Biology subjects

Bohra, N.

Publications and source records attributed to Bohra, N..

3 recordsLinked to original sources

bioRxiv2026

Combining evolution and machine learning-guided pathway optimization to engineer a novel methylsuccinate module for synthetic C1 metabolism in vivo

De novo metabolic pathways open possibilities for sustainable biotransformations in microbes. However, the in vivo-implementation of such new-to-nature pathways is highly challenging and heavily relies on adaptive laboratory evolution (ALE) of the hosts native metabolic network. Here, we assess how much this need for host-centric ALE can be overcome and/or complemented through the informed design of the newly introduced pathway. Exemplifying for a synthetic CO2-fixation module via methyl-succinate, we established methylsuccinate-dependent growth of Escherichia coli over six months by ALE of E. colis native metabolism. In parallel, we developed a machine-learning guided workflow (MEVIS) for the automated engineering of the synthetic pathway, resulting in methylsuccinate-dependent growth within three weeks. Critically, performing MEVIS in the background of the ALE-evolved strain is necessary to further approach wild-type like growth, demonstrating how ALE in combination with machine-learning-guided lab automation holds great potential to accelerate and improve design-build-test-learn cycles in contemporary metabolic engineering.

bioengineering↗

bioRxiv2024

In vitro transcription-based biosensing of glycolate for prototyping of a complex enzyme cascade

In vitro metabolic systems allow the reconstitution of natural and new-to-nature pathways outside of their cellular context and are of increasing interest in bottom-up synthetic biology, cell-free manufacturing and metabolic engineering. Yet, the prototyping of such in vitro networks is very often restricted by time- and cost-intensive analytical methods. To overcome these limitations, we sought to develop an in vitro transcription (IVT)-based biosensing workflow that offers fast results at low-cost, minimal volumes and high-throughput. As a proof-of-concept, we present an IVT biosensor for the so-called CETCH cycle, a complex in vitro metabolic system that converts CO2 into glycolate. To quantify glycolate production, we constructed a sensor module that is based on the glycolate repressor GlcR from Paracoccus denitrificans, and established an IVT biosensing off-line workflow that allows to measure glycolate from CETCH samples from the {micro}M to mM range. We characterized the influence of different cofactors on IVT output and further optimized our IVT biosensor against varying sample conditions. We show that availability of free Mg2+ is a critical factor in IVT biosensing and that IVT output is heavily influenced by ATP, NADPH and other phosphorylated metabolites frequently used in in vitro systems. Our final biosensor is highly robust and shows an excellent correlation between IVT output and classical LC-MS quantification, but notably at [~]10-fold lowered cost and [~]10 times faster turnover time. Our results demonstrate the potential of IVT-based biosensor systems to break current limitations in biological design-build-test cycles for the prototyping of individual enzymes, complex reaction cascades and in vitro metabolic networks.

synthetic biology↗

bioRxiv2023

An interdependent Metabolic and Genetic Network shows emergent properties in vitro

A hallmark of all living organisms is their ability for self-regeneration which requires a tight integration of metabolic and genetic networks. Here we constructed a metabolic and genetic linked in vitro network (MGLN) that shows life-like behavior outside of a cellular context and generates its own building blocks from non-living matter. To this end, we integrated the metabolism of the crotonyl-CoA/ethyl-malonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle with cell-free protein synthesis using recombinant elements (PURE). We demonstrate that the MGLN produces the essential amino acid glycine from inorganic carbon (CO2), and incorporates it into target proteins following DNA-encoded instructions. By programming genetically encoded response into metabolic networks our work opens new avenues for the development of advanced biomimetic systems with emergent properties, including decision-making, self-regeneration and evolution.

synthetic biology↗