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.

Distinct DNA methylation patterns associated with treatment resistance in metastatic castration resistant prostate cancer.

Androgens are a major driver of prostate cancer (PCa) and continue to be a critical treatment target for advanced disease, which includes castration therapy and antiandrogens. However, resistance to these therapies leading to metastatic castration-resistant prostate cancer (mCRPC), and the emergence of treatment-induced neuroendocrine disease (tNEPC) remains an ongoing challenge. Instability of the DNA methylome is well established as a major hallmark of PCa development and progression. Therefore, investigating the dynamics of the methylation changes going from the castration sensitive to the tNEPC state would provide insights into novel mechanisms of resistance. Using an established xenograft model of CRPC, genome-wide methylation analysis was performed on cell lines representing various stages of PCa progression. We confirmed extensive methylation changes with the development of CRPC and tNEPC using this model. This included key genes and pathways associated with cellular differentiation and neurodevelopment. Combined analysis of methylation and gene expression changes further highlighted genes that could potentially serve as therapeutic targets. Furthermore, tNEPC-related methylation signals from this model were detectable in circulating cell free DNA (cfDNA) from mCRPC patients undergoing androgen-targeting therapies and were associated with a faster time to clinical progression. These potential biomarkers could help with identifying patients with aggressive disease.

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.

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.

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.

Single-cell transcriptomic profiling of the aging mouse brain.

The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how each cell type is affected in aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide comprehensive datasets of aging-related genes, pathways and ligand-receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell-type specific manner, even at times in opposite directions. These data reveal that aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population, and they highlight key molecular processes, including ribosome biogenesis, underlying brain aging. Overall, these large-scale datasets (accessible online at https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain ) provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process.

Bmi1 regulates human glioblastoma stem cells through activation of differential gene networks in CD133+ brain tumor initiating cells.

PURPOSE: Glioblastoma (GBM) is the most aggressive adult brain cancer, with a 15 month median survivorship attributed to the existence of treatment-refractory brain tumor initiating cells (BTICs). In order to better understand the mechanisms regulating the tumorigenic properties of this population, we studied the role of the polycomb group member BMI1 in our patient-derived GBM BTICs and its relationship with CD133, a well-established marker of BTICs. METHODS: Using gain and loss-of-function studies for Bmi1 in neural stem cells (NSCs) and patient-derived GBM BTICs respectively, we assessed in vitro self-renewal and in vivo tumor formation in these two cell populations. We further explored the BMI1 transcriptional regulatory network through RNA sequencing of different GBM BTIC populations that were knocked down for Bmi1. RESULTS: There is a differential role of BMI1 in CD133-positive cells, notably involving cell metabolism. In addition, we identified pivotal targets downstream of BMI1 in CD133+ cells such as integrin alpha 2 (ITGA2), that may contribute to regulating GBM stem cell properties. CONCLUSIONS: Our work sheds light on the association of three genes with CD133-BMI1 circuitry, their importance as downstream effectors of the BMI1 signalling pathway, and their potential as future targets for tackling GBM treatment-resistant cell populations.

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.

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.

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.

Exercise-responsive phosphoproteins in the heart.

Endurance exercise improves cardiac performance and affords protection against cardiovascular diseases but the signalling events that mediate these benefits are largely unexplored. Phosphorylation is a widely studied post-translational modification involved in intracellular signalling, and to discover novel phosphorylation events associated with exercise we have profiled the cardiac phosphoproteome response to a standardised exercise test to peak oxygen uptake (VO2peak). Male Wistar rats (346±18g) were assigned to 3 independent groups (n=6, in each) that were familiarised with running on a motorised treadmill within a metabolic chamber. Animals performed a graded exercise test and were killed either immediately (0h) after or 3h after terminating the test at a standardised physiological end point (i.e. peak oxygen uptake; VO2peak). Control rats were killed at a similar time of day to the exercised animals, to minimise possible circadian effects. Cardiac proteins were digested with trypsin and phosphopeptides were enriched by selective binding to titanium dioxide (TiO2). Phosphopeptides were analysed by liquid chromatography and high-resolution tandem mass spectrometry, and phosphopeptides were quantified by MS1 intensities and identified against the UniProt knowledgebase using MaxQuant (data are available via ProteomeXchange, ID PXD006646). The VO2peak of rats in the 0h and 3h groups was 66±5mlkg-1min-1 and 69.8±5mlkg-1min-1, respectively. Proteome profiling detected 1169 phosphopeptides and one-way ANOVA found 141 significant (P<0.05 with a false discovery rate of 10%) differences. Almost all (97%) of the phosphosites that were responsive to exercise are annotated in the PhosphoSitePlus database but, importantly, the majority of these have not previously been associated with the cardiac response to exercise. More than two-thirds of the exercise-responsive phosphosites were different from those identified in previous phosphoproteome profiling of the cardiac response to ß1-adrenergic receptor stimulation. Moreover, we report entirely new phosphorylation sites on 4 cardiac proteins, including S81 of muscle LIM protein, and identified 7 exercise-responsive kinases, including myofibrillar protein kinases such as obscurin, titin and the striated-muscle-specific serine/threonine kinase (SPEG) that may be worthwhile targets for future investigation.

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.

Global phosphoproteomic profiling reveals perturbed signaling in a mouse model of dilated cardiomyopathy.

Phospholamban (PLN) plays a central role in Ca2+ homeostasis in cardiac myocytes through regulation of the sarco(endo)plasmic reticulum Ca2+-ATPase 2A (SERCA2A) Ca2+ pump. An inherited mutation converting arginine residue 9 in PLN to cysteine (R9C) results in dilated cardiomyopathy (DCM) in humans and transgenic mice, but the downstream signaling defects leading to decompensation and heart failure are poorly understood. Here we used precision mass spectrometry to study the global phosphorylation dynamics of 1,887 cardiac phosphoproteins in early affected heart tissue in a transgenic R9C mouse model of DCM compared with wild-type littermates. Dysregulated phosphorylation sites were quantified after affinity capture and identification of 3,908 phosphopeptides from fractionated whole-heart homogenates. Global statistical enrichment analysis of the differential phosphoprotein patterns revealed selective perturbation of signaling pathways regulating cardiovascular activity in early stages of DCM. Strikingly, dysregulated signaling through the Notch-1 receptor, recently linked to cardiomyogenesis and embryonic cardiac stem cell development and differentiation but never directly implicated in DCM before, was a prominently perturbed pathway. We verified alterations in Notch-1 downstream components in early symptomatic R9C transgenic mouse cardiomyocytes compared with wild type by immunoblot analysis and confocal immunofluorescence microscopy. These data reveal unexpected connections between stress-regulated cell signaling networks, specific protein kinases, and downstream effectors essential for proper cardiac function.

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.