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1. Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis

Author

Sonnenkalb, L, Carter, JJ, Spitaleri, A, Iqbal, Z, Hunt, M, Malone, KM, Utpatel, C, Cirillo, DM, Rodrigues, C, Nilgiriwala, KS, Fowler, PW, Merker, M, Niemann, S, Barilar, I, Battaglia, S, Borroni, E, Brandao, AP, Brankin, A, Cabibbe, AM, Carter, J, Claxton, P, Clifton, DA, Cohen, T, Coronel, J, Crook, DW, Dreyer, V, Earle, SG, Escuyer, V, Ferrazoli, L, Fu Gao, G, Gardy, J, Gharbia, S, Ghisi, KT, Ghodousi, A, Gibertoni Cruz, AL, Grandjean, L, Grazian, C, Groenheit, R, Guthrie, JL, He, W, Hoffmann, H, Hoosdally, SJ, Ismail, NA, Jarrett, L, Joseph, L, Jou, R, Kambli, P, Khot, R, Knaggs, J, Koch, A, Kohlerschmidt, D, Kouchaki, S, Lachapelle, AS, Lalvani, A, Grandjean Lapierre, S, Laurenson, IF, Letcher, B, Lin, W-H, Liu, C, Liu, D, Mandal, A, Mansjö, M, Matias, D, Meintjes, G, De Freitas Mendes, F, Mihalic, M, Millard, J, Miotto, P, Mistry, N, Moore, D, Musser, KA, Ngcamu, D, Hoang, NN, Nimmo, C, Okozi, N, Oliveira, RS, Omar, SV, Paton, N, Peto, TEA, Watanabe Pinhata, JM, Plesnik, S, Puyen, ZM, Rabodoarivelo, MS, Rakotosamimanana, N, Rancoita, PMV, Rathod, P, Rodger, G, Rodwell, TC, Roohi, E, Santos-Lazaro, D, Shah, S, Kohl, TA, Smith, G, Solano, W, Supply, P, Surve, U, Tahseen, S, Thuong, NTT, Thwaites, G, Todt, K, Trovato, A, Van Rie, A, Vijay, S, Walker, TM, Walker, SA, Warren, R, Werngren, J, Wijkander, M, Wilkinson, RJ, Wilson, DJ, Wintringer, P, Yu, XX, Yang, Y, Zhao, Y, Yao, S-Y, Zhu, B, and Consortium, Comprehensive Resistance Prediction for Tuberculosis: an International

Subjects
Microbiology (medical), Infectious Diseases, Virology, Microbiology
Abstract

Background Bedaquiline is a core drug for the treatment of multidrug-resistant tuberculosis; however, the understanding of resistance mechanisms is poor, which is hampering rapid molecular diagnostics. Some bedaquiline-resistant mutants are also cross-resistant to clofazimine. To decipher bedaquiline and clofazimine resistance determinants, we combined experimental evolution, protein modelling, genome sequencing, and phenotypic data. Methods For this in-vitro and in-silico data analysis, we used a novel in-vitro evolutionary model using subinhibitory drug concentrations to select bedaquiline-resistant and clofazimine-resistant mutants. We determined bedaquiline and clofazimine minimum inhibitory concentrations and did Illumina and PacBio sequencing to characterise selected mutants and establish a mutation catalogue. This catalogue also includes phenotypic and genotypic data of a global collection of more than 14 000 clinical Mycobacterium tuberculosis complex isolates, and publicly available data. We investigated variants implicated in bedaquiline resistance by protein modelling and dynamic simulations. Findings We discerned 265 genomic variants implicated in bedaquiline resistance, with 250 (94%) variants affecting the transcriptional repressor (Rv0678) of the MmpS5–MmpL5 efflux system. We identified 40 new variants in vitro, and a new bedaquiline resistance mechanism caused by a large-scale genomic rearrangement. Additionally, we identified in vitro 15 (7%) of 208 mutations found in clinical bedaquiline-resistant isolates. From our in-vitro work, we detected 14 (16%) of 88 mutations so far identified as being associated with clofazimine resistance and also seen in clinically resistant strains, and catalogued 35 new mutations. Structural modelling of Rv0678 showed four major mechanisms of bedaquiline resistance: impaired DNA binding, reduction in protein stability, disruption of protein dimerisation, and alteration in affinity for its fatty acid ligand. Interpretation Our findings advance the understanding of drug resistance mechanisms in M tuberculosis complex strains. We have established an extended mutation catalogue, comprising variants implicated in resistance and susceptibility to bedaquiline and clofazimine. Our data emphasise that genotypic testing can delineate clinical isolates with borderline phenotypes, which is essential for the design of effective treatments. Funding Leibniz ScienceCampus Evolutionary Medicine of the Lung, Deutsche Forschungsgemeinschaft, Research Training Group 2501 TransEvo, Rhodes Trust, Stanford University Medical Scientist Training Program, National Institute for Health and Care Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Bill & Melinda Gates Foundation, Wellcome Trust, and Marie Skłodowska-Curie Actions.

Published
2023

3. Nucleoside reverse-transcriptase inhibitor cross-resistance and outcomes from second-line antiretroviral therapy in the public health approach: an observational analysis within the randomised, open-label, EARNEST trial

Author

Agweng, E, Awio, P, Bakeinyaga, G, Isabirye, C, Kabuga, U, Kasuswa, S, Katuramu, M, Kityo, C, Kiweewa, F, Kyomugisha, H, Lutalo, E, Mugyenyi, P, Mulima, D, Musana, H, Musitwa, G, Musiime, V, Ndigendawan, M, Namata, H, Nkalubo, J, Labejja, P Ocitti, Okello, P, Olal, P, Pimundu, G, Segonga, P, Ssali, F, Tamale, Z, Tumukunde, D, Namala, W, Byaruhanga, R, Kayiwa, J, Tukamushaba, J, Abunyang, S, Eram, D, Denis, O, Lwalanda, R, Mugarura, L, Namusanje, J, Nankya, I, Ndashimye, E, Nabulime, E, Senfuma, O, Bihabwa, G, Buluma, E, Easterbrook, P, Elbireer, A, Kambugu, A, Kamya, D, Katwere, M, Kiggundu, R, Komujuni, C, Laker, E, Lubwama, E, Mambule, I, Matovu, J, Nakajubi, A, Nakku, J, Nalumenya, R, Namuyimbwa, L, Semitala, F, Wandera, B, Wanyama, J, Mugerwa, H, Lugemwa, A, Ninsiima, E, Ssenkindu, T, Mwebe, S, Atwine, L, William, H, Katemba, C, Acaku, M, Ssebutinde, P, Kitizo, H, Kukundakwe, J, Naluguza, M, Ssegawa, K, Namayanja, Nsibuka, F, Tuhirirwe, P, Fortunate, M, Acen, J, Achidri, J, Amone, A, Chamai, M, Ditai, J, Kemigisa, M, Kiconco, M, Matama, C, Mbanza, D, Nambaziira, F, Odoi, M Owor, Rweyora, A, Tumwebaze, G, Kalanzi, H, Katabaazi, J, Kiyingi, A, Mbidde, M, Mugenyi, M, Mwebaze, R, Okong, P, Senoga, I, Abwola, M, Baliruno, D, Bwomezi, J, Kasede, A, Mudoola, M, Namisi, R, Ssennono, F, Tuhirwe, S, Abongomera, G, Amone, G, Abach, J, Aciro, I, Arach, B, Kidega, P, Omongin, J, Ocung, E, Odong, W, Philliam, A, Alima, H, Ahimbisibwe, B, Atuhaire, E, Atukunda, F, Bekusike, G, Bulegyeya, A, Kahatano, D, Kamukama, S, Kyoshabire, J, Nassali, A, Mbonye, A, Naturinda, T M, Ndukukire, Nshabohurira, A, Ntawiha, H, Rogers, A, Tibyasa, M, Kiirya, S, Atwongyeire, D, Nankya, A, Draleku, C, Nakiboneka, D, Odoch, D, Lakidi, L, Ruganda, R, Abiriga, R, Mulindwa, M, Balmoi, F, Kafuma, S, Moriku, E, Hakim, J, Reid, A, Chidziva, E, Musoro, G, Warambwa, C, Tinago, G, Mutsai, S, Phiri, M, Mudzingwa, S, Bafana, T, Masore, V, Moyo, C, Nhema, R, Chitongo, S, Heyderman, Robert, Kabanga, Lucky, Kaunda, Symon, Kudzala, Aubrey, Lifa, Linly, Mallewa, Jane, Moore, Mike, Mtali, Chrissie, Musowa, George, Mwimaniwa, Grace, Sikwese, Rosemary, van Oosterhout, Joep, Ziwoya, Milton, Chimbaka, H, Chitete, B, Kamanga, S, Makwakwa, T Kayinga E, Mbiya, R, Mlenga, M, Mphande, T, Mtika, C, Mushani, G, Ndhlovu, O, Ngonga, M, Nkhana, I, Nyirenda, R, Cheruiyot, P, Kwobah, C, Ekiru, W Lokitala, Mokaya, M, Mudogo, A, Nzioka, A, Siika, A, Tanui, M, Wachira, S, Wools-Kaloustian, K, Alipalli, P, Chikatula, E, Kipaila, J, Kunda, I, Lakhi, S, Malama, J, Mufwambi, W, Mulenga, L, Mwaba, P, Mwamba, E, Mweemba, A, Namfukwe, M, Kerukadho, E, Ngwatu, B, Birungi, J, Paton, N, Boles, J, Burke, A, Castle, L, Ghuman, S, Kendall, L, Hoppe, A, Tebbs, S, Thomason, M, Thompson, J, Walker, S, Whittle, J, Wilkes, H, Young, N, Spyer, M, Kapuya, C, Kyomuhendo, F, Kyakundi, D, Mkandawire, N, Mulambo, S, Senyonjo, S, Angus, B, Arenas-Pinto, A, Palfreeman, A, Post, F, Ishola, D, Arribas, J, Colebunders, R, Floridia, M, Giuliano, M, Mallon, P, Walsh, P, De Rosa, M, Rinaldi, E, Weller, I, Gilks, C, Kangewende, A, Luyirika, E, Miiro, F, Mwamba, P, Ojoo, S, Phiri, S, van Oosterhout, J, Wapakabulo, A, Peto, T, French, N, Matenga, J, Cloherty, G, van Wyk, J, Norton, M, Lehrman, S, Lamba, P, Malik, K, Rooney, J, Snowden, W, Villacian, J, Paton, Nicholas I, Kityo, Cissy, Thompson, Jennifer, Nankya, Immaculate, Bagenda, Leonard, Hoppe, Anne, Hakim, James, Kambugu, Andrew, van Oosterhout, Joep J, Kiconco, Mary, Bertagnolio, Silvia, Easterbrook, Philippa J, Mugyenyi, Peter, and Walker, A Sarah

