Biology subjects

Middleditch, M.

Publications and source records attributed to Middleditch, M..

4 recordsLinked to original sources

bioRxiv2022

Investigating the Consistency of Extracellular Vesicle Production from Breast Cancer Subtypes Using CELLine Adherent Bioreactors

Extracellular vesicle (EV) research has grown rapidly in recent years, largely due to the potential use of EVs as liquid biopsy biomarkers or therapeutics. However, in-depth characterisation and validation of EVs produced using conventional in vitro cultures can be challenging due to the large area of cell monolayers and volumes of culture media required. To overcome this obstacle, multiple bioreactor designs have been tested for EV production with varying success, but the consistency of EVs produced over time in these systems has not been reported previously. In this study, we demonstrate that several breast cancer cell lines of different subtypes can be cultured simultaneously in space, resource, and time efficient manner using CELLine AD 1000 systems, allowing the consistent production of vast amounts of EVs for downstream experimentation. We report an improved workflow used for inoculating, maintaining, and monitoring the bioreactors, their EV production, and the characterisation of the EVs produced. Lastly, our proteomic analyses of the EVs produced throughout the lifetime of the bioreactors show that core EV-associated proteins are relatively consistent, with few minor variations over time, and that tracking the production of EVs may be a convenient method for indirectly monitoring the bioreactors health. These findings will aid future studies requiring the simultaneous production of large amounts of EVs from several cell lines of different subtypes of a disease and other EV biomanufacturing applications.

cancer biology↗

bioRxiv2019

Induction of microbial oxidative stress as a new strategy to enhance the enzymatic degradation of organic micropollutants in wastewater

Organic micropollutants (OMPs) are pervasive anthropogenic contaminants of fresh and marine waters with known potential to adversely affect aquatic life (e.g. endocrine disruption). Their ubiquitous environmental occurrence is primarily attributed to wastewater treatment plant discharges following their incomplete removal by common biological treatment processes. This study assesses a new strategy for promoting the degradation of six model OMPs (i.e. sulfamethoxazole, carbamazepine, tylosin, atrazine, naproxen and ibuprofen) by stimulating microbial oxidoreductase production to counter the effects of oxidative stress caused by oxygen perturbation. Microbial cultures from dairy farm wastewater were exposed to a cyclical ON-OFF perturbations of oxygen supply, ranging from 0.16 to 2 cycles per hour (i.e. 2, 1, 0.5, 0.25 and 0.16 cycles/hour), in laboratory bioreactors. The activity and relative abundances of microbial oxidoreductases (such as peroxidases, cytochromes P450) were upregulated by oxygen perturbation. In comparison to controls subjected to constant oxygen levels, OMP concentrations in perturbed cultures decreased by 70{+/-}9% (mean {+/-} SD). A distance-based linear model confirmed strong positive correlations between the relative abundance of the bacterial families, Rhodocyclaceae, Syntrophaceae and Syntrophobacteraceae, and oxygen perturbations. Our results confirm that intentional perturbation of oxygen supply to induce microbial oxidative stress can improve OMP removal efficiencies in wastewater treatment bioreactors.

bioengineering↗

bioRxiv2018

A revised biosynthetic pathway for the cofactor F420 in bacteria

Cofactor F420 plays critical roles in primary and secondary metabolism in a range of bacteria and archaea as a low-potential hydride transfer agent. It mediates a variety of important redox transformations involved in bacterial persistence, antibiotic biosynthesis, pro-drug activation and methanogenesis. However, the biosynthetic pathway for F420 has not been fully eluci-dated: neither the enzyme that generates the putative intermediate 2-phospho-L-lactate, nor the function of the FMN-binding C-terminal domain of the {gamma}-glutamyl ligase (FbiB) in bacteria are known. Here we show that the guanylyltransferases FbiD and CofC accept phosphoenolpyruvate, rather than 2-phospho-L-lactate, as their substrate, leading to the formation of the previously uncharacterized intermediate, dehydro-F420-0. The C-terminal domain of FbiB then utilizes FMNH2 to reduce dehydro-F420-0, which produces mature F420 species when combined with the {gamma}-glutamyl ligase activity of the N-terminal domain. This new insight has allowed the heterologous expression F420 from a recombinant F420 biosynthetic pathway in Escherichia coli.

molecular biology↗

bioRxiv2017

A Bayesian approach to multivariate and multilevel modelling with non-random missingness for hierarchical clinical proteomics data

High throughput mass-spectrometry-based proteomics data from clinical studies brings challenges to statistical analysis. The challenges originate from the hierarchical levels of protein abundance data and interactions between clinical study design and experimental design. The non-random missingness of the measurements from a vast amount of information also adds complexity in data analysis. We propose multivariate multilevel models to analyse protein abundances and to handle abundance-dependent missingness within a Bayesian framework. The proposed model enables the variance decomposition at different levels of the data hierarchy and provides shrinkage of protein-level estimates for a group of proteins. A logistic missingness and censored model with informative prior is used to handle incomplete data. Hamiltonian MC/No-U-Turn Sampling and Gibb MCMC algorithms are created to derive the posterior distribution of study parameters; Hamiltonian MC is demonstrated to gain more efficiency for these high-dimensional correlated data. Improvements of the proposed missing data model is compared to the univariate mixed effect model and the multivariate-multilevel model using complete data in a simulated study and a clinical proteomics study. The proposed model framework can be used in other types of data with similar structure and Non Random Missingness mechanism (MNAR).

bioinformatics↗