Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision.
Sci Adv
; 9(45): eadg5296, 2023 11 10.
Article
em En
| MEDLINE
| ID: mdl-37939173
ABSTRACT
Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.
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Base de dados:
MEDLINE
Assunto principal:
Proteínas de Escherichia coli
/
Aprendizado Profundo
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article