Notice NOT-OD-18-152 from the NIH Guide for Grants and Contracts

Funding Opportunity RFA-NS-18-020 from the NIH Guide for Grants and Contracts. Understanding the dynamic activity of neural circuits is central to the NIH BRAIN Initiative. This FOA seeks applications for proof-of-concept testing and development of new technologies and novel approaches for largescale recording and manipulation of neural activity to enable transformative understanding of dynamic signaling in the nervous system. In particular, we seek exceptionally creative approaches to address major challenges associated with recording and manipulating neural activity, at or near cellular resolution, at multiple spatial and/or temporal scales, in any region and throughout the entire depth of the brain. It is expected that the proposed research may be high-risk, but if successful could profoundly change the course of neuroscience research. Proposed technologies should be compatible with experiments in behaving animals, and should include advancements that enable or reduce major barriers to hypothesis-driven experiments. Technologies may engage diverse types of signaling beyond neuronal electrical activity for large-scale analysis, and may utilize any modality such as optical, electrical, magnetic, acoustic or genetic recording/manipulation. Applications that seek to integrate multiple approaches are encouraged. Where appropriate, applications are expected to integrate appropriate domains of expertise, including biological, chemical and physical sciences, engineering, computational modeling and statistical analysis.

Funding Opportunity RFA-NS-18-019 from the NIH Guide for Grants and Contracts. Although invention and proof-of-concept testing of new technologies are a key component of the BRAIN Initiative, to achieve their potential these technologies must also be optimized through feedback from end-users in the context of the intended experimental use. This seeks applications for the optimization of existing and emerging technologies and approaches that have potential to address major challenges associated with recording and manipulating neural activity, at or near cellular resolution, at multiple spatial and temporal scales, in any region and throughout the entire depth of the brain. This FOA is intended for the iterative refinement of emergent technologies and approaches that have already demonstrated their transformative potential through initial proof-of-concept testing, and are appropriate for accelerated development of hardware and software while scaling manufacturing techniques towards sustainable, broad dissemination and user-friendly incorporation into regular neuroscience practice. Proposed technologies should be compatible with experiments in behaving animals, and should include advancements that enable or reduce major barriers to hypothesis-driven experiments. Technologies may engage diverse types of signaling beyond neuronal electrical activity for large-scale analysis, and may utilize any modality such as optical, electrical, magnetic, acoustic or genetic recording/manipulation. Applications that seek to integrate multiple approaches are encouraged. Applications are expected to integrate appropriate domains of expertise, including where appropriate biological, chemical and physical sciences, engineering, computational modeling and statistical analysis.

Funding Opportunity RFA-DA-18-022 from the NIH Guide for Grants and Contracts. This FOA supports highly innovative R01 applications on HIV/AIDS and drug abuse and complements the Avant-Garde Award Program for HIV/AIDS and Drug Use Research and the Avenir Award Program for Research on Substance Abuse and HIV/AIDS. The Avant-Garde award supports individuals who conduct high-risk, high-reward research and does not require a detailed research plan. The Avenir award is similar to the Avant-Garde award but focuses on support for early stage investigators. Applications submitted under this FOA are required to have a detailed research plan and preliminary data. This FOA focuses on innovative research projects that have the potential to open new areas of HIV/AIDS research and/or lead to new avenues for prevention and treatment of HIV/AIDS among substance abusers. The nexus with drug abuse should be clearly described. This FOA is open to both individual researchers and research teams and is not limited to any one area of research on HIV and substance use, but all studies must focus on NIH HIV/AIDS Research Priorities https://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-137.html.

Funding Opportunity PAR-18-714 from the NIH Guide for Grants and Contracts. The purpose of this Academic Research Enhancement Award (AREA) for Undergraduate-Focused Institutions is to support small scale research grants at institutions that do not receive substantial funding from the NIH, with an emphasis on providing biomedical research experiences primarily for undergraduate students, and enhancing the research environment at these applicant institutions. Eligible institutions must award baccalaureate science degrees, and have received less than 6 million dollars per year of NIH support (total costs) in 4 of the last 7 fiscal years.

Notice NOT-AT-18-010 from the NIH Guide for Grants and Contracts

Notice NOT-AT-18-011 from the NIH Guide for Grants and Contracts

39 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2018/03/14

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

12 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("drug-induced" OR "drug-related") AND ("adverse events" OR "side effects" OR "side-effects")

These pubmed results were generated on 2018/03/14

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

A novel computational approach for drug repurposing using systems biology.