Published
2017
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6. Increased Indoleamine-2,3-Dioxygenase Activity Is Associated With Poor Clinical Outcome in Adults Hospitalized With Influenza in the INSIGHT FLU003Plus Study

Author

Pett, Sarah L, Kunisaki, Ken M, Wentworth, Deborah, Griffin, Timothy J, Kalomenidis, Ioannis, Nahra, Raquel, Montejano Sanchez, Rocio, Hodgson, Shane W, Ruxrungtham, Kiat, Dwyer, Dominic, Davey, Richard T, Wendt, Chris H, Lundgren, J, Jansson, P, Pearson, M, Aagaard, B, Hudson, F, Bennet, R, Pacciarini, F, Angus, B, Paton, N, Collaco Moraes, Y, Cooper, D, Pett, S, Emery, S, Courtney-Rogers, D, Robson, R, Gordin, F, Sanchez, A, Standridge, B, Vjecha, M, Moricz, A, Delfino, M, Belloso, W, Losso, M, Tillmann, K, Touloumi, G, Gioukari, V, Anagnostou, O, La Rosa, A, Saenz, M J, Lopez, P, Herrero, P, Portas, B, Avihingsanon, A, Ruxrungtham, K, Kaewon, P, Ubolyam, S, Brekke, K, Campbell, M, Denning, E, DuChene, A, Engen, N, George, M, Harrison, M, Neaton, J D, Nelson, R, Quan, S F, Schultz, T, Wentworth, D, Baxter, J, Brown, S, Hoover, M, Beigel, J, Davey, R T, Jr., Dewar, R, Gover, E, McConnell, R, Metcalf, J, Natarajan, V, Rehman, T, Voell, J, Dwyer, D E, Kok, J, Uyeki, T, Munroe, D, Paez, A, Bertrand, M, Temesgen, Z, Rizza, S, Wolfe, C, Carbonneau, J, Novak, R, Schwarber, M, Polenakovik, H, Clark, L, Patil, N, Riska, P, Omotosho, J, Faber, L, Markowitz, N, Glesby, M, Ham, K, Parenti, D, Simon, G, Baxter, J, Coburn, P, Freiberg, M, Koerbel, G, Dharan, N, Paez-Quinde, M, Gunter, J, Beilke, M, Lu, Z, Gunderson, E, Baker, J, Koletar, S, Harber, H, Hurt, C, Marcus, C, Allen, M, Cummins, S, Uslan, D, Bonam, T, Paez, A, Santiago, F, States, D, Gardner, E, DeHovitz, J, Holman, S, Watson, V, Nixon, D, Dwyer, D, Kabir, M, Pett, S, Kilkenny, F, Elliott, J, Garlick, J, McBride, J, Richmond, S, Barcan, L, Sanchez, M, Lopardo, G, Barcelona, L, Bonvehi, P, Temporiti, E R, Losso, M, Macias, L, Laplume, H, Daciuk, L, Warley, E, Tavella, S, Fernandez Cruz, E, Paño, J, Estrada, V, Lopetegui, P, Gimenez Julvez, T, Ryan, P, Sanz Moreno, J, Knobel, H, Soriano, V, Dalmau, D, Dockrell, D, Angus, B, Price, D, Newport, M, Chadwick, D, Østergaard, L, Yehdego, Y, Pedersen, C, Hergens, L, Joensen, Z, Aagaard, B, Kronborg, G, Collins, P, Nielsen, H, Gerstoft, J, Baadegaard, B, Koulouris, N, Antoniadou, A, Protopappas, K, Polixronopoulos, V, Diamantea, F, Sambatakou, H, Mariolis, I, Vassilopoulos, N, Gerogiannis, A, Pinedo Ramirez, Y, Cornelio Mauricio, E, Vega Bazalar, J, Castillo Cordova, R, Fãtkenhuerer, G, Thomas, E, Bergmann, F, Fõllmer, U, Rockstroh, J, Englehardt, A, Stephan, C, Thomas, E, Bogner, J, Brockmeyer, N, Klinker, H, Chetchotisakd, P, Jumpimai, T, Avihingsanon, A, Ruxrungtham, K, Clumeck, N, Kameya, K, Chu, M Y, Wu, T C, Horban, A, Bakowska, E, Burgmann, H, Tobudic, S, Maagaard, A, Wolff, M, and Allendes, G

Published
2018
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7. Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis

Author

Sonnenkalb, Lindsay, Carter, Joshua James, Spitaleri, Andrea, Iqbal, Zamin, Hunt, Martin, Malone, Kerri Marie, Utpatel, Christian, Cirillo, Daniela Maria, Rodrigues, Camilla, Nilgiriwala, Kayzad Soli, Fowler, Philip William, Merker, Matthias, Niemann, Stefan, Consortium, The Comprehensive Resistance Prediction for Tuberculosis: an International, Barilar, I, Battaglia, S, Borroni, E, Brandao, AP, Brankin, A, Cabibbe, AM, Carter, J, Claxton, P, Clifton, DA, Cohen, T, Coronel, J, Crook, DW, Dreyer, V, Earle, SG, Escuyer, V, Ferrazoli, L, Fowler, PW, Gao, G Fu, Gardy, J, Gharbia, S, Ghisi, KT, Ghodousi, A, Cruz, AL Gibertoni, Grandjean, L, Grazian, C, Groenheit, R, Guthrie, JL, He, W, Hoffmann, H, Hoosdally, SJ, Ismail, NA, Jarrett, L, Joseph, L, Jou, R, Kambli, P, Khot, R, Knaggs, J, Koch, A, Kohlerschmidt, D, Kouchaki, S, Lachapelle, AS, Lalvani, A, Lapierre, S Grandjean, Laurenson, IF, Letcher, B, Lin, WH, Liu, C, Liu, D, Malone, KM, Mandal, A, Mansjö, M, Matias, D, Meintjes, G, de Freitas Mendes, F, Mihalic, M, Millard, J, Miotto, P, Mistry, N, Moore, D, Musser, KA, Ngcamu, D, Hoang, NN, Nimmo, C, Okozi, N, Oliveira, RS, Omar, SV, Paton, N, Peto, TE, Pinhata, JM Watanabe, Plesnik, S, Puyen, ZM, Rabodoarivelo, MS, Rakotosamimanana, N, Rancoita, PM, Rathod, P, Rodger, G, Rodwell, TC, Roohi, E, Santos-Lazaro, D, Shah, S, Kohl, TA, Smith, G, Solano, W, Supply, P, Surve, U, Tahseen, S, and Thuong, NTT

Subjects
Model organisms, Human Biology & Physiology, FOS: Clinical medicine, Immunology, Infectious Disease
Abstract

Background: Bedaquiline is a core drug for the treatment of multidrug-resistant tuberculosis; however, the understanding of resistance mechanisms is poor, which is hampering rapid molecular diagnostics. Some bedaquiline-resistant mutants are also cross-resistant to clofazimine. To decipher bedaquiline and clofazimine resistance determinants, we combined experimental evolution, protein modelling, genome sequencing, and phenotypic data. Methods: For this in-vitro and in-silico data analysis, we used a novel in-vitro evolutionary model using subinhibitory drug concentrations to select bedaquiline-resistant and clofazimine-resistant mutants. We determined bedaquiline and clofazimine minimum inhibitory concentrations and did Illumina and PacBio sequencing to characterise selected mutants and establish a mutation catalogue. This catalogue also includes phenotypic and genotypic data of a global collection of more than 14 000 clinical Mycobacterium tuberculosis complex isolates, and publicly available data. We investigated variants implicated in bedaquiline resistance by protein modelling and dynamic simulations. Findings: We discerned 265 genomic variants implicated in bedaquiline resistance, with 250 (94%) variants affecting the transcriptional repressor (Rv0678) of the MmpS5–MmpL5 efflux system. We identified 40 new variants in vitro, and a new bedaquiline resistance mechanism caused by a large-scale genomic rearrangement. Additionally, we identified in vitro 15 (7%) of 208 mutations found in clinical bedaquiline-resistant isolates. From our in-vitro work, we detected 14 (16%) of 88 mutations so far identified as being associated with clofazimine resistance and also seen in clinically resistant strains, and catalogued 35 new mutations. Structural modelling of Rv0678 showed four major mechanisms of bedaquiline resistance: impaired DNA binding, reduction in protein stability, disruption of protein dimerisation, and alteration in affinity for its fatty acid ligand. Interpretation: Our findings advance the understanding of drug resistance mechanisms in M tuberculosis complex strains. We have established an extended mutation catalogue, comprising variants implicated in resistance and susceptibility to bedaquiline and clofazimine. Our data emphasise that genotypic testing can delineate clinical isolates with borderline phenotypes, which is essential for the design of effective treatments. Funding: Leibniz ScienceCampus Evolutionary Medicine of the Lung, Deutsche Forschungsgemeinschaft, Research Training Group 2501 TransEvo, Rhodes Trust, Stanford University Medical Scientist Training Program, National Institute for Health and Care Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Bill & Melinda Gates Foundation, Wellcome Trust, and Marie Skłodowska-Curie Actions.

