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Finnigan, J.

Publications and source records attributed to Finnigan, J..

2 recordsLinked to original sources

Ultrahigh-throughput screening for sialic acid-active enzymes in the microbial genetic diversity

Sialic acids (Sias) and related nonulosonic acids are critical components of glycoconjugates involved in host-pathogen interactions, immune regulation, and cell signalling. Despite their biotechnological relevance, the diversity of enzymes involved in Sia biosynthesis remains largely underexplored due to limitations in culture-dependent methods and the lack of (ultra)high-throughput screening strategies. Here, we report the development of a highly sensitive droplet-based microfluidic screening platform enabling the functional discovery of sialic acid aldolases in environmental metagenomes. The method integrates a fluorescence-coupled enzymatic cascade compatible with fluorescence-activated droplet sorting (FADS), allowing the screening of >10 droplets per experiment, as well as a downstream validation strategy for the selected hits. Although some limitations were identified, the system demonstrated high sensitivity and was utilised for the screening of a metagenomic library from garden soil. During this campaign, a potential new sialic acid aldolase enzyme was identified. This work establishes a generalizable framework for measuring complex, multi-step enzymatic functions at ultrahigh throughput using coupled cascades in droplets

microbiology↗

ro-crate-rs: Development of a Lightweight RO-Crate Rust Library for Automated Synthetic Biology

Advances in laboratory automation and AI-driven experimental design have increased the scale and complexity of data generated in synthetic biology. Whilst biofoundries provide significant resources and infrastructure to execute these experiments, most laboratories rely on isolated automated instruments and software systems that operate as disconnected silos, producing heterogeneous data formats with little structured metadata. This fragmentation hinders data integration, reproducibility, and downstream computational workflows. A potential solution is RO-Crate, which offers a lightweight, extensible framework for packaging research data with machine-readable metadata, but existing tooling remains immature for automation-orientated, cloud-native, or high-throughput laboratory workflows. Here, we introduce ro-crate-rs, a new suite of tools centred on a performant Rust library for constructing, validating and packaging RO-Crates across diverse compute environments and automated hardware. The library enforces RO-Crate 1.1 constraints through strong typing while enabling flexible extensions, and is complemented by a Python API and CLI for interactive use and pipeline integration. We demonstrate this combined approach through a semi-automated Old Yellow Enzyme characterisation workflow, showing how RO-Crates can capture data and metadata across multiple independent instruments. Together, these tools provide a robust foundation for FAIR-compliant, automation-ready data management and enable reproducible reconstruction of experimental workflows even in non-biofoundry settings. Availabilityhttps://github.com/intbio-ncl/ro-crate-rs

synthetic biology↗