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The functional engagement between an enhancer and its target promoter ensures precise gene transcription1. Understanding the basis of promoter choice by enhancers has important implications for health and disease. Here we report that functional loss of a preferred promoter can release its partner enhancer to loop to and activate an alternative promoter (or alternative promoters) in the neighbourhood. We refer to this target-switching process as 'enhancer release and retargeting'. Genetic deletion, motif perturbation or mutation, and dCas9-mediated CTCF tethering reveal that promoter choice by an enhancer can be determined by the binding of CTCF at promoters, in a cohesin-dependent manner-consistent with a model of 'enhancer scanning' inside the contact domain. Promoter-associated CTCF shows a lower affinity than that at chromatin domain boundaries and often lacks a preferred motif orientation or a partnering CTCF at the cognate enhancer, suggesting properties distinct from boundary CTCF. Analyses of cancer mutations, data from the GTEx project and risk loci from genome-wide association studies, together with a focused CRISPR interference screen, reveal that enhancer release and retargeting represents an overlooked mechanism that underlies the activation of disease-susceptibility genes, as exemplified by a risk locus for Parkinson's disease (NUCKS1-RAB7L1) and three loci associated with cancer (CLPTM1L-TERT, ZCCHC7-PAX5 and PVT1-MYC).

Although gene expansion plays an important role in evolution, its identification remains a challenge due to potential errors in genome assembly and annotation. Here, we describe a detailed step-by-step protocol for gene annotation, prediction of genomic gene expansion, and its computational and experimental validation. Finally, we also detail steps to discover functionality of each copy of replicated genes. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2021).

Alzheimer’s disease (AD) is a genetically complex, multifactorial neurodegenerative disease. It affects more than 45 million people worldwide and currently remains untreatable. Although genome-wide association studies (GWAS) have identified many AD-associated common variants, only about 25 genes are currently known to affect the risk of developing AD, despite its highly polygenic nature. Moreover, the risk variants underlying GWAS AD-association signals remain unknown. Here, we describe a deep post-GWAS analysis of AD-associated variants, using an integrated computational framework for predicting both disease genes and their risk variants. We identified 342 putative AD risk genes in 203 risk regions spanning 502 AD-associated common variants. 246 AD risk genes have not been identified as AD risk genes by previous GWAS collected in GWAS catalogs, and 115 of 342 AD risk genes are outside the risk regions, likely under the regulation of transcriptional regulatory elements contained therein. Even more significantly, for 109 AD risk genes, we predicted 150 risk variants, of both coding and regulatory (in promoters or enhancers) types, and 85 (57%) of them are supported by functional annotation. In-depth functional analyses showed that AD risk genes were overrepresented in AD-related pathways or GO terms—e.g., the complement and coagulation cascade and phosphorylation and activation of immune response—and their expression was relatively enriched in microglia, endothelia, and pericytes of the human brain. We found nine AD risk genes—e.g., IL1RAP, PMAIP1, LAMTOR4—as predictors for the prognosis of AD survival and genes such as ARL6IP5 with altered network connectivity between AD patients and normal individuals involved in AD progression. Our findings open new strategies for developing therapeutics targeting AD risk genes or risk variants to influence AD pathogenesis.

Gene variants associated with longevity are also associated with protection against cognitive decline, dementia and Alzheimer's disease, suggesting that common physiologic pathways act at the interface of longevity and cognitive function. To test the hypothesis that variants in genes implicated in cognitive function may promote exceptional longevity, we performed a comprehensive 3‐stage study to identify functional longevity‐associated variants in ~700 candidate genes in up to 450 centenarians and 500 controls by target capture sequencing analysis. We found an enrichment of longevity‐associated genes in the nPKC and NF‐κB signaling pathways by gene‐based association analyses. Functional analysis of the top three gene variants (NFKBIA, CLU, PRKCH) suggests that non‐coding variants modulate the expression of cognate genes, thereby reducing signaling through the nPKC and NF‐κB. This matches genetic studies in multiple model organisms, suggesting that the evolutionary conservation of reduced PKC and NF‐κB signaling pathways in exceptional longevity may include humans., To test the hypothesis that variants in genes implicated in cognitive function may promote exceptional longevity, we performed a 3‐stage study to identify functional longevity‐associated variants in ~700 candidate genes in up to 450 centenarians and 500 controls by target capture sequencing analysis. We found an enrichment of longevity‐associated genes in the nPKC and NF‐kB signaling pathways by gene‐based association analyses. Functional analysis of the top three gene variants (NFKBIA, CLU, PRKCH) suggests that non‐coding variants reduce signaling through the nPKC and NF‐kB.

