Search

Context Functional hypothalamic amenorrhea (HA) is a common, acquired form of hypogonadotropic hypogonadism that occurs in the setting of energy deficits and/or stress. Variability in individual susceptibility to these stressors, HA heritability, and previous identification of several rare sequence variants (RSVs) in genes associated with the rare disorder, isolated hypogonadotropic hypogonadism (IHH), in individuals with HA suggest a possible genetic contribution to HA susceptibility. Objective We sought to determine whether the burden of RSVs in IHH-related genes is greater in women with HA than controls. Design We compared patients with HA to control women. Setting The study was conducted at secondary referral centers. Patients and Other Participants Women with HA (n = 106) and control women (ClinSeq study; n = 468). Interventions We performed exome sequencing in all patients and controls. Main Outcome Measure(s) The frequency of RSVs in 53 IHH-associated genes was determined using rare variant burden and association tests. Results RSVs were overrepresented in women with HA compared with controls (P = .007). Seventy-eight heterozygous RSVs in 33 genes were identified in 58 women with HA (36.8% of alleles) compared to 255 RSVs in 41 genes among 200 control women (27.2%). Conclusions Women with HA are enriched for RSVs in genes that cause IHH, suggesting that variation in genes associated with gonadotropin-releasing hormone neuronal ontogeny and function may be a major determinant of individual susceptibility to developing HA in the face of diet, exercise, and/or stress.

Faithful transcription initiation is critical for accurate gene expression, yet the mechanisms underlying specific transcription start site (TSS) selection in mammals remain unclear. Here, we show that the histone-fold domain protein NF-Y, a ubiquitously expressed transcription factor, controls the fidelity of transcription initiation at gene promoters in mouse embryonic stem cells. We report that NF-Y maintains the region upstream of TSSs in a nucleosome-depleted state while simultaneously protecting this accessible region against aberrant and/or ectopic transcription initiation. We find that loss of NF-Y binding in mammalian cells disrupts the promoter chromatin landscape, leading to nucleosomal encroachment over the canonical TSS. Importantly, this chromatin rearrangement is accompanied by upstream relocation of the transcription pre-initiation complex and ectopic transcription initiation. Further, this phenomenon generates aberrant extended transcripts that undergo translation, disrupting gene expression profiles. These results suggest NF-Y is a central player in TSS selection in metazoans and highlight the deleterious consequences of inaccurate transcription initiation., The mechanisms underlying specific TSS selection in mammals remain unclear. Here the authors show that the ubiquitously expressed transcription factor NF-Y regulate fidelity of transcription initiation at gene promoters, maintaining the region upstream of TSSs in a nucleosome-depleted state, while protecting this region from ectopic transcription initiation.

Background Acquired human mitochondrial genome (mtDNA) deletions are symptoms and drivers of focal mitochondrial respiratory deficiency, a pathological hallmark of aging and late-onset mitochondrial disease. Results To decipher connections between these processes, we create LostArc, an ultrasensitive method for quantifying deletions in circular mtDNA molecules. LostArc reveals 35 million deletions (~ 470,000 unique spans) in skeletal muscle from 22 individuals with and 19 individuals without pathogenic variants in POLG. This nuclear gene encodes the catalytic subunit of replicative mitochondrial DNA polymerase γ. Ablation, the deleted mtDNA fraction, suffices to explain skeletal muscle phenotypes of aging and POLG-derived disease. Unsupervised bioinformatic analyses reveal distinct age- and disease-correlated deletion patterns. Conclusions These patterns implicate replication by DNA polymerase γ as the deletion driver and suggest little purifying selection against mtDNA deletions by mitophagy in postmitotic muscle fibers. Observed deletion patterns are best modeled as mtDNA deletions initiated by replication fork stalling during strand displacement mtDNA synthesis.

