Neurotensin accelerates atherosclerosis and increases circulating levels of short-chain and saturated triglycerides.

BACKGROUND AND AIMS: Obesity and type 2 diabetes are significant risk factors for atherosclerotic cardiovascular disease (CVD) worldwide, but the underlying pathophysiological links are poorly understood. Neurotensin (NT), a 13-amino-acid hormone peptide, facilitates intestinal fat absorption and contributes to obesity in mice fed a high-fat diet. Elevated levels of pro-NT (a stable NT precursor produced in equimolar amounts relative to NT) are associated with obesity, type 2 diabetes, and CVD in humans. Whether NT is a causative factor in CVD is unknown. METHODS: Nt+/+ and Nt-/- mice were either injected with adeno-associated virus encoding PCSK9 mutants or crossed with Ldlr-/- mice and fed a Western diet. Atherosclerotic plaques were analyzed by en face analysis, Oil Red O and CD68 staining. In humans, we evaluated the association between baseline pro-NT and growth of carotid bulb thickness after 16.4 years. Lipidomic profiles were analyzed. RESULTS: Atherosclerotic plaque formation is attenuated in Nt-deficient mice through mechanisms that are independent of reductions in circulating cholesterol and triglycerides but associated with remodeling of the plasma triglyceride pool. An increasing plasma concentration of pro-NT predicts atherosclerotic events in coronary and cerebral arteries independent of all major traditional risk factors, indicating a strong link between NT and atherosclerosis. This plasma lipid profile analysis confirms the association of pro-NT with remodeling of the plasma triglyceride pool in atherosclerotic events. CONCLUSIONS: Our findings are the first to directly link NT to increased atherosclerosis and indicate the potential role for NT in preventive and therapeutic strategies for CVD.

Bilirubin Nanoparticle Treatment in Obese Mice Inhibits Hepatic Ceramide Production and Remodels Liver Fat Content.

Studies have indicated that increasing plasma bilirubin levels might be useful for preventing and treating hepatic lipid accumulation that occurs with metabolic diseases such as obesity and diabetes. We have previously demonstrated that mice with hyperbilirubinemia had significantly less lipid accumulation in a diet-induced non-alcoholic fatty liver disease (NAFLD) model. However, bilirubin's effects on individual lipid species are currently unknown. Therefore, we used liquid chromatography-mass spectroscopy (LC-MS) to determine the hepatic lipid composition of obese mice with NAFLD treated with bilirubin nanoparticles or vehicle control. We placed the mice on a high-fat diet (HFD) for 24 weeks and then treated them with bilirubin nanoparticles or vehicle control for 4 weeks while maintaining the HFD. Bilirubin nanoparticles suppressed hepatic fat content overall. After analyzing the lipidomics data, we determined that bilirubin inhibited the accumulation of ceramides in the liver. The bilirubin nanoparticles significantly lowered the hepatic expression of two essential enzymes that regulate ceramide production, Sgpl1 and Degs1. Our results demonstrate that the bilirubin nanoparticles improve hepatic fat content by reducing ceramide production, remodeling the liver fat content, and improving overall metabolic health.

Polycomb deficiency drives a FOXP2-high aggressive state targetable by epigenetic inhibitors.

Inhibitors of the Polycomb Repressive Complex 2 (PRC2) histone methyltransferase EZH2 are approved for certain cancers, but realizing their wider utility relies upon understanding PRC2 biology in each cancer system. Using a genetic model to delete Ezh2 in KRAS-driven lung adenocarcinomas, we observed that Ezh2 haplo-insufficient tumors were less lethal and lower grade than Ezh2 fully-insufficient tumors, which were poorly differentiated and metastatic. Using three-dimensional cultures and in vivo experiments, we determined that EZH2-deficient tumors were vulnerable to H3K27 demethylase or BET inhibitors. PRC2 loss/inhibition led to de-repression of FOXP2, a transcription factor that promotes migration and stemness, and FOXP2 could be suppressed by BET inhibition. Poorly differentiated human lung cancers were enriched for an H3K27me3-low state, representing a subtype that may benefit from BET inhibition as a single therapy or combined with additional EZH2 inhibition. These data highlight diverse roles of PRC2 in KRAS-driven lung adenocarcinomas, and demonstrate the utility of three-dimensional cultures for exploring epigenetic drug sensitivities for cancer.

Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier Transform Mass Spectrometry Experiments.

