Bacterial systems biology articles from across Nature Portfolio

In this Review, Snyder et al. discuss the global impacts of food spoilage, mechanisms and causative agents, and strategies and emerging tools to control microbial food spoilage.

Single-cell expression data from bacteria are used to classify gene regulatory architectures in relation to gene expression dynamics and the cell cycle, revealing distinct categories of gene regulatory mechanisms.

An explainable deep learning model using a chemical substructure-based approach for the exploration of chemical compound libraries identified structural classes of compounds with antibiotic activity and low toxicity.

Research on the biology and pathogenicity of ‘Candidatus Liberibacter asiaticus’ (CLas), the bacterium that causes citrus Huanglongbing disease, is hampered by our inability to cultivate it in artificial media. Here, Carter et al. use a high-throughput yeast-two-hybrid screen to identify thousands of interactions between CLas proteins, thus providing insights into their potential functions.

Understanding how cells dynamically adapt to their environment is important, but temporal information about cellular behaviour is often limited. Here, Miano et al. apply unsupervised machine learning to a dataset describing the activity of over 1,800 promoters in E. coli, measured every 10 minutes, defining three primary stages of promoter activation in response to heavy metal stress.

Measuring gene expression responses for every transcription factor (TF)-gene pair in living prokaryotic cells is challenging. Here the authors report pooled promoter responses to TF perturbation sequencing (PPTP-seq) using CRISPRi, which they use to address this problem in E. coli.

Bacteria use CRISPR–Cas systems as adaptive defence weapons against attacking phages. A new study shows that under severe stress conditions, Serratia turn off their CRISPR immune system to increase the uptake of potentially beneficial plasmids.

Microbiome research has attracted considerable attention, partially because of the potential to manipulate the microbiome for human health. To fulfil this promise, tractable methods and cautious interpretation of results are needed.

A study in Cell incorporates metabolic networks into a machine-learning approach to provide mechanistic insights into bacterial antibiotic lethality.

It has been assumed that bacteria adapt to nutrient limitation by adjusting the number of ribosomes, no matter what they are being starved for. Instead, two recent studies show that Escherichia coli uses different approaches depending on whether its growth is limited by the availability of carbon, nitrogen or phosphate.