University of Dundee

UNLABELLED: Jalview Version 2 is a system for interactive WYSIWYG editing, analysis and annotation of multiple sequence alignments. Core features include keyboard and mouse-based editing, multiple views and alignment overviews, and linked structure display with Jmol. Jalview 2 is available in two forms: a lightweight Java applet for use in web applications, and a powerful desktop application that employs web services for sequence alignment, secondary structure prediction and the retrieval of alignments, sequences, annotation and structures from public databases and any DAS 1.53 compliant sequence or annotation server. AVAILABILITY: The Jalview 2 Desktop application and JalviewLite applet are made freely available under the GPL, and can be downloaded from www.jalview.org.

Abstract Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.

The fly Drosophila melanogaster is one of the most intensively studied organisms in biology and serves as a model system for the investigation of many developmental and cellular processes common to higher eukaryotes, including humans. We have determined the nucleotide sequence of nearly all of the approximately 120-megabase euchromatic portion of the Drosophila genome using a whole-genome shotgun sequencing strategy supported by extensive clone-based sequence and a high-quality bacterial artificial chromosome physical map. Efforts are under way to close the remaining gaps; however, the sequence is of sufficient accuracy and contiguity to be declared substantially complete and to support an initial analysis of genome structure and preliminary gene annotation and interpretation. The genome encodes approximately 13,600 genes, somewhat fewer than the smaller Caenorhabditis elegans genome, but with comparable functional diversity.