We present a novel, scan-centric method for characterizing peaks from direct injection multi-scan Fourier transform mass spectra of complex samples that utilizes frequency values derived directly from the spacing of raw m/z points in spectral scans. Our peak characterization method utilizes intensity-independent noise removal and normalization of scan-level data to provide a much better fit of relative intensity to natural abundance probabilities for low abundance isotopologues that are not present in all of the acquired scans. Moreover, our method calculates both peak- and scan-specific statistics incorporated within a series of quality control steps that are designed to robustly derive peak centers, intensities, and intensity ratios with their scan-level variances. These cross-scan characterized peaks are suitable for use in our previously published peak assignment methodology, Small Molecule Isotope Resolved Formula Enumeration (SMIRFE).

Hepatic kinome atlas: An in-depth identification of kinase pathways in liver fibrosis of humans and rodents.

BACKGROUND AND AIMS: Resolution of pathways that converge to induce deleterious effects in hepatic diseases, such as in the later stages, have potential antifibrotic effects that may improve outcomes. We aimed to explore whether humans and rodents display similar fibrotic signaling networks. APPROACH AND RESULTS: We assiduously mapped kinase pathways using 340 substrate targets, upstream bioinformatic analysis of kinase pathways, and over 2000 random sampling iterations using the PamGene PamStation kinome microarray chip technology. Using this technology, we characterized a large number of kinases with altered activity in liver fibrosis of both species. Gene expression and immunostaining analyses validated many of these kinases as bona fide signaling events. Surprisingly, the insulin receptor emerged as a considerable protein tyrosine kinase that is hyperactive in fibrotic liver disease in humans and rodents. Discoidin domain receptor tyrosine kinase, activated by collagen that increases during fibrosis, was another hyperactive protein tyrosine kinase in humans and rodents with fibrosis. The serine/threonine kinases found to be the most active in fibrosis were dystrophy type 1 protein kinase and members of the protein kinase family of kinases. We compared the fibrotic events over four models: humans with cirrhosis and three murine models with differing levels of fibrosis, including two models of fatty liver disease with emerging fibrosis. The data demonstrate a high concordance between human and rodent hepatic kinome signaling that focalizes, as shown by our network analysis of detrimental pathways. CONCLUSIONS: Our findings establish a comprehensive kinase atlas for liver fibrosis, which identifies analogous signaling events conserved among humans and rodents.

Untargeted Lipidomics of Non-Small Cell Lung Carcinoma Demonstrates Differentially Abundant Lipid Classes in Cancer vs. Non-Cancer Tissue.

Lung cancer remains the leading cause of cancer death worldwide and non-small cell lung carcinoma (NSCLC) represents 85% of newly diagnosed lung cancers. In this study, we utilized our untargeted assignment tool Small Molecule Isotope Resolved Formula Enumerator (SMIRFE) and ultra-high-resolution Fourier transform mass spectrometry to examine lipid profile differences between paired cancerous and non-cancerous lung tissue samples from 86 patients with suspected stage I or IIA primary NSCLC. Correlation and co-occurrence analysis revealed significant lipid profile differences between cancer and non-cancer samples. Further analysis of machine-learned lipid categories for the differentially abundant molecular formulas identified a high abundance sterol, high abundance and high m/z sphingolipid, and low abundance glycerophospholipid metabolic phenotype across the NSCLC samples. At the class level, higher abundances of sterol esters and lower abundances of cardiolipins were observed suggesting altered stearoyl-CoA desaturase 1 (SCD1) or acetyl-CoA acetyltransferase (ACAT1) activity and altered human cardiolipin synthase 1 or lysocardiolipin acyltransferase activity respectively, the latter of which is known to confer apoptotic resistance. The presence of a shared metabolic phenotype across a variety of genetically distinct NSCLC subtypes suggests that this phenotype is necessary for NSCLC development and may result from multiple distinct genetic lesions. Thus, targeting the shared affected pathways may be beneficial for a variety of genetically distinct NSCLC subtypes.

Cellular Origins of EGFR-Driven Lung Cancer Cells Determine Sensitivity to Therapy.

Targeting the epidermal growth factor receptor (EGFR) with tyrosine kinase inhibitors (TKIs) is one of the major precision medicine treatment options for lung adenocarcinoma. Due to common development of drug resistance to first- and second-generation TKIs, third-generation inhibitors, including osimertinib and rociletinib, have been developed. A model of EGFR-driven lung cancer and a method to develop tumors of distinct epigenetic states through 3D organotypic cultures are described here. It is discovered that activation of the EGFR T790M/L858R mutation in lung epithelial cells can drive lung cancers with alveolar or bronchiolar features, which can originate from alveolar type 2 (AT2) cells or bronchioalveolar stem cells, but not basal cells or club cells of the trachea. It is also demonstrated that these clones are able to retain their epigenetic differences through passaging orthotopically in mice and crucially that they have distinct drug vulnerabilities. This work serves as a blueprint for exploring how epigenetics can be used to stratify patients for precision medicine decisions.

