalling pathways.

A mitochondrial NADPH-cholesterol axis regulates extracellular vesicle biogenesis to support hematopoietic stem cell fate

Mitochondrial fatty acid oxidation (FAO) is essential for hematopoietic stem cell (HSC) self-renewal; however, the mechanism by which mitochondrial metabolism controls HSC fate remains unknown. Here, we show that within the hematopoietic lineage, HSCs have the largest mitochondrial NADPH pools, which are required for proper HSC cell fate and homeostasis. Bioinformatic analysis of the HSC transcriptome, biochemical assays, and genetic inactivation of FAO all indicate that FAO-generated NADPH fuels cholesterol synthesis in HSCs. Interference with FAO disturbs the segregation of mitochondrial NADPH toward corresponding daughter cells upon single HSC division. Importantly, we have found that the FAO-NADPH-cholesterol axis drives extracellular vesicle (EV) biogenesis and release in HSCs, while inhibition of EV signaling impairs HSC self-renewal. These data reveal the existence of a mitochondrial NADPH-cholesterol axis for EV biogenesis that is required for hematopoietic homeostasis and highlight the non-stochastic nature of HSC fate determination.

Differential effects of follicle-stimulating hormone glycoforms on the transcriptome profile of cultured rat granulosa cells as disclosed by RNA-seq

It has been documented that variations in glycosylation on glycoprotein hormones, confer distinctly different biological features to the corresponding glycoforms when multiple in vitro biochemical readings are analyzed. We here applied next generation RNA sequencing to explore changes in the transcriptome of rat granulosa cells exposed for 0, 6, and 12 h to 100 ng/ml of four highly purified follicle-stimulating hormone (FSH) glycoforms, each exhibiting different glycosylation patterns: human pituitary FSH18/21 and equine FSH (eqFSH) (hypo-glycosylated), and human FSH24 and chinese-hamster ovary cell-derived human recombinant FSH (recFSH) (fully-glycosylated). Total RNA from triplicate incubations was prepared from FSH glycoform-exposed cultured granulosa cells obtained from DES-pretreated immature female rats, and RNA libraries were sequenced in a HighSeq 2500 sequencer (2 × 125 bp paired-end format, 10-15 × 106 reads/sample). The computational workflow focused on investigating differences among the four FSH glycoforms at three levels: gene expression, enriched biological processes, and perturbed pathways. Among the top 200 differentially expressed genes, only 4 (0.6%) were shared by all 4 glycoforms at 6 h, whereas 118 genes (40%) were shared at 12 h. Follicle-stimulating hormone glycocoforms stimulated different patterns of exclusive and associated up regulated biological processes in a glycoform and time-dependent fashion with more shared biological processes after 12 h of exposure and fewer treatment-specific ones, except for recFSH, which exhibited stronger responses with more specifically associated processes at this time. Similar results were found for down-regulated processes, with a greater number of processes at 6 h or 12 h, depending on the particular glycoform. In general, there were fewer downregulated than upregulated processes at both 6 h and 12 h, with FSH18/21 exhibiting the largest number of down-regulated associated processes at 6 h while eqFSH exhibited the greatest number at 12 h. Signaling cascades, largely linked to cAMP-PKA, MAPK, and PI3/AKT pathways were detected as differentially activated by the glycoforms, with each glycoform exhibiting its own molecular signature. These data extend previous observations demonstrating glycosylation-dependent differential regulation of gene expression and intracellular signaling pathways triggered by FSH in granulosa cells. The results also suggest the importance of individual FSH glycoform glycosylation for the conformation of the ligand-receptor complex and induced signalling pathways.

Comparison of Two Experimental Mouse Dry Eye Models through Inflammatory Gene Set Enrichment Analysis Based on a Multiplexed Transcriptomic Approach

The goal of this study was to explore the specific signaling pathways related to inflammation in two experimental mouse dry eye (EDE) models. Female C57BL/6 mice housed for 10 days in a controlled desiccative environment were either treated with scopolamine (EDE-1; n = 18) or subjected to extraorbital lacrimal gland excision bilaterally (EDE-2; n = 10). Non-induced mice (n = 20) served as healthy controls. A corneal fluorescein staining (CFS) scoring was used at baseline through to day (D) 10 to evaluate epitheliopathy. At D10, corneas and conjunctivas were collected for multiplexed transcriptomic analysis with the NanoString^® mouse inflammatory CodeSet. Both EDE-1 and EDE-2 mice presented a change in corneal integrity, with a significant increase in CFS scores at D10. More gene transcripts were identified in EDE-2 compared with EDE-1 (116 vs. 96, respectively), and only a few were common to both models, 13 for the cornea and 6 for the conjunctiva. The gene functional annotation analysis revealed that the same inflammatory pathways were involved in both models. Comparative profiling of gene expression in the two EDE models leads to the identification of various targets and signaling pathways, which can be extrapolated to and confirmed in human disease.