Bioinformatics. 2018 Mar 09;:

Authors: Peyvandipour A, Saberian N, Shafi A, Donato M, Draghici S

Abstract
Motivation: Identification of novel therapeutic effects for existing U.S. Food and Drug Administration (FDA)-approved drugs, drug repurposing, is an approach aimed to dramatically shorten the drug discovery process, which is costly, slow and risky. Several computational approaches use transcriptional data to find potential repurposing candidates. The main hypothesis of such approaches is that if gene expression signature of a particular drug is opposite to the gene expression signature of a disease, that drug may have a potential therapeutic effect on the disease. However, this may not be optimal since it fails to consider the different roles of genes and their dependencies at the system level.
Results: We propose a systems biology approach to discover novel therapeutic roles for established drugs that addresses some of the issues in the current approaches. To do so, we use publicly available drug and disease data to build a drug-disease network (DDN) by considering all interactions between drug targets and disease-related genes in the context of all known signaling pathways. This network is integrated with gene-expression measurements to identify drugs with new desired therapeutic effects based on a system-level analysis method. We compare the proposed approach with the drug repurposing approach proposed by Sirota et al. on four human diseases: idiopathic pulmonary fibrosis (IPF), non-small cell lung cancer (NSCLC), prostate cancer, and breast cancer. We evaluate the proposed approach based on its ability to re-discover drugs that are already FDA-approved for a given disease.
Availability: The R package DrugDiseaseNet is under review for publication in Bioconductor and is available at https://github.com/azampvd/DrugDiseaseNet.
Contact: sorin@wayne.edu.
Supplementary information: Supplementary data are available at Bioinformatics online.

PMID: 29534151 [PubMed - as supplied by publisher]

POAP: A GNU parallel based multithreaded pipeline of open babel and AutoDock suite for boosted high throughput virtual screening.

Comput Biol Chem. 2018 Mar 01;74:39-48

Authors: Samdani A, Vetrivel U

Abstract
High throughput virtual screening plays a crucial role in hit identification during the drug discovery process. With the rapid increase in the chemical libraries, virtual screening process becomes computationally challenging, thereby posing a demand for efficiently parallelized software pipelines. Here we present a GNU Parallel based pipeline-POAP that is programmed to run Open Babel and AutoDock suite under highly optimized parallelization. The ligand preparation module is a unique feature in POAP, as it offers extensive options for geometry optimization, conformer generation, parallelization and also quarantines erroneous datasets for seamless operation. POAP also features multi receptor docking that can be utilized for comparative virtual screening and drug repurposing studies. As demonstrated using different structural datasets, POAP proves to be an efficient pipeline that enables high scalability, seamless operability, dynamic file handling and optimal utilization of CPU's for computationally demanding tasks. POAP is distributed freely under GNU GPL license and can be downloaded at https://github.com/inpacdb/POAP.

PMID: 29533817 [PubMed - as supplied by publisher]

Ketohexokinase C blockade ameliorates fructose-induced metabolic dysfunction in fructose-sensitive mice.

J Clin Invest. 2018 Mar 13;:

Authors: Lanaspa MA, Andres-Hernando A, Orlicky DJ, Cicerchi C, Jang C, Li N, Milagres T, Kuwabara M, Wempe MF, Rabinowitz JD, Johnson RJ, Tolan DR

Abstract
Increasing evidence suggests a role for excessive intake of fructose in the Western diet as a contributor to the current epidemics of metabolic syndrome and obesity. Hereditary fructose intolerance (HFI) is a difficult and potentially lethal orphan disease associated with impaired fructose metabolism. In HFI, the deficiency of a particular aldolase, aldolase B, results in the accumulation of intracellular phosphorylated fructose thus leading to phosphate sequestration and depletion, increased ATP turnover and a plethora of conditions leading to clinical manifestations including fatty liver, hyperuricemia, Fanconi syndrome and severe hypoglycemia. Unfortunately, to date, there is no treatment for HFI and avoiding sugar and fructose in our society has become quite challenging. In this report, through use of genetically modified mice and pharmacological inhibitors, we demonstrate that the absence or inhibition of ketohexokinase (Khk), an enzyme upstream of aldolase B, is sufficient to prevent hypoglycemia and liver and intestinal injury associated with HFI using aldolase B knockout mice. We thus provide evidence for the first time of a potential therapeutic approach for this condition. Mechanistically, our studies suggest that it is the inhibition of the Khk C isoform, not the A isoform, that protects animals from HFI.

