Control of tissue development and cell diversity by cell cycle-dependent transcriptional filtering

Cell cycle duration changes dramatically during development, starting out fast to generate cells quickly and slowing down over time as the organism matures. The cell cycle can also act as a transcriptional filter to control the expression of long gene transcripts, which are partially transcribed in short cycles. Using mathematical simulations of cell proliferation, we identify an emergent property that this filter can act as a tuning knob to control gene transcript expression, cell diversity, and the number and proportion of different cell types in a tissue. Our predictions are supported by comparison to single-cell RNA-seq data captured over embryonic development. Additionally, evolutionary genome analysis shows that fast-developing organisms have a narrow genomic distribution of gene lengths while slower developers have an expanded number of long genes. Our results support the idea that cell cycle dynamics may be important across multicellular animals for controlling gene transcript expression and cell fate.

netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features.

The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

Dynamics of the cell-free DNA methylome of metastatic prostate cancer during androgen-targeting treatment

Aim: We examined methylation changes in cell-free DNA (cfDNA) in metastatic castration-resistant prostate cancer (mCRPC) during treatment. Patients & methods: Genome-wide methylation analysis of sequentially collected cfDNA samples derived from mCRPC patients undergoing androgen-targeting therapy was performed. Results: Alterations in methylation states of genes previously implicated in prostate cancer progression were observed and patients that maintained methylation changes throughout therapy tended to have a longer time to clinical progression. Importantly, we also report that markers associated with a highly aggressive form of the disease, neuroendocrine-CRPC, were associated with a faster time to clinical progression. Conclusion: Our findings highlight the potential of monitoring the cfDNA methylome during therapy in mCRPC, which may serve as predictive markers of response to androgen-targeting agents.

RCy3: Network biology using Cytoscape from within R

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

RCy3: Network biology using Cytoscape from within R

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

RCy3: Network biology using Cytoscape from within R

RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.

Exploring targets of TET2-mediated methylation reprogramming as potential discriminators of prostate cancer progression

Background

Global DNA methylation alterations are hallmarks of cancer. The tumor-suppressive TET enzymes, which are involved in DNA demethylation, are decreased in prostate cancer (PCa); in particular, TET2 is specifically targeted by androgen-dependent mechanisms of repression in PCa and may play a central role in carcinogenesis. Thus, the identification of key genes targeted by TET2 dysregulation may provide further insight into cancer biology.

Results

Using a CRISPR/Cas9-derived TET2-knockout prostate cell line, and through whole-transcriptome and whole-methylome sequencing, we identified seven candidate genes—ASB2, ETNK2, MEIS2, NRG1, NTN1, NUDT10, and SRPX—exhibiting reduced expression and increased promoter methylation, a pattern characteristic of tumor suppressors. Decreased expression of these genes significantly discriminates between recurrent and non-recurrent prostate tumors from the Cancer Genome Atlas (TCGA) cohort (n = 423), and ASB2, NUDT10, and SRPX were significantly correlated with lower recurrence-free survival in patients by Kaplan-Meier analysis. ASB2, MEIS2, and SRPX also showed significantly lower expression in high-risk Gleason score 8 tumors as compared to low or intermediate risk tumors, suggesting that these genes may be particularly useful as indicators of PCa progression. Furthermore, methylation array probes in the TCGA dataset, which were proximal to the highly conserved, differentially methylated sites identified in our TET2-knockout cells, were able to significantly distinguish between matched prostate tumor and normal prostate tissues (n = 50 pairs). Except ASB2, all genes exhibited significantly increased methylation at these probes, and methylation status of at least one probe for each of these genes showed association with measures of PCa progression such as recurrence, stage, or Gleason score. Since ASB2 did not have any probes within the TET2-knockout differentially methylated region, we validated ASB2 methylation in an independent series of matched tumor-normal samples (n = 19) by methylation-specific qPCR, which revealed concordant and significant increases in promoter methylation within the TET2-knockout site.

Conclusions

Our study identifies seven genes governed by TET2 loss in PCa which exhibit an association between their methylation and expression status and measures of PCa progression. As differential methylation profiles and TET2 expression are associated with advanced PCa, further investigation of these specialized TET2 targets may provide important insights into patterns of carcinogenic gene dysregulation.

Electronic supplementary material

The online version of this article (10.1186/s13148-019-0651-z) contains supplementary material, which is available to authorized users.

netDx: interpretable patient classification using integrated patient similarity networks

Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis‐driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine‐learning approaches across most cancer types. Compared to traditional machine‐learning‐based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway‐level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows.

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap

Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes; however, the principles can be applied to diverse types of omics data. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training.