Published
2023
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9. A crowd of BashTheBug volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates

Author

Fowler, P.W., Wright, C., Spiers, H., Zhu, T., Baeten, E.M.L., Hoosdally, S.W., Cruz, A.L.G., Roohi, A., Kouchaki, S., Walker, T.M., Peto, T.E.A., Miller, G., Lintott, C., Clifton, D., Crook, D.W., Walker, A.S., Barilar, I., Battaglia, S., Borroni, E., Brandao, A.P., Brankin, A., Cabibbe, A.M., Carter, J., Chetty, D., Cirillo, D.M., Claxton, P., Clifton, D.A., Cohen, T., Coronel, Jorge, Dreyer, V., Earle, S.G., Escuyer, V., Ferrazoli, L., Gao, G.F., Gardy, J., Gharbia, S., Ghisi, K.T., Ghodousi, A., Grandjean, Louis, Grazian, C., Groenheit, R., Guthrie, J.L., He, W., Hoffmann, H., Hoosdally, S.J., Martinhunt, M., Iqbal, Z., Ismail, N.A., Jarrett, L., Joseph, L., Jou, R., Kambli, P., Khot, R., Knaggs, J., Koch, A., Kohlerschmidt, D., Lachapelle, A.S., Lalvani, A., Lapierre, S.G., Laurenson, I.F., Letcher, B., Lin, W.-H., Liu, C., Liu, D., Malone, K.M., Mandal, A., Mansjõ, M., Matias, D., Meintjes, G., Mendes, F.D.F., Merker, M., Mihalic, M., Millard, J., Miotto, P., Mistry, N., Moore, David Alexander James, Musser, K.A., Ngcamu, D., Nhung, H.N., Niemann, S., Nilgiriwala, K.S., Nimmo, C., O’Donnell, M., Okozi, N., Oliveira, R.S., Omar, S.V., Paton, N., Pinhata, J.M.W., Plesnik, S., Puyen, Z.M., Rabodoarivelo, M.S., Rakotosamimanana, N., Rancoita, P.M.V., Rathod, P., Robinson, E., Rodger, G., Rodrigues, C., Rodwell, T.C., Santos-Lazaro, D., Shah, S., Kohl, T.A., Smith, G., Solano, Walter, Spitaleri, A., Supply, P., Steyn, A.J.C., Surve, U., Tahseen, S., Thuong, N.T.T., Thwaites, G., Todt, K., Trovato, A., Utpatel, C., Van Rie, A., Vijay, S., Warren, R., Werngren, J., Wijkander, M., Wilkinson, R.J., Wilson, D.J., Wintringer, P., Xiao, Y.-X., Yang, Y., Yanlin, Z., Yao, S.-Y., Zhu, B., The Zooniverse Volunteer Community, The CRyPTIC Consortium, Community, The Zooniverse Volunteer, Consortium, The CRyPTIC, Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 (CIIL), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), and Wellcome Trust

Subjects
Volunteers, Model organisms, infectious disease, [SDV]Life Sciences [q-bio], Immunology, Antitubercular Agents, Infectious Disease, Microbial Sensitivity Tests, Zooniverse Volunteer Community, 0601 Biochemistry and Cell Biology, antibiotics, General Biochemistry, Genetics and Molecular Biology, Imaging, minimum inhibitory concentrations, antitubercular drugs, citizen science, M. tuberculosis, Humans, clinical microbiology, Human Biology & Physiology, General Immunology and Microbiology, CRyPTIC Consortium, Prevention, General Neuroscience, FOS: Clinical medicine, microbiology, BashTheBug, Mycobacterium tuberculosis, General Medicine, microdilution plates, Emerging Infectious Diseases, Infectious Diseases, Good Health and Well Being, tuberculosis, 5.1 Pharmaceuticals, photographs, Antimicrobial Resistance, Biochemistry and Cell Biology, Development of treatments and therapeutic interventions, Infection
Abstract

Tuberculosis is a respiratory disease that is treatable with antibiotics. An increasing prevalence of resistance means that to ensure a good treatment outcome it is desirable to test the susceptibility of each infection to different antibiotics. Conventionally, this is done by culturing a clinical sample and then exposing aliquots to a panel of antibiotics, each being present at a pre-determined concentration, thereby determining if the sample isresistant or susceptible to each sample. The minimum inhibitory concentration (MIC) of a drug is the lowestconcentration that inhibits growth and is a more useful quantity but requires each sample to be tested at a range ofconcentrations for each drug. Using 96-well broth micro dilution plates with each well containing a lyophilised pre-determined amount of an antibiotic is a convenient and cost-effective way to measure the MICs of several drugs at once for a clinical sample. Although accurate, this is still an expensive and slow process that requires highly-skilled and experienced laboratory scientists. Here we show that, through the BashTheBug project hosted on the Zooniverse citizen science platform, a crowd of volunteers can reproducibly and accurately determine the MICs for 13 drugs and that simply taking the median or mode of 11-17 independent classifications is sufficient. There is therefore a potential role for crowds to support (but not supplant) the role of experts in antibiotic susceptibility testing.Tuberculosis is a bacterial respiratory infection that kills about 1.4 million people worldwide each year. While antibiotics can cure the condition, the bacterium responsible for this disease

Published
2022

10. The European Far Right and Islamist Extremism on Twitter:From Radicalisation to Political Participation

Author

Paton, N., Nilsen, A.B., Dechesne, M., Sakellariou, A., Helm, G., Salord, T., Cabanac, G., Paton, N., Nilsen, A.B., Dechesne, M., Sakellariou, A., Helm, G., Salord, T., and Cabanac, G.

Abstract

This article explores the results of a study on media participation on Twitter in 2018/2019 perceived as contributing to far right and Islamist radical ideologies, in 7 European countries. By combining online ethnography and big data approaches, we see that online far right extremism in Europe is active and ranks are growing, while Islamist extremism has been incapacitated in sharing controversial forms of expression. We describe how the far right uses Twitter as a means for political activism, while Islamist extremists offer lifestyle information, related to local branches of faith, using Twitter as a storefront that re-routes users to other platforms. We consider resources for action, notably on the far right. World leaders influence followers online and support a global conversation between users that paves the way to a far right European milieu thriving on Twitter. To conclude, we delve into the notions of radicalisation and political participation to emphasise the asymmetry between the two forms of media participation in respect to academic discourses and big tech and States’ practises.

Published
2022

13. Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of

Author

Fowler, P.W., Barilar, I., Battaglia, S., Borroni, E., Brandao, A.P., Brankin, A., Cabibbe, A.M., Carter, J., Cirillo, D.M., Claxton, P., Clifton, D.A., Cohen, T., Coronel, Jorge, Crook, D.W., Dreyer, V., Earle, S.G., Escuyer, V., Ferrazoli, L., Gao, G.F., Gardy, J., Gharbia, S., Ghisi, K.T., Ghodousi, A., Cruz, A.L.G., Grandjean, Louis, Grazian, C., Groenheit, R., Guthrie, J.L., He, W., Hoffmann, H., Hoosdally, S.J., Hunt, M., Iqbal, Z., Ismail, N.A., Jarrett, L., Joseph, L., Jou, R., Kambli, P., Khot, R., Knaggs, J., Koch, A., Kohlerschmidt, D., Kouchaki, S., Lachapelle, A.S., Lalvani, A., Lapierre, S.G., Laurenson, I.F., Letcher, B., Lin, W.-H., Liu, C., Liu, D., Malone, K.M., Mandal, A., Mansjö, M., Matias, D., Meintjes, G., de Freitas Mendes, F., Merker, M., Mihalic, M., Millard, J., Miotto, P., Mistry, N., Moore, David Alexander James, Musser, K.A., Ngcamu, D., Nhung, H.N., Niemann, S., Nilgiriwala, K.S., Nimmo, C., Okozi, N., Oliveira, R.S., Omar, S.V., Paton, N., Peto, T.E.A., Pinhata, J.M.W., Plesnik, S., Puyen, Z.M., Rabodoarivelo, M.S., Rakotosamimanana, N., Rancoita, P.M.V., Rathod, P., Robinson, E., Rodger, G., Rodrigues, C., Rodwell, T.C., Roohi, A., Santos-Lazaro, D., Shah, S., Kohl, T.A., Smith, G., Solano, Walter, Spitaleri, A., Supply, P., Surve, U., Tahseen, S., Thuong, N.T.T., Thwaites, G., Todt, K., Trovato, A., Utpatel, C., Van Rie, A., Vijay, S., Walker, T.M., Walker, A.S., Warren, R., Werngren, J., Wijkander, M., Wilkinson, R.J., Wilson, D.J., Wintringer, P., Xiao, Y.-X., Yang, Y., Yanlin, Z., Yao, S.-Y., Zhu, B., Consoritum, CRyPTIC, Walker, AS, and CRyPTIC Consortium