PMID: 29533924 [PubMed - as supplied by publisher]

Related Articles

Man With a Bump on the Chest.

Ann Emerg Med. 2016 Aug;68(2):e53-4

Authors: Mohebbi MR, Bausano BJ, Vollstedt KA

PMID: 27451309 [PubMed - indexed for MEDLINE]

The use of the term "radiosensitivity" through history of radiation: from clarity to confusion.

Int J Radiat Biol. 2018 Mar 13;:1-31

Authors: Britel M, Bourguignon M, Foray N

Abstract
PURPOSES: The term "radiosensitivity" appeared for the first time at the beginning of the 20th century, few years after the discovery of X-rays. Initially used by French and German radiologists, it illustrated the risk of radiation-induced (RI) skin reactions. From the 1950's, "radiosensitivity" was progressively found to describe other features of RI response such as RI cancers or cataracts. To date, such confusion may raise legal issues and complexify the message addressed to general public. Here, through an historical review, we aimed to better understand how this confusion appeared.
METHODS: To support our historical review, a quantitative and qualitative wording analysis of the "radiosensitivity" occurrences and its derived terms was performed with Google books, Pubmed, Web of Science™ databases and in all the ICRP publications.
CONCLUSIONS: While "radiosensitivity" was historically related to RI adverse tissue events attributable to cell death, the first efforts to quantify the RI risk specific to each organ/tissue revealed some different semantic fields that are not necessarily compatible together (e.g. adverse tissue events for skin, cataracts for eyes, RI cancer for breast or thyroid). To avoid such confusion, we propose to keep the historical definition of "radiosensitivity" to any clinical and cellular consequences of radiation attributable to cell death and to introduce the term "radiosusceptibility" to describe the RI cancers or any feature that is attributable to cell transformation.

PMID: 29533136 [PubMed - as supplied by publisher]

Related Articles

Disease Compass- a navigation system for disease knowledge based on ontology and linked data techniques.

J Biomed Semantics. 2017 Jun 19;8(1):22

Authors: Kozaki K, Yamagata Y, Mizoguchi R, Imai T, Ohe K

Abstract
BACKGROUND: Medical ontologies are expected to contribute to the effective use of medical information resources that store considerable amount of data. In this study, we focused on disease ontology because the complicated mechanisms of diseases are related to concepts across various medical domains. The authors developed a River Flow Model (RFM) of diseases, which captures diseases as the causal chains of abnormal states. It represents causes of diseases, disease progression, and downstream consequences of diseases, which is compliant with the intuition of medical experts. In this paper, we discuss a fact repository for causal chains of disease based on the disease ontology. It could be a valuable knowledge base for advanced medical information systems.
METHODS: We developed the fact repository for causal chains of diseases based on our disease ontology and abnormality ontology. This section summarizes these two ontologies. It is developed as linked data so that information scientists can access it using SPARQL queries through an Resource Description Framework (RDF) model for causal chain of diseases.
RESULTS: We designed the RDF model as an implementation of the RFM for the fact repository based on the ontological definitions of the RFM. 1554 diseases and 7080 abnormal states in six major clinical areas, which are extracted from the disease ontology, are published as linked data (RDF) with SPARQL endpoint (accessible API). Furthermore, the authors developed Disease Compass, a navigation system for disease knowledge. Disease Compass can browse the causal chains of a disease and obtain related information, including abnormal states, through two web services that provide general information from linked data, such as DBpedia, and 3D anatomical images.
CONCLUSIONS: Disease Compass can provide a complete picture of disease-associated processes in such a way that fits with a clinician's understanding of diseases. Therefore, it supports user exploration of disease knowledge with access to pertinent information from a variety of sources.

PMID: 28629436 [PubMed - indexed for MEDLINE]

Related Articles

Learning from biomedical linked data to suggest valid pharmacogenes.

J Biomed Semantics. 2017 Apr 20;8(1):16

Authors: Dalleau K, Marzougui Y, Da Silva S, Ringot P, Ndiaye NC, Coulet A

Abstract
BACKGROUND: A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, only molecular networks or the biomedical literature were used, whereas many other resources are available.
METHOD: We propose here to consume a diverse and larger set of resources using linked data related either to genes, drugs or diseases. One of the advantages of linked data is that they are built on a standard framework that facilitates the joint use of various sources, and thus facilitates considering features of various origins. We propose a selection and linkage of data sources relevant to pharmacogenomics, including for example DisGeNET and Clinvar. We use machine learning to identify and prioritize pharmacogenes that are the most probably valid, considering the selected linked data. This identification relies on the classification of gene-drug pairs as either pharmacogenomically associated or not and was experimented with two machine learning methods -random forest and graph kernel-, which results are compared in this article.
RESULTS: We assembled a set of linked data relative to pharmacogenomics, of 2,610,793 triples, coming from six distinct resources. Learning from these data, random forest enables identifying valid pharmacogenes with a F-measure of 0.73, on a 10 folds cross-validation, whereas graph kernel achieves a F-measure of 0.81. A list of top candidates proposed by both approaches is provided and their obtention is discussed.

