Merging transcriptomics and metabolomics - advances in breast cancer profiling

Background Combining gene expression microarrays and high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) of the same tissue samples enables comparison of the transcriptional and metabolic profiles of breast cancer. The aim of this study was to explore the potential of combining these two different types of information. Methods Breast cancer tissue from 46 patients was analyzed by HR MAS MRS followed by gene expression microarrays. Two strategies were used to combine the gene expression and metabolic data; first using multivariate analyses to identify different groups based on gene expression and metabolic data; second correlating levels of specific metabolites to transcripts to suggest new hypotheses of connections between metabolite levels and the underlying biological processes. A parallel study was designed to address experimental issues of combining microarrays and HR MAS MRS. Results In the first strategy, using the microarray data and previously reported molecular classification methods, the majority of samples were classified as luminal A. Three subgroups of luminal A tumors were identified based on hierarchical clustering of the HR MAS MR spectra. The samples in one of the subgroups, designated A2, showed significantly lower glucose and higher alanine levels than the other luminal A samples, suggesting a higher glycolytic activity in these tumors. This group was also enriched for genes annotated with Gene Ontology (GO) terms related to cell cycle and DNA repair. In the second strategy, the correlations between concentrations of myo-inositol, glycine, taurine, glycerophosphocholine, phosphocholine, choline and creatine and all transcripts in the filtered microarray data were investigated. GO-terms related to the extracellular matrix were enriched among the genes that correlated the most to myo-inositol and taurine, while cell cycle related GO-terms were enriched for the genes that correlated the most to choline. Additionally, a subset of transcripts was identified to have slightly altered expression after HR MAS MRS and was therefore removed from all other analyses. Conclusions Combining transcriptional and metabolic data from the same breast carcinoma sample is feasible and may contribute to a more refined subclassification of breast cancers as well as reveal relations between metabolic and transcriptional levels. See Commentary: http://www.biomedcentral.com/1741-7015/8/7

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Pharmacogenetics Meets Metabolomics: Discovery of Tryptophan as a New Endogenous OCT2 Substrate Related to Metformin Disposition

Genetic polymorphisms of the organic cation transporter 2 (OCT2), encoded by SLC22A2, have been investigated in association with metformin disposition. A functional decrease in transport function has been shown to be associated with the OCT2 variants. Using metabolomics, our study aims at a comprehensive monitoring of primary metabolite changes in order to understand biochemical alteration associated with OCT2 polymorphisms and discovery of potential endogenous metabolites related to the genetic variation of OCT2. Using GC-TOF MS based metabolite profiling, clear clustering of samples was observed in Partial Least Square Discriminant Analysis, showing that metabolic profiles were linked to the genetic variants of OCT2. Tryptophan and uridine presented the most significant alteration in SLC22A2-808TT homozygous and the SLC22A2-808G>T heterozygous variants relative to the reference. Particularly tryptophan showed gene-dose effects of transporter activity according to OCT2 genotypes and the greatest linear association with the pharmacokinetic parameters (Clrenal, Clsec, Cl/F/kg, and Vd/F/kg) of metformin. An inhibition assay demonstrated the inhibitory effect of tryptophan on the uptake of 1-methyl-4-phenyl pyrinidium in a concentration dependent manner and subsequent uptake experiment revealed differential tryptophan-uptake rate in the oocytes expressing OCT2 reference and variant (808G>T). Our results collectively indicate tryptophan can serve as one of the endogenous substrate for the OCT2 as well as a biomarker candidate indicating the variability of the transport activity of OCT2

Adult Romantic Attachment, Negative Emotionality, and Depressive Symptoms in Middle Aged Men: A Multivariate Genetic Analysis

Adult romantic attachment styles reflect ways of relating in close relationships and are associated with depression and negative emotionality. We estimated the extent to which dimensions of romantic attachment and negative emotionality share genetic or environmental risk factors in 1,237 middle-aged men in the Vietnam Era Twin Study of Aging (VETSA). A common genetic factor largely explained the covariance between attachment-related anxiety, attachment-related avoidance, depressive symptoms, and two measures of negative emotionality: Stress-Reaction (anxiety), and Alienation. Multivariate results supported genetic and environmental differences in attachment. Attachment-related anxiety and attachment-related avoidance were each influenced by additional genetic factors not shared with other measures; the genetic correlation between the attachment measure-specific genetic factors was 0.41, indicating some, but not complete overlap of genetic factors. Genetically informative longitudinal studies on attachment relationship dimensions can help to illuminate the role of relationship-based risk factors in healthy aging

