Presenting ENCODE

TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning

MOTIVATION

Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of human diseases, we hypothesize that rare non-coding variants discovered in WGS play a regulatory role in predisposing disease risk.

RESULTS

With thousands of tissue- and cell-type-specific epigenomic features, we propose TVAR. This multi-label learning-based deep neural network predicts the functionality of non-coding variants in the genome based on eQTLs across 49 human tissues in the GTEx project. TVAR learns the relationships between high-dimensional epigenomics and eQTLs across tissues, taking the correlation among tissues into account to understand shared and tissue-specific eQTL effects. As a result, TVAR outputs tissue-specific annotations, with an average AUROC of 0.77 across these tissues. We evaluate TVAR's performance on four complex diseases (coronary artery disease, breast cancer, Type 2 diabetes and Schizophrenia), using TVAR's tissue-specific annotations, and observe its superior performance in predicting functional variants for both common and rare variants, compared with five existing state-of-the-art tools. We further evaluate TVAR's G-score, a scoring scheme across all tissues, on ClinVar, fine-mapped GWAS loci, Massive Parallel Reporter Assay (MPRA) validated variants and observe the consistently better performance of TVAR compared with other competing tools.

AVAILABILITY AND IMPLEMENTATION

The TVAR source code and its scores on the ClinVar catalog, fine mapped GWAS Loci, high confidence eQTLs from GTEx dataset, and MPRA validated functional variants are available at https://github.com/haiyang1986/TVAR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers

The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported enhancers in typical cell lines, are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell lines. Existing computational methods, capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation, and thus may provide useful information in identifying regulatory elements. Motivated by the aforementioned understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. Benefitting from the well-designed feature extraction mechanism and skip connection strategy, our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences, but also has the ability to self-adapt to different sizes of datasets. Besides, with the adoption of auto-encoder, our model is capable of making cross-cell line predictions. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers. The source code and detailed tutorial of DeepCAPE are freely available at https://github.com/ShengquanChen/DeepCAPE.

De novo pattern discovery enables robust assessment of functional consequences of non-coding variants

MOTIVATION

Given the complexity of genome regions, prioritize the functional effects of non-coding variants remains a challenge. Although several frameworks have been proposed for the evaluation of the functionality of non-coding variants, most of them used 'black boxes' methods that simplify the task as the pathogenicity/benign classification problem, which ignores the distinct regulatory mechanisms of variants and leads to less desirable performance. In this study, we developed DVAR, an unsupervised framework that leverage various biochemical and evolutionary evidence to distinguish the gene regulatory categories of variants and assess their comprehensive functional impact simultaneously.

RESULTS

DVAR performed de novo pattern discovery in high-dimensional data and identified five regulatory clusters of non-coding variants. Leveraging the new insights into the multiple functional patterns, it measures both the between-class and the within-class functional implication of the variants to achieve accurate prioritization. Compared to other two-class learning methods, it showed improved performance in identification of clinically significant variants, fine-mapped GWAS variants, eQTLs and expression-modulating variants. Moreover, it has superior performance on disease causal variants verified by genome-editing (like CRISPR-Cas9), which could provide a pre-selection strategy for genome-editing technologies across the whole genome. Finally, evaluated in BioVU and UK Biobank, two large-scale DNA biobanks linked to complete electronic health records, DVAR demonstrated its effectiveness in prioritizing non-coding variants associated with medical phenotypes.

AVAILABILITY AND IMPLEMENTATION

The C++ and Python source codes, the pre-computed DVAR-cluster labels and DVAR-scores across the whole genome are available at https://www.vumc.org/cgg/dvar.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Noncoding RNAs in the Vascular System Response to Oxidative Stress

SIGNIFICANCE

Redox homeostasis plays a pivotal role in vascular cell function and its imbalance has a causal role in a variety of vascular diseases. Accordingly, the response of mammalian cells to redox cues requires precise transcriptional and post-transcriptional modulation of gene expression patterns. Recent Advances: Mounting evidence shows that nonprotein-coding RNAs (ncRNAs) are important for the functional regulation of most, if not all, cellular processes and tissues. Not surprisingly, a prominent role of ncRNAs has been identified also in the vascular system response to oxidative stress.

