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Multimodal assays such as Patch-seq that simultaneously profile molecular and cellular phenotypes of cells enable the identification of molecular underpinnings of electrophysiological response patterns in neurons. Here we analyzed Patch-seq measurements of thousands of mouse interneurons to identify subclass-specific genes associated with different electrophysiological features. We found extensive subclass specificity: even for the same ephys feature, largely unique sets of genes are associated with that feature in different subclasses. Well established ephys genes such as Reln demonstrated subclass specificity that was previously not reported. Surprisingly, we found that ion channels explained significantly less variation in ephys response across interneurons compared to other genes; in particular, gene sets enriched in cell adhesion genes were amongst the most associated. We found our gene sets associated with action potential dV/dt measurements explained significant heritability of Schizophrenia risk, suggesting a novel role of single neuron electrophysiology in Schizophrenia risk. Finally, we observed significant ephys function switching of cell adhesion molecules across subclasses; the same adhesion molecule was observed to associate with different functional ephys measurements in distinct subclasses and co-express with different genes, suggesting re-purposing of adhesion molecules in different subclasses. Overall, our results yield novel insight into the specificity of roles that individual genes and adhesion molecules play in both single neuron ephys response and Schizophrenia risk.

Background: While next generation sequencing has enriched our knowledge about native microbial populations present in fresh produce, the loss of up to 50-70% of data during the alignment and denoising steps of data preprocessing may lead to the missing of important bacterial species information and decrease our ability to identify species associated with poor produce quality and contamination. Microbial studies are also often limited by small sample sizes, making generalization of results beyond individual studies difficult. Results: In this study, we explored separate strategies to mitigate the challenges of data preprocessing and small sample sizes. First, we explored an alignment-free analysis strategy using k-mer hashes to identify DNA signatures predictive of produce safety (contaminated vs. non-contaminated) and produce quality (good-quality vs. decreasing-quality), and compared it against the amplicon sequence variant (ASV) strategy that uses a typical alignment and denoising step. Random forests (RF)-based classifiers were trained on publicly available fresh produce microbiome datasets with data preprocessed using either the k-mer hash or ASV approach. RF-based classifiers for fresh produce safety and quality using 7-mer hash datasets had significantly higher classification accuracy than those using the ASV datasets, supporting the hypothesis that data preprocessing strategies that keep more data (k-mer hash) retain more useful information about bacterial species than approaches that lose data during preprocessing (ASV). We also demonstrated that integrating multiple datasets together also led to higher classification accuracy compared to those trained with individual datasets. Integrated datasets also enabled the identification of more consistent and generalizable biomarkers (ASV, 7-mer hash, or bacterial taxa) associated with fresh produce safety and quality. Conclusions:The proposed combination of integrating multiple datasets and leveraging an alignment-free 7-mer hash strategy substantially mitigates the loss of sequencing data due to the ASV denoising step and leads to better classification performance for fresh produce safety and quality. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing datasets (generated from different studies or different laboratories) for the application of machine learning in the area of microbial safety and quality of food.

Synopsis The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.

Tet1 protects against house dust mite (HDM)-induced lung inflammation in mice and alters the lung methylome and transcriptome. In order to explore the role of Tet1 in individual lung epithelial cell types in HDM-induced inflammation, we established a model of HDM-induced lung inflammation in Tet1 knockout and littermate wild-type mice, then studied EpCAM+ lung epithelial cells using single-cell RNA-seq analysis. We identified eight EpCAM+ lung epithelial cell types, among which AT2 cells were the most abundant. HDM challenge altered the relative abundance of epithelial cell types and resulted in cell type-specific transcriptomic changes. Bulk and cell type-specific analysis also showed that loss of Tet1 led to the altered expression of genes linked to augmented HDM-induced lung inflammation, including alarms, detoxification enzymes, oxidative stress response genes, and tissue repair genes. The transcriptomic regulation was accompanied by alterations in TF activities. Trajectory analysis supports that HDM may enhance the differentiation of AP and BAS cells into AT2 cells, independent of Tet1. Collectively, our data showed that lung epithelial cells had common and unique transcriptomic signatures of allergic lung inflammation. Tet1 deletion altered transcriptomic networks in various lung epithelial cells, which may promote allergen-induced lung inflammation.

© 2013 IEEE. Graph alignment refers to the problem of finding a bijective mapping across vertices of two graphs such that, if two nodes are connected in the first graph, their images are connected in the second graph. This problem arises in many fields, such as computational biology, social sciences, and computer vision and is often cast as a quadratic assignment problem (QAP). Most standard graph alignment methods consider an optimization that maximizes the number of matches between the two graphs, ignoring the effect of mismatches. We propose a generalized graph alignment formulation that considers both matches and mismatches in a standard QAP formulation. This modification can have a major impact in aligning graphs with different sizes and heterogeneous edge densities. Moreover, we propose two methods for solving the generalized graph alignment problem based on spectral decomposition of matrices. We compare the performance of proposed methods with some existing graph alignment algorithms including Natalie2, GHOST, IsoRank, NetAlign, Klau's approach as well as a semidefinite programming-based method over various synthetic and real graph models. Our proposed method based on simultaneous alignment of multiple eigenvectors leads to consistently good performance in different graph models. In particular, in the alignment of regular graph structures, which is one of the most difficult graph alignment cases, our proposed method significantly outperforms other methods.