Epistasis between COMT and MTHFR in Maternal-Fetal Dyads Increases Risk for Preeclampsia

Preeclampsia is a leading cause of perinatal morbidity and mortality. This disorder is thought to be multifactorial in origin, with multiple genes, environmental and social factors, contributing to disease. One proposed mechanism is placental hypoxia-driven imbalances in angiogenic and anti-angiogenic factors, causing endothelial cell dysfunction. Catechol-O-methyltransferase (Comt)-deficient pregnant mice have a preeclampsia phenotype that is reversed by exogenous 2-methoxyestradiol (2-ME), an estrogen metabolite generated by COMT. 2-ME inhibits Hypoxia Inducible Factor 1α, a transcription factor mediating hypoxic responses. COMT has been shown to interact with methylenetetrahydrofolate reductase (MTHFR), which modulates the availability of S-adenosylmethionine (SAM), a COMT cofactor. Variations in MTHFR have been associated with preeclampsia. By accounting for allelic variation in both genes, the role of COMT has been clarified. COMT allelic variation is linked to enzyme activity and four single nucleotide polymorphisms (SNPs) (rs6269, rs4633, rs4680, and rs4818) form haplotypes that characterize COMT activity. We tested for association between COMT haplotypes and the MTHFR 677 C→T polymorphism and preeclampsia risk in 1103 Chilean maternal-fetal dyads. The maternal ACCG COMT haplotype was associated with reduced risk for preeclampsia (P = 0.004), and that risk increased linearly from low to high activity haplotypes (P = 0.003). In fetal samples, we found that the fetal ATCA COMT haplotype and the fetal MTHFR minor “T” allele interact to increase preeclampsia risk (p = 0.022). We found a higher than expected number of patients with preeclampsia with both the fetal risk alleles alone (P = 0.052) and the fetal risk alleles in combination with a maternal balancing allele (P<0.001). This non-random distribution was not observed in controls (P = 0.341 and P = 0.219, respectively). Our findings demonstrate a role for both maternal and fetal COMT in preeclampsia and highlight the importance of including allelic variation in MTHFR

Metabolomic analysis of human disease and its application to the eye

Metabolomics, the analysis of the metabolite profile in body fluids or tissues, is being applied to the analysis of a number of different diseases as well as being used in following responses to therapy. While genomics involves the study of gene expression and proteomics the expression of proteins, metabolomics investigates the consequences of the activity of these genes and proteins. There is good reason to think that metabolomics will find particular utility in the investigation of inflammation, given the multi-layered responses to infection and damage that are seen. This may be particularly relevant to eye disease, which may have tissue specific and systemic components. Metabolomic analysis can inform us about ocular or other body fluids and can therefore provide new information on pathways and processes involved in these responses. In this review, we explore the metabolic consequences of disease, in particular ocular conditions, and why the data may be usefully and uniquely assessed using the multiplexed analysis inherent in the metabolomic approach

The discovery of I-BRD9, a selective cell active chemical probe for bromodomain containing protein 9 inhibition

Acetylation of histone lysine residues is one of the most well-studied post-translational modifications of chromatin, selectively recognized by bromodomain “reader” modules. Inhibitors of the bromodomain and extra terminal domain (BET) family of bromodomains have shown profound anticancer and anti-inflammatory properties, generating much interest in targeting other bromodomain-containing proteins for disease treatment. Herein, we report the discovery of I-BRD9, the first selective cellular chemical probe for bromodomain-containing protein 9 (BRD9). I-BRD9 was identified through structure-based design, leading to greater than 700-fold selectivity over the BET family and 200-fold over the highly homologous bromodomain-containing protein 7 (BRD7). I-BRD9 was used to identify genes regulated by BRD9 in Kasumi-1 cells involved in oncology and immune response pathways and to the best of our knowledge, represents the first selective tool compound available to elucidate the cellular phenotype of BRD9 bromodomain inhibition

