SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms.

SQANTI3 is a tool designed for the quality control, curation and annotation of long-read transcript models obtained with third-generation sequencing technologies. Leveraging its annotation framework, SQANTI3 calculates quality descriptors of transcript models, junctions and transcript ends. With this information, potential artifacts can be identified and replaced with reliable sequences. Furthermore, the integrated functional annotation feature enables subsequent functional iso-transcriptomics analyses.

Nucleotide-level distance metrics to quantify alternative splicing implemented in TranD.

Advances in affordable transcriptome sequencing combined with better exon and gene prediction has motivated many to compare transcription across the tree of life. We develop a mathematical framework to calculate complexity and compare transcript models. Structural features, i.e. intron retention (IR), donor/acceptor site variation, alternative exon cassettes, alternative 5'/3' UTRs, are compared and the distance between transcript models is calculated with nucleotide level precision. All metrics are implemented in a PyPi package, TranD and output can be used to summarize splicing patterns for a transcriptome (1GTF) and between transcriptomes (2GTF). TranD output enables quantitative comparisons between: annotations augmented by empirical RNA-seq data and the original transcript models; transcript model prediction tools for longread RNA-seq (e.g. FLAIR versus Isoseq3); alternate annotations for a species (e.g. RefSeq vs Ensembl); and between closely related species. In C. elegans, Z. mays, D. melanogaster, D. simulans and H. sapiens, alternative exons were observed more frequently in combination with an alternative donor/acceptor than alone. Transcript models in RefSeq and Ensembl are linked and both have unique transcript models with empirical support. D. melanogaster and D. simulans, share many transcript models and long-read RNAseq data suggests that both species are under-annotated. We recommend combined references.

Sex-Biased Expression Is Associated With Chromatin State in Drosophila melanogaster and Drosophila simulans.

In Drosophila melanogaster and D. simulans head tissue, 60% of orthologous genes show evidence of sex-biased expression in at least one species. Of these, ∼39% (2,192) are conserved in direction. We hypothesize enrichment of open chromatin in the sex where we see expression bias and closed chromatin in the opposite sex. Male-biased orthologs are significantly enriched for H3K4me3 marks in males of both species (∼89% of male-biased orthologs vs. ∼76% of unbiased orthologs). Similarly, female-biased orthologs are significantly enriched for H3K4me3 marks in females of both species (∼90% of female-biased orthologs vs. ∼73% of unbiased orthologs). The sex-bias ratio in female-biased orthologs was similar in magnitude between the two species, regardless of the closed chromatin (H3K27me2me3) marks in males. However, in male-biased orthologs, the presence of H3K27me2me3 in both species significantly reduced the correlation between D. melanogaster sex-bias ratio and the D. simulans sex-bias ratio. Male-biased orthologs are enriched for evidence of positive selection in the D. melanogaster group. There are more male-biased genes than female-biased genes in both species. For orthologs with gains/losses of sex-bias between the two species, there is an excess of male-bias compared to female-bias, but there is no consistent pattern in the relationship between H3K4me3 or H3K27me2me3 chromatin marks and expression. These data suggest chromatin state is a component of the maintenance of sex-biased expression and divergence of sex-bias between species is reflected in the complexity of the chromatin status.

High-resolution methylome analysis uncovers stress-responsive genomic hotspots and drought-sensitive TE superfamilies in the clonal Lombardy poplar.