CRITICAL ISSUES

The highly heterogeneous family of ncRNAs has been divided into several groups. In this article we focus on two classes of regulatory ncRNAs: microRNAs and long ncRNAs (lncRNAs). Although knowledge in many circumstances, and especially for lncRNAs, is still fragmentary, ncRNAs are clinically interesting because of their diagnostic and therapeutic potential. We outline ncRNAs that are regulated by oxidative stress as well as ncRNAs that modulate reactive oxygen species production and scavenging. More importantly, we describe the role of these ncRNAs in vascular physiopathology and specifically in disease conditions wherein oxidative stress plays a crucial role, such as hypoxia and ischemia, ischemia reperfusion, inflammation, diabetes mellitus, and atherosclerosis.

FUTURE DIRECTIONS

The therapeutic potential of ncRNAs in vascular diseases and in redox homeostasis is discussed.

Transcriptional-Readthrough RNAs Reflect the Phenomenon of "A Gene Contains Gene(s)" or "Gene(s) within a Gene" in the Human Genome, and Thus Are Not Chimeric RNAs

Tens of thousands of chimeric RNAs, i.e., RNAs with sequences of two genes, have been identified in human cells. Most of them are formed by two neighboring genes on the same chromosome and are considered to be derived via transcriptional readthrough, but a true readthrough event still awaits more evidence and trans-splicing that joins two transcripts together remains as a possible mechanism. We regard those genomic loci that are transcriptionally read through as unannotated genes, because their transcriptional and posttranscriptional regulations are the same as those of already-annotated genes, including fusion genes formed due to genetic alterations. Therefore, readthrough RNAs and fusion-gene-derived RNAs are not chimeras. Only those two-gene RNAs formed at the RNA level, likely via trans-splicing, without corresponding genes as genomic parents, should be regarded as authentic chimeric RNAs. However, since in human cells, procedural and mechanistic details of trans-splicing have never been disclosed, we doubt the existence of trans-splicing. Therefore, there are probably no authentic chimeras in humans, after readthrough and fusion-gene derived RNAs are all put back into the group of ordinary RNAs. Therefore, it should be further determined whether in human cells all two-neighboring-gene RNAs are derived from transcriptional readthrough and whether trans-splicing truly exists.

Living Organisms Author Their Read-Write Genomes in Evolution

Evolutionary variations generating phenotypic adaptations and novel taxa resulted from complex cellular activities altering genome content and expression: (i) Symbiogenetic cell mergers producing the mitochondrion-bearing ancestor of eukaryotes and chloroplast-bearing ancestors of photosynthetic eukaryotes; (ii) interspecific hybridizations and genome doublings generating new species and adaptive radiations of higher plants and animals; and, (iii) interspecific horizontal DNA transfer encoding virtually all of the cellular functions between organisms and their viruses in all domains of life. Consequently, assuming that evolutionary processes occur in isolated genomes of individual species has become an unrealistic abstraction. Adaptive variations also involved natural genetic engineering of mobile DNA elements to rewire regulatory networks. In the most highly evolved organisms, biological complexity scales with "non-coding" DNA content more closely than with protein-coding capacity. Coincidentally, we have learned how so-called "non-coding" RNAs that are rich in repetitive mobile DNA sequences are key regulators of complex phenotypes. Both biotic and abiotic ecological challenges serve as triggers for episodes of elevated genome change. The intersections of cell activities, biosphere interactions, horizontal DNA transfers, and non-random Read-Write genome modifications by natural genetic engineering provide a rich molecular and biological foundation for understanding how ecological disruptions can stimulate productive, often abrupt, evolutionary transformations.

It Is Imperative to Establish a Pellucid Definition of Chimeric RNA and to Clear Up a Lot of Confusion in the Relevant Research

There have been tens of thousands of RNAs deposited in different databases that contain sequences of two genes and are coined chimeric RNAs, or chimeras. However, "chimeric RNA" has never been lucidly defined, partly because "gene" itself is still ill-defined and because the means of production for many RNAs is unclear. Since the number of putative chimeras is soaring, it is imperative to establish a pellucid definition for it, in order to differentiate chimeras from regular RNAs. Otherwise, not only will chimeric RNA studies be misled but also characterization of fusion genes and unannotated genes will be hindered. We propose that only those RNAs that are formed by joining two RNA transcripts together without a fusion gene as a genomic basis should be regarded as authentic chimeras, whereas those RNAs transcribed as, and cis-spliced from, single transcripts should not be deemed as chimeras. Many RNAs containing sequences of two neighboring genes may be transcribed via a readthrough mechanism, and thus are actually RNAs of unannotated genes or RNA variants of known genes, but not chimeras. In today's chimeric RNA research, there are still several key flaws, technical constraints and understudied tasks, which are also described in this perspective essay.