PMID: 28427468 [PubMed - indexed for MEDLINE]

Intensive blood pressure lowering reduces adverse cardiovascular outcomes among patients with high-normal glucose: An analysis from the Systolic Blood Pressure Intervention Trial database.

J Clin Hypertens (Greenwich). 2018 Mar 13;:

Authors: Gong Y, Smith SM, Handberg EM, Pepine CJ, Cooper-DeHoff RM

Abstract
The objective of this analysis is to determine the effect of intensive (<120 mm Hg) versus standard (<140 mm Hg) systolic blood pressure (SBP) targets on cardiovascular (CV) outcomes among SPRINT participants with low-normal or high-normal fasting glucose (FG). We categorized the 5425 SPRINT participants with FG <100 mg/dL into 2 groups: <85 mg/dL (low-normal) and 85 to <100 mg/dL (high-normal). Among participants with low-normal glucose, there was no significant difference in the primary outcome (PO) between the 2 treatment arms (adjusted hazard ratio, HR: 1.27 (95% confidence interval [CI] 0.68-2.37, P = .46). However, the intensive SBP target was associated with 27% lower risk for the PO compared with the standard SBP target in those with high-normal glucose (HR 0.73, 0.57-0.93, P = .01). Our results indicate that hypertensive patients with high-normal FG may benefit from intensive SBP lowering, whereas benefits were inconclusive among those with low-normal FG.

PMID: 29532983 [PubMed - as supplied by publisher]

GRK2 moderates the acute mitochondrial damage to ionizing radiation exposure by promoting mitochondrial fission/fusion.

Cell Death Discov. 2018 Dec;4:25

Authors: Franco A, Sorriento D, Gambardella J, Pacelli R, Prevete N, Procaccini C, Matarese G, Trimarco B, Iaccarino G, Ciccarelli M

Abstract
The modern understanding of the G protein-coupled receptor kinase 2 has grown towards the definition of a stress protein, for its ability to rapidly compartmentalize within the cell in response to acute stimulation. Also, mitochondria can be regulated by GRK2 localization. We show that Ionizing Radiation (IR) exposure acutely damages mitochondria regarding mass, morphology, and respiration, with recovery in a framework of hours. This phenomenon is actively regulated by GRK2, whose overexpression results to be protective, and reciprocally, deletion accelerates degenerative processes. The regulatory effects of the kinase involve a new interactome that includes binding HSP90 and binding and phosphorylation of the key molecules involved in the process of mitochondrial fusion and recovery: MFN-1 and 2.

PMID: 29531822 [PubMed]

Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.

Nat Genet. 2018 Mar 12;:

Authors: Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, Rutten-Jacobs L, Giese AK, van der Laan SW, Gretarsdottir S, Anderson CD, Chong M, Adams HHH, Ago T, Almgren P, Amouyel P, Ay H, Bartz TM, Benavente OR, Bevan S, Boncoraglio GB, Brown RD, Butterworth AS, Carrera C, Carty CL, Chasman DI, Chen WM, Cole JW, Correa A, Cotlarciuc I, Cruchaga C, Danesh J, de Bakker PIW, DeStefano AL, den Hoed M, Duan Q, Engelter ST, Falcone GJ, Gottesman RF, Grewal RP, Gudnason V, Gustafsson S, Haessler J, Harris TB, Hassan A, Havulinna AS, Heckbert SR, Holliday EG, Howard G, Hsu FC, Hyacinth HI, Ikram MA, Ingelsson E, Irvin MR, Jian X, Jiménez-Conde J, Johnson JA, Jukema JW, Kanai M, Keene KL, Kissela BM, Kleindorfer DO, Kooperberg C, Kubo M, Lange LA, Langefeld CD, Langenberg C, Launer LJ, Lee JM, Lemmens R, Leys D, Lewis CM, Lin WY, Lindgren AG, Lorentzen E, Magnusson PK, Maguire J, Manichaikul A, McArdle PF, Meschia JF, Mitchell BD, Mosley TH, Nalls MA, Ninomiya T, O'Donnell MJ, Psaty BM, Pulit SL, Rannikmäe K, Reiner AP, Rexrode KM, Rice K, Rich SS, Ridker PM, Rost NS, Rothwell PM, Rotter JI, Rundek T, Sacco RL, Sakaue S, Sale MM, Salomaa V, Sapkota BR, Schmidt R, Schmidt CO, Schminke U, Sharma P, Slowik A, Sudlow CLM, Tanislav C, Tatlisumak T, Taylor KD, Thijs VNS, Thorleifsson G, Thorsteinsdottir U, Tiedt S, Trompet S, Tzourio C, van Duijn CM, Walters M, Wareham NJ, Wassertheil-Smoller S, Wilson JG, Wiggins KL, Yang Q, Yusuf S, Bis JC, Pastinen T, Ruusalepp A, Schadt EE, Koplev S, Björkegren JLM, Codoni V, Civelek M, Smith NL, Trégouët DA, Christophersen IE, Roselli C, Lubitz SA, Ellinor PT, Tai ES, Kooner JS, Kato N, He J, van der Harst P, Elliott P, Chambers JC, Takeuchi F, Johnson AD, Sanghera DK, Melander O, Jern C, Strbian D, Fernandez-Cadenas I, Longstreth WT, Rolfs A, Hata J, Woo D, Rosand J, Pare G, Hopewell JC, Saleheen D, Stefansson K, Worrall BB, Kittner SJ, Seshadri S, Fornage M, Markus HS, Howson JMM, Kamatani Y, Debette S, Dichgans M, Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, Rutten-Jacobs L, Giese AK, van der Laan SW, Gretarsdottir S, Anderson CD, Chong M, Adams HHH, Ago T, Almgren P, Amouyel P, Ay H, Bartz TM, Benavente OR, Bevan S, Boncoraglio GB, Brown RD, Butterworth AS, Carrera C, Carty CL, Chasman DI, Chen WM, Cole JW, Correa A, Cotlarciuc I, Cruchaga C, Danesh J, de Bakker PIW, DeStefano AL, Hoed MD, Duan Q, Engelter ST, Falcone GJ, Gottesman RF, Grewal RP, Gudnason V, Gustafsson S, Haessler J, Harris TB, Hassan A, Havulinna AS, Heckbert SR, Holliday EG, Howard G, Hsu FC, Hyacinth HI, Ikram MA, Ingelsson E, Irvin MR, Jian X, Jiménez-Conde J, Johnson JA, Jukema JW, Kanai M, Keene KL, Kissela BM, Kleindorfer DO, Kooperberg C, Kubo M, Lange LA, Langefeld CD, Langenberg C, Launer LJ, Lee JM, Lemmens R, Leys D, Lewis CM, Lin WY, Lindgren AG, Lorentzen E, Magnusson PK, Maguire J, Manichaikul A, McArdle PF, Meschia JF, Mitchell BD, Mosley TH, Nalls MA, Ninomiya T, O'Donnell MJ, Psaty BM, Pulit SL, Rannikmäe K, Reiner AP, Rexrode KM, Rice K, Rich SS, Ridker PM, Rost NS, Rothwell PM, Rotter JI, Rundek T, Sacco RL, Sakaue S, Sale MM, Salomaa V, Sapkota BR, Schmidt R, Schmidt CO, Schminke U, Sharma P, Slowik A, Sudlow CLM, Tanislav C, Tatlisumak T, Taylor KD, Thijs VNS, Thorleifsson G, Thorsteinsdottir U, Tiedt S, Trompet S, Tzourio C, van Duijn CM, Walters M, Wareham NJ, Wassertheil-Smoller S, Wilson JG, Wiggins KL, Yang Q, Yusuf S, Amin N, Aparicio HS, Arnett DK, Attia J, Beiser AS, Berr C, Buring JE, Bustamante M, Caso V, Cheng YC, Choi SH, Chowhan A, Cullell N, Dartigues JF, Delavaran H, Delgado P, Dörr M, Engström G, Ford I, Gurpreet WS, Hamsten A, Heitsch L, Hozawa A, Ibanez L, Ilinca A, Ingelsson M, Iwasaki M, Jackson RD, Jood K, Jousilahti P, Kaffashian S, Kalra L, Kamouchi M, Kitazono T, Kjartansson O, Kloss M, Koudstaal PJ, Krupinski J, Labovitz DL, Laurie CC, Levi CR, Li L, Lind L, Lindgren CM, Lioutas V, Liu YM, Lopez OL, Makoto H, Martinez-Majander N, Matsuda K, Minegishi N, Montaner J, Morris AP, Muiño E, Müller-Nurasyid M, Norrving B, Ogishima S, Parati EA, Peddareddygari LR, Pedersen NL, Pera J, Perola M, Pezzini A, Pileggi S, Rabionet R, Riba-Llena I, Ribasés M, Romero JR, Roquer J, Rudd AG, Sarin AP, Sarju R, Sarnowski C, Sasaki M, Satizabal CL, Satoh M, Sattar N, Sawada N, Sibolt G, Sigurdsson Á, Smith A, Sobue K, Soriano-Tárraga C, Stanne T, Stine OC, Stott DJ, Strauch K, Takai T, Tanaka H, Tanno K, Teumer A, Tomppo L, Torres-Aguila NP, Touze E, Tsugane S, Uitterlinden AG, Valdimarsson EM, van der Lee SJ, Völzke H, Wakai K, Weir D, Williams SR, Wolfe CDA, Wong Q, Xu H, Yamaji T, Sanghera DK, Melander O, Jern C, Strbian D, Fernandez-Cadenas I, Longstreth WT, Rolfs A, Hata J, Woo D, Rosand J, Pare G, Hopewell JC, Saleheen D, Stefansson K, Worrall BB, Kittner SJ, Seshadri S, Fornage M, Markus HS, Howson JMM, Kamatani Y, Debette S, Dichgans M, AFGen Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, International Genomics of Blood Pressure (iGEN-BP) Consortium, INVENT Consortium, STARNET, BioBank Japan Cooperative Hospital Group, COMPASS Consortium, EPIC-CVD Consortium, EPIC-InterAct Consortium, International Stroke Genetics Consortium (ISGC), METASTROKE Consortium, Neurology Working Group of the CHARGE Consortium, NINDS Stroke Genetics Network (SiGN), UK Young Lacunar DNA Study, MEGASTROKE Consortium, MEGASTROKE Consortium:

Abstract
Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454,450 controls) and discovered 22 new stroke risk loci, bringing the total to 32. We further found shared genetic variation with related vascular traits, including blood pressure, cardiac traits, and venous thromboembolism, at individual loci (n = 18), and using genetic risk scores and linkage-disequilibrium-score regression. Several loci exhibited distinct association and pleiotropy patterns for etiological stroke subtypes. Eleven new susceptibility loci indicate mechanisms not previously implicated in stroke pathophysiology, with prioritization of risk variants and genes accomplished through bioinformatics analyses using extensive functional datasets. Stroke risk loci were significantly enriched in drug targets for antithrombotic therapy.

PMID: 29531354 [PubMed - as supplied by publisher]

Structural patterns of the human ABCC4/MRP4 exporter in lipid bilayers rationalize clinically observed polymorphisms.

Pharmacol Res. 2018 Mar 09;:

Authors: Chantemargue B, Di Meo F, Berka K, Picard N, Arnion H, Essig M, Marquet P, Otyepka M, Trouillas P

Abstract
The ABCC4/MRP4 exporter has a clinical impact on membrane transport of a broad range of xenobiotics. It is expressed at key locations for drug disposition or effects such as in the liver, the kidney and blood cells. Several polymorphisms and mutations (e.g., p.Gly187Trp) leading to MRP4 dysfunction are associated with an increased risk of toxicity of some drugs. So far, no human MRP4 structure has been elucidated, precluding rationalization of these dysfunctions at a molecular level. We constructed atomistic model of the wild type (WT) MRP4 and the p.Gly187Trp mutant embedded in different lipid bilayers and relaxed them for hundreds of nanoseconds by molecular dynamics simulations. The WT MRP4 molecular structure confirmed and ameliorated the general knowledge about the transmembrane helices and the two nucleotide binding domains. Moreover, our model elucidated positions of three generally unresolved domains: L1 (linker between the two halves of the exporter); L0 (N-terminal domain); and the zipper helices (between the two NBDs). Each domain was thoroughly described in view of its function. The p.Gly187Trp mutation induced a huge structural impact on MRP4, mainly affecting NBD 1 structure and flexibility. The structure of transporter enabled rationalization of known dysfunctions associated with polymorphism of MRP4. This model is available to the pharmacology community to decipher the impact of any other clinically observed polymorphism and mutation on drug transport, giving rise to in silico predictive pharmacogenetics.

PMID: 29530601 [PubMed - as supplied by publisher]