AUTORES: Daniel J Klionsky1745,1749*, Kotb Abdelmohsen840, Akihisa Abe1237, Md Joynal Abedin1762, Hagai Abeliovich425,
\nAbraham Acevedo Arozena789, Hiroaki Adachi1800, Christopher M Adams1669, Peter D Adams57, Khosrow Adeli1981,
\nPeter J Adhihetty1625, Sharon G Adler700, Galila Agam67, Rajesh Agarwal1587, Manish K Aghi1537, Maria Agnello1826,
\nPatrizia Agostinis664, Patricia V Aguilar1960, Julio Aguirre-Ghiso784,786, Edoardo M Airoldi89,422, Slimane Ait-Si-Ali1376,
\nTakahiko Akematsu2010, Emmanuel T Akporiaye1097, Mohamed Al-Rubeai1394, Guillermo M Albaiceta1294,
\nChris Albanese363, Diego Albani561, Matthew L Albert517, Jesus Aldudo128, Hana Alg€ul1164, Mehrdad Alirezaei1198,
\nIraide Alloza642,888, Alexandru Almasan206, Maylin Almonte-Beceril524, Emad S Alnemri1212, Covadonga Alonso544,
\nNihal Altan-Bonnet848, Dario C Altieri1205, Silvia Alvarez1497, Lydia Alvarez-Erviti1395, Sandro Alves107,
\nGiuseppina Amadoro860, Atsuo Amano930, Consuelo Amantini1554, Santiago Ambrosio1458, Ivano Amelio756,
\nAmal O Amer918, Mohamed Amessou2089, Angelika Amon726, Zhenyi An1538, Frank A Anania291, Stig U Andersen6,
\nUsha P Andley2079, Catherine K Andreadi1690, Nathalie Andrieu-Abadie502, Alberto Anel2027, David K Ann58,
\nShailendra Anoopkumar-Dukie388, Manuela Antonioli832,858, Hiroshi Aoki1791, Nadezda Apostolova2007,
\nSaveria Aquila1500, Katia Aquilano1876, Koichi Araki292, Eli Arama2098, Agustin Aranda456, Jun Araya591,
\nAlexandre Arcaro1472, Esperanza Arias26, Hirokazu Arimoto1225, Aileen R Ariosa1749, Jane L Armstrong1930,
\nThierry Arnould1773, Ivica Arsov2120, Katsuhiko Asanuma675, Valerie Askanas1924, Eric Asselin1867, Ryuichiro Atarashi794,
\nSally S Atherton369, Julie D Atkin713, Laura D Attardi1131, Patrick Auberger1787, Georg Auburger379, Laure Aurelian1727,
\nRiccardo Autelli1992, Laura Avagliano1029,1755, Maria Laura Avantaggiati364, Limor Avrahami1166, Suresh Awale1986,
\nNeelam Azad404, Tiziana Bachetti568, Jonathan M Backer28, Dong-Hun Bae1933, Jae-sung Bae677, Ok-Nam Bae409,
\nSoo Han Bae2117, Eric H Baehrecke1729, Seung-Hoon Baek17, Stephen Baghdiguian1368,
\nAgnieszka Bagniewska-Zadworna2, Hua Bai90, Jie Bai667, Xue-Yuan Bai1133, Yannick Bailly884,
\nKithiganahalli Narayanaswamy Balaji473, Walter Balduini2002, Andrea Ballabio316, Rena Balzan1711, Rajkumar Banerjee239,
\nG abor B anhegyi1052, Haijun Bao2109, Benoit Barbeau1363, Maria D Barrachina2007, Esther Barreiro467, Bonnie Bartel997,
\nAlberto Bartolom e222, Diane C Bassham550, Maria Teresa Bassi1046, Robert C Bast Jr1273, Alakananda Basu1798,
\nMaria Teresa Batista1578, Henri Batoko1336, Maurizio Battino970, Kyle Bauckman2085, Bradley L Baumgarner1909,
\nK Ulrich Bayer1594, Rupert Beale1553, Jean-Fran¸cois Beaulieu1360, George R. Beck Jr48,294, Christoph Becker336,
\nJ David Beckham1595, Pierre-Andr e B edard749, Patrick J Bednarski301, Thomas J Begley1135, Christian Behl1419,
\nChristian Behrends757, Georg MN Behrens406, Kevin E Behrns1627, Eloy Bejarano26, Amine Belaid490,
\nFrancesca Belleudi1041, Giovanni B enard497, Guy Berchem706, Daniele Bergamaschi983, Matteo Bergami1401,
\nBen Berkhout1441, Laura Berliocchi714, Am elie Bernard1749, Monique Bernard1354, Francesca Bernassola1880,
\nAnne Bertolotti791, Amanda S Bess272, S ebastien Besteiro1351, Saverio Bettuzzi1828, Savita Bhalla913,
\nShalmoli Bhattacharyya973, Sujit K Bhutia838, Caroline Biagosch1159, Michele Wolfe Bianchi520,1378,1381,
\nMartine Biard-Piechaczyk210, Viktor Billes298, Claudia Bincoletto1314, Baris Bingol350, Sara W Bird1128, Marc Bitoun1112,
\nIvana Bjedov1258, Craig Blackstone843, Lionel Blanc1183, Guillermo A Blanco1496, Heidi Kiil Blomhoff1812,
\nEmilio Boada-Romero1297, Stefan B€ockler1464, Marianne Boes1423, Kathleen Boesze-Battaglia1835, Lawrence H Boise286,287,
\nAlessandra Bolino2063, Andrea Boman693, Paolo Bonaldo1823, Matteo Bordi897, J€urgen Bosch608, Luis M Botana1308,
\nJoelle Botti1375, German Bou1405, Marina Bouch e1038, Marion Bouchecareilh1331, Marie-Jos ee Boucher1901,
\nMichael E Boulton481, Sebastien G Bouret1926, Patricia Boya133, Micha€el Boyer-Guittaut1345, Peter V Bozhkov1141,
\nNathan Brady374, Vania MM Braga469, Claudio Brancolini1997, Gerhard H Braus353, Jos e M Bravo-San Pedro299,393,508,1374,
\nLisa A Brennan322, Emery H Bresnick2022, Patrick Brest490, Dave Bridges1939, Marie-Agn es Bringer124, Marisa Brini1822,
\nGlauber C Brito1311, Bertha Brodin631, Paul S Brookes1872, Eric J Brown352, Karen Brown1690, Hal E Broxmeyer480,
\nAlain Bruhat486,1339, Patricia Chakur Brum1893, John H Brumell446, Nicola Brunetti-Pierri315,1171,
\nRobert J Bryson-Richardson781, Shilpa Buch1777, Alastair M Buchan1819, Hikmet Budak1022, Dmitry V Bulavin118,505,1789,
\nScott J Bultman1792, Geert Bultynck665, Vladimir Bumbasirevic1470, Yan Burelle1356, Robert E Burke216,217,
\nMargit Burmeister1750, Peter B€utikofer1473, Laura Caberlotto1987, Ken Cadwell896, Monika Cahova112, Dongsheng Cai24,
\nJingjing Cai2099, Qian Cai1018, Sara Calatayud2007, Nadine Camougrand1343, Michelangelo Campanella1700,
\nGrant R Campbell1525, Matthew Campbell1249, Silvia Campello556,1876, Robin Candau1769, Isabella Caniggia1983,
\nLavinia Cantoni560, Lizhi Cao116, Allan B Caplan1656, Michele Caraglia1051, Claudio Cardinali1043, Sandra Morais Cardoso1579, Jennifer S Carew208, Laura A Carleton874, Cathleen R Carlin101, Silvia Carloni2002,
\nSven R Carlsson1267, Didac Carmona-Gutierrez1643, Leticia AM Carneiro312, Oliana Carnevali971, Serena Carra1318,
\nAlice Carrier120, Bernadette Carroll900, Caty Casas1324, Josefina Casas1116, Giuliana Cassinelli324, Perrine Castets1462,
\nSusana Castro-Obregon214, Gabriella Cavallini1841, Isabella Ceccherini568, Francesco Cecconi253,555,1884,
\nArthur I Cederbaum459, Valent ın Ce~na199,1281, Simone Cenci1323,2064, Claudia Cerella444, Davide Cervia1996,
\nSilvia Cetrullo1478, Hassan Chaachouay2028, Han-Jung Chae187, Andrei S Chagin634, Chee-Yin Chai626,628,
\nGopal Chakrabarti1502, Georgios Chamilos1601, Edmond YW Chan1142, Matthew TV Chan181, Dhyan Chandra1003,
\nPallavi Chandra548, Chih-Peng Chang818, Raymond Chuen-Chung Chang1653, Ta Yuan Chang345, John C Chatham1434,
\nSaurabh Chatterjee1910, Santosh Chauhan527, Yongsheng Che62, Michael E Cheetham1263, Rajkumar Cheluvappa1783,
\nChun-Jung Chen1153, Gang Chen598,1676, Guang-Chao Chen9, Guoqiang Chen1078, Hongzhuan Chen1077, Jeff W Chen1514,
\nJian-Kang Chen370,371, Min Chen249, Mingzhou Chen2104, Peiwen Chen1823, Qi Chen1674, Quan Chen172,
\nShang-Der Chen138, Si Chen325, Steve S-L Chen10, Wei Chen2125, Wei-Jung Chen829, Wen Qiang Chen979, Wenli Chen1113,
\nXiangmei Chen1133, Yau-Hung Chen1157, Ye-Guang Chen1250, Yin Chen1447, Yingyu Chen953,955, Yongshun Chen2135,
\nYu-Jen Chen712, Yue-Qin Chen1145, Yujie Chen1208, Zhen Chen339, Zhong Chen2123, Alan Cheng1702,
\nChristopher HK Cheng184, Hua Cheng1728, Heesun Cheong814, Sara Cherry1836, Jason Chesney1703,
\nChun Hei Antonio Cheung817, Eric Chevet1359, Hsiang Cheng Chi140, Sung-Gil Chi656, Fulvio Chiacchiera308,
\nHui-Ling Chiang958, Roberto Chiarelli1826, Mario Chiariello235,567,577, Marcello Chieppa835, Lih-Shen Chin290,
\nMario Chiong1285, Gigi NC Chiu878, Dong-Hyung Cho676, Ssang-Goo Cho650, William C Cho982, Yong-Yeon Cho105,
\nYoung-Seok Cho1064, Augustine MK Choi2095, Eui-Ju Choi656, Eun-Kyoung Choi387,400,685, Jayoung Choi1563,
\nMary E Choi2093, Seung-Il Choi2116, Tsui-Fen Chou412, Salem Chouaib395, Divaker Choubey1574, Vinay Choubey1936,
\nKuan-Chih Chow822, Kamal Chowdhury730, Charleen T Chu1856, Tsung-Hsien Chuang827, Taehoon Chun657,
\nHyewon Chung652, Taijoon Chung978, Yuen-Li Chung1194, Yong-Joon Chwae18, Valentina Cianfanelli254,
\nRoberto Ciarcia1775, Iwona A Ciechomska886, Maria Rosa Ciriolo1876, Mara Cirone1042, Sofie Claerhout1694,
\nMichael J Clague1698, Joan Cl aria1457, Peter GH Clarke1687, Robert Clarke361, Emilio Clementi1045,1398, C edric Cleyrat1781,
\nMiriam Cnop1366, Eliana M Coccia574, Tiziana Cocco1459, Patrice Codogno1375, J€orn Coers271, Ezra EW Cohen1533,
\nDavid Colecchia235,567,577, Luisa Coletto25, N uria S Coll123, Emma Colucci-Guyon516, Sergio Comincini1829,
\nMaria Condello578, Katherine L Cook2073, Graham H Coombs1929, Cynthia D Cooper2076, J Mark Cooper1395,
\nIsabelle Coppens601, Maria Tiziana Corasaniti1387, Marco Corazzari485,1884, Ramon Corbalan1566,
\nElisabeth Corcelle-Termeau251, Mario D Cordero1899, Cristina Corral-Ramos1289, Olga Corti507,1109, Andrea Cossarizza1767,
\nPaola Costelli1993, Safia Costes1518, Susan L Cotman721, Ana Coto-Montes946, Sandra Cottet566,1688, Eduardo Couve1301,
\nLori R Covey1015, L Ashley Cowart762, Jeffery S Cox1536, Fraser P Coxon1427, Carolyn B Coyne1846, Mark S Cragg1919,
\nRolf J Craven1679, Tiziana Crepaldi1995, Jose L Crespo1300, Alfredo Criollo1285, Valeria Crippa558, Maria Teresa Cruz1576,
\nAna Maria Cuervo26, Jose M Cuezva1277, Taixing Cui1907, Pedro R Cutillas987, Mark J Czaja27, Maria F Czyzyk-Krzeska1572,
\nRuben K Dagda2068, Uta Dahmen1404, Chunsun Dai800, Wenjie Dai1187, Yun Dai2059, Kevin N Dalby1940,
\nLuisa Dalla Valle1822, Guillaume Dalmasso1340, Marcello D’Amelio557, Markus Damme188, Arlette Darfeuille-Michaud1340,
\nCatherine Dargemont950, Victor M Darley-Usmar1433, Srinivasan Dasarathy205, Biplab Dasgupta202, Srikanta Dash1254,
\nCrispin R Dass242, Hazel Marie Davey8, Lester M Davids1560, David D avila227, Roger J Davis1731, Ted M Dawson604,
\nValina L Dawson606, Paula Daza1898, Jackie de Belleroche470, Paul de Figueiredo1180,1182,
\nRegina Celia Bressan Queiroz de Figueiredo135, Jos e de la Fuente1023, Luisa De Martino1775,
\nAntonella De Matteis1171, Guido RY De Meyer1443, Angelo De Milito631, Mauro De Santi2002,

Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments - including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances - are addressing such challenges and opening up new opportunities. Consequently, interest in natural products as drug leads is being revitalized, particularly for tackling antimicrobial resistance. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities.

The small-molecule topology generator PRODRG is described, which takes input from existing coordinates or various two-dimensional formats and automatically generates coordinates and molecular topologies suitable for X-ray refinement of protein-ligand complexes. Test results are described for automatic generation of topologies followed by energy minimization for a subset of compounds from the Cambridge Structural Database, which shows that, within the limits of the empirical GROMOS87 force field used, structures with good geometries are generated. X-ray refinement in X-PLOR/CNS, REFMAC and SHELX using PRODRG-generated topologies produces results comparable to refinement with topologies from the standard libraries. However, tests with distorted starting coordinates show that PRODRG topologies perform better, both in terms of ligand geometry and of crystallographic R factors.

Allergic rhinitis is a symptomatic disorder of the nose\ninduced after allergen exposure by an IgE-mediated\ninflammation of the membranes lining the nose. It is a\nglobal health problem that causes major illness and disability\nworldwide. Over 600 million patients from all\ncountries, all ethnic groups and of all ages suffer from\nallergic rhinitis. It affects social life, sleep, school and\nwork and its economic impact is substantial.\nRisk factors for allergic rhinitis are well identified.\nIndoor and outdoor allergens as well as occupational\nagents cause rhinitis and other allergic diseases.\nThe role of indoor and outdoor pollution is probably\nvery important, but has yet to be fully understood\nboth for the occurrence of the disease and its manifestations.\nIn 1999, during the Allergic Rhinitis and its Impact on\nAsthma (ARIA) WHO workshop, the expert panel\nproposed a new classification for allergic rhinitis which\nwas subdivided into _intermittent_ or _persistent_ disease.\nThis classification is now validated.\nThe diagnosis of allergic rhinitis is often quite easy, but\nin some cases it may cause problems and many patients\nare still under-diagnosed, often because they do not\nperceive the symptoms of rhinitis as a disease impairing\ntheir social life, school and work.\nThe management of allergic rhinitis is well established\nand the ARIA expert panel based its recommendations\non evidence using an extensive review of the literature\navailable up to December 1999. The statements of\nevidence for the development of these guidelines followed\nWHO rules and were based on those of Shekelle et al.\nA large number of papers have been published since 2000\nand are extensively reviewed in the 2008 Update using\nthe same evidence-based system. Recommendations for\nthe management of allergic rhinitis are similar in both the\nARIA workshop report and the 2008 Update. In the\nfuture, the GRADE approach will be used, but is not yet\navailable.\nAnother important aspect of the ARIA guidelines was\nto consider co-morbidities. Both allergic rhinitis and\nasthma are systemic inflammatory conditions and often\nco-exist in the same patients. In the 2008 Update, these\nlinks have been confirmed.\nTheARIAdocument is not intended to be a standard-ofcare\ndocument for individual countries. It is provided as a\nbasis for physicians, health care professionals and\norganizations involved in the treatment of allergic rhinitis\nand asthma in various countries to facilitate the\ndevelopment of relevant local standard-of-care documents\nfor patients.

Obesity is a serious international health problem that increases the risk of several common diseases. The genetic factors predisposing to obesity are poorly understood. A genome-wide search for type 2 diabetes-susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI). An additive association of the variant with BMI was replicated in 13 cohorts with 38,759 participants. The 16% of adults who are homozygous for the risk allele weighed about 3 kilograms more and had 1.67-fold increased odds of obesity when compared with those not inheriting a risk allele. This association was observed from age 7 years upward and reflects a specific increase in fat mass.

The specificities of 28 commercially available compounds reported to be relatively selective inhibitors of particular serine/threonine-specific protein kinases have been examined against a large panel of protein kinases. The compounds KT 5720, Rottlerin and quercetin were found to inhibit many protein kinases, sometimes much more potently than their presumed targets, and conclusions drawn from their use in cell-based experiments are likely to be erroneous. Ro 318220 and related bisindoylmaleimides, as well as H89, HA1077 and Y 27632, were more selective inhibitors, but still inhibited two or more protein kinases with similar potency. LY 294002 was found to inhibit casein kinase-2 with similar potency to phosphoinositide (phosphatidylinositol) 3-kinase. The compounds with the most impressive selectivity profiles were KN62, PD 98059, U0126, PD 184352, rapamycin, wortmannin, SB 203580 and SB 202190. U0126 and PD 184352, like PD 98059, were found to block the mitogen-activated protein kinase (MAPK) cascade in cell-based assays by preventing the activation of MAPK kinase (MKK1), and not by inhibiting MKK1 activity directly. Apart from rapamycin and PD 184352, even the most selective inhibitors affected at least one additional protein kinase. Our results demonstrate that the specificities of protein kinase inhibitors cannot be assessed simply by studying their effect on kinases that are closely related in primary structure. We propose guidelines for the use of protein kinase inhibitors in cell-based assays.