Minor salivary gland mesenchymal stromal cells derived from patients with Sjӧgren's syndrome deploy intact immune plasticity

BACKGROUND AIMS

Mesenchymal stromal cells (MSCs) provide minor salivary glands (MSGs) with support and niche cells for epithelial glandular tissue. Little is known about resident MSG-derived MSCs (MSG-MSCs) in primary Sjӧgren's syndrome (PSS). The authors' objective is to define the immunobiology of endogenous PSS MSG-MSCs.

METHODS

Using culture-adapted MSG-MSCs isolated from consenting PSS subjects (n = 13), the authors performed in vitro interrogation of PSS MSG-MSC immunobiology and global gene expression compared with controls. To this end, the authors performed phenotypic and immune functional analysis of indoleamine 2,3-dioxygenase (IDO), programmed death ligand 1 (PD-L1) and intercellular adhesion marker 1 (ICAM-1) before and after interferon γ (IFNγ) licensing as well as the effect of MSG-MSCs on T-cell proliferation. Considering the female predominance of PSS, the authors also addressed the influence of 17-β-estradiol on estrogen receptor α-positive-related MSC function.

RESULTS

The authors found that MSG-MSCs deployed normal immune regulatory functionality after IFNγ stimulation, as demonstrated by increased protein-level expression of IDO, PD-L1 and ICAM-1. The authors also found that MSG-MSCs suppressed T-cell proliferation in a dose-dependent manner independent of 17-β-estradiol exposure. Gene ontology and pathway analysis highlighted extracellular matrix deposition as a possible difference between PSS and control MSG-MSCs. MSG-MSCs demonstrated increased α-smooth muscle actin expression in PSS, indicating a partial myofibroblast-like adaptation.

CONCLUSIONS

These findings establish similar immune regulatory function of MSG-MSCs in both PSS and control patients, precluding intrinsic MSC immune regulatory defects in PSS. PSS MSG-MSCs show a partial imprinted myofibroblast-like phenotype that may arise in the setting of chronic inflammation, providing a plausible etiology for PSS-related glandular fibrosis.

Integrated analysis of transcriptome, methylome and copy number aberrations data of marginal zone lymphoma and follicular lymphoma in dog

Marginal zone lymphoma (MZL) and follicular lymphoma (FL) are classified as indolent B-cell lymphomas in dogs. Aside from the clinical and histopathological similarities with the human counterpart, the molecular pathogenesis remains unclear. We integrated transcriptome, genome-wide DNA methylation and copy number aberration analysis to provide insights on the pathogenesis of canine MZL (n = 5) and FL (n = 7), also comparing them with diffuse large B-cell lymphoma (DLBCL). Transcriptome profiling highlighted the presence of similar biological processes affecting both histotypes, including BCR and TLR signalling pathways. However, FLs showed an enrichment of E2F targets, whereas MZLs were characterized by MYC-driven transcriptional activation signatures. FLs showed a distinctive loss on chr1 containing CEACAM23 and 24, conversely MZLs presented multiple recurrent gains on chr13, where MYC is located. The distribution of methylation peaks was similar between the two histotypes. Integrating data from the three omics, FLs resulted clearly separated from MZLs and DLBCL dataset. MZLs showed the enrichment of FoxM1 network and TLR associated TICAM1-dependent IRFs activation pathway. However, no specific signatures differentiated MZLs from DLBCLs. In conclusion, our study presents the first comprehensive analysis of molecular and epigenetic pathogenesis of canine FL and MZL.

Targeting p53 and histone methyltransferases restores exhausted CD8+ T cells in HCV infection

Hepatitis C virus infection (HCV) represents a unique model to characterize, from early to late stages of infection, the T cell differentiation process leading to exhaustion of human CD8+ T cells. Here we show that in early HCV infection, exhaustion-committed virus-specific CD8+ T cells display a marked upregulation of transcription associated with impaired glycolytic and mitochondrial functions, that are linked to enhanced ataxia-telangiectasia mutated (ATM) and p53 signaling. After evolution to chronic infection, exhaustion of HCV-specific T cell responses is instead characterized by a broad gene downregulation associated with a wide metabolic and anti-viral function impairment, which can be rescued by histone methyltransferase inhibitors. These results have implications not only for treatment of HCV-positive patients not responding to last-generation antivirals, but also for other chronic pathologies associated with T cell dysfunction, including cancer.

Isolation and Transcriptomic Profiling of Single Myofibers from Mice

Skeletal muscle is composed of different cells and myofiber types, with distinct metabolic and structural features. Generally, transcriptomic analysis of skeletal muscle is performed using whole muscle, resulting in average information as all cells composing the organ contribute to the expression value detected for each gene with the loss of information about the distinctive features of each specific myofiber type. Since myofibers are the smallest complete contractile system of skeletal muscle influencing its contraction velocity and metabolism, it would be beneficial to have fiber-specific information about gene expression. Here, we describe a protocol for the isolation and the transcriptomic analysis of single individual myofibers. The protocol was set up using single myofibers isolated from soleus and Extensor Digitorum Longus (EDL) muscles, but it can be applied to all skeletal muscles. Briefly, muscles are enzymatically dissociated and individually collected. Long RNAs (> 200 nt) and short RNAs (< 200 nt) are separately purified from each myofiber and used to produce libraries for microarray or sequencing analysis. Through this approach, myofiber-specific transcriptional profiles can be produced, free from transcripts from other non-contractile cell types, in order to identify mRNA-miRNA-lncRNA regulatory networks specific for each myofiber type.

