604 research outputs found
EBV-positive Hodgkin lymphoma is associated with suppression of p21cip1/waf1 and a worse prognosis
- Author
- Publication venue
- BioMed Central
- Publication date
- 01/01/2010
- Field of study
<p>Abstract</p> <p>Background</p> <p>About 30-50% of Hodgkin lymphomas (HLs) harbor the Epstein-Barr virus (EBV), but the impact of EBV infection on clinical outcomes has been unclear. EBV-encoded small RNAs (<it>EBER</it>s) are presented in all EBV-infected cells, but their functions are still less understood.</p> <p>Results</p> <p><it>EBER1 </it>was transfected into two HL cell lines, KMH2 and L428, and microarrays were used to screen for <it>EBER1</it>-induced changes. We found that <it>EBER1 </it>suppressed <it>p21</it><sup>cip1/waf1 </sup>transcription in HL cell lines. In addition, positive regulators of <it>p21</it><sup>cip1/waf1 </sup>transcription, such as p53, EGR1, and STAT1, were decreased. Suppression of <it>p21</it><sup>cip1/waf1 </sup>in the <it>EBER1</it><sup>+ </sup>HL cell lines was associated with increased resistance to histone deacetylase inhibitors or proteasome inhibitors, drugs known to cause apoptosis by increasing p21<sup>cip1/waf1 </sup>levels. On biopsy specimens, EBV<sup>+ </sup>HLs had weaker expression of both p21<sup>cip1/waf1 </sup>and active caspase 3. Clinically, suppression of p21<sup>cip1/waf1 </sup>in EBV<sup>+ </sup>HLs was associated with a worse 2-year disease-free survival rate (45% for EBV<sup>+ </sup>HLs <it>vs</it>. 77% for EBV<sup>- </sup>HLs, <it>p </it>= 0.002).</p> <p>Conclusion</p> <p>Although the underlying mechanisms are still relatively unclear, <it>EBER1 </it>inhibits <it>p21</it><sup>cip1/waf1 </sup>transcription and prevents apoptosis through down-regulation of p53, EGR1, and STAT1. The anti-apoptotic activity of <it>EBER1 </it>may be important in the rescue of Reed-Sternberg cells from drug-induced apoptosis and in the clinical behaviors of EBV<sup>+ </sup>HLs.</p
The InterPro protein families database: the classification resource after 15 years
- Author
- Attwood Teresa K.
- Bateman Alex
- Bork Peer
- Chang Hsin-Yu
- Daugherty Louise
- Finn Robert D.
- Fraser Matthew
- Gough Julian
- Guyot Dominique
- Haft Daniel
- Huang Hongzhan
- Hunter Sarah
- Kahn Daniel
- Letunic Ivica
- Lopez Rodrigo
- McAnulla Craig
- McMenamin Conor
- Mi Huaiyu
- Mitchell Alex
- Natale Darren A.
- Nuka Gift
- Oates Matt
- Orengo Christine
- Pesseat Sebastien
- Punta Marco
- Rato Claudia
- Redaschi Nicole
- Rivoire Catherine
- Sangrador-Vegas Amaia
- Scheremetjew Maxim
- Sigrist Christian J.A.
- Sillitoe Ian
- Thomas Paul D.
- Wu Cathy H.
- Xenarios Ioannis
- Yong Siew-Yit
- Publication venue
- Publication date
- 02/08/2017
- Field of study
The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 201
InterPro in 2017-beyond protein family and domain annotations
- Author
- Attwood Teresa K.
- Babbitt Patricia C.
- Bateman Alex
- Bork Peer
- Bridge Alan J.
- Chang Hsin Yu
- Dosztanyi Zsuzsanna
- El-Gebali Sara
- Finn Robert D.
- Fraser Matthew
- Gough Julian
- Haft David
- Holliday Gemma L.
- Huang Hongzhan
- Huang Xiaosong
- Letunic Ivica
- Lopez Rodrigo
- Lu Shennan
- Marchler-Bauer Aron
- Mi Huaiyu
- Mistry Jaina
- Mitchell Alex L.