▪ Abstract This review describes the three mammalian glutathione transferase (GST) families, namely cytosolic, mitochondrial, and microsomal GST, the latter now designated MAPEG. Besides detoxifying electrophilic xenobiotics, such as chemical carcinogens, environmental pollutants, and antitumor agents, these transferases inactivate endogenous α,β-unsaturated aldehydes, quinones, epoxides, and hydroperoxides formed as secondary metabolites during oxidative stress. These enzymes are also intimately involved in the biosynthesis of leukotrienes, prostaglandins, testosterone, and progesterone, as well as the degradation of tyrosine. Among their substrates, GSTs conjugate the signaling molecules 15-deoxy-Δ 12,14 -prostaglandin J 2 (15d-PGJ 2 ) and 4-hydroxynonenal with glutathione, and consequently they antagonize expression of genes trans-activated by the peroxisome proliferator-activated receptor γ (PPARγ) and nuclear factor-erythroid 2 p45-related factor 2 (Nrf2). Through metabolism of 15d-PGJ 2 , GST may enhance gene expression driven by nuclear factor-κB (NF-κB). Cytosolic human GST exhibit genetic polymorphisms and this variation can increase susceptibility to carcinogenesis and inflammatory disease. Polymorphisms in human MAPEG are associated with alterations in lung function and increased risk of myocardial infarction and stroke. Targeted disruption of murine genes has demonstrated that cytosolic GST isoenzymes are broadly cytoprotective, whereas MAPEG proteins have proinflammatory activities. Furthermore, knockout of mouse GSTA4 and GSTZ1 leads to overexpression of transferases in the Alpha, Mu, and Pi classes, an observation suggesting they are part of an adaptive mechanism that responds to endogenous chemical cues such as 4-hydroxynonenal and tyrosine degradation products. Consistent with this hypothesis, the promoters of cytosolic GST and MAPEG genes contain antioxidant response elements through which they are transcriptionally activated during exposure to Michael reaction acceptors and oxidative stress.

The glutathione S-transferases (GST) represent a major group of detoxification enzymes. All eukaryotic species possess multiple cytosolic and membrane-bound GST isoenzymes, each of which displays distinct catalytic as well as noncatalytic binding properties: the cytosolic enzymes are encoded by at least five distantly related gene families (designated class alpha, mu, pi, sigma, and theta GST), whereas the membrane-bound enzymes, microsomal GST and leukotriene C4 synthetase, are encoded by single genes and both have arisen separately from the soluble GST. Evidence suggests that the level of expression of GST is a crucial factor in determining the sensitivity of cells to a broad spectrum of toxic chemicals. In this article the biochemical functions of GST are described to show how individual isoenzymes contribute to resistance to carcinogens, antitumor drugs, environmental pollutants, and products of oxidative stress. A description of the mechanisms of transcriptional and posttranscriptional regulation of GST isoenzymes is provided to allow identification of factors that may modulate resistance to specific noxious chemicals. The most abundant mammalian GST are the class alpha, mu, and pi enzymes and their regulation has been studied in detail. The biological control of these families is complex as they exhibit sex-, age-, tissue-, species-, and tumor-specific patterns of expression. In addition, GST are regulated by a structurally diverse range of xenobiotics and, to date, at least 100 chemicals have been identified that induce GST; a significant number of these chemical inducers occur naturally and, as they are found as nonnutrient components in vegetables and citrus fruits, it is apparent that humans are likely to be exposed regularly to such compounds. Many inducers, but not all, effect transcriptional activation of GST genes through either the antioxidant-responsive element (ARE), the xenobiotic-responsive element (XRE), the GST P enhancer 1(GPE), or the glucocorticoid-responsive element (GRE). Barbiturates may transcriptionally activate GST through a Barbie box element. The involvement of the Ah-receptor, Maf, Nrl, Jun, Fos, and NF-kappa B in GST induction is discussed. Many of the compounds that induce GST are themselves substrates for these enzymes, or are metabolized (by cytochrome P-450 monooxygenases) to compounds that can serve as GST substrates, suggesting that GST induction represents part of an adaptive response mechanism to chemical stress caused by electrophiles. It also appears probable that GST are regulated in vivo by reactive oxygen species (ROS), because not only are some of the most potent inducers capable of generating free radicals by redox-cycling, but H2O2 has been shown to induce GST in plant and mammalian cells: induction of GST by ROS would appear to represent an adaptive response as these enzymes detoxify some of the toxic carbonyl-, peroxide-, and epoxide-containing metabolites produced within the cell by oxidative stress. Class alpha, mu, and pi GST isoenzymes are overexpressed in rat hepatic preneoplastic nodules and the increased levels of these enzymes are believed to contribute to the multidrug-resistant phenotype observed in these lesions. The majority of human tumors and human tumor cell lines express significant amounts of class pi GST. Cell lines selected in vitro for resistance to anticancer drugs frequently overexpress class pi GST, although overexpression of class alpha and mu isoenzymes is also often observed. The mechanisms responsible for overexpression of GST include transcriptional activation, stabilization of either mRNA or protein, and gene amplification. In humans, marked interindividual differences exist in the expression of class alpha, mu, and theta GST. The molecular basis for the variation in class alpha GST is not known. (ABSTRACT TRUNCATED)