Tet1 protects against house dust mite (HDM)-induced lung inflammation in mice and alters the lung methylome and transcriptome. In order to explore the role of Tet1 in individual lung epithelial cell types in HDM-induced inflammation, we established a model of HDM-induced lung inflammation in Tet1 knockout and littermate wildtype mice and studied EpCAM+ lung epithelial cells using single-cell RNA-seq analysis. We identified eight EpCAM+ lung epithelial cell types, among which AT2 cells were the most abundant. HDM challenge increased the percentage of alveolar progenitor cells (AP), broncho alveolar stem cells (BAS), and goblet cells, and decreased the percentage of AT2 and ciliated cells. Bulk and cell-type-specific analysis identified genes subject to Tet1 regulation and linked to augmented lung inflammation, including alarms, detoxification enzymes, oxidative stress response genes, and genes in tissue repair. The transcriptomic regulation was accompanied by alterations in TF activities. Trajectory analysis supports that HDM may enhance the differentiation of AP and BAS cells into AT2 cells, independent of Tet1. Collectively, our data showed that lung epithelial cells had common and unique transcriptomic signatures of allergic lung inflammation. Tet1 deletion altered transcriptomic networks in various lung epithelial cells, with an overall effect of promoting allergen-induced lung inflammation.

Deep neural networks implementing generative models for dimensionality reduction have been extensively used for the visualization and analysis of genomic data. One of their key limitations is lack of interpretability: it is challenging to quantitatively identify which input features are used to construct the embedding dimensions, thus preventing insight into why cells are organized in a particular data visualization, for example. Here we present a scalable, interpretable variational autoencoder (siVAE) that is interpretable by design: it learns feature embeddings that guide the interpretation of the cell embeddings in a manner analogous to factor loadings of factor analysis. siVAE is as powerful and nearly as fast to train as the standard VAE but achieves full interpretability of the embedding dimensions. Using siVAE, we exploit a number of connections between dimensionality reduction and gene network inference to identify gene neighborhoods and gene hubs, without the explicit need for gene network inference. We observe a systematic difference in the gene neighborhoods identified by dimensionality reduction methods and gene network inference algorithms in general, suggesting they provide complementary information about the underlying structure of the gene co-expression network. Finally, we apply siVAE to implicitly learn gene networks for individual iPSC lines and uncover a correlation between neuronal differentiation efficiency and loss of co-expression of several mitochondrial complexes, including NADH dehydrogenase, cytochrome C oxidase, and cytochrome b.

Several significant models have been developed that enable the study of diffusion of signals across biological, social and engineered networks. Within these established frameworks, the inverse problem of identifying the source of the propagated signal is challenging, owing to the numerous alternative possibilities for signal progression through the network. In real world networks, the challenge of determining sources is compounded as the true propagation dynamics are typically unknown, and when they have been directly measured, they rarely conform to the assumptions of any of the well-studied models. In this paper we introduce a method called Network Infusion (NI) that has been designed to circumvent these issues, making source inference practical for large, complex real world networks. The key idea is that to infer the source node in the network, full characterization of diffusion dynamics, in many cases, may not be necessary. This objective is achieved by creating a diffusion kernel that well-approximates standard diffusion models, but lends itself to inversion, by design, via likelihood maximization or error minimization. We apply NI for both single-source and multi-source diffusion, for both single-snapshot and multi-snapshot observations, and for both homogeneous and heterogeneous diffusion setups. We prove the mean-field optimality of NI for different scenarios, and demonstrate its effectiveness over several synthetic networks. Moreover, we apply NI to a real-data application, identifying news sources in the Digg social network, and demonstrate the effectiveness of NI compared to existing methods. Finally, we propose an integrative source inference framework that combines NI with a distance centrality-based method, which leads to a robust performance in cases where the underlying dynamics are unknown., Comment: 21 pages, 13 figures

Gene regulatory elements are central drivers of phenotypic variation and thus of critical importance towards understanding the genetics of complex traits. The Functional Annotation of Animal Genomes consortium was formed to collaboratively annotate the functional elements in animal genomes, starting with domesticated animals. Here we present an expansive collection of datasets from eight diverse tissues in three important agricultural species: chicken (Gallus gallus), pig (Sus scrofa), and cattle (Bos taurus). Comparative analysis of these datasets and those from the human and mouse Encyclopedia of DNA Elements projects reveal that a core set of regulatory elements are functionally conserved independent of divergence between species, and that tissue-specific transcription factor occupancy at regulatory elements and their predicted target genes are also conserved. These datasets represent a unique opportunity for the emerging field of comparative epigenomics, as well as the agricultural research community, including species that are globally important food resources., In order to interpret non-coding variants, information about regulatory elements in the genome is essential. Here, the authors annotate regulatory elements in chicken, pig and cattle, and characterize conservation of these elements between species.

