National Center for Toxicological Research

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,

Nanotechnology is a rapidly developing field in the 21(st) century, and the commercial use of nanomaterials for novel applications is increasing exponentially. To date, the scientific basis for the cytotoxicity and genotoxicity of most manufactured nanomaterials are not understood. The mechanisms underlying the toxicity of nanomaterials have recently been studied intensively. An important mechanism of nanotoxicity is the generation of reactive oxygen species (ROS). Overproduction of ROS can induce oxidative stress, resulting in cells failing to maintain normal physiological redox-regulated functions. This in turn leads to DNA damage, unregulated cell signaling, change in cell motility, cytotoxicity, apoptosis, and cancer initiation. There are critical determinants that can affect the generation of ROS. These critical determinants, discussed briefly here, include: size, shape, particle surface, surface positive charges, surface-containing groups, particle dissolution, metal ion release from nanometals and nanometal oxides, UV light activation, aggregation, mode of interaction with cells, inflammation, and pH of the medium.

Graphitic nanomaterials such as graphene layers (G) and single-wall carbon nanotubes (SWCNT) are potential candidates in a large number of biomedical applications. However, little is known about the effects of these nanomaterials on biological systems. Here we show that the shape of these materials is directly related to their induced cellular toxicity. Both G and SWCNT induce cytotoxic effects, and these effects are concentration- and shape-dependent. Interestingly, at low concentrations, G induced stronger metabolic activity than SWCNT, a trend that reversed at higher concentrations. Lactate dehydrogenase levels were found to be significantly higher for SWCNT as compared to the G samples. Moreover, reactive oxygen species were generated in a concentration- and time-dependent manner after exposure to G, indicating an oxidative stress mechanism. Furthermore, time-dependent caspase 3 activation after exposure to G (10 microg/mL) shows evidence of apoptosis. Altogether these studies suggest different biological activities of the graphitic nanomaterials, with the shape playing a primary role.

Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.

'Viable yellow' (Avy/a) mice are larger, obese, hyperinsulinemic, more susceptible to cancer, and, on average, shorter lived than their non-yellow siblings. They are epigenetic mosaics ranging from a yellow phenotype with maximum ectopic agouti overexpression, through a continuum of mottled agouti/yellow phenotypes with partial agouti overexpression, to a pseudoagouti phenotype with minimal ectopic expression. Pseudoagouti Avy/a mice are lean, healthy, and longer lived than their yellow siblings. Here we report that feeding pregnant black a/a dams methyl-supplemented diets alters epigenetic regulation of agouti expression in their offspring, as indicated by increased agouti/black mottling in the direction of the pseudoagouti phenotype. We also present confirmatory evidence that epigenetic phenotypes are maternally heritable. Thus Avy expression, already known to be modulated by imprinting, strain-specific modification, and maternal epigenetic inheritance, is also modulated by maternal diet. These observations suggest, at least in this special case, that maternal dietary supplementation may positively affect health and longevity of the offspring. Therefore, this experimental system should be useful for identifying maternal factors that modulate epigenetic mechanisms, especially DNA methylation, in developing embryos.

Nanotechnology has gained a great deal of public interest because of the needs and applications of nanomaterials in many areas of human endeavors including industry, agriculture, business, medicine, and public health. Environmental exposure to nanomaterials is inevitable as nanomaterials become part of our daily life, and, as a result, nanotoxicity research is gaining attention. This review presents a summary of recent research efforts on fate, behavior, and toxicity of different classes of nanomaterials in the environment. A critical evaluation of challenges and future needs for the safe environmental nanotechnology are discussed.