The North American beaver (Castor canadensis) is an exceptionally long-lived and cancer-resistant rodent species, and thus an excellent model organism for comparative genomic studies of longevity. Here, we utilize a significantly improved beaver genome assembly to assess evolutionary changes in gene coding sequences, copy number, and expression. We found that the beaver Aldh1a1, a stem cell marker gene encoding an enzyme required for detoxification of ethanol and aldehydes, is expanded (~10 copies vs. two in mouse and one in human). We also show that the beaver cells are more resistant to ethanol, and beaver liver extracts show higher ability to metabolize aldehydes than the mouse samples. Furthermore, Hpgd, a tumor suppressor gene, is uniquely duplicated in the beaver among rodents. Our evolutionary analysis identified beaver genes under positive selection which are associated with tumor suppression and longevity. Genes involved in lipid metabolism show positive selection signals, changes in copy number and altered gene expression in beavers. Several genes involved in DNA repair showed a higher expression in beavers which is consistent with the trend observed in other long-lived mammals. In summary, we identified several genes that likely contribute to beaver longevity and cancer resistance, including increased ability to detoxify aldehydes, enhanced tumor suppression and DNA repair, and altered lipid metabolism.

Whether errors in protein synthesis play a role in aging has been a subject of intense debate. It has been suggested that rare mistakes in protein synthesis in young organisms may result in errors in the protein synthesis machinery, eventually leading to an increasing cascade of errors as organisms age. Studies that followed generally failed to identify a dramatic increase in translation errors with aging. However, whether translation fidelity plays a role in aging remained an open question. To address this issue, we examined the relationship between translation fidelity and maximum lifespan across 17 rodent species with diverse lifespans. To measure translation fidelity, we utilized sensitive luciferase-based reporter constructs with mutations in an amino acid residue critical to luciferase activity, wherein misincorporation of amino acids at this mutated codon re-activated the luciferase. The frequency of amino acid misincorporation at the first and second codon positions showed strong negative correlation with maximum lifespan. This correlation remained significant after phylogenetic correction, indicating that translation fidelity coevolves with longevity. These results give new life to the role of protein synthesis errors in aging: Although the error rate may not significantly change with age, the basal rate of translation errors is important in defining lifespan across mammals. Fil: Ke, Zhonghe. University of Rochester; Estados Unidos Fil: Mallik, Pramit. University of Rochester; Estados Unidos Fil: Johnson, Adam B.. University of Rochester; Estados Unidos Fil: Luna, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Nevo, Eviatar. University of Haifa; Israel Fil: Zhang, Zhengdong D.. Albert Einstein College of Medicine; Estados Unidos Fil: Gladyshev, Vadim N.. Harvard Medical School; Estados Unidos Fil: Seluanov, Andrei. University of Rochester; Estados Unidos Fil: Gorbunova, Vera. University of Rochester; Estados Unidos

Schizophrenia is a severe mental disorder with a large genetic component. Recent genome-wide association studies (GWAS) have identified many schizophrenia-associated common variants. For most of the reported associations, however, the underlying biological mechanisms are not clear. The critical first step for their elucidation is to identify the most likely disease genes as the source of the association signals. Here, we describe a general computational framework of post-GWAS analysis for complex disease gene prioritization. We identify 132 putative schizophrenia risk genes in 76 risk regions spanning 120 schizophrenia-associated common variants, 78 of which have not been recognized as schizophrenia disease genes by previous GWAS. Even more significantly, 29 of them are outside the risk regions, likely under regulation of transcriptional regulatory elements contained therein. These putative schizophrenia risk genes are transcriptionally active in both brain and the immune system, and highly enriched among cellular pathways, consistent with leading pathophysiological hypotheses about the pathogenesis of schizophrenia. With their involvement in distinct biological processes, these putative schizophrenia risk genes, with different association strengths, show distinctive temporal expression patterns, and play specific biological roles during brain development.

