Biology subjects

McNae, I.

Publications and source records attributed to McNae, I..

2 recordsLinked to original sources

bioRxiv2024

A new pathway in central metabolism mediates nutrient control of development and antibiotic production by Streptomyces

The amino sugar N-acetylglucosamine (GlcNAc) plays a central role in primary metabolism and is a key signaling molecule for the onset of morphological and chemical differentiation of Streptomyces. The global nutrient-sensory regulator DasR acts as the gatekeeper of development in streptomycetes, and its activity is modulated by aminosugar phosphates. Here, we report the discovery of a novel pathway in aminosugar metabolism that governs GlcNAc sensing. GlcNAc-6P is converted into a toxic metabolite via two new enzyme functions, namely dehydration of N-acetylglucosamine-6-phosphate by NagS to form 6P-Chromogen I, a reaction that has not yet been described in the textbooks, and its subsequent deacetylation by NagA producing a cytotoxic structural analogue of ribose. The latter reveals an unexpected promiscuous activity for GlcNAc-6P deacetylase NagA. The crystal structures of NagS apoenzyme and NagS in complex with its substrate GlcNAc-6P or its inhibitor 6-phosphogluconate were resolved at 2.3 [A], 2.6 [A], and 1.7 [A] resolution, respectively. Detailed in vivo and in vitro studies resolved the key residues of the NagS catalytic site. Thus, we have uncovered a novel pathway in aminosugar metabolism that sheds new light on nutrient-mediated control of development and antibiotic production in Streptomyces.

microbiology↗

bioRxiv2023

pyRBDome: A comprehensive computational platform for enhancing and interpreting RNA-binding proteome data

High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet the extent of noise, including false-positives, associated with these methodologies, is difficult to quantify as experimental approaches for validating the results are generally low throughput. To address this, we introduce pyRBDome, a pipeline for enhancing RNA-binding proteome data in silico. It aligns the experimental results with RNA-binding site (RBS) predictions from distinct machine learning tools and integrates high-resolution structural data when available. Its statistical evaluation of RBDome data enables quick identification of likely genuine RNA-binders in experimental datasets. Furthermore, by leveraging the pyRBDome results, we have enhanced the sensitivity and specificity of RBS detection through training new ensemble machine learning models. pyRBDome analysis of a human RBDome dataset, compared with known structural data, revealed that while UV cross-linked amino acids were more likely to contain predicted RBSs, they infrequently bind RNA in high-resolution structures. This discrepancy underscores the limitations of structural data as benchmarks, positioning pyRBDome as a valuable alternative for increasing confidence in RBDome datasets.

systems biology↗