Adrenal-permissive HSD3B1 genetic inheritance and risk of estrogen-driven postmenopausal breast cancer.

BACKGROUNDGenetics of estrogen synthesis and breast cancer risk has been elusive. The 1245A→C missense-encoding polymorphism in HSD3B1, which is common in White populations, is functionally adrenal permissive and increases synthesis of the aromatase substrate androstenedione. We hypothesized that homozygous inheritance of the adrenal-permissive HSD3B1(1245C) is associated with postmenopausal estrogen receptor-positive (ER-positive) breast cancer.METHODSA prospective study of postmenopausal ER-driven breast cancer was done for determination of HSD3B1 and circulating steroids. Validation was performed in 2 other cohorts. Adrenal-permissive genotype frequency was compared between postmenopausal ER-positive breast cancer, the general population, and postmenopausal ER-negative breast cancer.RESULTSProspective and validation studies had 157 and 538 patients, respectively, for the primary analysis of genotype frequency by ER status in White female breast cancer patients who were postmenopausal at diagnosis. The adrenal-permissive genotype frequency in postmenopausal White women with estrogen-driven breast cancer in the prospective cohort was 17.5% (21/120) compared with 5.4% (2/37) for ER-negative breast cancer (P = 0.108) and 9.6% (429/4451) in the general population (P = 0.0077). Adrenal-permissive genotype frequency for estrogen-driven postmenopausal breast cancer was validated using Cambridge and The Cancer Genome Atlas data sets: 14.4% (56/389) compared with 6.0% (9/149) for ER-negative breast cancer (P = 0.007) and the general population (P = 0.005). Circulating androstenedione concentration was higher with the adrenal-permissive genotype (P = 0.03).CONCLUSIONAdrenal-permissive genotype is associated with estrogen-driven postmenopausal breast cancer. These findings link genetic inheritance of endogenous estrogen exposure to estrogen-driven breast cancer.FUNDINGNational Cancer Institute, NIH (R01CA236780, R01CA172382, and P30-CA008748); and Prostate Cancer Foundation Challenge Award

An Approach for Protein-Ligand Binding Prediction Based on Docking Studies of Distinct, Low Energy Conformations

One aspect of drug design involves filtering libraries of existing compounds in order to select those that interact strongly with the protein of interest. An active area of drug design research is creating computational filters for this process. This work develops a machine learning approach for constructing a prediction model for the interactions of drugs with Cytochrome P450 2D6 (CYP2D6). This enzyme is responsible for metabolizing 20-25% of all clinically utilized drugs. To construct this model, a set of attributes are created for each drug compound. The novelty of this approach is the use of results from molecular docking simulations as attributes. Furthermore, these compounds are docked into multiple low energy conformations of CYP2D6 that are distinct in 3-dimensional space. The final model performed with a 78% accuracy when predicting whether a given compound is a binder, non-binder, or has a moderate interaction with CYP2D6, which is comparable with current machine learning approaches for this type of problem

Synergistic Use of Compound Properties and Docking Scores in Neural Network Modeling of CYP2D6 Binding: Predicting Affinity and Conformational Sampling

Cytochrome P450 2D6 (CYP2D6) is used to develop an approach for predicting affinity and relevant binding conformation(s) for highly flexible binding sites. The approach combines the use of docking scores and compound properties as attributes in building a neural network (NN) model. It begins by identifying segments of CYP2D6 that are important for binding specificity, based on structural variability among diverse CYP enzymes. A family of distinct, low-energy conformations of CYP2D6 are generated using simulated annealing (SA) and a collection of 82 compounds with known CYP2D6 affinities are docked. Interestingly, docking poses are observed on the backside of the heme as well as in the known actiVe site. Docking scores for the actiVe site binders, along with compound-specific attributes, are used to train a neural network model to properly bin compounds as strong binders, moderate binders, or nonbinders. Attribute selection is used to preselect the most important scores and compound-specific attributes for the model. A prediction accuracy of 85 ( 6 % is achieved. Dominant attributes include docking scores for three of the 20 conformations in the ensemble as well as the compound’s formal charge, number of aromatic rings, and AlogP. Although compound properties were highly predictive attributes (12 % improvement over baseline) in the NN-based prediction of CYP2D6 binders, their combined use with docking score attributes is synergistic (net increase of 23 % above baseline). Beyond prediction of affinity, attribute selection provides a way to identify the most relevant protein conformation(s), in terms of binding competence. In the case of CYP2D6, three out of the ensemble of 20 SA-generated structures are found to be the most predictive for binding