Sporadic colon cancer accounts for approximately 80% of colorectal cancer (CRC) with high incidence in Western societies strongly linked to long-term dietary patterns. A unique mouse model for sporadic CRC results from feeding a purified rodent Western-style diet (NWD1) recapitulating intake for the mouse of common nutrient risk factors each at its level consumed in higher risk Western populations. This causes sporadic large and small intestinal tumors in wild-type mice at an incidence and frequency similar to that in humans. NWD1 perturbs intestinal cell maturation and Wnt signaling throughout villi and colonic crypts and decreases mouse Lgr5hi intestinal stem cell contribution to homeostasis and tumor development. Here we establish that NWD1 transcriptionally reprograms Lgr5hi cells, and that nutrients are interactive in reprogramming. Furthermore, the DNA mismatch repair pathway is elevated in Lgr5hi cells by lower vitamin D3 and/or calcium in NWD1, paralleled by reduced accumulation of relevant somatic mutations detected by single-cell exome sequencing. In compensation, NWD1 also reprograms Bmi1+ cells to function and persist as stem-like cells in mucosal homeostasis and tumor development. The data establish the key role of the nutrient environment in defining the contribution of two different stem cell populations to both mucosal homeostasis and tumorigenesis. This raises important questions regarding impact of variable human diets on which and how stem cell populations function in the human mucosa and give rise to tumors. Moreover, major differences reported in turnover of human and mouse crypt base stem cells may be linked to their very different nutrient exposures.

The functional engagement between an enhancer and its target promoter ensures precise gene transcription

Gene regulatory analysis of highly diverse human tissues in vivo is essentially constrained by the challenge of performing genome-wide, integrated epigenetic and transcriptomic analysis in small selected groups of specific cell types. Here we performed genome-wide bisulfite sequencing and RNA-seq from the same small groups of bronchial and alveolar cells isolated by laser capture microdissection from flash-frozen lung tissue of 12 donors and their peripheral blood T cells. Methylation and transcriptome patterns differed between alveolar and bronchial cells, while each of these epithelia showed more differences from mesodermally-derived T cells. Differentially methylated regions (DMRs) between alveolar and bronchial cells tended to locate at regulatory regions affecting promoters of 4,350 genes. A large number of pathways enriched for these DMRs including GTPase signal transduction, cell death, and skeletal muscle. Similar patterns of transcriptome differences were observed: 4,108 differentially expressed genes (DEGs) enriched in GTPase signal transduction, inflammation, cilium assembly, and others. Prioritizing using DMR-DEG regulatory network, we highlighted genes, e.g., ETS1, PPARG, and RXRG, at prominent alveolar vs. bronchial cell discriminant nodes. Our results show that multi-omic analysis of small, highly specific cells is feasible and yields unique physiologic loci distinguishing human lung cell types in situ.

Differences in DNA repair capacity have been hypothesized to underlie the great range of maximum lifespans among mammals. However, measurements of individual DNA repair activities in cells and animals have not substantiated such a relationship because utilization of repair pathways among animals--depending on habitats, anatomical characteristics, and life styles--varies greatly between mammalian species. Recent advances in high-throughput genomics, in combination with increased knowledge of the genetic pathways involved in genome maintenance, now enable a comprehensive comparison of DNA repair transcriptomes in animal species with extreme lifespan differences. Here we compare transcriptomes of liver, an organ with high oxidative metabolism and abundant spontaneous DNA damage, from humans, naked mole rats, and mice, with maximum lifespans of ~120, 30, and 3 years, respectively, with a focus on genes involved in DNA repair. The results show that the longer-lived species, human and naked mole rat, share higher expression of DNA repair genes, including core genes in several DNA repair pathways. A more systematic approach of signaling pathway analysis indicates statistically significant upregulation of several DNA repair signaling pathways in human and naked mole rat compared with mouse. The results of this present work indicate, for the first time, that DNA repair is upregulated in a major metabolic organ in long-lived humans and naked mole rats compared with short-lived mice. These results strongly suggest that DNA repair can be considered a genuine longevity assurance system.

Genome maintenance (GM) is an essential defense system against aging and cancer, as both are characterized by increased genome instability. Here, we compared the copy number variation and mutation rate of 518 GM-associated genes in the naked mole rat (NMR), mouse, and human genomes. GM genes appeared to be strongly conserved, with copy number variation in only four genes. Interestingly, we found NMR to have a higher copy number of CEBPG, a regulator of DNA repair, and TINF2, a protector of telomere integrity. NMR, as well as human, was also found to have a lower rate of germline nucleotide substitution than the mouse. Together, the data suggest that the long-lived NMR, as well as human, has more robust GM than mouse and identifies new targets for the analysis of the exceptional longevity of the NMR.