PD 098059 has been shown previously to inhibit the dephosphorylated form of mitogen-activated protein kinase kinase-1 (MAPKK1) and a mutant MAPKK1(S217E,S221E), which has low levels of constitutive activity (Dudley, D. T., Pang, L., Decker, S. J., Bridges, A. J., and Saltiel, A. R.(1995) Proc. Natl. Acad. Sci. U. S. A. 92, 7686-7689). Here we report that PD 098059 does not inhibit Raf-activated MAPKK1 but that it prevents the activation of MAPKK1 by Raf or MEK kinase in vitro at concentrations (IC50 = 2-7 μM) similar to those concentrations that inhibit dephosphorylated MAPKK1 or MAPKK1(S217E,S221E). PD 098059 inhibited the activation of MAPKK2 by Raf with a much higher IC50 value (50 μM) and did not inhibit the phosphorylation of other Raf or MEK kinase substrates, indicating that it exerts its effect by binding to the inactive form of MAPKK1. PD 098059 also acts as a specific inhibitor of the activation of MAPKK in Swiss 3T3 cells, suppressing by 80-90% its activation by a variety of agonists. The high degree of specificity of PD 098059 in vitro and in vivo is indicated by its failure to inhibit 18 protein Ser/Thr kinases (including two other MAPKK homologues) in vitro by its failure to inhibit the in vivo activation of MAPKK and MAP kinase homologues that participate in stress and interleukin-1-stimulated kinase cascades in KB and PC12 cells, and by lack of inhibition of the activation of p70 S6 kinase by insulin or epidermal growth factor in Swiss 3T3 cells. PD 098059 (50 μM) inhibited the activation of p42MAPK and isoforms of MAP kinase-activated protein kinase-1 in Swiss 3T3 cells, but the extent of inhibition depended on how potently c-Raf and MAPKK were activated by any particular agonist and demonstrated the enormous amplification potential of this kinase cascade. PD 098059 not only failed to inhibit the activation of Raf by platelet-derived growth factor, serum, insulin, and phorbol esters in Swiss 3T3 cells but actually enhanced Raf activity. The rate of activation of Raf by platelet-derived growth factor was increased 3-fold, and the subsequent inactivation that occurred after 10 min was prevented. These results indicate that the activation of Raf is suppressed and that its inactivation is accelerated by a downstream component(s) of the MAP kinase pathway. PD 098059 has been shown previously to inhibit the dephosphorylated form of mitogen-activated protein kinase kinase-1 (MAPKK1) and a mutant MAPKK1(S217E,S221E), which has low levels of constitutive activity (Dudley, D. T., Pang, L., Decker, S. J., Bridges, A. J., and Saltiel, A. R.(1995) Proc. Natl. Acad. Sci. U. S. A. 92, 7686-7689). Here we report that PD 098059 does not inhibit Raf-activated MAPKK1 but that it prevents the activation of MAPKK1 by Raf or MEK kinase in vitro at concentrations (IC50 = 2-7 μM) similar to those concentrations that inhibit dephosphorylated MAPKK1 or MAPKK1(S217E,S221E). PD 098059 inhibited the activation of MAPKK2 by Raf with a much higher IC50 value (50 μM) and did not inhibit the phosphorylation of other Raf or MEK kinase substrates, indicating that it exerts its effect by binding to the inactive form of MAPKK1. PD 098059 also acts as a specific inhibitor of the activation of MAPKK in Swiss 3T3 cells, suppressing by 80-90% its activation by a variety of agonists. The high degree of specificity of PD 098059 in vitro and in vivo is indicated by its failure to inhibit 18 protein Ser/Thr kinases (including two other MAPKK homologues) in vitro by its failure to inhibit the in vivo activation of MAPKK and MAP kinase homologues that participate in stress and interleukin-1-stimulated kinase cascades in KB and PC12 cells, and by lack of inhibition of the activation of p70 S6 kinase by insulin or epidermal growth factor in Swiss 3T3 cells. PD 098059 (50 μM) inhibited the activation of p42MAPK and isoforms of MAP kinase-activated protein kinase-1 in Swiss 3T3 cells, but the extent of inhibition depended on how potently c-Raf and MAPKK were activated by any particular agonist and demonstrated the enormous amplification potential of this kinase cascade. PD 098059 not only failed to inhibit the activation of Raf by platelet-derived growth factor, serum, insulin, and phorbol esters in Swiss 3T3 cells but actually enhanced Raf activity. The rate of activation of Raf by platelet-derived growth factor was increased 3-fold, and the subsequent inactivation that occurred after 10 min was prevented. These results indicate that the activation of Raf is suppressed and that its inactivation is accelerated by a downstream component(s) of the MAP kinase pathway.

Activation of mammalian target of rapamycin 1 (mTORC1) by nutrients, insulin and leptin leads to appetite suppression (anorexia). Contrastingly, increased AMP-activated protein kinase (AMPK) activity by ghrelin promotes appetite (orexia). However, the interplay between these mechanisms remains poorly defined. The relationship between the anorexigenic hormones, insulin and leptin, and the orexigenic hormone, ghrelin, on mTORC1 signalling was examined using S6 kinase phosphorylation as a marker for changes in mTORC1 activity in mouse hypothalamic GT1-7 cells. Additionally, the contribution of AMPK and mTORC1 signalling in relation to insulin-, leptin- and ghrelin-driven alterations to mouse hypothalamic agouti-related protein (AgRP) mRNA levels was examined. Insulin and leptin increase mTORC1 activity in a phosphoinositide-3-kinase (PI3K)- and protein kinase B (PKB)-dependent manner, compared to vehicle controls, whereas increasing AMPK activity inhibits mTORC1 activity and blocks the actions of the anorexigenic hormones. Ghrelin mediates an AMPK-dependent decrease in mTORC1 activity and increases hypothalamic AgRP mRNA levels, the latter effect being prevented by insulin in an mTORC1-dependent manner. In conclusion, mTORC1 acts as an integration node in hypothalamic neurons for hormone-derived PI3K and AMPK signalling and mediates at least part of the assimilated output of anorexigenic and orexigenic hormone actions in the hypothalamus.

In interview studies, sample size is often justified by interviewing participants until reaching 'data saturation'. However, there is no agreed method of establishing this. We propose principles for deciding saturation in theory-based interview studies (where conceptual categories are pre-established by existing theory). First, specify a minimum sample size for initial analysis (initial analysis sample). Second, specify how many more interviews will be conducted without new ideas emerging (stopping criterion). We demonstrate these principles in two studies, based on the theory of planned behaviour, designed to identify three belief categories (Behavioural, Normative and Control), using an initial analysis sample of 10 and stopping criterion of 3. Study 1 (retrospective analysis of existing data) identified 84 shared beliefs of 14 general medical practitioners about managing patients with sore throat without prescribing antibiotics. The criterion for saturation was achieved for Normative beliefs but not for other beliefs or studywise saturation. In Study 2 (prospective analysis), 17 relatives of people with Paget's disease of the bone reported 44 shared beliefs about taking genetic testing. Studywise data saturation was achieved at interview 17. We propose specification of these principles for reporting data saturation in theory-based interview studies. The principles may be adaptable for other types of studies.

Takes as its departure point the criticism of Guthrie and Parker by Arnold and the Tinker et al. critique of Gray et al. Following an extensive review of the corporate social reporting literature, its major theoretical preoccupations and empirical conclusions, attempts to re‐examine the theoretical tensions that exist between “classical” political economy interpretations of social disclosure and those from more “bourgeois” perspectives. Argues that political economy, legitimacy theory and stakeholder theory need not be competitor theories but may, if analysed appropriately, be seen as alternative and mutually enriching theories from alternative levels of resolution. Offers evidence from 13 years of social disclosure by UK companies and attempts to interpret this from different levels of resolution. There is little doubt that social disclosure practice has changed dramatically in the period. The theoretical perspectives prove to offer different, but mutually enhancing, interpretations of these phenomena.