The Relative Contributions of Cell-Dependent Cortical Microcircuit Aging to Cognition and Anxiety

BACKGROUND

Aging is accompanied by altered thinking (cognition) and feeling (mood), functions that depend on information processing by brain cortical cell microcircuits. We hypothesized that age-associated long-term functional and biological changes are mediated by gene transcriptomic changes within neuronal cell types forming cortical microcircuits, namely excitatory pyramidal cells (PYCs) and inhibitory gamma-aminobutyric acidergic neurons expressing vasoactive intestinal peptide (Vip), somatostatin (Sst), and parvalbumin (Pvalb).

METHODS

To test this hypothesis, we assessed locomotor, anxiety-like, and cognitive behavioral changes between young (2 months of age, n = 9) and old (22 months of age, n = 12) male C57BL/6 mice, and performed frontal cortex cell type-specific molecular profiling, using laser capture microscopy and RNA sequencing. Results were analyzed by neuroinformatics and validated by fluorescent in situ hybridization.

RESULTS

Old mice displayed increased anxiety and reduced working memory. The four cell types displayed distinct age-related transcriptomes and biological pathway profiles, affecting metabolic and cell signaling pathways, and selective markers of neuronal vulnerability (Ryr3), resilience (Oxr1), and mitochondrial dynamics (Opa1), suggesting high age-related vulnerability of PYCs, and variable degree of adaptation in gamma-aminobutyric acidergic neurons. Correlations between gene expression and behaviors suggest that changes in cognition and anxiety associated with age are partly mediated by normal age-related cell changes, and that additional age-independent decreases in synaptic and signaling pathways, notably in PYCs and somatostatin neurons, further contribute to behavioral changes.

CONCLUSIONS

Our study demonstrates cell-dependent differential vulnerability and coordinated cell-specific cortical microcircuit molecular changes with age. Collectively, the results suggest intrinsic molecular links among aging, cognition, and mood-related behaviors, with somatostatin neurons contributing evenly to both behavioral conditions.

Mammary molecular portraits reveal lineage-specific features and progenitor cell vulnerabilities

Casey et al. integrate epigenomic, transcriptomic, and proteomic profiling of primary basal and luminal mammary cells to identify master epigenetic regulators of the mammary epithelium and uncover stem and progenitor cell vulnerabilities. They develop a pipeline to identify drugs that abrogate progenitor cell activity in normal and high-risk breast cancer patient samples in vitro and in vivo.

The mammary epithelium depends on specific lineages and their stem and progenitor function to accommodate hormone-triggered physiological demands in the adult female. Perturbations of these lineages underpin breast cancer risk, yet our understanding of normal mammary cell composition is incomplete. Here, we build a multimodal resource for the adult gland through comprehensive profiling of primary cell epigenomes, transcriptomes, and proteomes. We define systems-level relationships between chromatin–DNA–RNA–protein states, identify lineage-specific DNA methylation of transcription factor binding sites, and pinpoint proteins underlying progesterone responsiveness. Comparative proteomics of estrogen and progesterone receptor–positive and –negative cell populations, extensive target validation, and drug testing lead to discovery of stem and progenitor cell vulnerabilities. Top epigenetic drugs exert cytostatic effects; prevent adult mammary cell expansion, clonogenicity, and mammopoiesis; and deplete stem cell frequency. Select drugs also abrogate human breast progenitor cell activity in normal and high-risk patient samples. This integrative computational and functional study provides fundamental insight into mammary lineage and stem cell biology.

Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy

Identification of drug targets and mechanism of action (MoA) for new and uncharacterized anticancer drugs is important for optimization of treatment efficacy. Current MoA prediction largely relies on prior information including side effects, therapeutic indication, and chemoinformatics. Such information is not transferable or applicable for newly identified, previously uncharacterized small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary toward development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose here a new integrative computational pharmacogenomic approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that rely only on basic drug characteristics toward elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation, and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. DNF taxonomy succeeded in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. The scalability of DNF facilitated identification of key drug relationships across different drug categories, providing a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on compound MoA and potential insights for drug repurposing. Cancer Res; 77(11); 3057-69. ©2017 AACR.

AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations

Networks often contain regions of tightly connected nodes, or clusters, that highlight their shared relationships. An effective way to create a visual summary of a network is to identify clusters and annotate them with an enclosing shape and a summarizing label. Cytoscape provides the ability to annotate a network with shapes and labels, however these annotations must be created manually one at a time, which can be a laborious process. AutoAnnotate is a Cytoscape 3 App that automates the process of identifying clusters and visually annotating them. It greatly reduces the time and effort required to fully annotate clusters in a network, and provides freedom to experiment with different strategies for identifying and labelling clusters. Many customization options are available that enable the user to refine the generated annotations as required. Annotated clusters may be collapsed into single nodes using the Cytoscape groups feature, which helps simplify a network by making its overall structure more visible. AutoAnnotate is applicable to any type of network, including enrichment maps, protein-protein interactions, pathways, or social networks.