A comparison of mechanistic signaling pathway activity analysis methods

Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.

FXYD5 (Dysadherin) upregulation predicts shorter survival and reveals platinum resistance in high-grade serous ovarian cancer patients

BACKGROUND

High-grade serous ovarian carcinoma (HGSOC) is generally associated with a very dismal prognosis. Nevertheless, patients with similar clinicopathological characteristics can have markedly different clinical outcomes. Our aim was the identification of novel molecular determinants influencing survival.

METHODS

Gene expression profiles of extreme HGSOC survivors (training set) were obtained by microarray. Differentially expressed genes (DEGs) and enriched signalling pathways were determined. A prognostic signature was generated and validated on curatedOvarianData database through a meta-analysis approach. The best prognostic biomarker from the signature was confirmed by RT-qPCR and by immunohistochemistry on an independent validation set. Cox regression model was chosen for survival analysis.

RESULTS

Eighty DEGs and the extracellular matrix-receptor (ECM-receptor) interaction pathway were associated to extreme survival. A 10-gene prognostic signature able to correctly classify patients with 98% of accuracy was identified. By an 'in-silico' meta-analysis, overexpression of FXYD domain-containing ion transport regulator 5 (FXYD5), also known as dysadherin, was confirmed in HGSOC short-term survivors compared to long-term ones. Its prognostic and predictive power was then successfully validated, both at mRNA and protein level, first on training than on validation sample set.

CONCLUSION

We demonstrated the possible involvement of FXYD5 and ECM-receptor interaction signal pathway in HCSOC survival and prognosis.

Genetic association and functional characterization of MCPH1 gene variation in bipolar disorder and schizophrenia

A rare microcephalin 1 gene (MCPH1) variant rs61749465A>G (p.Asp61Gly) with prior evidence for association with schizophrenia (p = 3.78 × 10-7 ) was tested for association in 2,300 bipolar disorder (BPD) participants, 1,930 SCZ participants and 1,820 normal comparison subjects. We report evidence for association of rs61749465A>G with BPD (p = 0.0009). rs61749465 is located in the N-terminal of the BRCT1 domain of MCPH1. Bioinformatic analysis predicted the Asp61Gly substitution to be damaging to MCPH1 function. A second MCPH1 BRCT1 domain variant (rs199422124C>G; p.Thr27Arg), reported to cause autosomal recessive microcephaly, was not detected in the participants tested here. We sought to characterize the functional effects of these variants on MCPH1 function. Cell count assays indicated that rs199422124 allele G had a greater impact on cell survival compared to the G allele of rs61749465. Gene expression analysis combined with gene network and pathway analysis indicated that rs61749465 allele G may impact protein translation and cell cycle control. The evidence for association between rs61749465A>G and psychosis in both BPD and SCZ warrants further replication. Likewise, the data from the functional analyses point to molecular mechanisms that may underlie the proposed MCPH1 mediated risk of psychosis and pathogenesis in autosomal recessive microcephaly require additional experimental validation.

High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.

Rare Risk Variants Identification by Identity-by-Descent Mapping and Whole-Exome Sequencing Implicates Neuronal Development Pathways in Schizophrenia and Bipolar Disorder

Schizophrenia (SCZ) and bipolar disorder (BPD) are highly heritable disorders with an estimated co-heritability of 68%. Hundreds of common alleles have been implicated, but recently a role for rare, high-penetrant variants has been also suggested in both disorders. This study investigated a familial cohort of SCZ and BPD patients from a closed population sample, where the high recurrence of the disorders and the homogenous genetic background indicate a possible enrichment in rare risk alleles. A total of 230 subjects (161 cases, 22 unaffected relatives, and 47 controls) were genetically investigated through an innovative strategy that integrates identity-by-descent (IBD) mapping and whole-exome sequencing (WES). IBD analysis allowed to track high-risk haplotypes (IBD_risk) shared exclusively by multiple patients from different families and possibly carrying the most penetrant alleles. A total of 444 non-synonymous sequence variants, of which 137 disruptive, were identified in IBD_risk haplotypes by WES. Interestingly, gene sets previously implicated in SCZ (i.e., post-synaptic density (PSD) proteins, voltage-gated calcium channels (VGCCs), and fragile X mental retardation protein (FMRP) targets) were found significantly enriched in genes carrying IBD_risk variants. Further, IBD_risk variants were preferentially affecting genes involved in the extracellular matrix (ECM) biology and axon guidance processes which appeared to be functionally connected in the pathway-derived meta-network analysis. Results thus confirm rare risk variants as key factors in SCZ and BPD pathogenesis and highlight a role for the development of neuronal connectivity in the etiology of both disorders.

