Abstract 3451: Interpreting gene lists from -omics experiments

Understanding the mechanisms responsible for a cellular behavior often begins with observations of genes and gene products. Depending on the type of experiment, the number of resulting genes can be small, but increasingly, researchers are faced with many thousands of measurements, as in the case of transcriptomic or protein-DNA binding observations. Here, we describe ways to pair experimental results consisting of one or more genes with analysis tools with the overall aim being to make results more biologically interpretable. In certain cases, experimental approaches such as screens for essential genes can generate one or a few ‘genes of interest’ and there is a desire to understand their relationship to one another as well as discover links to additional, interesting genes. To this end, ‘GeneMANIA’ is a web tool that accepts gene names and returns a network visualization of related genes based on similarity in expression, localization, protein domains and those involved in physical interactions. Likewise, ‘PCViz’ is a web tool that displays a network of interactions drawn from Pathway Commons, a web resource for pathway and interaction knowledge. In cases where experiments generate a lengthy list of genes, for instance, transcriptomic measurements, there is a desire to understand their relevance to a phenotype of interest. Pathway enrichment analysis methods aim to summarize gene lists as pathways, which have a closer link to cell function. An online ‘Guide’ by Pathway Commons includes workflows that illustrate how to chain together software tools to identify pathways from the corresponding gene-level data then organize and summarize the pathway-level results in an interactive visualization known as an Enrichment Map. For those wishing to drill-down to individual pathways, Pathway Commons offers a set of web apps, including ‘Search’ that enables users to query by keyword and visualize ranked search results. Ongoing development of web apps aims to enhance the accessibility to pathways and integrate support for analysis and visualization of experimental data. The full complement of data, tools and resources offered by Pathway Commons in support of pathway analysis are described.

Citation Format: Augustin Luna, Jeffrey V. Wong, Emek Demir, Igor Rodchenkov, Özgün Babur, Chris Sander, Gary D. Bader. Interpreting gene lists from -omics experiments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3451.

The Mitochondrial Transacylase, Tafazzin, Regulates for AML Stemness by Modulating Intracellular Levels of Phospholipids

Tafazzin (TAZ) is a mitochondrial transacylase that remodels the mitochondrial cardiolipin into its mature form. Through a CRISPR screen, we identified TAZ as necessary for the growth and viability of acute myeloid leukemia (AML) cells. Genetic inhibition of TAZ reduced stemness and increased differentiation of AML cells both in vitro and in vivo. In contrast, knockdown of TAZ did not impair normal hematopoiesis under basal conditions. Mechanistically, inhibition of TAZ decreased levels of cardiolipin but also altered global levels of intracellular phospholipids, including phosphatidylserine, which controlled AML stemness and differentiation by modulating toll-like receptor (TLR) signaling.

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.

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and the fact that some dedicated methods are available only as web servers with limited cell type gene expression signatures. Methods: In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Results: Our results show that, in general, all four methods show a high performance in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.94, sd = 0.036), whereas precision-recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). Conclusions: CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

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.

Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic

A common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.

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.

Induction of rod versus cone photoreceptor-specific progenitors from retinal precursor cells

During development, multipotent progenitors undergo temporally-restricted differentiation into post-mitotic retinal cells; however, the mechanisms of progenitor division that occurs during retinogenesis remain controversial. Using clonal analyses (lineage tracing and single cell cultures), we identify rod versus cone lineage-specific progenitors derived from both adult retinal stem cells and embryonic neural retinal precursors. Taurine and retinoic acid are shown to act in an instructive and lineage-restricted manner early in the progenitor lineage hierarchy to produce rod-restricted progenitors from stem cell progeny. We also identify an instructive, but lineage-independent, mechanism for the specification of cone-restricted progenitors through the suppression of multiple differentiation signaling pathways. These data indicate that exogenous signals play critical roles in directing lineage decisions and resulting in fate-restricted rod or cone photoreceptor progenitors in culture. Additional factors may be involved in governing photoreceptor fates in vivo.

Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations

The liver is the largest solid organ in the body and is critical for metabolic and immune functions. However, little is known about the cells that make up the human liver and its immune microenvironment. Here we report a map of the cellular landscape of the human liver using single-cell RNA sequencing. We provide the transcriptional profiles of 8444 parenchymal and non-parenchymal cells obtained from the fractionation of fresh hepatic tissue from five human livers. Using gene expression patterns, flow cytometry, and immunohistochemical examinations, we identify 20 discrete cell populations of hepatocytes, endothelial cells, cholangiocytes, hepatic stellate cells, B cells, conventional and non-conventional T cells, NK-like cells, and distinct intrahepatic monocyte/macrophage populations. Together, our study presents a comprehensive view of the human liver at single-cell resolution that outlines the characteristics of resident cells in the liver, and in particular provides a map of the human hepatic immune microenvironment.

The development of single cell RNA sequencing technologies has been instrumental in advancing our understanding of tissue biology. Here, MacParland et al. performed single cell RNA sequencing of human liver samples, and identify distinct populations of intrahepatic macrophages that may play specific roles in liver disease.

scClustViz - Single-cell RNAseq cluster assessment and visualization

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data.

The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types.

scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.

scClustViz - Single-cell RNAseq cluster assessment and visualization

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.

Patient Similarity Networks for Precision Medicine

Clinical research and practice in the 21st century is poised to be transformed by analysis of computable electronic medical records and population-level genome-scale patient profiles. Genomic data capture genetic and environmental state, providing information on heterogeneity in disease and treatment outcome, but genomic-based clinical risk scores are limited. Achieving the goal of routine precision medicine that takes advantage of these rich genomics data will require computational methods that support heterogeneous data, have excellent predictive performance, and ideally, provide biologically interpretable results. Traditional machine-learning approaches excel at performance, but often have limited interpretability. Patient similarity networks are an emerging paradigm for precision medicine, in which patients are clustered or classified based on their similarities in various features, including genomic profiles. This strategy is analogous to standard medical diagnosis, has excellent performance, is interpretable, and can preserve patient privacy. We review new methods based on patient similarity networks, including Similarity Network Fusion for patient clustering and netDx for patient classification. While these methods are already useful, much work is required to improve their scalability for contemporary genetic cohorts, optimize parameters, and incorporate a wide range of genomics and clinical data. The coming 5 years will provide an opportunity to assess the utility of network-based algorithms for precision medicine.

Single-Cell RNA Sequencing: A New Window into Cell Scale Dynamics

Single-cell genomics has recently emerged as a powerful tool for observing multicellular systems at a much higher level of resolution and depth than previously possible. High-throughput single-cell RNA sequencing techniques are able to simultaneously quantify expression levels of several thousands of genes within individual cells for tens of thousands of cells within a complex tissue. This has led to development of novel computational methods to analyze this high-dimensional data, investigating longstanding and fundamental questions regarding the granularity of cell types, the definition of cell states, and transitions from one cell type to another along developmental trajectories. In this perspective, we outline this emerging field starting from the "input data" (e.g., quantifying transcription levels in single cells), which are analyzed to define "identities" (e.g., cell types, states, and key genes) and to build "interactions" using models that can infer relations and transitions between cells.

Abstract 1284: How can you interpret gene lists from -omics experiments

Understanding the mechanisms responsible for a cellular behaviour often begins with observations of genes and gene products. Depending on the type of experiment, the number of resulting genes can be small, but increasingly, researchers are faced with many thousands of measurements, as in the case of transcriptomic or protein-DNA binding observations. Here, we describe ways to pair experimental results consisting of one or more genes with analysis tools with the overall aim being to make results more biologically interpretable. In certain cases, experimental approaches such as screens for essential genes can generate one or a few ‘genes of interest' and there is a desire to understand their relationship to one another as well as discover links to additional, interesting genes. To this end, ‘GeneMANIA' is a web tool that accepts gene names and returns a network visualization of related genes based on similarity in expression, localization, protein domains and those involved in physical interactions. Likewise, ‘PCViz' is a web tool that displays a network of interactions drawn from Pathway Commons, a web resource for pathway and interaction knowledge. In cases where experiments generate a lengthy list of genes, for instance, transcriptomic measurements, there is a desire to understand their relevance to a phenotype of interest. Pathway enrichment analysis methods aim to summarize gene lists as pathways, which have a closer link to cell function. An online ‘Guide' by Pathway Commons includes workflows that illustrate how to chain together software tools to identify pathways from the corresponding gene-level data then organize and summarize the pathway-level results in an interactive visualization known as an Enrichment Map. For those wishing to drill-down to individual pathways, Pathway Commons offers a set of web apps, including ‘Search' that enables users to query by keyword and visualize ranked search results. Ongoing development of web apps aims to enhance the accessibility to pathways and integrate support for analysis and visualization of experimental data. The full complement of data, tools and resources offered by Pathway Commons in support of pathway analysis are described.