Ectopic miR-125a Expression Induces Long-Term Repopulating Stem Cell Capacity in Mouse and Human Hematopoietic Progenitors

Umbilical cord blood (CB) is a convenient and broadly used source of hematopoietic stem cells (HSCs) for allogeneic stem cell transplantation. However, limiting numbers of HSCs remain a major constraint for its clinical application. Although one feasible option would be to expand HSCs to improve therapeutic outcome, available protocols and the molecular mechanisms governing the self-renewal of HSCs are unclear. Here, we show that ectopic expression of a single microRNA (miRNA), miR-125a, in purified murine and human multipotent progenitors (MPPs) resulted in increased self-renewal and robust long-term multi-lineage repopulation in transplanted recipient mice. Using quantitative proteomics and western blot analysis, we identified a restricted set of miR-125a targets involved in conferring long-term repopulating capacity to MPPs in humans and mice. Our findings offer the innovative potential to use MPPs with enhanced self-renewal activity to augment limited sources of HSCs to improve clinical protocols.

Dynamic interplay between locus-specific DNA methylation and hydroxymethylation regulates distinct biological pathways in prostate carcinogenesis

BACKGROUND

Despite the significant global loss of DNA hydroxymethylation marks in prostate cancer tissues, the locus-specific role of hydroxymethylation in prostate tumorigenesis is unknown. We characterized hydroxymethylation and methylation marks by performing whole-genome next-generation sequencing in representative normal and prostate cancer-derived cell lines in order to determine functional pathways and key genes regulated by these epigenomic modifications in cancer.

RESULTS

Our cell line model shows disruption of hydroxymethylation distribution in cancer, with global loss and highly specific gain in promoter and CpG island regions. Significantly, we observed locus-specific retention of hydroxymethylation marks in specific intronic and intergenic regions which may play a novel role in the regulation of gene expression in critical functional pathways, such as BARD1 signaling and steroid hormone receptor signaling in cancer. We confirm a modest correlation of hydroxymethylation with expression in intragenic regions in prostate cancer, while identifying an original role for intergenic hydroxymethylation in differentially expressed regulatory pathways in cancer. We also demonstrate a successful strategy for the identification and validation of key candidate genes from differentially regulated biological pathways in prostate cancer.

CONCLUSIONS

Our results indicate a distinct function for aberrant hydroxymethylation within each genomic feature in cancer, suggesting a specific and complex role for the deregulation of hydroxymethylation in tumorigenesis, similar to methylation. Subsequently, our characterization of key cellular pathways exhibiting dynamic enrichment patterns for methylation and hydroxymethylation marks may allow us to identify differentially epigenetically modified target genes implicated in prostate cancer tumorigenesis.

miR-126 Regulates Distinct Self-Renewal Outcomes in Normal and Malignant Hematopoietic Stem Cells

To investigate miRNA function in human acute myeloid leukemia (AML) stem cells (LSC), we generated a prognostic LSC-associated miRNA signature derived from functionally validated subpopulations of AML samples. For one signature miRNA, miR-126, high bioactivity aggregated all in vivo patient sample LSC activity into a single sorted population, tightly coupling miR-126 expression to LSC function. Through functional studies, miR-126 was found to restrain cell cycle progression, prevent differentiation, and increase self-renewal of primary LSC in vivo. Compared with prior results showing miR-126 regulation of normal hematopoietic stem cell (HSC) cycling, these functional stem effects are opposite between LSC and HSC. Combined transcriptome and proteome analysis demonstrates that miR-126 targets the PI3K/AKT/MTOR signaling pathway, preserving LSC quiescence and promoting chemotherapy resistance.

Abstract B1-32: Genome-wide integrative analysis correlating locus-specific DNA methylation and hydroxymethylation to gene expression in normal prostate cells

Aberrant epigenetic modification in the form of regional hypermethylation and global hypomethylation has been implicated in the dysregulation of gene expression in various cancers, including prostate cancer (PCa). Recently discovered 5-hydroxymethylated marks (5hmC), considered to be key intermediates in the process of DNA demethylation, have also been shown to exhibit significant global loss in solid tumors as compared to normal tissue. Similar to cytosine base methylation (5mC), 5hmC may also play a key role in the regulation of gene expression and, consequently, gene function in PCa.

We hypothesize that dynamic interplay between 5mC and 5hmC enrichment in promoter, CpG island, and intragenic regions shows specific association patterns with gene expression. A systematic investigation of these methylation marks may provide novel insights into epigenomic architecture by elucidating novel pathways and candidate genes in prostate tumorigenesis.