Chronic pain is a major source of suffering. It interferes with daily functioning and often is accompanied by distress. Yet, in the International Classification of Diseases, chronic pain diagnoses are not represented systematically. The lack of appropriate codes renders accurate epidemiological investigations difficult and impedes health policy decisions regarding chronic pain such as adequate financing of access to multimodal pain management. In cooperation with the WHO, an IASP Working Group has developed a classification system that is applicable in a wide range of contexts, including pain medicine, primary care, and low-resource environments. Chronic pain is defined as pain that persists or recurs for more than 3 months. In chronic pain syndromes, pain can be the sole or a leading complaint and requires special treatment and care. In conditions such as fibromyalgia or nonspecific low-back pain, chronic pain may be conceived as a disease in its own right; in our proposal, we call this subgroup "chronic primary pain." In 6 other subgroups, pain is secondary to an underlying disease: chronic cancer-related pain, chronic neuropathic pain, chronic secondary visceral pain, chronic posttraumatic and postsurgical pain, chronic secondary headache and orofacial pain, and chronic secondary musculoskeletal pain. These conditions are summarized as "chronic secondary pain" where pain may at least initially be conceived as a symptom. Implementation of these codes in the upcoming 11th edition of International Classification of Diseases will lead to improved classification and diagnostic coding, thereby advancing the recognition of chronic pain as a health condition in its own right.

The specificities of 28 commercially available compounds reported to be relatively selective inhibitors of particular serine/threonine-specific protein kinases have been examined against a large panel of protein kinases. The compounds KT 5720, Rottlerin and quercetin were found to inhibit many protein kinases, sometimes much more potently than their presumed targets, and conclusions drawn from their use in cell-based experiments are likely to be erroneous. Ro 318220 and related bisindoylmaleimides, as well as H89, HA1077 and Y 27632, were more selective inhibitors, but still inhibited two or more protein kinases with similar potency. LY 294002 was found to inhibit casein kinase-2 with similar potency to phosphoinositide (phosphatidylinositol) 3-kinase. The compounds with the most impressive selectivity profiles were KN62, PD 98059, U0126, PD 184352, rapamycin, wortmannin, SB 203580 and SB 202190. U0126 and PD 184352, like PD 98059, were found to block the mitogen-activated protein kinase (MAPK) cascade in cell-based assays by preventing the activation of MAPK kinase (MKK1), and not by inhibiting MKK1 activity directly. Apart from rapamycin and PD 184352, even the most selective inhibitors affected at least one additional protein kinase. Our results demonstrate that the specificities of protein kinase inhibitors cannot be assessed simply by studying their effect on kinases that are closely related in primary structure. We propose guidelines for the use of protein kinase inhibitors in cell-based assays.

The International Prognostic Scoring System (IPSS) is an important standard for assessing prognosis of primary untreated adult patients with myelodysplastic syndromes (MDS). To refine the IPSS, MDS patient databases from international institutions were coalesced to assemble a much larger combined database (Revised-IPSS [IPSS-R], n = 7012, IPSS, n = 816) for analysis. Multiple statistically weighted clinical features were used to generate a prognostic categorization model. Bone marrow cytogenetics, marrow blast percentage, and cytopenias remained the basis of the new system. Novel components of the current analysis included: 5 rather than 3 cytogenetic prognostic subgroups with specific and new classifications of a number of less common cytogenetic subsets, splitting the low marrow blast percentage value, and depth of cytopenias. This model defined 5 rather than the 4 major prognostic categories that are present in the IPSS. Patient age, performance status, serum ferritin, and lactate dehydrogenase were significant additive features for survival but not for acute myeloid leukemia transformation. This system comprehensively integrated the numerous known clinical features into a method analyzing MDS patient prognosis more precisely than the initial IPSS. As such, this IPSS-R should prove beneficial for predicting the clinical outcomes of untreated MDS patients and aiding design and analysis of clinical trials in this disease.

Cyanobacteria can form dense and sometimes toxic blooms in freshwater and marine environments, which threaten ecosystem functioning and degrade water quality for recreation, drinking water, fisheries and human health. Here, we review evidence indicating that cyanobacterial blooms are increasing in frequency, magnitude and duration globally. We highlight species traits and environmental conditions that enable cyanobacteria to thrive and explain why eutrophication and climate change catalyse the global expansion of cyanobacterial blooms. Finally, we discuss management strategies, including nutrient load reductions, changes in hydrodynamics and chemical and biological controls, that can help to prevent or mitigate the proliferation of cyanobacterial blooms. Cyanobacteria can form large blooms that threaten the water quality of lakes and seas. In this Review, Huisman and colleagues discuss bloom formation, the impact of eutrophication and climate change, and measures to prevent and control cyanobacterial blooms.