Summary Although the genome-wide association study (GWAS) is a powerful method to identify disease-associated variants, it does not directly address the biological mechanisms underlying such genetic association signals. Here, we present PGA, a Perl- and Java-based program for post-GWAS analysis that predicts likely disease genes given a list of GWAS-reported variants. Designed with a command line interface, PGA incorporates genomic and eQTL data in identifying disease gene candidates and uses gene network and ontology data to score them based upon the strength of their relationship to the disease in question. Availability and implementation http://zdzlab.einstein.yu.edu/1/pga.html Supplementary information Supplementary data are available at Bioinformatics online.

Enhancers, as specialized genomic cis-regulatory elements, activate transcription of their target genes and play an important role in pathogenesis of many human complex diseases. Despite recent systematic identification of them in the human genome, currently there is an urgent need for comprehensive annotation databases of human enhancers with a focus on their disease connections. In response, we built the Human Enhancer Disease Database (HEDD) to facilitate studies of enhancers and their potential roles in human complex diseases. HEDD currently provides comprehensive genomic information for ∼2.8 million human enhancers identified by ENCODE, FANTOM5 and RoadMap with disease association scores based on enhancer–gene and gene–disease connections. It also provides Web-based analytical tools to visualize enhancer networks and score enhancers given a set of selected genes in a specific gene network. HEDD is freely accessible at http://zdzlab.einstein.yu.edu/1/hedd.php.

International audience; The naked mole rat ( Heterocephalus glaber ) is a long-lived and tumor-resistant rodent. Tumor resistance in the naked mole rat is mediated by the extracellular matrix component hyaluronan of very high molecular weight (HMW-HA). HMW-HA triggers hypersensitivity of naked mole rat cells to contact inhibition, which is associated with induction of the INK4 (inhibitors of cyclin dependent kinase 4) locus leading to cell-cycle arrest. The INK4a/b locus is among the most frequently mutated in human cancer. This locus encodes three distinct tumor suppressors: p15 INK4b , p16 INK4a , and ARF (alternate reading frame). Although p15 INK4b has its own ORF, p16 INK4a and ARF share common second and third exons with alternative reading frames. Here, we show that, in the naked mole rat, the INK4a/b locus encodes an additional product that consists of p15 INK4b exon 1 joined to p16 INK4a exons 2 and 3. We have named this isoform pALT INK4a/b (for alternative splicing). We show that pALT INK4a/b is present in both cultured cells and naked mole rat tissues but is absent in human and mouse cells. Additionally, we demonstrate that pALT INK4a/b expression is induced during early contact inhibition and upon a variety of stresses such as UV, gamma irradiation-induced senescence, loss of substrate attachment, and expression of oncogenes. When overexpressed in naked mole rat or human cells, pALT INK4a/b has stronger ability to induce cell-cycle arrest than either p15 INK4b or p16 INK4a . We hypothesize that the presence of the fourth product, pALT INK4a/b of the INK4a/b locus in the naked mole rat, contributes to the increased resistance to tumorigenesis of this species.

Mammals have evolved a remarkable diversity of ageing rates. Within the single order of Rodentia, maximum lifespans range from 4 years in mice to 32 years in naked mole rats. Cancer rates also differ substantially between cancer-prone mice and almost cancer-proof naked mole rats and blind mole rats. Recent progress in rodent comparative biology, together with the emergence of whole-genome sequence information, has opened opportunities for the discovery of genetic factors that control longevity and cancer susceptibility.

DNA repair has been hypothesized to be a longevity determinant, but the evidence for it is based largely on accelerated aging phenotypes of DNA repair mutants. Here, using a panel of 18 rodent species with diverse lifespans, we show that more robust DNA double-strand break (DSB) repair, but not nucleotide excision repair (NER), coevolves with longevity. Evolution of NER, unlike DSB, is shaped primarily by sunlight exposure. We further show that the capacity of the SIRT6 protein to promote DSB repair accounts for a major part of the variation in DSB repair efficacy between short- and long-lived species. We dissected the molecular differences between a weak (mouse) and a strong (beaver) SIRT6 protein and identified five amino acid residues that are fully responsible for their differential activities. Our findings demonstrate that DSB repair and SIRT6 have been optimized during the evolution of longevity, which provides new targets for anti-aging interventions.