Using Next Generation Sequencing (NGS), we characterized genome-wide 5mC and 5hmC marks and RNA expression data in a normal prostate tissue-derived representative cell line (RWPE-1). We have completed NGS following methyl-binding protein capture (MBD-seq) and hydroxymethyl-selective chemical labeling (hMe-Seal), and have performed integrative analysis correlating enriched genes stratified by region (intergenic regions proximal to DNase I hypersensitivity sites, as well as promoter, CpG island, and intragenic regions) to expression data obtained from RNA-seq and also validated using publicly available microarray expression data (GEO Accession Number: GSM375783)

We divided expression data into equal tiers representing genes with no expression, low expression, and high expression, and have performed pathway-based analysis on genes significantly enriched for either 5mC or 5hmC marks for each tier stratified by gene region. We found gene expression to exhibit a strong positive correlation with core promoter and CpG island hydroxymethylation, with a corresponding negative correlation to methylation of these regions. In contrast, gene expression was found to be positively correlated with both methylation and hydroxymethylation in both intragenic regions and intergenic regions proximal to ENCODE DNase I hypersensitivity sites (GEO Accession Number: GSM1008595), which are associated with open chromatin and may play a role in downstream gene regulation.

Interestingly, pathway-based analysis of these stratified tiers, grouped via the Molecular Complex Detection algorithm (MCODE) to identify pathway clusters with high biological significance, showed 5hmC and 5mC enrichment of the same pathways in each expression tier, suggesting a possible co-operative role for intragenic co-expression of these marks in regulating biological pathways. Clustered pathways co-enriched in intragenic 5mC and 5hmC showed strong enrichment in Gene Ontology terms related to core cellular functions within each expression tier, such as ion channel and transport regulation, primary metabolic processes, and protein and RNA binding. Currently, we are investigating these pathways to identify key candidate genes regulated by 5hmC and 5mC marks in normal prostate.

This is the first study to correlate locus-specific global 5hmC enrichment to expression in normal prostate cells. Our preliminary analysis has shown correlation between 5hmC and 5mC enrichment in genic, promoter, CpG island, and upstream DNase I hypersensitivity site-proximal regions and expression in RWPE-1 cells. Insights from this model will subsequently be explored via whole-genome differential analysis between normal prostate and PCa to determine the effect of epigenetic alterations in cancer on gene expression. Subsequently, our characterization of key cellular pathways exhibiting dynamic enrichment patterns for 5hmC or 5mC marks will potentially allow us to identify differentially epigenetically modified target genes implicated in prostate cancer tumorigenesis.

Citation Format: Shivani N. Kamdar, Linh T. Ho, Ruth Isserlin, Ken J. Kron, Theodorus van der Kwast, Alexandre R. Zlotta, Neil E. Fleshner, Gary Bader, Bharati Bapat. Genome-wide integrative analysis correlating locus-specific DNA methylation and hydroxymethylation to gene expression in normal prostate cells. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-32.

A Call for Systematic Research on Solute Carriers

Solute carrier (SLC) membrane transport proteins control essential physiological functions, including nutrient uptake, ion transport, and waste removal. SLCs interact with several important drugs, and a quarter of the more than 400 SLC genes are associated with human diseases. Yet, compared to other gene families of similar stature, SLCs are relatively understudied. The time is right for a systematic attack on SLC structure, specificity, and function, taking into account kinship and expression, as well as the dependencies that arise from the common metabolic space.

Unbiased phosphoproteomic method identifies the initial effects of a methacrylic acid copolymer on macrophages

An unbiased phosphoproteomic method was used to identify biomaterial-associated changes in the phosphorylation patterns of macrophage-like cells. The phosphorylation differences between differentiated THP1 (dTHP1) cells treated for 10, 20, or 30 min with a vascular regenerative methacrylic acid (MAA) copolymer or a control methyl methacrylate (MM) copolymer were determined by MS. There were 1,470 peptides (corresponding to 729 proteins) that were differentially phosphorylated in dTHP1 cells treated with the two materials with a greater cellular response to MAA treatment. In addition to identifying pathways (such as integrin signaling and cytoskeletal arrangement) that are well known to change with cell-material interaction, previously unidentified pathways, such as apoptosis and mRNA splicing, were also discovered.

Social Network: a Cytoscape app for visualizing co-authorship networks

Networks that represent connections between individuals can be valuable analytic tools. The Social Network Cytoscape app is capable of creating a visual summary of connected individuals automatically. It does this by representing relationships as networks where each node denotes an individual and an edge linking two individuals represents a connection. The app focuses on creating visual summaries of individuals connected by co-authorship links in academia, created from bibliographic databases like PubMed, Scopus and InCites. The resulting co-authorship networks can be visualized and analyzed to better understand collaborative research networks or to communicate the extent of collaboration and publication productivity among a group of researchers, like in a grant application or departmental review report. It can also be useful as a research tool to identify important research topics, researchers and papers in a subject area.