Subjects
microdilution, Pulmonary and Respiratory Medicine, tuberculosis, Respiratory, M. tuberculosis, Humans, Tuberculosis, Human medicine, infections, Microbial Sensitivity Tests, Mycobacterium tuberculosis, Anti-Bacterial Agents
Abstract

Drug susceptibility testing ofM. tuberculosisis rooted in a binary susceptible/resistant paradigm. While there are considerable advantages in measuring the minimum inhibitory concentrations (MICs) of a panel of drugs for an isolate, it is necessary to measure the epidemiological cut-off values (ECOFF/ECVs) to permit comparison with qualitative data. Here we present ECOFF/ECVs for 13 anti-tuberculosis compounds, including bedaquiline and delamanid, derived from 20 637 clinical isolates collected by 14 laboratories based in 11 countries on five continents. Each isolate was incubated for 14 days on a dry 96-well broth microdilution plate and then read. Resistance to most of the drugs due to prior exposure is expected and the MIC distributions for many of the compounds are complex, and therefore aphenotypicallywild-type population could not be defined. Since a majority of samples also underwent genetic sequencing, we defined agenotypicallywild-type population and measured the MIC of the 99th percentile by direct measurement andviafitting a Gaussian using interval regression. The proposed ECOFF/ECVs were then validated by comparing with the MIC distributions of high-confidence genetic variants that confer resistance and with qualitative drug susceptibility tests obtainedviathe Mycobacterial Growth Indicator Tube (MGIT) system or Microscopic-Observation Drug Susceptibility (MODS) assay. These ECOFF/ECVs will inform and encourage the more widespread adoption of broth microdilution: this is a cheap culture-based method that tests the susceptibility of 12–14 antibiotics on a single 96-well plate and so could help personalise the treatment of tuberculosis.

Published
2021

18. Population pharmacokinetics and pharmacogenetics of ritonavir-boosted darunavir in the presence of raltegravir or tenofovir disoproxil fumarate/emtricitabine in HIV-infected adults and the relationship with virological response:a sub-study of the NEAT001/ANRS143 randomized trial

Author

Dickinson L., Gurjar R., Stohr W., Bonora S., Owen A., D'Avolio A., Cursley A., Molina J. -M., Faetkenheuer G., Vandekerckhove L., Di Perri G., Pozniak A., Richert L., Raffi F., Boffito M., Dedes N., Chene G., Allavena C., Autran B., Antinori A., Bucciardini R., Vella S., Horban A., Arribas J., Babiker A. G., Pillay D., Franquet X., Schwarze S., Grarup J., Fischer A., Wallet C., Diallo A., Saillard J., Moecklinghoff C., Stellbrink H. -J., Vanleeuwen R., Gatell J., Sandstrom E., Flepp M., Ewings F., George E. C., Hudson F., Pearce G., Quercia R., Rogatto F., Leavitt R., Nguyen B. -Y., Peto T., Goebel F., Marcotullio S., Miller V., Sasieni P., Arnault F., Boucherie C., Jean D., Paniego V., Paraina F., Rouch E., Schwimmer C., Soussi M., Taieb A., Termote M., Touzeau G., Babiker A., Dodds W., Hoppe A., Kummeling I., Pacciarini F., Paton N., Russell C., Taylor K., Ward D., Aagaard B., Eid M., Gey D., Gramjensen B., Jakobsen M. -L., Jansson P. O., Jensen K., Mariajoensen Z., Moseholmlarsen E., Pahl C., Pearson M., Nielsen B. R., Reilev So. S., Christ I., Lathouwers D., Manting C., Van Leeuwen R., Mendy B., Metro A., Couffin-Cadiergues S., Knellwolf A. -L., Palmisiano L., Aznar E., Barea C., Cotarelo M., Esteban H., Girbau I., Moyano B., Ramirez M., Saiz C., Sanchez I., Yllescas M., Binelli A., Colasanti V., Massella M., Anagnostou O., Gioukari V., Touloumi G., Schmied B., Rieger A., Vetter N., Dewit S., Florence E., Gerstoft J., Mathiesen L., Katlama C., Cabie A., Cheret A., Dupon M., Ghosn J., Girard P. -M., Goujard C., Levy Y., Morlat P., Neau D., Obadia M., Perre P., Piroth L., Reynes J., Tattevin P., Ragnaud J. M., Weiss L., Yazdan Y., Yeni P., Zucman D., Esser S., Fatkenheuer G., Hoffmann C., Jessen H., Rockstroh J., Schmidt R., Stephan C., Unger S., Hatzakis A., Daikos G. L., Papadopoulos A., Skoutelis A., Banhegyi D., Mallon P., Mulcahy F., Andreoni M., Castelli F., D'Arminiomonforte A., Diperri G., Galli M., Lazzarin A., Mazzotta F., Torti C., Vullo V., Prins J., Richter C., Verhagen D., Vaneeden A., Doroana M., Antunes F., Maltez F., Sarmento-Castro R., Gonzalez Garcia J., Aldeguer J. L., Clotet B., Domingo P., Gatell J. M., Knobel H., Marquez M., Pilarmiralles M., Portilla J., Soriano V., Tellez M., Thalme A., Blaxhult A., Gisslen M., Winston A., Fox J., Gompels M., Herieka E., Johnson M., Leen C., Teague A., Williams I., Boyd M., Moller N. F., Moseholmlarsen E. F., Lemoing V., Wit F. W. N. M., Kowalska J., Berenguer J., Moreno S., Muller N. J., Torok E., Post F., Angus B., Calvez V., Boucher C., Collins S., Dunn D., Lambert S., Marcelin A. -G., Perno C. F., White E., Ammassari A., Schmidt R. E., Odermarsky M., Smith C., Thiebaut R., Delaserna J. I. B., Castagna A., De Wit S., Furrer H. -J., Mocroft A., Reiss P., Fragola V., Lauriola M., Murri R., Nieuwkerk P., Spire B., Volny-Anne A., West B., Amieva H., Llibre Codina J., Braggion M., Foca E., Dickinson, L., Gurjar, R., Stohr, W., Bonora, S., Owen, A., D'Avolio, A., Cursley, A., Molina, J. -M., Faetkenheuer, G., Vandekerckhove, L., Di Perri, G., Pozniak, A., Richert, L., Raffi, F., Boffito, M., Dedes, N., Chene, G., Allavena, C., Autran, B., Antinori, A., Bucciardini, R., Vella, S., Horban, A., Arribas, J., Babiker, A. G., Pillay, D., Franquet, X., Schwarze, S., Grarup, J., Fischer, A., Wallet, C., Diallo, A., Saillard, J., Moecklinghoff, C., Stellbrink, H. -J., Vanleeuwen, R., Gatell, J., Sandstrom, E., Flepp, M., Ewings, F., George, E. C., Hudson, F., Pearce, G., Quercia, R., Rogatto, F., Leavitt, R., Nguyen, B. -Y., Peto, T., Goebel, F., Marcotullio, S., Miller, V., Sasieni, P., Arnault, F., Boucherie, C., Jean, D., Paniego, V., Paraina, F., Rouch, E., Schwimmer, C., Soussi, M., Taieb, A., Termote, M., Touzeau, G., Babiker, A., Dodds, W., Hoppe, A., Kummeling, I., Pacciarini, F., Paton, N., Russell, C., Taylor, K., Ward, D., Aagaard, B., Eid, M., Gey, D., Gramjensen, B., Jakobsen, M. -L., Jansson, P. O., Jensen, K., Mariajoensen, Z., Moseholmlarsen, E., Pahl, C., Pearson, M., Nielsen, B. R., Reilev, So. S., Christ, I., Lathouwers, D., Manting, C., Van Leeuwen, R., Mendy, B., Metro, A., Couffin-Cadiergues, S., Knellwolf, A. -L., Palmisiano, L., Aznar, E., Barea, C., Cotarelo, M., Esteban, H., Girbau, I., Moyano, B., Ramirez, M., Saiz, C., Sanchez, I., Yllescas, M., Binelli, A., Colasanti, V., Massella, M., Anagnostou, O., Gioukari, V., Touloumi, G., Schmied, B., Rieger, A., Vetter, N., Dewit, S., Florence, E., Gerstoft, J., Mathiesen, L., Katlama, C., Cabie, A., Cheret, A., Dupon, M., Ghosn, J., Girard, P. -M., Goujard, C., Levy, Y., Morlat, P., Neau, D., Obadia, M., Perre, P., Piroth, L., Reynes, J., Tattevin, P., Ragnaud, J. M., Weiss, L., Yazdan, Y., Yeni, P., Zucman, D., Esser, S., Fatkenheuer, G., Hoffmann, C., Jessen, H., Rockstroh, J., Schmidt, R., Stephan, C., Unger, S., Hatzakis, A., Daikos, G. L., Papadopoulos, A., Skoutelis, A., Banhegyi, D., Mallon, P., Mulcahy, F., Andreoni, M., Castelli, F., D'Arminiomonforte, A., Diperri, G., Galli, M., Lazzarin, A., Mazzotta, F., Torti, C., Vullo, V., Prins, J., Richter, C., Verhagen, D., Vaneeden, A., Doroana, M., Antunes, F., Maltez, F., Sarmento-Castro, R., Gonzalez Garcia, J., Aldeguer, J. L., Clotet, B., Domingo, P., Gatell, J. M., Knobel, H., Marquez, M., Pilarmiralles, M., Portilla, J., Soriano, V., Tellez, M., Thalme, A., Blaxhult, A., Gisslen, M., Winston, A., Fox, J., Gompels, M., Herieka, E., Johnson, M., Leen, C., Teague, A., Williams, I., Boyd, M., Moller, N. F., Moseholmlarsen, E. F., Lemoing, V., Wit, F. W. N. M., Kowalska, J., Berenguer, J., Moreno, S., Muller, N. J., Torok, E., Post, F., Angus, B., Calvez, V., Boucher, C., Collins, S., Dunn, D., Lambert, S., Marcelin, A. -G., Perno, C. F., White, E., Ammassari, A., Schmidt, R. E., Odermarsky, M., Smith, C., Thiebaut, R., Delaserna, J. I. B., Castagna, A., De Wit, S., Furrer, H. -J., Mocroft, A., Reiss, P., Fragola, V., Lauriola, M., Murri, R., Nieuwkerk, P., Spire, B., Volny-Anne, A., West, B., Amieva, H., Llibre Codina, J., Braggion, M., Foca, E., Infectious diseases, AII - Infectious diseases, APH - Aging & Later Life, Global Health, APH - Personalized Medicine, APH - Mental Health, and Medical Psychology