minepath.org: a free interactive pathway analysis web server

Minepath

( www.minepath.org ) is a web-based platform that elaborates on, and radically extends the identification of differentially expressed sub-paths in molecular pathways. Besides the network topology, the underlying MinePath algorithmic processes exploit exact gene-gene molecular relationships (e.g. activation, inhibition) and are able to identify differentially expressed pathway parts. Each pathway is decomposed into all its constituent sub-paths, which in turn are matched with corresponding gene expression profiles. The highly ranked, and phenotype inclined sub-paths are kept. Apart from the pathway analysis algorithm, the fundamental innovation of the MinePath web-server concerns its advanced visualization and interactive capabilities. To our knowledge, this is the first pathway analysis server that introduces and offers visualization of the underlying and active pathway regulatory mechanisms instead of genes. Other features include live interaction, immediate visualization of functional sub-paths per phenotype and dynamic linked annotations for the engaged genes and molecular relations. The user can download not only the results but also the corresponding web viewer framework of the performed analysis. This feature provides the flexibility to immediately publish results without publishing source/expression data, and get all the functionality of a web based pathway analysis viewer.

Mechanism of action of trabectedin in desmoplastic small round cell tumor cells

Background

Desmoplastic small round cell tumor (DSRCT) is a rare and highly aggressive disease, that can be described as a member of the family of small round blue cell tumors. The molecular diagnostic marker is the t(11;22)(p13;q12) translocation, which creates an aberrant transcription factor, EWS-WT1, that underlies the oncogenesis of DSRCT. Current treatments are not very effective so new active drugs are needed. Trabectedin, now used as a single agent for the treatment of soft tissue sarcoma, was reported to be active in some pre-treated DSRCT patients. Using JN-DSRCT-1, a cell line derived from DSRCT expressing the EWS-WT1 fusion protein, we investigated the ability of trabectedin to modify the function of the chimeric protein, as in other sarcomas expressing fusion proteins. After detailed characterization of the EWS-WT1 transcripts structure, we investigated the mode of action of trabectedin, looking at the expression and function of the oncogenic chimera.

Methods

We characterized JN-DSRCT-1 cells using cellular approaches (FISH, Clonogenicity assay) and molecular approaches (Sanger sequencing, ChIP, GEP).

Results

JN-DSRCT-1 cells were sensitive to trabectedin at nanomolar concentrations. The cell line expresses different variants of EWS-WT1, some already identified in patients. EWS-WT1 mRNA expression was affected by trabectedin and chimeric protein binding on its target gene promoters was reduced. Expression profiling indicated that trabectedin affects the expression of genes involved in cell proliferation and apoptosis.

Conclusions

The JN-DSRCT-1 cell line, in vitro, is sensitive to trabectedin: after drug exposure, EWS-WT1 chimera expression decreases as well as binding on its target promoters. Probably the heterogeneity of chimera transcripts is an obstacle to precisely defining the molecular mode of action of drugs, calling for further cellular models of DSRCT, possibly growing in vivo too, to mimic the biological complexity of this disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3091-1) contains supplementary material, which is available to authorized users.

Prioritizing biological pathways by recognizing context in time-series gene expression data

Background

The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information.

Results

In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets.

Conclusions

Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1335-8) contains supplementary material, which is available to authorized users.

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

Altered Expression of Genes Encoding Neurotransmitter Receptors in GnRH Neurons of Proestrous Mice

Gonadotropin-releasing hormone (GnRH) neurons play a key role in the central regulation of reproduction. In proestrous female mice, estradiol triggers the pre-ovulatory GnRH surge, however, its impact on the expression of neurotransmitter receptor genes in GnRH neurons has not been explored yet. We hypothesized that proestrus is accompanied by substantial changes in the expression profile of genes coding for neurotransmitter receptors in GnRH neurons. We compared the transcriptome of GnRH neurons obtained from intact, proestrous, and metestrous female GnRH-GFP transgenic mice, respectively. About 1500 individual GnRH neurons were sampled from both groups and their transcriptome was analyzed using microarray hybridization and real-time PCR. In this study, changes in mRNA expression of genes involved in neurotransmitter signaling were investigated. Differential gene expression was most apparent in GABA-ergic (Gabbr1, Gabra3, Gabrb3, Gabrb2, Gabrg2), glutamatergic (Gria1, Gria2, Grin1, Grin3a, Grm1, Slc17a6), cholinergic (Chrnb2, Chrm4) and dopaminergic (Drd3, Drd4), adrenergic (Adra1b, Adra2a, Adra2c), adenosinergic (Adora2a, Adora2b), glycinergic (Glra), purinergic (P2rx7), and serotonergic (Htr1b) receptors. In concert with these events, expression of genes in the signaling pathways downstream to the receptors, i.e., G-proteins (Gnai1, Gnai2, Gnas), adenylate-cyclases (Adcy3, Adcy5), protein kinase A (Prkaca, Prkacb) protein kinase C (Prkca) and certain transporters (Slc1a4, Slc17a6, Slc6a17) were also changed. The marked differences found in the expression of genes involved in neurotransmitter signaling of GnRH neurons at pro- and metestrous stages of the ovarian cycle indicate the differential contribution of these neurotransmitter systems to the induction of the pre-ovulatory GnRH surge, the known prerequisite of the subsequent hormonal cascade inducing ovulation.

