Understanding VPAC receptor family peptide binding and selectivity

The vasoactive intestinal peptide (VIP) and pituitary adenylate cyclase-activating polypeptide (PACAP) receptors are key regulators of neurological processes. Despite recent structural data, a comprehensive understanding of peptide binding and selectivity among different subfamily receptors is lacking. Here, we determine structures of active, Gs-coupled, VIP-VPAC1R, PACAP27-VPAC1R, and PACAP27-PAC1R complexes. Cryo-EM structural analyses and molecular dynamics simulations (MDSs) reveal fewer stable interactions between VPAC1R and VIP than for PACAP27, more extensive dynamics of VIP interaction with extracellular loop 3, and receptor-dependent differences in interactions of conserved N-terminal peptide residues with the receptor core. MD of VIP modelled into PAC1R predicts more transient VIP-PAC1R interactions in the receptor core, compared to VIP-VPAC1R, which may underlie the selectivity of VIP for VPAC1R over PAC1R. Collectively, our work improves molecular understanding of peptide engagement with the PAC1R and VPAC1R that may benefit the development of novel selective agonists

Comparison of next generation sequencing technologies for transcriptome characterization

<p>Abstract</p> <p>Background</p> <p>We have developed a simulation approach to help determine the optimal mixture of sequencing methods for most complete and cost effective transcriptome sequencing. We compared simulation results for traditional capillary sequencing with "Next Generation" (NG) ultra high-throughput technologies. The simulation model was parameterized using mappings of 130,000 cDNA sequence reads to the <it>Arabidopsis </it>genome (NCBI Accession SRA008180.19). We also generated 454-GS20 sequences and <it>de novo </it>assemblies for the basal eudicot California poppy (<it>Eschscholzia californica</it>) and the magnoliid avocado (<it>Persea americana</it>) using a variety of methods for cDNA synthesis.</p> <p>Results</p> <p>The <it>Arabidopsis </it>reads tagged more than 15,000 genes, including new splice variants and extended UTR regions. Of the total 134,791 reads (13.8 MB), 119,518 (88.7%) mapped exactly to known exons, while 1,117 (0.8%) mapped to introns, 11,524 (8.6%) spanned annotated intron/exon boundaries, and 3,066 (2.3%) extended beyond the end of annotated UTRs. Sequence-based inference of relative gene expression levels correlated significantly with microarray data. As expected, NG sequencing of normalized libraries tagged more genes than non-normalized libraries, although non-normalized libraries yielded more full-length cDNA sequences. The <it>Arabidopsis </it>data were used to simulate additional rounds of NG and traditional EST sequencing, and various combinations of each. Our simulations suggest a combination of FLX and Solexa sequencing for optimal transcriptome coverage at modest cost. We have also developed ESTcalc <url>http://fgp.huck.psu.edu/NG_Sims/ngsim.pl</url>, an online webtool, which allows users to explore the results of this study by specifying individualized costs and sequencing characteristics.</p> <p>Conclusion</p> <p>NG sequencing technologies are a highly flexible set of platforms that can be scaled to suit different project goals. In terms of sequence coverage alone, the NG sequencing is a dramatic advance over capillary-based sequencing, but NG sequencing also presents significant challenges in assembly and sequence accuracy due to short read lengths, method-specific sequencing errors, and the absence of physical clones. These problems may be overcome by hybrid sequencing strategies using a mixture of sequencing methodologies, by new assemblers, and by sequencing more deeply. Sequencing and microarray outcomes from multiple experiments suggest that our simulator will be useful for guiding NG transcriptome sequencing projects in a wide range of organisms.</p

Dynamics of GLP-1R peptide agonist engagement are correlated with kinetics of G protein activation

The glucagon-like peptide-1 receptor (GLP-1R) has broad physiological roles and is a validated target for treatment of metabolic disorders. Despite recent advances in GLP-1R structure elucidation, detailed mechanistic understanding of how different peptides generate profound differences in G protein-mediated signalling is still lacking. Here we combine cryo-electron microscopy, molecular dynamics simulations, receptor mutagenesis and pharmacological assays, to interrogate the mechanism and consequences of GLP-1R binding to four peptide agonists; glucagon-like peptide-1, oxyntomodulin, exendin-4 and exendin-P5. These data reveal that distinctions in peptide N-terminal interactions and dynamics with the GLP-1R transmembrane domain are reciprocally associated with differences in the allosteric coupling to G proteins. In particular, transient interactions with residues at the base of the binding cavity correlate with enhanced kinetics for G protein activation, providing a rationale for differences in G protein-mediated signalling efficacy from distinct agonists