DNA methylation is environment-sensitive and can mediate stress responses. In long-lived trees, changing environments might cumulatively shape the methylome landscape over their lifetime. However, because high-resolution methylome studies usually focus on single environmental cues, it remains unclear to what extent the methylation responses are generic or stress-specific, and how this relates to their long-term stability. Here, we studied the methylome plasticity of a Populus nigra cv. 'Italica' clone that is widespread across Europe. Adult trees from a variety of geographic locations were clonally propagated in a common garden experiment, and the ramets were exposed to cold, heat, drought, herbivory, rust infection, and salicylic acid treatments. Through comprehensive whole-genome bisulfite sequencing, we analyzed stress-induced and naturally occurring DNA methylation variants. Stress-induced methylation changes predominantly targeted transposable elements. When occurring in CG/CHG contexts, the same regions were often affected by multiple stresses, suggesting a generic response of the methylome. Drought stress caused a distinct CHH hypermethylation response in transposable elements, affecting entire TE superfamilies near drought-responsive genes. Methylation differences in CG/CHG contexts that were induced by stress treatments showed striking overlap with methylation differences observed between trees from distinct geographical locations. Thus, we revealed genomic hotspots of methylation change that are not stress-specific and that contribute to natural DNA methylation variation, and we identified specific transposable element superfamilies that respond to a specific stress with possible functional consequences. Our results underscore the importance of studying the effects of multiple stressors in a single experiment for recognizing general versus stress-specific methylome responses.

PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases.

PaintOmics is a web server for the integrative analysis and visualisation of multi-omics datasets using biological pathway maps. PaintOmics 4 has several notable updates that improve and extend analyses. Three pathway databases are now supported: KEGG, Reactome and MapMan, providing more comprehensive pathway knowledge for animals and plants. New metabolite analysis methods fill gaps in traditional pathway-based enrichment methods. The metabolite hub analysis selects compounds with a high number of significant genes in their neighbouring network, suggesting regulation by gene expression changes. The metabolite class activity analysis tests the hypothesis that a metabolic class has a higher-than-expected proportion of significant elements, indicating that these compounds are regulated in the experiment. Finally, PaintOmics 4 includes a regulatory omics module to analyse the contribution of trans-regulatory layers (microRNA and transcription factors, RNA-binding proteins) to regulate pathways. We show the performance of PaintOmics 4 on both mouse and plant data to highlight how these new analysis features provide novel insights into regulatory biology. PaintOmics 4 is available at https://paintomics.org/.

Estimating transcriptome complexities across eukaryotes.

BACKGROUND: Genomic complexity is a growing field of evolution, with case studies for comparative evolutionary analyses in model and emerging non-model systems. Understanding complexity and the functional components of the genome is an untapped wealth of knowledge ripe for exploration. With the "remarkable lack of correspondence" between genome size and complexity, there needs to be a way to quantify complexity across organisms. In this study, we use a set of complexity metrics that allow for evaluating changes in complexity using TranD. RESULTS: We ascertain if complexity is increasing or decreasing across transcriptomes and at what structural level, as complexity varies. In this study, we define three metrics - TpG, EpT, and EpG- to quantify the transcriptome's complexity that encapsulates the dynamics of alternative splicing. Here we compare complexity metrics across 1) whole genome annotations, 2) a filtered subset of orthologs, and 3) novel genes to elucidate the impacts of orthologs and novel genes in transcript model analysis. Effective Exon Number (EEN) issued to compare the distribution of exon sizes within transcripts against random expectations of uniform exon placement. EEN accounts for differences in exon size, which is important because novel gene differences in complexity for orthologs and whole-transcriptome analyses are biased towards low-complexity genes with few exons and few alternative transcripts. CONCLUSIONS: With our metric analyses, we are able to quantify changes in complexity across diverse lineages with greater precision and accuracy than previous cross-species comparisons under ortholog conditioning. These analyses represent a step toward whole-transcriptome analysis in the emerging field of non-model evolutionary genomics, with key insights for evolutionary inference of complexity changes on deep timescales across the tree of life. We suggest a means to quantify biases generated in ortholog calling and correct complexity analysis for lineage-specific effects. With these metrics, we directly assay the quantitative properties of newly formed lineage-specific genes as they lower complexity.

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry.

Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insights into areas requiring further improvement.

Human auditory ossicles as an alternative optimal source of ancient DNA.