Bioconductor's EnrichmentBrowser: seamless navigation through combined results of set- & network-based enrichment analysis

BACKGROUND

Enrichment analysis of gene expression data is essential to find functional groups of genes whose interplay can explain experimental observations. Numerous methods have been published that either ignore (set-based) or incorporate (network-based) known interactions between genes. However, the often subtle benefits and disadvantages of the individual methods are confusing for most biological end users and there is currently no convenient way to combine methods for an enhanced result interpretation.

RESULTS

We present the EnrichmentBrowser package as an easily applicable software that enables (1) the application of the most frequently used set-based and network-based enrichment methods, (2) their straightforward combination, and (3) a detailed and interactive visualization and exploration of the results. The package is available from the Bioconductor repository and implements additional support for standardized expression data preprocessing, differential expression analysis, and definition of suitable input gene sets and networks.

CONCLUSION

The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. It combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways.

LENS: web-based lens for enrichment and network studies of human proteins

Background

Network analysis is a common approach for the study of genetic view of diseases and biological pathways. Typically, when a set of genes are identified to be of interest in relation to a disease, say through a genome wide association study (GWAS) or a different gene expression study, these genes are typically analyzed in the context of their protein-protein interaction (PPI) networks. Further analysis is carried out to compute the enrichment of known pathways and disease-associations in the network. Having tools for such analysis at the fingertips of biologists without the requirement for computer programming or curation of data would accelerate the characterization of genes of interest. Currently available tools do not integrate network and enrichment analysis and their visualizations, and most of them present results in formats not most conducive to human cognition.

Results

We developed the tool Lens for Enrichment and Network Studies of human proteins (LENS) that performs network and pathway and diseases enrichment analyses on genes of interest to users. The tool creates a visualization of the network, provides easy to read statistics on network connectivity, and displays Venn diagrams with statistical significance values of the network's association with drugs, diseases, pathways, and GWASs. We used the tool to analyze gene sets related to craniofacial development, autism, and schizophrenia.

Conclusion

LENS is a web-based tool that does not require and download or plugins to use. The tool is free and does not require login for use, and is available at http://severus.dbmi.pitt.edu/LENS.

Integrated analysis of oral tongue squamous cell carcinoma identifies key variants and pathways linked to risk habits, HPV, clinical parameters and tumor recurrence

Oral tongue squamous cell carcinomas (OTSCC) are a homogeneous group of tumors characterized by aggressive behavior, early spread to lymph nodes and a higher rate of regional failure. Additionally, the incidence of OTSCC among younger population (<50yrs) is on the rise; many of whom lack the typical associated risk factors of alcohol and/or tobacco exposure. We present data on single nucleotide variations (SNVs), indels, regions with loss of heterozygosity (LOH), and copy number variations (CNVs) from fifty-paired oral tongue primary tumors and link the significant somatic variants with clinical parameters, epidemiological factors including human papilloma virus (HPV) infection and tumor recurrence. Apart from the frequent somatic variants harbored in TP53, CASP8, RASA1, NOTCH and CDKN2A genes, significant amplifications and/or deletions were detected in chromosomes 6-9, and 11 in the tumors. Variants in CASP8 and CDKN2A were mutually exclusive. CDKN2A, PIK3CA, RASA1 and DMD variants were exclusively linked to smoking, chewing, HPV infection and tumor stage. We also performed a whole-genome gene expression study that identified matrix metalloproteases to be highly expressed in tumors and linked pathways involving arachidonic acid and NF-k-B to habits and distant metastasis, respectively. Functional knockdown studies in cell lines demonstrated the role of CASP8 in a HPV-negative OTSCC cell line. Finally, we identified a 38-gene minimal signature that predicts tumor recurrence using an ensemble machine-learning method. Taken together, this study links molecular signatures to various clinical and epidemiological factors in a homogeneous tumor population with a relatively high HPV prevalence.

A basal stem cell signature identifies aggressive prostate cancer phenotypes

Evidence from numerous cancers suggests that increased aggressiveness is accompanied by up-regulation of signaling pathways and acquisition of properties common to stem cells. It is unclear if different subtypes of late-stage cancer vary in stemness properties and whether or not these subtypes are transcriptionally similar to normal tissue stem cells. We report a gene signature specific for human prostate basal cells that is differentially enriched in various phenotypes of late-stage metastatic prostate cancer. We FACS-purified and transcriptionally profiled basal and luminal epithelial populations from the benign and cancerous regions of primary human prostates. High-throughput RNA sequencing showed the basal population to be defined by genes associated with stem cell signaling programs and invasiveness. Application of a 91-gene basal signature to gene expression datasets from patients with organ-confined or hormone-refractory metastatic prostate cancer revealed that metastatic small cell neuroendocrine carcinoma was molecularly more stem-like than either metastatic adenocarcinoma or organ-confined adenocarcinoma. Bioinformatic analysis of the basal cell and two human small cell gene signatures identified a set of E2F target genes common between prostate small cell neuroendocrine carcinoma and primary prostate basal cells. Taken together, our data suggest that aggressive prostate cancer shares a conserved transcriptional program with normal adult prostate basal stem cells.