- Natale Darren A.
- Necci Marco
- Nuka Gift
- Orengo Christine A.
- Park Youngmi
- Pesseat Sebastien
- Piovesan Damiano
- Potter Simon C.
- Rawlings Neil D.
- Redaschi Nicole
- Richardson Lorna
- Rivoire Catherine
- Sangrador-Vegas Amaia
- Sigrist Christian
- Sillitoe Ian
- Smithers Ben
- Squizzato Silvano
- Sutton Granger
- Thanki Narmada
- Thomas Paul D.
- Tosatto Silvio C.E.
- Wu Cathy H.
- Xenarios Ioannis
- Yeh Lai Su
- Young Siew Yit
- Publication venue
- 'Oxford University Press (OUP)'
- Publication date
- 28/11/2016
- Field of study
InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPro's predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences
Recommended from our members
InterPro in 2019: improving coverage, classification and access to protein sequence annotations
- Author
- Attwood Teresa K.
- Babbitt Patricia C.
- Blum Matthias
- Bork Peer
- Bridge Alan
- Brown Shoshana D.
- Chang Hsin-Yu
- El-Gebali Sara
- Finn Robert D.
- Fraser Matthew I.
- Gough Julian
- Haft David R.
- Huang Hongzhan
- Letunic Ivica
- Lopez Rodrigo
- Luciani Aur\ue9lien
- Madeira Fabio
- Marchler-Bauer Aron
- Mi Huaiyu
- Mitchell Alex L.
- Natale Darren A.
- Necci Marco
- Nuka Gift
- Orengo Christine
- Pandurangan Arun P.
- Paysan-Lafosse Typhaine
- Pesseat Sebastien
- Potter Simon C.
- Qureshi Matloob A.
- Rawlings Neil D.
- Redaschi Nicole
- Richardson Lorna J.
- Rivoire Catherine
- Salazar Gustavo A.
- Sangrador-Vegas Amaia
- Sigrist Christian J A
- Sillitoe Ian
- Sutton Granger G.
- Thanki Narmada
- Thomas Paul D.
- Tosatto Silvio C E
- Yong Siew-Yit
- Publication venue
- 'Oxford University Press (OUP)'
- Publication date
- 01/01/2019
- Field of study
The InterPro database (http://www.ebi.ac.uk/interpro/) classifies protein sequences into families and predicts the presence of functionally important domains and sites. Here, we report recent developments with InterPro (version 70.0) and its associated software, including an 18% growth in the size of the database in terms on new InterPro entries, updates to content, the inclusion of an additional entry type, refined modelling of discontinuous domains, and the development of a new programmatic interface and website. These developments extend and enrich the information provided by InterPro, and provide greater flexibility in terms of data access. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB, and discuss how our evaluation of residue coverage may help guide future curation activities
The InterPro protein families database: the classification resource after 15 years
- Author
- Attwood Teresa K
- Bateman Alex
- Bork Peer
- Chang Hsin-Yu
- Daugherty Louise
- Finn Robert D
- Fraser Matthew
- Gough Julian
- Guyot Dominique
- Haft Daniel
- Huang Hongzhan
- Hunter Sarah
- Kahn Daniel
- Letunic Ivica
- Lopez Rodrigo
- McAnulla Craig
- McMenamin Conor
- Mi Huaiyu
- Mitchell Alex
- Natale Darren A
- Nuka Gift
- Oates Matt
- Orengo Christine
- Pesseat Sebastien
- Punta Marco
- Rato Claudia
- Redaschi Nicole
- Rivoire Catherine
- Sangrador-Vegas Amaia
- Scheremetjew Maxim
- Sigrist Christian J A
- Sillitoe Ian
- Thomas Paul D
- Wu Cathy H
- Xenarios Ioannis
- Yong Siew-Yit
- Publication venue
- 'Oxford University Press (OUP)'
- Publication date
- 26/11/2014
- Field of study
The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36,766 member database signatures integrated into 26,238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012
Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels
- Author
- Absher Devin
- Adair Linda S.
- Akiyama Koichi
- Assimes Themistocles L.