Discriminative identification of transcriptional responses of promoters and enhancers after stimulus

Promoters and enhancers regulate the initiation of gene expression and maintenance of expression levels in spatial and temporal manner. Recent findings stemming from the Cap Analysis of Gene Expression (CAGE) demonstrate that promoters and enhancers, based on their expression profiles after stimulus, belong to different transcription response subclasses. One of the most promising biological features that might explain the difference in transcriptional response between subclasses is the local chromatin environment. We introduce a novel computational framework, PEDAL, for distinguishing effectively transcriptional profiles of promoters and enhancers using solely histone modification marks, chromatin accessibility and binding sites of transcription factors and co-activators. A case study on data from MCF-7 cell-line reveals that PEDAL can identify successfully the transcription response subclasses of promoters and enhancers from two different stimulations. Moreover, we report subsets of input markers that discriminate with minimized classification error MCF-7 promoter and enhancer transcription response subclasses. Our work provides a general computational approach for identifying effectively cell-specific and stimulation-specific promoter and enhancer transcriptional profiles, and thus, contributes to improve our understanding of transcriptional activation in human.

The canine era: the rise of a biomedical model

Since the annotation of its genome a decade ago, the dog has proven to be an excellent model for the study of inherited diseases. A large variety of spontaneous simple and complex phenotypes occur in dogs, providing physiologically relevant models to corresponding human conditions. In addition, gene discovery is facilitated in clinically less heterogeneous purebred dogs with closed population structures because smaller study cohorts and fewer markers are often sufficient to expose causal variants. Here, we review the development of genomic resources from microsatellites to whole-genome sequencing and give examples of successful findings that have followed the technological progress. The increasing amount of whole-genome sequence data warrants better functional annotation of the canine genome to more effectively utilise this unique model to understand genetic contributions in morphological, behavioural and other complex traits.

Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits

BACKGROUND

Dense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants.

RESULTS

We introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits.

CONCLUSIONS

BayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species.

Progress and challenges in bioinformatics approaches for enhancer identification

Enhancers are cis-acting DNA elements that play critical roles in distal regulation of gene expression. Identifying enhancers is an important step for understanding distinct gene expression programs that may reflect normal and pathogenic cellular conditions. Experimental identification of enhancers is constrained by the set of conditions used in the experiment. This requires multiple experiments to identify enhancers, as they can be active under specific cellular conditions but not in different cell types/tissues or cellular states. This has opened prospects for computational prediction methods that can be used for high-throughput identification of putative enhancers to complement experimental approaches. Potential functions and properties of predicted enhancers have been catalogued and summarized in several enhancer-oriented databases. Because the current methods for the computational prediction of enhancers produce significantly different enhancer predictions, it will be beneficial for the research community to have an overview of the strategies and solutions developed in this field. In this review, we focus on the identification and analysis of enhancers by bioinformatics approaches. First, we describe a general framework for computational identification of enhancers, present relevant data types and discuss possible computational solutions. Next, we cover over 30 existing computational enhancer identification methods that were developed since 2000. Our review highlights advantages, limitations and potentials, while suggesting pragmatic guidelines for development of more efficient computational enhancer prediction methods. Finally, we discuss challenges and open problems of this topic, which require further consideration.

To Know How a Gene Works, We Need to Redefine It First but then, More Importantly, to Let the Cell Itself Decide How to Transcribe and Process Its RNAs

Recent genomic and ribonomic research reveals that our genome produces a stupendous amount of non-coding RNAs (ncRNAs), including antisense RNAs, and that many genes contain other gene(s) in their introns. Since ncRNAs either regulate the transcription, translation or stability of mRNAs or directly exert cellular functions, they should be regarded as the fourth category of RNAs, after ribosomal, messenger and transfer RNAs. These and other research advances challenge the current concept of gene and raise a question as to how we should redefine gene. We can either consider each tiny part of the classically-defined gene, such as each mRNA variant, as a “gene”, or, alternatively and oppositely, regard a whole genomic locus as a “gene” that may contain intron-embedded genes and produce different types of RNAs and proteins. Each of the two ways to redefine gene not only has its strengths and weaknesses but also has its particular concern on the methodology for the determination of the gene's function: Ectopic expression of complementary DNA (cDNA) in cells has in the past decades provided us with great deal of detail about the functions of individual mRNA variants, and will make the data less conflicting with each other if just a small part of a classically-defined gene is considered as a “gene”. On the other hand, genomic DNA (gDNA) will better help us in understanding the collective function of a genomic locus. In our opinion, we need to be more cautious in the use of cDNA and in the explanation of data resulting from cDNA, and, instead, should make delivery of gDNA into cells routine in determination of genes' functions, although this demands some technology renovation.