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.

Traditional high-throughput drug combination screening requires automatic pipetting of drugs into high-density microtiter plates. Here, a drug-on-pillar platform is proposed for efficient combination drug screening. Using the proposed approach, combination drug screening can be carried out in a plug-and-play manner, allowing for high-throughput screening of large permutations of drug combinations at various concentrations, such that drug dispensing and cell-based screening can be temporally separated and therefore can potentially be performed at distant laboratories. The dispensing is implemented using our recently developed microfluidic pneumatic printing platform, which features a low-cost disposable cartridge that minimizes cross contamination. Moreover, our previously developed drug nanoformulation method with amphiphilic telodendrimers has been utilized to maintain drug stability in a dry form, allowing for convenient drug storage, shipping, and subsequent rehydration. Combining the features described above, we have implemented a 1260-spot drug combination array to study the effect of paired drugs against MDA-MB-231 triple negative human breast cancer cells. This study supports the feasibility of the drug-on-pillar platform for combination drug screening and has provided valuable insight into drug combination efficacy against breast cancer.

Mapping the molecular circuits that are perturbed by genetic variants underlying complex traits and diseases remains a great challenge. We present a comprehensive resource of 394 cell type and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity between transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) shows that disease-associated genetic variants — including variants that do not reach genome-wide significance — often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues. Our resource opens the door to systematic analysis of regulatory programs across hundreds of human cell types and tissues.

Intratumoral heterogeneity is associated with aggressive tumor behavior, therapy resistance, and poor patient outcomes. Such heterogeneity is thought to be dynamic, shifting over periods of minutes to hours in response to signaling inputs from the tumor microenvironment. However, models of this process have been inferred from indirect or post-hoc measurements of cell state, leaving the temporal details of signaling-driven heterogeneity undefined. Here, we developed a live-cell model system in which microenvironment-driven signaling dynamics can be directly observed and linked to variation in gene expression. Our analysis reveals that paracrine signaling between two cell types is sufficient to drive continual diversification of gene expression programs. This diversification emerges from systems-level properties of the EGFR/RAS/ERK signaling cascade, including intracellular amplification of amphiregulin-mediated paracrine signals and differential kinetic filtering by target genes including Fra-1, c-Myc, and Egr1. Our data enable more precise modeling of paracrine-driven transcriptional variation as a generator of gene expression heterogeneity. A record of this paper’s Transparent Peer Review process is included in the Supplemental Information.

Genomic regions associated with complex traits and diseases are primarily located in non-coding regions of the genome and have unknown mechanism of action. A critical step to understanding the genetics of complex traits is to fine-map each associated locus; that is, to find the causal variant(s) that underlie genetic associations with a trait. Fine-mapping approaches are currently focused on identifying genomic annotations, such as transcription factor binding sites, which are enriched in direct overlap with candidate causal variants. We introduce CONVERGE, the first computational tool to search for co-localization of GWAS causal variants with transcription factor binding sites in the same regulatory regions, without requiring direct overlap. As a proof of principle, we demonstrate that CONVERGE is able to identify five novel regulators of type 2 diabetes which subsequently validated in knockdown experiments in pancreatic beta cells, while existing fine-mapping methods were unable to find any statistically significant regulators. CONVERGE also recovers more established regulators for total cholesterol compared to other fine-mapping methods. CONVERGE is therefore unique and complementary to existing fine-mapping methods and is useful for exploring the regulatory architecture of complex traits.

The EGFR/Ras/ERK signalling pathway is a driver of cancer cell proliferation and metastasis in tumours that exhibit high cell-to-cell heterogeneity. While the signalling activity of this pathway is frequently amplified in tumours, it is not understood how the kinetic aspects of its activation in tumours differ from normal cellular signalling. Using live-cell reporters of ERK signalling in the breast cancer progression series HMT-3522, we found that ERK activity in invasive cells is similar in amplitude to isogenic non-malignant cells but is highly dynamic and more disordered, leading to more heterogeneous expression of ERK target genes. Our analysis reveals that this diversification arises from systems-level functions of the pathway, including intracellular amplification of amphiregulin-mediated paracrine signalling and differential kinetic filtering by genes including Fra-1, c-Myc, and Egr1. Our findings establish a mechanism for the generation of non-genetic tumour cell plasticity arising from the specific quantitative properties of a signal transduction pathway.