ABSTRACT ‘Viable yellow’ (A vy /a) mice are larger, obese, hyperinsulinemic, more susceptible to cancer, and, on average, shorter lived than their non‐yellow siblings. They are epigenetic mosaics ranging from a yellow phenotype with maximum ectopic agouti overexpression, through a continuum of mottled agouti/yellow phenotypes with partial agouti overexpression, to a pseudoagouti phenotype with minimal ectopic expression. Pseudoagouti A vy /a mice are lean, healthy, and longer lived than their yellow siblings. Here we report that feeding pregnant black a/a dams methyl‐supplemented diets alters epigenetic regulation of agouti expression in their offspring, as indicated by increased agouti/black mottling in the direction of the pseudoagouti phenotype. We also present confirmatory evidence that epigenetic phenotypes are maternally heritable. Thus A vy expression, already known to be modulated by imprinting, strain‐specific modification, and maternal epigenetic inheritance, is also modulated by maternal diet. These observations suggest, at least in this special case, that maternal dietary supplementation may positively affect health and longevity of the offspring. Therefore, this experimental system should be useful for identifying maternal factors that modulate epigenetic mechanisms, especially DNA methylation, in developing embryos.—Wolff, G. L., Kodell, R. L., Moore, S. R., Cooney, C. A. Maternal epigenetics and methyl supplements affect agouti gene expression in A vy / a mice. FASEB J. 12, 949–957 (1998)

This is the 52nd report of a series of workshops organised by the European Centre for the Validation of Alternative Methods (ECVAM). The main objective of ECVAM, as defined in 1993 by its Scientific Advisory Committee, is to promote the scientific and regulatory acceptance of alternative methods which are of importance to the biosciences, and that reduce, refine or replace the use of laboratory animals. The ECVAM workshop on the quantitative structure-activity relationship applicability domain was held at ECVAM on 29 September–1 October 2004, under the chairmanship of Andrew Worth. The workshop was attended by experts from academia, industry, international organisations and regulatory authorities. The aim of the workshop was to review the state of the art of methods for identifying the domain of applicability of structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs), collectively referred to as (Q)SARs. The report is intended to provide a source of input to the development of an OECD Guidance Document on (Q)SAR Validation. The report also makes recommendations for further research needed to understand and apply the concept of the (Q)SAR applicability domain (AD).

We have utilized a validated (standardized) estrogen receptor (ER) competitive-binding assay to determine the ER affinity for a large, structurally diverse group of chemicals. Uteri from ovariectomized Sprague-Dawley rats were the ER source for the competitive-binding assay. Initially, test chemicals were screened at high concentrations to determine whether a chemical competed with [3H]-estradiol for the ER. Test chemicals that exhibited affinity for the ER in the first tier were subsequently assayed using a wide range of concentrations to characterize the binding curve and to determine each chemical's IC50 and relative binding affinity (RBA) values. Overall, we assayed 188 chemicals, covering a 1 x 10(6)-fold range of RBAs from several different chemical or use categories, including steroidal estrogens, synthetic estrogens, antiestrogens, other miscellaneous steroids, alkylphenols, diphenyl derivatives, organochlorines, pesticides, alkylhydroxybenzoate preservatives (parabens), phthalates, benzophenone compounds, and a number of other miscellaneous chemicals. Of the 188 chemicals tested, 100 bound to the ER while 88 were non-binders. Included in the 100 chemicals that bound to the ER were 4-benzyloxyphenol, 2,4-dihydroxybenzophenone, and 2,2'-methylenebis(4-chlorophenol), compounds that have not been shown previously to bind the ER. It was also evident that certain structural features, such as an overall ring structure, were important for ER binding. The current study provides the most structurally diverse ER RBA data set with the widest range of RBA values published to date.

Nanoparticles (NPs) can potentially cause adverse effects on organ, tissue, cellular, subcellular, and protein levels due to their unusual physicochemical properties (e.g., small size, high surface area to volume ratio, chemical composition, crystallinity, electronic properties, surface structure reactivity and functional groups, inorganic or organic coatings, solubility, shape, and aggregation behavior). Metal NPs, in particular, have received increasing interest due to their widespread medical, consumer, industrial, and military applications. However, as particle size decreases, some metal-based NPs are showing increased toxicity, even if the same material is relatively inert in its bulk form (e.g., Ag, Au, and Cu). NPs also interact with proteins and enzymes within mammalian cells and they can interfere with the antioxidant defense mechanism leading to reactive oxygen species generation, the initiation of an inflammatory response and perturbation and destruction of the mitochondria causing apoptosis or necrosis. As a result, there are many challenges to overcome before we can determine if the benefits outweigh the risks associated with NPs.