Subjects
0301 basic medicine, Microbiology (medical), Adult, Male, medicine.medical_specialty, Anti-HIV Agents, 030106 microbiology, HIV Infections, Emtricitabine, 030226 pharmacology & pharmacy, Gastroenterology, 03 medical and health sciences, 0302 clinical medicine, Pharmacokinetics, immune system diseases, Internal medicine, Raltegravir Potassium, medicine, Humans, Pharmacology (medical), Tenofovir, Darunavir, Constitutive Androstane Receptor, Pharmacology, Ritonavir, biology, business.industry, Liver-Specific Organic Anion Transporter 1, virus diseases, Lopinavir, Viral Load, Raltegravir, Multidrug Resistance-Associated Protein 2, 3. Good health, NONMEM, SISTM, Infectious Diseases, Pharmacogenetics, biology.protein, Female, SLCO1B1, business, medicine.drug
Abstract

Objectives NEAT001/ANRS143 demonstrated non-inferiority of once-daily darunavir/ritonavir (800/100 mg) + twice-daily raltegravir (400 mg) versus darunavir/ritonavir + tenofovir disoproxil fumarate/emtricitabine (245/200 mg once daily) in treatment-naive patients. We investigated the population pharmacokinetics of darunavir, ritonavir, tenofovir and emtricitabine and relationships with demographics, genetic polymorphisms and virological failure. Methods Non-linear mixed-effects models (NONMEM v. 7.3) were applied to determine pharmacokinetic parameters and assess demographic covariates and relationships with SNPs (SLCO3A1, SLCO1B1, NR1I2, NR1I3, CYP3A5*3, CYP3A4*22, ABCC2, ABCC10, ABCG2 and SCL47A1). The relationship between model-predicted darunavir AUC0–24 and C24 with time to virological failure was evaluated by Cox regression. Results Of 805 enrolled, 716, 720, 347 and 361 were included in the darunavir, ritonavir, tenofovir and emtricitabine models, respectively (11% female, 83% Caucasian). No significant effect of patient demographics or SNPs was observed for darunavir or tenofovir apparent oral clearance (CL/F); coadministration of raltegravir did not influence darunavir or ritonavir CL/F. Ritonavir CL/F decreased by 23% in NR1I2 63396C>T carriers and emtricitabine CL/F was linearly associated with creatinine clearance (P Conclusions Darunavir concentrations were unaltered in the presence of raltegravir and not associated with virological failure. Polymorphisms investigated had little impact on study-drug pharmacokinetics. Darunavir/ritonavir + raltegravir may be an appropriate option for patients experiencing NRTI-associated toxicity.

Published
2020

23. Blockchain-Enabled Fish Provenance and Quality Tracking System

Author

Wang, X, Yu, G, Liu, RP, Zhang, J, Wu, Q, Su, S, He, Y, Zhang, Z, Yu, L, Liu, T, Zhang, W, Loneragan, P, Dutkiewicz, E, Poole, E, Paton, N, Wang, X, Yu, G, Liu, RP, Zhang, J, Wu, Q, Su, S, He, Y, Zhang, Z, Yu, L, Liu, T, Zhang, W, Loneragan, P, Dutkiewicz, E, Poole, E, and Paton, N

Published
2021

24. Holding a Conference Online and Live due to COVID-19

Author

Bonifati, A., Guerrini, G., Lutz, C., Martens, W., Mazilu, L., Paton, N. W., Vaz Salles, M. A., Scholl, M. H., and Zhou, Y.

Subjects
FOS: Computer and information sciences, Computer Science - Databases, Computer Science - Human-Computer Interaction, Databases (cs.DB), Human-Computer Interaction (cs.HC)
Abstract

The joint EDBT/ICDT conference (International Conference on Extending Database Technology/International Conference on Database Theory) is a well established conference series on data management, with annual meetings in the second half of March that attract 250 to 300 delegates. Three weeks before EDBT/ICDT 2020 was planned to take place in Copenhagen, the rapidly developing Covid-19 pandemic led to the decision to cancel the face-to-face event. In the interest of the research community, it was decided to move the conference online while trying to preserve as much of the real-life experience as possible. As far as we know, we are one of the first conferences that moved to a fully synchronous online experience due to the COVID-19 outbreak. With fully synchronous, we mean that participants jointly listened to presentations, had live Q&A, and attended other live events associated with the conference. In this report, we share our decisions, experiences, and lessons learned.

Published
2020
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25. Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking

Author

Kouchaki, S, Yang, Y, Lachapelle, A, Walker, T, Walker, SA, Hoosdally, S, Gibertoni Cruz, AL, Carter, J, Grazian, C, Earle, SG, Fowler, P, Iqbal, Z, Hunt, M, Knaggs, J, Smith, GE, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaler, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Merker, M, Hoffmann, H, Todt, K, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Thwaites, G, Thuong, TNT, Ngoc, NH, Srinivasan, V, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Liu, C, Ma, A, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, Siqueira de Oliveira, R, Arandjelovic, I, Chaiprasert, A, Comas, I, Roig, CR, Drobniewski, FA, Farhat, MR, Gao, Q, Hee, ROT, Sintchenko, V, Supply, P, van Soolingen, D, Peto, TEA, Crook, D, Clifton, D, Kouchaki, S, Yang, Y, Lachapelle, A, Walker, T, Walker, SA, Hoosdally, S, Gibertoni Cruz, AL, Carter, J, Grazian, C, Earle, SG, Fowler, P, Iqbal, Z, Hunt, M, Knaggs, J, Smith, GE, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaler, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Merker, M, Hoffmann, H, Todt, K, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Thwaites, G, Thuong, TNT, Ngoc, NH, Srinivasan, V, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Liu, C, Ma, A, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, Siqueira de Oliveira, R, Arandjelovic, I, Chaiprasert, A, Comas, I, Roig, CR, Drobniewski, FA, Farhat, MR, Gao, Q, Hee, ROT, Sintchenko, V, Supply, P, van Soolingen, D, Peto, TEA, Crook, D, and Clifton, D

Published
2020

26. GenomegaMap: within-species genome-wide d_N/d_S estimation from over 10,000 genomes

Author

Wilson, D, Crook, DW, Peto, TEA, Walker, AS, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, C, Earle, SG, Kouchaki, S, Lachapelle, A, Yang, Y, Clifton, DA, Fowler, PW, Iqbal, Z, Hunt, M, Knaggs, J, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, NIemann, S, Kohl, TA, Merker, M, Hoffman, H, Todt, K, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Thwaites, G, Thoung, TNT, Ngoc, NH, Srinivasan, V, Walker, TM, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Liu, C, Ma, A, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, Siqueira de Oliveira, R, Arandjelovic, I, Chaipresert, A, Comas, I, Roig, CJ, Drobniewski, FA, Farhat, MR, Gao, Q, Hee, ROT, Sintchenko, V, Wilson, D, Crook, DW, Peto, TEA, Walker, AS, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, C, Earle, SG, Kouchaki, S, Lachapelle, A, Yang, Y, Clifton, DA, Fowler, PW, Iqbal, Z, Hunt, M, Knaggs, J, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, NIemann, S, Kohl, TA, Merker, M, Hoffman, H, Todt, K, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Thwaites, G, Thoung, TNT, Ngoc, NH, Srinivasan, V, Walker, TM, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Liu, C, Ma, A, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, Siqueira de Oliveira, R, Arandjelovic, I, Chaipresert, A, Comas, I, Roig, CJ, Drobniewski, FA, Farhat, MR, Gao, Q, Hee, ROT, and Sintchenko, V