High-throughput sequencing has become ubiquitous in biomedical sciences. As new technologies emerge and sequencing costs decline, the diversity and volume of available data increases exponentially, and successfully navigating the data becomes more challenging. Though datasets are often hosted by public repositories, scientists must rely on inconsistent annotation to identify and interpret meaningful data. Moreover, the experimental heterogeneity and wide-ranging quality of high-throughput biological data means that even data with desired cell lines, tissue types, or molecular targets may not be readily interpretable or integrated. We have developed ORSO (Online Resource for Social Omics) as an easy-to-use web application to connect life scientists with genomics data. In ORSO, users interact within a data-driven social network, where they can favorite datasets and follow other users. In addition to more than 30,000 datasets hosted from major biomedical consortia, users may contribute their own data to ORSO, facilitating its discovery by other users. Leveraging user interactions, ORSO provides a novel recommendation system to automatically connect users with hosted data. In addition to social interactions, the recommendation system considers primary read coverage information and annotated metadata. Similarities used by the recommendation system are presented by ORSO in a graph display, allowing exploration of dataset associations. The topology of the network graph reflects established biology, with samples from related systems grouped together. We tested the recommendation system using an RNA-seq time course dataset from differentiation of embryonic stem cells to cardiomyocytes. The ORSO recommendation system correctly predicted early data point sources as embryonic stem cells and late data point sources as heart and muscle samples, resulting in recommendation of related datasets. By connecting scientists with relevant data, ORSO provides a critical new service that facilitates wide-ranging research interests., Author summary New sequencing technologies have rapidly transformed biomedical research. Public data repositories now contain millions of datasets, which have the potential to accelerate and bolster research projects. However, the sheer magnitude of available data makes navigation difficult. We created ORSO (Online Resource for Social Omics) to address these challenges. ORSO is a social network where entries are not status updates or tweets, but biological datasets. Users may add their own data to ORSO, joining 30,000 validated datasets that are already hosted, and other users may find these data through intuitive search functions and informative analytics. Users can then favorite datasets relevant to their interests or follow contributing users. ORSO also uses a recommendation system like those used on commercial websites to automatically recommend data to users based on user interactions and dataset similarities. By making data more accessible and by connecting users to relevant data, we anticipate that ORSO will be an important resource for scientists. ORSO may be the first of many applications that use methods originating in social media and ecommerce to enhance and further research projects in the life sciences.

p53 transcriptional networks are well-characterized in many organisms. However, a global understanding of requirements for in vivo p53 interactions with DNA and relationships with transcription across human biological systems in response to various p53 activating situations remains limited. Using a common analysis pipeline, we analyzed 41 data sets from genome-wide ChIP-seq studies of which 16 have associated gene expression data, including our recent primary data with normal human lymphocytes. The resulting extensive analysis, accessible at p53 BAER hub via the UCSC browser, provides a robust platform to characterize p53 binding throughout the human genome including direct influence on gene expression and underlying mechanisms. We establish the impact of spacers and mismatches from consensus on p53 binding in vivo and propose that once bound, neither significantly influences the likelihood of expression. Our rigorous approach revealed a large p53 genome-wide cistrome composed of >900 genes directly targeted by p53. Importantly, we identify a core cistrome signature composed of genes appearing in over half the data sets, and we identify signatures that are treatment- or cell-specific, demonstrating new functions for p53 in cell biology. Our analysis reveals a broad homeostatic role for human p53 that is relevant to both basic and translational studies.

Regulation by gene-distal enhancers is critical for cell type-specific and condition-specific patterns of gene expression. Thus, to understand the basis of gene activity in a given cell type or tissue, we must identify the precise locations of enhancers and functionally characterize their behaviors. Here, we demonstrate that transcription is a nearly universal feature of enhancers in Drosophila and mammalian cells and that nascent RNA sequencing strategies are optimal for identification of both enhancers and superenhancers. We dissect the mechanisms governing enhancer transcription and discover remarkable similarities to transcription at protein-coding genes. We show that RNA polymerase II (RNAPII) undergoes regulated pausing and release at enhancers. However, as compared with mRNA genes, RNAPII at enhancers is less stable and more prone to early termination. Furthermore, we found that the level of histone H3 Lys4 (H3K4) methylation at enhancers corresponds to transcriptional activity such that highly active enhancers display H3K4 trimethylation rather than the H3K4 monomethylation considered a hallmark of enhancers. Finally, our work provides insights into the unique characteristics of superenhancers, which stimulate high-level gene expression through rapid pause release; interestingly, this property renders associated genes resistant to the loss of factors that stabilize paused RNAPII.

