| Autor: |
Arianna Miano, Kevin Rychel, Andrew Lezia, Anand Sastry, Bernhard Palsson, Jeff Hasty |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Nature Communications, Vol 14, Iss 1, Pp 1-10 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-023-43173-7 |
| Popis: |
Abstract Understanding how cells dynamically adapt to their environment is a primary focus of biology research. Temporal information about cellular behavior is often limited by both small numbers of data time-points and the methods used to analyze this data. Here, we apply unsupervised machine learning to a data set containing the activity of 1805 native promoters in E. coli measured every 10 minutes in a high-throughput microfluidic device via fluorescence time-lapse microscopy. Specifically, this data set reveals E. coli transcriptome dynamics when exposed to different heavy metal ions. We use a bioinformatics pipeline based on Independent Component Analysis (ICA) to generate insights and hypotheses from this data. We discovered three primary, time-dependent stages of promoter activation to heavy metal stress (fast, intermediate, and steady). Furthermore, we uncovered a global strategy E. coli uses to reallocate resources from stress-related promoters to growth-related promoters following exposure to heavy metal stress. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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