Biology subjects

Dahiya, G. S.

Publications and source records attributed to Dahiya, G. S..

4 recordsLinked to original sources

bioRxiv2025

Engineering Artificial 5' Regulatory Sequences for Thermostable Protein Expression in the Extremophile Thermus thermophilus

The utilisation of biocatalysts in biotechnological applications often necessitates their heterologous expression in suitable host organisms. However, the range of standardised microbial hosts for recombinant protein production remains limited, with most being mesophilic and suboptimal for certain protein types. Although the thermophilic bacterium Thermus thermophilus has long been established as a valuable extremophile host, thanks to its high-temperature tolerance, robust growth, and extensively characterised proteome, its genetic toolkit has predominantly depended on a limited set of native promoters. To overcome this bottleneck, we have expanded the available regulatory repertoire in T. thermophilus by developing novel artificial 5' regulatory sequences. In this study, we applied our Gene Expression Engineering platform to engineer 53 artificial 5' regulatory sequences (ARES) in T. thermophilus. These ARES, which comprise both promoter and 5' untranslated regions (UTRs), were functionally characterised in both T. thermophilus and Escherichia coli, revealing distinct host-specific expression patterns. Furthermore, we demonstrated the utility of these ARES by demonstrating high-level expression of thermostable proteins, including {beta}-galactosidase, a superfolder citrine fluorescent protein, and phytoene synthase. A bioinformatic analysis of the novel sequences has also being carried out indicating that the ARES possess markedly lower GC content compared to native promoters. This study contributes to expanding the genetic toolkit for recombinant protein production by providing a set of functionally validated ARES, enhancing the versatility of T. thermophilus as a synthetic biology chassis for thermostable protein expression.

synthetic biology↗

bioRxiv2025

Genetic and materials engineering to enhance inducible gene expression in lactobacilli

Lactiplantibacillus plantarum is known for its potential in healthcare, food production, and environmental biotechnology. However, its broader utility is constrained by a limited genetic toolbox, particularly lacking robust genetic switches for inducible gene expression. Addressing this gap, we developed a novel genetic switch for L. plantarum based on a strong bacteriophage-derived promoter and the food-grade inducer, cumate. However, the switch was susceptible to leaky expression in the late log phase of bacterial growth, which was correlated to a reduction in the culture pH. This leakiness was partially resolved by regulating culture conditions (temperature and nutrients) to limit growth below a certain bacterial density. More interestingly, leaky expression could be stably suppressed by encapsulating the bacteria in alginate as an engineered living material. This physically restricted growth and limited the pHdrop, thereby enhancing the switch performance. The possibilities to regulate protein secretion over several days, reversibly switch protein production, and establish dual functionalities by co-encapsulating strains with different switches were demonstrated. Thus, for the first time, we show a material-based strategy to enhance the performance of a genetic switch in bacteria. This strategy facilitates the development of L. plantarum for advanced applications in biotechnology, pharmaceutics, and living therapeutics.

bioengineering↗

bioRxiv2024

High-resolution mapping of Sigma Factor DNA Binding Sequences using Artificial Promoters, RNA Aptamers and Deep Sequencing

The variable sigma ({sigma}) subunit of the bacterial RNA polymerase holoenzyme determines promoter specificity and facilitate open complex formation during transcription initiation. Understanding {sigma}-factor binding sequences is therefore crucial for deciphering bacterial gene regulation. Here, we present a data-driven high-throughput approach that utilizes an extensive library of 1.54 million DNA templates providing artificial promoters and 5' UTR sequences for {sigma}-factor DNA binding motif discovery. This method combines the generation of extensive DNA libraries, in vitro transcription, RNA aptamer selection, and deep DNA and RNA sequencing. It allows direct assessment of promoter activity, identification of transcription start sites, and quantification of promoter strength based on mRNA production levels. We applied this approach to map {sigma}54 DNA binding sequences in Pseudomonas putida. Deep sequencing of the enriched RNA pool revealed 64,966 distinct {sigma}54 binding motifs, significantly expanding the known repertoire. This data-driven approach surpasses traditional methods by directly evaluating promoter function and avoiding selection bias based solely on binding affinity. This comprehensive dataset enhances our understanding of {sigma}-factor binding sequences and their regulatory roles, opening avenues for new research in biology and biotechnology.

biochemistry↗

bioRxiv2023

From Context to Code: Rational De Novo DNA Design and Predicting Cross-Species DNA Functionality Using Deep Learning Transformer Models

Synthetic biology currently operates under a framework dominated by trial-and-error approaches, which hinders the effective engineering of organisms and the expansion of large-scale biomanufacturing. Motivated by the success of computational designs in areas like architecture and aeronautics, we aspire to transition to a more efficient and predictive methodology in synthetic biology. In this study, we report a DNA Design Platform that relies on the predictive power of Transformer-based deep learning architectures. The platform transforms the conventional paradigms in synthetic biology by enabling the context-sensitive and host-specific engineering of 5' regulatory elements--promoters and 5' untranslated regions (UTRs) along with an array of codon-optimised coding sequence (CDS) variants. This allows us to generate context-sensitive 5' regulatory sequences and CDSs, achieving an unparalleled level of specificity and adaptability in different target hosts. With context-aware design, we significantly broaden the range of possible gene expression profiles and phenotypic outcomes, substantially reducing the need for laborious high-throughput screening efforts. Our context-aware, AI-driven design strategy marks a significant advancement in synthetic biology, offering a scalable and refined approach for gene expression optimisation across a diverse range of expression hosts. In summary, this study represents a substantial leap forward in the field, utilising deep learning models to transform the conventional design, build, test, learn-cycle into a more efficient and predictive framework.

synthetic biology↗