Loss of CLN3, the gene mutated in juvenile neuronal ceroid lipofuscinosis, leads to metabolic impairment and autophagy induction in retinal pigment epithelium.

Juvenile neuronal ceroid lipofuscinosis (JNCL, aka. juvenile Batten disease or CLN3 disease) is a lysosomal storage disease characterized by progressive blindness, seizures, cognitive and motor failures, and premature death. JNCL is caused by mutations in the Ceroid Lipofuscinosis, Neuronal 3 (CLN3) gene, whose function is unclear. Although traditionally considered a neurodegenerative disease, CLN3 disease displays eye-specific effects: Vision loss not only is often one of the earliest symptoms of JNCL, but also has been reported in non-syndromic CLN3 disease. Here we described the roles of CLN3 protein in maintaining healthy retinal pigment epithelium (RPE) and normal vision. Using electroretinogram, fundoscopy and microscopy, we showed impaired visual function, retinal autofluorescent lesions, and RPE disintegration and metaplasia/hyperplasia in a Cln3 ~ 1 kb-deletion mouse model [1] on C57BL/6J background. Utilizing a combination of biochemical analyses, RNA-Seq, Seahorse XF bioenergetic analysis, and Stable Isotope Resolved Metabolomics (SIRM), we further demonstrated that loss of CLN3 increased autophagic flux, suppressed mTORC1 and Akt activities, enhanced AMPK activity, and up-regulated gene expression of the autophagy-lysosomal system in RPE-1 cells, suggesting autophagy induction. This CLN3 deficiency induced autophagy induction coincided with decreased mitochondrial oxygen consumption, glycolysis, the tricarboxylic acid (TCA) cycle, and ATP production. We also reported for the first time that loss of CLN3 led to glycogen accumulation despite of impaired glycogen synthesis. Our comprehensive analyses shed light on how loss of CLN3 affect autophagy and metabolism. This work suggests possible links among metabolic impairment, autophagy induction and lysosomal storage, as well as between RPE atrophy/degeneration and vision loss in JNCL.

Deriving Lipid Classification Based on Molecular Formulas.

Despite instrument and algorithmic improvements, the untargeted and accurate assignment of metabolites remains an unsolved problem in metabolomics. New assignment methods such as our SMIRFE algorithm can assign elemental molecular formulas to observed spectral features in a highly untargeted manner without orthogonal information from tandem MS or chromatography. However, for many lipidomics applications, it is necessary to know at least the lipid category or class that is associated with a detected spectral feature to derive a biochemical interpretation. Our goal is to develop a method for robustly classifying elemental molecular formula assignments into lipid categories for an application to SMIRFE-generated assignments. Using a Random Forest machine learning approach, we developed a method that can predict lipid category and class from SMIRFE non-adducted molecular formula assignments. Our methods achieve high average predictive accuracy (>90%) and precision (>83%) across all eight of the lipid categories in the LIPIDMAPS database. Classification performance was evaluated using sets of theoretical, data-derived, and artifactual molecular formulas. Our methods enable the lipid classification of non-adducted molecular formula assignments generated by SMIRFE without orthogonal information, facilitating the biochemical interpretation of untargeted lipidomics experiments. This lipid classification appears insufficient for validating single-spectrum assignments, but could be useful in cross-spectrum assignment validation.

Dataset for dose and time-dependent transcriptional response to ionizing radiation exposure.

Exposure to ionizing radiation associated with highly energetic and charged heavy particles is an inherent risk astronauts face in long duration space missions. We have previously considered the transcriptional effects that three levels of radiation (0.3 Gy, 1.5 Gy, and 3.0 Gy) have at an immediate time point (1 hr) post-exposure [1]. Our analysis of these results suggest effects on transcript levels that could be modulated at lower radiation doses [2]. In addition, a time dependent effect is likely to be present. Therefore, in order to develop a lab-on-a-chip approach for detection of radiation exposure in terms of both radiation level and time since exposure, we developed a time- and dose-course study to determine appropriate sensitive and specific transcript biomarkers that are detectable in blood samples. The data described herein was developed from a study measuring exposure to 0.15 Gy, 0.30 Gy, and 1.5 Gy of radiation at 1 hr, 2 hr, and 6 hr post-exposure using Affymetrix® GeneChip® PrimeView™ microarrays. This report includes raw gene expression data files from the resulting microarray experiments representing typical radiation exposure levels an astronaut may experience as part of a long duration space mission. The data described here is available in NCBI's Gene Expression Omnibus (GEO), accession GSE63952.

