240 results on '"Jolly MK"'
Search Results
2. X upregulation is not global and extent of upregulation differs between ancestral and acquired X-linked genes
- Author
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Naik, C H, primary, Hari, K, additional, Chandel, D, additional, Jolly, MK, additional, and Gayen, S, additional
- Published
- 2021
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3. Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)
- Author
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Klionsky, DJ, 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, Álvarez, ÉMC, Alves, S, Alves da Costa, C, 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, Araújo, WL, Araya, J, Arden, C, Arévalo, M-A, Arguelles, S, Arias, E, Arikkath, J, Arimoto, H, Ariosa, AR, Armstrong-James, D, Arnauné-Pelloquin, L, Aroca, A, Arroyo, DS, Arsov, I, Artero, R, Asaro, DML, Aschner, M, Ashrafizadeh, M, Ashur-Fabian, O, Atanasov, AG, Au, AK, Auberger, P, Auner, HW, Aurelian, L, Autelli, R, Avagliano, L, Ávalos, Y, 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, Balbi, T, Baldari, CT, Balduini, W, Ballabio, A, Ballester, M, Balazadeh, S, Balzan, R, Bandopadhyay, R, Banerjee, S, Bánréti, Á, 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, Behrends, C, Behrens, GMN, Bei, R, Bejarano, E, Bel, S, Behl, C, Belaid, A, Belgareh-Touzé, N, Bellarosa, C, Belleudi, F, Belló Pérez, M, Bello-Morales, R, 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, 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, Buchrieser, C, Bueno, M, Buitrago-Molina, LE, Buraschi, S, Buch, S, Buchan, JR, Buckingham, EM, Budak, H, Budini, M, Bultynck, G, Burada, F, Burgoyne, JR, Burón, MI, Bustos, V, Büttner, S, Butturini, E, Byrd, A, Cabas, I, Cabrera-Benitez, S, Cadwell, K, Cai, J, Cai, L, 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, Casanova, M, Casas, C, Casas, J, Cassioli, C, Castillo, EF, Castillo, K, 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, Chami, M, Chamilos, G, Chan, DW, Chan, EYW, Chan, ED, Chan, HYE, Chan, HH, Chan, H, Chan, MTV, Chan, YS, Chandra, PK, Chang, C-P, Chang, C, 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, Chen, LL, Chen, L, Chen, M, Chen, M-K, Chen, N, Chen, Q, Chen, R-H, Chen, S, Chen, W, Chen, X-M, Chen, X-W, Chen, X, Chen, Y, Chen, Y-G, Chen, Y-J, Chen, Y-Q, Chen, ZS, Chen, Z, Chen, Z-H, Chen, ZJ, 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, JP, Chua, JJE, 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, Čolić, M, 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, Corkery, DP, Cordes, N, 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, 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, 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, Dávila, VA, 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, DeBosch, BJ, Decuypere, J-P, Deehan, MA, Deflorian, G, DeGregori, J, Dehay, B, Del Rio, G, Delaney, JR, Delbridge, LMD, Delorme-Axford, E, Delpino, MV, Demarchi, F, Dembitz, V, Demers, ND, Deng, H, Deng, Z, Dengjel, J, Dent, P, Denton, D, DePamphilis, ML, Der, CJ, Deretic, V, Descoteaux, A, Devis, L, Devkota, S, Devuyst, O, Dewson, G, Dharmasivam, M, Dhiman, R, di Bernardo, D, Di Cristina, M, Di Domenico, F, Di Fazio, P, Di Fonzo, A, Di Guardo, G, Di Guglielmo, GM, Di Leo, L, Di Malta, C, Di Nardo, A, Di Rienzo, M, Di Sano, F, Diallinas, G, Diao, J, Diaz-Araya, G, Díaz-Laviada, I, Dickinson, JM, Diederich, M, Dieudé, M, Dikic, I, Ding, S, Ding, W-X, Dini, L, Dinić, J, Dinic, M, Dinkova-Kostova, AT, Dionne, MS, Distler, JHW, Diwan, A, Dixon, IMC, Djavaheri-Mergny, M, Dobrinski, I, Dobrovinskaya, O, Dobrowolski, R, Dobson, RCJ, Đokić, J, Dokmeci Emre, S, Donadelli, M, Dong, B, Dong, X, Dong, Z, Dorn Ii, GW, Dotsch, V, Dou, H, Dou, J, Dowaidar, M, Dridi, S, Drucker, L, Du, A, Du, C, Du, G, Du, H-N, Du, L-L, du Toit, A, Duan, S-B, Duan, X, Duarte, SP, Dubrovska, A, Dunlop, EA, Dupont, N, Durán, RV, Dwarakanath, BS, Dyshlovoy, SA, Ebrahimi-Fakhari, D, Eckhart, L, Edelstein, CL, Efferth, T, Eftekharpour, E, Eichinger, L, Eid, N, Eisenberg, T, Eissa, NT, Eissa, S, Ejarque, M, El Andaloussi, A, El-Hage, N, El-Naggar, S, Eleuteri, AM, El-Shafey, ES, Elgendy, M, Eliopoulos, AG, Elizalde, MM, Elks, PM, Elsasser, H-P, Elsherbiny, ES, Emerling, BM, Emre, NCT, Eng, CH, Engedal, N, Engelbrecht, A-M, Engelsen, AST, Enserink, JM, Escalante, R, Esclatine, A, Escobar-Henriques, M, Eskelinen, E-L, Espert, L, Eusebio, M-O, Fabrias, G, Fabrizi, C, Facchiano, A, Facchiano, F, Fadeel, B, Fader, C, Faesen, AC, Fairlie, WD, Falcó, A, Falkenburger, BH, Fan, D, Fan, J, Fan, Y, Fang, EF, Fang, Y, Fanto, M, Farfel-Becker, T, Faure, M, Fazeli, G, Fedele, AO, Feldman, AM, Feng, D, Feng, J, Feng, L, Feng, Y, Feng, W, Fenz Araujo, T, Ferguson, TA, Fernández, ÁF, Fernandez-Checa, JC, Fernández-Veledo, S, Fernie, AR, Ferrante, AW, Ferraresi, A, Ferrari, MF, Ferreira, JCB, Ferro-Novick, S, Figueras, A, Filadi, R, Filigheddu, N, Filippi-Chiela, E, Filomeni, G, Fimia, GM, Fineschi, V, Finetti, F, Finkbeiner, S, Fisher, EA, Fisher, PB, Flamigni, F, Fliesler, SJ, Flo, TH, Florance, I, Florey, O, Florio, T, Fodor, E, Follo, C, Fon, EA, Forlino, A, Fornai, F, Fortini, P, Fracassi, A, Fraldi, A, Franco, B, Franco, R, Franconi, F, Frankel, LB, Friedman, SL, Fröhlich, LF, Frühbeck, G, Fuentes, JM, Fujiki, Y, Fujita, N, Fujiwara, Y, Fukuda, M, Fulda, S, Furic, L, Furuya, N, Fusco, C, Gack, MU, Gaffke, L, Galadari, S, Galasso, A, Galindo, MF, Gallolu Kankanamalage, S, Galluzzi, L, Galy, V, Gammoh, N, Gan, B, Ganley, IG, Gao, F, Gao, H, Gao, M, Gao, P, Gao, S-J, Gao, W, Gao, X, Garcera, A, Garcia, MN, Garcia, VE, García-Del Portillo, F, Garcia-Escudero, V, Garcia-Garcia, A, Garcia-Macia, M, García-Moreno, D, Garcia-Ruiz, C, García-Sanz, P, Garg, AD, Gargini, R, Garofalo, T, Garry, RF, Gassen, NC, Gatica, D, Ge, L, Ge, W, Geiss-Friedlander, R, Gelfi, C, Genschik, P, Gentle, IE, Gerbino, V, Gerhardt, C, Germain, K, Germain, M, Gewirtz, DA, Ghasemipour Afshar, E, Ghavami, S, Ghigo, A, Ghosh, M, Giamas, G, Giampietri, C, Giatromanolaki, A, Gibson, GE, Gibson, SB, Ginet, V, Giniger, E, Giorgi, C, Girao, H, Girardin, SE, Giridharan, M, Giuliano, S, Giulivi, C, Giuriato, S, Giustiniani, J, Gluschko, A, Goder, V, Goginashvili, A, Golab, J, Goldstone, DC, Golebiewska, A, Gomes, LR, Gomez, R, Gómez-Sánchez, R, Gomez-Puerto, MC, Gomez-Sintes, R, Gong, Q, Goni, FM, González-Gallego, J, Gonzalez-Hernandez, T, Gonzalez-Polo, RA, Gonzalez-Reyes, JA, González-Rodríguez, P, Goping, IS, Gorbatyuk, MS, Gorbunov, NV, Görgülü, K, Gorojod, RM, Gorski, SM, Goruppi, S, Gotor, C, Gottlieb, RA, Gozes, I, Gozuacik, D, Graef, M, Gräler, MH, Granatiero, V, Grasso, D, Gray, JP, Green, DR, Greenhough, A, Gregory, SL, Griffin, EF, Grinstaff, MW, Gros, F, Grose, C, Gross, AS, Gruber, F, Grumati, P, Grune, T, Gu, X, Guan, J-L, Guardia, CM, Guda, K, Guerra, F, Guerri, C, Guha, P, Guillén, C, Gujar, S, Gukovskaya, A, Gukovsky, I, Gunst, J, Günther, A, Guntur, AR, Guo, C, Guo, H, Guo, L-W, Guo, M, Gupta, P, Gupta, SK, Gupta, S, Gupta, VB, Gupta, V, Gustafsson, AB, Gutterman, DD, H B, R, Haapasalo, A, Haber, JE, Hać, A, Hadano, S, Hafrén, AJ, Haidar, M, Hall, BS, Halldén, G, Hamacher-Brady, A, Hamann, A, Hamasaki, M, Han, W, Hansen, M, Hanson, PI, Hao, Z, Harada, M, Harhaji-Trajkovic, L, Hariharan, N, Haroon, N, Harris, J, Hasegawa, T, Hasima Nagoor, N, Haspel, JA, Haucke, V, Hawkins, WD, Hay, BA, Haynes, CM, Hayrabedyan, SB, Hays, TS, He, C, He, Q, He, R-R, He, Y-W, He, Y-Y, Heakal, Y, Heberle, AM, Hejtmancik, JF, Helgason, GV, Henkel, V, Herb, M, Hergovich, A, Herman-Antosiewicz, A, Hernández, A, Hernandez, C, Hernandez-Diaz, S, Hernandez-Gea, V, Herpin, A, Herreros, J, Hervás, JH, Hesselson, D, Hetz, C, Heussler, VT, Higuchi, Y, Hilfiker, S, Hill, JA, Hlavacek, WS, Ho, EA, Ho, IHT, Ho, PW-L, Ho, S-L, Ho, WY, Hobbs, GA, Hochstrasser, M, Hoet, PHM, Hofius, D, Hofman, P, Höhn, A, Holmberg, CI, Hombrebueno, JR, Yi-Ren Hong, C-WH, Hooper, LV, Hoppe, T, Horos, R, Hoshida, Y, Hsin, I-L, Hsu, H-Y, Hu, B, Hu, D, Hu, L-F, Hu, MC, Hu, R, Hu, W, Hu, Y-C, Hu, Z-W, Hua, F, Hua, J, Hua, Y, Huan, C, Huang, C, Huang, H, Huang, K, Huang, MLH, Huang, R, Huang, S, Huang, T, Huang, X, Huang, YJ, Huber, TB, Hubert, V, Hubner, CA, Hughes, SM, Hughes, WE, Humbert, M, Hummer, G, Hurley, JH, Hussain, S, Hussey, PJ, Hutabarat, M, Hwang, H-Y, Hwang, S, Ieni, A, Ikeda, F, Imagawa, Y, Imai, Y, Imbriano, C, Imoto, M, Inman, DM, Inoki, K, Iovanna, J, Iozzo, RV, Ippolito, G, Irazoqui, JE, Iribarren, P, Ishaq, M, Ishikawa, M, Ishimwe, N, Isidoro, C, Ismail, N, Issazadeh-Navikas, S, Itakura, E, Ito, D, Ivankovic, D, Ivanova, S, Iyer, AKV, Izquierdo, JM, Izumi, M, Jäättelä, M, Jabir, MS, Jackson, WT, Jacobo-Herrera, N, Jacomin, A-C, Jacquin, E, Jadiya, P, Jaeschke, H, Jagannath, C, Jakobi, AJ, Jakobsson, J, Janji, B, Jansen-Dürr, P, Jansson, PJ, Jantsch, J, Januszewski, S, Jassey, A, Jean, S, Jeltsch-David, H, Jendelova, P, Jenny, A, Jensen, TE, Jessen, N, Jewell, JL, Ji, J, Jia, L, Jia, R, Jiang, L, Jiang, Q, Jiang, R, Jiang, T, Jiang, X, Jiang, Y, Jimenez-Sanchez, M, Jin, E-J, Jin, F, Jin, H, Jin, L, Jin, M, Jin, S, Jo, E-K, Joffre, C, Johansen, T, Johnson, GVW, Johnston, SA, Jokitalo, E, Jolly, MK, Joosten, LAB, Jordan, J, Joseph, B, Ju, D, Ju, J-S, Ju, J, Juárez, E, Judith, D, Juhász, G, Jun, Y, Jung, CH, Jung, S-C, Jung, YK, Jungbluth, H, Jungverdorben, J, Just, S, Kaarniranta, K, Kaasik, A, Kabuta, T, Kaganovich, D, Kahana, A, Kain, R, Kajimura, S, Kalamvoki, M, Kalia, M, Kalinowski, DS, Kaludercic, N, Kalvari, I, Kaminska, J, Kaminskyy, VO, Kanamori, H, Kanasaki, K, Kang, C, Kang, R, Kang, SS, Kaniyappan, S, Kanki, T, Kanneganti, T-D, Kanthasamy, AG, Kanthasamy, A, Kantorow, M, Kapuy, O, Karamouzis, MV, Karim, MR, Karmakar, P, Katare, RG, Kato, M, Kaufmann, SHE, Kauppinen, A, Kaushal, GP, Kaushik, S, Kawasaki, K, Kazan, K, Ke, P-Y, Keating, DJ, Keber, U, Kehrl, JH, Keller, KE, Keller, CW, Kemper, JK, Kenific, CM, Kepp, O, Kermorgant, S, Kern, A, Ketteler, R, Keulers, TG, Khalfin, B, Khalil, H, Khambu, B, Khan, SY, Khandelwal, VKM, Khandia, R, Kho, W, Khobrekar, NV, Khuansuwan, S, Khundadze, M, Killackey, SA, Kim, D, Kim, DR, Kim, D-H, Kim, D-E, Kim, EY, Kim, E-K, Kim, H-R, Kim, H-S, Hyung-Ryong