DNA recovery from ancient human remains has revolutionized our ability to reconstruct the genetic landscape of the past. Ancient DNA research has benefited from the identification of skeletal elements, such as the cochlear part of the osseous inner ear, that provides optimal contexts for DNA preservation; however, the rich genetic information obtained from the cochlea must be counterbalanced against the loss of morphological information caused by its sampling. Motivated by similarities in developmental processes and histological properties between the cochlea and auditory ossicles, we evaluate the ossicles as an alternative source of ancient DNA. We show that ossicles perform comparably to the cochlea in terms of DNA recovery, finding no substantial reduction in data quantity and minimal differences in data quality across preservation conditions. Ossicles can be sampled from intact skulls or disarticulated petrous bones without damage to surrounding bone, and we argue that they should be used when available to reduce damage to human remains. Our results identify another optimal skeletal element for ancient DNA analysis and add to a growing toolkit of sampling methods that help to better preserve skeletal remains for future research while maximizing the likelihood that ancient DNA analysis will produce useable results.

Phylogenomic Resolution of Sea Spider Diversification through Integration of Multiple Data Classes.

Despite significant advances in invertebrate phylogenomics over the past decade, the higher-level phylogeny of Pycnogonida (sea spiders) remains elusive. Due to the inaccessibility of some small-bodied lineages, few phylogenetic studies have sampled all sea spider families. Previous efforts based on a handful of genes have yielded unstable tree topologies. Here, we inferred the relationships of 89 sea spider species using targeted capture of the mitochondrial genome, 56 conserved exons, 101 ultraconserved elements, and 3 nuclear ribosomal genes. We inferred molecular divergence times by integrating morphological data for fossil species to calibrate 15 nodes in the arthropod tree of life. This integration of data classes resolved the basal topology of sea spiders with high support. The enigmatic family Austrodecidae was resolved as the sister group to the remaining Pycnogonida and the small-bodied family Rhynchothoracidae as the sister group of the robust-bodied family Pycnogonidae. Molecular divergence time estimation recovered a basal divergence of crown group sea spiders in the Ordovician. Comparison of diversification dynamics with other marine invertebrate taxa that originated in the Paleozoic suggests that sea spiders and some crustacean groups exhibit resilience to mass extinction episodes, relative to mollusk and echinoderm lineages.

Risk Factors for Recurrent Staphylococcus aureus Bacteremia.

BACKGROUND: To understand the clinical, bacterial, and host characteristics associated with recurrent Staphylococcus aureus bacteremia (R-SAB), patients with R-SAB were compared to contemporaneous patients with a single episode of SAB (S-SAB). METHODS: All SAB isolates underwent spa genotyping. All isolates from R-SAB patients underwent pulsed-field gel electrophoresis (PFGE). PFGE-indistinguishable pairs from 40 patients underwent whole genome sequencing (WGS). Acute phase plasma from R-SAB and S-SAB patients was matched 1:1 for age, race, sex, and bacterial genotype, and underwent cytokine quantification using 25-analyte multiplex bead array. RESULTS: R-SAB occurred in 69 (9.1%) of the 756 study patients. Of the 69 patients, 30 experienced relapse (43.5%) and 39 reinfection (56.5%). Age, race, hemodialysis dependence, presence of foreign body, methicillin-resistant Staphyloccus aureus, and persistent bacteremia were individually associated with likelihood of recurrence. Multivariate risk modeling revealed that black hemodialysis patients were nearly 2 times more likely (odds ratio [OR] = 9.652 [95% confidence interval [CI], 5.402-17.418]) than white hemodialysis patients (OR = 4.53 [95% CI, 1.696-10.879]) to experience R-SAB. WGS confirmed PFGE interpretations in all cases. Median RANTES (regulated on activation, normal T cell expressed and secreted) levels in acute phase plasma from the initial episode of SAB were higher in R-SAB than in matched S-SAB controls (P = .0053, false discovery rate < 0.10). CONCLUSION: This study identified several risk factors for R-SAB. The largest risk for R-SAB is among black hemodialysis patients. Higher RANTES levels in R-SAB compared to matched controls warrants further study.

Long-Term Metabolomics Reference Material.