- Aung Tin
- Cai Hui
- Cai Qiuyin
- Cannon Maren E.
- Chang Li-Ching
- Chen Chien-Hsiun
- Chen Peng
- Chen Yuan-Tsong
- Cheng Ching-Yu
- Chien Li-Hsin
- Cho Yoon Shin
- Chuang Lee-Ming
- Chuang Shu-Chun
- Dorajoo Rajkumar
- Du Shufa
- Fan Qiao
- Fann Cathy S.J.
- Feranil Alan B.
- Friedlander Yechiel
- Gordon-Larsen Penny
- Gu Dongfeng
- Gui Lixuan
- Guo Xiuqing
- Guo Zhirong
- Han Bok-Ghee
- He Jiang
- He Meian
- Heng Chew-Kiat
- Hixson James
- Hou Xuhong
- Hsiung Chao Agnes
- Hu Cheng
- Hu Yao
- Huang Wei
- Hwang Mi Yeong
- Hwu Chii-Min
- Ida Chen Yii-Der
- Isono Masato
- Jee Sun-Ha
- Jia Weiping
- Jimmy Juang Jyh-Ming
- Johnson Todd A.
- Jonas Jost B.
- Jung Keum-Ji
- Kato Norihiro
- Katsuya Tomohiro
- Khor Chiea-Chuen
- Kim Bong-Jo
- Kim Young Jin
- Kim Yun Kyoung
- Koh Woon-Puay
- Kubo Michiaki
- Lee I-Te
- Lee Sun-Ju
- Lee Wen-Jane
- Li Huaixing
- Li Shengxu
- Liang Kae-Woei
- Lim Blanche
- Lim Sing-Hui
- Lin Keng-Hung
- Lin Xu
- Liu Jianjun
- Long Jirong
- Mohlke Karen L.
- Moon Sanghoon
- Nabika Toru
- Pan Wen-Harn
- Peng Hao
- Quertermous Thomas
- Rich Stephen S.
- Roman Tamara S.
- Rotter Jerome I.
- Sabanayagam Charumathi
- Salfati Elias
- Sandow Kevin
- Sheu Wayne H.H.
- Shi Jinxiu
- Shu Xiao-Ou
- Sim Xueling
- Spracklen Cassandra N.
- Sun Liang
- Tai E-Shyong
- Takeuchi Fumihiko
- Tan Pok Chien
- Tan Shu-Pei
- Tanaka Toshihiro
- Taylor Kent D.
- Teo Yik-Ying
- Toh Sue-Anne
- Tsunoda Tatsuhiko
- van Dam Rob M.
- Wang Aili
- Wang Feijie
- Wang Jie
- Wang Tzung-Dau
- Wang Xu
- Wang Ya Xing
- Wei Wen Bin
- Wong Tien-Yin
- Wu Jer-Yuarn
- Wu Tangchun
- Wu Ying
- Xiang Yong-Bing
- Yao Jie
- Yuan Jian-Min
- Zhang Rong
- Zhang Yonghong
- Zhao Wanting
- Zheng Wei
- Publication venue
- Publication date
- 01/01/2017
- Field of study
Large-scale meta-analyses of genome-wide association studies (GWAS) have identified >175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P < 5 × 10-8) three novel loci associated with HDL-C near CD163-APOBEC1 (P = 7.4 × 10-9), NCOA2 (P = 1.6 × 10-8), and NID2-PTGDR (P = 4.2 × 10-8), and one novel locus associated with TG near WDR11-FGFR2 (P = 2.7 × 10-10). Conditional analyses identified a second signal near CD163-APOBEC1. We then combined results from the East Asian meta-analysis with association results from up to 187,365 European individuals from the Global Lipids Genetics Consortium in a trans-ancestry meta-analysis. This analysis identified (log10Bayes Factor ≥6.1) eight additional novel lipid loci. Among the twelve total loci identified, the index variants at eight loci have demonstrated at least nominal significance with other metabolic traits in prior studies, and two loci exhibited coincident eQTLs (P < 1 × 10-5) in subcutaneous adipose tissue for BPTF and PDGFC. Taken together, these analyses identified multiple novel lipid loci, providing new potential therapeutic targets
Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.