Abstract

The dN/dS ratio provides evidence of adaptation or functional constraint in protein-coding genes by quantifying the relative excess or deficit of amino acid-replacing versus silent nucleotide variation. Inexpensive sequencing promises a better understanding of parameters such as dN/dS, but analysing very large datasets poses a major statistical challenge. Here I introduce genomegaMap for estimating within-species genome-wide variation in dN/dS, and I apply it to 3,979 genes across 10,209 tuberculosis genomes to characterize the selection pressures shaping this global pathogen. GenomegaMap is a phylogeny-free method that addresses two major problems with existing approaches: (i) it is fast no matter how large the sample size and (ii) it is robust to recombination, which causes phylogenetic methods to report artefactual signals of adaptation. GenomegaMap uses population genetics theory to approximate the distribution of allele frequencies under general, parent-dependent mutation models. Coalescent simulations show that substitution parameters are well-estimated even when genomegaMap’s simplifying assumption of independence among sites is violated. I demonstrate the ability of genomegaMap to detect genuine signatures of selection at antimicrobial resistance-conferring substitutions in M. tuberculosis and describe a novel signature of selection in the cold-shock DEAD-box protein A gene deaD/csdA. The genomegaMap approach helps accelerate the exploitation of big data for gaining new insights into evolution within species.

Published
2020

30. 463P The prognostic role of tumour inflammation markers in patients (pts) with colorectal cancer (CRC) treated with trifluridine/tipiuracil hydrochloride: Real-world data (RWD) from Greater Manchester

Author

Alchawaf, A., primary, Dawood, M., additional, Al-Ani, M., additional, Ho, A., additional, Ferrera, A., additional, Saunders, M., additional, Tinsley, N., additional, Nasralla, M., additional, Paton, N., additional, Wilson, G., additional, Braun, M., additional, Alam, N., additional, Hasan, J.H., additional, Marti, F. Marti, additional, Kamposioras, K., additional, Mullamitha, S., additional, and Barriuso, J., additional

Published
2020
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31. P-339 Real-world data (RWD) of the use of trifluridine/tipiracil hydrochloride (TFT) in patients with metastatic colorectal cancer: The Greater Manchester experience

Author

Alchawaf, A., primary, Dawod, M., additional, Al-Ani, M., additional, Barriuso, J., additional, Ferrera, A., additional, Ho, A., additional, Braun, M., additional, Paton, N., additional, Saunders, M., additional, Wilson, G., additional, Alam, N., additional, Hasan, J., additional, Marti, F. Marti, additional, Kamposioras, K., additional, and Mullamitha, S., additional

Published
2020
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33. Mapping the medical outcomes study HIV health survey (MOS-HIV) to the EuroQoL 5 Dimension (EQ-5D-3 L) utility index

Author

Shi, Yuan, Thompson, Jennifer, Walker, A Sarah, Paton, Nicholas I, Cheung, Yin Bun, Agweng, E, Awio, P, Bakeinyaga, G, Isabirye, C, Kabuga, U, Kasuswa, S, Katuramu, M, Kityo, C, Kiweewa, F, Kyomugisha, H, Lutalo, E, Mugyenyi, P, Mulima, D, Musana, H, Musitwa, G, Musiime, V, Ndigendawan, M, Namata, H, Nkalubo, J, Labejja, P Ocitti, Okello, P, Olal, P, Pimundu, G, Segonga, P, Ssali, F, Tamale, Z, Tumukunde, D, Namala, W, Byaruhanga, R, Kayiwa, J, Tukamushaba, J, Abunyang, S, Eram, D, Denis, O, Lwalanda, R, Mugarura, L, Namusanje, J, Nankya, I, Ndashimye, E, Nabulime, E, Senfuma, O, Bihabwa, G, Buluma, E, Easterbrook, P, Elbireer, A, Kambugu, A, Kamya, D, Katwere, M, Kiggundu, R, Komujuni, C, Laker, E, Lubwama, E, Mambule, I, Matovu, J, Nakajubi, A, Nakku, J, Nalumenya, R, Namuyimbwa, L, Semitala, F, Wandera, B, Wanyama, J, Mugerwa, H, Lugemwa, A, Ninsiima, E, Ssenkindu, T, Mwebe, S, Atwine, L, William, H, Katemba, C, Acaku, M, Ssebutinde, P, Kitizo, H, Kukundakwe, J, Naluguza, M, Ssegawa, K, Namayanja, Nsibuka, F, Tuhirirwe, P, Fortunate, M, Acen, J, Achidri, J, Amone, A, Chamai, M, Ditai, J, Kemigisa, M, Kiconco, M, Matama, C, Mbanza, D, Nambaziira, F, Odoi, M Owor, Rweyora, A, Tumwebaze, G, Kalanzi, H, Katabaazi, J, Kiyingi, A, Mbidde, M, Mugenyi, M, Mwebaze, R, Okong, P, Senoga, I, Abwola, M, Baliruno, D, Bwomezi, J, Kasede, A, Mudoola, M, Namisi, R, Ssennono, F, Tuhirwe, S, Abongomera, G, Amone, G, Abach, J, Aciro, I, Arach, B, Kidega, P, Omongin, J, Ocung, E, Odong, W, Philliam, A, Alima, H, Ahimbisibwe, B, Atuhaire, E, Atukunda, F, Bekusike, G, Bulegyeya, A, Kahatano, D, Kamukama, S, Kyoshabire, J, Nassali, A, Mbonye, A, Naturinda, TM, Ndukukire, Nshabohurira, A, Ntawiha, H, Rogers, A, Tibyasa, M, Kiirya, S, Atwongyeire, D, Nankya, A, Draleku, C, Nakiboneka, D, Odoch, D, Lakidi, L, Ruganda, R, Abiriga, R, Mulindwa, M, Balmoi, F, Kafuma, S, Moriku, E, Hakim, J, Reid, A, Chidziva, E, Musoro, G, Warambwa, C, Tinago, G, Mutsai, S, Phiri, M, Mudzingwa, S, Bafana, T, Masore, V, Moyo, C, Nhema, R, Chitongo, S, Heyderman, Robert, Kabanga, Lucky, Kaunda, Symon, Kudzala, Aubrey, Lifa, Linly, Mallewa, Jane, Moore, Mike, Mtali, Chrissie, Musowa, George, Mwimaniwa, Grace, Sikwese, Rosemary, van Oosterhout, Joep, Ziwoya, Milton, Chimbaka, H, Chitete, B, Kamanga, S, Kayinga, T, Makwakwa, E, Mbiya, R, Mlenga, M, Mphande, T, Mtika, C, Mushani, G, Ndhlovu, O, Ngonga, M, Nkhana, I, Nyirenda, R, Cheruiyot, P, Kwobah, C, Ekiru, W Lokitala, Mokaya, M, Mudogo, A, Nzioka, A, Siika, A, Tanui, M, Wachira, S, Wools-Kaloustian, K, Alipalli, P, Chikatula, E, Kipaila, J, Kunda, I, Lakhi, S, Malama, J, Mufwambi, W, Mulenga, L, Mwaba, P, Mwamba, E, Mweemba, A, Namfukwe, M, Kerukadho, E, Ngwatu, B, Birungi, J, Paton, N, Boles, J, Burke, A, Castle, L, Ghuman, S, Kendall, L, Hoppe, A, Tebbs, S, Thomason, M, Thompson, J, Walker, S, Whittle, J, Wilkes, H, Young, N, Kapuya, C, Kyomuhendo, F, Kyakundi, D, Mkandawire, N, Mulambo, S, Senyonjo, S, Angus, B, Arenas-Pinto, A, Palfreeman, A, Post, F, Ishola, D, Arribas, J, Colebunders, R, Floridia, M, Giuliano, M, Mallon, P, Walsh, P, De Rosa, M, Rinaldi, E, Weller, I, Gilks, C, Kangewende, A, Luyirika, E, Miiro, F, Mwamba, P, Ojoo, S, Phiri, S, van Oosterhout, J, Wapakabulo, A, Peto, T, French, N, Matenga, J, Cloherty, G, van Wyk, J, Norton, M, Lehrman, S, Lamba, P, Malik, K, Rooney, J, Snowden, W, Villacian, J, Team, EARNEST Trial, UAM. Departamento de Medicina, and Instituto de Investigación Sanitaria Hospital Universitario de La Paz (IdiPAZ)

Subjects
Adult, Male, Mean squared error, Medicina, Intraclass correlation, HIV Infections, lcsh:Computer applications to medicine. Medical informatics, Standard deviation, 03 medical and health sciences, 0302 clinical medicine, EQ-5D, Outcome Assessment, Health Care, Statistics, Covariate, Humans, 030212 general & internal medicine, Least-Squares Analysis, Africa South of the Sahara, Health utility, Mathematics, Medical outcomes study HIV health survey, Research, 030503 health policy & services, 1. No poverty, Public Health, Environmental and Occupational Health, General Medicine, Health Surveys, Regression, 3. Good health, Mapping, Ordinary least squares, Quality of Life, lcsh:R858-859.7, Female, 0305 other medical science, Body mass index
Abstract