Advances in gene ontology utilization improve statistical power of annotation enrichment.

Gene-annotation enrichment is a common method for utilizing ontology-based annotations in gene and gene-product centric knowledgebases. Effective utilization of these annotations requires inferring semantic linkages by tracing paths through edges in the ontological graph, referred to as relations. However, some relations are semantically problematic with respect to scope, necessitating their omission or modification lest erroneous term mappings occur. To address these issues, we created the Gene Ontology Categorization Suite, or GOcats-a novel tool that organizes the Gene Ontology into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. Here, we demonstrate the improvements in annotation enrichment by re-interpreting edges that would otherwise be omitted by traditional ancestor path-tracing methods. Specifically, we show that GOcats' unique handling of relations improves enrichment over conventional methods in the analysis of two different gene-expression datasets: a breast cancer microarray dataset and several horse cartilage development RNAseq datasets. With the breast cancer microarray dataset, we observed significant improvement (one-sided binomial test p-value = 1.86E-25) in 182 of 217 significantly enriched GO terms identified from the conventional path traversal method when GOcats' path traversal was used. We also found new significantly enriched terms using GOcats, whose biological relevancy has been experimentally demonstrated elsewhere. Likewise, on the horse RNAseq datasets, we observed a significant improvement in GO term enrichment when using GOcat's path traversal: one-sided binomial test p-values range from 1.32E-03 to 2.58E-44.

Small Molecule Isotope Resolved Formula Enumeration: A Methodology for Assigning Isotopologues and Metabolite Formulas in Fourier Transform Mass Spectra.

Improvements in Fourier transform mass spectrometry (FT-MS) enable increasingly more complex experiments in the field of metabolomics. What is directly detected in FT-MS spectra are spectral features (peaks) that correspond to sets of adducted and charged forms of specific molecules in the sample. The robust assignment of these features is an essential step for MS-based metabolomics experiments, but the sheer complexity of what is detected and a variety of analytically introduced variance, errors, and artifacts has hindered the systematic analysis of complex patterns of observed peaks with respect to isotope content. We have developed a method called SMIRFE that detects small biomolecules and determines their elemental molecular formula (EMF) using detected sets of isotopologue peaks sharing the same EMF. SMIRFE does not use a database of known metabolite formulas; instead a nearly comprehensive search space of all isotopologues within a mass range is constructed and used for assignment. This search space can be tailored for different isotope labeling patterns expected in different stable isotope tracing experiments. Using consumer-level computing equipment, a large search space of 2000 Da was constructed, and assignment performance was evaluated and validated using verified assignments on a pair of peak lists derived from spectra containing unlabeled and 15N-labeled versions of amino acids derivatized using ethylchloroformate. SMIRFE identified 18 of 18 predicted derivatized EMFs, and each assignment was evaluated statistically and assigned an e-value representing the probability to occur by chance.

Deregulation of a Network of mRNA and miRNA Genes Reveals That CK2 and MEK Inhibitors May Synergize to Induce Apoptosis KRAS-Active NSCLC.

KRAS-activation mutations occur in 25% to 40% of lung adenocarcinomas and are a known mechanism of epidermal growth factor receptor inhibitor (EGFRI) resistance. There are currently no targeted therapies approved specifically for the treatment of KRAS-active non-small cell lung cancers (NSCLC). Attempts to target mutant KRAS have failed in clinical studies leaving no targeted therapy option for these patients. To circumvent targeting KRAS directly, we hypothesized that targeting proteins connected to KRAS function rather than targeting KRAS directly could induce cell death in KRAS-active NSCLC cells. To identify potential targets, we leveraged 2 gene expression data sets derived from NSCLC cell lines either resistant and sensitive to EGFRI treatment. Using a Feasible Solutions Algorithm, we identified genes with deregulated expression in KRAS-active cell lines and used STRING as a source for known protein-protein interactions. This process generated a network of 385 deregulated proteins including KRAS and other known mechanisms of EGFRI resistance. To identify candidate drug targets from the network for further study, we selected proteins with the greatest number of connections within the network and possessed an enzymatic activity that could be inhibited with an existing pharmacological agent. Of the potential candidates, the pharmacological impact of targeting casein kinase 2 (CK2) as a single target was tested, and we found a modest reduction in viability in KRAS-active NSCLC cells. MEK was chosen as a second target from outside the network because it lies downstream of KRAS and MEK inhibition can overcome resistance to CK2 inhibitors. We found that CK2 and MEK inhibition demonstrates moderate synergy in inducing apoptosis in KRAS-active NSCLC cells. These results suggest promise for a combination inhibitor strategy for treating KRAS-active NSCLC.