While mitochondria have been linked to many human diseases through genetic association and functional studies, the precise role of mitochondria in specific pathologies, such as cardiovascular, neurodegenerative, and metabolic diseases, is often unclear. Here, we take advantage of the catalog of human genome-wide associations, whole-genome tissue expression and expression quantitative trait loci datasets, and annotated mitochondrial proteome databases to examine the role of common genetic variation in mitonuclear genes in human disease. Through pathway-based analysis we identified distinct functional pathways and tissue expression profiles associated with each of the major human diseases. Among our most striking findings, we observe that mitonuclear genes associated with cancer are broadly expressed among human tissues and largely represent one functional process, intrinsic apoptosis, while mitonuclear genes associated with other diseases, such as neurodegenerative and metabolic diseases, show tissue-specific expression profiles and are associated with unique functional pathways. These results provide new insight into human diseases using unbiased genome-wide approaches. Electronic supplementary material The online version of this article (doi:10.1007/s00439-016-1736-9) contains supplementary material, which is available to authorized users.

Although studies over the last decades have firmly connected a number of genes and molecular pathways to aging, the aging process as a whole still remains poorly understood. To gain novel insights into the mechanisms underlying aging, instead of considering aging genes individually, we studied their characteristics at the systems level in the context of biological networks. We calculated a comprehensive set of network characteristics for human aging-related genes from the GenAge database. By comparing them with other functional groups of genes, we identified a robust group of aging-specific network characteristics. To find the structural basis and the molecular mechanisms underlying this aging-related network specificity, we also analyzed protein domain interactions and gene expression patterns across different tissues. Our study revealed that aging genes not only tend to be network hubs, playing important roles in communication among different functional modules or pathways, but also are more likely to physically interact and be co-expressed with essential genes. The high expression of aging genes across a large number of tissue types also points to a high level of connectivity among aging genes. Unexpectedly, contrary to the depletion of interactions among hub genes in biological networks, we observed close interactions among aging hubs, which renders the aging subnetworks vulnerable to random attacks and thus may contribute to the aging process. Comparison across species reveals the evolution process of the aging subnetwork. As the organisms become more complex, the complexity of its aging mechanisms increases and their aging hub genes are more functionally connected.

Differences in the way human and mouse fibroblasts experience senescence in culture had long puzzled researchers. While senescence of human cells is mediated by telomere shortening, Parrinello et al. demonstrated that senescence of mouse cells is caused by extreme oxygen sensitivity. It was hypothesized that the striking difference in oxygen sensitivity between mouse and human cells explains their different rates of aging. To test if this hypothesis is broadly applicable, we cultured cells from 16 rodent species with diverse lifespans in 3% and 21% oxygen and compared their growth rates. Unexpectedly, fibroblasts derived from laboratory mouse strains were the only cells demonstrating extreme sensitivity to oxygen. Cells from hamster, muskrat, woodchuck, capybara, blind mole rat, paca, squirrel, beaver, naked mole rat and wild‐caught mice were mildly sensitive to oxygen, while cells from rat, gerbil, deer mouse, chipmunk, guinea pig and chinchilla showed no difference in the growth rate between 3% and 21% oxygen. We conclude that, although the growth of primary fibroblasts is generally improved by maintaining cells in 3% oxygen, the extreme oxygen sensitivity is a peculiarity of laboratory mouse strains, possibly related to their very long telomeres, and fibroblast oxygen sensitivity does not directly correlate with species’ lifespan.

Defective Gene Detective Identifying genes that give rise to diseases is one of the major goals of sequencing human genomes. However, putative loss-of-function genes, which are often some of the first identified targets of genome and exome sequencing, have often turned out to be sequencing errors rather than true genetic variants. In order to identify the true scope of loss-of-function genes within the human genome, MacArthur et al. (p. 823 ; see the Perspective by Quintana-Murci ) extensively validated the genomes from the 1000 Genomes Project, as well as an additional European individual, and found that the average person has about 100 true loss-of-function alleles of which approximately 20 have two copies within an individual. Because many known disease-causing genes were identified in “normal” individuals, the process of clinical sequencing needs to reassess how to identify likely causative alleles.

