Transcription Factors as Evolvable Biosensors

Microbial Transcription Factor-Based Biosensors: Innovations from Design to Applications in Synthetic Biology

Transcription factor-based biosensors (TFBs) are powerful tools in microbial biosensor applications, enabling dynamic control of metabolic pathways, real-time monitoring of intracellular metabolites, and high-throughput screening (HTS) for strain engineering. These systems use transcription factors (TFs) to convert metabolite concentrations into quantifiable outputs, enabling precise regulation of metabolic fluxes and biosynthetic efficiency in microbial cell factories. Recent advancements in TFB, including improved sensitivity, specificity, and dynamic range, have broadened their applications in synthetic biology and industrial biotechnology. Computational tools such as Cello have further revolutionized TFB design, enabling in silico optimization and construction of complex genetic circuits for integrating multiple signals and achieving precise gene regulation. This review explores innovations in TFB systems for microbial biosensors, their role in metabolic engineering and adaptive evolution, and their future integration with artificial intelligence and advanced screening technologies to overcome critical challenges in synthetic biology and industrial bioproduction.

Biological Switches: Past and Future Milestones of Transcription Factor-Based Biosensors

Since the description of the lac operon in 1961 by Jacob and Monod, transcriptional regulation in prokaryotes has been studied extensively and has led to the development of transcription factor-based biosensors. Due to the broad variety of detectable small molecules and their various applications across biotechnology, biosensor research and development have increased exponentially over the past decades. Throughout this period, key milestones in fundamental knowledge, synthetic biology, analytical tools, and computational learning have led to an immense expansion of the biosensor repertoire and its application portfolio. Over the years, biosensor engineering became a more multidisciplinary discipline, combining high-throughput analytical tools, DNA randomization strategies, forward engineering, and advanced protein engineering workflows. Despite these advances, many obstacles remain to fully unlock the potential of biosensor technology. This review analyzes the timeline of key milestones on fundamental research (1960s to 2000s) and engineering strategies (2000s onward), on both the DNA and protein level of biosensors. Moreover, insights into the future perspectives, remaining hurdles, and unexplored opportunities of this promising field are discussed.

Transcription factor-based biosensors for screening and dynamic regulation

Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.

Down-regulation of KLF9 ameliorates LPS-caused acute lung injury and inflammation in mice via reducing GSDMD expression

Acute lung injury (ALI) is considered as a severe respiratory disease with aggravated inflammatory responses. Krüppel-like factor 9 (KLF9), a member of KLF family, has been reported to be involved in inflammatory disorders. However, the effect of KLF9 in ALI has not been elucidated. Here the present study was to clarify the role of KLF9 and its mechanism in ALI. The ALI in vitro model was established with lipopolysaccharide (LPS)-treated RAW264.7 cells. Mice were injected with LPS to conduct an ALI in vivo model. The expression of KLF9 and gasdermin D (GSDMD) was examined using quantitative reverse transcription-PCR, haematoxylin-eosin/immunohistochemistry staining and western blot assays. Enzyme-linked immunosorbent assay was employed to detect the levels of inflammatory cytokines. JASPAR database was used to predict the recognition motif of KLF9, and the relationship between KLF9 and GSDMD was determined by luciferase reporter assay and chromatin immunoprecipitation analysis. The number of neutrophils in bronchoalveolar lavage fluid as well as the wet/dry weight ratio was caculated. The results showed that The expression of KLF9 in lung was significantly increased in LPS-stimulated mice. Moreover, KLF9 knockout relieved the lung injury in ALI mice. GSDMD is one of targets genes of the transcription factor KLF9. KLF9 knockout induced a decreased expression of GSDMD in LPS-treated mice. Furthermore, in RAW264.7 cells after LPS administration, KLF9 knockdown reduced the levels of inflammatory factors and suppressed the expression of GSDMD. In summarise, these findings exhibited that KLF9 knockout could mitigate the lung injury and inflammatory responses in ALI mice by directly regulating GSDMD.

Engineering of Synthetic Transcriptional Switches in Yeast

Transcriptional switches can be utilized for many purposes in synthetic biology, including the assembly of complex genetic circuits to achieve sophisticated cellular systems and the construction of biosensors for real-time monitoring of intracellular metabolite concentrations. Although to date such switches have mainly been developed in prokaryotes, those for eukaryotes are increasingly being reported as both rational and random engineering technologies mature. In this review, we describe yeast transcriptional switches with different modes of action and how to alter their properties. We also discuss directed evolution technologies for the rapid and robust construction of yeast transcriptional switches.