YamiPred: A Novel Evolutionary Method for Predicting Pre-miRNAs and Selecting Relevant Features

MicroRNAs (miRNAs) are small non-coding RNAs, which play a significant role in gene regulation. Predicting miRNA genes is a challenging bioinformatics problem and existing experimental and computational methods fail to deal with it effectively. We developed YamiPred, an embedded classification method that combines the efficiency and robustness of support vector machines (SVM) with genetic algorithms (GA) for feature selection and parameters optimization. YamiPred was tested in a new and realistic human dataset and was compared with state-of-the-art computational intelligence approaches and the prevalent SVM-based tools for miRNA prediction. Experimental results indicate that YamiPred outperforms existing approaches in terms of accuracy and of geometric mean of sensitivity and specificity. The embedded feature selection component selects a compact feature subset that contributes to the performance optimization. Further experimentation with this minimal feature subset has achieved very high classification performance and revealed the minimum number of samples required for developing a robust predictor. YamiPred also confirmed the important role of commonly used features such as entropy and enthalpy, and uncovered the significance of newly introduced features, such as %A-U aggregate nucleotide frequency and positional entropy. The best model trained on human data has successfully predicted pre-miRNAs to other organisms including the category of viruses.

Ribosomal proteins as novel players in tumorigenesis

Ribosome biogenesis is the most demanding energetic and metabolic expenditure of the cell. The nucleolus, a nuclear compartment, coordinates rRNA transcription, maturation, and assembly into ribosome subunits. The transcription process is highly coordinated with ribosome biogenesis. In this context, ribosomal proteins (RPs) play a crucial role. In the last decade, an increasing number of studies have associated RPs with extraribosomal functions related to proliferation. Importantly, the expression of RPs appears to be deregulated in several human disorders due, at least in part, to genetic mutations. Although the deregulation of RPs in human malignancies is commonly observed, a more complex mechanism is believed to be involved, favoring the tumorigenic process, its progression and metastasis. This review explores the roles of the most frequently mutated oncogenes and tumor suppressor genes in human cancer that modulate ribosome biogenesis, including their interaction with RPs. In this regard, we propose a new focus for novel therapies.

Hypothesis: Artifacts, Including Spurious Chimeric RNAs with a Short Homologous Sequence, Caused by Consecutive Reverse Transcriptions and Endogenous Random Primers

Recent RNA-sequencing technology and associated bioinformatics have led to identification of tens of thousands of putative human chimeric RNAs, i.e. RNAs containing sequences from two different genes, most of which are derived from neighboring genes on the same chromosome. In this essay, we redefine "two neighboring genes" as those producing individual transcripts, and point out two known mechanisms for chimeric RNA formation, i.e. transcription from a fusion gene or trans-splicing of two RNAs. By our definition, most putative RNA chimeras derived from canonically-defined neighboring genes may either be technical artifacts or be cis-splicing products of 5'- or 3'-extended RNA of either partner that is redefined herein as an unannotated gene, whereas trans-splicing events are rare in human cells. Therefore, most authentic chimeric RNAs result from fusion genes, about 1,000 of which have been identified hitherto. We propose a hypothesis of "consecutive reverse transcriptions (RTs)", i.e. another RT reaction following the previous one, for how most spurious chimeric RNAs, especially those containing a short homologous sequence, may be generated during RT, especially in RNA-sequencing wherein RNAs are fragmented. We also point out that RNA samples contain numerous RNA and DNA shreds that can serve as endogenous random primers for RT and ensuing polymerase chain reactions (PCR), creating artifacts in RT-PCR.

Future translational applications from the contemporary genomics era: a scientific statement from the American Heart Association

The field of genetics and genomics has advanced considerably with the achievement of recent milestones encompassing the identification of many loci for cardiovascular disease and variable drug responses. Despite this achievement, a gap exists in the understanding and advancement to meaningful translation that directly affects disease prevention and clinical care. The purpose of this scientific statement is to address the gap between genetic discoveries and their practical application to cardiovascular clinical care. In brief, this scientific statement assesses the current timeline for effective translation of basic discoveries to clinical advances, highlighting past successes. Current discoveries in the area of genetics and genomics are covered next, followed by future expectations, tools, and competencies for achieving the goal of improving clinical care.

DEEP: a general computational framework for predicting enhancers

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

Just like the rest of evolution in Mother Nature, the evolution of cancers may be driven by natural selection, and not by haphazard mutations

Sporadic carcinogenesis starts from immortalization of a differentiated somatic cell or an organ-specific stem cell. The immortalized cell incepts a new or quasinew organism that lives like a parasite in the patient and usually proceeds to progressive simplification, constantly engendering intermediate organisms that are simpler than normal cells. Like organismal evolution in Mother Nature, this cellular simplification is a process of Darwinian selection of those mutations with growth- or survival-advantages, from numerous ones that occur randomly and stochastically. Therefore, functional gain of growth- or survival-sustaining oncogenes and functional loss of differentiation-sustaining tumor suppressor genes, which are hallmarks of cancer cells and contribute to phenotypes of greater malignancy, are not drivers of carcinogenesis but are results from natural selection of advantageous mutations. Besides this mutation-load dependent survival mechanism that is evolutionarily low and of an asexual nature, cancer cells may also use cell fusion for survival, which is an evolutionarily-higher mechanism and is of a sexual nature. Assigning oncogenes or tumor suppressor genes or their mutants as drivers to induce cancer in animals may somewhat coerce them to create man-made oncogenic pathways that may not really be a course of sporadic cancer formations in the human.

The IRF5-TNPO3 association with systemic lupus erythematosus has two components that other autoimmune disorders variably share

Exploiting genotyping, DNA sequencing, imputation and trans-ancestral mapping, we used Bayesian and frequentist approaches to model the IRF5-TNPO3 locus association, now implicated in two immunotherapies and seven autoimmune diseases. Specifically, in systemic lupus erythematosus (SLE), we resolved separate associations in the IRF5 promoter (all ancestries) and with an extended European haplotype. We captured 3230 IRF5-TNPO3 high-quality, common variants across 5 ethnicities in 8395 SLE cases and 7367 controls. The genetic effect from the IRF5 promoter can be explained by any one of four variants in 5.7 kb (P-valuemeta = 6 × 10(-49); OR = 1.38-1.97). The second genetic effect spanned an 85.5-kb, 24-variant haplotype that included the genes IRF5 and TNPO3 (P-valuesEU = 10(-27)-10(-32), OR = 1.7-1.81). Many variants at the IRF5 locus with previously assigned biological function are not members of either final credible set of potential causal variants identified herein. In addition to the known biologically functional variants, we demonstrated that the risk allele of rs4728142, a variant in the promoter among the lowest frequentist probability and highest Bayesian posterior probability, was correlated with IRF5 expression and differentially binds the transcription factor ZBTB3. Our analytical strategy provides a novel framework for future studies aimed at dissecting etiological genetic effects. Finally, both SLE elements of the statistical model appear to operate in Sjögren's syndrome and systemic sclerosis whereas only the IRF5-TNPO3 gene-spanning haplotype is associated with primary biliary cirrhosis, demonstrating the nuance of similarity and difference in autoimmune disease risk mechanisms at IRF5-TNPO3.

Genome-wide association analysis of eosinophilic esophagitis provides insight into the tissue specificity of this allergic disease

Eosinophilic esophagitis (EoE) is a chronic inflammatory disorder associated with allergic hypersensitivity to food. We interrogated >1.5 million genetic variants in EoE cases of European ancestry and subsequently in a multi-site cohort with local and out-of-study control subjects. In addition to replicating association of the 5q22 locus (meta-analysis P=1.9×10(-16)), we identified an association at 2p23 spanning CAPN14 (P=2.5×10(-10)). CAPN14 was specifically expressed in the esophagus, was dynamically upregulated as a function of disease activity and genetic haplotype and after exposure of epithelial cells to interleukin (IL)-13, and was located in an epigenetic hotspot modified by IL-13. Genes neighboring the top 208 EoE-associated sequence variants were enriched for esophageal expression, and multiple loci for allergic sensitization were associated with EoE susceptibility (4.8×10(-2) Collapse