The number of cells to be sequenced is one of the most important considerations in single cell RNA experiment design, and we find that experiments designed to maximize the number of sequenced cells tends to also increase the measurement noise within individual cells. This noise is detrimental to the performance of downstream scRNA-seq analysis tools, but can be mitigated by ignoring precise expression measurements and focusing on the detection of a gene. This observation that throwing away data can increase signal is observed across multiple capture technologies, can be addressed using as little as one line of code, and results in execution times that are up to three orders of magnitude faster, therefore making this observation important as newer capture technologies continue to drive the number of sequenced cells higher.

Alzheimer’s disease (AD) is a severe age-related neurodegenerative disorder characterized by accumulation of amyloid-β plaques and neurofibrillary tangles, synaptic and neuronal loss, and cognitive decline. Several genes have been implicated in AD, but chromatin state alterations during neurodegeneration remain uncharacterized. Here we profile transcriptional and chromatin state dynamics across early and late pathology in the hippocampus of an inducible mouse model of AD-like neurodegeneration. We find a coordinated downregulation of synaptic plasticity genes and regulatory regions, and upregulation of immune response genes and regulatory regions, which are targeted by factors that belong to the ETS family of transcriptional regulators, including PU.1. Human regions orthologous to increasing-level enhancers show immune-cell-specific enhancer signatures as well as immune cell expression quantitative trait loci, while decreasing-level enhancer orthologues show fetal-brain-specific enhancer activity. Notably, AD-associated genetic variants are specifically enriched in increasing-level enhancer orthologues, implicating immune processes in AD predisposition. Indeed, increasing enhancers overlap known AD loci lacking protein-altering variants, and implicate additional loci that do not reach genome-wide significance. Our results reveal new insights into the mechanisms of neurodegeneration and establish the mouse as a useful model for functional studies of AD regulatory regions., Belfer Neurodegeneration Consortium, National Institutes of Health (U.S.) (National Institute of Neurological Disorders and Stroke (U.S.)/National Institute on Aging RO1NS078839), Swiss National Science Foundation (Early Postdoc Mobility Fellowship P2BSP3_151885), National Institutes of Health (U.S.) (National Human Genome Research Institute (U.S.) R01HG004037-07), National Institutes of Health (U.S.) (National Human Genome Research Institute (U.S.) RC1HG005334)

Technical variation in feature measurements, such as gene expression and locus accessibility, is a key challenge of large-scale single-cell genomic datasets. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by analyzing feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference. Performance gains can also be realized in one line of R code in existing pipelines. Electronic supplementary material The online version of this article (10.1186/s13059-019-1806-0) contains supplementary material, which is available to authorized users.

Understanding the genetics of gene regulation provides information on the cellular mechanisms through which genetic variation influences complex traits. Expression quantitative trait loci, or eQTLs, are enriched for polymorphisms that have been found to be associated with disease risk. While most analyses of human data has focused on regulation of expression by nearby variants (cis-eQTLs), distal or trans-eQTLs may have broader effects on the transcriptome and important phenotypic consequences, necessitating a comprehensive study of the effects of genetic variants on distal gene transcription levels. In this work, we identify trans-eQTLs in the Genotype Tissue Expression (GTEx) project data1, consisting of 449 individuals with RNA-sequencing data across 44 tissue types. We find 81 genes with a trans-eQTL in at least one tissue, and we demonstrate that trans-eQTLs are more likely than cis-eQTLs to have effects specific to a single tissue. We evaluate the genomic and functional properties of trans-eQTL variants, identifying strong enrichment in enhancer elements and Piwi-interacting RNA clusters. Finally, we describe three tissue-specific regulatory loci underlying relevant disease associations: 9q22 in thyroid that has a role in thyroid cancer, 5q31 in skeletal muscle, and a previously reported master regulator near KLF14 in adipose. These analyses provide a comprehensive characterization of trans-eQTLs across human tissues, which contribute to an improved understanding of the tissue-specific cellular mechanisms of regulatory genetic variation.

We propose a unified Bayesian framework for detecting genetic variants associated with disease by exploiting image-based features as an intermediate phenotype. The use of imaging data for examining genetic associations promises new directions of analysis, but currently the most widely used methods make sub-optimal use of the richness that these data types can offer. Currently, image features are most commonly selected based on their relevance to the disease phenotype. Then, in a separate step, a set of genetic variants is identified to explain the selected features. In contrast, our method performs these tasks simultaneously in order to jointly exploit information in both data types. The analysis yields probabilistic measures of clinical relevance for both imaging and genetic markers. We derive an efficient approximate inference algorithm that handles the high dimensionality of image and genetic data. We evaluate the algorithm on synthetic data and demonstrate that it outperforms traditional models. We also illustrate our method on Alzheimer's Disease Neuroimaging Initiative data., National Alliance for Medical Image Computing (U.S.) (U54-EB005149), National Center for Research Resources (U.S.) (NAC P41-RR13218), National Institute for Biomedical Imaging and Bioengineering (U.S.) (NAC P41-EB-015902), National Institute for Biomedical Imaging and Bioengineering (U.S.) (1K25EB013649-01), BrightFocus Foundation (AHAF-A2012333), Natural Sciences and Engineering Research Council of Canada (CGS-D), Barbara J. Weedon Fellowship, Wistron Corporation