Kim, Kim, JH, Kim, JK, Kim, J-H, Kim, J, Kim, KI, Kim, PK, Kim, S-J, Kimball, SR, Kimchi, A, Kimmelman, AC, Kimura, T, King, MA, Kinghorn, KJ, Kinsey, CG, Kirkin, V, Kirshenbaum, LA, Kiselev, SL, Kishi, S, Kitamoto, K, Kitaoka, Y, Kitazato, K, Kitsis, RN, Kittler, JT, Kjaerulff, O, Klein, PS, Klopstock, T, Klucken, J, Knævelsrud, H, Knorr, RL, Ko, BCB, Ko, F, Ko, J-L, Kobayashi, H, Kobayashi, S, Koch, I, Koch, JC, Koenig, U, Kögel, D, Koh, YH, Koike, M, Kohlwein, SD, Kocaturk, NM, Komatsu, M, König, J, Kono, T, Kopp, BT, Korcsmaros, T, Korkmaz, G, Korolchuk, VI, Korsnes, MS, Koskela, A, Kota, J, Kotake, Y, Kotler, ML, Kou, Y, Koukourakis, MI, Koustas, E, Kovacs, AL, Kovács, T, Koya, D, Kozako, T, Kraft, C, Krainc, D, Krämer, H, Krasnodembskaya, AD, Kretz-Remy, C, Kroemer, G, Ktistakis, NT, Kuchitsu, K, Kuenen, S, Kuerschner, L, Kukar, T, Kumar, A, Kumar, D, Kumar, S, Kume, S, Kumsta, C, Kundu, CN, Kundu, M, Kunnumakkara, AB, Kurgan, L, Kutateladze, TG, Kutlu, O, Kwak, S, Kwon, HJ, Kwon, TK, Kwon, YT, Kyrmizi, I, La Spada, A, Labonté, P, Ladoire, S, Laface, I, Lafont, F, Lagace, DC, Lahiri, V, Lai, Z, Laird, AS, Lakkaraju, A, Lamark, T, Lan, S-H, Landajuela, A, Lane, DJR, Lane, JD, Lang, CH, Lange, C, Langel, Ü, Langer, R, Lapaquette, P, Laporte, J, LaRusso, NF, Lastres-Becker, I, Lau, WCY, Laurie, GW, Lavandero, S, Law, BYK, Law, HK-W, Layfield, R, Le, W, Le Stunff, H, Leary, AY, Lebrun, J-J, Leck, LYW, Leduc-Gaudet, J-P, Lee, C, Lee, C-P, Lee, D-H, Lee, EB, Lee, EF, Lee, GM, Lee, H-J, Lee, HK, Lee, JM, Lee, JS, Lee, J-A, Lee, J-Y, Lee, JH, Lee, M, Lee, MG, Lee, MJ, Lee, M-S, Lee, SY, Lee, S-J, Lee, SB, Lee, WH, Lee, Y-R, Lee, Y-H, Lee, Y, Lefebvre, C, Legouis, R, Lei, YL, Lei, Y, Leikin, S, Leitinger, G, Lemus, L, Leng, S, Lenoir, O, Lenz, G, Lenz, HJ, Lenzi, P, León, Y, Leopoldino, AM, Leschczyk, C, Leskelä, S, Letellier, E, Leung, C-T, Leung, PS, Leventhal, JS, Levine, B, Lewis, PA, Ley, K, Li, B, Li, D-Q, Li, J, Li, K, Li, L, Li, M, Li, P-L, Li, M-Q, Li, Q, Li, S, Li, T, Li, W, Li, X, Li, Y-P, Li, Y, Li, Z, Lian, J, Liang, C, Liang, Q, Liang, W, Liang, Y, Liao, G, Liao, L, Liao, M, Liao, Y-F, Librizzi, M, Lie, PPY, Lilly, MA, Lim, HJ, Lima, TRR, Limana, F, Lin, C, Lin, C-W, Lin, D-S, Lin, F-C, Lin, JD, Lin, KM, Lin, K-H, Lin, L-T, Lin, P-H, Lin, Q, Lin, S, Lin, S-J, Lin, W, Lin, X, Lin, Y-X, Lin, Y-S, Linden, R, Lindner, P, Ling, S-C, Lingor, P, Linnemann, AK, Liou, Y-C, Lipinski, MM, Lipovšek, S, Lira, VA, Lisiak, N, Liton, PB, Liu, C, Liu, C-H, Liu, C-F, Liu, CH, Liu, F, Liu, H, Liu, H-S, Liu, H-F, Liu, J, Liu, L, Liu, M, Liu, Q, Liu, W, Liu, X-H, Liu, X, Liu, Y, Livingston, JA, Lizard, G, Lizcano, JM, Ljubojevic-Holzer, S, LLeonart, ME, Llobet-Navàs, D, Llorente, A, Lo, CH, Lobato-Márquez, D, Long, Q, Long, YC, Loos, B, Loos, JA, López, MG, López-Doménech, G, López-Guerrero, JA, López-Jiménez, AT, López-Pérez, Ó, López-Valero, I, Lorenowicz, MJ, Lorente, M, Lorincz, P, Lossi, L, Lotersztajn, S, Lovat, PE, Lovell, JF, Lovy, A, Lőw, P, Lu, G, Lu, H, Lu, J-H, Lu, J-J, Lu, M, Lu, S, Luciani, A, Lucocq, JM, Ludovico, P, Luftig, MA, Luhr, M, Luis-Ravelo, D, Lum, JJ, Luna-Dulcey, L, Lund, AH, Lund, VK, Lünemann, JD, Lüningschrör, P, Luo, H, Luo, R, Luo, S, Luo, Z, Luparello, C, Lüscher, B, Luu, L, Lyakhovich, A, Lyamzaev, KG, Lystad, AH, Lytvynchuk, L, Ma, AC, Ma, C, Ma, M, Ma, N-F, Ma, Q-H, Ma, X, Ma, Y, Ma, Z, MacDougald, OA, Macian, F, MacIntosh, GC, MacKeigan, JP, Macleod, KF, Maday, S, Madeo, F, Madesh, M, Madl, T, Madrigal-Matute, J, Maeda, A, Maejima, Y, Magarinos, M, Mahavadi, P, Maiani, E, Maiese, K, Maiti, P, Maiuri, MC, Majello, B, Major, MB, Makareeva, E, Malik, F, Mallilankaraman, K, Malorni, W, Maloyan, A, Mammadova, N, Man, GCW, Manai, F, Mancias, JD, Mandelkow, E-M, Mandell, MA, Manfredi, AA, Manjili, MH, Manjithaya, R, Manque, P, Manshian, BB, Manzano, R, Manzoni, C, Mao, K, Marchese, C, Marchetti, S, Marconi, AM, Marcucci, F, Mardente, S, Mareninova, OA, Margeta, M, Mari, M, Marinelli, S, Marinelli, O, Mariño, G, Mariotto, S, Marshall, RS, Marten, MR, Martens, S, Martin, APJ, Martin, KR, Martin, S, Martín-Segura, A, Martín-Acebes, MA, Martin-Burriel, I, Martin-Rincon, M, Martin-Sanz, P, Martina, JA, Martinet, W, Martinez, A, Martinez, J, Martinez Velazquez, M, Martinez-Lopez, N, Martinez-Vicente, M, Martins, DO, Martins, JO, Martins, WK, Martins-Marques, T, Marzetti, E, Masaldan, S, Masclaux-Daubresse, C, Mashek, DG, Massa, V, Massieu, L, Masson, GR, Masuelli, L, Masyuk, AI, Masyuk, TV, Matarrese, P, Matheu, A, Matoba, S, Matsuzaki, S, Mattar, P, Matte, A, Mattoscio, D, Mauriz, JL, Mauthe, M, Mauvezin, C, Maverakis, E, Maycotte, P, Mayer, J, Mazzoccoli, G, Mazzoni, C, Mazzulli, JR, McCarty, N, McDonald, C, McGill, MR, McKenna, SL, McLaughlin, B, McLoughlin, F, McNiven, MA, McWilliams, TG, Mechta-Grigoriou, F, Medeiros, TC, Medina, DL, Megeney, LA, Megyeri, K, Mehrpour, M, Mehta, JL, Meijer, AJ, Meijer, AH, Mejlvang, J, Meléndez, A, Melk, A, Memisoglu, G, Mendes, AF, Meng, D, Meng, F, Meng, T, Menna-Barreto, R, Menon, MB, Mercer, C, Mercier, AE, Mergny, J-L, Merighi, A, Merkley, SD, Merla, G, Meske, V, Mestre, AC, Metur, SP, Meyer, C, Meyer, H, Mi, W, Mialet-Perez, J, Miao, J, Micale, L, Miki, Y, Milan, E, Milczarek, M, Miller, DL, Miller, SI, Miller, S, Millward, SW, Milosevic, I, Minina, EA, Mirzaei, H, Mirzaei, HR, Mirzaei, M, Mishra, A, Mishra, N, Mishra, PK, Misirkic Marjanovic, M, Misasi, R, Misra, A, Misso, G, Mitchell, C, Mitou, G, Miura, T, Miyamoto, S, Miyazaki, M, Miyazaki, T, Miyazawa, K, Mizushima, N, Mogensen, TH, Mograbi, B, Mohammadinejad, R, Mohamud, Y, Mohanty, A, Mohapatra, S, Möhlmann, T, Mohmmed, A, Moles, A, Moley, KH, Molinari, M, Mollace, V, Møller, AB, Mollereau, B, Mollinedo, F, Montagna, C, Monteiro, MJ, Montella, A, Montes, LR, Montico, B, Mony, VK, Monzio Compagnoni, G, Moore, MN, Moosavi, MA, Mora, AL, Mora, M, Morales-Alamo, D, Moratalla, R, Moreira, PI, Morelli, E, Moreno, S, Moreno-Blas, D, Moresi, V, Morga, B, Morgan, AH, Morin, F, Morishita, H, Moritz, OL, Moriyama, M, Moriyasu, Y, Morleo, M, Morselli, E, Moruno-Manchon, JF, Moscat, J, Mostowy, S, Motori, E, Moura, AF, Moustaid-Moussa, N, Mrakovcic, M, Muciño-Hernández, G, Mukherjee, A, Mukhopadhyay, S, Mulcahy Levy, JM, Mulero, V, Muller, S, Münch, C, Munjal, A, Munoz-Canoves, P, Muñoz-Galdeano, T, Münz, C, Murakawa, T, Muratori, C, Murphy, BM, Murphy, JP, Murthy, A, Myöhänen, TT, Mysorekar, IU, Mytych, J, Nabavi, SM, Nabissi, M, Nagy, P, Nah, J, Nahimana, A, Nakagawa, I, Nakamura, K, Nakatogawa, H, Nandi, SS, Nanjundan, M, Nanni, M, Napolitano, G, Nardacci, R, Narita, M, Nassif, M, Nathan, I, Natsumeda, M, Naude, RJ, Naumann, C, Naveiras, O, Navid, F, Nawrocki, ST, Nazarko, TY, Nazio, F, Negoita, F, Neill, T, Neisch, AL, Neri, LM, Netea, MG, Neubert, P, Neufeld, TP, Neumann, D, Neutzner, A, Newton, PT, Ney, PA, Nezis, IP, Ng, CCW, Ng, TB, Nguyen, HTT, Nguyen, LT, Ni, H-M, Ní Cheallaigh, C, Ni, Z, Nicolao, MC, Nicoli, F, Nieto-Diaz, M, Nilsson, P, Ning, S, Niranjan, R, Nishimune, H, Niso-Santano, M, Nixon, RA, Nobili, A, Nobrega, C, Noda, T, Nogueira-Recalde, U, Nolan, TM, Nombela, I, Novak, I, Novoa, B, Nozawa, T, Nukina, N, Nussbaum-Krammer, C, Nylandsted, J, O'Donovan, TR, O'Leary, SM, O'Rourke, EJ, O'Sullivan, MP, O'Sullivan, TE, Oddo, S, Oehme, I, Ogawa, M, Ogier-Denis, E, Ogmundsdottir, MH, Ogretmen, B, Oh, GT, Oh, S-H, Oh, YJ, Ohama, T, Ohashi, Y, Ohmuraya, M, Oikonomou, V, Ojha, R, Okamoto, K, Okazawa, H, Oku, M, Oliván, S, Oliveira, JMA, Ollmann, M, Olzmann, JA, Omari, S, Omary, MB, Önal, G, Ondrej, M, Ong, S-B, Ong, S-G, Onnis, A, Orellana, JA, Orellana-Muñoz, S, Ortega-Villaizan, MDM, Ortiz-Gonzalez, XR, Ortona, E, Osiewacz, HD, Osman, A-HK, Osta, R, Otegui, MS, Otsu, K, Ott, C, Ottobrini, L, Ou, J-HJ, Outeiro, TF, Oynebraten, I, Ozturk, M, Pagès, G, Pahari, S, Pajares, M, Pajvani, UB, Pal, R, Paladino, S, Pallet, N, Palmieri, M, Palmisano, G, Palumbo, C, Pampaloni, F, Pan, L, Pan, Q, Pan, W, Pan, X, Panasyuk, G, Pandey, R, Pandey, UB, Pandya, V, Paneni, F, Pang, SY, Panzarini, E, Papademetrio, DL, Papaleo, E, Papinski, D, Papp, D, Park, EC, Park, HT, Park, J-M, Park, J-I, Park, JT, Park, J, Park, SC, Park, S-Y, Parola, AH, Parys, JB, Pasquier, A, Pasquier, B, Passos, JF, Pastore, N, Patel, HH, Patschan, D, Pattingre, S, Pedraza-Alva, G, Pedraza-Chaverri, J, Pedrozo, Z, Pei, G, Pei, J, Peled-Zehavi, H, Pellegrini, JM, Pelletier, J, Peñalva, MA, Peng, D, Peng, Y, Penna, F, Pennuto, M, Pentimalli, F, Pereira, CM, Pereira, GJS, Pereira, LC, Pereira de Almeida, L, Perera, ND, Pérez-Lara, Á, Perez-Oliva, AB, Pérez-Pérez, ME, Periyasamy, P, Perl, A, Perrotta, C, Perrotta, I, Pestell, RG, Petersen, M, Petrache, I, Petrovski, G, Pfirrmann, T, Pfister, AS, Philips, JA, Pi, H, Picca, A, Pickrell, AM, Picot, S, Pierantoni, GM, Pierdominici, M, Pierre, P, Pierrefite-Carle, V, Pierzynowska, K, Pietrocola, F, Pietruczuk, M, Pignata, C, Pimentel-Muiños, FX, Pinar, M, Pinheiro, RO, Pinkas-Kramarski, R, Pinton, P, Pircs, K, Piya, S, Pizzo, P, Plantinga, TS, Platta, HW, Plaza-Zabala, A, Plomann, M, Plotnikov, EY, Plun-Favreau, H, Pluta, R, Pocock, R, Pöggeler, S, Pohl, C, Poirot, M, Poletti, A, Ponpuak, M, Popelka, H, Popova, B, Porta, H, Porte Alcon, S, Portilla-Fernandez, E, Post, M, Potts, MB, Poulton, J, Powers, T, Prahlad, V, Prajsnar, TK, Praticò, D, Prencipe, R, Priault, M, Proikas-Cezanne, T, Promponas, VJ, Proud, CG, Puertollano, R, Puglielli, L, Pulinilkunnil, T, Puri, D, Puri, R, Puyal, J, Qi, X, Qi, Y, Qian, W, Qiang, L, Qiu, Y, Quadrilatero, J, Quarleri, J, Raben, N, Rabinowich, H, Ragona, D, Ragusa, MJ, Rahimi, N, Rahmati, M, Raia, V, Raimundo, N, Rajasekaran, N-S, Ramachandra Rao, S, Rami, A, Ramírez-Pardo, I, Ramsden, DB, Randow, F, Rangarajan, PN, Ranieri, D, Rao, H, Rao, L, Rao, R, Rathore, S, Ratnayaka, JA, Ratovitski, EA, Ravanan, P, Ravegnini, G, Ray, SK, Razani, B, Rebecca, V, Reggiori, F, Régnier-Vigouroux, A, Reichert, AS, Reigada, D, Reiling, JH, Rein, T, Reipert, S, Rekha, RS, Ren, H, Ren, J, Ren, W, Renault, T, Renga, G, Reue, K, Rewitz, K, Ribeiro de Andrade Ramos, B, Riazuddin, SA, Ribeiro-Rodrigues, TM, Ricci, J-E, Ricci, R, Riccio, V, Richardson, DR, Rikihisa, Y, Risbud, MV, Risueño, RM, Ritis, K, Rizza, S, Rizzuto, R, Roberts, HC, Roberts, LD, Robinson, KJ, Roccheri, MC, Rocchi, S, Rodney, GG, Rodrigues, T, Rodrigues