- Author
- Abdel-Aziz AK
- Abdelfatah S
- Abdellatif M
- Abdoli A
- Abel S
- Abeliovich H
- Abildgaard MH
- Abudu YP
- Acevedo-Arozena A
- Adamopoulos IE
- Adeli K
- Adolph TE
- Adornetto A
- Aflaki E
- Agam G
- Agarwal A
- Aggarwal BB
- Agnello M
- Agostinis P
- Agrewala JN
- Agrotis A
- Aguilar PV
- Ahmad ST
- Ahmed ZM
- Ahumada-Castro U
- Aits S
- Aizawa S
- Akkoc Y
- Akoumianaki T
- Akpinar HA
- Al-Abd AM
- Al-Akra L
- Al-Gharaibeh A
- Alaoui-Jamali MA
- Alberti S
- Alcocer-Gómez E
- Alessandri C
- Ali M
- Alim Al-Bari MA
- Aliwaini S
- Alizadeh J
- Almacellas E
- Almasan A
- Alonso A
- Alonso GD
- Altan-Bonnet N
- Altieri DC
- Alves da Costa C
- Alves S
- Alzaharna MM
- Amadio M
- Amantini C
- Amaral C
- Ambrosio S
- Amer AO
- Ammanathan V
- An Z
- Andersen SU
- Andrabi SA
- Andrade-Silva M
- Andres AM
- Angelini S
- Ann D
- Anozie UC
- Ansari MY
- Antas P
- Antebi A
- Antón Z
- Anwar T
- Apetoh L
- Apostolova N
- Araki T
- Araki Y
- Arasaki K
- Araya J
- Araújo WL
- Arden C
- Arguelles S
- Arias E
- Arikkath J
- Arimoto H
- Ariosa AR
- Armstrong-James D
- Arnauné-Pelloquin L
- Aroca A
- Arroyo DS
- Arsov I
- Artero R
- Arévalo M-A
- Asaro DML
- Aschner M
- Ashrafizadeh M
- Ashur-Fabian O
- Atanasov AG
- Au AK
- Auberger P
- Auner HW
- Aurelian L
- Autelli R
- Avagliano L
- Aveic S
- Aveleira CA
- Avin-Wittenberg T
- Aydin Y
- Ayton S
- Ayyadevara S
- Azzopardi M
- Baba M
- Backer JM
- Backues SK
- Bae D-H
- Bae O-N
- Bae SH
- Baehrecke EH
- Baek A
- Baek S-H
- Baek SH
- Bagetta G
- Bagniewska-Zadworna A
- Bai H
- Bai J
- Bai X
- Bai Y
- Bairagi N
- Baksi S
- Balazadeh S
- Balbi T
- Baldari CT
- Balduini W
- Ballabio A
- Ballester M
- Balzan R
- Bandopadhyay R
- Banerjee S
- Banerjee S
- Bao Y
- Baptista MS
- Baracca A
- Barbati C
- Bargiela A
- Barilà D
- Barlow PG
- Barmada SJ
- Barreiro E
- Barreto GE
- Bartek J
- Bartel B
- Bartolome A
- Barve GR
- Basagoudanavar SH
- Bassham DC
- Bast RC
- Basu A
- Batoko H
- Batten I
- Baulieu EE
- Baumgarner BL
- Bayry J
- Beale R
- Beau I
- Beaumatin F
- Bechara LRG
- Beck GR
- Beers MF
- Begun J
- Behl C
- Behrends C
- Behrens GMN
- Bei R
- Bejarano E
- Bel S
- Belaid A
- Belgareh-Touzé N
- Bellarosa C
- Belleudi F
- Bello-Morales R
- Belló Pérez M
- Beltran JSDO
- Beltran S
- Benbrook DM
- Bendorius M
- Benitez BA
- Benito-Cuesta I
- Bensalem J
- Berchtold MW
- Berezowska S
- Bergamaschi D
- Bergami M
- Bergmann A
- Berliocchi L
- Berlioz-Torrent C
- Bernard A
- Berthoux L
- Besirli CG
- Besteiro S
- Betin VM
- Beyaert R
- Bezbradica JS
- Bhaskar K
- Bhatia-Kissova I
- Bhattacharya R
- Bhattacharya S
- Bhattacharyya S
- Bhuiyan MS
- Bhutia SK
- Bi L
- Bi X
- Biden TJ
- Bijian K
- Billes VA
- Binart N
- Bincoletto C