Activation of the GLP-1 receptor by a non-peptidic agonist

Class B G-protein-coupled receptors are major targets for the treatment of chronic diseases, including diabetes and obesity1. Structures of active receptors reveal peptide agonists engage deep within the receptor core, leading to an outward movement of extracellular loop 3 and the tops of transmembrane helices 6 and 7, an inward movement of transmembrane helix 1, reorganization of extracellular loop 2 and outward movement of the intracellular side of transmembrane helix 6, resulting in G-protein interaction and activation2,3,4,5,6. Here we solved the structure of a non-peptide agonist, TT-OAD2, bound to the glucagon-like peptide-1 (GLP-1) receptor. Our structure identified an unpredicted non-peptide agonist-binding pocket in which reorganization of extracellular loop 3 and transmembrane helices 6 and 7 manifests independently of direct ligand interaction within the deep transmembrane domain pocket. TT-OAD2 exhibits biased agonism, and kinetics of G-protein activation and signalling that are distinct from peptide agonists. Within the structure, TT-OAD2 protrudes beyond the receptor core to interact with the lipid or detergent, providing an explanation for the distinct activation kinetics that may contribute to the clinical efficacy of this compound series. This work alters our understanding of the events that drive the activation of class B receptors

Cryo-EM structure of the active, Gs-protein complexed, human CGRP receptor

Calcitonin gene-related peptide (CGRP) is a widely expressed neuropeptide that plays a major role in sensory neurotransmission. The CGRP receptor is a heterodimer of the calcitonin receptor-like receptor (CLR) class B G-protein-coupled receptor and the type 1 transmembrane domain protein, receptor activity modifying protein (RAMP) 1. Herein, we report the 3.3 Å structure of the human CGRP receptor in complex with CGRP and the Gs40 protein heterotrimer determined by Volta phase plate cryo-electron microscopy. The RAMP transmembrane domain sits at the interface between transmembrane domains 3, 4 and 5 of CLR, and stabilises CLR extracellular loop 2. RAMP1 makes only limited direct interaction with CGRP, consistent with allosteric modulation of CLR as its key function. Molecular dynamics simulations indicate that RAMP1 provides stability to the receptor complex, particularly the location of the CLR extracellular domain. The work provides novel insight into the control of G-protein-coupled receptor function

The Molecular Control of Calcitonin Receptor Signaling

The calcitonin receptor (CTR) is a class B G protein-coupled receptor (GPCR) that responds to the peptide hormone calcitonin (CT). CTs are clinically approved for the treatment of bone diseases. We previously reported a 4.1 Å structure of the activated CTR bound to salmon CT (sCT) and heterotrimeric Gs protein by cryo-electron microscopy (Liang, Y.-L., et al. Phase-plate cryo- EM structure of a class B GPCR-G protein complex. Nature 2017, 546, 118–123). In the current study, we have reprocessed the electron micrographs to yield a 3.3 Å map of the complex. This has allowed us to model extracellular loops (ECLs) 2 and 3, and the peptide N-terminus that previously could not be resolved. We have also performed alanine scanning mutagenesis of ECL1 and the upper segment of transmembrane helix 1 (TM1) and its extension into the receptor extracellular domain (TM1 stalk), with effects on peptide binding and function assessed by cAMP accumulation and ERK1/2 phosphorylation. These data were combined with previously published alanine scanning mutagenesis of ECL2 and ECL3 and the new structural information to provide a comprehensive 3D map of the molecular surface of the CTR that controls binding and signaling of distinct CT and related peptides. The work highlights distinctions in how different, related, class B receptors may be activated. The new mutational data on the TM1 stalk and ECL1 have also provided critical insights into the divergent control of cAMP versus pERK signaling and, collectively with previous mutagenesis data, offer evidence that the conformations linked to these different signaling pathways are, in many ways, mutually exclusive. This study furthers our understanding of the complex nature of signaling elicited by GPCRs and, in particular, that of the therapeutically important class B subfamily

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment