Max Perutz Labs

Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%. IQ-TREE is freely available at http://www.cibiv.at/software/iqtree.

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) systems provide bacteria and archaea with adaptive immunity against viruses and plasmids by using CRISPR RNAs (crRNAs) to guide the silencing of invading nucleic acids. We show here that in a subset of these systems, the mature crRNA that is base-paired to trans-activating crRNA (tracrRNA) forms a two-RNA structure that directs the CRISPR-associated protein Cas9 to introduce double-stranded (ds) breaks in target DNA. At sites complementary to the crRNA-guide sequence, the Cas9 HNH nuclease domain cleaves the complementary strand, whereas the Cas9 RuvC-like domain cleaves the noncomplementary strand. The dual-tracrRNA:crRNA, when engineered as a single RNA chimera, also directs sequence-specific Cas9 dsDNA cleavage. Our study reveals a family of endonucleases that use dual-RNAs for site-specific DNA cleavage and highlights the potential to exploit the system for RNA-programmable genome editing.

IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.

Several reactive oxygen species (ROS) are continuously produced in plants as byproducts of aerobic metabolism. Depending on the nature of the ROS species, some are highly toxic and rapidly detoxified by various cellular enzymatic and nonenzymatic mechanisms. Whereas plants are surfeited with mechanisms to combat increased ROS levels during abiotic stress conditions, in other circumstances plants appear to purposefully generate ROS as signaling molecules to control various processes including pathogen defense, programmed cell death, and stomatal behavior. This review describes the mechanisms of ROS generation and removal in plants during development and under biotic and abiotic stress conditions. New insights into the complexity and roles that ROS play in plants have come from genetic analyses of ROS detoxifying and signaling mutants. Considering recent ROS-induced genome-wide expression analyses, the possible functions and mechanisms for ROS sensing and signaling in plants are compared with those in animals and yeast.

The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches. Here, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.

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,

This article presents W-IQ-TREE, an intuitive and user-friendly web interface and server for IQ-TREE, an efficient phylogenetic software for maximum likelihood analysis. W-IQ-TREE supports multiple sequence types (DNA, protein, codon, binary and morphology) in common alignment formats and a wide range of evolutionary models including mixture and partition models. W-IQ-TREE performs fast model selection, partition scheme finding, efficient tree reconstruction, ultrafast bootstrapping, branch tests, and tree topology tests. All computations are conducted on a dedicated computer cluster and the users receive the results via URL or email. W-IQ-TREE is available at http://iqtree.cibiv.univie.ac.at It is free and open to all users and there is no login requirement.

Nonparametric bootstrap has been a widely used tool in phylogenetic analysis to assess the clade support of phylogenetic trees. However, with the rapidly growing amount of data, this task remains a computational bottleneck. Recently, approximation methods such as the RAxML rapid bootstrap (RBS) and the Shimodaira-Hasegawa-like approximate likelihood ratio test have been introduced to speed up the bootstrap. Here, we suggest an ultrafast bootstrap approximation approach (UFBoot) to compute the support of phylogenetic groups in maximum likelihood (ML) based trees. To achieve this, we combine the resampling estimated log-likelihood method with a simple but effective collection scheme of candidate trees. We also propose a stopping rule that assesses the convergence of branch support values to automatically determine when to stop collecting candidate trees. UFBoot achieves a median speed up of 3.1 (range: 0.66-33.3) to 10.2 (range: 1.32-41.4) compared with RAxML RBS for real DNA and amino acid alignments, respectively. Moreover, our extensive simulations show that UFBoot is robust against moderate model violations and the support values obtained appear to be relatively unbiased compared with the conservative standard bootstrap. This provides a more direct interpretation of the bootstrap support. We offer an efficient and easy-to-use software (available at http://www.cibiv.at/software/iqtree) to perform the UFBoot analysis with ML tree inference.

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

In phylogenomics the analysis of concatenated gene alignments, the so-called supermatrix, is commonly accompanied by the assumption of partition models. Under such models each gene, or more generally partition, is allowed to evolve under its own evolutionary model. Although partition models provide a more comprehensive analysis of supermatrices, missing data may hamper the tree search algorithms due to the existence of phylogenetic (partial) terraces. Here, we introduce the phylogenetic terrace aware (PTA) data structure for the efficient analysis under partition models. In the presence of missing data PTA exploits (partial) terraces and induced partition trees to save computation time. We show that an implementation of PTA in IQ-TREE leads to a substantial speedup of up to 4.5 and 8 times compared with the standard IQ-TREE and RAxML implementations, respectively. PTA is generally applicable to all types of partition models and common topological rearrangements thus can be employed by all phylogenomic inference software.

Alternative splicing (AS) of precursor mRNAs (pre-mRNAs) from multiexon genes allows organisms to increase their coding potential and regulate gene expression through multiple mechanisms. Recent transcriptome-wide analysis of AS using RNA sequencing has revealed that AS is highly pervasive in plants. Pre-mRNAs from over 60% of intron-containing genes undergo AS to produce a vast repertoire of mRNA isoforms. The functions of most splice variants are unknown. However, emerging evidence indicates that splice variants increase the functional diversity of proteins. Furthermore, AS is coupled to transcript stability and translation through nonsense-mediated decay and microRNA-mediated gene regulation. Widespread changes in AS in response to developmental cues and stresses suggest a role for regulated splicing in plant development and stress responses. Here, we review recent progress in uncovering the extent and complexity of the AS landscape in plants, its regulation, and the roles of AS in gene regulation. The prevalence of AS in plants has raised many new questions that require additional studies. New tools based on recent technological advances are allowing genome-wide analysis of RNA elements in transcripts and of chromatin modifications that regulate AS. Application of these tools in plants will provide significant new insights into AS regulation and crosstalk between AS and other layers of gene regulation.

and the cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825 and ONO 5334. Notably, MDL-28170, ONO 5334 and apilimod were found to antagonize viral replication in human pneumocyte-like cells derived from induced pluripotent stem cells, and apilimod also demonstrated antiviral efficacy in a primary human lung explant model. Since most of the molecules identified in this study have already advanced into the clinic, their known pharmacological and human safety profiles will enable accelerated preclinical and clinical evaluation of these drugs for the treatment of COVID-19.

Landscape genomics is an emerging research field that aims to identify the environmental factors that shape adaptive genetic variation and the gene variants that drive local adaptation. Its development has been facilitated by next-generation sequencing, which allows for screening thousands to millions of single nucleotide polymorphisms in many individuals and populations at reasonable costs. In parallel, data sets describing environmental factors have greatly improved and increasingly become publicly accessible. Accordingly, numerous analytical methods for environmental association studies have been developed. Environmental association analysis identifies genetic variants associated with particular environmental factors and has the potential to uncover adaptive patterns that are not discovered by traditional tests for the detection of outlier loci based on population genetic differentiation. We review methods for conducting environmental association analysis including categorical tests, logistic regressions, matrix correlations, general linear models and mixed effects models. We discuss the advantages and disadvantages of different approaches, provide a list of dedicated software packages and their specific properties, and stress the importance of incorporating neutral genetic structure in the analysis. We also touch on additional important aspects such as sampling design, environmental data preparation, pooled and reduced-representation sequencing, candidate-gene approaches, linearity of allele-environment associations and the combination of environmental association analyses with traditional outlier detection tests. We conclude by summarizing expected future directions in the field, such as the extension of statistical approaches, environmental association analysis for ecological gene annotation, and the need for replication and post hoc validation studies.

Alternative splicing (AS) is a key regulatory mechanism that contributes to transcriptome and proteome diversity. As very few genome-wide studies analyzing AS in plants are available, we have performed high-throughput sequencing of a normalized cDNA library which resulted in a high coverage transcriptome map of Arabidopsis. We detect ∼150,000 splice junctions derived mostly from typical plant introns, including an eightfold increase in the number of U12 introns (2069). Around 61% of multiexonic genes are alternatively spliced under normal growth conditions. Moreover, we provide experimental validation of 540 AS transcripts (from 256 genes coding for important regulatory factors) using high-resolution RT-PCR and Sanger sequencing. Intron retention (IR) is the most frequent AS event (∼40%), but many IRs have relatively low read coverage and are less well-represented in assembled transcripts. Additionally, ∼51% of Arabidopsis genes produce AS transcripts which do not involve IR. Therefore, the significance of IR in generating transcript diversity was generally overestimated in previous assessments. IR analysis allowed the identification of a large set of cryptic introns inside annotated coding exons. Importantly, a significant fraction of these cryptic introns are spliced out in frame, indicating a role in protein diversity. Furthermore, we show extensive AS coupled to nonsense-mediated decay in AFC2, encoding a highly conserved LAMMER kinase which phosphorylates splicing factors, thus establishing a complex loop in AS regulation. We provide the most comprehensive analysis of AS to date which will serve as a valuable resource for the plant community to study transcriptome complexity and gene regulation.

Background & Aims: Recent studies have highlighted the role of noncoding RNAs (ncRNAs) in carcinogenesis, and suggested that this class of genes might be used as biomarkers in cancer. We searched the human genome for novel genes including ncRNAs related to hepatocellular carcinoma (HCC). Methods: An HCC-specific gene library was generated and screened for deregulated genes with 46 HCCs, 4 focal nodular hyperplasias, and 7 cirrhoses utilizing cDNA arrays. Sequencing of library clones identified a novel ncRNA as the most up-regulated gene in HCC. This gene was also cloned from different monkeys and characterized by quantitative RT-PCR, Northern blot analysis and in situ hybridization. Structural and functional studies included comparative sequence and protein expression analyses, quantitative RT-PCR of polysomal preparations, and siRNA-mediated knockdown experiments. Results: The most up-regulated gene in HCC named highly up-regulated in liver cancer (HULC) was characterized as a novel mRNA-like ncRNA. HULC RNA is spliced and polyadenlyated, and resembles the mammalian LTR transposon 1A. It does not contain substantial open reading frames, and no native translation product was detected. HULC is present in the cytoplasm, where it copurifies with ribosomes. siRNA-mediated knockdown of HULC RNA in 2 HCC cell lines altered the expression of several genes, 5 of which were known to be affected in HCC, suggesting a role for HULC in post-transcriptional modulation of gene expression. Conclusions: HULC is the first ncRNA with highly specific up-regulation in HCC. Because HULC was detected in blood of HCC patients, a potential use as novel biomarker can be envisaged. Background & Aims: Recent studies have highlighted the role of noncoding RNAs (ncRNAs) in carcinogenesis, and suggested that this class of genes might be used as biomarkers in cancer. We searched the human genome for novel genes including ncRNAs related to hepatocellular carcinoma (HCC). Methods: An HCC-specific gene library was generated and screened for deregulated genes with 46 HCCs, 4 focal nodular hyperplasias, and 7 cirrhoses utilizing cDNA arrays. Sequencing of library clones identified a novel ncRNA as the most up-regulated gene in HCC. This gene was also cloned from different monkeys and characterized by quantitative RT-PCR, Northern blot analysis and in situ hybridization. Structural and functional studies included comparative sequence and protein expression analyses, quantitative RT-PCR of polysomal preparations, and siRNA-mediated knockdown experiments. Results: The most up-regulated gene in HCC named highly up-regulated in liver cancer (HULC) was characterized as a novel mRNA-like ncRNA. HULC RNA is spliced and polyadenlyated, and resembles the mammalian LTR transposon 1A. It does not contain substantial open reading frames, and no native translation product was detected. HULC is present in the cytoplasm, where it copurifies with ribosomes. siRNA-mediated knockdown of HULC RNA in 2 HCC cell lines altered the expression of several genes, 5 of which were known to be affected in HCC, suggesting a role for HULC in post-transcriptional modulation of gene expression. Conclusions: HULC is the first ncRNA with highly specific up-regulation in HCC. Because HULC was detected in blood of HCC patients, a potential use as novel biomarker can be envisaged. Noncoding RNAs (ncRNAs) have emerged as a new class of functional transcripts in eukaryotic cells, and were grouped into 3 subclasses according to their number of nucleotides.1Mattick J.S. Non-coding RNAs: the architects of eukaryotic complexity.EMBO Rep. 2001; 2: 986-991Crossref PubMed Scopus (626) Google Scholar, 2Costa F.F. Non-coding RNAs: new players in eukaryotic biology.Gene. 2005; 357: 83-94Crossref PubMed Scopus (282) Google Scholar The growing class of micro-RNAs (mRNAs) (21–25 nt) has been related to cell differentiation and cancer in recent publications.3Lu J. Getz G. Miska E.A. Alvarez-Saavedra E. Lamb J. Peck D. Sweet-Cordero A. Ebert B.L. Mak R.H. Ferrando A.A. Downing J.R. Jacks T. Horvitz H.R. Golub T.R. MicroRNA expression profiles classify human cancers.Nature. 2005; 435: 834-838Crossref PubMed Scopus (8237) Google Scholar, 4Bentwich I. Avniel A. Karov Y. Aharonov R. Gilad S. Barad O. Barzilai A. Einat P. Einav U. Meiri E. Sharon E. Spector Y. Bentwich Z. Identification of hundreds of conserved and nonconserved human microRNAs.Nat Genet. 2005; 37: 766-770Crossref PubMed Scopus (1566) Google Scholar, 5Jopling C. Yi M. Lancaster A. Lemon S. Sarnow P. Modulation of hepatitis C virus RNA abundance by a liver-specific microRNA.Science. 2005; 309: 1577-1581Crossref PubMed Scopus (2140) Google Scholar Small ncRNAs with a length of 100–200 nt are commonly found as translational repressors, and long ncRNAs (>10.000 nt) are involved in gene silencing.2Costa F.F. Non-coding RNAs: new players in eukaryotic biology.Gene. 2005; 357: 83-94Crossref PubMed Scopus (282) Google Scholar According to their structural features, these 3 subclasses of heterogeneous transcriptional units can be further subcategorized. mRNA-like ncRNAs, for example, usually lack extensive open reading frames (ORF) and are, therefore, difficult to predict from genomic sequences. In general, they are more than 200 nt long and, in most of the cases, they are spliced and polyadenylated.6Erdmann V.A. Szymanski M. Hochberg A. de Groot N. Barciszewski J. Collection of mRNA-like non-coding RNAs.Nucleic Acids Res. 1999; 27: 192-195Crossref PubMed Scopus (63) Google Scholar Recent studies imply ncRNAs in the regulation of gene expression by a variety of mechanisms such as RNA interference, gene silencing, imprinting, and DNA demethylation, indicating that this novel class of transcripts plays a central role in development and cell differentiation,6Erdmann V.A. Szymanski M. Hochberg A. de Groot N. Barciszewski J. Collection of mRNA-like non-coding RNAs.Nucleic Acids Res. 1999; 27: 192-195Crossref PubMed Scopus (63) Google Scholar although to most ncRNAs no specific function has been ascribed. Increasing evidence relates changes in expression levels of ncRNAs to complex diseases such as cancer.7Tam W. Ben-Yehuda D. Hayward W.S. Bic, a novel gene activated by proviral insertions in avian leukosis virus-induced lymphomas, is likely to function through its non-coding RNA.Mol Cell Biol. 1997; 3: 1490-1502Google Scholar, 8Ji P. Diederichs S. 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Elevated expression of PCGEM1, a prostate-specific gene with cell growth-promoting function, is associated with high-risk prostate cancer patients.Oncogene. 2004; 23: 605-611Crossref PubMed Scopus (220) Google Scholar BC200 RNA overexpression has recently been correlated with the progression of breast tumors and proposed as a new molecular marker for breast carcinomas.14Iacoangeli A. Lin Y. Morley E.J. Muslimov I.A. Bianchi R. Reilly J. Weedon J. Diallo R. Bocker W. Tiedge H. BC200 RNA in invasive and preinvasive breast cancer.Carcinogenesis. 2004; 25: 2125-2133Crossref PubMed Scopus (136) Google Scholar Increased expression of the MALAT-1 gene indicates a worse clinical outcome in lung cancer patients, and further the potential role of ncRNAs in P. Diederichs S. Wang W. Boing S. Metzger R. Schneider P.M. Tidow N. Brandt B. Buerger H. Bulk E. Thomas M. Berdel W.E. Serve H. Muller-Tidow C. MALAT-1, a novel non-coding RNA, and thymosin beta4 predict metastasis and survival in early-stage non-small cell lung cancer.Oncogene. 2003; 22: 8031-8041Crossref PubMed Scopus (1799) Google Scholar carcinoma is of the of and a J. P. the cancer J 2001; PubMed Scopus Google Scholar a and with a complex most the of liver by hepatitis C virus liver a variety of J.W. of human hepatocellular Genet. PubMed Scopus Google Scholar The of these liver diseases to the development of liver which HCC the highly HCC, several also in the The most in the liver is focal nodular which is characterized by of and that are to be related to liver T. R. V. in focal nodular of the PubMed Scopus Google Scholar the evidence for a ncRNAs and and a for novel which are associated with the molecular of HCC, not in gene and HCC-specific gene and cDNA identified a novel ncRNA as the most up-regulated gene in the and named it HULC up-regulated in liver to its expression a of this first ncRNA associated with HCC. were from the the of of from tumors and from were in with in and in The was by the of the of RNA was from 3 HCC and from 3 liver and was cDNA and were the cDNA were cloned into and into For and the was were from the HCC cDNA cDNA clones genes with in cell and were from the for and were included as for clones were and according to P. R. K. C. S. R. E. N. J. to cDNA 2000; PubMed Scopus Google Scholar and a were the to of were used for RNA with included a liver as In a of RNA of were with different of The RNAs were with 3 5 by were to the for to P. R. K. C. S. R. E. N. J. to cDNA 2000; PubMed Scopus Google Scholar and were a were by and and for were in the 3 and 5 by were by from the According to their the in the 2 was were of was by the The was used to for with In and and 7 the the of of were and a RNA The first the RNA not to RNA were by quantitative RT-PCR and of HULC and RNA were from RNA genes expression levels in the with where the first were of siRNA-mediated HULC knockdown in the and cell were with 2 different and 2 different for HULC RNA expression levels from 2 were by quantitative RT-PCR in the knockdown to the present the of The of cDNA cDNA was used for the of and from RNA, from RNA from a of HCC and from RNA with The sequence for the HULC has been into the with the number of RNA were a and to a by The HULC to was with J. Xu T. Zhang J. W. G. G. M. Xu W. J.R. Z. into hepatocellular by gene expression profiles of hepatocellular carcinoma with of Natl Acad Sci U S A. 2001; PubMed Scopus Google the was used for and and were detected by For the of blood from blood for which was 5 of blood was in 5 were in and to in a in a for and RNA from cell from the by of and for and was the For the of RNA from were the to of were used for RNA with according to the RNA from was as A. B. K. H. gene in 2005; PubMed Scopus Google Scholar cDNA was from of RNA from 3 including the liver from the cDNA were used with of cDNA and were in were generated in with and and the were The were HULC HULC HULC were from to the gene, RNA were as in situ was as P. M. A. C. K. H. as for J Pathol. PubMed Scopus Google Scholar The was generated by in of were in and of the were with a and with were in with and the and were a in for 2 were from the and RNA were cDNA was from RNA and to quantitative RT-PCR were in 2 and was and a with a were with the and for The were to the putative were detected with the were in with and in and were with 4 2 were into the in were from and analysis 2 different of the HULC RNA were used for the for HULC and for HULC involved a and a as and sequence as was according to the were to with and in For of a for in were by of and was to were of a were and to RNA of RNA was and the of was by quantitative were to hybridization. The of RNA from was the with a RNA were to of RNA the The cDNA product was to genes according to the were the For cell and for knockdown 2 were were the and analysis were used to genes affected by siRNA-mediated Expression were by and the up-regulated genes were to functional to and affected by the For a gene expression generated HCC-specific cDNA by Z. gene expression Natl Acad Sci U S A. PubMed Scopus Google Scholar from the HCC cDNA library in with and genes were used for the of HCC-specific cDNA a of cDNA of 46 HCCs, 4 7 and 2 were with a of liver be as The most of up-regulated genes a with and 2 genes that are known to be highly up-regulated the of human HCC J.W. of human hepatocellular Genet. PubMed Scopus Google Scholar, L. J.P. R. C. C. of in human liver PubMed Google Scholar, E. of the and of are in liver and Res. Google Scholar the liver in of HCCs, and by 3 HCC-specific library this was named HULC expression levels were to a up-regulated in In to the in liver HULC was up-regulated in liver HULC is commonly overexpressed in neoplastic of a of normal and their were for HULC RNA expression by quantitative RT-PCR HULC was in most of the normal and not in the of the neoplastic The of HULC RNA levels in prostate and was as as in HCC, where the HULC RNA expression normal and neoplastic by a of The quantitative RT-PCR the cDNA although the levels of up-regulation were in the cDNA these a highly specific up-regulation of HULC RNA expression levels in HCC. In situ the specific and expression of HULC RNA in the of HCC, it was not detected in and liver Northern blot analysis identified HULC with a length of HULC RNA levels were in HCC than in liver which is with cDNA and quantitative RT-PCR a gene and to the HULC transcriptional and and of cDNA cDNA were Sequencing of the HULC sequence to a human analysis of the sequence to the of the HULC transcriptional The HULC gene a and and 2 and from the and The genomic the HULC is of LTR mammalian LTR transposon indicating the of a this A. Identification of a of mammalian Acids Res. 2: Scopus Google Scholar that to no cDNA in the as from the abundance of the HULC identified 5 to HULC in the genomic and and the genome of these were from a of liver and cDNA a of from including in of not HULC in for a in for HULC in cloned and HULC from 3 monkeys and HULC were with quantitative RT-PCR from liver RNA from these human Sequencing of the that HULC were highly conserved in these the of and the and in the In analysis of was J. D. T.R. J. Brosius J. The BC200 RNA gene and its neural expression are conserved in PubMed Scopus Google Scholar The human HULC a of with of 5 in In the to was and to 2 was from the HULC This indicates of HULC and that it is transcriptional to have a HULC The sequence and of the HULC transcriptional in are highly for several to to for the and for the no is for the HULC transcriptional of the HULC RNA sequence is the of in the potential reading frames the HULC in translation of the HULC RNA not the of a substantial in the HULC The with a functional translation is nt and a gene product of the sequence does not contain known protein does it to in the lack of than suggested that the HULC RNA might not be J.S. the the of RNAs in complex 2003; 25: PubMed Scopus Google Scholar in translation to protein product was generated from the HULC RNA In translation as as overexpression of HULC in not not protein product to than 4 generated to 2 from this potential sequence to a putative protein by this these studies were of HCC and and cell with the putative HULC expression In was a HULC protein not be detected by analysis of from levels of HULC RNA from with a HULC expression the putative HULC sequence was to the of to a the detected the sequence in the protein indicating that were to to the used for The of HULC was further by analysis of with quantitative In with the by the of HULC were detected in the not Because 2 human ncRNAs, and were to be associated with and to J. D. T.R. J. Brosius J. The BC200 RNA gene and its neural expression are conserved in PubMed Scopus Google Scholar, H. P. S. Y. of in cancer to of and Res. Google Scholar, M. B. A. A. S. B. C. The protein with RNA and the translation of specific 2003; PubMed Scopus Google Scholar further HULC RNA also to the ribosomes. of RNA from the by and quantitative RT-PCR analysis of RNA from a of HULC RNA in the a first into a role of HULC in hepatocellular carcinogenesis, the of siRNA-mediated knockdown of HULC expression was in 2 cell lines by transcriptional For that and were for with 2 different and HULC different of the HULC different HULC and a were included in this to the and of HULC to C was used as and for of the not RNA from 2 of these was to quantitative RT-PCR for of HULC knockdown of and were for HULC and 2 in cells, HULC knockdown in was and HULC expression by and with the of the not these the expression of the marker genes and protein B. G. Zhang Y. Wang J.R. Lin Z. G. of by PubMed Scopus Google Scholar was not affected not For transcriptional RNAs from 2 knockdown in and with the 2 different HULC and the 2 different were to the of genes that were in as as in to the 2 which several have been in the of liver different features, searched for these and HULC and these the which not in Cell with to in HULC cell cell associated protein cell protein gene protein protein to protein RNA 4 that have been in the of liver cancer. in cell lines with to protein protein protein cell and with cell and in HULC associated protein are grouped with their and function according to that have been in the of liver cancer. in cell lines with to in a new are grouped with their and function according to In a blood from with liver and 4 HCC were by quantitative RT-PCR for the of HULC RNA in of The for 3 of 4 HCC of HULC RNA HULC expression levels in blood were in blood 3 and in blood 4 than in the of known liver For 2 of the HCC of blood and blood and from tumors and liver were for a of HULC expression in blood with HULC expression in liver HCC were from the and liver the and HULC expression levels in were by quantitative HULC expression in HCC was 5 and than in the liver not HCC-specific cDNA identified a novel mRNA-like as of the most up-regulated genes in HCC. 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Identification of a of mammalian Acids Res. 2: Scopus Google Scholar We that a mammalian LTR transposon from J. D. T.R. J. Brosius J. The BC200 RNA gene and its neural expression are conserved in PubMed Scopus Google Scholar a of HULC was not the the HULC gene to and This that HULC has been by the monkeys from J. D. T.R. J. Brosius J. The BC200 RNA gene and its neural expression are conserved in PubMed Scopus Google Scholar by J. and from the RNA to the 2003; 3: PubMed Scopus Google Scholar are into that expression of J. a for and Natl Acad Sci U S A. PubMed Scopus Google Scholar might be for The of this is further by the conserved of are most to of the of the sequence is of a functional of this more than 2 of the HULC were for their the and these not the of which are for in the the functional of conserved in the HULC sequence the function of HULC were from siRNA-mediated knockdown of HULC in and cells, which in a and of several genes, of which have been in the of liver cancer. This indicates that not be HULC gene, a more role for The of HULC with the a to its of as it is of the of which have recently been found to be associated with the the complex that is to and can as translational Verhaegh G.W. D. Schalken J.A. a and specific marker to prostate Res. Google Scholar In to such ncRNAs, for which a to the has been F.F. Non-coding RNAs: new players in eukaryotic biology.Gene. 2005; 357: 83-94Crossref PubMed Scopus (282) Google Scholar and which have as involved in carcinogenesis, HULC as for the of mRNA-like ncRNA with ribosomes. studies no HULC and its putative indicating that the of HULC its not be the by which HULC of a to be HULC expression was also in which is a liver from of to T. R. V. in focal nodular of the PubMed Scopus Google Scholar does not to HCC, are several affected in which also a role in in cell cell of a cell and The up-regulation of HULC expression in also HULC expression with suggesting that HULC to modulation of gene expression in than involved in the of the highly HCC is no of are no in to this in more the of HULC with the and knockdown a role of HULC in the post-transcriptional of gene expression. The recent of new of ncRNAs in of cancer and progression the role of these transcripts in the of and several potential for and biomarkers for for example, is the most prostate ncRNA M.J. van Bokhoven A. Verhaegh G.W. Smit F.P. Karthaus H.F. Schalken J.A. Debruyne F.M. Ru N. Isaacs W.B. DD3: a new prostate-specific gene, highly overexpressed in prostate cancer.Cancer Res. 1999; 59: 5975-5979PubMed Google Scholar and a and specific marker for the of J.W. E.J. complex in 2004; PubMed Scopus Google Scholar is deregulated in breast W. Bocker W. Brosius J. Tiedge H. Expression of neural BC200 RNA in human tumours.J Pathol. 1997; 183: 345-351Crossref PubMed Scopus (174) Google Scholar and BC200 RNA overexpression was recently as a new molecular marker for a in breast carcinomas.14Iacoangeli A. Lin Y. Morley E.J. Muslimov I.A. Bianchi R. Reilly J. Weedon J. Diallo R. Bocker W. Tiedge H. BC200 RNA in invasive and preinvasive breast cancer.Carcinogenesis. 2004; 25: 2125-2133Crossref PubMed Scopus (136) Google Scholar In this the potential role of HULC as a novel biomarker is its expression and by the that HULC RNA can be detected in the blood of HCC and in by H. P. S. Y. of in cancer to of and Res. Google Scholar, R. R. L. R. J.P. M. development the of 2003; PubMed Scopus Google Scholar The A. J. and E. for J. K. K. H. M. D. M. A. R. and M. for and W. and M. G. for the We also and of the of of for human and associated

Abstract IQ-TREE ( http://www.iqtree.org ) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.

Selective autophagy of damaged mitochondria requires autophagy receptors optineurin (OPTN), NDP52 (CALCOCO2), TAX1BP1, and p62 (SQSTM1) linking ubiquitinated cargo to autophagic membranes. By using quantitative proteomics, we show that Tank-binding kinase 1 (TBK1) phosphorylates all four receptors on several autophagy-relevant sites, including the ubiquitin- and LC3-binding domains of OPTN and p62/SQSTM1 as well as the SKICH domains of NDP52 and TAX1BP1. Constitutive interaction of TBK1 with OPTN and the ability of OPTN to bind to ubiquitin chains are essential for TBK1 recruitment and kinase activation on mitochondria. TBK1 in turn phosphorylates OPTN's UBAN domain at S473, thereby expanding the binding capacity of OPTN to diverse Ub chains. In combination with phosphorylation of S177 and S513, this posttranslational modification promotes recruitment and retention of OPTN/TBK1 on ubiquitinated, damaged mitochondria. Moreover, phosphorylation of OPTN on S473 enables binding to pS65 Ub chains and is also implicated in PINK1-driven and Parkin-independent mitophagy. Thus, TBK1-mediated phosphorylation of autophagy receptors creates a signal amplification loop operating in selective autophagy of damaged mitochondria.

The ADAR RNA-editing enzymes deaminate adenosine bases to inosines in cellular RNAs. Aberrant interferon expression occurs in patients in whom ADAR1 mutations cause Aicardi-Goutières syndrome (AGS) or dystonia arising from striatal neurodegeneration. Adar1 mutant mouse embryos show aberrant interferon induction and die by embryonic day E12.5. We demonstrate that Adar1 embryonic lethality is rescued to live birth in Adar1; Mavs double mutants in which the antiviral interferon induction response to cytoplasmic double-stranded RNA (dsRNA) is prevented. Aberrant immune responses in Adar1 mutant mouse embryo fibroblasts are dramatically reduced by restoring the expression of editing-active cytoplasmic ADARs. We propose that inosine in cellular RNA inhibits antiviral inflammatory and interferon responses by altering RLR interactions. Transfecting dsRNA oligonucleotides containing inosine-uracil base pairs into Adar1 mutant mouse embryo fibroblasts reduces the aberrant innate immune response. ADAR1 mutations causing AGS affect the activity of the interferon-inducible cytoplasmic isoform more severely than the nuclear isoform.

Oxidative and replication stress underlie genomic instability of cancer cells. Amplifying genomic instability through radiotherapy and chemotherapy has been a powerful but nonselective means of killing cancer cells. Precision medicine has revolutionized cancer therapy by putting forth the concept of selective targeting of cancer cells. Poly(ADP-ribose) polymerase (PARP) inhibitors represent a successful example of precision medicine as the first drugs targeting DNA damage response to have entered the clinic. PARP inhibitors act through synthetic lethality with mutations in DNA repair genes and were approved for the treatment of BRCA mutated ovarian and breast cancer. PARP inhibitors destabilize replication forks through PARP DNA entrapment and induce cell death through replication stress-induced mitotic catastrophe. Inhibitors of poly(ADP-ribose) glycohydrolase (PARG) exploit and exacerbate replication deficiencies of cancer cells and may complement PARP inhibitors in targeting a broad range of cancer types with different sources of genomic instability. Here I provide an overview of the molecular mechanisms and cellular consequences of PARP and PARG inhibition. I highlight clinical performance of four PARP inhibitors used in cancer therapy (olaparib, rucaparib, niraparib, and talazoparib) and discuss the predictive biomarkers of inhibitor sensitivity, mechanisms of resistance as well as the means of overcoming them through combination therapy.

The family Picornaviridae comprises small non-enveloped viruses with RNA genomes of 6.7 to 10.1 kb, and contains >30 genera and >75 species. Most of the known picornaviruses infect mammals and birds, but some have also been detected in reptiles, amphibians and fish. Many picornaviruses are important human and veterinary pathogens and may cause diseases of the central nervous system, heart, liver, skin, gastrointestinal tract or upper respiratory tract. Most picornaviruses are transmitted by the faecal-oral or respiratory routes. This is a summary of the International Committee on Taxonomy of Viruses (ICTV) Report on the taxonomy of the Picornaviridae, which is available at www.ictv.global/report/picornaviridae.