Motivation: Lung cancer is often discovered long after its onset, making identifying genes important in its initiation and progression a challenge. By the time the tumors are discovered, we only observe the final sum of changes of the few genes that initiated cancer and thousands of genes that they have influenced. Gene interactions and heterogeneity of samples make it difficult to identify genes consistent between different cohorts. Using gene and gene–product interaction networks, we propose a principled approach to identify a small subset of genes whose network neighbors exhibit consistently high expression change (in cancerous tissue versus normal) regardless of their own expression. We hypothesize that these genes can shed light on the larger scale perturbations in the overall landscape of expression levels. Results: We benchmark our method on simulated data, and show that we can recover a true gene list in noisy measurement data. We then apply our method to four non-small cell lung cancer and two pancreatic cancer cohorts, finding several genes that are consistent within all cohorts of the same cancer type. Conclusion: Our model is flexible, robust and identifies gene sets that are more consistent across cohorts than several other approaches. Additionally, our method can be applied on a per-patient basis not requiring large cohorts of patients to find genes of influence. Our approach is generally applicable to gene expression studies where the goal is to identify a small set of influential genes that may in turn explain the much larger set of genome-wide expression changes. Availability: The code is available at http://morrislab.med.utoronto.ca/~anna/cannet.zip Contact: anna.goldenberg@utoronto.ca Supplementary Information: Supplementary data are available at Bioinformatics online.

While many RNA-binding proteins (RBPs) bind RNA in a sequence-specific manner, their sequence preferences alone do not distinguish known target RNAs from other potential targets that are coexpressed and contain the same sequence motifs. Recently, the mRNA targets of dozens of RNA-binding proteins have been identified, facilitating a systematic study of the features of target transcripts. Using these data, we demonstrate that calculating the predicted structural accessibility of a putative RBP binding site allows one to significantly improve the accuracy of predicting in vivo binding for the majority of sequence-specific RBPs. In our new in silico approach, accessibility is predicted based solely on the mRNA sequence without consideration of the locations of bound trans-factors; as such, our results suggest a greater than previously anticipated role for intrinsic mRNA secondary structure in determining RBP binding target preference. Target site accessibility aids in predicting target transcripts and the binding sites for RBPs with a range of RNA-binding domains and subcellular functions. Based on this work, we introduce a new motif-finding algorithm that identifies accessible sequence-specific RBP motifs from in vivo binding data.

Gene expression-based signatures help identify pathways relevant to diseases and treatments, but are challenging to construct when there is a diversity of disease mechanisms and treatments in patients with complex diseases. To overcome this challenge, we present a new application of an in silico gene expression deconvolution method, ISOpure-S1, and apply it to identify a common gene expression signature corresponding to response to treatment in 33 juvenile idiopathic arthritis (JIA) patients. Using pre- and post-treatment gene expression profiles only, we found a gene expression signature that significantly correlated with a reduction in the number of joints with active arthritis, a measure of clinical outcome (Spearman rho = 0.44, p = 0.040, Bonferroni correction). This signature may be associated with a decrease in T-cells, monocytes, neutrophils and platelets. The products of most differentially expressed genes include known biomarkers for JIA such as major histocompatibility complexes and interleukins, as well as novel biomarkers including α-defensins. This method is readily applicable to expression datasets of other complex diseases to uncover shared mechanistic patterns in heterogeneous samples.

Background Genomewide association studies can be used to identify disease-relevant genomic regions, but interpretation of the data is challenging. The FTO region harbors the strongest genetic association with obesity, yet the mechanistic basis of this association remains elusive. Methods We examined epigenomic data, allelic activity, motif conservation, regulator expression, and gene coexpression patterns, with the aim of dissecting the regulatory circuitry and mechanistic basis of the association between the FTO region and obesity. We validated our predictions with the use of directed perturbations in samples from patients and from mice and with endogenous CRISPR-Cas9 genome editing in samples from patients. Results Our data indicate that the FTO allele associated with obesity represses mitochondrial thermogenesis in adipocyte precursor cells in a tissue-autonomous manner. The rs1421085 T-to-C single-nucleotide variant disrupts a conserved motif for the ARID5B repressor, which leads to derepression of a potent preadipocyte enhancer and a doubling of IRX3 and IRX5 expression during early adipocyte differentiation. This results in a cell-autonomous developmental shift from energy-dissipating beige (brite) adipocytes to energy-storing white adipocytes, with a reduction in mitochondrial thermogenesis by a factor of 5, as well as an increase in lipid storage. Inhibition of Irx3 in adipose tissue in mice reduced body weight and increased energy dissipation without a change in physical activity or appetite. Knockdown of IRX3 or IRX5 in primary adipocytes from participants with the risk allele restored thermogenesis, increasing it by a factor of 7, and overexpression of these genes had the opposite effect in adipocytes from nonrisk-allele carriers. Repair of the ARID5B motif by CRISPR-Cas9 editing of rs1421085 in primary adipocytes from a patient with the risk allele restored IRX3 and IRX5 repression, activated browning expression programs, and restored thermogenesis, increasing it by a factor of 7. Conclusions Our results point to a pathway for adipocyte thermogenesis regulation involving ARID5B, rs1421085, IRX3, and IRX5, which, when manipulated, had pronounced pro-obesity and anti-obesity effects. (Funded by the German Research Center for Environmental Health and others.).