The use of quality control samples in metabolomics ensures data quality, reproducibility, and comparability between studies, analytical platforms, and laboratories. Long-term, stable, and sustainable reference materials (RMs) are a critical component of the quality assurance/quality control (QA/QC) system; however, the limited selection of currently available matrix-matched RMs reduces their applicability for widespread use. To produce an RM in any context, for any matrix that is robust to changes over the course of time, we developed iterative batch averaging method (IBAT). To illustrate this method, we generated 11 independently grown Escherichia coli batches and made an RM over the course of 10 IBAT iterations. We measured the variance of these materials by nuclear magnetic resonance (NMR) and showed that IBAT produces a stable and sustainable RM over time. This E. coli RM was then used as a food source to produce a Caenorhabditis elegans RM for a metabolomics experiment. The metabolite extraction of this material, alongside 41 independently grown individual C. elegans samples of the same genotype, allowed us to estimate the proportion of sample variation in preanalytical steps. From the NMR data, we found that 40% of the metabolite variance is due to the metabolite extraction process and analysis and 60% is due to sample-to-sample variance. The availability of RMs in untargeted metabolomics is one of the predominant needs of the metabolomics community that reach beyond quality control practices. IBAT addresses this need by facilitating the production of biologically relevant RMs and increasing their widespread use.

Disease-specific biases in alternative splicing and tissue-specific dysregulation revealed by multitissue profiling of lymphocyte gene expression in type 1 diabetes.

Genome-wide association studies (GWAS) have identified multiple, shared allelic associations with many autoimmune diseases. However, the pathogenic contributions of variants residing in risk loci remain unresolved. The location of the majority of shared disease-associated variants in noncoding regions suggests they contribute to risk of autoimmunity through effects on gene expression in the immune system. In the current study, we test this hypothesis by applying RNA sequencing to CD4+, CD8+, and CD19+ lymphocyte populations isolated from 81 subjects with type 1 diabetes (T1D). We characterize and compare the expression patterns across these cell types for three gene sets: all genes, the set of genes implicated in autoimmune disease risk by GWAS, and the subset of these genes specifically implicated in T1D. We performed RNA sequencing and aligned the reads to both the human reference genome and a catalog of all possible splicing events developed from the genome, thereby providing a comprehensive evaluation of the roles of gene expression and alternative splicing (AS) in autoimmunity. Autoimmune candidate genes displayed greater expression specificity in the three lymphocyte populations relative to other genes, with significantly increased levels of splicing events, particularly those predicted to have substantial effects on protein isoform structure and function (e.g., intron retention, exon skipping). The majority of single-nucleotide polymorphisms within T1D-associated loci were also associated with one or more cis-expression quantitative trait loci (cis-eQTLs) and/or splicing eQTLs. Our findings highlight a substantial, and previously underrecognized, role for AS in the pathogenesis of autoimmune disorders and particularly for T1D.

Uncovering hidden genetic variation in photosynthesis of field-grown maize under ozone pollution.

Ozone is the most damaging air pollutant to crops, currently reducing Midwest US maize production by up to 10%, yet there has been very little effort to adapt germplasm for ozone tolerance. Ozone enters plants through stomata, reacts to form reactive oxygen species in the apoplast and ultimately decreases photosynthetic C gain. In this study, 10 diverse inbred parents were crossed in a half-diallel design to create 45 F1 hybrids, which were tested for ozone response in the field using free air concentration enrichment (FACE). Ozone stress increased the heritability of photosynthetic traits and altered genetic correlations among traits. Hybrids from parents Hp301 and NC338 showed greater sensitivity to ozone stress, and disrupted relationships among photosynthetic traits. The physiological responses underlying sensitivity to ozone differed in hybrids from the two parents, suggesting multiple mechanisms of response to oxidative stress. FACE technology was essential to this evaluation because genetic variation in photosynthesis under elevated ozone was not predictable based on performance at ambient ozone. These findings suggest that selection under elevated ozone is needed to identify deleterious alleles in the world's largest commodity crop.

Kinetic Analysis of Hepatic Metabolism Using Hyperpolarized Dihydroxyacetone.