Citation Format: Jeffrey V. Wong, Augustin Luna, Emek Demir, Igor Rodchenkov, Özgün Babur, Chris Sander, Gary D. Bader. How can you interpret gene lists from -omics experiments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1284.

The Cytoscape Automation app article collection

Cytoscape is the premiere platform for interactive analysis, integration and visualization of network data. While Cytoscape itself delivers much basic functionality, it relies on community-written apps to deliver specialized functions and analyses. To date, Cytoscape's CyREST feature has allowed researchers to write workflows that call basic Cytoscape functions, but provides no access to its high value app-based functions. With Cytoscape Automation, workflows can now call apps that have been upgraded to expose their functionality. This article collection is a resource to assist readers in quickly and economically leveraging such apps in reproducible workflows that scale independently to large data sets and production runs.

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2017

Standards are essential to the advancement of Systems and Synthetic Biology. COMBINE provides a formal body and a centralised platform to help develop and disseminate relevant standards and related resources. The regular special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards by providing unified, easily citable access. This paper provides an overview of existing COMBINE standards and presents developments of the last year.

Whole transcriptome analysis reveals differential gene expression profile reflecting macrophage polarization in response to influenza A H5N1 virus infection

Background

Avian influenza A H5N1 virus can cause lethal disease in humans. The virus can trigger severe pneumonia and lead to acute respiratory distress syndrome. Data from clinical, in vitro and in vivo suggest that virus-induced cytokine dysregulation could be a contributory factor to the pathogenesis of human H5N1 disease. However, the precise mechanism of H5N1 infection eliciting the unique host response are still not well understood.

Methods

To obtain a better understanding of the molecular events at the earliest time points, we used RNA-Seq to quantify and compare the host mRNA and miRNA transcriptomes induced by the highly pathogenic influenza A H5N1 (A/Vietnam/3212/04) or low virulent H1N1 (A/Hong Kong/54/98) viruses in human monocyte-derived macrophages at 1-, 3-, and 6-h post infection.

Results

Our data reveals that two macrophage populations corresponding to M1 (classically activated) and M2 (alternatively activated) macrophage subtypes respond distinctly to H5N1 virus infection when compared to H1N1 virus or mock infection, a distinction that could not be made from previous microarray studies. When this confounding variable is considered in our statistical model, a clear set of dysregulated genes and pathways emerges specifically in H5N1 virus-infected macrophages at 6-h post infection, whilst was not found with H1N1 virus infection. Furthermore, altered expression of genes in these pathways, which have been previously implicated in viral host response, occurs specifically in the M1 subtype. We observe a significant up-regulation of genes in the RIG-I-like receptor signaling pathway. In particular, interferons, and interferon-stimulated genes are broadly affected. The negative regulators of interferon signaling, the suppressors of cytokine signaling, SOCS-1 and SOCS-3, were found to be markedly up-regulated in the initial round of H5N1 virus replication. Elevated levels of these suppressors could lead to the eventual suppression of cellular antiviral genes, contributing to pathophysiology of H5N1 virus infection.

Conclusions

Our study provides important mechanistic insights into the understanding of H5N1 viral pathogenesis and the multi-faceted host immune responses. The dysregulated genes could be potential candidates as therapeutic targets for treating H5N1 disease.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0335-0) contains supplementary material, which is available to authorized users.

Abstract PR07: MicroRNA-130a regulates hematopoietic stem cell self-renewal and erythroid differentiation

Hematopoietic homeostasis is tightly regulated by controlling the balance between quiescence, self-renewal, and lineage-commitment of hematopoietic stem cells (HSCs). Deregulation of these processes and aberrant acquisition of stem cell-like properties is believed to be central to the pathogenesis of hematologic malignancies, such as myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). However, little is known about the molecular networks maintaining the stem cell state and the epigenetic and post-transcriptional regulation of determinants that control these programs. MicroRNAs (miRNAs) represent a large class of post-transcriptional regulators that mediate repression of multiple target mRNAs. We have previously shown that miR-126 and miR-125a are differentially expressed across the human hematopoietic hierarchy and function to control self-renewal and cell fate decisions by reinforcing gene expression programs in a developmental stage-specific manner (Lechman et al. Cell Stem Cell, 2012; Wojtowitz et al. Cell Stem Cell, 2016).

