Biology subjects

Walsh, A.

Publications and source records attributed to Walsh, A..

24 records · Page 2Linked to original sources

bioRxiv2022

Machine learning prediction of cancer cell metabolism from autofluorescence lifetime images

Metabolic reprogramming at a cellular level contributes to many diseases including cancer, yet few assays are capable of measuring metabolic pathway usage by individual cells within living samples. Here, we combine autofluorescence lifetime imaging with single-cell segmentation and machine-learning models to predict the metabolic pathway usage of cancer cells. The metabolic activities of MCF7 breast cancer cells and HepG2 liver cancer cells were controlled by growing the cells in culture media with specific substrates and metabolic inhibitors. Fluorescence lifetime images of two endogenous metabolic coenzymes, reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD), were acquired by a multi-photon fluorescence lifetime microscope and analyzed at the cellular level. Quantitative changes of NADH and FAD lifetime components were observed for cells using glycolysis, oxidative phosphorylation, and glutaminolysis. Conventional machine learning models trained with the autofluorescence features classified cells as dependent on glycolytic or oxidative metabolism with 90 - 92% accuracy. Furthermore, adapting convolutional neural networks to predict cancer cell metabolic perturbations from the autofluorescence lifetime images provided improved performance, 95% accuracy, over traditional models trained via extracted features. In summary, autofluorescence lifetime imaging combined with machine learning models can detect metabolic perturbations between glycolysis and oxidative metabolism of living samples at a cellular level, providing a label-free technology to study cellular metabolism and metabolic heterogeneity.

bioengineering↗

bioRxiv2021

An extremes of phenotype approach confirms significant genetic heterogeneity in patients with ulcerative colitis.

Ulcerative colitis (UC) is a major form of inflammatory bowel disease with increasing global incidence. There is significant phenotypic heterogeneity defined by a range of clinical variables including age of onset and disease extent. Clinical outcomes range from long-term remission on minimal therapy to surgical resection. Close to 70% of UC risk can be attributed to genetics and understanding the genetic mechanisms contributing to this risk and disease heterogeneity is vital for understanding disease pathogenesis and improving patient outcomes through targeted screening and therapies. This study aims to characterise the genetic heterogeneity of UC by identifying genomic risk variants specific to mild and/or severe forms of UC, exploring variations in the effect size of known risk variants and assessing the clinical value of a genetic risk score (GRS). We conducted genome-wide association (GWA) analyses in 287 patients with mild UC, 311 patients with severe UC and 583 age- and gender-matched controls. Odds ratios (OR) for mild vs control, severe vs control and combined mild and severe UC vs control were calculated. Using the combined UC data, two independent loci in the HLA region reached genome-wide significance. An additional genome-wide significant signal on chromosome 1 was identified in severe cases only. OR for known risk loci varied between mild and severe patients and were similar to previously published results. Effect estimates from the most recent UC GWA meta-analysis were used to calculate a GRS for each individual. A higher mean GRS was observed in both mild and severe UC cases compared to controls however, there was no difference between the mean GRS for mild and severe UC. Heterogeneity in effect sizes of UC associated variants between mild and severe disease burden suggests the presence of genetically distinct signatures. While large consortium data are needed to identify genome-wide significant variants, additional risk loci may be identified by targeted recruitment of individuals with a history of severe disease. Author SummaryUlcerative colitis (UC) is a chronic and often debilitating form of inflammatory bowel disease affecting approximately 0.3% of the population in industrialized economies. The disease displays significant clinical heterogeneity including age at presentation, disease severity, and the propensity to develop disease-related complications. Several previous studies have demonstrated the heritability of UC, identifying over 30 loci specific to the disease. The majority of these loci have small to modest effect sizes other than those within the Human Leucocyte Antigen (HLA) region on chromosome 6. Using stringent clinical criteria for defining mild and severe forms of UC in an extremes of phenotype approach, we undertook a genome wide association study in a dataset of 1222 participants to investigate genetic heterogeneity in this disease. We demonstrated substantial differences in genetic associations in severe UC as compared to mild UC. While over 2,000 SNPs achieved genome-wide significance in the severe UC analysis, none reached significance for mild UC. These results were reflected in significant differences in odds ratios. We identified Complement Factor B (CFB) as a potential susceptibility gene for severe UC in the Caucasian population with additional tissue gene expression demonstrating a positive correlation with disease severity.

genetics↗

bioRxiv2021

Cysteine induces mitochondrial reductive stress in glioblastoma through hydrogen peroxide production

