skip to main content

Search for: All records

Award ID contains: 1934568

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration ofEscherichia coli’s populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data. Analysis of the proposed experiments shows that broad exploration of the experimental space followed by fine-tuning emerges as the optimal strategy. Additionally, analysis of the experimental data reveals 29 cases of cross-stress protection andmore »4 cases of cross-stress vulnerability. Further validation reveals the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to guide omics data collection for training predictive models, making evidence-driven decisions and accelerating knowledge discovery in life sciences.

    « less
  2. Free, publicly-accessible full text available March 1, 2023
  3. Free, publicly-accessible full text available March 1, 2023
  4. Free, publicly-accessible full text available January 1, 2023
  5. Free, publicly-accessible full text available January 1, 2023
  6. Free, publicly-accessible full text available January 1, 2023
  7. Free, publicly-accessible full text available January 1, 2023
  8. Free, publicly-accessible full text available September 5, 2022
  9. Glutaraldehyde is a widely used biocide on the market for about 50 years. Despite its broad application, several reports on the emergence of bacterial resistance, and occasional outbreaks caused by poorly disinfection, there is a gap of knowledge on the bacterial adaptation, tolerance, and resistance mechanisms to glutaraldehyde. Here, we analyze the effects of the independent selection of mutations in the transcriptional regulator yqhC for biological replicates of Escherichia coli cells subjected to adaptive laboratory evolution (ALE) in the presence of glutaraldehyde. The evolved strains showed improved survival in the biocide (11–26% increase in fitness) as a result of mutationsmore »in the activator yqhC , which led to the overexpression of the yqhD aldehyde reductase gene by 8 to over 30-fold (3.1–5.2 log2FC range). The protective effect was exclusive to yqhD as other aldehyde reductase genes of E. coli , such as yahK , ybbO , yghA , and ahr did not offer protection against the biocide. We describe a novel mechanism of tolerance to glutaraldehyde based on the activation of the aldehyde reductase YqhD by YqhC and bring attention to the potential for the selection of such tolerance mechanism outside the laboratory, given the existence of YqhD homologs in various pathogenic and opportunistic bacterial species.« less