Measurement of substructure-dependent jet suppression in Pb+Pb collisions at 5.02 TeV with the ATLAS detector

The ATLAS detector at the Large Hadron Collider has been used to measure jet substructure modification and suppression in Pb+Pb collisions at a nucleon–nucleon center-of-mass energy √sNN = 5.02 TeV in comparison with proton–proton (pp) collisions at √s = 5.02 TeV. The Pb+Pb data, collected in 2018, have an integrated luminosity of 1.72 nb−1, while the ppdata, collected in 2017, have an integrated luminosity of 260 pb−1. Jets used in this analysis are clustered using the anti-kt algorithm with a radius parameter R = 0.4. The jet constituents, defined by both tracking and calorimeter information, are used to determine the angular scale rg of the first hard splitting inside the jet by reclustering them using the Cambridge–Aachen algorithm and employing the soft-drop grooming technique. The nuclear modification factor, RAA, used to characterize jet suppression in Pb+Pb collisions, is presented differentially in rg, jet transverse momentum, and in intervals of collision centrality. The RAA value is observed to depend significantly on jet rg. Jets produced with the largest measured rg are found to be twice as suppressed as those with the smallest rg in central Pb+Pb collisions. The RAA values do not exhibit a strong variation with jet pT in any of the rg intervals. The rg and pT dependence of jet RAA is qualitatively consistent with a picture of jet quenching arising from coherence and provides the most direct evidence in support of this approach

Anomaly detection search for new resonances decaying into a Higgs boson and a generic new particle X in hadronic final states using Formula Presented pp collisions with the ATLAS detector

A search is presented for a heavy resonance Formula Presented decaying into a Standard Model Higgs boson Formula Presented and a new particle Formula Presented in a fully hadronic final state. The full Large Hadron Collider run 2 dataset of proton-proton collisions at Formula Presented collected by the ATLAS detector from 2015 to 2018 is used and corresponds to an integrated luminosity of Formula Presented. The search targets the high Formula Presented-mass region, where the Formula Presented and Formula Presented have a significant Lorentz boost in the laboratory frame. A novel application of anomaly detection is used to define a general signal region, where events are selected solely because of their incompatibility with a learned background-only model. It is constructed using a jet-level tagger for signal-model-independent selection of the boosted Formula Presented particle, representing the first application of fully unsupervised machine learning to an ATLAS analysis. Two additional signal regions are implemented to target a benchmark Formula Presented decay into two quarks, covering topologies where the Formula Presented is reconstructed as either a single large-radius jet or two small-radius jets. The analysis selects Higgs boson decays into Formula Presented, and a dedicated neural-network-based tagger provides sensitivity to the boosted heavy-flavor topology. No significant excess of data over the expected background is observed, and the results are presented as upper limits on the production cross section Formula Presented) for signals with Formula Presented between 1.5 and 6 TeV and Formula Presented between 65 and 3000 GeV. A search is presented for a heavy resonance Y decaying into a Standard Model Higgs boson H and a new particle X in a fully hadronic final state. The full Large Hadron Collider run 2 dataset of proton-proton collisions at √ s = 13     TeV collected by the ATLAS detector from 2015 to 2018 is used and corresponds to an integrated luminosity of 139     fb − 1 . The search targets the high Y -mass region, where the H and X have a significant Lorentz boost in the laboratory frame. A novel application of anomaly detection is used to define a general signal region, where events are selected solely because of their incompatibility with a learned background-only model. It is constructed using a jet-level tagger for signal-model-independent selection of the boosted X particle, representing the first application of fully unsupervised machine learning to an ATLAS analysis. Two additional signal regions are implemented to target a benchmark X decay into two quarks, covering topologies where the X is reconstructed as either a single large-radius jet or two small-radius jets. The analysis selects Higgs boson decays into b ¯ b , and a dedicated neural-network-based tagger provides sensitivity to the boosted heavy-flavor topology. No significant excess of data over the expected background is observed, and the results are presented as upper limits on the production cross section σ ( p p → Y → X H → q ¯ q b ¯ b ) for signals with m Y between 1.5 and 6 TeV and m X between 65 and 3000 GeV