Fecal Microbiome Composition Does Not Predict Diet-Induced TMAO Production in Healthy Adults.

Background Trimethylamine-N-oxide (TMAO) is a small molecule derived from the metabolism of dietary nutrients by gut microbes and contributes to cardiovascular disease. Plasma TMAO increases following consumption of red meat. This metabolic change is thought to be partly because of the expansion of gut microbes able to use nutrients abundant in red meat. Methods and Results We used data from a randomized crossover study to estimate the degree to which TMAO can be estimated from fecal microbial composition. Healthy participants received a series of 3 diets that differed in protein source (red meat, white meat, and non-meat), and fecal, plasma, and urine samples were collected following 4 weeks of exposure to each diet. TMAO was quantitated in plasma and urine, while shotgun metagenomic sequencing was performed on fecal DNA. While the cai gene cluster was weakly correlated with plasma TMAO (rho=0.17, P=0.0007), elastic net models of TMAO were not improved by abundances of bacterial genes known to contribute to TMAO synthesis. A global analysis of all taxonomic groups, genes, and gene families found no meaningful predictors of TMAO. We postulated that abundances of known genes related to TMAO production do not predict bacterial metabolism, and we measured choline- and carnitine-trimethylamine lyase activity during fecal culture. Trimethylamine lyase genes were only weakly correlated with the activity of the enzymes they encode. Conclusions Fecal microbiome composition does not predict systemic TMAO because, in this case, gene copy number does not predict bacterial metabolic activity. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT01427855

Plant U13 orthologues and orphan snoRNAs identified by RNomics of RNA from Arabidopsis nucleoli

Small nucleolar RNAs (snoRNAs) and small Cajal body-specific RNAs (scaRNAs) are non-coding RNAs whose main function in eukaryotes is to guide the modification of nucleotides in ribosomal and spliceosomal small nuclear RNAs, respectively. Full-length sequences of Arabidopsis snoRNAs and scaRNAs have been obtained from cDNA libraries of capped and uncapped small RNAs using RNA from isolated nucleoli from Arabidopsis cell cultures. We have identified 31 novel snoRNA genes (9 box C/D and 22 box H/ACA) and 15 new variants of previously described snoRNAs. Three related capped snoRNAs with a distinct gene organization and structure were identified as orthologues of animal U13snoRNAs. In addition, eight of the novel genes had no complementarity to rRNAs or snRNAs and are therefore putative orphan snoRNAs potentially reflecting wider functions for these RNAs. The nucleolar localization of a number of the snoRNAs and the localization to nuclear bodies of two putative scaRNAs was confirmed by in situ hybridization. The majority of the novel snoRNA genes were found in new gene clusters or as part of previously described clusters. These results expand the repertoire of Arabidopsis snoRNAs to 188 snoRNA genes with 294 gene variants

Animal snoRNAs and scaRNAs with exceptional structures

The overwhelming majority of small nucleolar RNAs (snoRNAs) fall into two clearly defined classes characterized by distinctive secondary structures and sequence motifs. A small group of diverse ncRNAs, however, shares the hallmarks of one or both classes of snoRNAs but differs substantially from the norm in some respects. Here, we compile the available information on these exceptional cases, conduct a thorough homology search throughout the available metazoan genomes, provide improved and expanded alignments, and investigate the evolutionary histories of these ncRNA families as well as their mutual relationships