To identify additional miRNA(s) that play a functional role in hematopoiesis, we performed an in vivo competitive repopulation screen in which candidate miRNAs were overexpressed (OE) in human CD34+CD38- umbilical cord blood (CB) cells and subsequently transplanted into immune-deficient mice for 24 weeks. miR-130a was shown to enhance long-term hematopoietic reconstitution and chosen for further investigation. At 12 and 24 weeks after transplantation, enforced miR-130a expression (including an mOrange-mO+ indicator) conferred a competitive advantage over untransduced CB cells demonstrated by increased CD45+ human chimerism in the injected femur (IF), bone marrow (BM), and spleen of recipient mice. miR-130 enforced expression (miR-130a OE) increased the proportion of mO+/hCD45+ cells by approximately 2- and 5-fold after 12 and 24 weeks of repopulation, respectively. miR-130a OE xenografts showed multilineage engraftment with increased myeloid lineage output and significantly enhanced erythroid development at the expense of B-lymphoid lineage output in BM and spleen of recipient mice. Detailed flow cytometry analysis of xenografts revealed accumulation of immature GlyA+/CD71+/CD36+ erythroid progenitors, suggesting a differentiation block at the polychromic erythroblast stage. Notably, miR-130a OE induced the expansion of CD34+CD38- Lin- compartment and increased proportion of CD34+CD38-CD90+CD45RA- immuno-phenotypic HSC. Secondary transplantation involving limiting dilution analysis revealed approximately a 10-fold increase in HSC frequency, consistent with a role of miR-130a in HSC self-renewal. The lineage potential of miR-130OE primitive cells was assessed in vitro using single-cell stromal-based myelo-erythroid differentiation assay. Enforced expression of miR-130a in human HSC and multipotent progenitors (MPP) resulted in the decreased frequency of unipotent myeloid output (M colonies) and increased multipotent output (M/E/Meg, E/Meg colonies), supporting a role of miR-130a in erythroid-megakaryocytic fate specification. Label-free semiquantitative proteomics and subsequent gene set enrichment pathway analysis (GSEA) were performed on miR-130a OE and control transduced CD34+ CB cells to elucidate molecular mechanism(s) of miR-130a function. We identified that miR-130a modulated pathways centered on translational regulation and chromatin modification. Together, our data suggest that miR-130a plays a role in the regulation of the HSC self-renewal and erythroid differentiation. Given that several studies showed aberrant expression of miR-130a in MDS and some AML subtypes, it is important to delineate the role of miR-130a in normal hematopoiesis to comprehend its potential contribution to the development of hematologic malignancies.

This abstract is also being presented as Poster 40.

Citation Format: Gabriela Krivdova, Eric R. Lechman, Erwin M. Schoof, Veronique Voisin, Olga I. Gan, Aaron Trotman-Grant, Karin G. Hermans, Gary D. Bader, John E. Dick. MicroRNA-130a regulates hematopoietic stem cell self-renewal and erythroid differentiation [abstract]. In: Proceedings of the Second AACR Conference on Hematologic Malignancies: Translating Discoveries to Novel Therapies; May 6-9, 2017; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(24_Suppl):Abstract nr PR07.

Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10-8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.

A Map of Human Mitochondrial Protein Interactions Linked to Neurodegeneration Reveals New Mechanisms of Redox Homeostasis and NF-κB Signaling

Mitochondrial protein (MP) dysfunction has been linked to neurodegenerative disorders (NDs); however, the discovery of the molecular mechanisms underlying NDs has been impeded by the limited characterization of interactions governing MP function. Here, using mass spectrometry (MS)-based analysis of 210 affinity-purified mitochondrial (mt) fractions isolated from 27 epitope-tagged human ND-linked MPs in HEK293 cells, we report a high-confidence MP network including 1,964 interactions among 772 proteins (>90% previously unreported). Nearly three-fourths of these interactions were confirmed in mouse brain and multiple human differentiated neuronal cell lines by primary antibody immunoprecipitation and MS, with many linked to NDs and autism. We show that the SOD1-PRDX5 interaction, critical for mt redox homeostasis, can be perturbed by amyotrophic lateral sclerosis-linked SOD1 allelic variants and establish a functional role for ND-linked factors coupled with IκBɛ in NF-κB activation. Our results identify mechanisms for ND-linked MPs and expand the human mt interaction landscape.