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MESH Headings

• Adolescent
• Adult
• Calpain/genetics
• Child
• Child, Preschool
• Eosinophilic Esophagitis/genetics
• Epithelial Cells/metabolism
• Esophagus/metabolism
• Female
• Genetic Predisposition to Disease
• Genetic Variation
• Genome-Wide Association Study/methods
• Genotype
• Haplotypes
• Humans
• Interleukin-13/genetics
• Male
• Meta-Analysis as Topic
• Middle Aged
• Models, Genetic
• Organ Specificity/genetics
• Up-Regulation
• Young Adult

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Grants

• U01 AI066560 NIAID NIH HHS
• UL1 TR000039 NCATS NIH HHS
• U01 HG006828-S1 NHGRI NIH HHS
• T32 AI060515 NIAID NIH HHS
• P30 DK078392 NIDDK NIH HHS
• UL1 TR-000067 NCATS NIH HHS
• T32 HL7752-19 NHLBI NIH HHS
• T32 HL007752 NHLBI NIH HHS
• UL1 TR-000039 NCATS NIH HHS
• P01 AI083194 NIAID NIH HHS
• UL1 TR-000083 NCATS NIH HHS
• UL1 TR-000424 NCATS NIH HHS
• U01 HG006828-S2 NHGRI NIH HHS
• T32 GM063483 NIGMS NIH HHS
• U01 HG006828 NHGRI NIH HHS
• U19 AI066738 NIAID NIH HHS
• R37 AI024717 NIAID NIH HHS
• UL1 TR000424 NCATS NIH HHS
• UL1 TR000067 NCATS NIH HHS
• UL1 TR001425 NCATS NIH HHS
• I01 BX001834 BLRD VA
• UL1 TR000083 NCATS NIH HHS
• UL1 TR001082 NCATS NIH HHS
• UL1 RR029887 NCRR NIH HHS
• P01 AR049084 NIAMS NIH HHS
• P30 AR047363 NIAMS NIH HHS
• K23 AI099083 NIAID NIH HHS

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Affiliation(s)

Leah C. Kottyan Center for Autoimmune Genomics and Etiology, Division of Rheumatology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA United States Department of Veterans Affairs Medical Center, Cincinnati, Ohio, USA Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Benjamin P. Davis Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Joseph D. Sherrill Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Kan Liu Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Mark Rochman Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Kenneth Kaufman Center for Autoimmune Genomics and Etiology, Division of Rheumatology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA United States Department of Veterans Affairs Medical Center, Cincinnati, Ohio, USA
Matthew T. Weirauch Center for Autoimmune Genomics and Etiology, Division of Rheumatology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Samuel Vaughn Center for Autoimmune Genomics and Etiology, Division of Rheumatology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Sara Lazaro Center for Autoimmune Genomics and Etiology, Division of Rheumatology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA United States Department of Veterans Affairs Medical Center, Cincinnati, Ohio, USA
Andrew M. Rupert Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Mojtaba Kohram Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Emily M. Stucke Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Katherine A. Kemme Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Albert Magnusen Center for Autoimmune Genomics and Etiology, Division of Rheumatology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA United States Department of Veterans Affairs Medical Center, Cincinnati, Ohio, USA
Hua He Division of Human Genetics, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Phillip Dexheimer Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Mirna Chehade Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
Robert A. Wood Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
Robbie D. Pesek Department of Pediatrics, University of Arkansas for Medical Sciences and Arkansas Children’s Hospital, Little Rock, Arkansas, USA
Brian P. Vickery Department of Pediatrics, University of North Carolina, Chapel Hill, North Carolina, USA
David M. Fleischer Department of Pediatrics, National Jewish Health, Denver, Colorado, USA
Robert Lindbad The EMMES Corporation, Rockville, Maryland, USA
Hugh A. Sampson Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
Vince Mukkada Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Phil E. Putnam Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
J. Pablo Abonia Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
Lisa J. Martin Division of Human Genetics, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
John B. Harley Center for Autoimmune Genomics and Etiology, Division of Rheumatology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA United States Department of Veterans Affairs Medical Center, Cincinnati, Ohio, USA
Marc E. Rothenberg Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA

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Lysophospholipid receptor nomenclature review: IUPHAR Review 8