The collaborative Interagency Agreement between the National Center for Toxicological Research (NCTR) and the National Institute on Aging (NIA) was aimed at identifying and validating a panel of biomarkers of aging in rodents in order to rapidly test the efficacy and safety of interventions designed to slow aging. Another aim was to provide a basis for developing biomarkers of aging in humans, using the assumption that biomarkers that were useful across different genotypes and species were sensitive to fundamental processes that would extrapolate to humans. Caloric restriction (CR), the only intervention that consistently extends both mean and maximal life span in a variety of species, was used to provide a model with extended life span. C57BI/6NNia, DBA/2JNia, B6D2F1, and B6C3F1 mice and Brown Norway (BN/RijNia), Fischer (F344/NNia) and Fischer x Brown Norway hybrid (F344 x BN F1) rats were bred and maintained on study. NCTR generated data from over 60,000 individually housed animals of the seven different genotypes and both sexes, approximately half ad libitum (AL) fed, the remainder CR. Approximately half the animals were shipped to offsite NIA investigators internationally, with the majority of the remainder maintained at NCTR until they died. The collaboration supplied a choice of healthy, long-lived rodent models to investigators, while allowing for the development of some of the most definitive information on life span, food consumption, and growth characteristics in these genotypes under diverse feeding paradigms.

S-Adenosylmethionine and S-adenosylhomocysteine (SAH), as the substrate and product of essential cellular methyltransferase reactions, are important metabolic indicators of cellular methylation status. Chronic elevation of SAH, secondary to the homocysteine-mediated reversal of the SAH hydrolase reaction, reduces methylation of DNA, RNA, proteins, and phospholipids. High affinity binding of SAH to the active site of cellular methyltransferases results in product inhibition of the enzyme. Using a sensitive new high pressure liquid chromatography method with coulometric electrochemical detection, plasma SAH levels in healthy young women were found to increase linearly with mild elevation in homocysteine levels (r = 0.73; p < 0.001); however, S-adenosylmethionine levels were not affected. Plasma SAH levels were positively correlated with intracellular lymphocyte SAH levels (r = 0.81; p < 0.001) and also with lymphocyte DNA hypomethylation (r = 0.74, p < 0.001). These results suggest that chronic elevation in plasma homocysteine levels, such as those associated with nutritional deficiencies or genetic polymorphisms in the folate pathway, may have an indirect and negative effect on cellular methylation reactions through a concomitant increase in intracellular SAH levels.

Aromatic amines are well known as occupational carcinogens and are found in cooked foods, tobacco smoke, synthetic fuels, and agricultural chemicals. For the primary arylamines, metabolic N-oxidation by hepatic cytochromes P-450 is generally regarded as an initial activation step leading to carcinogenesis. The metabolic activation of 4-aminobiphenyl, 2-naphthylamine, and several heterocyclic amines has been shown recently to be catalyzed by rat cytochrome P-450ISF-G and by its human ortholog, cytochrome P-450PA. We now report that human hepatic microsomal caffeine 3-demethylation, the initial major step in caffeine biotransformation in humans, is selectively catalyzed by cytochrome P-450PA. Caffeine 3-demethylation was highly correlated with 4-aminobiphenyl N-oxidation (r = 0.99; P less than 0.0005) in hepatic microsomal preparations obtained from 22 human organ donors, and both activities were similarly decreased by the selective inhibitor, 7,8-benzoflavone. The rates of microsomal caffeine 3-demethylation, 4-aminobiphenyl N-oxidation, and phenacetin O-deethylation were also significantly correlated with each other and with the levels of immunoreactive human cytochrome P-450PA. Moreover, a rabbit polyclonal antibody raised to human cytochrome P-450PA was shown to inhibit strongly all three of these activities and to inhibit the N-oxidation of the carcinogen 2-naphthylamine and the heterocyclic amines, 2-amino-6-methyldipyrido-[1,2-a:3',2'-d]imidazole and 2-amino-3-methylimidazo[4,5-f]-quinoline. Human liver cytochrome P-450PA was also shown to catalyze caffeine 3-demethylation, 4-aminobiphenyl N-oxidation, and phenacetin O-deethylation. Thus, estimation of caffeine 3-demethylation activity in humans may be useful in the characterization of arylamine N-oxidation phenotypes and in the assessment of whether or not the hepatic levels of cytochrome P-450PA, as affected by environmental or genetic factors, contribute to interindividual differences in susceptibility to arylamine-induced cancers.