Background: Mapping of health-related quality-of-life measures to health utility values can facilitate cost-utility evaluation. Regression-based methods tend to lead to shrinkage of variance. This study aims to map the Medical Outcomes Study HIV Health Survey (MOS-HIV) to EuroQoL 5 Dimensions (EQ-5D-3 L) utility index, and to characterize the performance of three mapping methods, including ordinary least squares (OLS), equi-percentile method (EPM), and a recently proposed method called Mean Rank Method (MRM). Methods: This is a secondary analysis of data from a randomized HIV treatment trial. Baseline data from 421 participants were used to develop mapping functions. Follow-up data from 236 participants was used to validate the mapping functions. Results: In the training dataset, MRM and OLS, but not EPM, reproduced the observed mean utility (0.731). MRM, OLS and EPM under-estimated the standard deviation by 0.3, 26.6 and 1.7%, respectively. MRM had the lowest mean absolute error (0.143) and highest intraclass correlation coefficient (0.723) with the observed utility values, whereas OLS had the lowest mean squared error (0.038) and highest R-squared (0.542). Regressing the MRM- and OLS-mapped utility values upon body mass index and log-viral load gave covariate associations comparable to those estimated from the observed utility data (all P > 0.10). EPM did not achieve this property. Findings from the validation data were similar. Conclusions: Functions are available for mapping the MOS-HIV to the EQ-5D-3 L utility values. MRM and OLS were comparable in terms of agreement with the observed utility values at the individual level. MRM had better performance at the group level in terms of describing the utility distribution. Trial registration: NCT00988039. Registered 30 September 2009., The EARNEST trial was funded by the European and Developing Countries Clinical Trials Partnership (EDCTP, Grant Code: IP.2007.33011.003) with contributions from the Medical Research Council, UK; Institito de Salud Carlos III, Spain (Grant A107/90015); Irish Aid, Ireland; Swedish International Development Cooperation Agency (SIDA), Sweden; Instituto Superiore di Sanita (ISS), Italy; The World Health Organisation; and Merck, USA. Substantive in-kind contributions were made by the Medical Research Council Clinical Trials Unit, UK [MC_UU_12023/23], CINECA, Bologna, Italy, Janssen Diagnostics, Beerse, Belgium; GSK/ViiV Healthcare Ltd., UK; Abbott Laboratories, USA. Trial medication was donated by AbbVie, Merck, Pfizer, GSK and Gilead. The Malawi-Liverpool-Wellcome Trust Clinical Research Programme, University of Malawi

Published
2019

34. Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis

Author

Ratmann, O., Grabowski, M.K., Hall, M., Golubchik, T., Wymant, C., Abeler-Dörner, L., Bonsall, D., Hoppe, A., Brown, A.L., de Oliveira, T., Gall, A., Kellam, P., Pillay, D., Kagaayi, J., Kigozi, G., Quinn, T.C., Wawer, M.J., Laeyendecker, O., Serwadda, D., Gray, R.H., Fraser, C., Ayles, H., Bowden, R., Calvez, V., Cohen, M., Dennis, A., Essex, M., Fidler, S., Frampton, D., Hayes, R., Herbeck, J.T., Kaleebu, P., Kityo, C., Lingappa, J., Novitsky, V., Paton, N., Rambaut, A., Seeley, J., Ssemwanga, D., Tanser, F., Nakigozi, G., Ssekubugu, R., Nalugoda, F., Lutalo, T., Galiwango, R., Makumbi, F., Sewankambo, N.K., R. Tobian, A.A., Reynolds, S.J., Chang, L.W., Nabukalu, D., Ndyanabo, A., Ssekasanvu, J., Nakawooya, H., Nakukumba, J., Kigozi, G.N., Nantume, B.S., Resty, N., Kambasu, J., Nalugemwa, M., Nakabuye, R., Ssebanobe, L., Nankinga, J., Kayiira, A., Nanfuka, G., Ahimbisibwe, R., Tomusange, S., Galiwango, R.M., Kalibbali, S., Nakalanzi, M., Otobi, J.O., Ankunda, D., Ssembatya, J.L., Ssemanda, J.B., Kairania, R., Kato, E., Kisakye, A., Batte, J., Ludigo, J., Nampijja, A., Watya, S., Nehemia, K., Anyokot, M., Mwinike, J., Kibumba, G., Ssebowa, P., Mondo, G., Wasswa, F., Nantongo, A., Kakembo, R., Galiwango, J., Ssemango, G., Redd, A.D., Santelli, J., Kennedy, C.E., and Wagman, J.

Abstract

To prevent new infections with human immunodeficiency virus type 1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting interventions to populations that are at high risk of acquiring and passing on the virus. Yet it is often unclear who and where these ‘source’ populations are. Here we demonstrate how viral deep-sequencing can be used to reconstruct HIV-1 transmission networks and to infer the direction of transmission in these networks. We are able to deep-sequence virus from a large population-based sample of infected individuals in Rakai District, Uganda, reconstruct partial transmission networks, and infer the direction of transmission within them at an estimated error rate of 16.3% [8.8–28.3%]. With this error rate, deep-sequence phylogenetics cannot be used against individuals in legal contexts, but is sufficiently low for population-level inferences into the sources of epidemic spread. The technique presents new opportunities for characterizing source populations and for targeting of HIV-1 prevention interventions in Africa.

Published
2019

35. DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis

Author

Yang, Y, Walker, TM, Walker, AS, Wilson, DJ, Peto, TEA, Crook, DW, Shamout, F, Zhu, T, Clifton, DA, Arandjelovic, I, Comas, I, Farhat, MR, Gao, Q, Sintchenko, V, Van Soolingen, D, Hoosdally, S, Cruz, ALG, Carter, J, Grazian, C, Earle, SG, Kouchaki, S, Fowler, PW, Iqbal, Z, Hunt, M, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Kohl, T, Merker, M, Hoffmann, H, Molodtsov, N, Plesnik, S, Ismail, N, Omar, SV, Thwaites, G, Thuong, NTT, Nhung, HN, Srinivasan, V, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Ma, A, Liu, C, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, M-H, Lin, W-H, Ferrazoli, L, De Oliveira, RS, and Wellcome Trust

Subjects
DRUG-RESISTANCE, Technology, Biochemistry & Molecular Biology, Science & Technology, INFORMATION, MUTATIONS, CRyPTIC Consortium, Bioinformatics, Statistics & Probability, SUSCEPTIBILITY, 06 Biological Sciences, GENE, Biochemical Research Methods, CLASSIFICATION, DIMENSIONALITY REDUCTION, Biotechnology & Applied Microbiology, Physical Sciences, Computer Science, Computer Science, Interdisciplinary Applications, Mathematical & Computational Biology, 08 Information and Computing Sciences, Life Sciences & Biomedicine, Mathematics, 01 Mathematical Sciences
Abstract

Motivation Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. Availability and implementation The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php.