Lysophospholipids encompass a diverse range of small, membrane-derived phospholipids that act as extracellular signals. The signalling properties are mediated by 7-transmembrane GPCRs, constituent members of which have continued to be identified after their initial discovery in the mid-1990s. Here we briefly review this class of receptors, with a particular emphasis on their protein and gene nomenclatures that reflect their cognate ligands. There are six lysophospholipid receptors that interact with lysophosphatidic acid (LPA): protein names LPA1 - LPA6 and italicized gene names LPAR1-LPAR6 (human) and Lpar1-Lpar6 (non-human). There are five sphingosine 1-phosphate (S1P) receptors: protein names S1P1 -S1P5 and italicized gene names S1PR1-S1PR5 (human) and S1pr1-S1pr5 (non-human). Recent additions to the lysophospholipid receptor family have resulted in the proposed names for a lysophosphatidyl inositol (LPI) receptor - protein name LPI1 and gene name LPIR1 (human) and Lpir1 (non-human) - and three lysophosphatidyl serine receptors - protein names LyPS1 , LyPS2 , LyPS3 and gene names LYPSR1-LYPSR3 (human) and Lypsr1-Lypsr3 (non-human) along with a variant form that does not appear to exist in humans that is provisionally named LyPS2L . This nomenclature incorporates previous recommendations from the International Union of Basic and Clinical Pharmacology, the Human Genome Organization, the Gene Nomenclature Committee, and the Mouse Genome Informatix.

The other side of the coin: the tumor-suppressive aspect of oncogenes and the oncogenic aspect of tumor-suppressive genes, such as those along the CCND-CDK4/6-RB axis

Although cancer-regulatory genes are dichotomized to oncogenes and tumor-suppressor gene s, in reality they can be oncogenic in one situation but tumor-suppressive in another. This dual-function nature, which sometimes hampers our understanding of tumor biology, has several manifestations: (1) Most canonically defined genes have multiple mRNAs, regulatory RNAs, protein isoforms, and posttranslational modifications; (2) Genes may interact at different levels, such as by forming chimeric RNAs or by forming different protein complexes; (3) Increased levels of tumor-suppressive genes in normal cells drive proliferation of cancer progenitor cells in the same organ or tissue by imposing compensatory proliferation pressure, which presents the dual-function nature as a cell-cell interaction. All these manifestations of dual functions can find examples in the genes along the CCND-CDK4/6-RB axis. The dual-function nature also underlies the heterogeneity of cancer cells. Gene-targeting chemotherapies, including that targets CDK4, are effective to some cancer cells but in the meantime may promote growth or progression of some others in the same patient. Redefining "gene" by considering each mRNA, regulatory RNA, protein isoform, and posttranslational modification from the same genomic locus as a "gene" may help in better understanding tumor biology and better selecting targets for different sub-populations of cancer cells in individual patients for personalized therapy.

Cyclin-dependent kinase 4 may be expressed as multiple proteins and have functions that are independent of binding to CCND and RB and occur at the S and G 2/M phases of the cell cycle

Cyclin-dependent kinase 4 (CDK4) is known to be a 33 kD protein that drives G 1 phase progression of the cell cycle by binding to a CCND protein to phosphorylate RB proteins. Using different CDK4 antibodies in western blot, we detected 2 groups of proteins around 40 and 33 kD, respectively, in human and mouse cells; each group often appeared as a duplet or triplet of bands. Some CDK4 shRNAs could decrease the 33 kD wild-type (wt) CDK4 but increase some 40 kD proteins, whereas some other shRNAs had the opposite effects. Liquid chromatography-mass spectrometry/mass spectrometry analysis confirmed the existence of CDK4 isoforms smaller than 33 kD but failed to identify CDK4 at 40 kD. We cloned one CDK4 mRNA variant that lacks exon 2 and encodes a 26 kD protein without the first 74 amino acids of the wt CDK4, thus lacking the ATP binding sequence and the PISTVRE domain required for binding to CCND. Co-IP assay confirmed that this ΔE2 protein lost CCND1- and RB1-binding ability. Moreover, we found, surprisingly, that the wt CDK4 and the ΔE2 could inhibit G 1-S progression, accelerate S-G 2/M progression, and enhance or delay apoptosis in a cell line-specific manner in a situation where the cells were treated with a CDK4 inhibitor or the cells were serum-starved and then replenished. Hence, CDK4 seems to be expressed as multiple proteins that react differently to different CDK4 antibodies, respond differently to different shRNAs, and, in some situations, have previously unrecognized functions at the S-G 2/M phases of the cell cycle via mechanisms independent of binding to CCND and RB.

RCytoscape: tools for exploratory network analysis

BACKGROUND

Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand.

