ReviewMicrobial cell factories2024
Engineering of bacteria towards programmed autolysis: why, how, and when?
Review in Microbial cell factories, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Genetically Modified Lactic Acid Bacteria in the EU Food Chain: Applications, Benefits, and Risk Assessment.International journal of molecular sciences · 2026Review
- Engineered Escherichia coli as a microbial cell factory for intracellular protein delivery: strains, vectors, mechanisms, and therapeutic applications.Microbial cell factories · 2026Review
- Update of the list of QPS-recommended biological agents intentionally added to food or feeds as notified to EFSA.EFSA journal. European Food Safety Authority · 2026Article
- Engineering bacteria for enhanced tumor therapy: from surface modification to synthetic genetic circuits.Journal of hematology & oncology · 2025Review
- Bacterial programmed cell death and toxin-antitoxin system in bacteria.Archives of microbiology · 2025Review
- Prophage-mediated endolysis of Latilactobacillus Sakei TMW 1.1290 induced by exogenous stress arising from food technologies.BMC microbiology · 2025Article
- Deletion ofFrontiers in cellular and infection microbiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Programmed autolytic bacteria, also termed controlled self-disruptive or self-destructive bacteria, are bacterial systems that express certain lytic genes and undergo cell lysis at a predetermined time point to release the intracellular contents or to commit suicide. Such systems have wide applications in high-throughput screening of protein libraries, synthesis and recovery of bio-products, population control of heterogeneous cultures or synthetic co-cultures, drug delivery, and food fermentation. Recently, great achievements have been reported regarding on-demand control of cell autolysis for different purposes, highlighting the potential of autolytic strains in biomanufacturing and biomedicine. In this review article, we first introduce the various applications of such bacteria, followed by a summarization of the approaches used in the establishment of autolytic bacterial systems, including cell autolysis mediated by cell wall hydrolases with or without facilitating proteins and by membrane-disturbing proteins. Next, we describe in detail the methodologies adopted to control and initiate cell lysis, including induction by chemical inducers, stimulation by physical signals, auto-induction by metabolic status or nutrient limitation, and constitutive expression of the lytic genes. This article is ended with discussions on the remaining problems and possible future directions. This review provides comprehensive information on autolytic bacteria and insightful guidance to the development of highly efficient, robust, and smart autolytic bacterial platforms.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.