- Birgisdottir AB
- Bjorkoy G
- Blanco G
- Blas-Garcia A
- Blasiak J
- Blomgran R
- Blomgren K
- Blum JS
- Boada-Romero E
- Boban M
- Boesze-Battaglia K
- Boeuf P
- Boland B
- Bomont P
- Bonaldo P
- Bonam SR
- Bonfili L
- Bonifacino JS
- Boone BA
- Bootman MD
- Bordi M
- Borner C
- Bornhauser BC
- Borthakur G
- Bosch J
- Bose S
- Botana LM
- Botas J
- Boulanger CM
- Boulton ME
- Bourdenx M
- Bourgeois B
- Bourke NM
- Bousquet G
- Boya P
- Bozhkov PV
- Bozi LHM
- Bozkurt TO
- Brackney DE
- Brand-Saberi B
- Brandts CH
- Braun RJ
- Braus GH
- Bravo-Sagua R
- Bravo-San Pedro JM
- Brest P
- Bringer M-A
- Briones-Herrera A
- Broaddus VC
- Brodersen P
- Brodsky JL
- Brody SL
- Bronson PG
- Bronstein JM
- Brown CN
- Brown RE
- Brum PC
- Brumell JH
- Brunetti-Pierri N
- Bruno D
- Bryson-Richardson RJ
- Bucci C
- Buch S
- Buchan JR
- Buchrieser C
- Buckingham EM
- Budak H
- Budini M
- Bueno M
- Buitrago-Molina LE
- Bultynck G
- Burada F
- Buraschi S
- Burgoyne JR
- Burón MI
- Bustos V
- Butturini E
- Byrd A
- Bánréti Á
- Büttner S
- Cabas I
- Cabrera-Benitez S
- Cadwell K
- Cai J
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- Cai Q
- Cairó M
- Calbet JA
- Caldwell GA
- Caldwell KA
- Call JA
- Calvani R
- Calvo AC
- Calvo-Rubio Barrera M
- Camara NO
- Camonis JH
- Camougrand N
- Campanella M
- Campbell EM
- Campbell-Valois F-X
- Campello S
- Campesi I
- Campos JC
- Camuzard O
- Cancino J
- Candido de Almeida D
- Canesi L
- Caniggia I
- Canonico B
- Cantí C
- Cao B
- Caraglia M
- Caramés B
- Carchman EH
- Cardenal-Muñoz E
- Cardenas C
- Cardenas L
- Cardoso SM
- Carew JS
- Carle GF
- Carleton G
- Carloni S
- Carmona-Gutierrez D
- Carneiro LA
- Carnevali O
- Carosi JM
- Carra S
- Carrier A
- Carrier L
- Carroll B
- Carter AB
- Carvalho AN
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- Casas C
- Casas J
- Cassioli C
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- Castillo-Lluva S
- Castoldi F
- Castori M
- Castro AF
- Castro-Caldas M
- Castro-Hernandez J
- Castro-Obregon S
- Catz SD
- Cavadas C
- Cavaliere F
- Cavallini G
- Cavinato M
- Cayuela ML
- Cebollada Rica P
- Cecarini V
- Cecconi F
- Cechowska-Pasko M
- Cenci S
- Ceperuelo-Mallafré V
- Cerqueira JJ
- Cerutti JM
- Cervia D
- Cetintas VB
- Cetrullo S
- Chae H-J
- Chagin AS
- Chai C-Y
- Chakrabarti G
- Chakrabarti O
- Chakraborty T
- Chakraborty T
- Chami M
- Chamilos G
- Chan DW
- Chan ED
- Chan EYW
- Chan H
- Chan HH
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- Chan MTV
- Chan YS
- Chandra PK
- Chang C
- Chang C-P
- Chang H-C
- Chang K
- Chao J
- Chapman T
- Charlet-Berguerand N
- Chatterjee S
- Chaube SK
- Chaudhary A
- Chauhan S
- Chaum E
- Checler F
- Cheetham ME
- Chen C-S
