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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
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2. PARATHYROID ADENOMA PRESENTING AS MULTIPLE BROWN TUMORS AND SEVERE ASYMPTOMATIC HYPERCALCEMIA.

Author

Wasnik, D. V., Khot, R. S., Joshi, P. P., Rathod, B. D., Narang, U., and Ratnaparkhi, C.

Subjects
*PARATHYROID glands, *HYPERCALCEMIA, *ADENOMA, *CEREBRAL cortical thinning, *BONE metabolism
Abstract

Introduction. Primary hyperparathyroidism may have several presentations, varying from an incidental asymptomatic biochemical finding to gastrointestinal, psychiatric, renal and bone manifestations. Brown tumors are rare non-neoplastic lesions because of abnormal bone metabolism. Herein, we describe a patient who presented with lytic bony lesions and severe asymptomatic hypercalcemia due to parathyroid adenoma. Case presentation. A 38-year-old male presented with multiple painful bony lesions over upper and lower limbs. Radiographs of long bones showed multiple lytic lesions with cortical thinning. Investigations revealed hypercalcemia and hyperparathyroidism. A radionuclide scan showed parathyroid adenoma. The patient was treated for hypercalcemia and a parathyroidectomy was performed. Conclusions. In a patient presenting with multiple bony swellings and asymptomatic hypercalcemia, hyperparathyroidism should be suspected. Parathyroid adenoma is a treatable cause of primary hyperparathyroidism. [ABSTRACT FROM AUTHOR]

Published
2023
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3. 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
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4. 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

5. 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, Clara ; https://orcid.org/0000-0002-4967-7720, 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, Clara ; https://orcid.org/0000-0002-4967-7720, 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

Abstract

Resistance prediction and mutation ranking are important tasks in the analysis of Tuberculosis sequence data. Due to standard regimens for the use of first-line antibiotics, resistance co-occurrence, in which samples are resistant to multiple drugs, is common. Analysing all drugs simultaneously should therefore enable patterns reflecting resistance co-occurrence to be exploited for resistance prediction. Here, multi-label random forest (MLRF) models are compared with single-label random forest (SLRF) for both predicting phenotypic resistance from whole genome sequences and identifying important mutations for better prediction of four first-line drugs in a dataset of 13402 Mycobacterium tuberculosis isolates. Results confirmed that MLRFs can improve performance compared to conventional clinical methods (by 18.10%) and SLRFs (by 0.91%). In addition, we identified a list of candidate mutations that are important for resistance prediction or that are related to resistance co-occurrence. Moreover, we found that retraining our analysis to a subset of top-ranked mutations was sufficient to achieve satisfactory performance. The source code can be found at http://www.robots.ox.ac.uk/~davidc/code.php.

Published
2020

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

Author

Rosenberg, Michael, Wilson, D, Crook, DW, Peto, TEA, Walker, AS, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, Clara ; https://orcid.org/0000-0002-4967-7720, 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, Rosenberg, Michael, Wilson, D, Crook, DW, Peto, TEA, Walker, AS, Hoosdally, SJ, Gibertoni Cruz, AL, Carter, J, Grazian, Clara ; https://orcid.org/0000-0002-4967-7720, 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

7. 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

8. 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
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9. Application of machine learning techniques to tuberculosis drug resistance analysis

Author

Wren, Jonathan, 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 ; https://orcid.org/0000-0002-4967-7720, 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, Wren, Jonathan, 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 ; https://orcid.org/0000-0002-4967-7720, 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

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

Author

Hancock, John, 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, Soolingen, D, Hoosdally, S, Cruz, ALG, Carter, J, Grazian, C ; https://orcid.org/0000-0002-4967-7720, 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, 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, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, de Oliveira, RS, Hancock, John, 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, Soolingen, D, Hoosdally, S, Cruz, ALG, Carter, J, Grazian, C ; https://orcid.org/0000-0002-4967-7720, 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, 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, Koch, A, Wilkinson, R, Lalvani, A, Posey, J, Gardy, J, Werngren, J, Paton, N, Jou, R, Wu, MH, Lin, WH, Ferrazoli, L, and de Oliveira, RS

Abstract

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. Supplementary information: Supplementary data are available at Bioinformatics online.

Published
2019

12. Game jam

Author

Chatham, A., Schouten, B.A.M., Toprak, C., Mueller, F., Deen, M., Bernhaupt, R., Khot, R., Pijnappel, S., Chatham, A., Schouten, B.A.M., Toprak, C., Mueller, F., Deen, M., Bernhaupt, R., Khot, R., and Pijnappel, S.