Silva, VR, Rodriguez, A, Rodriguez-Barrueco, R, Rodriguez-Henche, N, Rodriguez-Rocha, H, Roelofs, J, Rogers, RS, Rogov, VV, Rojo, AI, Rolka, K, Romanello, V, Romani, L, Romano, A, Romano, PS, Romeo-Guitart, D, Romero, LC, Romero, M, Roney, JC, Rongo, C, Roperto, S, Rosenfeldt, MT, Rosenstiel, P, Rosenwald, AG, Roth, KA, Roth, L, Roth, S, Rouschop, KMA, Roussel, BD, Roux, S, Rovere-Querini, P, Roy, A, Rozieres, A, Ruano, D, Rubinsztein, DC, Rubtsova, MP, Ruckdeschel, K, Ruckenstuhl, C, Rudolf, E, Rudolf, R, Ruggieri, A, Ruparelia, AA, Rusmini, P, Russell, RR, Russo, GL, Russo, M, Russo, R, Ryabaya, OO, Ryan, KM, Ryu, K-Y, Sabater-Arcis, M, Sachdev, U, Sacher, M, Sachse, C, Sadhu, A, Sadoshima, J, Safren, N, Saftig, P, Sagona, AP, Sahay, G, Sahebkar, A, Sahin, M, Sahin, O, Sahni, S, Saito, N, Saito, S, Saito, T, Sakai, R, Sakai, Y, Sakamaki, J-I, Saksela, K, Salazar, G, Salazar-Degracia, A, Salekdeh, GH, Saluja, AK, Sampaio-Marques, B, Sanchez, MC, Sanchez-Alcazar, JA, Sanchez-Vera, V, Sancho-Shimizu, V, Sanderson, JT, Sandri, M, Santaguida, S, Santambrogio, L, Santana, MM, Santoni, G, Sanz, A, Sanz, P, Saran, S, Sardiello, M, Sargeant, TJ, Sarin, A, Sarkar, C, Sarkar, S, Sarrias, M-R, Sarmah, DT, Sarparanta, J, Sathyanarayan, A, Sathyanarayanan, R, Scaglione, KM, Scatozza, F, Schaefer, L, Schafer, ZT, Schaible, UE, Schapira, AHV, Scharl, M, Schatzl, HM, Schein, CH, Scheper, W, Scheuring, D, Schiaffino, MV, Schiappacassi, M, Schindl, R, Schlattner, U, Schmidt, O, Schmitt, R, Schmidt, SD, Schmitz, I, Schmukler, E, Schneider, A, Schneider, BE, Schober, R, Schoijet, AC, Schott, MB, Schramm, M, Schröder, B, Schuh, K, Schüller, C, Schulze, RJ, Schürmanns, L, Schwamborn, JC, Schwarten, M, Scialo, F, Sciarretta, S, Scott, MJ, Scotto, KW, Scovassi, AI, Scrima, A, Scrivo, A, Sebastian, D, Sebti, S, Sedej, S, Segatori, L, Segev, N, Seglen, PO, Seiliez, I, Seki, E, Selleck, SB, Sellke, FW, Selsby, JT, Sendtner, M, Senturk, S, Seranova, E, Sergi, C, Serra-Moreno, R, Sesaki, H, Settembre, C, Setty, SRG, Sgarbi, G, Sha, O, Shacka, JJ, Shah, JA, Shang, D, Shao, C, Shao, F, Sharbati, S, Sharkey, LM, Sharma, D, Sharma, G, Sharma, K, Sharma, P, Sharma, S, Shen, H-M, Shen, H, Shen, J, Shen, M, Shen, W, Shen, Z, Sheng, R, Sheng, Z, Sheng, Z-H, Shi, J, Shi, X, Shi, Y-H, Shiba-Fukushima, K, Shieh, J-J, Shimada, Y, Shimizu, S, Shimozawa, M, Shintani, T, Shoemaker, CJ, Shojaei, S, Shoji, I, Shravage, BV, Shridhar, V, Shu, C-W, Shu, H-B, Shui, K, Shukla, AK, Shutt, TE, Sica, V, Siddiqui, A, Sierra, A, Sierra-Torre, V, Signorelli, S, Sil, P, Silva, BJDA, Silva, JD, Silva-Pavez, E, Silvente-Poirot, S, Simmonds, RE, Simon, AK, Simon, H-U, Simons, M, Singh, A, Singh, LP, Singh, R, Singh, SV, Singh, SK, Singh, SB, Singh, S, Singh, SP, Sinha, D, Sinha, RA, Sinha, S, Sirko, A, Sirohi, K, Sivridis, EL, Skendros, P, Skirycz, A, Slaninová, I, Smaili, SS, Smertenko, A, Smith, MD, Soenen, SJ, Sohn, EJ, Sok, SPM, Solaini, G, Soldati, T, Soleimanpour, SA, Soler, RM, Solovchenko, A, Somarelli, JA, Sonawane, A, Song, F, Song, HK, Song, J-X, Song, K, Song, Z, Soria, LR, Sorice, M, Soukas, AA, Soukup, S-F, Sousa, D, Sousa, N, Spagnuolo, PA, Spector, SA, Srinivas Bharath, MM, St Clair, D, Stagni, V, Staiano, L, Stalnecker, CA, Stankov, MV, Stathopulos, PB, Stefan, K, Stefan, SM, Stefanis, L, Steffan, JS, Steinkasserer, A, Stenmark, H, Sterneckert, J, Stevens, C, Stoka, V, Storch, S, Stork, B, Strappazzon, F, Strohecker, AM, Stupack, DG, Su, H, Su, L-Y, Su, L, Suarez-Fontes, AM, Subauste, CS, Subbian, S, Subirada, PV, Sudhandiran, G, Sue, CM, Sui, X, Summers, C, Sun, G, Sun, J, Sun, K, Sun, M-X, Sun, Q, Sun, Y, Sun, Z, Sunahara, KKS, Sundberg, E, Susztak, K, Sutovsky, P, Suzuki, H, Sweeney, G, Symons, JD, Sze, SCW, Szewczyk, NJ, Tabęcka-Łonczynska, A, Tabolacci, C, Tacke, F, Taegtmeyer, H, Tafani, M, Tagaya, M, Tai, H, Tait, SWG, Takahashi, Y, Takats, S, Talwar, P, Tam, C, Tam, SY, Tampellini, D, Tamura, A, Tan, CT, Tan, E-K, Tan, Y-Q, Tanaka, M, Tang, D, Tang, J, Tang, T-S, Tanida, I, Tao, Z, Taouis, M, Tatenhorst, L, Tavernarakis, N, Taylor, A, Taylor, GA, Taylor, JM, Tchetina, E, Tee, AR, Tegeder, I, Teis, D, Teixeira, N, Teixeira-Clerc, F, Tekirdag, KA, Tencomnao, T, Tenreiro, S, Tepikin, AV, Testillano, PS, Tettamanti, G, Tharaux, P-L, Thedieck, K, Thekkinghat, AA, Thellung, S, Thinwa, JW, Thirumalaikumar, VP, Thomas, SM, Thomes, PG, Thorburn, A, Thukral, L, Thum, T, Thumm, M, Tian, L, Tichy, A, Till, A, Timmerman, V, Titorenko, VI, Todi, SV, Todorova, K, Toivonen, JM, Tomaipitinca, L, Tomar, D, Tomas-Zapico, C, Tomić, S, Tong, BC-K, Tong, C, Tong, X, Tooze, SA, Torgersen, ML, Torii, S, Torres-López, L, Torriglia, A, Towers, CG, Towns, R, Toyokuni, S, Trajkovic, V, Tramontano, D, Tran, Q-G, Travassos, LH, Trelford, CB, Tremel, S, Trougakos, IP, Tsao, BP, Tschan, MP, Tse, H-F, Tse, TF, Tsugawa, H, Tsvetkov, AS, Tumbarello, DA, Tumtas, Y, Tuñón, MJ, Turcotte, S, Turk, B, Turk, V, Turner, BJ, Tuxworth, RI, Tyler, JK, Tyutereva, EV, Uchiyama, Y, Ugun-Klusek, A, Uhlig, HH, Ułamek-Kozioł, M, Ulasov, IV, Umekawa, M, Ungermann, C, Unno, R, Urbe, S, Uribe-Carretero, E, Üstün, S, Uversky, VN, Vaccari, T, Vaccaro, MI, Vahsen, BF, Vakifahmetoglu-Norberg, H, Valdor, R, Valente, MJ, Valko, A, Vallee, RB, Valverde, AM, Van den Berghe, G, van der Veen, S, Van Kaer, L, van Loosdregt, J, van Wijk, SJL, Vandenberghe, W, Vanhorebeek, I, Vannier-Santos, MA, Vannini, N, Vanrell, MC, Vantaggiato, C, Varano, G, Varela-Nieto, I, Varga, M, Vasconcelos, MH, Vats, S, Vavvas, DG, Vega-Naredo, I, Vega-Rubin-de-Celis, S, Velasco, G, Velázquez, AP, Vellai, T, Vellenga, E, Velotti, F, Verdier, M, Verginis, P, Vergne, I, Verkade, P, Verma, M, Verstreken, P, Vervliet, T, Vervoorts, J, Vessoni, AT, Victor, VM, Vidal, M, Vidoni, C, Vieira, OV, Vierstra, RD, Viganó, S, Vihinen, H, Vijayan, V, Vila, M, Vilar, M, Villalba, JM, Villalobo, A, Villarejo-Zori, B, Villarroya, F, Villarroya, J, Vincent, O, Vindis, C, Viret, C, Viscomi, MT, Visnjic, D, Vitale, I, Vocadlo, DJ, Voitsekhovskaja, OV, Volonté, C, Volta, M, Vomero, M, Von Haefen, C, Vooijs, MA, Voos, W, Vucicevic, L, Wade-Martins, R, Waguri, S, Waite, KA, Wakatsuki, S, Walker, DW, Walker, MJ, Walker, SA, Walter, J, Wandosell, FG, Wang, B, Wang, C-Y, Wang, C, Wang, D, Wang, F, Wang, G, Wang, H, Wang, H-G, Wang, J, Wang, K, Wang, L, Wang, MH, Wang, M, Wang, N, Wang, P, Wang, QJ, Wang, Q, Wang, QK, Wang, QA, Wang, W-T, Wang, W, Wang, X, Wang, Y, Wang, Y-Y, Wang, Z, Warnes, G, Warnsmann, V, Watada, H, Watanabe, E, Watchon, M, Wawrzyńska, A, Weaver, TE, Wegrzyn, G, Wehman, AM, Wei, H, Wei, L, Wei, T, Wei, Y, Weiergräber, OH, Weihl, CC, Weindl, G, Weiskirchen, R, Wells, A, Wen, RH, Wen, X, Werner, A, Weykopf, B, Wheatley, SP, Whitton, JL, Whitworth, AJ, Wiktorska, K, Wildenberg, ME, Wileman, T, Wilkinson, S, Willbold, D, Williams, B, Williams, RSB, Williams, RL, Williamson, PR, Wilson, RA, Winner, B, Winsor, NJ, Witkin, SS, Wodrich, H, Woehlbier, U, Wollert, T, Wong, E, Wong, JH, Wong, RW, Wong, VKW, Wong, WW-L, Wu, A-G, Wu, C, Wu, J, Wu, KK, Wu, M, Wu, S-Y, Wu, S, Wu, WKK, Wu, X, Wu, Y-W, Wu, Y, Xavier, RJ, Xia, H, Xia, L, Xia, Z, Xiang, G, Xiang, J, Xiang, M, Xiang, W, Xiao, B, Xiao, G, Xiao, H, Xiao, H-T, Xiao, J, Xiao, L, Xiao, S, Xiao, Y, Xie, B, Xie, C-M, Xie, M, Xie, Y, Xie, Z, Xilouri, M, Xu, C, Xu, E, Xu, H, Xu, J, Xu, L, Xu, WW, Xu, X, Xue, Y, Yakhine-Diop, SMS, Yamaguchi, M, Yamaguchi, O, Yamamoto, A, Yamashina, S, Yan, S, Yan, S-J, Yan, Z, Yanagi, Y, Yang, C, Yang, D-S, Yang, H, Yang, H-T, Yang, J-M, Yang, J, Yang, L, Yang, M, Yang, P-M, Yang, Q, Yang, S, Yang, S-F, Yang, W, Yang, WY, Yang, X, Yang, Y, Yao, H, Yao, S, Yao, X, Yao, Y-G, Yao, Y-M, Yasui, T, Yazdankhah, M, Yen, PM, Yi, C, Yin, X-M, Yin, Y, Yin, Z, Ying, M, Ying, Z, Yip, CK, Yiu, SPT, Yoo, YH, Yoshida, K, Yoshii, SR, Yoshimori, T, Yousefi, B, Yu, B, Yu, H, Yu, J, Yu, L, Yu, M-L, Yu, S-W, Yu, VC, Yu, WH, Yu, Z, Yuan, J, Yuan, L-Q, Yuan, S, Yuan, S-SF, Yuan, Y, Yuan, Z, Yue, J, Yue, Z, Yun, J, Yung, RL, Zacks, DN, Zaffagnini, G, Zambelli, VO, Zanella, I, Zang, QS, Zanivan, S, Zappavigna, S, Zaragoza, P, Zarbalis, KS, Zarebkohan, A, Zarrouk, A, Zeitlin, SO, Zeng, J, Zeng, J-D, Žerovnik, E, Zhan, L, Zhang, B, Zhang, DD, Zhang, H, Zhang, H-L, Zhang, J, Zhang, J-P, Zhang, KYB, Zhang, LW, Zhang, L, Zhang, M, Zhang, P, Zhang, S, Zhang, W, Zhang, X, Zhang, X-W, Zhang, XD, Zhang, Y, Zhang, Y-D, Zhang, Y-Y, Zhang, Z, Zhao, H, Zhao, L, Zhao, S, Zhao, T, Zhao, X-F, Zhao, Y, Zheng, G, Zheng, K, Zheng, L, Zheng, S, Zheng, X-L, Zheng, Y, Zheng, Z-G, Zhivotovsky, B, Zhong, Q, Zhou, A, Zhou, B, Zhou, C, Zhou, G, Zhou, H, Zhou, J, Zhou, K, Zhou, R, Zhou, X-J, Zhou, Y, Zhou, Z-Y, Zhou, Z, Zhu, B, Zhu, C, Zhu, G-Q, Zhu, H, Zhu, W-G, Zhu, Y, Zhuang, H, Zhuang, X, Zientara-Rytter, K, Zimmermann, CM, Ziviani, E, Zoladek, T, Zong, W-X, Zorov, DB, Zorzano, A, Zou, W, Zou, Z, Zuryn, S, Zwerschke, W, Brand-Saberi, B, Dong, XC, Kenchappa, CS, Lin, Y, Oshima, S, Rong, Y, Sluimer, JC, Stallings, CL, and Tong, C-K
- Abstract
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.
- Published
- 2021
4. Chronic Obstructive Pulmonary Disease and Lung Cancer: Underlying Pathophysiology and New Therapeutic Modalities
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Eapen, MS, Hansbro, PM, Larsson‑Callerfelt, AK, Jolly, MK, Myers, S, Sharma, P, Jones, B, Rahman, MA, Markos, J, Chia, C, Larby, J, Haug, G, Hardikar, A, Weber, HC, Mabeza, G, Cavalheri, V, Khor, YH, McDonald, CF, and Sohal, SS
- Subjects
Pulmonary Disease, Chronic Obstructive ,Oxidative Stress ,Lung Neoplasms ,Smoking ,Animals ,Humans ,Pharmacology & Pharmacy ,Molecular Targeted Therapy ,Combined Modality Therapy ,respiratory tract diseases - Abstract
© 2018, Springer Nature Switzerland AG. Chronic obstructive pulmonary disease (COPD) and lung cancer are major lung diseases affecting millions worldwide. Both diseases have links to cigarette smoking and exert a considerable societal burden. People suffering from COPD are at higher risk of developing lung cancer than those without, and are more susceptible to poor outcomes after diagnosis and treatment. Lung cancer and COPD are closely associated, possibly sharing common traits such as an underlying genetic predisposition, epithelial and endothelial cell plasticity, dysfunctional inflammatory mechanisms including the deposition of excessive extracellular matrix, angiogenesis, susceptibility to DNA damage and cellular mutagenesis. In fact, COPD could be the driving factor for lung cancer, providing a conducive environment that propagates its evolution. In the early stages of smoking, body defences provide a combative immune/oxidative response and DNA repair mechanisms are likely to subdue these changes to a certain extent; however, in patients with COPD with lung cancer the consequences could be devastating, potentially contributing to slower postoperative recovery after lung resection and increased resistance to radiotherapy and chemotherapy. Vital to the development of new-targeted therapies is an in-depth understanding of various molecular mechanisms that are associated with both pathologies. In this comprehensive review, we provide a detailed overview of possible underlying factors that link COPD and lung cancer, and current therapeutic advances from both human and preclinical animal models that can effectively mitigate this unholy relationship.