Published
2019

36. GenomegaMap: within-species genome-widedN/dSestimation from over 10,000 genomes

Author

Wilson, DJ, Crook, DW, Peto, TEA, Walker, AS, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, C, Earle, SG, Kouchaki, S, Lachapelle, A, Yang, Y, Clifton, DA, Fowler, PW, Iqbal, Z, Hunt, M, Knaggs, J, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Kohl, TA, Merker, M, Hoffmann, H, Todt, K, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Thwaites, G, Thuong, TNT, Ngoc, NH, Srinivasan, V, Walker, TM, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Liu, C, Ma, A, Zhu, B, Laurenson, I, Claxton, P, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, M-H, Lin, W-H, Ferrazoli, L, De Oliveira, RS, Arandjelovic, I, Chaiprasert, A, Comas, I, Roig, CJ, Drobniewski, FA, Farhat, MR, Gao, Q, Hee, ROT, Sintchenko, V, Supply, P, Van Soolingen, D, University of Oxford, Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 (CIIL), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), D.J.W. is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (grant no. 101237/Z/13/B) and is a Big Data Institute Robertson Fellow. The CRyPTIC Consortium was supported by grants from the Bill and Melinda Gates Foundation (OPP1133541) and a Wellcome Trust/Newton Fund-MRC Collaborative Award (200205/Z/15/Z). F.A.D. was supported by the Imperial Biomedical Research Centre., Members of the CRyPTIC Consortium : Derrick W. Crook, Timothy E.A. Peto, A. Sarah Walker, Sarah J. Hoosdally, Ana L. Gibertoni Cruz, Joshua Carter, Clara Grazian, Sarah G. Earle, Samaneh Kouchaki, Alexander Lachapelle, Yang Yang, David A. Clifton, and Philip W. Fowler, University of Oxford, Zamin Iqbal, Martin Hunt, and Jeffrey Knaggs, European Bioinformatics Institute, E. Grace Smith, Priti Rathod, Lisa Jarrett, and Daniela Matias, Public Health England, Birmingham, Daniela M. Cirillo, Emanuele Borroni, Simone Battaglia, Arash Ghodousi, Andrea Spitaleri, and Andrea Cabibbe, Emerging Bacterial Pathogens Unit, IRCCS San Raffaele Scientific Institute, Milan, Sabira Tahseen, National Tuberculosis Control Program Pakistan, Islamabad, Kayzad Nilgiriwala and Sanchi Shah, The Foundation for Medical Research, Mumbai, Camilla Rodrigues, Priti Kambli, Utkarsha Surve, and Rukhsar Khot, P.D. Hinduja National Hospital and Medical Research Centre, Mumbai, Stefan Niemann, Thomas A. Kohl, and Matthias Merker, Research Center Borstel, Harald Hoffmann, Katharina Todt, and Sara Plesnik, Institute of Microbiology & Laboratory Medicine, IML Red, Gauting, Nazir Ismail, Shaheed Vally Omar, and Lavania Joseph, National Institute for Communicable Diseases, Johannesburg, Guy Thwaites, Thuong Nguyen Thuy Thuong, Nhung Hoang Ngoc, Vijay Srinivasan, and Timothy M. Walker, Oxford University Clinical Research Unit, Ho Chi Minh City, David Moore, Jorge Coronel and Walter Solano, London School of Hygiene and Tropical Medicine and Universidad Peruana Cayetano Heredá, Lima, George F. Gao, Guangxue He, Yanlin Zhao, and Chunfa Liu, China CDC, Beijing, Aijing Ma, Shenzhen Third People’s Hospital, Shenzhen, Baoli Zhu, Institute of Microbiology, CAS, Beijing, Ian Laurenson and Pauline Claxton, Scottish Mycobacteria Reference Laboratory, Edinburgh, Anastasia Koch, Robert Wilkinson, University of Cape Town, Ajit Lalvani, Imperial College London, James Posey, CDC Atlanta, Jennifer Gardy, University of British Columbia, Jim Werngren, Public Health Agency of Sweden, Nicholas Paton, National University of Singapore, Ruwen Jou, Mei-Hua Wu, Wan-Hsuan Lin, CDC Taiwan, Lucilaine Ferrazoli, Rosangela Siqueira de Oliveira, Institute Adolfo Lutz, São Paulo. Authors contributing to the CRyPTIC Consortium are (in alphabetical order): Irena Arandjelovic (Institute of Microbiology and Immunology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia), Angkana Chaiprasert (Faculty of Medicine Siriraj Hospital, Mahidol University, Thailand), Iñaki Comas (Instituto de Biomedicina de Valencia [IBV-CSIC], Calle Jaime Roig, Valencia, Spain, FISABIO Public Health, Valencia, Spain, CIBER in Epidemiology and Public Health, Madrid, Spain), Francis A. Drobniewski (Imperial College, London, UK), Maha R. Farhat (Harvard Medical School, Boston, USA), Qian Gao (Shanghai Medical College, Fudan University, Shanghai, China), Rick Ong Twee Hee (Saw Swee Hock School of Public Health, National University of Singapore, Singapore), Vitali Sintchenko (Centre for Infectious Diseases and Microbiology—Public Health, University of Sydney, Sydney, Australia), Philip Supply (Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019—UMR 8204—CIIL—Centre d’Infection et d’Immunité de Lille, F-59000 Lille, France), and Dick van Soolingen (National Institute for Public Health and the Environment [RIVM], Bilthoven, The Netherlands)., Supply, Philip, Consortium, CRyPTIC, University of Oxford [Oxford], Centre National de la Recherche Scientifique (CNRS)-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Royal Society (UK), Bill & Melinda Gates Foundation, Newton Fund, Comas, Iñaki [0000-0001-5504-9408], and Comas, Iñaki

Subjects
Natural selection, [SDV]Life Sciences [q-bio], Population genetics, adaptation, Computational biology, Biology, AcademicSubjects/SCI01180, 0601 Biochemistry and Cell Biology, Genome, Coalescent theory, DEAD-box RNA Helicases, Big data, 03 medical and health sciences, 0603 Evolutionary Biology, big data, Parent-dependent mutation, Genetics, dN/dS, Adaptation, Selection, Genetic, Molecular Biology, Allele frequency, Ecology, Evolution, Behavior and Systematics, Silent Mutation, Selection (genetic algorithm), 030304 developmental biology, Evolutionary Biology, 0604 Genetics, 0303 health sciences, Models, Genetic, Phylogenetic tree, 030306 microbiology, AcademicSubjects/SCI01130, natural selection, Mycobacterium tuberculosis, Recombination, Resources, recombination, 3. Good health, [SDV] Life Sciences [q-bio], Genetic Techniques, Mutation (genetic algorithm), parent-dependent mutation, Genome, Bacterial
Abstract

11 págs, 4 figuras y fórmulas matemáticas. Material suplementario en: http://dx.doi.org/10.1093/molbev/msaa069, The dN/dS ratio provides evidence of adaptation or functional constraint in protein-coding genes by quantifying the relative excess or deficit of amino acid-replacing versus silent nucleotide variation. Inexpensive sequencing promises a better understanding of parameters, such as dN/dS, but analyzing very large data sets poses a major statistical challenge. Here, I introduce genomegaMap for estimating within-species genome-wide variation in dN/dS, and I apply it to 3,979 genes across 10,209 tuberculosis genomes to characterize the selection pressures shaping this global pathogen. GenomegaMap is a phylogeny-free method that addresses two major problems with existing approaches: 1) It is fast no matter how large the sample size and 2) it is robust to recombination, which causes phylogenetic methods to report artefactual signals of adaptation. GenomegaMap uses population genetics theory to approximate the distribution of allele frequencies under general, parent-dependent mutation models. Coalescent simulations show that substitution parameters are well estimated even when genomegaMap's simplifying assumption of independence among sites is violated. I demonstrate the ability of genomegaMap to detect genuine signatures of selection at antimicrobial resistance-conferring substitutions in Mycobacterium tuberculosis and describe a novel signature of selection in the cold-shock DEAD-box protein A gene deaD/csdA. The genomegaMap approach helps accelerate the exploitation of big data for gaining new insights into evolution within species., D.J.W. is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (grant no. 101237/Z/13/B) and is a Big Data Institute Robertson Fellow. The CRyPTIC Consortium was supported by grants from the Bill and Melinda Gates Foundation (OPP1133541) and a Wellcome Trust/Newton Fund-MRC Collaborative Award (200205/Z/15/Z). F.A.D. was supported by the Imperial Biomedical Research Centre

Published
2019

46. Application of machine learning techniques to tuberculosis drug resistance analysis

Author

Kouchaki, S, Yang, YY, Walker, TM, Walker, AS, Wilson, DJ, Peto, TEA, Crook, DW, Clifton, DA, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, C, Fowler, PW, Iqbal, Z, Hunt, M, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Kohl, T, Merker, M, Hoffmann, H, Molodtsov, N, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Marubini, E, Thwaites, G, Thuong, TNT, Ngoc, NH, Srinivasan, V, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Ma, A, Liu, C, Zhu, B, Laurenson, I, Claxton, P, Wilkinson, RJ, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, De Oliveira, RS, Arandjelovic, I, Comas, I, Drobniewski, F, Gao, Q, Sintchenko, V, Supply, P, Van Soolingen, D, Kouchaki, S, Yang, YY, Walker, TM, Walker, AS, Wilson, DJ, Peto, TEA, Crook, DW, Clifton, DA, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, C, Fowler, PW, Iqbal, Z, Hunt, M, Smith, EG, Rathod, P, Jarrett, L, Matias, D, Cirillo, DM, Borroni, E, Battaglia, S, Ghodousi, A, Spitaleri, A, Cabibbe, A, Tahseen, S, Nilgiriwala, K, Shah, S, Rodrigues, C, Kambli, P, Surve, U, Khot, R, Niemann, S, Kohl, T, Merker, M, Hoffmann, H, Molodtsov, N, Plesnik, S, Ismail, N, Omar, SV, Joseph, L, Marubini, E, Thwaites, G, Thuong, TNT, Ngoc, NH, Srinivasan, V, Moore, D, Coronel, J, Solano, W, Gao, GF, He, G, Zhao, Y, Ma, A, Liu, C, Zhu, B, Laurenson, I, Claxton, P, Wilkinson, RJ, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, De Oliveira, RS, Arandjelovic, I, Comas, I, Drobniewski, F, Gao, Q, Sintchenko, V, Supply, P, and Van Soolingen, D

Abstract

Motivation: Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance prediction and resistance marker identification. Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13 402 isolates collected from 16 countries across 6 continents and tested 11 drugs. Results: Compared to conventional molecular diagnostic test, area under curve of the best machine learning classifier increased for all drugs especially by 23.11%, 15.22% and 10.14% for pyrazinamide, ciprofloxacin and ofloxacin, respectively (P < 0.01). Logistic regression and gradient tree boosting found to perform better than other techniques. Moreover, logistic regression/gradient tree boosting with a sparse principal component analysis/non-negative matrix factorization step compared with the classifier alone enhanced the best performance in terms of F1-score by 12.54%, 4.61%, 7.45% and 9.58% for amikacin, moxifloxacin, ofloxacin and capreomycin, respectively, as well increasing area under curve for amikacin and capreomycin. Results provided a comprehensive comparison of various techniques and confirmed the application of machine learning for better prediction of the large diverse tuberculosis data. Furthermore, mutation ranking showed the possibility of finding new resistance/susceptible markers.

Published
2019

48. CADRE: the Central Aspergillus Data REpository

Author

Mabey, J. E., Anderson, M. J., Giles, P. F., Miller, C. J., Attwood, T. K., Paton, N. W., Bornberg-Bauer, E., Robson, G. D., Oliver, S. G., and Denning, D. W.

Published
2004

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