Laboratory evolution studies, particularly with , have yielded invaluable insights into the mechanisms of antimicrobial resistance (AMR). Recent investigations have illuminated that, with repetitive antibiotic exposures, bacterial populations will adapt and eventually become tolerant and resistant to the drugs. Through intensive analyses, these inquiries have unveiled instances of convergent evolution across diverse antibiotics, the pleiotropic effects of resistance mutations, and the role played by loss-of-function mutations in the evolutionary landscape. Moreover, a quantitative analysis of multidrug combinations has shed light on collateral sensitivity, revealing specific drug combinations capable of suppressing the acquisition of resistance. This review article introduces the methodologies employed in the laboratory evolution of AMR in bacteria and presents recent discoveries concerning AMR mechanisms derived from laboratory evolution. Additionally, the review outlines the application of laboratory evolution in endeavors to formulate rational treatment strategies.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10812413 | PMC |
http://dx.doi.org/10.3390/antibiotics13010094 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Laboratory of Precision Medicine and Biopharmaceuticals, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Recurrent missense mutations in the human epidermal growth factor receptor 2 (HER2) have been identified across various human cancers. Among these mutations, the active S310F mutation in the HER2 extracellular domain stands out as not only oncogenic but also confers resistance to pertuzumab, an antibody drug widely used in clinical cancer therapy, by impeding its binding. In this study, we have successfully employed computational-aided rational design to undertake directed evolution of pertuzumab, resulting in the creation of an evolved pertuzumab variant named Ptz-SA.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310058, China.
Photonic manipulation of large-capacity data with the advantages of high speed and low power consumption is a promising solution for explosive growth demands in the era of post-Moore. A well-developed lithium-niobate-on-insulator (LNOI) platform has been widely explored for high-performance electro-optic (EO) modulators to bridge electrical and optical signals. However, the photonic waveguides on the x-cut LNOI platform suffer serious polarization-mode conversion/coupling issues because of strong birefringence, making it hard to realize large-scale integration.
View Article and Find Full Text PDFSci Adv
January 2025
State Key Laboratory of Continental Dynamics, Shaanxi Key Laboratory of Early Life & Environments and Department of Geology, Northwest University, Xi'an, China.
Ecdysozoan worms (Nematoida + Scalidophora) are typified by disparate grades of neural organization reflecting a complex evolutionary history. The fossil record offers a unique opportunity to reconstruct the early character evolution of the nervous system via the exceptional preservation of extinct representatives. We focus on their nervous system as it appears in early and mid-Cambrian fossils.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Chemistry and Biochemistry, Queens College, Flushing, New York 11367, United States.
Semiconductor nanomaterials and nanostructured interfaces have important technological applications, ranging from fuel production to electrosynthesis. Their photocatalytic activity is known to be highly heterogeneous, both in an ensemble of nanomaterials and within a single entity. Photoelectrochemical imaging techniques are potentially useful for high-resolution mapping of photo(electro)catalytic active sites; however, the nanoscale spatial resolution required for such experiments has not yet been attained.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.
Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential use of sequence information for enzyme engineering. In this study, we demonstrated that sequence information can predict the rate of the S2 step of a haloalkane dehalogenase using a generative maximum-entropy (MaxEnt) model.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!