Biology subjects

Tanaka, R. J.

Publications and source records attributed to Tanaka, R. J..

4 recordsLinked to original sources

bioRxiv2026

Inferring antifungal drug synergy from Candidozyma auris optical density data using Bayesian mechanistic modelling

Antifungal drug-resistant Candidozyma auris (C. auris) is a threat to human health worldwide. Combination antifungal drug therapy has emerged as a promising approach to combat drug-resistant C. auris because some drugs interact synergistically to increase fungal clearance when co-administered. Moreover, combination regimens that either rapidly act or completely kill C. auris could mitigate development of on-treatment resistance. However, traditional checkerboard methods to identify synergistic drug combinations only inspect fungal growth at a single timepoint. As a result, they cannot be used to estimate the rate of drug-action or to hypothesise on fungicidal or fungistatic drug-action. Mechanistic modelling would allow us to quantify time-dependent drug-action and infer killing or inhibitory action, but these models are usually fit to direct measurements of fungal growth whose collection is currently not scalable to many time-points and drug combinations. In this paper, we propose a Bayesian mechanistic modelling approach that could detect drug-synergy, estimate drug-action over time and investigate fungicidal or fungistatic drug-activity from optical density (OD600) data alone. OD600 is quicker and easier to collect than direct measurements of fungal growth and therefore more amenable to high-throughput susceptibility testing. By fitting our model to time-course OD600 data of a multi-drug-resistant C. auris isolate growing in mono- and combination drug regimens, we successfully inferred synergy between previously confirmed synergistic antifungal drugs (anidulafungin with manogepix or with 5-flucytosine) and linked our models inferred kinetic parameters to fungicidal and fungistatic action on C. auris growth, which matched drug-activity reported in literature where known. We validated that our model outperformed baseline logistic and Gompertz models using cross validation stratified by OD600 replicates. Our results represent the much-needed groundwork for identifying drug combinations for subsequent experimental testing for use in clinics based on their synergy, temporal drug-action and fungicidal or fungistatic activities inferred from OD600 data alone. Author SummaryThere is an urgent need to locate novel treatments to better treat antifungal drug-resistant Candidozyma auris infections. Combination therapy is a promising approach where two or more antifungal drugs are administered and interact synergistically to enhance fungal clearance. If these combinations are fast acting or eradicate fungi through killing, then they could also reduce the chance of resistance developing during treatment. The synergy of antifungal drug combinations is currently assessed by checkerboard methodologies that compare fungal growth under drug combinations to that under a single drug. However, checkerboard methodologies record only one time-point. Hence, they cannot evaluate drug combinations timeframe of action and follow-up studies are required to determine which combinations could optimally enhance killing. We developed a Bayesian mechanistic model that could detect synergy between drugs, estimate rates of drug-action and investigate killing and inhibition drug-action using only optical density (OD600) data of C. auris. OD600-based measurement of fungal growth is more amenable to large-scale drug testing than data typically used for mechanistic modelling, such as microscopy data. This work serves as a foundation for more targeted drug testing that identifies promising drug combinations based on their inferred drug-synergy and hypothesised killing (or inhibition) rates.

systems biology↗

bioRxiv2024

Design principles for engineering bacteria to maximise chemical production from batch cultures

Bacteria can be engineered to manufacture chemicals, but it is unclear how to optimally engineer a single cell to maximise production performance from batch cultures. Moreover, the performance of engineered production pathways is affected by competition for the hosts native resources. Here, using a "host-aware" computational framework which captures competition for both metabolic and gene expression resources, we uncover design principles for engineering the expression of host and production enzymes in a cell to maximise volumetric productivity and yield from batch cultures. Our results suggest that selecting strains in the lab for maximum growth and product synthesis can achieve close to maximum culture productivity and yield, but the growth-synthesis trade-off fundamentally limits production performance. We show that engineering genetic circuits to switch cells to a high synthesis-low growth state after first growing to a large population can further improve performance. By analysing different circuit topologies, we show that optimal performance is achieved by circuits that inhibit host metabolism to redirect it to product synthesis. Our results should facilitate construction of microbial cell factories with high and efficient production capabilities.

synthetic biology↗

bioRxiv2024

Inferring fungal growth rates from optical density data

Quantifying fungal growth underpins our ability to effectively treat severe fungal infections. Current methods quantify fungal growth rates from time-course morphology-specific data, such as hyphal length data. However, automated large-scale collection of such data lies beyond the scope of most clinical microbiology laboratories. In this paper, we propose a mathematical model of fungal growth to estimate morphology-specific growth rates from easy-to-collect, but indirect, optical density (OD600) data of Aspergillus fumigatus growth (filamentous fungus). Our method accounts for OD600 being an indirect measure by explicitly including the relationship between the indirect OD600 measurements and the calibrating true fungal growth in the model. Therefore, the method does not require de novo generation of calibration data. Our model outperformed reference models at fitting to and predicting OD600 growth curves and overcame observed discrepancies between morphology-specific rates inferred from OD600 versus directly measured data in reference models that did not include calibration. Author summaryQuantifying fungal growth is essential for antifungal drug discovery and monitoring antifungal resistance. As fungal growth is complex, with fungal morphology (shape) dynamically changing over time, previous studies have quantified fungal growth by estimating growth rates during specific fungal morphologies (morphology-specific growth rates) or by mathematically modelling fungal growth. However, collecting time-series data that captures the morphological information required for mathematical model fitting or estimating morphology-specific growth rates is prohibitively time consuming for large-scale drug testing in most microbiology laboratories. Alternatively, fungal growth can be quickly, although indirectly, quantified by measuring the optical density (OD) of a broth culture. However, changes in OD are not always reflective of true changes in fungal growth because OD is an indirect measure. This paper proposes a method to model fungal growth and estimate a morphology-specific growth rate from indirect OD600 measurements of the major mould pathogen, Aspergillus fumigatus. We explicitly model the relationship between measured indirect OD600 data and true fungal growth (calibration). The presented work serves as the much-needed foundation for estimating and comparing morphology-specific fungal growth rates in varying antifungal drug concentrations using only OD600 data.

systems biology↗

bioRxiv2021

A suite of activity-based probes to dissect the KLK activome in drug-resistant prostate cancer

Kallikrein-related peptidases (KLKs) are a family of secreted serine proteases, which form a network - the KLK activome - with an important role in proteolysis and signaling. In prostate cancer (PCa), increased KLK activity promotes tumor growth and metastasis through multiple biochemical pathways, and specific quantification and tracking of changes in the KLK activome could contribute to validation of KLKs as potential drug targets. Herein we report a technology platform based on novel activity-based probes (ABPs) and inhibitors with unprecedented potency and selectivity enabling simultaneous orthogonal analysis of KLK2, KLK3 and KLK14 activity in hormone-responsive PCa cell lines and tumor homogenates. Using selective inhibitors and multiplexed fluorescent activity-based protein profiling (ABPP) we dissect the KLK activome in PCa cells and show that increased KLK14 activity leads to a migratory phenotype. Furthermore, using biotinylated ABPs we show that active KLK molecules are secreted into the bone microenvironment by PCa cells following stimulation by osteoblasts suggesting KLK-mediated signaling mechanisms could contribute to PCa metastasis to bone. Together our findings show that ABPP is a powerful approach to dissect dysregulation of the KLK activome as a promising and previously underappreciated therapeutic target in advanced PCa.

biochemistry↗