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- 2018
5. Rationale, design, implementation, and baseline characteristics of patients in the dig trial: A large, simple, long-term trial to evaluate the effect of digitalis on mortality in heart failure
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Abernathy, GT, Abrams, J, Akhtar, S, Albitar, I, Amidi, M, Anand, IS, Arnold, JMO, Ashton, T, Aubrey, B, Auger, P, Babb, J, Baigrie, R, Baird, MG, Baitz, T, Barber, NC, Barbour, DJ, Barr, DM, Basu, AK, Baughman, KL, Beckham, V, BekheitSaad, S, Berkson, DM, Bertoglio, M, Bessoudo, R, Beaudoin, J, Bhaskar, G, Binder, A, Bloomfield, D, Bodine, K, Boehmer, JP, Borgersen, K, Borts, D, Bouchard, G, Bourassa, MG, Boutros, G, Bozek, B, Brisbin, D, Brophy, J, Brossoit, R, Brown, E, Brown, J, Bruinsma, N, Burton, G, Cameron, A, Campbell, R, Campeau, J, Campos, EE, Cardello, FP, Carter, RP, Chan, YK, Charles, FR, Chaudhry, MA, Chiaramida, A, Chiaramida, S, Chohan, A, Christie, LG, Clemson, BS, Collin, R, Cook, TH, Copen, DL, Cossett, J, Costantino, T, Crawford, MH, Croke, RP, Crowell, R, DAmours, G, Dagenais, GR, Danisa, K, Davidson, S, Davies, ML, Davies, R, Davies, RA, DeLarochelliere, R, DeLeon, AC, Delage, F, Denes, P, Dennish, GW, Denny, DM, DeVilla, MA, DeYoung, JP, Dhurandhar, RW, DibnerDunlap, M, Dodek, A, Doherty, JE, Dominguez, J, Dubbin, J, Dufton, J, Effron, MB, ElSherif, N, Eladasari, B, Fly, D, Ericson, K, Fahrenholtz, D, Fast, A, Fell, DA, Fishman, S, Fitchett, D, Fleg, JL, Flint, E, Folger, JS, Folkins, D, Forker, AD, Fowles, RE, Fraker, TD, Francis, G, Frerking, TR, Friesinger, GC, Fulop, JC, Gagnon, J, Gamble, L, Ganjavi, F, Garrou, BW, Gervais, PB, Gheorghiade, M, Gilbert, L, Gillie, E, Glatter, TR, Godley, ML, Goeres, M, Goldberger, MH, Gollapudi, A, Goode, JE, Goodman, LS, Gordon, R, Gossard, D, Gosselin, G, Goulet, C, Grant, C, Graettinger, WF, Greene, JG, Greenwood, PV, Gregoratos, G, Gregory, JJ, Groden, DL, Grover, J, Gudapati, R, Guess, MA, Gupta, SC, Habib, N, Hack, I, Hamilton, WP, Hankey, TL, Hanna, M, Harper, D, Harris, DE, Hassapoyannes, CA, Hatheway, RJ, Heinsimer, J, Pequignot, MH, Heiselman, DE, Hess, AR, Hickner, J, Hickey, JE, Higgins, T, Higginson, L, Hill, L, Hobbs, RE, Honos, G, Horner, BA, Horwitz, L, Hsieh, A, Hsueh, JT, Hubbard, J, Hughes, DF, Hui, W, Imrie, JR, Jacobs, MH, Jarmukli, N, Johnson, TH, Johnstone, D, Jutila, CK, Kadri, N, Kahl, FR, Kaimal, PK, Karnegis, J, Kay, R, Kelly, KJ, Kenefick, G, Kennelly, BM, Kent, E, Khan, AH, Khanijo, V, Khouri, M, Kinloch, D, Kirlin, PC, Kiwan, GS, Kline, MD, Kohn, RM, Koilpillai, C, Kornder, JM, Kouz, S, Kumar, VA, Kumar, U, Kuntz, A, Kuritzky, RA, Kuruvilla, G, Kwok, KK, Lader, E, Laforest, M, LaForge, D, Lalonde, G, Lalonde, L, Lang, RM, Latour, Y, Lawal, O, LeBlanc, MH, Lee, AB, Lee, RW, Legault, C, Lemay, M, Lenis, JHF, Lepage, S, Letarte, P, Levesque, C, LevinoffRoth, SN, Lewis, BK, Lipshutz, H, Loungani, RR, Lowery, ML, Lubell, DL, Lucariello, R, LugoRodriguez, JE, Lui, C, Lutterodt, AT, Lutz, L, Machel, T, Macina, G, MacLellan, K, Magnan, O, Mansuri, M, Manyari, DE, Mallis, GI, Marr, D, Mast, DJ, Mathew, J, McBarron, FD, McIntyre, KM, McLean, RW, McMahon, DP, Mercier, M, Methe, M, Miller, AB, Minkowitz, J, Milton, JR, Mizgala, HF, Mohanty, PK, Mohiuddin, S, Montero, A, Mookherjee, S, Morris, A, Morris, L, Morrison, J, Moten, M, Nafziger, A, Nair, PH, Nawaz, S, Neiman, JC, Nutting, P, NguyenPho, HT, OBrien, TK, OKelly, RL, OReilly, MV, Okerson, D, Patel, G, Pande, PN, Papa, LA, Patrick, L, Payne, RM, Perry, G, Philbin, EF, Pierpont, G, Pitt, WA, Poirier, C, Pollak, EM, Popio, K, Poulin, JF, Probst, PA, Pruneau, G, Pu, C, Puram, BS, Putatunda, B, Quinn, B, Rabkin, SW, Racine, N, Raco, DL, Radant, L, Radford, MJ, Radwany, S, Rajachar, M, Ramanathan, KB, Rashkow, A, Rausch, DC, Read, L, Reddy, KR, Reid, R, Rich, MW, Ricci, AJ, Richman, HG, Riley, A, Rim, DA, Rinne, C, Roberge, G, Roberts, DK, Robinson, V, Rodeheffer, RT, Rosenstein, R, Roth, DL, Rothbart, R, Rouleau, JL, Ruble, P, Sacco, J, Safford, RE, Salmon, D, Sahay, BM, Sarma, RJ, Sayeed, MAR, Schick, EC, Schroeder, GS, Seifert, M, Senaratne, MPJ, Sestier, F, Shah, A, Shanes, JG, Sheesley, K, Silverman, A, Shiva, T, Shrestha, DD, Silver, MA, Silverberg, L, Simard, L, Singh, BN, Small, RS, Smith, MR, Smith, S, Sochowski, RA, Southern, RF, Sridharan, MR, StHilaire, R, Stein, M, Stewart, JW, Stillabower, ME, Sullivan, BHM, Sturrock, WA, Sussex, BA, Swan, J, Swenson, L, Talbot, P, Talibi, T, Tamilia, M, Tan, A, Tanser, PH, Tarry, L, Teo, KK, Thadani, U, Thagirisa, S, Thompson, B, Thornton, R, Timmis, GC, Tobin, M, Tommaso, C, Toren, M, Tsuyuki, R, Turek, M, Utley, K, Vanderbush, EJ, VanVoorhees, L, Ventura, H, Vertes, G, Vizel, S, Wagner, KR, Wagner, S, Weeks, A, Weingert, ME, Weinstein, C, Weiss, MM, Weiss, R, Wickemeyer, W, Wielgoz, A, Willens, HJ, Williams, WL, Wong, D, Yarows, SA, Yao, L, Shalev, Y, Young, JB, Yousefian, M, Zajac, EJ, Zatuchni, J, Ziperman, DB, Zoble, RG, Zoneraich, S, Gorlin, R, Sleight, P, Cohn, JN, Collins, R, Deykin, D, Hennekens, C, Kjekshus, J, Smith, TW, Tognoni, G, Collins, JF, Williford, WO, Fye, C, Sather, R, Jolly, MK, Held, CP, Verter, J, Yusuf, S, Egan, D, Garg, R, Johnstone, DE, Montague, T, Bristow, D, Engelhardt, HT, Gent, M, Hood, WB, Jones, S, Meier, P, Pitt, B, Waters, D, Baker, A, Barnhill, S, Carew, B, Hagar, S, Liuni, C, Martin, S, Miles, R, Arthur, MM, Feldbush, MW, Highfield, DA, Hobbins, TE, Kurz, R, Leviton, SP, Libonati, JP, Moore, M, Perez, E, Mills, P, Geller, N, Hunsberger, S, Gold, J, Huang, PC, Burns, A, Caleb, H, Cline, DR, Harris, S, Hockenbrock, R, Horney, RA, Jadwin, LM, King, J, Sexton, P, Spence, ME, Chacon, F, Gagne, W, Maple, S, and Martinez, G
- Subjects
Heart Failure ,Male ,Pharmacology ,Digoxin ,Treatment Outcome ,Patient Selection ,Digitalis Glycosides ,Humans ,Multicenter Studies as Topic ,Female ,Middle Aged ,Aged ,Randomized Controlled Trials as Topic - Abstract
This article provides a detailed overview of the rationale for key aspects of the protocol of the Digitalis Investigation Group (DIG) trial. It also highlights unusual aspects of the study implementation and the baseline characteristics. The DIG trial is a large, simple, international placebo-controlled trial whose primary objective is to determine the effect of digoxin on all cause mortality in patients with clinical heart failure who are in sinus rhythm and whose ejection fraction isor = 0.45. An ancillary study examines the effect in those with an ejection fraction0.45. Key aspects of the trial include the simplicity of the design, broad eligibility criteria, essential data collection, and inclusion of various types of centers. A total of 302 centers in the United States and Canada enrolled 7788 patients between February 1991 and September 1993. Follow-up continued until December 1995 with the results available in Spring 1996.
- Published
- 1996
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6. Lineage-specific dynamics of loss of X upregulation during inactive-X reactivation.
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Naik HC, Chandel D, Majumdar S, Arava M, Baro R, Bv H, Hari K, Ayyamperumal P, Manhas A, Jolly MK, and Gayen S
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- Animals, Mice, Humans, Female, Cell Lineage genetics, Induced Pluripotent Stem Cells metabolism, Induced Pluripotent Stem Cells cytology, Germ Layers metabolism, Germ Layers cytology, Gene Expression Regulation, Developmental, B-Lymphocytes metabolism, B-Lymphocytes cytology, Male, X Chromosome Inactivation, Up-Regulation, X Chromosome genetics
- Abstract
In mammals, X chromosome dosage is balanced between sexes through the silencing of one X chromosome in females. Recent single-cell RNA sequencing analysis demonstrated that the inactivation of the X chromosome is accompanied by the upregulation of the active X chromosome (Xa) during mouse embryogenesis. Here, we have investigated if the reactivation of inactive-X (Xi) leads to the loss of Xa upregulation in different cellular or developmental contexts. We find that while Xi reactivation and loss of Xa upregulation are tightly coupled in mouse embryonic epiblast and induced pluripotent stem cells, that is not the case in germ cells. Moreover, we demonstrate that partial reactivation of Xi in mouse extra-embryonic endoderm stem cells and human B cells does not result in the loss of Xa upregulation. Finally, we have established a mathematical model for the transcriptional coordination of two X chromosomes. Together, we conclude that the reactivation of Xi is not always synchronized with the loss of Xa upregulation., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
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- 2024
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7. Drug tolerance and persistence in bacteria, fungi and cancer cells: Role of non-genetic heterogeneity.
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El Meouche I, Jain P, Jolly MK, and Capp JP
- Abstract
A common feature of bacterial, fungal and cancer cell populations upon treatment is the presence of tolerant and persistent cells able to survive, and sometimes grow, even in the presence of usually inhibitory or lethal drug concentrations, driven by non-genetic differences among individual cells in a population. Here we review and compare data obtained on drug survival in bacteria, fungi and cancer cells to unravel common characteristics and cellular pathways, and to point their singularities. This comparative work also allows to cross-fertilize ideas across fields. We particularly focus on the role of gene expression variability in the emergence of cell-cell non-genetic heterogeneity because it represents a possible common basic molecular process at the origin of most persistence phenomena and could be monitored and tuned to help improve therapeutic interventions., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024. Published by Elsevier Inc.)
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- 2024
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8. Emergence of planar cell polarity from the interplay of local interactions and global gradients.
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Singh D, Ramaswamy S, Jolly MK, and Rizvi MS
- Abstract
Planar cell polarity (PCP) - tissue-scale alignment of the direction of asymmetric localization of proteins at the cell-cell interface - is essential for embryonic development and physiological functions. Abnormalities in PCP can result in developmental imperfections, including neural tube closure defects and misaligned hair follicles. Decoding the mechanisms responsible for PCP establishment and maintenance remains a fundamental open question. While the roles of various molecules - broadly classified into "global" and "local" modules - have been well-studied, their necessity and sufficiency in explaining PCP and connecting their perturbations to experimentally observed patterns have not been examined. Here, we develop a minimal model that captures the proposed features of PCP establishment - a global tissue-level gradient and local asymmetric distribution of protein complexes. The proposed model suggests that while polarity can emerge without a gradient, the gradient not only acts as a global cue but also increases the robustness of PCP against stochastic perturbations. We also recapitulated and quantified the experimentally observed features of swirling patterns and domineering non-autonomy, using only three free model parameters - the rate of protein binding to membrane, the concentration of PCP proteins, and the gradient steepness. We explain how self-stabilizing asymmetric protein localizations in the presence of tissue-level gradient can lead to robust PCP patterns and reveal minimal design principles for a polarized system., Competing Interests: DS, SR, MJ, MR The authors declare that no competing interests exist., (© 2024, Singh et al.)
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- 2024
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9. Multistability and predominant hybrid phenotypes in a four node mutually repressive network of Th1/Th2/Th17/Treg differentiation.
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Duddu AS, Andreas E, Bv H, Grover K, Singh VR, Hari K, Jhunjhunwala S, Cummins B, Gedeon T, and Jolly MK
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- Humans, Th2 Cells immunology, Transcription Factors genetics, Gene Regulatory Networks genetics, Computer Simulation, Cell Differentiation genetics, Phenotype, T-Lymphocytes, Regulatory immunology, Th17 Cells immunology, Th1 Cells immunology
- Abstract
Elucidating the emergent dynamics of cellular differentiation networks is crucial to understanding cell-fate decisions. Toggle switch - a network of mutually repressive lineage-specific transcription factors A and B - enables two phenotypes from a common progenitor: (high A, low B) and (low A, high B). However, the dynamics of networks enabling differentiation of more than two phenotypes from a progenitor cell has not been well-studied. Here, we investigate the dynamics of a four-node network A, B, C, and D inhibiting each other, forming a toggle tetrahedron. Our simulations show that this network is multistable and predominantly allows for the co-existence of six hybrid phenotypes where two of the nodes are expressed relatively high as compared to the remaining two, for instance (high A, high B, low C, low D). Finally, we apply our results to understand naïve CD4
+ T cell differentiation into Th1, Th2, Th17 and Treg subsets, suggesting Th1/Th2/Th17/Treg decision-making to be a two-step process., (© 2024. The Author(s).)- Published
- 2024
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10. Metastatic organotropism in small cell lung cancer.