Hyperpolarized carbon-13 magnetic resonance (HP-MR) is a new metabolic imaging method the does not use ionizing radiation. Due to the inherent chemical specificity of MR, not only tracer uptake but also downstream metabolism of the agent is detected in a straightforward manner. HP [2-13C] dihydroxyacetone (DHA) is a promising new agent that directly interrogates hepatic glucose metabolism. DHA has three metabolic fates in the liver: glucose production, glycerol production and potential inclusion into triglycerides, and oxidation in the tricarboxylic acid cycle. Each pathway is regulated by flux through multiple enzymes. Using Duhamel's formula, the kinetics of DHA metabolism is modeled, resulting in estimates of specific reaction rate constants. The multiple enzymatic steps that control DHA metabolism make more simplified methods for extracting kinetic data less than satisfactory. The described modeling paradigm effectively identifies changes in metabolism between gluconeogenic and glycogenolytic models of hepatic function.

SECIMTools: a suite of metabolomics data analysis tools.

BACKGROUND: Metabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic tools that are broadly accessible to the biological community need to be developed. While technology used in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access platform that enables scientists at all levels to interact with big data. Galaxy promotes reproducibility by saving histories and enabling the sharing workflows among scientists. RESULTS: SECIMTools (SouthEast Center for Integrated Metabolomics) is a set of Python applications that are available both as standalone tools and wrapped for use in Galaxy. The suite includes a comprehensive set of quality control metrics (retention time window evaluation and various peak evaluation tools), visualization techniques (hierarchical cluster heatmap, principal component analysis, modular modularity clustering), basic statistical analysis methods (partial least squares - discriminant analysis, analysis of variance, t-test, Kruskal-Wallis non-parametric test), advanced classification methods (random forest, support vector machines), and advanced variable selection tools (least absolute shrinkage and selection operator LASSO and Elastic Net). CONCLUSIONS: SECIMTools leverages the Galaxy platform and enables integrated workflows for metabolomics data analysis made from building blocks designed for easy use and interpretability. Standard data formats and a set of utilities allow arbitrary linkages between tools to encourage novel workflow designs. The Galaxy framework enables future data integration for metabolomics studies with other omics data.

Intergenerational environmental effects: functional signals in offspring transcriptomes and metabolomes after parental jasmonic acid treatment in apomictic dandelion.

Parental environments can influence offspring traits. However, the magnitude of the impact of parental environments on offspring molecular phenotypes is poorly understood. Here, we test the direct effects and intergenerational effects of jasmonic acid (JA) treatment, which is involved in herbivory-induced defense signaling, on transcriptomes and metabolomes in apomictic common dandelion (Taraxacum officinale). In a full factorial crossed design with parental and offspring JA and control treatments, we performed leaf RNA-seq gene expression analysis, LC-MS metabolomics and total phenolics assays in offspring plants. Expression analysis, leveraged by a de novo assembled transcriptome, revealed an induced response to JA exposure that is consistent with known JA effects. The intergenerational effect of treatment was considerable: 307 of 858 detected JA-responsive transcripts were affected by parental JA treatment. In terms of the numbers of metabolites affected, the magnitude of the chemical response to parental JA exposure was c. 10% of the direct JA treatment response. Transcriptome and metabolome analyses both identified the phosphatidylinositol signaling pathway as a target of intergenerational JA effects. Our results highlight that parental environments can have substantial effects in offspring generations. Transcriptome and metabolome assays provide a basis for zooming in on the potential mechanisms of inherited JA effects.

High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance.

High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (λ = 500-2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO2]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress.

A comprehensive analysis of myocardial substrate preference emphasizes the need for a synchronized fluxomic/metabolomic research design.