Systems analysis reveals down-regulation of a network of pro-survival miRNAs drives the apoptotic response in dilated cardiomyopathy

Apoptosis is a hallmark of multiple etiologies of heart failure, including dilated cardiomyopathy. Since microRNAs are master regulators of cardiac development and key effectors of intracellular signaling, they represent novel candidates for understanding the mechanisms driving the increased dysfunction and loss of cardiomyocytes during cardiovascular disease progression. To determine the role of cardiac miRNAs in the apoptotic response, we used microarray technology to monitor miRNA levels in a validated murine phospholambam mutant model of dilated cardiomyopathy. 24 miRNAs were found to be differentially expressed, most of which have not been previously linked to dilated cardiomyopathy. We showed that individual silencing of 7 out of 8 significantly down-regulated miRNAs (mir-1, -29c, -30c, -30d, -149, -486, -499) led to a strong apoptotic phenotype in cell culture, suggesting they repress pro-apoptotic factors. To identify putative miRNA targets most likely relevant to cell death, we computationally integrated transcriptomic, proteomic and functional annotation data. We showed the dependency of prioritized target abundance on miRNA expression using RNA interference and quantitative mass spectrometry. We concluded that down regulation of key pro-survival miRNAs causes up-regulation of apoptotic signaling effectors that contribute to cardiac cell loss, potentially leading to system decompensation and heart failure.

Enrichment Map - a Cytoscape app to visualize and explore OMICs pathway enrichment results

High-throughput OMICs experiments generate signals for millions of entities (i.e. genes, proteins, metabolites or any measurable biological entity) in the cell. In an effort to summarize and explore these signals, expression results are examined in the context of known pathways and processes, through enrichment analysis to generate a set of pathways and processes that is significantly enriched. Due to the high redundancy in annotation resources this often results in hundreds of sets. To facilitate the analysis of these results, we have developed the Enrichment Map app to visualize enrichments as a network. We have updated Enrichment Map to support Cytoscape 3, and have added additional features including new data formats and command line access.

Discovery of novel disease-specific and membrane-associated candidate markers in a mouse model of multiple sclerosis

Multiple sclerosis is a chronic demyelinating disorder characterized by the infiltration of auto-reactive immune cells from the periphery into the central nervous system resulting in axonal injury and neuronal cell death. Experimental autoimmune encephalomyelitis represents the best characterized animal model as common clinical, histological, and immunological features are recapitulated. A label-free mass spectrometric proteomics approach was used to detect differences in protein abundance within specific fractions of disease-affected tissues including the soluble lysate derived from the spinal cord and membrane protein-enriched peripheral blood mononuclear cells. Tissues were harvested from actively induced experimental autoimmune encephalomyelitis mice and sham-induced ("vehicle" control) counterparts at the disease peak followed by subsequent analysis by nanoflow liquid chromatography tandem mass spectrometry. Relative protein quantitation was performed using both intensity- and fragmentation-based approaches. After statistical evaluation of the data, over 500 and 250 differentially abundant proteins were identified in the spinal cord and peripheral blood mononuclear cell data sets, respectively. More than half of these observations have not previously been linked to the disease. The biological significance of all candidate disease markers has been elucidated through rigorous literature searches, pathway analysis, and validation studies. Results from comprehensive targeted mass spectrometry analyses have confirmed the differential abundance of ∼ 200 candidate markers (≥ twofold dysregulated expression) at a 70% success rate. This study is, to our knowledge, the first to examine the cell-surface proteome of peripheral blood mononuclear cells in experimental autoimmune encephalomyelitis. These data provide a unique mechanistic insight into the dynamics of peripheral immune cell infiltration into CNS-privileged sites at a molecular level and has identified several candidate markers, which represent promising targets for future multiple sclerosis therapies. The mass spectrometry proteomics data associated with this manuscript have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000255.

Integrative network analysis of signaling in human CD34(+) hematopoietic progenitor cells by global phosphoproteomic profiling using TiO2 enrichment combined with 2D LC-MS/MS and pathway mapping

Protein kinase signaling regulates human hematopoietic stem/progenitor cell (HSPC) fate, yet little is known about critical pathway substrates. To address this, we have developed and applied a large-scale, empirically optimized phosphopeptide affinity enrichment strategy with high-throughput 2D LC-MS/MS screening to evaluate the phosphoproteome of an isolated human CD34(+) HSPC population. We first used hydrophilic interaction chromatography as a first dimension separation to separate and simplify protein digest mixtures into discrete fractions. Phosphopeptides were then enriched off-line using TiO2 -coated magnetic beads and subsequently detected online by C18 RP nanoflow HPLC using data-dependent MS/MS high-energy collision-activated dissociation fragmentation on a high-performance Orbitrap hybrid tandem mass spectrometer. We identified 15 533 unique phosphopeptides in 3574 putative phosphoproteins. Systematic computational analysis revealed biological pathways and phosphopeptide motifs enriched in CD34(+) HSPC that are markedly different from those observed in an analogous parallel analysis of isolated human T cells, pointing to the possible involvement of specific kinase-substrate relationships within activated cascades driving hematopoietic renewal, commitment, and differentiation.