Background Identification of mutations from next-generation sequencing data typically requires a balance between sensitivity and accuracy. This is particularly true of DNA insertions and deletions (indels), that can impart significant phenotypic consequences on cells but are harder to call than substitution mutations from whole genome mutation accumulation experiments. To overcome these difficulties, we present muver, a computational framework that integrates established bioinformatics tools with novel analytical methods to generate mutation calls with the extremely low false positive rates and high sensitivity required for accurate mutation rate determination and comparison. Results Muver uses statistical comparison of ancestral and descendant allelic frequencies to identify variant loci and assigns genotypes with models that include per-sample assessments of sequencing errors by mutation type and repeat context. Muver identifies maximally parsimonious mutation pathways that connect these genotypes, differentiating potential allelic conversion events and delineating ambiguities in mutation location, type, and size. Benchmarking with a human gold standard father-son pair demonstrates muver’s sensitivity and low false positive rates. In DNA mismatch repair (MMR) deficient Saccharomyces cerevisiae, muver detects multi-base deletions in homopolymers longer than the replicative polymerase footprint at rates greater than predicted for sequential single-base deletions, implying a novel multi-repeat-unit slippage mechanism. Conclusions Benchmarking results demonstrate the high accuracy and sensitivity achieved with muver, particularly for indels, relative to available tools. Applied to an MMR-deficient Saccharomyces cerevisiae system, muver mutation calls facilitate mechanistic insights into DNA replication fidelity. Electronic supplementary material The online version of this article (10.1186/s12864-018-4753-3) contains supplementary material, which is available to authorized users.

Histone H3 lysine 36 methylation (H3K36me) is thought to participate in a host of co-transcriptional regulatory events. To study the function of this residue independent from the enzymes that modify it, we used a ‘histone replacement’ system in Drosophila to generate a non-modifiable H3K36 lysine-to-arginine (H3K36R) mutant. We observed global dysregulation of mRNA levels in H3K36R animals that correlates with the incidence of H3K36me3. Similar to previous studies, we found that mutation of H3K36 also resulted in H4 hyperacetylation. However, neither cryptic transcription initiation, nor alternative pre-mRNA splicing, contributed to the observed changes in expression, in contrast with previously reported roles for H3K36me. Interestingly, knockdown of the RNA surveillance nuclease, Xrn1, and members of the CCR4-Not deadenylase complex, restored mRNA levels for a class of downregulated, H3K36me3-rich genes. We propose a post-transcriptional role for modification of replication-dependent H3K36 in the control of metazoan gene expression. DOI: http://dx.doi.org/10.7554/eLife.23249.001, eLife digest In a single human cell there is enough DNA to stretch over a meter if laid end to end. To fit this DNA inside the cell – which is less than 20 micrometers in diameter – the DNA is tightly wrapped around millions of proteins known as histones, which look like “beads” along a “string” of DNA. These histones can prevent other proteins from binding to DNA and activating specific genes. Therefore, cells use enzymes to chemically modify histones to allow particular stretches of DNA to be unwrapped at specific times. Proteins are made up of building blocks called amino acids. A specific amino acid on histones known as H3K36 is modified in certain sections of DNA that suggest it affects the activities of many genes. However, the precise role of this amino acid remains unclear. Previous studies have tried to investigate this by removing the enzymes that modify it, but these enzymes can also modify many other proteins, making it difficult to know what exactly causes the changes in gene activity. Fruit flies are often used in experiments as models of how genetic processes work in humans and other animals. Like us, fruit flies also package their DNA using histones. To investigate the role of H3K36, Meers et al. generated a mutant fruit fly that has a version of the amino acid that cannot be chemically modified by the normal enzymes. Unexpectedly, the experiments suggest that some changes in gene activity that have been previously reported to be caused by modifying H3K36 might actually be due to other factors. Meers et al. found that H3K36 modifications may instead “mark” certain genes to be more active than they otherwise would be. These findings provide a starting point for understanding exactly how H3K36 regulates gene activity. The next challenge is to refine our understanding of how H3K36 modification affects genes in cancer and other diseases, which may aid the development of new therapies to treat these conditions. DOI: http://dx.doi.org/10.7554/eLife.23249.002

Established and emerging next generation sequencing (NGS)-based technologies allow for genome-wide interrogation of diverse biological processes. However, accessibility of NGS data remains a problem, and few user-friendly resources exist for integrative analysis of NGS data from different sources and experimental techniques. Here, we present Online Resource for Integrative Omics (ORIO; https://orio.niehs.nih.gov/), a web-based resource with an intuitive user interface for rapid analysis and integration of NGS data. To use ORIO, the user specifies NGS data of interest along with a list of genomic coordinates. Genomic coordinates may be biologically relevant features from a variety of sources, such as ChIP-seq peaks for a given protein or transcription start sites from known gene models. ORIO first iteratively finds read coverage values at each genomic feature for each NGS dataset. Data are then integrated using clustering-based approaches, giving hierarchical relationships across NGS datasets and separating individual genomic features into groups. In focusing its analysis on read coverage, ORIO makes limited assumptions about the analyzed data; this allows the tool to be applied across data from a variety of experiments and techniques. Results from analysis are presented in dynamic displays alongside user-controlled statistical tests, supporting rapid statistical validation of observed results. We emphasize the versatility of ORIO through diverse examples, ranging from NGS data quality control to characterization of enhancer regions and integration of gene expression information. Easily accessible on a public web server, we anticipate wide use of ORIO in genome-wide investigations by life scientists.

