Ribosome profiling reveals the fine-tuned response of Escherichia coli to mild and severe acid stress

Abstract

ABSTRACT The ability to respond to acidic environments is crucial for neutralophilic bacteria. Escherichia coli has a well-characterized regulatory network that triggers a multitude of defense mechanisms to counteract excess protons. Nevertheless, systemic studies of the transcriptional and translational reprogramming of E. coli to different degrees of acid stress have not yet been performed. Here, we used ribosome profiling and RNA sequencing to compare the response of E. coli (pH 7.6) to sudden mild (pH 5.8) and severe near-lethal acid stress (pH 4.4) conditions that mimic passage through the gastrointestinal tract. We uncovered new differentially regulated genes and pathways, key transcriptional regulators, and 18 novel acid-induced candidate small open reading frames. By using machine learning and leveraging large compendia of publicly available E. coli expression data, we were able to distinguish between the response to acid stress and general stress. These results expand the acid resistance network and provide new insights into the fine-tuned response of E. coli to mild and severe acid stress. IMPORTANCE Bacteria react very differently to survive in acidic environments, such as the human gastrointestinal tract. Escherichia coli is one of the extremely acid-resistant bacteria and has a variety of acid-defense mechanisms. Here, we provide the first genome-wide overview of the adaptations of E. coli K-12 to mild and severe acid stress at both the transcriptional and translational levels. Using ribosome profiling and RNA sequencing, we uncover novel adaptations to different degrees of acidity, including previously hidden stress-induced small proteins and novel key transcription factors for acid defense, and report mRNAs with pH-dependent differential translation efficiency. In addition, we distinguish between acid-specific adaptations and general stress response mechanisms using denoising autoencoders. This workflow represents a powerful approach that takes advantage of next-generation sequencing techniques and machine learning to systematically analyze bacterial stress responses.