Meta-Analysis of Placental Transcriptome Data Identifies a Novel Molecular Pathway Related to Preeclampsia

Studies using the placental transcriptome to identify key molecules relevant for preeclampsia are hampered by a relatively small sample size. In addition, they use a variety of bioinformatics and statistical methods, making comparison of findings challenging. To generate a more robust preeclampsia gene expression signature, we performed a meta-analysis on the original data of 11 placenta RNA microarray experiments, representing 139 normotensive and 116 preeclamptic pregnancies. Microarray data were pre-processed and analyzed using standardized bioinformatics and statistical procedures and the effect sizes were combined using an inverse-variance random-effects model. Interactions between genes in the resulting gene expression signature were identified by pathway analysis (Ingenuity Pathway Analysis, Gene Set Enrichment Analysis, Graphite) and protein-protein associations (STRING). This approach has resulted in a comprehensive list of differentially expressed genes that led to a 388-gene meta-signature of preeclamptic placenta. Pathway analysis highlights the involvement of the previously identified hypoxia/HIF1A pathway in the establishment of the preeclamptic gene expression profile, while analysis of protein interaction networks indicates CREBBP/EP300 as a novel element central to the preeclamptic placental transcriptome. In addition, there is an apparent high incidence of preeclampsia in women carrying a child with a mutation in CREBBP/EP300 (Rubinstein-Taybi Syndrome). The 388-gene preeclampsia meta-signature offers a vital starting point for further studies into the relevance of these genes (in particular CREBBP/EP300) and their concomitant pathways as biomarkers or functional molecules in preeclampsia. This will result in a better understanding of the molecular basis of this disease and opens up the opportunity to develop rational therapies targeting the placental dysfunction causal to preeclampsia.

iCOSSY: An Online Tool for Context-Specific Subnetwork Discovery from Gene Expression Data

Pathway analyses help reveal underlying molecular mechanisms of complex biological phenotypes. Biologists tend to perform multiple pathway analyses on the same dataset, as there is no single answer. It is often inefficient for them to implement and/or install all the algorithms by themselves. Online tools can help the community in this regard. Here we present an online gene expression analytical tool called iCOSSY which implements a novel pathway-based COntext-specific Subnetwork discoverY (COSSY) algorithm. iCOSSY also includes a few modifications of COSSY to increase its reliability and interpretability. Users can upload their gene expression datasets, and discover important subnetworks of closely interacting molecules to differentiate between two phenotypes (context). They can also interactively visualize the resulting subnetworks. iCOSSY is a web server that finds subnetworks that are differentially expressed in two phenotypes. Users can visualize the subnetworks to understand the biology of the difference.

Gene expression changes of single skeletal muscle fibers in response to modulation of the mitochondrial calcium uniporter (MCU)

The mitochondrial calcium uniporter (MCU) gene codifies for the inner mitochondrial membrane (IMM) channel responsible for mitochondrial Ca^2 + uptake. Cytosolic Ca^2 + transients are involved in sarcomere contraction through cycles of release and storage in the sarcoplasmic reticulum. In addition cytosolic Ca^2 + regulates various signaling cascades that eventually lead to gene expression reprogramming. Mitochondria are strategically placed in close contact with the ER/SR, thus cytosolic Ca^2 + transients elicit large increases in the [Ca^2 +] of the mitochondrial matrix ([Ca^2 +]mt). Mitochondrial Ca^2 + uptake regulates energy production and cell survival. In addition, we recently showed that MCU-dependent mitochondrial Ca^2 + uptake controls skeletal muscle trophism. In the same report, we dissected the effects of MCU-dependent mitochondrial Ca^2 + uptake on gene expression through microarray gene expression analysis upon modulation of MCU expression by in vivo AAV infection. Analyses were performed on single skeletal muscle fibers at two time points (7 and 14 days post-AAV injection). Raw and normalized data are available on the GEO database (http://www.ncbi.nlm.nih.gov/geo/) (GSE60931).