The heart oxidizes fatty acids, carbohydrates, and ketone bodies inside the tricarboxylic acid (TCA) cycle to generate the reducing equivalents needed for ATP production. Competition between these substrates makes it difficult to estimate the extent of pyruvate oxidation. Previously, hyperpolarized pyruvate detected propionate-mediated activation of carbohydrate oxidation, even in the presence of acetate. In this report, the optimal concentration of propionate for the activation of glucose oxidation was measured in mouse hearts perfused in Langendorff mode. This study was performed with a more physiologically relevant perfusate than the previous work. Increasing concentrations of propionate did not cause adverse effects on myocardial metabolism, as evidenced by unchanged O2 consumption, TCA cycle flux, and developed pressures. Propionate at 1 mM was sufficient to achieve significant increases in pyruvate dehydrogenase flux (3×), and anaplerosis (6×), as measured by isotopomer analysis. These results further demonstrate the potential of propionate as an aid for the correct estimation of total carbohydrate oxidative capacity in the heart. However, liquid chromotography/mass spectroscopy-based metabolomics detected large changes (~30-fold) in malate and fumarate pool sizes. This observation leads to a key observation regarding mass balance in the TCA cycle; flux through a portion of the cycle can be drastically elevated without changing the O2 consumption.

Elevated ozone reduces photosynthetic carbon gain by accelerating leaf senescence of inbred and hybrid maize in a genotype-specific manner.

Exposure to elevated tropospheric ozone concentration ([O3 ]) accelerates leaf senescence in many C3 crops. However, the effects of elevated [O3 ] on C4 crops including maize (Zea mays L.) are poorly understood in terms of physiological mechanism and genetic variation in sensitivity. Using free air gas concentration enrichment, we investigated the photosynthetic response of 18 diverse maize inbred and hybrid lines to season-long exposure to elevated [O3 ] (~100 nl L-1 ) in the field. Gas exchange was measured on the leaf subtending the ear throughout the grain filling period. On average over the lifetime of the leaf, elevated [O3 ] led to reductions in photosynthetic CO2 assimilation of both inbred (-22%) and hybrid (-33%) genotypes. There was significant variation among both inbred and hybrid lines in the sensitivity of photosynthesis to elevated [O3 ], with some lines showing no change in photosynthesis at elevated [O3 ]. Based on analysis of inbred line B73, the reduced CO2 assimilation at elevated [O3 ] was associated with accelerated senescence decreasing photosynthetic capacity and not altered stomatal limitation. These findings across diverse maize genotypes could advance the development of more O3 tolerant maize and provide experimental data for parameterization and validation of studies modeling how O3 impacts crop performance.

LANA binds to multiple active viral and cellular promoters and associates with the H3K4methyltransferase hSET1 complex.

Kaposi's sarcoma-associated herpesvirus (KSHV) is a γ-herpesvirus associated with KS and two lymphoproliferative diseases. Recent studies characterized epigenetic modification of KSHV episomes during latency and determined that latency-associated genes are associated with H3K4me3 while most lytic genes are associated with the silencing mark H3K27me3. Since the latency-associated nuclear antigen (LANA) (i) is expressed very early after de novo infection, (ii) interacts with transcriptional regulators and chromatin remodelers, and (iii) regulates the LANA and RTA promoters, we hypothesized that LANA may contribute to the establishment of latency through epigenetic control. We performed a detailed ChIP-seq analysis in cells of lymphoid and endothelial origin and compared H3K4me3, H3K27me3, polII, and LANA occupancy. On viral episomes LANA binding was detected at numerous lytic and latent promoters, which were transactivated by LANA using reporter assays. LANA binding was highly enriched at H3K4me3 peaks and this co-occupancy was also detected on many host gene promoters. Bioinformatic analysis of enriched LANA binding sites in combination with biochemical binding studies revealed three distinct binding patterns. A small subset of LANA binding sites showed sequence homology to the characterized LBS1/2 sequence in the viral terminal repeat. A large number of sites contained a novel LANA binding motif (TCCAT)3 which was confirmed by gel shift analysis. Third, some viral and cellular promoters did not contain LANA binding sites and are likely enriched through protein/protein interaction. LANA was associated with H3K4me3 marks and in PEL cells 86% of all LANA bound promoters were transcriptionally active, leading to the hypothesis that LANA interacts with the machinery that methylates H3K4. Co-immunoprecipitation demonstrated LANA association with endogenous hSET1 complexes in both lymphoid and endothelial cells suggesting that LANA may contribute to the epigenetic profile of KSHV episomes.