Complex higher-order RNA structures play critical roles in all facets of gene expression; however, the through-space interaction networks that define tertiary structures and govern sampling of multiple conformations are poorly understood. Here we describe single-molecule RNA structure analysis in which multiple sites of chemical modification are identified in single RNA strands by massively parallel sequencing and then analyzed for correlated and clustered interactions. The strategy thus identifies RNA interaction groups by mutational profiling (RING-MaP) and makes possible two expansive applications. First, we identify through-space interactions, create 3D models for RNAs spanning 80-265 nucleotides, and characterize broad classes of intramolecular interactions that stabilize RNA. Second, we distinguish distinct conformations in solution ensembles and reveal previously undetected hidden states and large-scale structural reconfigurations that occur in unfolded RNAs relative to native states. RING-MaP single-molecule nucleic acid structure interrogation enables concise and facile analysis of the global architectures and multiple conformations that govern function in RNA.

Antisense transcription is a prevalent feature at mammalian promoters. Previous studies have primarily focused on antisense transcription initiating upstream of genes. Here, we characterize promoter-proximal antisense transcription downstream of gene transcription starts sites in human breast cancer cells, investigating the genomic context of downstream antisense transcription. We find extensive correlations between antisense transcription and features associated with the chromatin environment at gene promoters. Antisense transcription downstream of promoters is widespread, with antisense transcription initiation observed within 2 kb of 28% of gene transcription start sites. Antisense transcription initiates between nucleosomes regularly positioned downstream of these promoters. The nucleosomes between gene and downstream antisense transcription start sites carry histone modifications associated with active promoters, such as H3K4me3 and H3K27ac. This region is bound by chromatin remodeling and histone modifying complexes including SWI/SNF subunits and HDACs, suggesting that antisense transcription or resulting RNA transcripts contribute to the creation and maintenance of a promoter-associated chromatin environment. Downstream antisense transcription overlays additional regulatory features, such as transcription factor binding, DNA accessibility, and the downstream edge of promoter-associated CpG islands. These features suggest an important role for antisense transcription in the regulation of gene expression and the maintenance of a promoter-associated chromatin environment., Author Summary Gene transcription is regulated by the coordinated interaction of genetic, epigenetic and trans-acting factors. The chromatin environment at gene promoters, including positioned nucleosomes that may display functional histone modifications, is a key regulator of gene expression, contributing to transcriptional activation and repression. In addition to sense-strand transcription of gene sequences, antisense transcription is prevalent at gene promoters. Often resulting in a short-lived non-coding RNA transcript, the function of antisense transcription is poorly understood. Using next-generation sequencing techniques, we characterized transcription in human breast cancer cells and found extensive correlations between antisense transcription and the chromatin environment at promoters. We found that downstream antisense transcription initiates from between regularly positioned nucleosomes and that those nucleosomes between sense and downstream antisense transcription start sites display histone modifications associated with active gene promoters. Chromatin remodelers and other protein complexes responsible for creation and maintenance of the promoter chromatin environment associate with this same region, suggesting an important role of antisense transcription in the regulation of gene expression.

We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

It was shown decades ago that purified 30S ribosome subunits readily interconvert between “active” and “inactive” conformations in a switch that involves changes in the functionally important neck and decoding regions. However, the physiological significance of this conformational change had remained unknown. In exponentially growing Escherichia coli cells, RNA SHAPE probing revealed that 16S rRNA largely adopts the inactive conformation in stably assembled, mature 30S subunits and the active conformation in translating (70S) ribosomes. Inactive 30S subunits bind mRNA as efficiently as active subunits but initiate translation more slowly. Mutations that inhibited interconversion between states compromised translation in vivo. Binding by the small antibiotic paromomycin induced the inactive-to-active conversion, consistent with a low-energy barrier between the two states. Despite the small energetic barrier between states, but consistent with slow translation initiation and a functional role in vivo, interconversion involved large-scale changes in structure in the neck region that likely propagate across the 30S body via helix 44. These findings suggest the inactive state is a biologically relevant alternate conformation that regulates ribosome function as a conformational switch.