We have adapted the Java Molecular Viewer (JMV) to virtual reality display environments, through a number of extensions to the Java 3D code. Phylogenetic information derived from multiple alignments (temporal information) can be overlaid onto molecule structures (spatial information). The number of sequences included in the underlying multiple alignment can be changed instantaneously, resulting in dynamical updates of the displayed information. JMV was also extended to handle an infinite number of objects (molecules) in the same display. The objects can be manipulated in six degrees of freedom simultaneously or independently. We have used the small subunit ribosomal RNA to demonstrate the system (http:// cave.ucalgary.ca), which can be used for any molecule with a resolved structure.

We report an in-depth characterization of patient-derived orthotopic xenograft (PDOX) models of triple-negative breast cancer (TNBC) regarding their molecular profile at the single cell level, tumor heterogeneity, 3D organoid generation, and ability to generate circulating tumor cells (CTCs). A panel of seven TNBC PDOX tumors were grown orthotopically in Nod scid gamma mice and used in this study. Blood obtained via cardiac puncture from tumor bearing animals was processed on the Vortex microfluidic platform, for label-free, size-based enrichment of circulating tumor cells (CTCs). Enriched cell populations were stained for human-specific cytokeratin (CK) and Vimentin (Vim), mouse-specific CD45, and DAPI; CTCs were identified as cells that were CD45 negative and positive for CK or Vim. Bulk tumor growing in the mammary fat pads was dissociated to single cells and characterized using Fluidigm’s® PolarisTM platform for single cell biological experimentation and cDNA generation within an integrated fluidic circuit (IFC). From the cell suspension, Polaris identified single cells that were then processed for mRNA-seq. The resulting cDNA libraries were then multiplexed using Nextera XT® (Illumina®) and sequenced on Illumina systems. Data generated from mRNA-seq was processed to correct for confounding factors such as cell size, cell cycle and read depth and then analyzed to screen for heterogeneity between different populations of cells. Tumors were analyzed by flow cytometry for both tumor and immune cells and additionally the single cell suspension was seeded into 3-D culture to generate organoids. Finally, organs from tumor bearing animals were analyzed for metastases. With the Vortex platform, we detected CTCs from a majority of our PDOX tumor-bearing mice. The total number of CTCs varied over a wide range between different PDOX tumors. There was a clear heterogeneity in CTCs in terms of CK and Vim expression. In CTCs from one of the PDOX tumors, we detected a small population of CTCs that were either CK+ or Vim+ but the major fraction that was double positive (Vim+ CK+). Probing the bulk tumor from different PDOX models revealed heterogeneity in the levels and number of cells positive for cell surface markers like EpCAM and a difference in the levels of infiltrating myeloid cells (CD11b+). mRNA-seq analyses of individual tumor cells from the bulk tumor belonging to different PDOX models will be described. Additionally, lung and brain metastases were identified. 3D organoid cultures from our PDOX models were successfully grown and their gene expression profiles will be analyzed. In summary, PDOX models of TNBC will help advance our understanding of the molecular basis of this deadly cancer. Citation Format: Vishnu C. Ramani, Rakhi Gupta, Gerald Quon, Melanie Triboulet, Corinne Renier, Cassandra Greene, Chad Sanada, Tracy Lu, Lukasz Szpankowski, Naveen Ramalingam, Ameen A. Salahudeen, Sean de la O, Ranjani Rajapaksa, Shoshana Levy, Anne A. Leyrat, Jay A. West, Elodie Sollier-Christen, Calvin J. Kuo, George W. Sledge, Stefanie S. Jeffrey. Evaluating the metastatic potential and the molecular heterogeneity of patient-derived orthotopic xenograft models of triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1847. doi:10.1158/1538-7445.AM2017-1847

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease., National Human Genome Research Institute (U.S.) (RC1HG005334), National Human Genome Research Institute (U.S.) (R01HG004037), National Human Genome Research Institute (U.S.) (R01HG004037-S1), National Human Genome Research Institute (U.S.) (RO1NS078839), National Science Foundation (U.S.) (CAREER Award 1254200)