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Krishnamurthy M, Dhall A, Schultz CW, Baird MA, Desai P, Odell J, Sahoo S, Takahashi N, Nirula M, Zhuang S, Huang Y, Schroeder B, Zhang Y, Thomas MS, Redon C, Robinson C, Thang L, Ileva L, Patel NL, Kalen JD, Varlet AA, Zuela-Sopilniak N, Jha A, Wangsa D, Butcher D, Morgan T, Afzal AN, Chari R, Baktiar K, Kumar S, Pongor L, Difilippantonio S, Aladjem MI, Pommier Y, Jolly MK, Lammerding J, Sharma AK, and Thomas A
- Abstract
Metastasis is the leading cause of cancer-related deaths, yet its regulatory mechanisms are not fully understood. Small-cell lung cancer (SCLC) is the most metastatic form of lung cancer, with most patients presenting with widespread disease, making it an ideal model for studying metastasis. However, the lack of suitable preclinical models has limited such studies. We utilized well-annotated rapid autopsy-derived tumors to develop xenograft models that mimic key features of SCLC, including histopathology, rapid and widespread development of metastasis to the liver, brain, adrenal, bone marrow, and kidneys within weeks, and response to chemotherapy. By integrating in vivo lineage selection with comprehensive transcriptomic and epigenomic analyses, we identified critical cellular programs driving metastatic organotropism to the liver and brain, the most common sites of SCLC metastasis. Our findings reveal the key role of nuclear-cytoskeletal interactions in SCLC liver metastasis. Specifically, the loss of the nuclear envelope protein lamin A/C, encoded by the LMNA gene, increased nuclear deformability and significantly increased the incidence of liver metastasis. Human liver metastases exhibited reduced LMNA expression compared to other metastatic sites, correlating with poorer patient outcomes and increased mortality. This study introduces novel preclinical models for SCLC metastasis and highlights pathways critical for organ-specific metastasis, offering new avenues for the development of targeted therapies to prevent or treat metastatic disease., Competing Interests: Conflict of Interest disclosure: A.T. received grants to NCI from EMD Serono Research & Development, AstraZeneca, Gilead Sciences, and ProLynx during the conduct of the study.
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- 2024
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11. Mutually exclusive teams-like patterns of gene regulation characterize phenotypic heterogeneity along the noradrenergic-mesenchymal axis in neuroblastoma.
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Sehgal M, Nayak SP, Sahoo S, Somarelli JA, and Jolly MK
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- Child, Humans, Gene Expression Regulation, Neoplastic, Phenotype, Neoplasm Recurrence, Local, Neuroblastoma genetics, Neuroblastoma pathology
- Abstract
Neuroblastoma is the most frequent extracranial pediatric tumor and leads to 15% of all cancer-related deaths in children. Tumor relapse and therapy resistance in neuroblastoma are driven by phenotypic plasticity and heterogeneity between noradrenergic (NOR) and mesenchymal (MES) cell states. Despite the importance of this phenotypic plasticity, the dynamics and molecular patterns associated with these bidirectional cell-state transitions remain relatively poorly understood. Here, we analyze multiple RNA-seq datasets at both bulk and single-cell resolution, to understand the association between NOR- and MES-specific factors. We observed that NOR-specific and MES-specific expression patterns are largely mutually exclusive, exhibiting a "teams-like" behavior among the genes involved, reminiscent of our earlier observations in lung cancer and melanoma. This antagonism between NOR and MES phenotypes was also associated with metabolic reprogramming and with immunotherapy targets PD-L1 and GD2 as well as with experimental perturbations driving the NOR-MES and/or MES-NOR transition. Further, these "teams-like" patterns were seen only among the NOR- and MES-specific genes, but not in housekeeping genes, possibly highlighting a hallmark of network topology enabling cancer cell plasticity.
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- 2024
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12. Microenvironmental entropy dynamics analysis reveals novel insights into Notch-Delta-Jagged decision-making mechanism.
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Pujar AA, Barua A, Dey PS, Singh D, Roy U, Jolly MK, and Hatzikirou H
- Abstract
Notch-Delta-Jagged (NDJ) signaling among neighboring cells contributes crucially to spatiotemporal pattern formation and developmental decision-making. Despite numerous detailed mathematical models, their high-dimensionality parametric space limits analytical treatment, especially regarding local microenvironmental fluctuations. Using the low-dimensional dynamics of the recently postulated least microenvironmental uncertainty principle (LEUP) framework, we showcase how the LEUP formalism recapitulates a noisy NDJ spatial patterning. Our LEUP simulations show that local phenotypic entropy increases for lateral inhibition but decreases for lateral induction. This distinction allows us to identify a critical parameter that captures the transition from a Notch-Delta-driven lateral inhibition to a Notch-Jagged-driven lateral induction phenomenon and suggests random phenotypic patterning in the case of lack of dominance of either Notch-Delta or Notch-Jagged signaling. Our results enable an analytical treatment to map the high-dimensional dynamics of NDJ signaling on tissue-level patterning and can possibly be generalized to decode operating principles of collective cellular decision-making., Competing Interests: The authors declared no competing interests., (© 2024 The Author(s).)
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- 2024
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13. Distinct melanocyte subpopulations defined by stochastic expression of proliferation or maturation programs enable a rapid and sustainable pigmentation response.
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Aggarwal A, Nasreen A, Sharma B, Sahoo S, Aswin K, Faruq M, Pandey R, Jolly MK, Singh A, Gokhale RS, and Natarajan VT
- Subjects
- Animals, Humans, Stochastic Processes, Cell Differentiation genetics, Histones metabolism, Acetylation, Ultraviolet Rays, Single-Cell Analysis, Pigmentation genetics, Enhancer Elements, Genetic genetics, Epigenesis, Genetic, Skin metabolism, Skin cytology, Melanocytes metabolism, Zebrafish genetics, Cell Proliferation, Gene Regulatory Networks, Skin Pigmentation genetics, Skin Pigmentation physiology
- Abstract
The ultraviolet (UV) radiation triggers a pigmentation response in human skin, wherein, melanocytes rapidly activate divergent maturation and proliferation programs. Using single-cell sequencing, we demonstrate that these 2 programs are segregated in distinct subpopulations in melanocytes of human and zebrafish skin. The coexistence of these 2 cell states in cultured melanocytes suggests possible cell autonomy. Luria-Delbrück fluctuation test reveals that the initial establishment of these states is stochastic. Tracking of pigmenting cells ascertains that the stochastically acquired state is faithfully propagated in the progeny. A systemic approach combining single-cell multi-omics (RNA+ATAC) coupled to enhancer mapping with H3K27 acetylation successfully identified state-specific transcriptional networks. This comprehensive analysis led to the construction of a gene regulatory network (GRN) that under the influence of noise, establishes a bistable system of pigmentation and proliferation at the population level. This GRN recapitulates melanocyte behaviour in response to external cues that reinforce either of the states. Our work highlights that inherent stochasticity within melanocytes establishes dedicated states, and the mature state is sustained by selective enhancers mark through histone acetylation. While the initial cue triggers a proliferation response, the continued signal activates and maintains the pigmenting subpopulation via epigenetic imprinting. Thereby our study provides the basis of coexistence of distinct populations which ensures effective pigmentation response while preserving the self-renewal capacity., Competing Interests: RSG is the co-founder of Vyome Biosciences Pvt Ltd., a biopharmaceutical company working in the area of dermatology. Other authors declare no competing interest., (Copyright: © 2024 Aggarwal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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- 2024
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14. Reconstruction of single-cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition.
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Cheng YC, Zhang Y, Tripathi S, Harshavardhan BV, Jolly MK, Schiebinger G, Levine H, McDonald TO, and Michor F
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- Humans, Cell Lineage genetics, Transforming Growth Factor beta metabolism, Enhancer of Zeste Homolog 2 Protein metabolism, Enhancer of Zeste Homolog 2 Protein genetics, Epithelial-Mesenchymal Transition genetics, Single-Cell Analysis methods
- Abstract
Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single-cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along the partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of the EED and EZH2 genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointing ITGB4 , LAMA3 , and LAMB3 as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found that CENPF , CKS1B , and MKI67 showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the identification of the latter group of genes sheds light on potential cell cycle targets for modulating EMT trajectories., Competing Interests: Competing interests statement:F.M. is a co-founder of and has equity in Harbinger Health, has equity in Zephyr AI, and serves as a consultant for both companies. She is also on the board of directors of Exscientia Plc. F.M. declares that none of these relationships are directly or indirectly related to the content of this manuscript. One of the authors (H.L.) was co-author on a review paper (in 2021) with one of the referees (Y.K.), and a different author (M.K.J.) was co-author on a different review paper (in 2023) with the other referee (Q.N.). All other authors declare no conflicts.
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- 2024
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15. Differential role of glucocorticoid receptor based on its cell type specific expression on tumor cells and infiltrating lymphocytes.
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Snijesh VP, Nimbalkar VP, Patil S, Rajarajan S, Anupama CE, Mahalakshmi S, Alexander A, Soundharya R, Ramesh R, Srinath BS, Jolly MK, and Prabhu JS
- Abstract
Background: The glucocorticoid receptor (GR) is frequently expressed in breast cancer (BC), and its prognostic implications are contingent on estrogen receptor (ER) status. To address conflicting reports and explore therapeutic potential, a GR signature (GRsig) independent of ER status was developed. We also investigated cell type-specific GR protein expression in BC tumor epithelial cells and infiltrating lymphocytes., Methods: GRsig was derived from Dexamethasone treated cell lines through a bioinformatic pipeline. Immunohistochemistry assessed GR protein expression. Associations between GRsig and tumor phenotypes (proliferation, cytolytic activity (CYT), immune cell distribution, and epithelial-to-mesenchymal transition (EMT) were explored in public datasets. Single-cell RNA sequencing data evaluated context-dependent GR roles, and a cell type-specific prognostic role was assessed in an independent BC cohort., Results: High GRsig levels were associated with a favorable prognosis across BC subtypes. Tumor-specific high GRsig correlated with lower proliferation, increased CYT, and anti-tumorigenic immune cells. Single-cell data analysis revealed higher GRsig expression in immune cells, negatively correlating with EMT while a positive correlation was observed with EMT primarily in tumor and stromal cells. Univariate and multivariate analyses demonstrated the robust and independent predictive capability of GRsig for favorable prognosis. GR protein expression on immune cells in triple-negative tumors indicated a favorable prognosis., Conclusion: This study underscores the cell type-specific role of GR, where its expression on tumor cells is associated with aggressive features like EMT, while in infiltrating lymphocytes, it predicts a better prognosis, particularly within TNBC tumors. The GRsig emerges as a promising independent prognostic indicator across diverse BC subtypes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)
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- 2024
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16. Cell-state transitions and density-dependent interactions together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity.
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Jain P, Kizhuttil R, Nair MB, Bhatia S, Thompson EW, George JT, and Jolly MK
- Abstract
Cancer cell populations comprise phenotypes distributed among the epithelial-mesenchymal (E-M) spectrum. However, it remains unclear which population-level processes give rise to the observed experimental distribution and dynamical changes in E-M heterogeneity, including (1) differential growth, (2) cell-state switching, and (3) population density-dependent growth or state-transition rates. Here, we analyze the necessity of these three processes in explaining the dynamics of E-M population distributions as observed in PMC42-LA and HCC38 breast cancer cells. We find that, while cell-state transition is necessary to reproduce experimental observations of dynamical changes in E-M fractions, including density-dependent growth interactions (cooperation or suppression) better explains the data. Further, our models predict that treatment of HCC38 cells with transforming growth factor β (TGF-β) signaling and Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/3) inhibitors enhances the rate of mesenchymal-epithelial transition (MET) instead of lowering that of E-M transition (EMT). Overall, our study identifies the population-level processes shaping the dynamics of spontaneous E-M heterogeneity in breast cancer cells., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)
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- 2024
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17. Microenvironment shapes small-cell lung cancer neuroendocrine states and presents therapeutic opportunities.
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Desai P, Takahashi N, Kumar R, Nichols S, Malin J, Hunt A, Schultz C, Cao Y, Tillo D, Nousome D, Chauhan L, Sciuto L, Jordan K, Rajapakse V, Tandon M, Lissa D, Zhang Y, Kumar S, Pongor L, Singh A, Schroder B, Sharma AK, Chang T, Vilimas R, Pinkiert D, Graham C, Butcher D, Warner A, Sebastian R, Mahon M, Baker K, Cheng J, Berger A, Lake R, Abel M, Krishnamurthy M, Chrisafis G, Fitzgerald P, Nirula M, Goyal S, Atkinson D, Bateman NW, Abulez T, Nair G, Apolo A, Guha U, Karim B, El Meskini R, Ohler ZW, Jolly MK, Schaffer A, Ruppin E, Kleiner D, Miettinen M, Brown GT, Hewitt S, Conrads T, and Thomas A
- Subjects
- Humans, Cancer-Associated Fibroblasts metabolism, Cancer-Associated Fibroblasts pathology, Neuroendocrine Tumors pathology, Neuroendocrine Tumors genetics, Neuroendocrine Tumors metabolism, Neuroendocrine Cells pathology, Neuroendocrine Cells metabolism, Female, Male, Prognosis, Tumor Microenvironment, Small Cell Lung Carcinoma pathology, Small Cell Lung Carcinoma genetics, Small Cell Lung Carcinoma metabolism, Lung Neoplasms pathology, Lung Neoplasms metabolism
- Abstract
Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states., Competing Interests: Declaration of interests A.T. received grants to NCI from EMD Serono Research & Development, AstraZeneca, Gilead Sciences, and ProLynx during the conduct of the study., (Published by Elsevier Inc.)
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- 2024
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18. Spatial heterogeneity in tumor adhesion qualifies collective cell invasion.
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Prasanna CVS, Jolly MK, and Bhat R
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- Humans, Neoplasms pathology, Neoplasms metabolism, Cell Adhesion, Neoplasm Invasiveness, Models, Biological, Extracellular Matrix metabolism
- Abstract
Collective cell invasion (CCI), a canon of most invasive solid tumors, is an emergent property of the interactions between cancer cells and their surrounding extracellular matrix (ECM). However, tumor populations invariably consist of cells expressing variable levels of adhesive proteins that mediate such interactions, disallowing an intuitive understanding of how tumor invasiveness at a multicellular scale is influenced by spatial heterogeneity of cell-cell and cell-ECM adhesion. Here, we have used a Cellular Potts model-based multiscale computational framework that is constructed on the histopathological principles of glandular cancers. In earlier efforts on homogenous cancer cell populations, this framework revealed the relative ranges of interactions, including cell-cell and cell-ECM adhesion that drove collective, dispersed, and mixed multimodal invasion. Here, we constitute a tumor core of two separate cell subsets showing distinct intra- and inter-subset cell-cell or cell-ECM adhesion strengths. These two subsets of cells are arranged to varying extents of spatial intermingling, which we call the heterogeneity index (HI). We observe that low and high inter-subset cell adhesion favors invasion of high-HI and low-HI intermingled populations with distinct intra-subset cell-cell adhesion strengths, respectively. In addition, for explored values of cell-ECM adhesion strengths, populations with high HI values collectively invade better than those with lower HI values. We then asked how spatial invasion is regulated by progressively intermingled cellular subsets that are epithelial, i.e., showed high cell-cell but poor cell-ECM adhesion, and mesenchymal, i.e., with reversed adhesion strengths to the former. Here too, inter-subset adhesion plays an important role in contextualizing the proportionate relationship between HI and invasion. An exception to this relationship is seen for cases of heterogeneous cell-ECM adhesion where sub-maximal HI patterns with higher outer localization of cells with stronger ECM adhesion collectively invade better than their relatively higher-HI counterparts. Our simulations also reveal how adhesion heterogeneity qualifies collective invasion, when either cell-cell or cell-ECM adhesion type is varied but results in an invasive dispersion when both adhesion types are simultaneously altered., Competing Interests: Declaration of interests The authors do not declare any conflicts of interest., (Copyright © 2024 Biophysical Society. Published by Elsevier Inc. All rights reserved.)
