Dataset Information

High-resolution temporal profiling of E. coli transcriptional response.

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.

SUBMITTER: Miano A

PROVIDER: S-EPMC10665441 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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High-resolution temporal profiling of E. coli transcriptional response.

Miano Arianna A Rychel Kevin K Lezia Andrew A Sastry Anand A Palsson Bernhard B Hasty Jeff J

Nature communications 20231122 1

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 ...[more]

PMID: 37993418

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Project description:Although of high priority for the development of genetic tools to control malaria-transmitting mosquitoes, only a few germline-specific regulatory regions have been characterised to date and the presence of global regulatory mechanisms, such as dosage compensation and meiotic sex chromosome inactivation (MSCI), are mostly assumed from transcriptomic analyses of reproductive tissues or whole gonads. In such studies, samples include a significant portion of somatic tissues inevitably complicating the reconstruction of a defined transcriptional map of gametogenesis. By exploiting recent advances in transgenic technologies and gene editing tools, combined with fluorescence-activated cell sorting and RNA sequencing, we have separated four distinct cell lineages from the Anopheles gambiae male gonads: premeiotic, meiotic (primary and secondary spermatocytes) and postmeiotic. By comparing the overall expression levels of X-linked and autosomal genes across the four populations, we revealed a striking transcriptional repression of the X chromosome coincident with the meiotic phase, classifiable as MSCI, and highlighted genes that may evade silencing. In addition, chromosome-wide median expression ratios of the premeiotic population confirmed the absence of dosage compensation in the male germline. Applying differential expression analysis, we highlighted genes and transcript isoforms enriched at specific timepoints and reconstructed the expression dynamics of the main biological processes regulating the key stages of sperm development and maturation. We generated the first transcriptomic atlas of A. gambiae spermatogenesis that will expand the available toolbox for the genetic engineering of vector control technologies. We also describe an innovative and multidimensional approach to isolate specific cell lineages that can be used for the targeted analysis of other A. gambiae organs or transferred to other medically relevant species and model organisms.

Dataset Information

High-resolution temporal profiling of E. coli transcriptional response.

Publications

High-resolution temporal profiling of E. coli transcriptional response.

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