Biology subjects

Tu, M. M.

Publications and source records attributed to Tu, M. M..

4 recordsLinked to original sources

bioRxiv2025

A ubiquitous Streptomyces biosynthetic megacluster encodes an arsenal of synergistic biotin-targeting antibiotics

The rise of multidrug-resistant pathogens underscores the urgent need for antibiotics that act through new targets and mechanisms. Biotin metabolism, essential in most bacteria, remains underexploited therapeutically. Here, we uncover a highly conserved, co-located biosynthetic megacluster in Streptomyces, a striking "cluster of clusters", that encodes four distinct natural product families: acidomycin, stravidins, dapamycins, and -methyl-KAPA, and is flanked by genes that encode streptavidin, a high-affinity biotin-binding protein. Remarkably, all molecules target different steps in bacterial biotin metabolism, revealing a multi-pronged natural strategy for biotin starvation. This arrangement of four functionally convergent biosynthetic gene clusters at a single genomic locus is without precedent. Even more surprisingly, we find that this anti-biotin megacluster is widespread across Streptomyces bacteria, suggesting a deeply conserved evolutionary solution to microbial competition. Mechanistically, the compounds inhibit biotin biosynthesis through enzyme blockade, prodrug activation, covalent cofactor mimicry, and biotin sequestration via co-expressed streptavidin. Stravidin S2 and -methyl-KAPA are effective in a murine model of multidrug-resistant E. coli infection. These findings expose a coordinated biosynthetic logic in microbial secondary metabolites and point to higher-order biosynthetic architectures as promising reservoirs of antibiotic innovation.

microbiology↗

bioRxiv2025

SyntheMol-RL: a flexible reinforcement learning framework for designing novel and synthesizable antibiotics

The rise of antibiotic-resistant pathogens such as Staphylococcus aureus has created an urgent need for new antibiotics. Generative artificial intelligence (AI) has shown promise in drug discovery, but existing models often fail to propose compounds that are both effective and synthetically tractable. To address these challenges, we introduce SyntheMol-RL, a reinforcement learning-based generative model that can rapidly design synthetically accessible small molecule drug candidates from a massive chemical space of 46 billion compounds. SyntheMol-RL improves upon our prior Monte Carlo Tree Search (MCTS)-based SyntheMol model by generalizing across chemically similar building blocks and enabling multi-parameter optimization. We applied SyntheMol-RL to generate candidate antibiotics against S. aureus by optimizing for both antibacterial activity and aqueous solubility, and we found that SyntheMol-RL generated molecules with improved predicted properties compared to both the previous MCTS version of SyntheMol as well as an AI-based virtual screening baseline. We synthesized 79 SyntheMol-RL compounds that were unique relative to the training dataset and found that 13 showed potent in vitro activity, of which seven were structurally novel after detailed literature searches. Furthermore, one hit compound, synthecin, demonstrated efficacy in a murine wound infection model of methicillin-resistant S. aureus (MRSA). These results validate SyntheMol-RLs ability to generate novel and synthetically accessible candidate antibiotics and position SyntheMol-RL as a powerful tool for drug design across therapeutic domains.

biochemistry↗

bioRxiv2025

Identifying Modulators of the Post-Antibiotic Effect

The post-antibiotic effect (PAE) is the delay in bacterial regrowth following antibiotic removal. It has important implications for dosing regimens since drugs that have extended activity following their elimination can be dosed less frequently, widening the therapeutic window. While the PAE has been associated with target vulnerability and the rate of target turnover, little is known about the genetic components that modulate the PAE. Here, we developed a high-throughput assay to screen the Escherichia coli Keio collection of [~]4000 deletion strains, identifying genes that enhance the PAE for CHIR-090, an inhibitor of UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase (LpxC). This screen revealed approximately 400 gene knockouts that enhanced the PAE of CHIR-090. The list of PAE enhancers was enriched for genes involved in transmembrane transport and outer membrane synthesis. Notably, deletion of the rfaE gene, which is involved in lipopolysaccharide (LPS) biosynthesis, increased the PAE of the LpxC inhibitors CHIR-090 and LPC-058 by 2 h and 3 h, respectively. Consistent with this phenotype, co-treatment of wild-type E. coli with an RfaE inhibitor increased the PAE of CHIR-090 or LPC-058 by 1 h. To probe the mechanism of this interaction, we measured the rate of LpxC turnover and found that knocking out rfaE reduced its half-life by 2-fold, suggesting that disrupting RfaE increases the stability of LpxC, increasing target vulnerability and enhancing the PAE of LpxC inhibitors. SIGNIFICANCEAntibiotics are generally dosed at very high levels leading to unwanted side effects and non-compliance, which in turn results in the emergence of drug-resistant bacterial infections. The goal of this work was to develop strategies that will enable antibiotics to be dosed at lower levels, thereby improving safety and compliance. In the present work, we have screened 4,000 strains of Escherichia coli to identify compounds that result in an increase in the post-antibiotic effect (PAE), which is the delay in bacterial regrowth following antibiotic exposure and removal. Drugs that cause a PAE are dosed less frequently, and the method we describe will provide a new approach to developing safer drugs.

microbiology↗

bioRxiv2023

A mobile CRISPRi collection enables genetic interaction studies for the essential genes of Escherichia coli

Ordered collections of precise gene deletions in E. coli and other model microbes have proven to be invaluable resources to study the dispensability of the non-essential genome in different environmental and genetic contexts. The dispensability of canonically essential genes, however, has been largely unstudied in such contexts. Advances in gene editing, in particular CRISPR interference (CRISPRi), have enabled depletion of essential cellular machinery to study the downstream effects on bacterial physiology. Here, we describe the construction of an ordered E. coli CRISPRi collection, designed to knock down the expression of 356 essential genes with the induction of a catalytically inactive Cas9, harbored on the conjugative plasmid pFD152, that also encodes for expression of specific guide RNA scaffolds. This mobile CRISPRi library can be conjugated into other ordered genetic libraries, to assess combined effects of essential gene knockdowns with non-essential gene deletions. As proof of concept, we probed cell envelope synthesis with two crosses. First, we crossed a deletion in the gene lpp with the entire CRISPRi library. Similarly, we crossed the CRISPRi knockdown of lolA with [~]4,000 deletion strains of the Keio collection. These experiments revealed a number of notable genetic interactions for the essential phenotype probed and, in particular, showed supressing interactions for the loci in question.

systems biology↗