- Chen G-C
- Chen J-F
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- Chen LL
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- Chen M-K
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- Chen R-H
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- Chen W
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- Chen X-W
- Chen Y
- Chen Y
- Chen Y
- Chen Y-G
- Chen Y-J
- Chen Y-Q
- Chen Z
- Chen Z
- Chen Z
- Chen Z-H
- Chen ZJ
- Chen ZS
- Cheng H
- Cheng J
- Cheng S-Y
- Cheng W
- Cheng X
- Cheng X-T
- Cheng Y
- Cheng Z
- Cheong H
- Cheong JK
- Chernyak BV
- Cherry S
- Cheung CFR
- Cheung CHA
- Cheung K-H
- Chevet E
- Chi RJ
- Chiang AKS
- Chiaradonna F
- Chiarelli R
- Chiariello M
- Chica N
- Chiocca S
- Chiong M
- Chiou S-H
- Chiramel AI
- Chiurchiù V
- Cho D-H
- Choe S-K
- Choi AMK
- Choi ME
- Choudhury KR
- Chow NS
- Chu CT
- Chua JJE
- Chua JP
- Chung H
- Chung KP
- Chung S
- Chung S-H
- Chung Y-L
- Cianfanelli V
- Ciechomska IA
- Cifuentes M
- Cinque L
- Cirak S
- Cirone M
- Clague MJ
- Clarke R
- Clementi E
- Coccia EM
- Codogno P
- Cohen E
- Cohen MM
- Colasanti T
- Colasuonno F
- Colbert RA
- Colell A
- Coll NS
- Collins MO
- Colombo MI
- Colón-Ramos DA
- Combaret L
- Comincini S
- Cominetti MR
- Consiglio A
- Conte A
- Conti F
- Contu VR
- Cookson MR
- Coombs KM
- Coppens I
- Corasaniti MT
- Cordes N
- Corkery DP
- Cortese K
- Costa MDC
- Costantino S
- Costelli P
- Coto-Montes A
- Crack PJ
- Crespo JL
- Criollo A
- Crippa V
- Cristofani R
- Csizmadia T
- Cuadrado A
- Cui B
- Cui J
- Cui Y
- Cui Y
- Culetto E
- Cumino AC
- Cybulsky AV
- Czaja MJ
- Czuczwar SJ
- D'Adamo S
- D'Amelio M
- D'Arcangelo D
- D'Lugos AC
- D'Orazi G
- da Silva JA
- Dafsari HS
- Dagda RK
- Dagdas Y
- Daglia M
- Dai X
- Dai Y
- Dai Y
- Dal Col J
- Dalhaimer P
- Dalla Valle L
- Dallenga T
- Dalmasso G
- Damme M
- Dando I
- Dantuma NP
- Darling AL
- Das H
- Dasarathy S
- Dasari SK
- Dash S
- Daumke O
- Dauphinee AN
- Davies JS
- Davis RJ
- Davis T
- Dayalan Naidu S
- De Amicis F
- De Bosscher K
- De Felice F
- De Franceschi L
- De Leonibus C
- de Mattos Barbosa MG
- De Meyer GRY
- De Milito A
- De Nunzio C
- De Palma C
- De Santi M
- De Virgilio C
- De Zio D
- Debnath J
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- Álvarez ÉMC
- Ávalos Y
- Önal G
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- Čolić M
- Đokić J
- Žerovnik E
- Publication venue
- 'Informa UK Limited'
- Publication date
- 01/01/2021
- Field of study
In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide 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
Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)
- Author
- A. -G. Wu
- A. C. Ma
- A. K. Au
- Abdel-Aziz A. K.
- Abdelfatah S.
