Biomedical Engineering Research

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    Supplemental Data from: Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide
    Diebold, Peter J.; Rhee, Matthew; Shi, Qiaojuan; Nguyen, Vinh Trung; Umrani, Fayaz; Ahmed, Sheraz; Kulkarni, Vandana; Deshpande, Prasad; Alexander, Mallika; Ngo, Thi Hoa; Christakis, Nicholas; Iqbal, Najeeha; Ali, Asad; Mathad, Jyoti; Brito, Ilana Lauren (2023-11)
    The acquisition of antimicrobial resistance (AR) genes has rendered important pathogens nearly or fully unresponsive to antibiotics. It has been suggested that pathogens acquire AR traits from the gut microbiota, which collectively serve as a global reservoir for AR genes conferring resistance to all classes of antibiotics. However, only a subset of AR genes confers resistance to clinically relevant antibiotics, and, although these AR gene profiles are well-characterized for common pathogens, less is known about their taxonomic associations and transfer potential within diverse members of the gut microbiota. We examined a collection of 14,850 human metagenomes and 1,666 environmental metagenomes from 33 countries, in addition to nearly 600,000 isolate genomes, to gain insight into the global prevalence and taxonomic range of clinically relevant AR genes. We find that several of the most concerning AR genes, such as those encoding the cephalosporinase CTX-M and carbapenemases KPC, IMP, NDM, and VIM, remain taxonomically restricted to Proteobacteria. Even cfiA, the most common carbapenemase gene within the human gut microbiome, remains tightly restricted to Bacteroides, despite being found on a mobilizable plasmid. We confirmed these findings in gut microbiome samples from India, Honduras, Pakistan, and Vietnam, using a high-sensitivity singlecell fusion PCR approach. Focusing on a set of genes encoding carbapenemases and cephalosporinases, thus far restricted to Bacteroides species, we find that few mutations are required for efficacy in a different phylum, raising the question of why these genes have not spread more widely. Overall, these data suggest that globally prevalent, clinically relevant AR genes have not yet established themselves across diverse commensal gut microbiota.
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    Data from: An in silico-in vitro pipeline for drug cardiotoxicity screening identifies ionic proarrhythmia mechanisms
    Clark, Alexander P.; Wei, Siyu; Kalola, Darshan; Krogh-Madsen, Trine; Christini, David J. (2022-06-23)
    Background and Purpose: Before advancing to clinical trials, new drugs are screened for their proarrhythmic potential using a method that is overly conservative and provides limited mechanistic insight. The shortcomings of this approach can lead to the misclassification of beneficial drugs as proarrhythmic. Experimental Approach: An in silico-in vitro pipeline was developed to circumvent these shortcomings. A computational human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CMs) model was used as part of a genetic algorithm to design experiments, specifically electrophysiological voltage-clamp (VC) protocols, to identify which of several cardiac ion channels were blocked during in vitro drug studies. Such VC data, along with dynamically clamped action potentials (AP), were acquired from iPSC-CMs before and after treatment with a control solution or a low- (verapamil), intermediate- (cisapride), or high-risk (quinidine or quinine) drug. Key Results: Significant AP prolongation (a proarrhythmia marker) was seen in response to both high-risk drugs. The VC protocol identified block of IKr (a source of arrhythmias) by all strong IKr blockers, including cisapride, quinidine, and quinine. The protocol also detected block of ICaL by verapamil and Ito by quinidine. Further demonstrating the power of the approach, the VC data uncovered a previously unidentified funny current (If) block by quinine, which was confirmed with experiments using a HEK-293 expression system and automated patch-clamp. Conclusion and Implications: We developed an in silico-in vitro pipeline that simultaneously identifies proarrhythmia risk and mechanism of ion channel-blocking drugs. The approach offers a new tool for evaluating cardiotoxicity in the preclinical drug screening phase.