Applications of linear mixed models (LMMs) to problems in genomics include phenotype prediction, correction for confounding in genome-wide association studies, estimation of narrow sense heritability, and testing sets of variants (e.g., rare variants) for association. In each of these applications, the LMM uses a genetic similarity matrix, which encodes the pairwise similarity between every two individuals in a cohort. Although ideally these similarities would be estimated using strictly variants relevant to the given phenotype, the identity of such variants is typically unknown. Consequently, relevant variants are excluded and irrelevant variants are included, both having deleterious effects. For each application of the LMM, we review known effects and describe new effects showing how variable selection can be used to mitigate them., National Institute on Aging (Brain eQTL Study (dbGaP phs000249.v1.p1))

Tumor heterogeneity is a limiting factor in cancer treatment and in the discovery of biomarkers to personalize it. We describe a computational purification tool, ISOpure, which directly addresses the effects of variable contamination by normal tissue in clinical tumor specimens. ISOpure uses a set of tumor expression profiles and a panel of healthy tissue expression profiles to generate a purified cancer profile for each tumor sample, and an estimate of the proportion of RNA originating from cancerous cells. Applying ISOpure before identifying gene signatures leads to significant improvements in the prediction of prognosis and other clinical variables in lung and prostate cancer.

DNA methylation has been implicated in a number of diseases and other phenotypes. It is, therefore, of interest to identify and understand the genetic determinants of methylation and epigenomic variation. We investigated the extent to which genetic variation in cis-DNA sequence explains variation in CpG dinucleotide methylation in publicly available data for four brain regions from unrelated individuals, finding that 3–4% of CpG loci assayed were heritable, with a mean estimated narrow-sense heritability of 30% over the heritable loci. Over all loci, the mean estimated heritability was 3%, as compared with a recent twin-based study reporting 18%. Heritable loci were enriched for open chromatin regions and binding sites of CTCF, an influential regulator of transcription and chromatin architecture. Additionally, heritable loci were proximal to genes enriched in several known pathways, suggesting a possible functional role for these loci. Our estimates of heritability are conservative, and we suspect that the number of identified heritable loci will increase as the methylome is assayed across a broader range of cell types and the density of the tested loci is increased. Finally, we show that the number of heritable loci depends on the window size parameter commonly used to identify candidate cis-acting single-nucleotide polymorphism variants.

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity., Author Summary The cellular composition of heterogeneous samples can be predicted from reference gene expression profiles that represent the homogeneous, constituent populations of the heterogeneous samples. However, existing methods fail when the reference profiles are not representative of the constituent populations. We developed PERT, a new probabilistic expression deconvolution method, to address this limitation. PERT was used to deconvolve the cellular composition of variably sourced and treated heterogeneous human blood samples. Our results indicate that even after batch correction is applied, cells presenting the same cell surface antigens display different transcriptional programs when they are uncultured versus culture-derived. Given gene expression profiles of culture-derived heterogeneous samples and profiles of uncultured reference populations, PERT was able to accurately recover proportions of the constituent populations composing the heterogeneous samples. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity.

Background Cancer gene expression profiling is an indispensable tool for identifying drivers of tumor progression, identifying subtypes, and predicting clinical outcome. An outstanding challenge faced by cancer gene expression studies is the limited concordance between studies [1], driven in part by lack of statistical power [2]. Part of this lack of statistical power is due to the fact that tumor samples from some solid cancers contain between 30%-70% healthy tissue [3]. This healthy tissue contaminates tumor expression profiles and variable amounts of healthy tissue leads to increased variability between tumor expression profiles. Physical purification of these tumor samples before profiling is often not feasible.

Motivation: One of the most deadly cancer diagnoses is the carcinoma of unknown primary origin. Without the knowledge of the site of origin, treatment regimens are limited in their specificity and result in high mortality rates. Though supervised classification methods have been developed to predict the site of origin based on gene expression data, they require large numbers of previously classified tumors for training, in part because they do not account for sample heterogeneity, which limits their application to well-studied cancers. Results: We present ISOLATE, a new statistical method that simultaneously predicts the primary site of origin of cancers and addresses sample heterogeneity, while taking advantage of new high-throughput sequencing technology that promises to bring higher accuracy and reproducibility to gene expression profiling experiments. ISOLATE makes predictions de novo, without having seen any training expression profiles of cancers with identified origin. Compared with previous methods, ISOLATE is able to predict the primary site of origin, de-convolve and remove the effect of sample heterogeneity and identify differentially expressed genes with higher accuracy, across both synthetic and clinical datasets. Methods such as ISOLATE are invaluable tools for clinicians faced with carcinomas of unknown primary origin. Availability: ISOLATE is available for download at: http://morrislab.med.utoronto.ca/software Contact: gerald.quon@utoronto.ca; quaid.morris@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.