Abstract 1432: The heterogeneous genomic landscape of posterior fossa ependymoma

Brain tumors are the most common cause of cancer-related death in childhood. Ependymomas, are the third most common pediatric brain tumor. The disease remains incurable for about 45% of patients even after gross total resection and radiotherapy. Despite showing a very homogeneous histological picture, ependymomas display distinct molecular behavior, which supports the existence of several independent entities of the disease. We examined two non-overlapping cohorts of 102 and 75 ependymomas by mRNA expression profiling, on two different array platforms (Affymetrix, Agilent). When performing multiple statistical clustering methods (unsupervised consensus NMF and consensus HCL), we could consistently identify three major clusters, including two subgroups of posterior fossa (PF) ependymoma, a variant common in children and associated with heterogeneous clinical outcome. Subgroup-specific chromosome aberrations of PF tumors were detected by aCGH, and biological signaling pathways distinguishing PF subgroups were identified by gene set enrichment analysis and visualized in Cytoscape. We validated the most significantly classifying markers of each subgroup by immunohistochemistry on a tissue microarray containing an independent set of 265 PF ependymomas. Our findings delineate two subgroups of PF ependymoma (groups A and B) which are demographically, transcriptionally, genetically, and clinically distinct. Group A patients are younger, have laterally located tumors with a balanced genome, more frequently develop secondary metastases and are much more likely to have an extremely poor outcome as compared with group B patients. Based on a multi-variate Cox proportional-hazards model, our identified markers have the strongest independent prognostic value among demographic and molecular variables with Hazard ratios of 8.45 (PFS) and 10.55 (OS). Prognostic significance and predictive impact is being validated in the GPOH HIT2000 Ependymoma study. The identification of two distinct subgroups of PF ependymoma, and markers applicable for their clinical distinction, will allow for better prognostication of individual cases, independent of age, level of resection and WHO grade, and also for stratification in future ependymoma clinical trials.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1432. doi:1538-7445.AM2012-1432

Delineation of two clinically and molecularly distinct subgroups of posterior fossa ependymoma

Despite the histological similarity of ependymomas from throughout the neuroaxis, the disease likely comprises multiple independent entities, each with a distinct molecular pathogenesis. Transcriptional profiling of two large independent cohorts of ependymoma reveals the existence of two demographically, transcriptionally, genetically, and clinically distinct groups of posterior fossa (PF) ependymomas. Group A patients are younger, have laterally located tumors with a balanced genome, and are much more likely to exhibit recurrence, metastasis at recurrence, and death compared with Group B patients. Identification and optimization of immunohistochemical (IHC) markers for PF ependymoma subgroups allowed validation of our findings on a third independent cohort, using a human ependymoma tissue microarray, and provides a tool for prospective prognostication and stratification of PF ependymoma patients.

WordCloud: a Cytoscape plugin to create a visual semantic summary of networks

BACKGROUND

When biological networks are studied, it is common to look for clusters, i.e. sets of nodes that are highly inter-connected. To understand the biological meaning of a cluster, the user usually has to sift through many textual annotations that are associated with biological entities.

FINDINGS

The WordCloud Cytoscape plugin generates a visual summary of these annotations by displaying them as a tag cloud, where more frequent words are displayed using a larger font size. Word co-occurrence in a phrase can be visualized by arranging words in clusters or as a network.

CONCLUSIONS

WordCloud provides a concise visual summary of annotations which is helpful for network analysis and interpretation. WordCloud is freely available at http://baderlab.org/Software/WordCloudPlugin.

The Biomolecular Interaction Network Database in PSI-MI 2.5

The Biomolecular Interaction Network Database (BIND) is a major source of curated biomolecular interactions, which has been unmaintained for the last few years, a trend which will eventually result in the loss of a significant amount of unique biomolecular interaction information, mostly as database identifiers become out of date. To help reverse this trend, we converted BIND to a standard format, Proteomics Standard Initiative-Molecular Interaction 2.5, starting from the last curated data release (from 2005) available in a custom XML format and made the core components (interactions and complexes) plus additional valuable curated information available for download (http://download.baderlab.org/BINDTranslation/). Major work during the conversion process was required to update out of date molecule identifiers resulting in a more comprehensive conversion of BIND, by measures including number of species and interactor types covered, than what is currently accessible elsewhere. This work also highlights issues of data modeling, controlled vocabulary adoption and data cleaning that can serve as a general case study on the future compatibility of interaction databases. Database URL: http://download.baderlab.org/BINDTranslation/

Visualizing gene-set enrichment results using the Cytoscape plug-in enrichment map

Gene-set enrichment analysis finds functionally coherent gene-sets, such as pathways, that are statistically overrepresented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of -gene-sets used by many current enrichment analysis resources work against this ideal. "Enrichment Map" is a Cytoscape plug-in that helps overcome gene-set redundancy and aids in the interpretation of enrichment results. Gene-sets are organized in a network, where each set is a node and links represent gene overlap between sets. Automated network layout groups related gene-sets into -network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret enrichment results.