1. Introduction Chronic pain has been recognized as pain that persists past normal healing time5 and hence lacks the acute warning function of physiological nociception.35 Usually pain is regarded as chronic when it lasts or recurs for more than 3 to 6 months.29 Chronic pain is a frequent condition, affecting an estimated 20% of people worldwide6,13,14,18 and accounting for 15% to 20% of physician visits.25,28 Chronic pain should receive greater attention as a global health priority because adequate pain treatment is a human right, and it is the duty of any health care system to provide it.4,13 The current version of the International Classification of Diseases (ICD) of the World Health Organization (WHO) includes some diagnostic codes for chronic pain conditions, but these diagnoses do not reflect the actual epidemiology of chronic pain, nor are they categorized in a systematic manner. The ICD is the preeminent tool for coding diagnoses and documenting investigations or therapeutic measures within the health care systems of many countries. In addition, ICD codes are commonly used to report target diseases and comorbidities of participants in clinical research. Consequently, the current lack of adequate coding in the ICD makes the acquisition of accurate epidemiological data related to chronic pain difficult, prevents adequate billing for health care expenses related to pain treatment, and hinders the development and implementation of new therapies.10,11,16,23,27,31,37 Responding to these shortcomings, the International Association for the Study of Pain (IASP) contacted the WHO and established a Task Force for the Classification of Chronic Pain. The IASP Task Force, which comprises pain experts from across the globe,19 has developed a new and pragmatic classification of chronic pain for the upcoming 11th revision of the ICD. The goal is to create a classification system that is applicable in primary care and in clinical settings for specialized pain management. A major challenge in this process was finding a rational principle of classification that suits the different types of chronic pain and fits into the general ICD-11 framework. Pain categories are variably defined based on the perceived location (headache), etiology (cancer pain), or the primarily affected anatomical system (neuropathic pain). Some diagnoses of pain defy these classification principles (fibromyalgia). This problem is not unique to the classification of pain, but exists throughout the ICD. The IASP Task Force decided to give first priority to pain etiology, followed by underlying pathophysiological mechanisms, and finally the body site. Developing this multilayered classification was greatly facilitated by a novel principle of assigning diagnostic codes in ICD-11, termed “multiple parenting.” Multiple parenting allows the same diagnosis to be subsumed under more than 1 category (for a glossary of ICD terms refer to Table 1). Each diagnosis retains 1 category as primary parent, but is cross-referenced to other categories that function as secondary parents.Table 1: Glossary of ICD-11 terms.The new ICD category for “Chronic Pain” comprises the most common clinically relevant disorders. These disorders were divided into 7 groups (Fig. 1): (1) chronic primary pain, (2) chronic cancer pain, (3) chronic posttraumatic and postsurgical pain, (4) chronic neuropathic pain, (5) chronic headache and orofacial pain, (6) chronic visceral pain, and (7) chronic musculoskeletal pain. Experts assigned to each group are responsible for the definition of diagnostic criteria and the selection of the diagnoses to be included under these subcategories of chronic pain. Thanks to Bedirhan Üstün and Robert Jakob of the WHO, these pain diagnoses are now integrated in the beta version of ICD-11 (http://id.who.int/icd/entity/1581976053). The Task Force is generating content models for single entities to describe their clinical characteristics. After peer review overseen by the WHO Steering Committee,39 the classification of chronic pain will be voted into action by the World Health Assembly in 2017.Figure 1: Organizational chart of Task Force, IASP, and WHO interactions. The IASP Task Force was created by the IASP council and its scope defined in direct consultation of the chairs (R.D.T. and W.R.) with WHO representatives in 2012. The Task Force reports to the IASP Council on an annual basis.2. Classification of chronic pain Chronic pain was defined as persistent or recurrent pain lasting longer than 3 months. This definition according to pain duration has the advantage that it is clear and operationalized. Optional specifiers for each diagnosis record evidence of psychosocial factors and the severity of the pain. Pain severity can be graded based on pain intensity, pain-related distress, and functional impairment. 2.1. Chronic primary pain Chronic primary pain is pain in 1 or more anatomic regions that persists or recurs for longer than 3 months and is associated with significant emotional distress or significant functional disability (interference with activities of daily life and participation in social roles) and that cannot be better explained by another chronic pain condition. This is a new phenomenological definition, created because the etiology is unknown for many forms of chronic pain. Common conditions such as, eg, back pain that is neither identified as musculoskeletal or neuropathic pain, chronic widespread pain, fibromyalgia, and irritable bowel syndrome will be found in this section and biological findings contributing to the pain problem may or may not be present. The term “primary pain” was chosen in close liaison with the ICD-11 revision committee, who felt this was the most widely acceptable term, in particular, from a nonspecialist perspective. 2.2. Chronic cancer pain Pain is a frequent and debilitating accompaniment of cancer8 that as yet has not been represented in the ICD. The Task Force decided to list it as a separate entity because there are specific treatment guidelines.7,38 Chronic cancer pain includes pain caused by the cancer itself (the primary tumor or metastases) and pain that is caused by the cancer treatment (surgical, chemotherapy, radiotherapy, and others). Cancer-related pain will be subdivided based on location into visceral, bony (or musculoskeletal), and somatosensory (neuropathic). It will be described as either continuous (background pain) or intermittent (episodic pain) if associated with physical movement or clinical procedures. The treatment-related pain will be cross-referenced from the chapters on postsurgical pain and neuropathic pain. 2.3. Chronic postsurgical and posttraumatic pain Because pain that persists beyond normal healing is frequent after surgery and some types of injuries, the entity of postsurgical and posttraumatic pain was created. This is defined as pain that develops after a surgical procedure or a tissue injury (involving any trauma, including burns) and persists at least 3 months after surgery or tissue trauma26; this is a definition of exclusion, as all other causes of pain (infection, recurring malignancy) as well as pain from a pre-existing pain problem need to be excluded. In view of the different causality, as well as from a medicolegal point of view, a separation between postsurgical pain and pain after all other trauma is regarded as useful. Depending on the type of surgery, chronic postsurgical pain is often neuropathic pain (on average 30% of cases with a range from 6% to 54% and more).15 Pain including such a neuropathic component is usually more severe than nociceptive pain and often affects the quality of life more adversely.21 2.4. Chronic neuropathic pain Chronic neuropathic pain is caused by a lesion or disease of the somatosensory nervous system.20,22 The somatosensory nervous system provides information about the body including skin, musculoskeletal, and visceral organs. Neuropathic pain may be spontaneous or evoked, as an increased response to a painful stimulus (hyperalgesia) or a painful response to a normally nonpainful stimulus (allodynia). The diagnosis of neuropathic pain requires a history of nervous system injury, for example, by a stroke, nerve trauma, or diabetic neuropathy, and a neuroanatomically plausible distribution of the pain.22 For the identification of definite neuropathic pain, it is necessary to demonstrate the lesion or disease involving the nervous system, for example, by imaging, biopsy, neurophysiological, or laboratory tests. In addition, negative or positive sensory signs compatible with the innervation territory of the lesioned nervous structure must be present.36 Diagnostic entities within this category will be divided into conditions of peripheral or central neuropathic pain. 2.5. Chronic headache and orofacial pain The International Headache Society (IHS) has created a headache classification17 that is implemented in full in the chapter on neurology. This classification differentiates between primary (idiopathic), secondary (symptomatic) headache, and orofacial pain including cranial neuralgias. In the section on chronic pain, only chronic headache and chronic orofacial pain will be included. Chronic headache and chronic orofacial pain is defined as headaches or orofacial pains that occur on at least 50% of the days