Many chemotherapy regiments are successfully used to treat breast cancer; however, often breast cancer cells develop drug resistance that usually leads to a relapse and worsening of prognosis. We have shown recently that epigenetic changes such as DNA methylation and histone modifications play an important role in breast cancer cell resistance to chemotherapeutic agents. Another mechanism of gene expression control is mediated via the function of small regulatory RNA, particularly microRNA (miRNA); its role in cancer cell drug resistance still remains unexplored. In the present study, we investigated the role of miRNA in the resistance of human MCF-7 breast adenocarcinoma cells to doxorubicin (DOX). Here, we for the first time show that DOX-resistant MCF-7 cells (MCF-7/DOX) exhibit a considerable dysregulation of the miRNAome profile and altered expression of miRNA processing enzymes Dicer and Argonaute 2. The mechanistic link of miRNAome deregulation and the multidrug-resistant phenotype of MCF-7/DOX cells was evidenced by a remarkable correlation between specific miRNA expression and corresponding changes in protein levels of their targets, specifically those ones that have a documented role in cancer drug resistance. Furthermore, we show that microRNA-451 regulates the expression of multidrug resistance 1 gene. More importantly, transfection of the MCF-7/DOX-resistant cells with microRNA-451 resulted in the increased sensitivity of cells to DOX, indicating that correction of altered expression of miRNA may have significant implications for therapeutic strategies aiming to overcome cancer cell resistance.

Matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) was investigated as a method for the rapid identification of whole bacteria, either by comparison with archived reference spectra or by co-analysis with cultures of known bacteria. Bacteria were sampled from colonies on an agar plate, mixed with the matrix, air-dried, and introduced in batches into the mass spectrometer for analysis. In the first experiment, both bacterial strains that had been previously analyzed to obtain reference spectra and other strains that had not been analyzed were blind-numbered and their spectra were obtained. Those strains that matched reference spectra were found to be correctly identified. A second experiment involved co-analysis of reference strains and bind-numbered strains under identical conditions; species-specific identification was demonstrated by comparison of spectra of the blind-numbered strains with those of the standards. In all of the spectra obtained in these experiments, each bacterial strain showed a few characteristic high-mass ions which are thought to be derived from bacterial proteins. This work represents the first reported instance of successful bacterial chemotaxonomy by MALDI-TOFMS analysis of whole cells. For the strains tested, the method is rapid and simple.

Hyperhomocysteinemia, a risk factor for cardiovascular disease, is caused by nutritional and/or genetic disruptions in homocysteine metabolism. The most common genetic cause of hyperhomocysteinemia is the 677C-->T mutation in the methylenetetrahydrofolate reductase (MTHFR) gene. This variant, with mild enzymatic deficiency, is associated with an increased risk for neural tube defects and pregnancy complications and with a decreased risk for colon cancer and leukemia. Although many studies have reported that this variant is also a risk factor for vascular disease, this area of investigation is still controversial. Severe MTHFR deficiency results in homocystinuria, an inborn error of metabolism with neurological and vascular complications. To investigate the in vivo pathogenetic mechanisms of MTHFR deficiency, we generated mice with a knockout of MTHFR: Plasma total homocysteine levels in heterozygous and homozygous knockout mice are 1.6- and 10-fold higher than those in wild-type littermates, respectively. Both heterozygous and homozygous knockouts have either significantly decreased S-adenosylmethionine levels or significantly increased S-adenosylhomocysteine levels, or both, with global DNA hypomethylation. The heterozygous knockout mice appear normal, whereas the homozygotes are smaller and show developmental retardation with cerebellar pathology. Abnormal lipid deposition in the proximal portion of the aorta was observed in older heterozygotes and homozygotes, alluding to an atherogenic effect of hyperhomocysteinemia in these mice.