Preclinical evaluation of novel fatty acid synthase inhibitors in primary colorectal cancer cells and a patient-derived xenograft model of colorectal cancer.

Fatty Acid Synthase (FASN), a key enzyme of de novo lipogenesis, is upregulated in many cancers including colorectal cancer (CRC); increased FASN expression is associated with poor prognosis. Potent FASN inhibitors (TVBs) developed by 3-V Biosciences demonstrate anti-tumor activity in vitro and in vivo and a favorable tolerability profile in a Phase I clinical trial. However, CRC characteristics associated with responsiveness to FASN inhibition are not fully understood. We evaluated the effect of TVB-3664 on tumor growth in nine CRC patient-derived xenografts (PDXs) and investigated molecular and metabolic changes associated with CRC responsiveness to FASN inhibition. CRC cells and PDXs showed a wide range of sensitivity to FASN inhibition. TVB-3664 treatment showed significant response (reduced tumor volume) in 30% of cases. Anti-tumor effect of TVB-3664 was associated with a significant decrease in a pool of adenine nucleotides and alterations in lipid composition including a significant reduction in fatty acids and phospholipids and an increase in lactosylceramide and sphingomyelin in PDXs sensitive to FASN inhibition. Moreover, Akt, Erk1/2 and AMPK were major oncogenic pathways altered by TVBs. In summary, we demonstrated that novel TVB inhibitors show anti-tumor activity in CRC and this activity is associated with a decrease in activation of Akt and Erk1/2 oncogenic pathways and significant alteration of lipid composition of tumors. Further understanding of genetic and metabolic characteristics of tumors susceptible to FASN inhibition may enable patient selection and personalized medicine approaches in CRC.

New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis.

INTRODUCTION: Direct injection Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolite-associated isotopologues. However, spectral artifacts can generate large numbers of spectral features (peaks) that do not correspond to known compounds. Misassignment of these artifactual features creates interpretive errors and limits our ability to discern the role of representative features within living systems. OBJECTIVES: Our goal is to develop rigorous methods that identify and handle spectral artifacts within the context of high-throughput FT-MS-based metabolomics studies. RESULTS: We observed three types of artifacts unique to FT-MS that we named high peak density (HPD) sites: fuzzy sites, ringing and partial ringing. While ringing artifacts are well-known, fuzzy sites and partial ringing have not been previously well-characterized in the literature. We developed new computational methods based on comparisons of peak density within a spectrum to identify regions of spectra with fuzzy sites. We used these methods to identify and eliminate fuzzy site artifacts in an example dataset of paired cancer and non-cancer lung tissue samples and evaluated the impact of these artifacts on classification accuracy and robustness. CONCLUSION: Our methods robustly identified consistent fuzzy site artifacts in our FT-MS metabolomics spectral data. Without artifact identification and removal, 91.4% classification accuracy was achieved on an example lung cancer dataset; however, these classifiers rely heavily on artifactual features present in fuzzy sites. Proper removal of fuzzy site artifacts produces a more robust classifier based on non-artifactual features, with slightly improved accuracy of 92.4% in our example analysis.

Perspectives and expectations in structural bioinformatics of metalloproteins.

Recent papers highlight the presence of large numbers of compressed angles in metal ion coordination geometries for metalloprotein entries in the worldwide Protein Data Bank, due mainly to multidentate coordination. The prevalence of these compressed angles has raised the controversial idea that significantly populated aberrant or even novel coordination geometries may exist. Some of these papers have undergone severe criticism, apparently due to views held that only canonical coordination geometries exist in significant numbers. While criticism of controversial ideas is warranted and to be expected, we believe that a line was crossed where unfair criticism was put forth to discredit an inconvenient result that compressed angles exist in large numbers, which does not support the dogmatic canonical coordination geometry view. We present a review of the major controversial results and their criticisms, pointing out both good suggestions that have been incorporated in new analyses, but also unfair criticism that was put forth to support a particular view. We also suggest that better science is enabled through: (i) a more collegial and collaborative approach in future critical reviews and (ii) the requirement for a description of methods and data including source code and visualizations that enables full reproducibility of results. Proteins 2017; 85:938-944. © 2016 Wiley Periodicals, Inc.