during at least 3 months. For most purposes, patients receive a diagnosis according to the headache phenotypes or orofacial pains that they currently present. The section will list the most frequent chronic headache conditions. The most common chronic orofacial pains are temporomandibular disorders,32 which have been included in this subchapter of chronic pain. Chronic orofacial pain can be a localized presentation of a primary headache.2 This is common in the trigeminal autonomic cephalalgias, less common in migraines, and rare in tension-type headache. Several chronic orofacial pains such as post-traumatic trigeminal neuropathic pain,3 persistent idiopathic orofacial pain, and burning mouth syndrome are cross-referenced to, eg, primary chronic pain and neuropathic pain. The temporal definition of “chronic” has been extrapolated from that of chronic headaches.1 2.6. Chronic visceral pain Chronic visceral pain is persistent or recurrent pain that originates from the internal organs of the head and neck region and the thoracic, abdominal, and pelvic cavities.24,33,34 The pain is usually perceived in the somatic tissues of the body wall (skin, subcutis, muscle) in areas that receive the same sensory innervation as the internal organ at the origin of the symptom (referred visceral pain).12 In these areas, secondary hyperalgesia (increased sensitivity to painful stimuli in areas other than the primary site of the nociceptive input) often occurs30; the intensity of the symptom may bear no relationship with the extent of the internal damage or noxious visceral stimulation.9 The section on visceral pain will be subdivided according to the major underlying mechanisms, ie, persistent inflammation, vascular mechanisms (ischemia, thrombosis), obstruction and distension, traction and compression, combined mechanisms (eg, obstruction and inflammation concurrently), and referral from other locations. Pain due to cancer will be cross-referenced to the chapter chronic cancer pain and pain due to functional or unexplained mechanisms to chronic primary pain. 2.7. Chronic musculoskeletal pain Chronic musculoskeletal pain is defined as persistent or recurrent pain that arises as part of a disease process directly affecting bone(s), joint(s), muscle(s), or related soft tissue(s). According to the constraints of the approach as described in the Introduction, this category is therefore limited to nociceptive pain and does not include pain that may be perceived in musculoskeletal tissues but does not arise therefrom, such as the pain of compression neuropathy or somatic referred pain. The entities subsumed in this approach include those characterized by persistent inflammation of infectious, autoimmune or metabolic etiology, such as rheumatoid arthritis, and by structural changes affecting bones, joints, tendons, or muscles, such as symptomatic osteoarthrosis. Musculoskeletal pain of neuropathic origin will be cross-referenced to neuropathic pain. Well-described apparent musculoskeletal conditions for which the causes are incompletely understood, such as nonspecific back pain or chronic widespread pain, will be included in the section on chronic primary pain. 3. Outlook Irrespective of its etiology, chronic pain is a major source of suffering and requires special treatment and care. Our proposal may not represent a perfect solution for the classification of all manifestations of chronic pain. However, it does represent the first systematic approach to implementing a classification of chronic pain in the ICD. It is based on international expertise and agreement, and consistent with the requirements of the ICD regarding the structure and format of content models. The 7 major categories of chronic pain were identified after considerable research and discussion. They represent a compromise between comprehensiveness and practical applicability of the classification system. Several clinically important conditions that were neglected in former ICD revisions will now be mentioned, eg, chronic cancer pain or chronic neuropathic pain. Etiological factors, pain intensity, and disability related to pain will be reflected. With the introduction of chronic primary pain as a new diagnostic entity, the classification recognizes conditions that affect a broad group of patients with pain and would be neglected in etiologically defined categories. We hope that this classification strengthens the representation of chronic pain conditions in clinical practice and research and welcome comments to improve it further. Conflict of interest statement Q. Aziz has attended advisory board meetings for Almirall pharmaceuticals and Grunenthal. He has also received funding for clinical trials from Ono Pharmaceutical and Protexin. M.I. Bennett has received consultancy or speaker fees from Pfizer, Bayer, Astellas, and Grunenthal in the last 5 years. M. Cohen has received honoraria for contributions to educational programs from Mundipharma Pty Limited and Pfizer. S. Evers received honoraria (as speaker and/or member of advisory boards) and research grants within the past 5 years by AGA Medical (now St Jude), Allergan, Almirall, Astra Zeneca, Berlin-Chemie, CoLucid, Desitin, Eisai, GlaxoSmithKline, Ipsen Pharma, Menarini, MSD, Novartis, Pfizer, Reckitt-Benckiser, UCB. N.B. Finnerup has received speaker's honoraria from Pfizer, Grunenthal, and Norpharma, research grant from Grünenthal, and consultancy fee from Astellas and is member of the IMI “Europain” collaboration where industry members of this are: Astra Zeneca, Pfizer, Esteve, UCB-Pharma, Sanofi Aventis, Grünenthal, Eli Lilly, Boehringer Ingelheim, Astellas, Abbott, and Lundbeck. M.B. First on the faculty of the Lundbeck International Neuroscience Foundation. In the past 2 years, M.A. Giamberardino received research funding or honoraria (participation in Advisory Board) from Bayer Healthcare, Helsinn, and Epitech Group. S. Kaasa declares no conflict of interest related to this work. In the past year he received honoraria from Helsinn related to participation in Advisory Board. E. Kosek has received consultancy and speaker fees in the past 24 months from Eli Lilly and Company and Orion and has ongoing research collaborations with Eli Lilly and Company and Abbott and Pierre Fabre. M. Nicholas received honoraria for contributing to educational sessions for Mundipharma and Pfizer in the last 5 years. S. Perrot received honoraria as a speaker and/or member of the advisory board in the past 5 years from Pfizer, BMS, Grunenthal, Elli Lilly, Sanofi, Daichi-Sankyo, Astellas, and Mundipharma. He has received grant support from BMS. W. Rief received honoraria (as speaker and/or member of advisory boards on topics such as adherence, placebo mechanisms) within the past 5 years from Berlin Chemie, Astra Zeneca, Bayer, Heel (research grant). J. Scholz has received speaker fees from Convergence, GlaxoSmithKline, Pfizer, St Jude Medical, and Zalicus. He has served on advisory boards or consulted for Convergence, Pfizer, Sanofi Aventis, and Zalicus Pharmaceuticals. He has received grant support from GlaxoSmithKline and Pfizer. In the last 5 years, the Anaesthesiology Unit of the University of Western Australia, but not S. Schug personally, has received research and travel funding and speaking and consulting honoraria from bioCSL, Bionomics, Eli Lilly, Grunenthal, Janssen, Mundipharma, Pfizer, Phosphagenics and iX Biopharma within the last 2 years. B.H. Smith has received lecture and consultancy fees, on behalf of his institution, from Pfizer, Grunenthal, Eli Lilly, and Napp. He has received unconditional educational grants from Pfizer Ltd; and he has received travel and accommodation support from Napp. P. Svensson served as a paid consultant for Sunstar Suisse SA. R.-D. Treede has received speaker's honoraria, research grants or consultancy fees from AbbVie, Acron, Astellas, Bauerfeind, Boehringer Ingelheim, Grünenthal, Hydra, Mundipharma, and Pfizer and is a member of the IMI “Europain” collaboration where industry members of this are: Astra Zeneca, Pfizer, Esteve, UCB-Pharma, Sanofi Aventis, Grünenthal, Eli Lilly, Boehringer Ingelheim, Astellas, Abbott, and Lundbeck. J.W.S. Vlaeyen is a member of the PHILIPS advisory board on pain management and declares no conflicts of interest with regard to this work. S.-J. Wang has served on the advisory boards of Allergan and Eli Lilly, Taiwan. He has received speaking honoraria from local companies (Taiwan branches) of Pfizer, Elli Lilly, and GSK. He has received research grants from the Novartis Taiwan, Taiwan Ministry of Science and Technology, Taipei-Veterans General Hospital and Taiwan Headache Society. The other authors have no conflicts of interest to declare. Acknowledgements The authors are members of the Classification of Pain Diseases Task Force of the International Association for the Study of Pain (IASP), which gave logistical and financial support to perform this work. We acknowledge the contributions of the following IASP Special Interest Groups (SIGs): Abdominal & Pelvic Pain SIG, Acute Pain SIG, Cancer Pain SIG, Neuropathic Pain SIG and the Orofacial Pain SIG, and the Classification Committee of the International Headache Society (IHS). Author contributions: R.-D. Treede, W. Rief, and A. Barke contributed equally to this topical review.