RESULTS

RCytoscape integrates R (an open-ended programming environment rich in statistical power and data-handling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape's functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance.

CONCLUSIONS

Progress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. Network visualizations--molecular maps--created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression.

Heterosis in elite hybrid rice: speculation on the genetic and biochemical mechanisms

Because of the tremendous advances in functional genomics and the current availability of a large number of superior hybrids, rice is an excellent model crop system for heterosis research. Genetic dissection of yield and yield component traits of an elite rice hybrid using an ultra-high density linkage map identified overdominance as the principal genetic basis of heterosis in this hybrid. This is not an expected finding based on the reported effects of single genes. Here we propose a gene expression and protein quality control hypothesis as one possible explanation for the overdominance in hybrids bred for yield. Future studies will be directed toward the identification of the genetic and biochemical mechanisms underlying the biology of hybrid vigor.

Non-Coding RNAs: Functional Aspects and Diagnostic Utility in Oncology

Noncoding RNAs (ncRNAs) have been found to have roles in a large variety of biological processes. Recent studies indicate that ncRNAs are far more abundant and important than initially imagined, holding great promise for use in diagnostic, prognostic, and therapeutic applications. Within ncRNAs, microRNAs (miRNAs) are the most widely studied and characterized. They have been implicated in initiation and progression of a variety of human malignancies, including major pathologies such as cancers, arthritis, neurodegenerative disorders, and cardiovascular diseases. Their surprising stability in serum and other bodily fluids led to their rapid ascent as a novel class of biomarkers. For example, several properties of stable miRNAs, and perhaps other classes of ncRNAs, make them good candidate biomarkers for early cancer detection and for determining which preneoplastic lesions are likely to progress to cancer. Of particular interest is the identification of biomarker signatures, which may include traditional protein-based biomarkers, to improve risk assessment, detection, and prognosis. Here, we offer a comprehensive review of the ncRNA biomarker literature and discuss state-of-the-art technologies for their detection. Furthermore, we address the challenges present in miRNA detection and quantification, and outline future perspectives for development of next-generation biodetection assays employing multicolor alternating-laser excitation (ALEX) fluorescence spectroscopy.

EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era

The Eukaryotic Promoter Database (EPD), available online at http://epd.vital-it.ch, is a collection of experimentally defined eukaryotic POL II promoters which has been maintained for more than 25 years. A promoter is represented by a single position in the genome, typically the major transcription start site (TSS). EPD primarily serves biologists interested in analysing the motif content, chromatin structure or DNA methylation status of co-regulated promoter subsets. Initially, promoter evidence came from TSS mapping experiments targeted at single genes and published in journal articles. Today, the TSS positions provided by EPD are inferred from next-generation sequencing data distributed in electronic form. Traditionally, EPD has been a high-quality database with low coverage. The focus of recent efforts has been to reach complete gene coverage for important model organisms. To this end, we introduced a new section called EPDnew, which is automatically assembled from multiple, carefully selected input datasets. As another novelty, we started to use chromatin signatures in addition to mRNA 5′tags to locate promoters of weekly expressed genes. Regarding user interfaces, we introduced a new promoter viewer which enables users to explore promoter-defining experimental evidence in a UCSC genome browser window.

A dictionary based informational genome analysis

Background

In the post-genomic era several methods of computational genomics are emerging to understand how the whole information is structured within genomes. Literature of last five years accounts for several alignment-free methods, arisen as alternative metrics for dissimilarity of biological sequences. Among the others, recent approaches are based on empirical frequencies of DNA k-mers in whole genomes.

Results

Any set of words (factors) occurring in a genome provides a genomic dictionary. About sixty genomes were analyzed by means of informational indexes based on genomic dictionaries, where a systemic view replaces a local sequence analysis. A software prototype applying a methodology here outlined carried out some computations on genomic data. We computed informational indexes, built the genomic dictionaries with different sizes, along with frequency distributions. The software performed three main tasks: computation of informational indexes, storage of these in a database, index analysis and visualization. The validation was done by investigating genomes of various organisms. A systematic analysis of genomic repeats of several lengths, which is of vivid interest in biology (for example to compute excessively represented functional sequences, such as promoters), was discussed, and suggested a method to define synthetic genetic networks.

Conclusions

We introduced a methodology based on dictionaries, and an efficient motif-finding software application for comparative genomics. This approach could be extended along many investigation lines, namely exported in other contexts of computational genomics, as a basis for discrimination of genomic pathologies.