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Item The global distribution and environmental drivers of the soil antibiotic resistomeAutores: Delgado-Baquerizo, Manuel; Hu, Hang-Wei; Maestre, Fernando T.; Guerra, Carlos A.; Eisenhauer, Nico; Eldridge, David J.; Zhu, Yong-Guan; Chen, Qing-Lin; Trivedi, Pankaj; Du, Shuai; Makhalanyane, Thulani P.; Verma, Jay Prakash; Gozalo, Beatriz; Ochoa, Victoria; Asensio, Sergio; Wang, Ling; Zaady, Eli; Illan, Javier G.; Siebe, Christina; Grebenc, Tine; Zhou, Xiaobing; Liu, Yu-Rong; Bamigboye, Adebola R.; Blanco-Pastor, José L.; Durán, Jorge; Rodríguez, Alexandra; Mamet, Steven; Alfaro, Fernando; Abades, Sebastián; Teixido, Alberto L.; Penaloza-Bojaca, Gabriel F.; Molina-Montenegro, Marco A.; Torres-Díaz, Cristian; Pérez, Cecilia; Gallardo, Antonio; García-Velazquez, Laura; Hayes, Patrick E.; Neuhauser, Sigrid; He, Ji-ZhengBackground: Little is known about the global distribution and environmental drivers of key microbial functional traits such as antibiotic resistance genes (ARGs). Soils are one of Earth's largest reservoirs of ARGs, which are integral for soil microbial competition, and have potential implications for plant and human health. Yet, their diversity and global patterns remain poorly described. Here, we analyzed 285 ARGs in soils from 1012 sites across all continents and created the first global atlas with the distributions of topsoil ARGs. Results: We show that ARGs peaked in high latitude cold and boreal forests. Climatic seasonality and mobile genetic elements, associated with the transmission of antibiotic resistance, were also key drivers of their global distribution. Dominant ARGs were mainly related to multidrug resistance genes and efflux pump machineries. We further pinpointed the global hotspots of the diversity and proportions of soil ARGs. Conclusions: Together, our work provides the foundation for a better understanding of the ecology and global distribution of the environmental soil antibiotic resistome.Item Rational Design of Hydrogels for Cationic Antimicrobial Peptide Delivery: A Molecular Modeling ApproachAutores: Pereira, Alfredo; Valdés-Muñoz, Elizabeth; Marican, Adolfo; Cabrera-Barjas, Gustavo; Vijayakumar, Sekar; Valdés, Óscar; Rafael, Diana; Andrade, Fernanda; Abaca, Paulina; Bustos, Daniel; Durán-Lara, Esteban F.In light of the growing bacterial resistance to antibiotics and in the absence of the development of new antimicrobial agents, numerous antimicrobial delivery systems over the past decades have been developed with the aim to provide new alternatives to the antimicrobial treatment of infections. However, there are few studies that focus on the development of a rational design that is accurate based on a set of theoretical-computational methods that permit the prediction and the understanding of hydrogels regarding their interaction with cationic antimicrobial peptides (cAMPs) as potential sustained and localized delivery nanoplatforms of cAMP. To this aim, we employed docking and Molecular Dynamics simulations (MDs) that allowed us to propose a rational selection of hydrogel candidates based on the propensity to form intermolecular interactions with two types of cAMPs (MP-L and NCP-3a). For the design of the hydrogels, specific building blocks were considered, named monomers (MN), co-monomers (CM), and cross-linkers (CL). These building blocks were ranked by considering the interaction with two peptides (MP-L and NCP-3a) as receptors. The better proposed hydrogel candidates were composed of MN3-CM7-CL1 and MN4-CM5-CL1 termed HG1 and HG2, respectively. The results obtained by MDs show that the biggest differences between the hydrogels are in the CM, where HG2 has two carboxylic acids that allow the forming of greater amounts of hydrogen bonds (HBs) and salt bridges (SBs) with both cAMPs. Therefore, using theoretical-computational methods allowed for the obtaining of the best virtual hydrogel candidates according to affinity with the specific cAMP. In conclusion, this study showed that HG2 is the better candidate for future in vitro or in vivo experiments due to its possible capacity as a depot system and its potential sustained and localized delivery system of cAMP.Item Integrating Machine Learning with MALDI-TOF Mass Spectrometry for Rapid and Accurate Antimicrobial Resistance Detection in Clinical PathogensAutores: López Cortés, Xaviera A.; Manríquez Troncoso, José M.; Yáñez Sepúlveda, Alejandra; Suazo Soto, Patricio MaximilianoAntimicrobial resistance (AMR) is one of the most pressing public health challenges of the 21st century. This study aims to evaluate the efficacy of mass spectral data generated by VITEK® MS instruments for predicting antibiotic resistance in Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae using machine learning algorithms. Additionally, the potential of pre-trained models was assessed through transfer learning analysis. A dataset comprising 2229 mass spectra was collected, and classification algorithms, including Support Vector Machines, Random Forest, Logistic Regression, and CatBoost, were applied to predict resistance. CatBoost demonstrated a clear advantage over the other models, effectively handling complex non-linear relationships within the spectra and achieving an AUROC of 0.91 and an F1 score of 0.78 for E. coli. In contrast, transfer learning yielded suboptimal results. These findings highlight the potential of gradient-boosting techniques to enhance resistance prediction, particularly with data from less conventional platforms like VITEK® MS. Furthermore, the identification of specific biomarkers using SHAP values indicates promising potential for clinical applications in early diagnosis. Future efforts focused on standardizing data and refining algorithms could expand the utility of these approaches across diverse clinical environments, supporting the global fight against AMR.