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- 2024
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19. Increased prevalence of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer.
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Sahoo S, Ramu S, Nair MG, Pillai M, San Juan BP, Milioli HZ, Mandal S, Naidu CM, Mavatkar AD, Subramaniam H, Neogi AG, Chaffer CL, Prabhu JS, Somarelli JA, and Jolly MK
- Abstract
Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. Decoding the interconnections among different biological axes of plasticity is crucial to understand the molecular origins of phenotypic heterogeneity. Here, we use multi-modal transcriptomic data-bulk, single-cell, and spatial transcriptomics-from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity-two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. Mathematical modeling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and identify interventions to restrict it., Competing Interests: The authors declare no conflicts of interest., (© 2024 The Author(s).)
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- 2024
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20. Elucidating the Role of MicroRNA-18a in Propelling a Hybrid Epithelial-Mesenchymal Phenotype and Driving Malignant Progression in ER-Negative Breast Cancer.
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Nair MG, Mavatkar AD, Naidu CM, V P S, C E A, Rajarajan S, Sahoo S, Mohan G, Jaikumar VS, Ramesh RS, B S S, Jolly MK, Maliekal TT, and Prabhu JS
- Subjects
- Humans, Female, Disease Progression, Receptors, Estrogen metabolism, Receptors, Estrogen genetics, Cell Line, Tumor, Hypoxia-Inducible Factor 1, alpha Subunit metabolism, Hypoxia-Inducible Factor 1, alpha Subunit genetics, Phenotype, Animals, Mice, Cell Movement genetics, MicroRNAs genetics, MicroRNAs metabolism, Epithelial-Mesenchymal Transition genetics, Breast Neoplasms genetics, Breast Neoplasms pathology, Breast Neoplasms metabolism, Gene Expression Regulation, Neoplastic
- Abstract
Epigenetic alterations that lead to differential expression of microRNAs (miRNAs/miR) are known to regulate tumour cell states, epithelial-mesenchymal transition (EMT) and the progression to metastasis in breast cancer. This study explores the key contribution of miRNA-18a in mediating a hybrid E/M cell state that is pivotal to the malignant transformation and tumour progression in the aggressive ER-negative subtype of breast cancer. The expression status and associated effects of miR-18a were evaluated in patient-derived breast tumour samples in combination with gene expression data from public datasets, and further validated in in vitro and in vivo breast cancer model systems. The clinical relevance of the study findings was corroborated against human breast tumour specimens (n = 446 patients). The down-regulated expression of miR-18a observed in ER-negative tumours was found to drive the enrichment of hybrid epithelial/mesenchymal (E/M) cells with luminal attributes, enhanced traits of migration, stemness, drug-resistance and immunosuppression. Further analysis of the miR-18a targets highlighted possible hypoxia-inducible factor 1-alpha (HIF-1α)-mediated signalling in these tumours. This is a foremost report that validates the dual role of miR-18a in breast cancer that is subtype-specific based on hormone receptor expression. The study also features a novel association of low miR-18a levels and subsequent enrichment of hybrid E/M cells, increased migration and stemness in a subgroup of ER-negative tumours that may be attributed to HIF-1α mediated signalling. The results highlight the possibility of stratifying the ER-negative disease into clinically relevant groups by analysing miRNA signatures.
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- 2024
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21. An Endosomal Acid-Regulatory Feedback System Rewires Cytosolic cAMP Metabolism and Drives Tumor Progression.
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Prasad H, Mandal S, Mathew JKK, Cherukunnath A, Duddu AS, Banerjee M, Ramani H, Bhat R, Jolly MK, and Visweswariah SS
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- Humans, Colorectal Neoplasms metabolism, Colorectal Neoplasms pathology, Colorectal Neoplasms genetics, Animals, Cytosol metabolism, Disease Progression, Mice, Hydrogen-Ion Concentration, Cell Line, Tumor, Endosomes metabolism, Cyclic AMP metabolism, Sodium-Hydrogen Exchangers metabolism, Sodium-Hydrogen Exchangers genetics
- Abstract
Although suppressed cAMP levels have been linked to cancer for nearly five decades, the molecular basis remains uncertain. Here, we identify endosomal pH as a novel regulator of cytosolic cAMP homeostasis and a promoter of transformed phenotypic traits in colorectal cancer. Combining experiments and computational analysis, we show that the Na+/H+ exchanger NHE9 contributes to proton leak and causes luminal alkalinization, which induces resting [Ca2+], and in consequence, represses cAMP levels, creating a feedback loop that echoes nutrient deprivation or hypoxia. Higher NHE9 expression in cancer epithelia is associated with a hybrid epithelial-mesenchymal (E/M) state, poor prognosis, tumor budding, and invasive growth in vitro and in vivo. These findings point to NHE9-mediated cAMP suppression as a pseudostarvation-induced invasion state and potential therapeutic vulnerability in colorectal cancer. Our observations lay the groundwork for future research into the complexities of endosome-driven metabolic reprogramming and phenotype switching and the biology of cancer progression., Implications: Endosomal pH regulator NHE9 actively controls cytosolic Ca2+ levels to downregulate the adenylate cyclase-cAMP system, enabling colorectal cancer cells to acquire hybrid E/M characteristics and promoting metastatic progression., (©2024 American Association for Cancer Research.)
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- 2024
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22. Loss of p53 epigenetically modulates epithelial to mesenchymal transition in colorectal cancer.
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Sharma S, Rani H, Mahesh Y, Jolly MK, Dixit J, and Mahadevan V
- Abstract
Epithelial to Mesenchymal transition (EMT) drives cancer metastasis and is governed by genetic and epigenetic alterations at multiple levels of regulation. It is well established that loss/mutation of p53 confers oncogenic function to cancer cells and promotes metastasis. Though transcription factors like ZEB1, SLUG, SNAIL and TWIST have been implied in EMT signalling, p53 mediated alterations in the epigenetic machinery accompanying EMT are not clearly understood. This work attempts to explore epigenetic signalling during EMT in colorectal cancer (CRC) cells with varying status of p53. Towards this, we have induced EMT using TGFβ on CRC cell lines with wild type, null and mutant p53 and have assayed epigenetic alterations after EMT induction. Transcriptomic profiling of the four CRC cell lines revealed that the loss of p53 confers more mesenchymal phenotype with EMT induction than its mutant counterparts. This was also accompanied by upregulation of epigenetic writer and eraser machinery suggesting an epigenetic signalling cascade triggered by TGFβ signalling in CRC. Significant agonist and antagonistic relationships observed between EMT factor SNAI1 and SNAI2 with epigenetic enzymes KDM6A/6B and the chromatin organiser SATB1 in p53 null CRC cells suggest a crosstalk between epigenetic and EMT factors. The observed epigenetic regulation of EMT factor SNAI1 correlates with poor clinical outcomes in 270 colorectal cancer patients taken from TCGA-COAD. This unique p53 dependent interplay between epigenetic enzymes and EMT factors in CRC cells may be exploited for development of synergistic therapies for CRC patients presenting to the clinic with loss of p53., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)
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- 2024
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23. Protocol for inferring epithelial-to-mesenchymal transition trajectories from single-cell RNA sequencing data using R.
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Najafi A, Jolly MK, and George JT
- Subjects
- Humans, Sequence Analysis, RNA, Epithelial-Mesenchymal Transition genetics, Neoplasms pathology
- Abstract
The epithelial-to-mesenchymal transition (EMT) provides crucial insights into the metastatic process and possesses prognostic value within the cancer context. Here, we present COMET, an R package for inferring EMT trajectories and inter-state transition rates from single-cell RNA sequencing data. We describe steps for finding the optimal number of EMT genes for a specific context, estimating EMT-related trajectories, optimal fitting of continuous-time Markov chain to inferred trajectories, and estimating inter-state transition rates., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)
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- 2024
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24. Perspectives on polarity - exploring biological asymmetry across scales.
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Glazenburg MM, Hettema NM, Laan L, Remy O, Laloux G, Brunet T, Chen X, Tee YH, Wen W, Rizvi MS, Jolly MK, and Riddell M
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- Animals, Humans, Biophysics, Cell Differentiation, Saccharomyces cerevisiae, Cell Polarity, Research Personnel
- Abstract
In this Perspective, Journal of Cell Science invited researchers working on cell and tissue polarity to share their thoughts on unique, emerging or open questions relating to their field. The goal of this article is to feature 'voices' from scientists around the world and at various career stages, to bring attention to innovative and thought-provoking topics of interest to the cell biology community. These voices discuss intriguing questions that consider polarity across scales, evolution, development and disease. What can yeast and protists tell us about the evolution of cell and tissue polarity in animals? How are cell fate and development influenced by emerging dynamics in cell polarity? What can we learn from atypical and extreme polarity systems? How can we arrive at a more unified biophysical understanding of polarity? Taken together, these pieces demonstrate the broad relevance of the fascinating phenomenon of cell polarization to diverse fundamental biological questions., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)
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- 2024
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25. Zinc(II) Complexes of SIRTi1/2 Analogues Transmetallating with Copper Ions and Inducing ROS Mediated Paraptosis.
- Author
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Kumar A, Chaudhary A, Sonker H, Subhadarshini S, Jolly MK, and Singh RG
- Abstract
As the SIRT i analogue series (HL1-HL6) show potent antitumor activity in vitro, we synthesized their corresponding zinc(II) complexes (ZnL1-ZnL6) and investigated their potential as anticancer agents. The Zn(II) complexes showed substantially greater cytotoxicity than HL1-HL6 alone in several cancer cell-types. Notably, distinct structure-activity relationships confirmed the significance of tert -butyl (ZnL2) pharmacophore inclusion in their activity. ZnL2 complexes were found to transmetalate with copper ions inside cells, causing the formation of redox-active copper complexes that induced reactive oxygen species (ROS) production, mitochondrial membrane depolarization, ATP decay, and cell death. This is the first study to exhibit Zn(II) complexes that mediate their activity via transmetalation with copper ions to undergo paraptosis cell death pathway. To further confirm if the SIRT1/2 inhibitory property of SIRTi analogues is conserved, a docking simulation study is performed. The binding affinity and specific interactions of the Cu(II) complex obtained after transmetalation with ZnL2 were found to be higher for SIRT2 ( K
i = 0.06 μM) compared to SIRT1 ( Ki = 0.25 μM). Thus, the concurrent regulation of several biological targets using a single drug has been shown to have synergistic therapeutic effects, which are crucial for the effective treatment of cancer., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)- Published
- 2024
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26. Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma.
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Bv H and Jolly MK
- Abstract
The aggressive nature of glioblastoma (GBM) - one of the deadliest forms of brain tumors - is majorly attributed to underlying phenotypic heterogeneity. Early attempts to classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed the existence of four distinct molecular subtypes: Proneural, Neural, Classical, and Mesenchymal. Further, a single-cell RNA sequencing (scRNA-seq) analysis of primary tumors also reported similar four subtypes mimicking neurodevelopmental lineages. However, it remains unclear whether these four subtypes identified via bulk and single-cell transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes and gene signatures corresponding to these proposed subtypes and show that the subtypes are not distinctly mutually antagonistic in either TCGA or scRNA-seq data. We observed that the proneural (or neural progenitor-like)-mesenchymal axis is the most prominent antagonistic pair, with the other two subtypes lying on this spectrum. These results are reinforced through a meta-analysis of over 100 single-cell and bulk transcriptomic datasets as well as in terms of functional association with metabolic switching, cell cycle, and immune evasion pathways. Finally, this proneural-mesenchymal antagonistic trend percolates to the association of relevant transcription factors with patient survival. These results suggest rethinking GBM phenotypic characterization for more effective therapeutic targeting efforts., Competing Interests: The authors declare no conflict of interest., (© 2024 The Authors.)
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- 2024
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27. Characterizing heterogeneity along EMT and metabolic axes in colorectal cancer reveals underlying consensus molecular subtype-specific trends.
- Author
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Sehgal M, Ramu S, Vaz JM, Ganapathy YR, Muralidharan S, Venkatraghavan S, and Jolly MK
- Abstract
Colorectal cancer (CRC) is highly heterogeneous with variable survival outcomes and therapeutic vulnerabilities. A commonly used classification system in CRC is the Consensus Molecular Subtypes (CMS) based on gene expression patterns. However, how these CMS categories connect to axes of phenotypic plasticity and heterogeneity remains unclear. Here, in our analysis of CMS-specific TCGA data and 101 bulk transcriptomic datasets, we found the epithelial phenotype score to be consistently positively correlated with scores of glycolysis, OXPHOS and FAO pathways, while PD-L1 activity scores positively correlated with mesenchymal phenotype scoring, revealing possible interconnections among plasticity axes. Single-cell RNA-sequencing analysis of patient samples revealed that that CMS2 and CMS3 subtype samples were relatively more epithelial as compared to CMS1 and CMS4. CMS1 revealed two subpopulations: one close to CMS4 (more mesenchymal) and the other closer to CMS2 or CMS3 (more epithelial), indicating a partial EMT-like behavior. Consistent observations were made in single-cell analysis of metabolic axes and PD-L1 activity scores. Together, our results quantify the patterns of two functional interconnected axes of phenotypic heterogeneity - EMT and metabolic reprogramming - in a CMS-specific manner in CRC., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2023. Published by Elsevier Inc.)
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- 2024
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28. Spatiotemporal modulation of growth factors directs the generation of multilineage mouse embryonic stem cell-derived mammary organoids.
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Sahu S, Sahoo S, Sullivan T, O'Sullivan TN, Turan S, Albaugh ME, Burkett S, Tran B, Salomon DS, Kozlov SV, Koehler KR, Jolly MK, and Sharan SK
- Subjects
- Mice, Animals, Mammary Glands, Animal, Epithelial Cells, Cell Differentiation, Organoids, Mouse Embryonic Stem Cells, Hedgehog Proteins metabolism
- Abstract
Ectodermal appendages, such as the mammary gland (MG), are thought to have evolved from hair-associated apocrine glands to serve the function of milk secretion. Through the directed differentiation of mouse embryonic stem cells (mESCs), here, we report the generation of multilineage ESC-derived mammary organoids (MEMOs). We adapted the skin organoid model, inducing the dermal mesenchyme to transform into mammary-specific mesenchyme via the sequential activation of Bone Morphogenetic Protein 4 (BMP4) and Parathyroid Hormone-related Protein (PTHrP) and inhibition of hedgehog (HH) signaling. Using single-cell RNA sequencing, we identified gene expression profiles that demonstrate the presence of mammary-specific epithelial cells, fibroblasts, and adipocytes. MEMOs undergo ductal morphogenesis in Matrigel and can reconstitute the MG in vivo. Further, we demonstrate that the loss of function in placode regulators LEF1 and TBX3 in mESCs results in impaired skin and MEMO generation. In summary, our MEMO model is a robust tool for studying the development of ectodermal appendages, and it provides a foundation for regenerative medicine and disease modeling., Competing Interests: Declaration of interests The authors declare no competing interests., (Published by Elsevier Inc.)
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- 2024
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29. The exostosin glycosyltransferase 1/STAT3 axis is a driver of breast cancer aggressiveness.