Discovery and characterization of functional RNA structures remains challenging due to deficiencies in de novo secondary structure modeling. Here we describe a dynamic programming approach for model-free sequence comparison that incorporates high-throughput chemical probing data. Based on SHAPE probing data alone, ribosomal RNAs (rRNAs) from three diverse organisms – the eubacteria E. coli and C. difficile and the archeon H. volcanii – could be aligned with accuracies comparable to alignments based on actual sequence identity. When both base sequence identity and chemical probing reactivities were considered together, accuracies improved further. Derived sequence alignments and chemical probing data from protein-free RNAs were then used as pseudo-free energy constraints to model consensus secondary structures for the 16S and 23S rRNAs. There are critical differences between these experimentally-informed models and currently accepted models, including in the functionally important neck and decoding regions of the 16S rRNA. We infer that the 16S rRNA has evolved to undergo large-scale changes in base pairing as part of ribosome function. As high-quality RNA probing data become widely available, structurally-informed sequence alignment will become broadly useful for de novo motif and function discovery., Author Summary Despite the clear functional importance of structure in RNA molecules, it remains very difficult to correctly identify and annotate similar RNA structures because their sequences are often poorly conserved even for RNAs that form very similar higher-order structures. A solution is to use a metric that identifies structural motifs but this, too, is difficult because RNA structure modeling based on sequence alone is generally not very accurate. In this work, we use SHAPE chemical probing to obtain model-free information about RNA structure and then exploit this information to align RNAs by sequence. We show for ribosomal RNAs that sequence alignments based on SHAPE experimental information alone are as accurate as those that actually use sequence information. In addition, we identify regions in the 16S ribosomal RNA that form conserved secondary structures that are different from currently accepted models. These differences, rather than being errors, reveal sites of conformational flexibility that may underlie mechanistic functions in ribosome assembly and translation regulation. We anticipate that structure-informed sequence alignment and structure modeling will become broadly useful tools in RNA function analysis.

Molecular modeling guided by experimentally derived structural information is an attractive approach for three-dimensional structure determination of complex RNAs that are not amenable to study by high-resolution methods. Hydroxyl radical probing (HRP), which is performed routinely in many laboratories, provides a measure of solvent accessibility at individual nucleotides. HRP measurements have, to date, only been used to evaluate RNA models qualitatively. Here we report the development of a quantitative structure refinement approach using HRP measurements to drive discrete molecular dynamics simulations for RNAs ranging in size from 80 to 230 nucleotides. We first used HRP reactivities to identify RNAs that form extensive helical packing interactions. For these RNAs, we achieved highly significant structure predictions given the inputs of RNA sequence and base pairing. This HRP-directed tertiary structure refinement approach generates robust structural hypotheses that are useful for guiding explorations of structure-function inter-relationships in RNA.

RNA function is dependent on its structure, yet three-dimensional folds for most biologically important RNAs are unknown. We develop a generic discrete molecular dynamics-based modeling system that uses long-range constraints inferred from diverse biochemical or bioinformatic analyses to create statistically significant (p < 0.01) nativelike folds for RNAs of known structure ranging from 45 to 158 nucleotides. We then predict the unknown structure of the hepatitis C virus internal ribosome entry site (IRES) pseudoknot domain. The resulting RNA model rationalizes independent solvent accessibility and cryo-electron microscopy structure information. The pseudoknot domain positions the AUG start codon near the mRNA channel and is tRNA-like, suggesting the IRES employs molecular mimicry as a functional strategy.

RNA function is dependent on its structure, yet three-dimensional folds for most biologically important RNAs are unknown. We develop a generic discrete molecular dynamics (DMD)-based modeling system that uses long-range constraints inferred from diverse biochemical or bioinformatic analyses to create statistically significant (p < 0.01) native-like folds for RNAs of known structure ranging from 45 to 158 nucleotides. We then predict the unknown structure of the hepatitis C virus IRES pseudoknot domain. The resulting RNA model rationalizes independent solvent accessibility and cryo-electron microscopy structure information. The pseudoknot positions the AUG start codon near the mRNA channel and is tRNA-like, suggesting the IRES employs molecular mimicry as a functional strategy.