PathCards: multi-source consolidation of human biological pathways

The study of biological pathways is key to a large number of systems analyses. However, many relevant tools consider a limited number of pathway sources, missing out on many genes and gene-to-gene connections. Simply pooling several pathways sources would result in redundancy and the lack of systematic pathway interrelations. To address this, we exercised a combination of hierarchical clustering and nearest neighbor graph representation, with judiciously selected cutoff values, thereby consolidating 3215 human pathways from 12 sources into a set of 1073 SuperPaths. Our unification algorithm finds a balance between reducing redundancy and optimizing the level of pathway-related informativeness for individual genes. We show a substantial enhancement of the SuperPaths’ capacity to infer gene-to-gene relationships when compared with individual pathway sources, separately or taken together. Further, we demonstrate that the chosen 12 sources entail nearly exhaustive gene coverage. The computed SuperPaths are presented in a new online database, PathCards, showing each SuperPath, its constituent network of pathways, and its contained genes. This provides researchers with a rich, searchable systems analysis resource.Database URL:http://pathcards.genecards.org/

Pathway-based analysis tools for complex diseases: a review

Genetic studies are traditionally based on single-gene analysis. The use of these analyses can pose tremendous challenges for elucidating complicated genetic interplays involved in complex human diseases. Modern pathway-based analysis provides a technique, which allows a comprehensive understanding of the molecular mechanisms underlying complex diseases. Extensive studies utilizing the methods and applications for pathway-based analysis have significantly advanced our capacity to explore large-scale omics data, which has rapidly accumulated in biomedical fields. This article is a comprehensive review of the pathway-based analysis methods—the powerful methods with the potential to uncover the biological depths of the complex diseases. The general concepts and procedures for the pathway-based analysis methods are introduced and then, a comprehensive review of the major approaches for this analysis is presented. In addition, a list of available pathway-based analysis software and databases is provided. Finally, future directions and challenges for the methodological development and applications of pathway-based analysis techniques are discussed. This review will provide a useful guide to dissect complex diseases.

Understanding disease mechanisms with models of signaling pathway activities

BACKGROUND

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.

RESULTS

Here we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.

CONCLUSIONS

The proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.

Leigh syndrome in Drosophila melanogaster: morphological and biochemical characterization of Surf1 post-transcriptional silencing

Leigh Syndrome (LS) is the most common early-onset, progressive mitochondrial encephalopathy usually leading to early death. The single most prevalent cause of LS is occurrence of mutations in the SURF1 gene, and LS(Surf1) patients show a ubiquitous and specific decrease in the activity of mitochondrial respiratory chain complex IV (cytochrome c oxidase, COX). SURF1 encodes an inner membrane mitochondrial protein involved in COX assembly. We established a Drosophila melanogaster model of LS based on the post-transcriptional silencing of CG9943, the Drosophila homolog of SURF1. Knockdown of Surf1 was induced ubiquitously in larvae and adults, which led to lethality; in the mesodermal derivatives, which led to pupal lethality; or in the central nervous system, which allowed survival. A biochemical characterization was carried out in knockdown individuals, which revealed that larvae unexpectedly displayed defects in all complexes of the mitochondrial respiratory chain and in the F-ATP synthase, while adults had a COX-selective impairment. Silencing of Surf1 expression in Drosophila S2R(+) cells led to selective loss of COX activity associated with decreased oxygen consumption and respiratory reserve. We conclude that Surf1 is essential for COX activity and mitochondrial function in D. melanogaster, thus providing a new tool that may help clarify the pathogenic mechanisms of LS.

Altered gene transcription in human cells treated with Ludox® silica nanoparticles

Silica (SiO₂) nanoparticles (NPs) have found extensive applications in industrial manufacturing, biomedical and biotechnological fields. Therefore, the increasing exposure to such ultrafine particles requires studies to characterize their potential cytotoxic effects in order to provide exhaustive information to assess the impact of nanomaterials on human health. The understanding of the biological processes involved in the development and maintenance of a variety of pathologies is improved by genome-wide approaches, and in this context, gene set analysis has emerged as a fundamental tool for the interpretation of the results. In this work we show how the use of a combination of gene-by-gene and gene set analyses can enhance the interpretation of results of in vitro treatment of A549 cells with Ludox^® colloidal amorphous silica nanoparticles. By gene-by-gene and gene set analyses, we evidenced a specific cell response in relation to NPs size and elapsed time after treatment, with the smaller NPs (SM30) having higher impact on inflammatory and apoptosis processes than the bigger ones. Apoptotic process appeared to be activated by the up-regulation of the initiator genes TNFa and IL1b and by ATM. Moreover, our analyses evidenced that cell treatment with Ludox^® silica nanoparticles activated the matrix metalloproteinase genes MMP1, MMP10 and MMP9. The information derived from this study can be informative about the cytotoxicity of Ludox^® and other similar colloidal amorphous silica NPs prepared by solution processes.

Analysis of differential miRNA expression in primary tumor and stroma of colorectal cancer patients

Microarray technology was used to profile miRNA expression in primary tumor and stromal tissue from paraffin embedded material of 51 patients with colorectal cancer. 26 miRNAs resulted differentially expressed with at least 2-fold change in tumor tissue with respect to stroma (16 more expressed in the tumor and 10 more expressed in the stroma). 10/26 were confirmed as differentially expressed at qRTPCR: miR-200c-3p, miR-141-3p, miR-200b-3p, miR-200a-3p, miR-1246, miR-92a-3p, miR-194-5p, miR-192-5p, miR-3651-5p, and miR-574-3p. No significant association was found between miRNA expressions and stage at diagnosis, site of primary tumor, first site of metastasis, progression-free, or overall survival.