Epstein–Barr virus (EBV) is associated with several types of lymphomas and epithelial tumors including Burkitt's lymphoma (BL), HIV-associated lymphoma, posttransplant lymphoproliferative disorder, and nasopharyngeal carcinoma. EBV nuclear antigen 1 (EBNA1) is expressed in all EBV associated tumors and is required for latency and transformation. EBNA1 initiates latent viral replication in B cells, maintains the viral genome copy number, and regulates transcription of other EBV-encoded latent genes. These activities are mediated through the ability of EBNA1 to bind viral-DNA. To further elucidate the role of EBNA1 in the host cell, we have examined the effect of EBNA1 on cellular gene expression by microarray analysis using the B cell BJAB and the epithelial 293 cell lines transfected with EBNA1. Analysis of the data revealed distinct profiles of cellular gene changes in BJAB and 293 cell lines. Subsequently, chromatin immune-precipitation revealed a direct binding of EBNA1 to cellular promoters. We have correlated EBNA1 bound promoters with changes in gene expression. Sequence analysis of the 100 promoters most enriched revealed a DNA motif that differs from the EBNA1 binding site in the EBV genome.

Schizophrenia (SCZ) is a severe mental disorder with a large genetic component. While recent large-scale microarray- and sequencing-based genome wide association studies have made significant progress toward finding SCZ risk variants and genes of subtle effect, the interactions among them were not considered in those studies. Using a protein-protein interaction network both in our regression model and to generate a SCZ gene subnetwork, we developed an analytical framework with Logit-Lapnet, the graphical Laplacian-regularized logistic regression, for whole exome sequencing (WES) data analysis to detect SCZ gene subnetworks. Using simulated data from sequencing-based association study, we compared the performances of Logit-Lapnet with other logistic regression (LR)-based models. We use Logit-Lapnet to prioritize genes according to their coefficients and select top-ranked genes as seeds to generate the gene sub-network that is associated to SCZ. The comparison demonstrated not only the applicability but also better performance of Logit-Lapnet to score disease risk genes using sequencing-based association data. We applied our method to SCZ whole exome sequencing data and selected top-ranked risk genes, the majority of which are either known SCZ genes or genes potentially associated with SCZ. We then used the seed genes to construct SCZ gene subnetworks. This result demonstrates that by ranking gene according to their disease contributions our method scores and thus prioritizes disease risk genes for further investigation. An implementation of our approach in MATLAB is freely available for download at: http://zdzlab.einstein.yu.edu/1/publications/LapNet-MATLAB.zip.

Characterization of interspecies differences in gene regulation is crucial for understanding the molecular basis of both phenotypic diversity and evolution. By means of chromatin immunoprecipitation and DNA microarray analysis, the divergence in the binding sites of the pseudohyphal regulators Ste12 and Tec1 was determined in the yeasts Saccharomyces cerevisiae , S. mikatae , and S. bayanus under pseudohyphal conditions. We have shown that most of these sites have diverged across these species, far exceeding the interspecies variation in orthologous genes. A group of Ste12 targets was shown to be bound only in S. mikatae and S. bayanus under pseudohyphal conditions. Many of these genes are targets of Ste12 during mating in S. cerevisiae , indicating that specialization between the two pathways has occurred in this species. Transcription factor binding sites have therefore diverged substantially faster than ortholog content. Thus, gene regulation resulting from transcription factor binding is likely to be a major cause of divergence between related species.

In recent years, techniques have been developed to map transcription factor binding sites using chromatin immunoprecipitation combined with DNA microarrays (chIP chip). Initially, polymerase chain reaction (PCR)-based DNA arrays were used for the chIP chip procedure, however, high-density oligonucleotide (HDO) arrays, which allow for the production of thousands more features per array, have emerged as a competing array platform. To compare the two platforms, data from chIP chip analysis performed for three factors (Tec1, Ste12, and Sok2) using both HDO and PCR arrays under identical experimental conditions were compared. HDO arrays provided increased reproducibility and sensitivity, detecting approximately three times more binding events than the PCR arrays while also showing increased accuracy. The increased resolution provided by the HDO arrays also allowed for the identification of multiple binding peaks in close proximity and of novel binding events such as binding within ORFs. The HDO array platform provides a far more robust array system by all measures than PCR-based arrays, all of which is directly attributable to the large number of probes available.