Enrichment map: a network-based method for gene-set enrichment visualization and interpretation

Background

Gene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal.

Principal Findings

To overcome gene-set redundancy and help in the interpretation of large gene lists, we developed “Enrichment Map”, a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results.

Conclusions

Enrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http://baderlab.org/Software/EnrichmentMap/).

Pathway analysis of dilated cardiomyopathy using global proteomic profiling and enrichment maps

Global protein expression profiling can potentially uncover perturbations associated with common forms of heart disease. We have used shotgun MS/MS to monitor the state of biological systems in cardiac tissue correlating with disease onset, cardiac insufficiency and progression to heart failure in a time-course mouse model of dilated cardiomyopathy. However, interpreting the functional significance of the hundreds of differentially expressed proteins has been challenging. Here, we utilize improved enrichment statistical methods and an extensive collection of functionally related gene sets, gaining a more comprehensive understanding of the progressive alterations associated with functional decline in dilated cardiomyopathy. We visualize the enrichment results as an Enrichment Map, where significant gene sets are grouped based on annotation similarity. This approach vastly simplifies the interpretation of the large number of enriched gene sets found. For pathways of specific interest, such as Apoptosis and the MAPK (mitogen-activated protein kinase) cascade, we performed a more detailed analysis of the underlying signaling network, including experimental validation of expression patterns.

A lentiviral functional proteomics approach identifies chromatin remodeling complexes important for the induction of pluripotency

Protein complexes and protein-protein interactions are essential for almost all cellular processes. Here, we establish a mammalian affinity purification and lentiviral expression (MAPLE) system for characterizing the subunit compositions of protein complexes. The system is flexible (i.e. multiple N- and C-terminal tags and multiple promoters), is compatible with Gateway cloning, and incorporates a reference peptide. Its major advantage is that it permits efficient and stable delivery of affinity-tagged open reading frames into most mammalian cell types. We benchmarked MAPLE with a number of human protein complexes involved in transcription, including the RNA polymerase II-associated factor, negative elongation factor, positive transcription elongation factor b, SWI/SNF, and mixed lineage leukemia complexes. In addition, MAPLE was used to identify an interaction between the reprogramming factor Klf4 and the Swi/Snf chromatin remodeling complex in mouse embryonic stem cells. We show that the SWI/SNF catalytic subunit Smarca2/Brm is up-regulated during the process of induced pluripotency and demonstrate a role for the catalytic subunits of the SWI/SNF complex during somatic cell reprogramming. Our data suggest that the transcription factor Klf4 facilitates chromatin remodeling during reprogramming.

Interpretation of large-scale quantitative shotgun proteomic profiles for biomarker discovery

Large-scale quantitative shotgun tandem mass spectrometry serves as a flexible proteomic platform for the systematic investigation of molecular processes perturbed by disease and the potential discovery of clinically relevant protein biomarkers associated with a particular pathology. Multiple innovative profiling techniques have been introduced with the aims of comprehensively identifying and quantifying the protein complements of human tissues and blood and enabling the systematic comparison of clinically relevant specimens. In this review, the novel computational methods that have been developed to maximize the amount of information inferred from the raw spectral datasets are explored, including innovative database search programs for more complete and confident protein identifications, improved normalization and data processing techniques to enhance statistical accuracy, and innovative algorithms for improved pattern discrimination and biomarker candidate ranking. Ultimately, integrative biomarker analyses that amalgamate the protein expression data generated by these approaches, together with data from high-throughput functional genomics, offer the possibility of identifying the mechanisms and causative factors that underlie complex human diseases, thereby improving both clinical outcomes and personalized treatment options.

