Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Antimicrobial resistance prediction by bacterial genome-wide-association-study in non-fermenting bacilli with critical priority (Pseudomonas aeruginosa and acinetobacter baumannii).(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-07) Barlandas Quintana, Erick Alan; MARTINEZ LEDESMA, JUAN EMMANUEL; 200096; Martinez Ledesma, Juan Emmanuel; puemcuervo; González Mendoza, Miguel; Garza González, Elvira; School of Engineering and Sciences; Campus Estado de México; Cuevas Díaz Durán, RaquelAntimicrobial resistance (AMR) (or drug resistance) is a natural phenomenon where microor- ganisms change their molecular, physical, or chemical structures to resist the drugs created by infections. The World Health Organization (WHO) had released for the first time a list of Multidrug-Resistant Bacteria (MRB) that pose the greatest threat to human health and for which new antibiotics are desperately needed. Acinetobacter baumannii and Pseudomonas aeruginosa resistant to carbapenems are part of the Gram-negative non-fermenting bacilli group with critical priority according to the WHO. For this, the final research purpose was to create and train a bioinformatic study capable of finding critical k-mers that could differentiate those strains of P. aeruginosa and A. baumannii resistant to carbapenems. Four k-mers sizes were performed for each bacterium (12, 14, 16, and 18), and two training and testing (70:30 and 80:20) schemas were used over seven different machine learning algorithms: Random Forrest, Adaboost, Xgboost, Decision Trees, Bagging Classifier, Support Vector Machine, and KNN. For both bacteria, the best models were obtained when using a k-mer length of 12. In the case of Acinetobacter baumannii, the best models obtained an accuracy of 0.99 for testing. Moreover, for Pseudomonas aeruginosa, the best accuracy obtained was 0.93 when us- ing Bagging Classifier. To investigate the sequences of the k-mers obtained, the National Cen- ter for Biotechnology Information (NCBI) Basic Local Alignment Search Tool BLAST was used. Ten to twenty sequences built with the k-mers were investigated for each model. When using a k-mer length of 12 for A. baumannii, 18 out of 20 sequences represented a crucial sequence in carbapenems (meropenem and imipenem) resistance. In the case of P. aerugi- nosa, 16 out of 20 sequences represented a key sequence. To complement this research, a Dynamic Programming algorithm was used to find changes over the reference genome that could explain the carbapenems resistance within the resistant genomes. Not all the resistant k-mer sequences were found over the reference genome, as some of them could be acquired by horizontal transference (Conjugation, Transformation, or Transduction inheritance). Fur- ther investigation over these sequences can be applied in creating new directed antibiotics or detecting easily resistant strains of Pseudomonas aeruginosa or Acinetobacter baumannii resistant to carbapenems.