The regulation of transcriptional initiation in the human genome is a critical component of global gene regulation, but a complete catalog of human promoters currently does not exist. In order to identify regulatory regions, we developed four computational methods to integrate 129 sets of ENCODE-wide chromatin immunoprecipitation data. They collectively predicted 1393 regions. Roughly 47% of the regions were unique to one method, as each method makes different assumptions about the data. Overall, predicted regions tend to localize to highly conserved, DNase I hypersensitive, and actively transcribed regions in the genome. Interestingly, a significant portion of the regions overlaps with annotated 3′-UTRs, suggesting that some of them might regulate anti-sense transcription. The majority of the predicted regions are >2 kb away from the 5′-ends of previously annotated human cDNAs and hence are novel. These novel regions may regulate unannotated transcripts or may represent new alternative transcription start sites of known genes. We tested 163 such regions for promoter activity in four cell lines using transient transfection assays, and 25% of them showed transcriptional activity above background in at least one cell line. We also performed 5′-RACE experiments on 62 novel regions, and 76% of the regions were associated with the 5′-ends of at least two RACE products. Our results suggest that there are at least 35% more functional promoters in the human genome than currently annotated.

Background RNA:DNA hybrids represent a non-canonical nucleic acid structure that has been associated with a range of human diseases and potential transcriptional regulatory functions. Mapping of RNA:DNA hybrids in human cells reveals them to have a number of characteristics that give insights into their functions. Results We find RNA:DNA hybrids to occupy millions of base pairs in the human genome. A directional sequencing approach shows the RNA component of the RNA:DNA hybrid to be purine-rich, indicating a thermodynamic contribution to their in vivo stability. The RNA:DNA hybrids are enriched at loci with decreased DNA methylation and increased DNase hypersensitivity, and within larger domains with characteristics of heterochromatin formation, indicating potential transcriptional regulatory properties. Mass spectrometry studies of chromatin at RNA:DNA hybrids shows the presence of the ILF2 and ILF3 transcription factors, supporting a model of certain transcription factors binding preferentially to the RNA:DNA conformation. Conclusions Overall, there is little to indicate a dependence for RNA:DNA hybrids forming co-transcriptionally, with results from the ribosomal DNA repeat unit instead supporting the intriguing model of RNA generating these structures intrans. The results of the study indicate heterogeneous functions of these genomic elements and new insights into their formation and stability in vivo. Electronic supplementary material The online version of this article (doi:10.1186/s13072-015-0040-6) contains supplementary material, which is available to authorized users.

Gene expression is a fundamental biological process under tight regulation at all levels in normal cells. Its dysregulation can cause abnormal cell behaviors and result in diseases, and thus gene expression profiling and analysis have been widely used to provide the first clue about the molecular mechanisms of human diseases. Because genes and their products interact with and regulate one another, it is essential to analyze gene expression data and understand the genetics of disease in a biological network context. In this chapter, we first introduce the state-of-the-art gene expression analysis (GEA) with network integration and the joint analysis of mRNA and miRNA expression to understand disease regulatory mechanisms and then discuss how disease genes are predicted by incorporating knowledge of gene regulation and characterized in biological networks.

The 22q11.2 deletion syndrome (22q11DS) affects 1:4000 live births and presents with highly variable phenotype expressivity. In this study, we developed an analytical approach utilizing whole genome sequencing and integrative analysis to discover genetic modifiers. Our pipeline combined available tools in order to prioritize rare, predicted deleterious, coding and non-coding single nucleotide variants (SNVs) and insertion/deletions (INDELs) from whole genome sequencing (WGS). We sequenced two unrelated probands with 22q11DS, with contrasting clinical findings, and their unaffected parents. Proband P1 had cognitive impairment, psychotic episodes, anxiety, and tetralogy of Fallot (TOF); while proband P2 had juvenile rheumatoid arthritis but no other major clinical findings. In P1, we identified common variants in COMT and PRODH on 22q11.2 as well as rare potentially deleterious DNA variants in other behavioral/neurocognitive genes. We also identified a de novo SNV in ADNP2 (NM_014913.3:c.2243G>C), encoding a neuroprotective protein that may be involved in behavioral disorders. In P2, we identified a novel non-synonymous SNV in ZFPM2 (NM_012082.3:c.1576C>T), a known causative gene for TOF, which may act as a protective variant downstream of TBX1, haploinsufficiency of which is responsible for congenital heart disease in individuals with 22q11DS.