Glucose and amino acid metabolism are critical for glioblastoma (GBM) growth, but little is known about the specific metabolic alterations in GBM that are targetable with FDA-approved compounds. To investigate tumor metabolism signatures unique to GBM, we interrogated The Cancer Genome Atlas for alterations in glucose and amino acid signatures in GBM relative to other human cancers and found that GBM exhibits the highest levels of cysteine and methionine pathway gene expression of 32 human cancers. Treatment of patient-derived GBM cells with the FDA-approved cysteine compound N-acetylcysteine (NAC) reduce GBM cell growth and mitochondrial oxygen consumption, which was worsened by glucose starvation. Mechanistic experiments revealed that cysteine compounds induce rapid mitochondrial H2O2 production and reductive stress in GBM cells, an effect blocked by oxidized glutathione, thioredoxin, and redox enzyme overexpression. These findings indicate that GBM is uniquely susceptible to NAC-driven reductive stress and could synergize with glucose-lowering treatments for GBM.

cancer biology↗

bioRxiv2020

Nano-Emulsion Adjuvant Enhances Cross-presentation by Lipid Body Formation Independent of Glycolysis

Here, we report that a carbomer-based adjuvant, Adjuplex(R) (ADJ), stimulated robust CD8 T-cell responses to subunit antigens by modulating multiple steps in the cytosolic pathway of cross-presentation, and afforded effective immunity against virus and intracellular bacteria. Cross-presentation induced by TLR agonists requires a critical switch to anabolic metabolism, but ADJ enhanced cross presentation without this metabolic switch in DCs and NLRP3-driven caspase 1 activity. Instead, ADJ induced in DCs, an unique metabolic state, typified by dampened oxidative phosphorylation and basal levels of glycolysis. In the absence of increased glycolytic flux, induction of ROS and lipid bodies (LBs) and alterations in LB composition mediated by ADJ were critical for DC cross-presentation. These findings challenge the prevailing metabolic paradigm by suggesting that DCs can perform effective DC cross-presentation, independent of glycolysis to induce robust T cell-dependent protective immunity to intracellular pathogens. These findings have implications in the rational development of novel adjuvants.

immunology↗

bioRxiv2019

Label-free Method for Classification of T cell Activation

T cells have a range of cytotoxic and immune-modulating functions, depending on activation state and subtype. However, current methods to assess T cell function use exogenous labels that often require cell permeabilization, which is limiting for time-course studies of T cell activation and non-destructive quality control of immunotherapies. Label-free optical imaging is an attractive solution. Here, we use autofluorescence imaging of NAD(P)H and FAD, co-enzymes of metabolism, to quantify optical imaging endpoints in quiescent and activated T cells. Machine learning classification models were developed for label-free, non-destructive determination of T cell activation state. T cells were isolated from the peripheral blood of human donors, and a subset were activated with a tetrameric antibody against CD2/CD3/CD28 surface ligands. NAD(P)H and FAD autofluorescence intensity and lifetime of the T cells were imaged using a multiphoton fluorescence lifetime microscope. Significant differences in autofluorescence imaging end-points were observed between quiescent and activated T cells. Feature selection methods revealed that the contribution of the short NAD(P)H lifetime (1) is the most important feature for classification of activation state, across multiple donors and T cell subsets. Logistic regression models achieved 97-99% accuracy for classification of T cell activation from the autofluorescence imaging endpoints. Additionally, autofluorescence imaging revealed NAD(P)H and FAD autofluorescence differences between CD3+CD8+ and CD3+CD4+ T cells, and random forest models of the autofluorescence imaging endpoints achieved 97+% accuracy for four-group classification of quiescent and activated CD3+CD8+ and CD3+CD4+ T cells. Altogether these results indicate that autofluorescence imaging of NAD(P)H and FAD is a powerful method for label-free, non-destructive determination of T cell activation and subtype, which could have important applications for the treatment of cancer, autoimmune, infectious, and other diseases.

bioengineering↗

bioRxiv2017

Time-resolved analyses of elemental distribution and concentration in living plants: An example using manganese toxicity in cowpea leaves

O_LIKnowledge of elemental distribution and concentration within plant tissues is crucial in the understanding of almost every process that occurs within plants. However, analytical limitations have hindered the microscopic determination of changes over time in the location and concentration of nutrients and contaminants in living plant tissues.\nC_LIO_LIWe developed a novel method using synchrotron-based micro X-ray fluorescence (-XRF) that allows for laterally-resolved, multi-element, kinetic analyses of plant leaf tissues in vivo. To test the utility of this approach, we examined changes in the accumulation of Mn in unifoliate leaves of 7-d-old cowpea (Vigna unguiculata) plants grown for 48 h at 0.2 and 30 M Mn in solution.\nC_LIO_LIRepeated -XRF scanning did not damage leaf tissues demonstrating the validity of the method. Exposure to 30 M Mn for 48 h increased the initial number of small spots of localized high Mn and their concentration rose from 40 to 670 mg Mn kg-1 fresh mass. Extension of the two-dimensional -XRF scans to a three-dimensional geometry provided further assessment of Mn localization and concentration.\nC_LIO_LIThis method shows the value of synchrotron-based -XRF analyses for time-resolved in vivo analysis of elemental dynamics in plant sciences.\nC_LI

plant biology↗