Integration analysis of microRNA and mRNA expression profiles in human peripheral blood lymphocytes cultured in modeled microgravity

We analyzed miRNA and mRNA expression profiles in human peripheral blood lymphocytes (PBLs) incubated in microgravity condition, simulated by a ground-based rotating wall vessel (RWV) bioreactor. Our results show that 42 miRNAs were differentially expressed in MMG-incubated PBLs compared with 1 g incubated ones. Among these, miR-9-5p, miR-9-3p, miR-155-5p, miR-150-3p, and miR-378-3p were the most dysregulated. To improve the detection of functional miRNA-mRNA pairs, we performed gene expression profiles on the same samples assayed for miRNA profiling and we integrated miRNA and mRNA expression data. The functional classification of miRNA-correlated genes evidenced significant enrichment in the biological processes of immune/inflammatory response, signal transduction, regulation of response to stress, regulation of programmed cell death, and regulation of cell proliferation. We identified the correlation of miR-9-3p, miR-155-5p, miR-150-3p, and miR-378-3p expression with that of genes involved in immune/inflammatory response (e.g., IFNG and IL17F), apoptosis (e.g., PDCD4 and PTEN), and cell proliferation (e.g., NKX3-1 and GADD45A). Experimental assays of cell viability and apoptosis induction validated the results obtained by bioinformatics analyses demonstrating that in human PBLs the exposure to reduced gravitational force increases the frequency of apoptosis and decreases cell proliferation.

timeClip: pathway analysis for time course data without replicates

Background

Time-course gene expression experiments are useful tools for exploring biological processes. In this type of experiments, gene expression changes are monitored along time. Unfortunately, replication of time series is still costly and usually long time course do not have replicates. Many approaches have been proposed to deal with this data structure, but none of them in the field of pathway analysis. Pathway analyses have acquired great relevance for helping the interpretation of gene expression data. Several methods have been proposed to this aim: from the classical enrichment to the more complex topological analysis that gains power from the topology of the pathway. None of them were devised to identify temporal variations in time course data.

Results

Here we present timeClip, a topology based pathway analysis specifically tailored to long time series without replicates. timeClip combines dimension reduction techniques and graph decomposition theory to explore and identify the portion of pathways that is most time-dependent. In the first step, timeClip selects the time-dependent pathways; in the second step, the most time dependent portions of these pathways are highlighted. We used timeClip on simulated data and on a benchmark dataset regarding mouse muscle regeneration model. Our approach shows good performance on different simulated settings. On the real dataset, we identify 76 time-dependent pathways, most of which known to be involved in the regeneration process. Focusing on the 'mTOR signaling pathway' we highlight the timing of key processes of the muscle regeneration: from the early pathway activation through growth factor signals to the late burst of protein production needed for the fiber regeneration.

Conclusions

timeClip represents a new improvement in the field of time-dependent pathway analysis. It allows to isolate and dissect pathways characterized by time-dependent components. Furthermore, using timeClip on a mouse muscle regeneration dataset we were able to characterize the process of muscle fiber regeneration with its correct timing.

R-based software for the integration of pathway data into bioinformatic algorithms

Putting new findings into the context of available literature knowledge is one approach to deal with the surge of high-throughput data results. Furthermore, prior knowledge can increase the performance and stability of bioinformatic algorithms, for example, methods for network reconstruction. In this review, we examine software packages for the statistical computing framework R, which enable the integration of pathway data for further bioinformatic analyses. Different approaches to integrate and visualize pathway data are identified and packages are stratified concerning their features according to a number of different aspects: data import strategies, the extent of available data, dependencies on external tools, integration with further analysis steps and visualization options are considered. A total of 12 packages integrating pathway data are reviewed in this manuscript. These are supplemented by five R-specific packages for visualization and six connector packages, which provide access to external tools.

Systems biology approach to the dissection of the complexity of regulatory networks in the S. scrofa cardiocirculatory system

Genome-wide experiments are routinely used to increase the understanding of the biological processes involved in the development and maintenance of a variety of pathologies. Although the technical feasibility of this type of experiment has improved in recent years, data analysis remains challenging. In this context, gene set analysis has emerged as a fundamental tool for the interpretation of the results. Here, we review strategies used in the gene set approach, and using datasets for the pig cardiocirculatory system as a case study, we demonstrate how the use of a combination of these strategies can enhance the interpretation of results. Gene set analyses are able to distinguish vessels from the heart and arteries from veins in a manner that is consistent with the different cellular composition of smooth muscle cells. By integrating microRNA elements in the regulatory circuits identified, we find that vessel specificity is maintained through specific miRNAs, such as miR-133a and miR-143, which show anti-correlated expression with their mRNA targets.