Motivation: Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array datasets into ‘active regions’ (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling array data itself. Here we propose a supervised framework for doing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing. Methodology: In particular, we use a hidden Markov model (HMM) framework, which is capable of explicitly modeling the dependency between neighboring probes and whose extended version (the generalized HMM) also allows explicit description of state duration density. We introduce a formal definition of the tiling-array analysis problem, and explain how we can use this to describe sampling small genomic regions for experimental validation to build up a gold-standard set for training and testing. We then describe various ideal and practical sampling strategies (e.g. maximizing signal entropy within a selected region versus using gene annotation or known promoters as positives for transcription or ChIP-chip data, respectively). Results: For the practical sampling and training strategies, we show how the size and noise in the validated training data affects the performance of an HMM applied to the ENCODE transcriptional and ChIP-chip experiments. In particular, we show that the HMM framework is able to efficiently process tiling array data as well as or better than previous approaches. For the idealized sampling strategies, we show how we can assess their performance in a simulation framework and how a maximum entropy approach, which samples sub-regions with very different signal intensities, gives the maximally performing gold-standard. This latter result has strong implications for the optimum way medium-scale validation experiments should be carried out to verify the results of the genome-scale tiling array experiments. Supplementary information: The supplementary data are available at Contact: mark.gerstein@yale.edu

Udgivelsesdato: 2006-Apr-27 After the completion of a draft human genome sequence, the International Human Genome Sequencing Consortium has proceeded to finish and annotate each of the 24 chromosomes comprising the human genome. Here we describe the sequencing and analysis of human chromosome 3, one of the largest human chromosomes. Chromosome 3 comprises just four contigs, one of which currently represents the longest unbroken stretch of finished DNA sequence known so far. The chromosome is remarkable in having the lowest rate of segmental duplication in the genome. It also includes a chemokine receptor gene cluster as well as numerous loci involved in multiple human cancers such as the gene encoding FHIT, which contains the most common constitutive fragile site in the genome, FRA3B. Using genomic sequence from chimpanzee and rhesus macaque, we were able to characterize the breakpoints defining a large pericentric inversion that occurred some time after the split of Homininae from Ponginae, and propose an evolutionary history of the inversion.

Rare variants of major effect play an important role in human complex diseases and can be discovered by sequencing-based genome-wide association studies. Here, we introduce an integrated approach that combines the rare variant association test with gene network and phenotype information to identify risk genes implicated by rare variants for human complex diseases. Our data integration method follows a 'discovery-driven' strategy without relying on prior knowledge about the disease and thus maintains the unbiased character of genome-wide association studies. Simulations reveal that our method can outperform a widely-used rare variant association test method by 2 to 3 times. In a case study of a small disease cohort, we uncovered putative risk genes and the corresponding rare variants that may act as genetic modifiers of congenital heart disease in 22q11.2 deletion syndrome patients. These variants were missed by a conventional approach that relied on the rare variant association test alone., Author summary Case-control sequencing studies are a promising design to uncover risk genes of human complex diseases implicated by rare variants. The recent development of different types of rare variant association tests has improved the statistical power to identify disease genes that harbor risk rare variants. However, none of the recent sequencing-based genome-wide association studies identified robust disease association of rare variants or genes based on them. Due to limited sample sizes that can be feasibly achieved in real applications, current rare variant association tests can only generate marginal association signals for most risk genes. Here we proposed an integrated method that combined association signals with orthogonal biological evidence to uncover risk genes in sequencing studies. Designed to address the lack-of-power issue, our method was shown to effectively uncover risk genes with marginal association signals in data simulation. Indeed, in a real application demonstrated in our case study our method disclosed important risk genes of congenital heart disease in 22q11.2 deletion syndrome that were missed by the previous study.

We have implemented aggregation and correlation toolbox (ACT), an efficient, multifaceted toolbox for analyzing continuous signal and discrete region tracks from high-throughput genomic experiments, such as RNA-seq or ChIP-chip signal profiles from the ENCODE and modENCODE projects, or lists of single nucleotide polymorphisms from the 1000 genomes project. It is able to generate aggregate profiles of a given track around a set of specified anchor points, such as transcription start sites. It is also able to correlate related tracks and analyze them for saturation–i.e. how much of a certain feature is covered with each new succeeding experiment. The ACT site contains downloadable code in a variety of formats, interactive web servers (for use on small quantities of data), example datasets, documentation and a gallery of outputs. Here, we explain the components of the toolbox in more detail and apply them in various contexts. Availability: ACT is available at http://act.gersteinlab.org Contact: gro.balnietsreg@ip