Chromatin structure is tightly regulated in cells and its dysregulation is associated with diseases such as cancer. The Mi-2/NuRD (Nucleosome Remodeling Deacetylase) complex is postulated to organize chromatin structure using its nucleosome remodeling and histone deacetylase activities. MBD3 is an integral component of NuRD, which potentially targets the complex to the specific sites of the genome. Recently, we have demonstrated that MBD3/NuRD targets regulatory elements of active genes, a finding diametrically opposed to historical models depicting NuRD as a co-repressor localized to transcriptionally non-permissive chromatin. Moreover, MBD3 co-localized with RNA polymerase II (pol II) at loci where promoter-enhancer loops are formed. Although our previous findings shed light on the role of NuRD in the regulation of active genes, the detailed mechanism of how NuRD is involved in transcriptional regulation is yet not fully understood. To investigate this question, we have developed a next generation, tagmentation based chromatin immunoprecipitation method followed by massively parallel sequencing (ChIP-nexo), which substantially increased the resolution of the MBD3/NuRD mapping. ChIP-nexo defined MBD3/NuRD localization at high resolution, showing accumulation of MBD3 at active promoters with a dip at transcription start site, suggesting an active role of NuRD in modulation of the cell-type specific transcriptional network. To interrogate the detailed functions of NuRD at active promoters, we conducted MNase-seq using conditions which measures both nucleosome positioning and sensitivity, in MBD3 depleted cells. Depletion of MBD3 changed the accessibility of nucleosomes at promoters, while maintaining overall nucleosome positioning. These data indicate an active regulatory function of NuRD in fine tuning the transcriptional network by modulating transcription rates of pol II. We propose that NuRD may be involved in establishing cell type specific gene expression patterns in diverse cell types by organizing promoter nucleoprotein architecture. Citation Format: Takashi Shimbo, Christopher Lavender, Sara A. Grimm, Makiko I. Doi, Telmo Henriques, Kimberly R. Cannady, Kevin J. Murphy, Daniel A. Gilchrist, Adam Burkholder, Jeffrey J. Hayes, Karen Adelman, Trevor K. Archer, Kenneth S. Zaret, Paul A. Wade. MBD3 regulates chromatin accessibility at active promoters. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2862. doi:10.1158/1538-7445.AM2015-2862

HIV and related primate lentiviruses possess single-stranded RNA genomes. Multiple regions of these genomes participate in critical steps in the viral replication cycle, and the functions of many RNA elements are dependent on the formation of defined structures. The structures of these elements are still not fully understood, and additional functional elements likely exist that have not been identified. In this work, we compared three full-length HIV-related viral genomes: HIV-1NL4-3, SIVcpz, and SIVmac (the latter two strains are progenitors for all HIV-1 and HIV-2 strains, respectively). Model-free RNA structure comparisons were performed using whole-genome structure information experimentally derived from nucleotide-resolution SHAPE reactivities. Consensus secondary structures were constructed for strongly correlated regions by taking into account both SHAPE probing structural data and nucleotide covariation information from structure-based alignments. In these consensus models, all known functional RNA elements were recapitulated with high accuracy. In addition, we identified multiple previously unannotated structural elements in the HIV-1 genome likely to function in translation, splicing and other replication cycle processes; these are compelling targets for future functional analyses. The structure-informed alignment strategy developed here will be broadly useful for efficient RNA motif discovery., Author Summary Human immunodeficiency virus (HIV) is a persistent and critical threat to human health. Replication and pathogenesis of HIV is governed by information encoded in its single-stranded RNA genome. In addition to coding for viral proteins, the HIV genomic RNA forms base paired and higher-order structures that are critical for viral replication. It is likely that only a subset of functional RNA motifs has been identified. Here, we interrogate the structures of three diverse HIV-related viral genomes by nucleotide-resolution chemical probing. The three genomes include HIV-1, the virus that infects humans, and SIVcpz and SIVmac, which are progenitors for the main branches of the two HIV evolutionary groups. We used a structure-informed alignment approach to generate consensus models for base-paired secondary structures that are shared by these three HIV-related genomes. With this approach, we were able to recapitulate all known RNA structures and, additionally, discovered multiple previously undescribed structural elements that are clearly conserved among major HIV groups. We anticipate that the methods described here will be broadly useful for RNA structure motif discovery and, more immediately, for identification of RNA targets in HIV that are promising sites for therapeutic intervention.