The aim of this paper is to use the Java 3D ConfiguredUniverse utilities to create a set of behaviors that we can integrate into existing Java 3D programs, as long as these programs use the ConfiguredUniverse. We call this behavior package Jabiru (Java 3D Application Behavior Immersive Virtual Reality Utilities). The Jabiru set of behaviors, accessible from a master menu, facilitates moving Java 3D applications originally created for desktop environments to immersive VR environments and vice versa. Jabiru also provides a six-degrees-of-freedom (6DOF) device emulator for a conventional mouse, meant to facilitate offsite testing of immersive VR behaviors on a desktop. The main focus of our work with a CAVE is in relation to bioinformatics, which has embraced Java and Java 3D as one of the choice programming environments. By creating this package, we help bring 3D graphics to both the bioinformatics community and the casual Java 3D developer or user. We discuss the design and implementation of Jabiru.

Macromolecular crystallization has been identified as the rate-limiting step in the structural biology field. With the advent of genomic-wide approaches to structural biology, efforts to streamline and automate the process of crystal growth are mandatory. In the last eighteen months we have developed a high throughput laboratory capable of executing a million crystallization experiments a year. We will describe the efforts and comment on the first 717,312 crystal growth experiments.

INTRODUCTION: Molecular-based diagnostics tools such as gene expression signatures for predicting patient outcome or response to treatment are cornerstones of personalized medicine. However, they have a reputation for poor validation performance on patient cohorts independent of those on which they were developed, a combined result of small patient cohorts and tumor heterogeneity. We quantitatively assess the impact of tumor heterogeneity on gene-expression based predictive models of patient outcome for non small cell lung cancer. EXPERIMENTS: We developed a fully automated pipeline to computationally purify tumor gene expression profiles using a tool called ISOLATE (Quon et al., 2009), identify a prognostic gene signature on a training cohort (Friedman et al., 2010), and validate the signature on independent cohorts. ISOLATE accepts heterogeneous tumor profiles and healthy tissue profiles, and outputs purified tumor profiles and percentage tumor content for each heterogeneous sample. We used 1 training cohort of 86 patients (Beer et al., 2002), and 4 independent validation cohorts totaling 443 patients (Shedden et al., 2008). The computational pipeline was run on the training and validation cohorts in two identical scenarios with the exception of either performing computational purification on all samples or not. RESULTS: We first validated the estimates of % tumor content of ISOLATE on 32 lung adenocarcinomas on which two pathologists independently estimated % content. For the 23 samples on which the two pathologists agreed strongly, ISOLATE estimates had 0.51 correlation with their average, compared with an overall correlation of 0.59 between the two pathologists. Our computational pipeline identified an 82-gene signature (‘unpurified-sig’) on the unpurified tumor profiles, and a 110-gene signature (‘ISOLATE-sig’) on the purified tumor profiles. We used these signatures to identify low and high risk patient groups within the validation cohorts of 443 patients. ISOLATE-sig achieved a very significant difference in survival between the identified low and high risk patients (Hazard Ratio (HR)=1.92, p=10⁁(−6)), compared to unpurified-sig (HR=1.53, p=10⁁(−3)). We separately examined all 227 Stage I patients, and found consistent results: ISOLATE-sig performance (HR=1.80, p=10⁁(−3)) compared very favorably to the original profiles (HR=1.45, p=10⁁(−2)). Here, only ISOLATE-sig achieves a hazard ratio whose 95% CI strictly exceeds 1. CONCLUSION: This is the first study to date that quantitatively assesses the effect of tumor heterogeneity on models for predicting patient outcome. We also present a fully automated computational pipeline for purifying, identifying, and validating gene expression based signatures. We showed purification lead to much better prognostic models of lung cancer, and anticipate similar results for other solid cancers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4847. doi:10.1158/1538-7445.AM2011-4847

Background The statistical analysis of protein structures requires datasets in which structural features can be considered independently distributed, i.e. not related through common ancestry, and that fulfil minimal requirements regarding the experimental quality of the structures it contains. However, non-redundant datasets based on sequence similarity invariably contain distantly related homologues. Here we provide a reference dataset of non-homologous protein domains, assuming that structural dissimilarity at the topology level is incompatible with recognizable common ancestry. The dataset is based on domains at the Topology level of the CATH database which hierarchically classifies all protein structures. It contains the best refined representatives of each Topology level, validates structural dissimilarity and removes internally duplicated fragments. The compilation of Nh3D is fully scripted. Results The current Nh3D list contains 570 domains with a total of 90780 residues. It covers more than 70% of folds at the Topology level of the CATH database and represents more than 90% of the structures in the PDB that have been classified by CATH. We observe that even though all protein pairs are structurally dissimilar, some pairwise sequence identities after global alignment are greater than 30%. Conclusion Nh3D is freely available as a reference dataset for the statistical analysis of sequence and structure features of proteins in the PDB. Regularly updated versions of Nh3D and the corresponding PDB-formatted coordinate sets are accessible from our Web site http://www.schematikon.org.