Aberrant coordination geometries discovered in the most abundant metalloproteins.

Metalloproteins bind and utilize metal ions for a variety of biological purposes. Due to the ubiquity of metalloprotein involvement throughout these processes across all domains of life, how proteins coordinate metal ions for different biochemical functions is of great relevance to understanding the implementation of these biological processes. Toward these ends, we have improved our methodology for structurally and functionally characterizing metal binding sites in metalloproteins. Our new ligand detection method is statistically much more robust, producing estimated false positive and false negative rates of ∼0.11% and ∼1.2%, respectively. Additional improvements expand both the range of metal ions and their coordination number that can be effectively analyzed. Also, the inclusion of additional quality control filters has significantly improved structure-function Spearman correlations as demonstrated by rho values greater than 0.90 for several metal coordination analyses and even one rho value above 0.95. Also, improvements in bond-length distributions have revealed bond-length modes specific to chemical functional groups involved in multidentation. Using these improved methods, we analyzed all single metal ion binding sites with Zn, Mg, Ca, Fe, and Na ions in the wwPDB, producing statistically rigorous results supporting the existence of both a significant number of unexpected compressed angles and subsequent aberrant metal ion coordination geometries (CGs) within structurally known metalloproteins. By recognizing these aberrant CGs in our clustering analyses, high correlations are achieved between structural and functional descriptions of metal ion coordination. Moreover, distinct biochemical functions are associated with aberrant CGs versus nonaberrant CGs. Proteins 2017; 85:885-907. © 2016 Wiley Periodicals, Inc.

Transcriptional profile of immediate response to ionizing radiation exposure.

Astronauts participating in long duration space missions are likely to be exposed to ionizing radiation associated with highly energetic and charged heavy particles. Previously proposed gene biomarkers for radiation exposure include phosphorylated H2A Histone Family, Member X (γH2AX), Tumor Protein 53 (TP53), and Cyclin-Dependent Kinase Inhibitor 1A (CDKN1A). However, transcripts of these genes may not be the most suitable biomarkers for radiation exposure due to a lack of sensitivity or specificity. As part of a larger effort to develop lab-on-a-chip methods for detecting radiation exposure events using blood samples, we designed a dose-course microarray study in order to determine coding and non-coding RNA transcripts undergoing differential expression immediately following radiation exposure. The main goal was to elicit a small set of sensitive and specific radiation exposure biomarkers at low, medium, and high levels of ionizing radiation exposure. Four separate levels of radiation were considered: 0 Gray (Gy) control; 0.3 Gy; 1.5 Gy; and 3.0 Gy with four replicates at each radiation level. This report includes raw gene expression data files from the resulting microarray experiments from all three radiation levels ranging from a lower, typical exposure than an astronaut might see (0.3 Gy) to high, potentially lethal, levels of radiation (3.0 Gy). The data described here is available in NCBI's Gene Expression Omnibus (GEO), accession GSE64375.

Genome-Wide Profiling of PARP1 Reveals an Interplay with Gene Regulatory Regions and DNA Methylation.

Poly (ADP-ribose) polymerase-1 (PARP1) is a nuclear enzyme involved in DNA repair, chromatin remodeling and gene expression. PARP1 interactions with chromatin architectural multi-protein complexes (i.e. nucleosomes) alter chromatin structure resulting in changes in gene expression. Chromatin structure impacts gene regulatory processes including transcription, splicing, DNA repair, replication and recombination. It is important to delineate whether PARP1 randomly associates with nucleosomes or is present at specific nucleosome regions throughout the cell genome. We performed genome-wide association studies in breast cancer cell lines to address these questions. Our studies show that PARP1 associates with epigenetic regulatory elements genome-wide, such as active histone marks, CTCF and DNase hypersensitive sites. Additionally, the binding of PARP1 to chromatin genome-wide is mutually exclusive with DNA methylation pattern suggesting a functional interplay between PARP1 and DNA methylation. Indeed, inhibition of PARylation results in genome-wide changes in DNA methylation patterns. Our results suggest that PARP1 controls the fidelity of gene transcription and marks actively transcribed gene regions by selectively binding to transcriptionally active chromatin. These studies provide a platform for developing our understanding of PARP1's role in gene regulation.