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Solaimuthu B, Khatib A, Tanna M, Karmi A, Hayashi A, Abu Rmaileh A, Lichtenstein M, Takoe S, Jolly MK, and Shaul YD
- Subjects
- Humans, Female, Heparan Sulfate Proteoglycans metabolism, STAT3 Transcription Factor metabolism, Cell Line, Glycosyltransferases genetics, Glycosyltransferases metabolism, Epithelial-Mesenchymal Transition, Cell Line, Tumor, Cell Movement, Breast Neoplasms genetics
- Abstract
The epithelial-mesenchymal transition (EMT) program is crucial for transforming carcinoma cells into a partially mesenchymal state, enhancing their chemoresistance, migration, and metastasis. This shift in cell state is tightly regulated by cellular mechanisms that are not yet fully characterized. One intriguing EMT aspect is the rewiring of the proteoglycan landscape, particularly the induction of heparan sulfate proteoglycan (HSPG) biosynthesis. This proteoglycan functions as a co-receptor that accelerates cancer-associated signaling pathways through its negatively-charged residues. However, the precise mechanisms through which EMT governs HSPG biosynthesis and its role in cancer cell plasticity remain elusive. Here, we identified exostosin glycosyltransferase 1 (EXT1), a central enzyme in HSPG biosynthesis, to be selectively upregulated in aggressive tumor subtypes and cancer cell lines, and to function as a key player in breast cancer aggressiveness. Notably, ectopic expression of EXT1 in epithelial cells is sufficient to induce HSPG levels and the expression of known mesenchymal markers, subsequently enhancing EMT features, including cell migration, invasion, and tumor formation. Additionally, EXT1 loss in MDA-MB-231 cells inhibits their aggressiveness-associated traits such as migration, chemoresistance, tumor formation, and metastasis. Our findings reveal that EXT1, through its role in HSPG biosynthesis, governs signal transducer and activator of transcription 3 (STAT3) signaling, a known regulator of cancer cell aggressiveness. Collectively, we present the EXT1/HSPG/STAT3 axis as a central regulator of cancer cell plasticity that directly links proteoglycan synthesis to oncogenic signaling pathways., Competing Interests: Competing interests statement:The authors declare no competing interest.
- Published
- 2024
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30. A Multistep Tumor Growth Model of High-Grade Serous Ovarian Carcinoma Identifies Hypoxia-Associated Signatures.
- Author
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More MH, Varankar SS, Naik RR, Dhake RD, Ray P, Bankar RM, Mali AM, Subbalakshmi AR, Chakraborty P, Jolly MK, and Bapat SA
- Subjects
- Female, Humans, Epithelial Cells metabolism, Ovarian Neoplasms metabolism, Ovarian Neoplasms pathology, Cystadenocarcinoma, Serous metabolism, Cystadenocarcinoma, Serous pathology
- Abstract
High-grade serous ovarian carcinoma (HGSC) is associated with late-stage disease presentation and poor prognosis, with a limited understanding of early transformation events. Our study analyzes HGSC tumor progression and organ-specific metastatic dissemination to identify hypoxia-associated molecular, cellular, and histological alterations. Clinical characteristics of the HGSC were replicated in orthotopic xenografts, which involve metastatic dissemination and the prevalence of group B tumors (volume: >0.0625 ≤ 0.5 cm3). Enhanced hyaluronic acid (HA) deposition, expanded tumor vasculature, and increased necrosis contributed to the remodeling of tumor tissue architecture. The proliferative potential of tumor cells and the ability to form glands were also altered during tumor growth. Flow cytometry and label chase-based molecular profiling across the tumor regenerative hierarchy identified the hypoxia-vasculogenic niche and the hybrid epithelial-mesenchymal tumor-cell state as determinants of self-renewal capabilities of progenitors and cancer stem cells. A regulatory network and mathematical model based on tumor histology and molecular signatures predicted hypoxia-inducible factor 1-alpha (HIF1A) as a central node connecting HA synthesis, epithelial-mesenchymal transition, metabolic, vasculogenic, inflammatory, and necrotic pathways in HGSC tumors. Thus, our findings provide a temporal resolution of hypoxia-associated events that sculpt HGSC tumor growth; an in-depth understanding of it may aid in the early detection and treatment of HGSC., (© 2022 S. Karger AG, Basel.)
- Published
- 2024
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31. Dual role of CASP8AP2/FLASH in regulating epithelial-to-mesenchymal transition plasticity (EMP).
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Catalanotto M, Vaz JM, Abshire C, Youngblood R, Chu M, Levine H, Jolly MK, and Dragoi AM
- Abstract
Background: Epithelial-to-mesenchymal transition (EMT) is a developmental program that consists of the loss of epithelial features concomitant with the acquisition of mesenchymal features. Activation of EMT in cancer facilitates the acquisition of aggressive traits and cancer invasion. EMT plasticity (EMP), the dynamic transition between multiple hybrid states in which cancer cells display both epithelial and mesenchymal markers, confers survival advantages for cancer cells in constantly changing environments during metastasis., Methods: RNAseq analysis was performed to assess genome-wide transcriptional changes in cancer cells depleted for histone regulators FLASH, NPAT, and SLBP. Quantitative PCR and Western blot were used for the detection of mRNA and protein levels. Computational analysis was performed on distinct sets of genes to determine the epithelial and mesenchymal score in cancer cells and to correlate FLASH expression with EMT markers in the CCLE collection., Results: We demonstrate that loss of FLASH in cancer cells gives rise to a hybrid E/M phenotype with high epithelial scores even in the presence of TGFβ, as determined by computational methods using expression of predetermined sets of epithelial and mesenchymal genes. Multiple genes involved in cell-cell junction formation are similarly specifically upregulated in FLASH-depleted cells, suggesting that FLASH acts as a repressor of the epithelial phenotype. Further, FLASH expression in cancer lines is inversely correlated with the epithelial score. Nonetheless, subsets of mesenchymal markers were distinctly up-regulated in FLASH, NPAT, or SLBP-depleted cells., Conclusions: The ZEB1
low /SNAILhigh /E-cadherinhigh phenotype described in FLASH-depleted cancer cells is driving a hybrid E/M phenotype in which epithelial and mesenchymal markers coexist., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2023. Published by Elsevier Inc.)- Published
- 2024
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32. Coupled Mutual Inhibition and Mutual Activation Motifs as Tools for Cell-Fate Control.
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Sabuwala B, Hari K, Shanmuga Vengatasalam A, and Jolly MK
- Subjects
- Humans, Models, Biological, Feedback, Physiological, Gene Regulatory Networks, Computer Simulation, Cell Differentiation
- Abstract
Multistability is central to biological systems. It plays a crucial role in adaptation, evolvability, and differentiation. The presence of positive feedback loops can enable multistability. The simplest of such feedback loops are (a) a mutual inhibition (MI) loop, (b) a mutual activation (MA) loop, and (c) self-activation. While it is established that all three motifs can give rise to bistability, the characteristic differences in the bistability exhibited by each of these motifs is relatively less understood. Here, we use dynamical simulations across a large ensemble of parameter sets and initial conditions to study the bistability characteristics of these motifs. Furthermore, we investigate the utility of these motifs for achieving coordinated expression through cyclic and parallel coupling amongst them. Our analysis revealed that MI-based architectures offer discrete and robust control over gene expression, multistability, and coordinated expression among multiple genes, as compared to MA-based architectures. We then devised a combination of MI and MA architectures to improve coordination and multistability. Such designs help enhance our understanding of the control structures involved in robust cell-fate decisions and provide a way to achieve controlled decision-making in synthetic systems., (© 2023 S. Karger AG, Basel.)
- Published
- 2024
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33. Transcriptional state dynamics lead to heterogeneity and adaptive tumor evolution in urothelial bladder carcinoma.
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Biswas A, Sahoo S, Riedlinger GM, Ghodoussipour S, Jolly MK, and De S
- Subjects
- Humans, Urinary Bladder, PPAR gamma, Disease Progression, Urinary Bladder Neoplasms therapy, Carcinoma, Transitional Cell pathology
- Abstract
Intra-tumor heterogeneity contributes to treatment failure and poor survival in urothelial bladder carcinoma (UBC). Analyzing transcriptome from a UBC cohort, we report that intra-tumor transcriptomic heterogeneity indicates co-existence of tumor cells in epithelial and mesenchymal-like transcriptional states and bi-directional transition between them occurs within and between tumor subclones. We model spontaneous and reversible transition between these partially heritable states in cell lines and characterize their population dynamics. SMAD3, KLF4 and PPARG emerge as key regulatory markers of the transcriptional dynamics. Nutrient limitation, as in the core of large tumors, and radiation treatment perturb the dynamics, initially selecting for a transiently resistant phenotype and then reconstituting heterogeneity and growth potential, driving adaptive evolution. Dominance of transcriptional states with low PPARG expression indicates an aggressive phenotype in UBC patients. We propose that phenotypic plasticity and dynamic, non-genetic intra-tumor heterogeneity modulate both the trajectory of disease progression and adaptive treatment response in UBC., (© 2023. The Author(s).)
- Published
- 2023
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34. Development of adaptive anoikis resistance promotes metastasis that can be overcome by CDK8/19 Mediator kinase inhibition.
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Monavarian M, Page EF, Rajkarnikar R, Kumari A, Macias LQ, Massicano F, Lee NY, Sahoo S, Hempel N, Jolly MK, Ianov L, Worthey E, Singh A, Broude EV, and Mythreye K
- Abstract
Anoikis resistance or evasion of cell death triggered by cell detachment into suspension is a hallmark of cancer that is concurrent with cell survival and metastasis. The effects of frequent matrix detachment encounters on the development of anoikis resistance in cancer remains poorly defined. Here we show using a panel of ovarian cancer models, that repeated exposure to suspension stress in vitro followed by attached recovery growth leads to the development of anoikis resistance paralleling in vivo development of anoikis resistance in ovarian cancer ascites. This resistance is concurrent with enhanced invasion, chemoresistance and the ability of anoikis adapted cells to metastasize to distant sites. Adapted anoikis resistant cells show a heightened dependency on oxidative phosphorylation and can also evade immune surveillance. We find that such acquired anoikis resistance is not genetic, as acquired resistance persists for a finite duration in the absence of suspension stress. Transcriptional reprogramming is however essential to this process, as acquisition of adaptive anoikis resistance in vitro and in vivo is exquisitely sensitive to inhibition of CDK8/19 Mediator kinase, a pleiotropic regulator of transcriptional reprogramming. Our data demonstrate that growth after recovery from repeated exposure to suspension stress is a direct contributor to metastasis and that inhibition of CDK8/19 Mediator kinase during such adaptation provides a therapeutic opportunity to prevent both local and distant metastasis in cancer., Competing Interests: Conflict of interest statement : E.V.B. is a consultant of Senex Biotechnology, Inc
- Published
- 2023
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35. Effects of microRNA-mediated negative feedback on gene expression noise.
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Adhikary R, Roy A, Jolly MK, and Das D
- Subjects
- Feedback, RNA, Messenger genetics, RNA, Messenger metabolism, Feedback, Physiological, Gene Regulatory Networks, Gene Expression, MicroRNAs genetics
- Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression post-transcriptionally in eukaryotes by binding with target mRNAs and preventing translation. miRNA-mediated feedback motifs are ubiquitous in various genetic networks that control cellular decision making. A key question is how such a feedback mechanism may affect gene expression noise. To answer this, we have developed a mathematical model to study the effects of a miRNA-dependent negative-feedback loop on mean expression and noise in target mRNAs. Combining analytics and simulations, we show the existence of an expression threshold demarcating repressed and expressed regimes in agreement with earlier studies. The steady-state mRNA distributions are bimodal near the threshold, where copy numbers of mRNAs and miRNAs exhibit enhanced anticorrelated fluctuations. Moreover, variation of negative-feedback strength shifts the threshold locations and modulates the noise profiles. Notably, the miRNA-mRNA binding affinity and feedback strength collectively shape the bimodality. We also compare our model with a direct auto-repression motif, where a gene produces its own repressor. Auto-repression fails to produce bimodal mRNA distributions as found in miRNA-based indirect repression, suggesting the crucial role of miRNAs in creating phenotypic diversity. Together, we demonstrate how miRNA-dependent negative feedback modifies the expression threshold and leads to a broader parameter regime of bimodality compared to the no-feedback case., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 Biophysical Society. Published by Elsevier Inc. All rights reserved.)
- Published
- 2023
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36. Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity.
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Jain P, Pillai M, Duddu AS, Somarelli JA, Goyal Y, and Jolly MK
- Subjects
- Humans, Epithelial-Mesenchymal Transition genetics, Cell Differentiation genetics, Phenotype, Melanoma genetics, Carcinoma
- Abstract
Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)
- Published
- 2023
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37. A systems-level analysis of the mutually antagonistic roles of RKIP and BACH1 in dynamics of cancer cell plasticity.
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Shyam S, Ramu S, Sehgal M, and Jolly MK
- Subjects
- Humans, Female, Cell Plasticity, Epithelial-Mesenchymal Transition, Basic-Leucine Zipper Transcription Factors genetics, Basic-Leucine Zipper Transcription Factors metabolism, Phosphatidylethanolamine Binding Protein genetics, Phosphatidylethanolamine Binding Protein metabolism, Breast Neoplasms
- Abstract
Epithelial-mesenchymal transition (EMT) is an important axis of phenotypic plasticity-a hallmark of cancer metastasis. Raf kinase-B inhibitor protein (RKIP) and BTB and CNC homology 1 (BACH1) are reported to influence EMT. In breast cancer, they act antagonistically, but the exact nature of their roles in mediating EMT and associated other axes of plasticity remains unclear. Here, analysing transcriptomic data, we reveal their antagonistic trends in a pan-cancer manner in terms of association with EMT, metabolic reprogramming and immune evasion via PD-L1. Next, we developed and simulated a mechanism-based gene regulatory network that captures how RKIP and BACH1 engage in feedback loops with drivers of EMT and stemness. We found that RKIP and BACH1 belong to two antagonistic 'teams' of players-while BACH1 belonged to the one driving pro-EMT, stem-like and therapy-resistant cell states, RKIP belonged to the one enabling pro-epithelial, less stem-like and therapy-sensitive phenotypes. Finally, we observed that low RKIP levels and upregulated BACH1 levels associated with worse clinical outcomes in many cancer types. Together, our systems-level analysis indicates that the emergent dynamics of underlying regulatory network enable the antagonistic patterns of RKIP and BACH1 with various axes of cancer cell plasticity, and with patient survival data.
- Published
- 2023
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38. An androgen receptor regulated gene score is associated with epithelial to mesenchymal transition features in triple negative breast cancers.
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Rajarajan S, Snijesh VP, Anupama CE, Nair MG, Mavatkar AD, Naidu CM, Patil S, Nimbalkar VP, Alexander A, Pillai M, Jolly MK, Sabarinathan R, Ramesh RS, Bs S, and Prabhu JS
- Abstract
Background: Androgen receptor (AR) is considered a marker of better prognosis in hormone receptor positive breast cancers (BC), however, its role in triple negative breast cancer (TNBC) is controversial. This may be attributed to intrinsic molecular differences or scoring methods for AR positivity. We derived AR regulated gene score and examined its utility in BC subtypes., Methods: AR regulated genes were derived by applying a bioinformatic pipeline on publicly available microarray data sets of AR+ BC cell lines and gene score was calculated as average expression of six AR regulated genes. Tumors were divided into AR high and low based on gene score and associations with clinical parameters, circulating androgens, survival and epithelial to mesenchymal transition (EMT) markers were examined, further evaluated in invitro models and public datasets., Results: 53% (133/249) tumors were classified as AR gene score high and were associated with significantly better clinical parameters, disease-free survival (86.13 vs 72.69 months, log rank p = 0.032) when compared to AR low tumors. 36% of TNBC (N = 66) were AR gene score high with higher expression of EMT markers (p = 0.024) and had high intratumoral levels of 5α-reductase, enzyme involved in intracrine androgen metabolism. In MDA-MB-453 treated with dihydrotestosterone, SLUG expression increased, E-cadherin decreased with increase in migration and these changes were reversed with bicalutamide. Similar results were obtained in public datasets., Conclusion: Deciphering the role of AR in BC is difficult based on AR protein levels alone. Our results support the context dependent function of AR in driving better prognosis in ER positive tumors and EMT features in TNBC tumors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)
- Published
- 2023
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39. Synthetic Gene Circuits Combining CRISPR Interference and CRISPR Activation in E. coli : Importance of Equal Guide RNA Binding Affinities to Avoid Context-Dependent Effects.