Abstract

Game Jams - energized, fast-paced get-togethers of developers and artists to make digital games - have recently emerged as a way to generate and inspire novel game ideas and new ways of thinking. We intend to introduce the CHI community to this collaborative, fast-paced method of design by holding a game jam with an emphasis on developing novel user inputs as a way to explore HCI and to connect participants from diverse backgrounds. This game jam will introduce a successful model from a related field to the HCI agenda while developing collaborations between the two growing areas.

Published
2013

18. A multi-species occupancy modeling approach to access the impacts of land use and land cover on terrestrial vertebrates in the Mumbai Metropolitan Region (MMR), Western Ghats, India.

Author

Bajaru S, Pal S, Prabhu M, Patel P, Khot R, and Apte D

Subjects
Amphibians, Animals, Bayes Theorem, Biodiversity, Birds, Body Size, Carnivora, Ecosystem, Environmental Monitoring, Forests, India, Life History Traits, Mammals, Models, Biological, Models, Theoretical, Reptiles, Urbanization, Vertebrates
Abstract

Urbanization is one of the main drivers in the conversion of natural habitats into different land use and land cover types (LULC) which threaten the local as well as global biodiversity. This impact is particularly alarming in tropical countries like India, where ~18% of the world's population live, and its ever-growing economy (i.e., industrial development) expanded urban areas by several folds. We undertook this study to examine the impacts of urbanization (i.e., LULC) on terrestrial vertebrates (mammals, birds, reptiles, and amphibians) in the Mumbai Metropolitan Region (MMR), Western Ghats, India. We sampled different habitats ranged from highly disturbed urban areas to less disturbed forested areas. Multiple sampling methods such as quadrat sampling, line transect, point count, and camera trapping were used to quantify the target taxa. We used multi-species occupancy modeling in the Bayesian framework to estimate detection probability and occupancy and to assess the effect of various LULC on different species. All four groups showed a significant negative impact of increasing anthropogenic habitat cover on occupancy. Out of 213 species detected in this study, 96% of mammals, 85% of birds, 93.75% of amphibians, and 69.43% of reptiles showed a negative effect of anthropogenic habitat cover. Evidence suggests that historical and recent human disturbances could have played an important role in transforming this area from semi-evergreen and moist deciduous forest to open, scrubby, dry deciduous, and fire-prone landscape. This might be the reason for the high occupancy of open and degraded forest habitat preferring species in our study area. We recommend species-rich areas in the MMR, e.g., Karnala Bird Sanctuary (KBS) and Prabalgad-Matheran-Malanggad Hill Range (PMMHR), must be conserved through habitat restoration, ecotourism, public awareness, and policymaking., Competing Interests: The authors have declared that no competing interests exist.

Published
2020
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19. Rapid detection of the commonly encountered carbapenemases (New Delhi metallo-β-lactamase, OXA-48/181) directly from various clinical samples using multiplex real-time polymerase chain reaction assay.

Author

Kazi M, Khot R, Shetty A, and Rodrigues C

Subjects
Bacterial Proteins genetics, Gram-Negative Bacterial Infections microbiology, Humans, Multiplex Polymerase Chain Reaction methods, Sensitivity and Specificity, Time Factors, beta-Lactamases genetics, Bacterial Proteins analysis, Real-Time Polymerase Chain Reaction methods, Specimen Handling methods, beta-Lactamases analysis
Abstract

Background: Resistance due to New Delhi metallo-β-lactamase (NDM) and OXA-48/181 continues to emerge as a threat which is associated with nosocomial outbreaks and is a serious healthcare concern. Phenotypic detection being laborious and time-consuming requires rapid detection of NDM and OXA-48/181, which is achieved through real-time polymerase chain reaction (RT-PCR)., Materials and Methods: In this study, RT-PCR assay was developed to simultaneously detect NDM and OXA-48/181. The assay was validated on 102 non-duplicate, phenotypically characterised clinical samples., Results: The assay showed a sensitivity and specificity of 97% and 100% for the detection of carbapenemases in comparison to conventional PCR. The in-house developed multiplex RT-PCR would help to rule-in the presence of NDM and OXA-48/181., Conclusions: Rapid detection of these carbapenemases would be assist in better patient management, in terms of accurate antimicrobial treatment, help in cohorting infected from uninfected patient to prevent spread., Competing Interests: There are no conflicts of interest

Published
2018
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