Biology subjects

Bryson, K.

Publications and source records attributed to Bryson, K..

6 recordsLinked to original sources

bioRxiv2025

Comprehensive LC-MS/MS Data Acquisition in Metabolomics via Maximum Bipartite Matching

BackgroundIn untargeted metabolomics studies, liquid chromatography tandem mass spectrometry (LC-MS/MS) is a powerful analytical platform. The fragmentation spectra produced can be used as "molecular fingerprints" to identify unknown metabolites. However, the high number of analytes that may be co-eluting limits the number of fragmentation spectra that can be collected and potentially identified, presenting a serious bottleneck for many studies. There is a need for new fragmentation strategies which are comprehensive, interpretable and robust, meaning they produce high-quality fragmentation spectra for as many analytes as possible while operating within the constraints of notoriously noisy mass spectrometry data. ResultsWe present a data acquisition workflow which uses a bipartite graph to represent the relationship between opportunities for fragmentation and desired fragmentation targets. This method allows a schedule for data acquisition to be optimally allocated by a standard algorithm. We augment this existing technique by allowing it to solve for multiple samples collectively, allowing it to optimise target intensity (and hence spectral quality) via the use of a weighted matching and by assigning leftover scans redundantly to improve robustness. We also show how this workflow can be used flexibly to generate inclusion windows for Data-Dependent Acquisition (DDA) methods. Our experiments show that several thousand peaks identified in a realistic biological sample can be targeted using only two LC-MS/MS runs. We also further investigate the trade-off between offline workflows and DDA methods by exposing our target list of peaks to realistic variation across samples. We find in those circumstances that our new method has performance (measured by number of peaks targeted comparable to state-of-the-art DDA methods). However, this competitive performance is only possible with our additions to the base maximum matching technique, which provide extra resistance against inter-sample variations. ConclusionsWe have proposed a workflow for LC-MS/MS data acquisition which can be used flexibly for entirely pre-scheduled acquisition or which may generate inclusion windows for online DDA methods. Our results show that the maximum matching workflow with our improvements is state-of-the-art where pre-scheduling is concerned, and in future this foundation may be developed to build more powerful DDA methods which can action the promise of truly comprehensive data acquisition.

bioinformatics↗

bioRxiv2023

Discovery of multi-state gene cluster switches determining the adaptive mitochondrial and metabolic landscape of breast cancer

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution, where they represent important therapeutic targets. However, the full spectrum, characteristics and regulation of existing metabolic switches are unknown. We propose that metabolic switches can be recognised by locating large alternating gene expression patterns and associate them with specific metabolic states. We developed a method to identify interspersed genesets by massive correlated biclustering (MCbiclust) and to predict their metabolic wiring. Testing the method on major breast cancer transcriptome datasets we discovered a series of gene sets with switch-like behaviour, predicting mitochondrial content, activity and central carbon fluxes in tumours associated with different switch positions. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labelled substrates and were ultimately extended by geneset analysis to link metabolic alterations to cellular states, thus predicting tumour pathology, prognosis and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Statement of significanceWe present a novel method to identify the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation. We illustrate the power of the method by stratifying breast cancer into metabolic subtypes, predicting their biology, architecture and clinical outcome.

systems biology↗

bioRxiv2023

TopNEXt: Automatic DDA Exclusion Framework for Multi-Sample Mass Spectrometry Experiments

MotivationLiquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) experiments aim to produce high quality fragmentation spectra which can be used to identify metabolites. However, current Data-Dependent Acquisition (DDA) approaches may fail to collect spectra of sufficient quality and quantity for experimental outcomes, and extend poorly across multiple samples by failing to share information across samples or by requiring manual expert input. ResultsWe present TopNEXt, a real-time scan prioritisation framework that improves data acquisition in multi-sample LC-MS/MS metabolomics experiments. TopNEXt extends traditional DDA exclusion methods across multiple samples by using a Region of Interest (RoI) and intensity-based scoring system. Through both simulated and lab experiments we show that methods incorporating these novel concepts acquire fragmentation spectra for an additional 10% of our set of target peaks and with an additional 20% of acquisition intensity. By increasing the quality and quantity of fragmentation spectra, TopNEXt can help improve metabolite identification with a potential impact across a variety of experimental contexts. AvailabilityTopNEXt is implemented as part of the ViMMS framework and the latest version can be found at https://github.com/glasgowcompbio/vimms. A stable version used to produce our results can be found at 10.5281/zenodo.7468914. Data can be found at 10.5525/gla.researchdata.1382. Contactr.mcbride.1@research.gla.ac.uk or vinny.davies@glasgow.ac.uk Supplementary informationSupplementary data are available at Bioarxiv online.

molecular biology↗

bioRxiv2023

Simulated-to-real Benchmarking of Acquisition Methods in Metabolomics

Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their performance due to the difficulty and cost of performing comparisons with real experimental data due to the lack of ground truth and the costs involved in running large number of acquisitions. Here, we present a systematic in-silico comparison of these two acquisition methods. To do so, we extended our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. Embedding this work within ViMMS also allows for researchers to easily simulate DDA and DIA LC-MS/MS runs, validate them on actual instrument, and potentially prototype novel methods that best combine the characteristics of both approaches. We believe that this work provides a useful guide to the choice of DIA or DDA for scientists involved in metabolomics data acquisition.

systems biology↗

bioRxiv2019

The breast cancer oncogene IKKε coordinates mitochondrial function and serine metabolism

The I{kappa}B kinase {varepsilon} (IKK{varepsilon}) is a key molecule at the crossroads of inflammation and cancer. Known for its role as an activator of NF{kappa}B and IRF3 signalling leading to cytokine secretion, the kinase is also a breast cancer oncogene, overexpressed in a variety of tumours. However, to what extent IKK{varepsilon} remodels cellular metabolism is currently unknown. Here we used a combination of metabolomics and phosphoproteomics to show that IKK{varepsilon} orchestrates a complex metabolic reprogramming that affects mitochondrial metabolism and serine biosynthesis. Acting independently of its canonical signalling role, IKK{varepsilon} upregulates the serine biosynthesis pathway (SBP) mainly by limiting glucose and pyruvate derived anaplerosis of the TCA cycle. In turn, this elicits activation of the transcription factor ATF4 and upregulation of the SBP genes. Importantly, pharmacological inhibition of the IKK{varepsilon}-induced metabolic phenotype reduces proliferation of breast cancer cells. Finally, we show that in a set of basal ER negative and highly proliferative human breast cancer tumours, IKK{varepsilon} and PSAT1 expression levels are positively correlated corroborating the link between IKK{varepsilon} and the SBP in the clinical context.

cancer biology↗

bioRxiv2019

Adversarial generation of gene expression data

The problem of reverse engineering gene regulatory networks from high-throughput expression data is one of the biggest challenges in bioinformatics. In order to benchmark network inference algorithms, simulators of well-characterized expression datasets are often required. However, existing simulators have been criticized because they fail to emulate key properties of gene expression data. In this study we address two problems. First, we propose mechanisms to faithfully assess the realism of a synthetic gene expression dataset. Second, we design an adversarial simulator of expression data, gGAN, based on a Generative Adversarial Network. We show that our model outperforms existing simulators by a large margin, achieving realism scores that are up to 17 times higher than those of GeneNetWeaver and SynTReN. More importantly, our results show that gGAN is, to our best knowledge, the first simulator that passes the Turing test for gene expression data proposed by Maier et al. (2013).

bioinformatics↗