- Author
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Barbier I, Kusumawardhani H, Chauhan L, Harlapur PV, Jolly MK, and Schaerli Y
- Subjects
- Clustered Regularly Interspaced Short Palindromic Repeats genetics, Genes, Synthetic, RNA metabolism, Escherichia coli genetics, Escherichia coli metabolism, CRISPR-Cas Systems genetics
- Abstract
Gene expression control based on clustered regularly interspaced short palindromic repeats (CRISPR) has emerged as a powerful approach for constructing synthetic gene circuits. While the use of CRISPR interference (CRISPRi) is already well-established in prokaryotic circuits, CRISPR activation (CRISPRa) is less mature, and a combination of the two in the same circuits is only just emerging. Here, we report that combining CRISPRi with SoxS-based CRISPRa in Escherichia coli can lead to context-dependent effects due to different affinities in the formation of CRISPRa and CRISPRi complexes, resulting in loss of predictable behavior. We show that this effect can be avoided by using the same scaffold guide RNA structure for both complexes.
- Published
- 2023
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40. Collective heterogeneity of mitochondrial potential in contact inhibition of proliferation.
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Thurakkal B, Hari K, Marwaha R, Karki S, Jolly MK, and Das T
- Subjects
- Epithelial Cells metabolism, Epithelium metabolism, Cell Proliferation, Contact Inhibition, Actomyosin metabolism
- Abstract
In the epithelium, cell density and cell proliferation are closely connected to each other through contact inhibition of proliferation (CIP). Depending on cell density, CIP proceeds through three distinct stages: the free-growing stage at low density, the pre-epithelial transition stage at medium density, and the post-epithelial transition stage at high density. Previous studies have elucidated how cell morphology, motion, and mechanics vary in these stages. However, it remains unknown whether cellular metabolism also has a density-dependent behavior. By measuring the mitochondrial membrane potential at different cell densities, here we reveal a heterogeneous landscape of metabolism in the epithelium, which appears qualitatively distinct in three stages of CIP and did not follow the trend of other CIP-associated parameters, which increases or decreases monotonically with increasing cell density. Importantly, epithelial cells established a collective metabolic heterogeneity exclusively in the pre-epithelial transition stage, where the multicellular clusters of high- and low-potential cells emerged. However, in the post-epithelial transition stage, the metabolic potential field became relatively homogeneous. Next, to study the underlying dynamics, we constructed a system biology model, which predicted the role of cell proliferation in metabolic potential toward establishing collective heterogeneity. Further experiments indeed revealed that the metabolic pattern spatially correlated with the proliferation capacity of cells, as measured by the nuclear localization of a pro-proliferation protein, YAP. Finally, experiments perturbing the actomyosin contractility revealed that, while metabolic heterogeneity was maintained in the absence of actomyosin contractility, its ab initio emergence depended on the latter. Taken together, our results revealed a density-dependent collective heterogeneity in the metabolic field of a pre-epithelial transition-stage epithelial monolayer, which may have significant implications for epithelial form and function., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 Biophysical Society. Published by Elsevier Inc. All rights reserved.)
- Published
- 2023
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41. Multi-modal transcriptomic analysis unravels enrichment of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer.
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Sahoo S, Ramu S, Nair MG, Pillai M, San Juan BP, Milioli HZ, Mandal S, Naidu CM, Mavatkar AD, Subramaniam H, Neogi AG, Chaffer CL, Prabhu JS, Somarelli JA, and Jolly MK
- Abstract
Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. It manifests along multiple phenotypic axes and decoding the interconnections among these different axes is crucial to understand its molecular origins and to develop novel therapeutic strategies to control it. Here, we use multi-modal transcriptomic data analysis - bulk, single-cell and spatial transcriptomics - from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity - two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. These patterns were inherent in methylation profiles, suggesting an epigenetic crosstalk between EMT and lineage plasticity in breast cancer. Mathematical modelling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes recapitulate and thus elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and to identify possible interventions to restrict it., Competing Interests: Conflict of Interest The authors declare no conflicts of interest.
- Published
- 2023
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42. Phenotypic heterogeneity drives differential disease outcome in a mouse model of triple negative breast cancer.
- Author
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Thankamony AP, Ramkomuth S, Ramesh ST, Murali R, Chakraborty P, Karthikeyan N, Varghese BA, Jaikumar VS, Jolly MK, Swarbrick A, and Nair R
- Abstract
The triple negative breast cancer (TNBC) subtype is one of the most aggressive forms of breast cancer that has poor clinical outcome and is an unmet clinical challenge. Accumulating evidence suggests that intratumoral heterogeneity or the presence of phenotypically distinct cell populations within a tumor play a crucial role in chemoresistance, tumor progression and metastasis. An increased understanding of the molecular regulators of intratumoral heterogeneity is crucial to the development of effective therapeutic strategies in TNBC. To this end, we used an unbiased approach to identify a molecular mediator of intratumoral heterogeneity in breast cancer by isolating two tumor cell populations (T1 and T2) from the 4T1 TNBC model. Phenotypic characterization revealed that the cells are different in terms of their morphology, proliferation and self-renewal ability in vitro as well as primary tumor formation and metastatic potential in vivo . Bioinformatic analysis followed by Kaplan Meier survival analysis in TNBC patients identified Metastasis associated colon cancer 1 (Macc1) as one of the top candidate genes mediating the aggressive phenotype in the T1 tumor cells. The role of Macc1 in regulating the proliferative phenotype was validated and taken forward in a therapeutic context with Lovastatin, a small molecule transcriptional inhibitor of Macc1 to target the T1 cell population. This study increases our understanding of the molecular underpinnings of intratumoral heterogeneity in breast cancer that is critical to improve the treatment of women currently living with the highly aggressive TNBC subtype., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Thankamony, Ramkomuth, Ramesh, Murali, Chakraborty, Karthikeyan, Varghese, Jaikumar, Jolly, Swarbrick and Nair.)
- Published
- 2023
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43. p53 amyloid pathology is correlated with higher cancer grade irrespective of the mutant or wild-type form.
- Author
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Sengupta S, Singh N, Paul A, Datta D, Chatterjee D, Mukherjee S, Gadhe L, Devi J, Mahesh Y, Jolly MK, and Maji SK
- Subjects
- Humans, Cell Nucleus, Cytoplasm, Mutation genetics, Stomach Neoplasms, Tumor Suppressor Protein p53 genetics
- Abstract
p53 (also known as TP53) mutation and amyloid formation are long associated with cancer pathogenesis; however, the direct demonstration of the link between p53 amyloid load and cancer progression is lacking. Using multi-disciplinary techniques and 59 tissues (53 oral and stomach cancer tumor tissue samples from Indian individuals with cancer and six non-cancer oral and stomach tissue samples), we showed that p53 amyloid load and cancer grades are highly correlated. Furthermore, next-generation sequencing (NGS) data suggest that not only mutant p53 (e.g. single-nucleotide variants, deletions, and insertions) but wild-type p53 also formed amyloids either in the nucleus (50%) and/or in the cytoplasm in most cancer tissues. Interestingly, in all these cancer tissues, p53 displays a loss of DNA-binding and transcriptional activities, suggesting that the level of amyloid load correlates with the degree of loss and an increase in cancer grades. The p53 amyloids also sequester higher amounts of the related p63 and p73 (also known as TP63 and TP73, respectively) protein in higher-grade tumor tissues. The data suggest p53 misfolding and/or aggregation, and subsequent amyloid formation, lead to loss of the tumor-suppressive function and the gain of oncogenic function, aggravation of which might determine the cancer grade., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2023. Published by The Company of Biologists Ltd.)
- Published
- 2023
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44. Dynamical modeling of proliferative-invasive plasticity and IFNγ signaling in melanoma reveals mechanisms of PD-L1 expression heterogeneity.
- Author
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Subhadarshini S, Sahoo S, Debnath S, Somarelli JA, and Jolly MK
- Subjects
- Humans, B7-H1 Antigen genetics, Immune Checkpoint Inhibitors, Cell Line, Melanoma drug therapy, Melanoma genetics, Neoplasms, Second Primary
- Abstract
Background: Phenotypic heterogeneity of melanoma cells contributes to drug tolerance, increased metastasis, and immune evasion in patients with progressive disease. Diverse mechanisms have been individually reported to shape extensive intra-tumor and inter-tumor phenotypic heterogeneity, such as IFNγ signaling and proliferative to invasive transition, but how their crosstalk impacts tumor progression remains largely elusive., Methods: Here, we integrate dynamical systems modeling with transcriptomic data analysis at bulk and single-cell levels to investigate underlying mechanisms behind phenotypic heterogeneity in melanoma and its impact on adaptation to targeted therapy and immune checkpoint inhibitors. We construct a minimal core regulatory network involving transcription factors implicated in this process and identify the multiple 'attractors' in the phenotypic landscape enabled by this network. Our model predictions about synergistic control of PD-L1 by IFNγ signaling and proliferative to invasive transition were validated experimentally in three melanoma cell lines-MALME3, SK-MEL-5 and A375., Results: We demonstrate that the emergent dynamics of our regulatory network comprising MITF, SOX10, SOX9, JUN and ZEB1 can recapitulate experimental observations about the co-existence of diverse phenotypes (proliferative, neural crest-like, invasive) and reversible cell-state transitions among them, including in response to targeted therapy and immune checkpoint inhibitors. These phenotypes have varied levels of PD-L1, driving heterogeneity in immunosuppression. This heterogeneity in PD-L1 can be aggravated by combinatorial dynamics of these regulators with IFNγ signaling. Our model predictions about changes in proliferative to invasive transition and PD-L1 levels as melanoma cells evade targeted therapy and immune checkpoint inhibitors were validated in multiple RNA-seq data sets from in vitro and in vivo experiments., Conclusion: Our calibrated dynamical model offers a platform to test combinatorial therapies and provide rational avenues for the treatment of metastatic melanoma. This improved understanding of crosstalk among PD-L1 expression, proliferative to invasive transition and IFNγ signaling can be leveraged to improve the clinical management of therapy-resistant and metastatic melanoma., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
- Published
- 2023
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45. Theoretical and computational tools to model multistable gene regulatory networks.
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Bocci F, Jia D, Nie Q, Jolly MK, and Onuchic J
- Subjects
- Nonlinear Dynamics, Gene Regulatory Networks, Models, Biological
- Abstract
The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics., (© 2023 IOP Publishing Ltd.)
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- 2023
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46. Editorial: Organoids, organs-on-chip, nanoparticles and in silico approaches to dissect the tumor-immune dynamics and to unveil the drug resistance mechanisms to therapy in the tumor microenvironment.
- Author
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Mattei F, George JT, and Jolly MK
- Subjects
- Humans, Organoids, Microphysiological Systems, Tumor Microenvironment, Neoplasms
- Abstract
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
- Published
- 2023
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47. Aberrations in ion channels interacting with lipid metabolism and epithelial-mesenchymal transition in esophageal squamous cell carcinoma.
- Author
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Parthasarathi KTS, Mandal S, George JP, Gaikwad KB, Sasidharan S, Gundimeda S, Jolly MK, Pandey A, and Sharma J
- Abstract
Esophageal squamous cell carcinoma (ESCC) is the most prevalent malignant gastrointestinal tumor. Ion channels contribute to tumor growth and progression through interactions with their neighboring molecules including lipids. The dysregulation of membrane ion channels and lipid metabolism may contribute to the epithelial-mesenchymal transition (EMT), leading to metastatic progression. Herein, transcriptome profiles of patients with ESCC were analyzed by performing differential gene expression and weighted gene co-expression network analysis to identify the altered ion channels, lipid metabolism- and EMT-related genes in ESCC. A total of 1,081 differentially expressed genes, including 113 ion channels, 487 lipid metabolism-related, and 537 EMT-related genes, were identified in patients with ESCC. Thereafter, EMT scores were correlated with altered co-expressed genes. The altered co-expressed genes indicated a correlation with EMT signatures. Interactions among 22 ion channels with 3 hub lipid metabolism- and 13 hub EMT-related proteins were determined using protein-protein interaction networks. A pathway map was generated to depict deregulated signaling pathways including insulin resistance and the estrogen receptor-Ca
2+ signaling pathway in ESCC. The relationship between potential ion channels and 5-year survival rates in ESCC was determined using Kaplan-Meier plots and Cox proportional hazard regression analysis. Inositol 1,4,5-trisphosphate receptor type 3 ( ITPR3 ) was found to be associated with poor prognosis of patients with ESCC. Additionally, drugs interacting with potential ion channels, including GJA1 and ITPR3 , were identified. Understanding alterations in ion channels with lipid metabolism and EMT in ESCC pathophysiology would most likely provide potential targets for the better treatment of patients with ESCC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Parthasarathi, Mandal, George, Gaikwad, Sasidharan, Gundimeda, Jolly, Pandey and Sharma.)- Published
- 2023
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48. Assessing biological network dynamics: comparing numerical simulations with analytical decomposition of parameter space.
- Author
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Hari K, Duncan W, Ibrahim MA, Jolly MK, Cummins B, and Gedeon T
- Subjects
- Computer Simulation, Gene Regulatory Networks genetics, Models, Theoretical
- Abstract
Mathematical modeling of the emergent dynamics of gene regulatory networks (GRN) faces a double challenge of (a) dependence of model dynamics on parameters, and (b) lack of reliable experimentally determined parameters. In this paper we compare two complementary approaches for describing GRN dynamics across unknown parameters: (1) parameter sampling and resulting ensemble statistics used by RACIPE (RAndom CIrcuit PErturbation), and (2) use of rigorous analysis of combinatorial approximation of the ODE models by DSGRN (Dynamic Signatures Generated by Regulatory Networks). We find a very good agreement between RACIPE simulation and DSGRN predictions for four different 2- and 3-node networks typically observed in cellular decision making. This observation is remarkable since the DSGRN approach assumes that the Hill coefficients of the models are very high while RACIPE assumes the values in the range 1-6. Thus DSGRN parameter domains, explicitly defined by inequalities between systems parameters, are highly predictive of ODE model dynamics within a biologically reasonable range of parameters., (© 2023. The Author(s).)
- Published
- 2023
- Full Text
- View/download PDF
49. Editorial: Heterogeneity and plasticity of prostate cancer.
- Author
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Culig Z, Jolly MK, and Souček K
- Abstract
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
- Published
- 2023
- Full Text
- View/download PDF
50. Link updating strategies influence consensus decisions as a function of the direction of communication.
- Author
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Kunjar S, Strandburg-Peshkin A, Giese H, Minasandra P, Sarkar S, Jolly MK, and Gradwohl N
- Abstract
Consensus decision-making in social groups strongly depends on communication links that determine to whom individuals send, and from whom they receive, information. Here, we ask how consensus decisions are affected by strategic updating of links and how this effect varies with the direction of communication. We quantified the coevolution of link and opinion dynamics in a large population with binary opinions using mean-field numerical simulations of two voter-like models of opinion dynamics: an incoming model (IM) (where individuals choose who to receive opinions from) and an outgoing model (OM) (where individuals choose who to send opinions to). We show that individuals can bias group-level outcomes in their favour by breaking disagreeing links while receiving opinions (IM) and retaining disagreeing links while sending opinions (OM). Importantly, these biases can help the population avoid stalemates and achieve consensus. However, the role of disagreement avoidance is diluted in the presence of strong preferences-highly stubborn individuals can shape decisions to favour their preferences, giving rise to non-consensus outcomes. We conclude that collectively changing communication structures can bias consensus decisions, as a function of the strength of preferences and the direction of communication., Competing Interests: We declare we have no competing interests., (© 2023 The Authors.)
- Published
- 2023
- Full Text
- View/download PDF
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