Search PubMed⌕ Search

PubMed · 15342489

Regulatory circuit design and evolution using phage lambda.

Abstract

Bistable gene regulatory circuits can adopt more than one stable epigenetic state. To understand how natural circuits have this and other systems properties, several groups have designed regulatory circuits de novo. Here we describe an alternative approach. We have modified an existing bistable circuit, that of phage lambda. With this approach, we used powerful genetic selections to identify functional circuits and selected for variants with altered behavior. The lambda circuit involves two antagonistic repressors, CI and Cro. We replaced lambda Cro with a module that included Lac repressor and several lac operators. Using a combinatorial approach, we isolated variants with different types of regulatory behavior. Several resembled wild-type lambda--they could grow lytically, could form highly stable lysogens, and carried out prophage induction. Another variant could form stable lysogens in the presence of a ligand for Lac repressor but switched to the lytic state when the ligand was removed. Several isolates evolved toward a desired behavior under selective pressure. These results strongly support the idea that complex circuits can arise during the course of evolution by a combination of simpler regulatory modules. They also underscore the advantages of modifying a natural circuit as an approach to understanding circuit design, systems behavior, and circuit evolution.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shota Atsumi, John W Little. 2004-09-01. Regulatory circuit design and evolution using phage lambda.. https://doi.org/10.1101/gad.1226004

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Collateral sensitivity-harnessing microbial vulnerabilities as a solution to antimicrobial resistance.

Bacteria exhibit an evolutionary trade-off through their development of collateral sensitivity (CS) which allows them to resist one antibiotic while becoming more vulnerable to another. This vulnerability offers a compelling therapeutic opportunity by selecting against resistant isolates. Laboratory evolution studies, genome sequencing, deep mutagenesis and use of artificial intelligence and machine learning can design the bespoke strategy against multi-drug-resistant bacteria. This review discusses about recent studies that are rationally designed to harness this evolutionary trade-off for the development of alternative antimicrobial strategies. The translational barriers to the clinical implementation of CS are addressed and evidence-based design principles for optimization of CS-guided therapy are discussed.

Bacteria↗

How the social lives of bacteria affect their pangenome.

Although the study of microbes started with type strains and reference genomes, advances in sequencing technology and new interest in mixed microbial communities have made us aware that a single genome cannot and does not reflect the diversity of a given bacterial species. Bacteria rarely occupy an environmental or host niche alone and quickly diversify into strains upon colonization of a new niche. The genetic diversity present within a phylogenetically related set of bacterial strains (the 'pangenome') is influenced by the niche that they occupy and how they interact with the other microorganisms that they share that niche with. In this review, I examine how the social lives of bacteria can affect their genetic diversity and the bioinformatic techniques that we use to detect that diversity.

Bacteria↗

DURABLE: A Workflow for Determining Corrosion-Driving and Protective Microbial Mechanisms.

Microbiologically influenced corrosion (MIC) threatens global infrastructure, causing billions of dollars in annual losses. Its persistence stems from unresolved mechanisms─particularly the metabolites produced by microorganisms that drive or inhibit corrosion─and the microbial community structures. Progress has been hindered by the absence of systematic workflows to rapidly and accurately identify MIC-relevant microorganisms and their functions. Here, we present DURABLE (Detection of Unique Corrosion Resistant or Accelerating Biologics in a Laboratory Environment), a pipeline that couples high-throughput microbial screening with genomic and metabolic workflows. We applied the DURABLE workflow to six diesel tank samples and revealed fuel-dependent microbial community structures, which showed greater diversity and evenness in bacterial communities than their fungal counterparts. The workflow used carbon steel beads to rapidly screen over 80 bacterial isolates for corrosive activity, reducing assay time to approximately 2 days compared with the conventional 30-day metal coupon test. More than 40 isolates were identified as corrosive. Further testing using mass spectrometry analysis revealed corrosion-associated metabolites, which were further validated using electrochemical assays. Thus, DURABLE achieved a ∼15-fold increase in screening speed and provided a scalable and mechanistic framework for dissecting MIC dynamics. We expect this advance will enable the development of precision mitigation strategies in hydrocarbon fuel infrastructure.

Bacteria↗