INTRODUCTION BACKGROUND TO METABOLOMICS: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

Ketamine is widely used as a pediatric anesthetic. Studies in developing rodents have indicated that ketamine-induced anesthesia results in brain cell death. Additional studies are needed to determine if ketamine anesthesia results in brain cell death in the nonhuman primate and if so, to begin to define the stage of development and the duration of ketamine anesthesia necessary to produce brain cell death. Rhesus monkeys (N = 3 for each treatment and control group) at three stages of development (122 days of gestation and 5 and 35 postnatal days [PNDs]) were administered ketamine intravenously for 24 h to maintain a surgical anesthetic plane, followed by a 6-h withdrawal period. Similar studies were performed in PND 5 animals with 3 h of ketamine anesthesia. Animals were subsequently perfused and brain tissue processed for analyses. Ketamine (24-h infusion) produced a significant increase in the number of caspase 3-, Fluoro-Jade C- and silver stain-positive cells in the cortex of gestational and PND 5 animals but not in PND 35 animals. Electron microscopy indicated typical nuclear condensation and fragmentation in some neuronal cells, and cell body swelling was observed in others indicating that ketamine-induced neuronal cell death is most likely both apoptotic and necrotic in nature. Ketamine increased N-methyl-D-aspartate (NMDA) receptor NR1 subunit messenger RNA in the frontal cortex where enhanced cell death was apparent. Earlier developmental stages (122 days of gestation and 5 PNDs) appear more sensitive to ketamine-induced neuronal cell death than later in development (35 PNDs). However, a shorter duration of ketamine anesthesia (3 h) did not result in neuronal cell death in the 5-day-old monkey.

MicroRNAs (miRs) are conserved small non-coding RNAs that negatively regulate gene expression. The miR profiles are markedly altered in cancers and some of them have a causal role in tumorigenesis. Here, we report changes in miR expression profile in hepatocellular carcinomas (HCCs) developed in male Fisher rats-fed folic acid, methionine, and choline-deficient (FMD) diet. Comparison of the miR profile by microarray analysis showed altered expression of some miRs in hepatomas compared to the livers from age-matched rats on the normal diet. While let-7a, miR-21, miR-23, miR-130, miR-190, and miR-17-92 family of genes was upregulated, miR-122, an abundant liver-specific miR, was downregulated in the tumors. The decrease in hepatic miR-122 was a tumor-specific event because it did not occur in the rats switched to the folate and methyl-adequate diet after 36 weeks on deficient diet, which did not lead to hepatocarcinogenesis. miR-122 was also silent in a transplanted rat hepatoma. Extrapolation of this study to human primary HCCs revealed that miR-122 expression was significantly (P = 0.013) reduced in 10 out of 20 tumors compared to the pair-matched control tissues. These findings suggest that the downregulation of miR-122 is associated with hepatocarcinogenesis and could be a potential biomarker for liver cancers.

Multi-walled carbon nanotubes (CNTs) can affect plant phenotype and the composition of soil microbiota. Tomato plants grown in soil supplemented with CNTs produce two times more flowers and fruit compared to plants grown in control soil. The effect of carbon nanotubes on microbial community of CNT-treated soil is determined by denaturing gradient gel electrophoresis and pyrosequencing analysis. Phylogenetic analysis indicates that Proteobacteria and Bacteroidetes are the most dominant groups in the microbial community of soil. The relative abundances of Bacteroidetes and Firmicutes are found to increase, whereas Proteobacteria and Verrucomicorbia decrease with increasing concentration of CNTs. The results of comparing diversity indices and species level phylotypes (OTUs) between samples showed that there is not a significant affect on bacterial diversity.