Comparative proteomics profiling of a phospholamban mutant mouse model of dilated cardiomyopathy reveals progressive intracellular stress responses

Defective mobilization of Ca2+ by cardiomyocytes can lead to cardiac insufficiency, but the causative mechanisms leading to congestive heart failure (HF) remain unclear. In the present study we performed exhaustive global proteomics surveys of cardiac ventricle isolated from a mouse model of cardiomyopathy overexpressing a phospholamban mutant, R9C (PLN-R9C), and exhibiting impaired Ca2+ handling and death at 24 weeks and compared them with normal control littermates. The relative expression patterns of 6190 high confidence proteins were monitored by shotgun tandem mass spectrometry at 8, 16, and 24 weeks of disease progression. Significant differential abundance of 593 proteins was detected. These proteins mapped to select biological pathways such as endoplasmic reticulum stress response, cytoskeletal remodeling, and apoptosis and included known biomarkers of HF (e.g. brain natriuretic peptide/atrial natriuretic factor and angiotensin-converting enzyme) and other indicators of presymptomatic functional impairment. These altered proteomic profiles were concordant with cognate mRNA patterns recorded in parallel using high density mRNA microarrays, and top candidates were validated by RT-PCR and Western blotting. Mapping of our highest ranked proteins against a human diseased explant and to available data sets indicated that many of these proteins could serve as markers of disease. Indeed we showed that several of these proteins are detectable in mouse and human plasma and display differential abundance in the plasma of diseased mice and affected patients. These results offer a systems-wide perspective of the dynamic maladaptions associated with impaired Ca2+ homeostasis that perturb myocyte function and ultimately converge to cause HF.

Structure-templated predictions of novel protein interactions from sequence information

The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain–motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information.

Many functions performed within a living cell are mediated by specific interactions between proteins. Precise geometric and chemical matches between segments of the protein structures facilitate those interactions. Such binding surfaces are often evolutionarily conserved elements of protein structures known as conserved domains that recognize specific binding elements on the interacting proteins. Binding domains and their corresponding interacting profiles constitute basic interacting modules that are replicated in multiple protein pairs, where they mediate similar interactions. Although many conserved domains are identified, only a handful have known, well-characterized binding elements. This paper describes a computational method that aims to elucidate the binding specificity of many domains. The utility of the derived binding specificity is demonstrated by predicting new interactions between yeast proteins. The predictions are based solely on sequence information by identifying the conserved domains and their corresponding binding sequences. A number of the predicted interactions were confirmed experimentally, demonstrating the feasibility of this approach.

The Biomolecular Interaction Network Database and related tools 2005 update

The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machine-readable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues.

Analysis of domain correlations in yeast protein complexes

MOTIVATION

A growing body of research has concentrated on the identification and definition of conserved sequence motifs. It is widely recognized that these conserved sequence and structural units often mediate protein functions and interactions. The continuing advancements in high-throughput experiments necessitate the development of computational methods to critically assess the results. In this work, we analyzed high-throughput protein complexes using the domain composition of their protein constituents. Domains that mediate similar or related functions may consistently co-occur in protein complexes.

RESULTS

We analyzed Saccharomyces cerevisiae protein complexes from curated and high-throughput experimental datasets to identify statistically significant functional associations between domains. The resulting correlations are represented as domain networks that form the basis of comparison between the datasets, as well as to binary protein interactions. The results show that the curated datasets produce domain networks that map to known biological assemblies, such as ribosome, RNA polymerase, proteasome regulators, transcription initiation and histones. Furthermore, many of these domain correlations were also found in binary protein interactions. In contrast, the high-throughput datasets contain one large network of domain associations. High connectivity of RNA processing and binding domains in the high-throughput datasets reflects the abundance of RNA binding proteins in yeast, in agreement with a previous report that identified a nucleolar protein cluster, possibly mediated by rRNA, from these complexes.

AVAILABILITY

The software is available upon request from the authors and is dependent on the NCBI C++ toolkit.

SeqHound: biological sequence and structure database as a platform for bioinformatics research

BACKGROUND

SeqHound has been developed as an integrated biological sequence, taxonomy, annotation and 3-D structure database system. It provides a high-performance server platform for bioinformatics research in a locally-hosted environment.

RESULTS

SeqHound is based on the National Center for Biotechnology Information data model and programming tools. It offers daily updated contents of all Entrez sequence databases in addition to 3-D structural data and information about sequence redundancies, sequence neighbours, taxonomy, complete genomes, functional annotation including Gene Ontology terms and literature links to PubMed. SeqHound is accessible via a web server through a Perl, C or C++ remote API or an optimized local API. It provides functionality necessary to retrieve specialized subsets of sequences, structures and structural domains. Sequences may be retrieved in FASTA, GenBank, ASN.1 and XML formats. Structures are available in ASN.1, XML and PDB formats. Emphasis has been placed on complete genomes, taxonomy, domain and functional annotation as well as 3-D structural functionality in the API, while fielded text indexing functionality remains under development. SeqHound also offers a streamlined WWW interface for simple web-user queries.

CONCLUSIONS

The system has proven useful in several published bioinformatics projects such as the BIND database and offers a cost-effective infrastructure for research. SeqHound will continue to develop and be provided as a service of the Blueprint Initiative at the Samuel Lunenfeld Research Institute. The source code and examples are available under the terms of the GNU public license at the Sourceforge site http://sourceforge.net/projects/slritools/ in the SLRI Toolkit.