- Abdellatif M.
- Abdoli A.
- Abel S.
- Abeliovich H.
- Abildgaard M. H.
- Abudu Y. P.
- Acevedo-Arozena A.
- Adamopoulos I. E.
- Adeli K.
- Adolph T. E.
- Adornetto A.
- Aflaki E.
- Agam G.
- Agarwal A.
- Aggarwal B. B.
- Agnello M.
- Agostinis P.
- Agrewala J. N.
- Agrotis A.
- Aguilar P. V.
- Ahmad S. T.
- Ahmed Z. M.
- Ahumada-Castro U.
- Aits S.
- Aizawa S.
- Akkoc Y.
- Akoumianaki T.
- Akpinar H. A.
- Al-Abd A. M.
- Al-Akra L.
- Al-Gharaibeh A.
- Alaoui-Jamali M. A.
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- Publication venue
- 'Informa UK Limited'
- Publication date
- 01/01/2021
- Field of study
Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors
- Author
- Abdellaoui Abdel
- Abramova Liliya
- Adams Mark
- Adams Mark J.
- Adan Roger A. H.
- Adolfsson Rolf
- Adolfsson Rolf
- Adorjan Kristina
- Agartz Ingrid
- Agartz Ingrid
- Agerbo Esben
- Agerbo Esben
- Agerbo Esben
- Aguayo Samuel
- Ahuja Sunil
- Air Tracy M.
- Air Tracy M.
- Al Eissa Mariam
- Albani Diego
- Alda Martin
- Alda Martin
- Alfredsson Lars
- Alfredsson Lars
- Alfredsson Lars
- Alliey-Rodriguez Ney
- Als Thomas D.
- Andlauer Till F. M.
- Ando Tetsuya
- Andreassen Ole A.
- Andreassen Ole A.
- Andreassen Ole A.
- Anjorin Adebayo
- Anjorin Adebayo
- Antilla Verneri
- Antoniou Anastasia
- Appadurai Vivek
- Argyres Dean P.
- Arolt Volker
- Artigas Maria Soler
- Aschauer Harald
- Ashley-Koch Allison E.
- Aslan Mihaela
- Awasthi Swapnil
- Awasthi Swapnil
- Awasthi Swapnil
- Azevedo M. Helena
- Babadjanova Gulja
- Bacanu Silviu-Alin
- Backlund Lena
- Baek Ji Hyun
- Baker Jessica H.
- Ballas Zuhair
- Bass Nicholas
- Bass Nicholas
- Bau Claiton H. D.
- Bauer Michael
- Baune Bernhard T.
- Baune Bernhard T.
- Baune Bernhard T.
- Beckham Jean
- Beckham Jean
- Beckham Jean C.
- Beekman Aartjan T. F.
- Beins Eva C.
- Bellivier Frank
- Bellivier Frank
- Bencko Vladimir
- Bengesser Susanne
- Bergen Andrew W.
- Bergen Andrew W.
- Bergen Sarah E.
- Berger Klaus
- Berger Klaus
- Berrettini Wade H.
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- Witt Stephanie H.
- Witt Stephanie H.
- Witt Stephanie H.
- Won Hong-Hee
- Woodside D. Blake
- Woodside D. Blake
- Wray Naomi R.
- Wray Naomi R.
- Wray Naomi R.
- Wu Yang
- Xi Hualin S.
- Xi Simon
- Xu Junzhe
- Xu Wei
- Yang Jian
- Yao Shuyang
- Yeh Shing Shing
- Yilmaz Zeynep
- Yilmaz Zeynep
- Young Allan H.
- Young Hannah
- Zablocki Clement J.
- Zai Clement C.
- Zandi Peter P.
- Zeggini Eleftheria
- Zerwas Stephanie
- Zhang Futao
- Zhao Hongyu
- Zhou Hang
- Zillich Lea
- Zillich Lea
- Zipfel Stephan
- Zwart John-Anker
- Świątkowska Beata
- Świątkowska Beata
- Świątkowska Beata
- Publication venue
- Publication date
- 01/01/2021
- Field of study
Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe
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