Biology subjects

Taylor, G. P.

Publications and source records attributed to Taylor, G. P..

4 recordsLinked to original sources

bioRxiv2022

Transcriptional reprogramming from innate immune functions to a pro-thrombotic signature upon SARS-CoV-2 sensing by monocytes in COVID-19

Alterations in the myeloid immune compartment have been observed in COVID-19, but the specific mechanisms underlying these impairments are not completely understood. Here we examined the functionality of classical CD14+ monocytes as a main myeloid cell component in well-defined cohorts of patients with mild and moderate COVID-19 during the acute phase of infection and compared them to that of healthy individuals. We found that ex vivo isolated CD14+ monocytes from mild and moderate COVID-19 patients display specific patterns of costimulatory and inhibitory receptors that clearly distinguish them from healthy monocytes, as well as altered expression of histone marks and a dysfunctional metabolic profile. Decreased NF{kappa}B activation in COVID-19 monocytes ex vivo is accompanied by an intact type I IFN antiviral response. Subsequent pathogen sensing ex vivo led to a state of functional unresponsiveness characterized by a defect in pro-inflammatory cytokine expression, NF{kappa}B-driven cytokine responses and defective type I IFN response in moderate COVID-19 monocytes. Transcriptionally, COVID-19 monocytes switched their gene expression signature from canonical innate immune functions to a pro-thrombotic phenotype characterized by increased expression of pathways involved in hemostasis and immunothrombosis. In response to SARS-CoV-2 or other viral or bacterial components, monocytes displayed defects in the epigenetic remodelling and metabolic reprogramming that usually occurs upon pathogen sensing in innate immune cells. These results provide a potential mechanism by which innate immune dysfunction in COVID-19 may contribute to disease pathology.

immunology↗

bioRxiv2021

Retention Time Standardization and Registration (RTStaR): An algorithm that matches corresponding and identifies unique species in nanoliquid chromatography-nanoelectrospray ionization-mass spectrometry lipidomic datasets

Bioinformatic tools capable of registering, rapidly and reproducibly, large numbers of nanoliquid chromatography-nanoelectrospray ionization-tandem mass spectrometry (nLC-nESI-MS/MS) lipidomic datasets are lacking. We provide here a freely available Retention Time Standardization and Registration (RTStaR) algorithm that aligns nLC-nESI-MS/MS spectra within a single dataset and compares these aligned retention times across multiple datasets. This two-step calibration matches corresponding and identifies unique lipid species in different lipidomes from different matrices and organisms. RTStaR was developed using a population-based study of 1001 human serum samples composed of 71 distinct glycerophosphocholine metabolites comprising a total of 68,572 analytes. Platform and matrix independence were validated using different MS instruments, nLC methodologies, and mammalian lipidomes. The complete algorithm is packaged in two modular ExcelTM workbook templates for easy implementation. RTStaR is freely available from the India Taylor Lipidomics Research Platform http://www.neurolipidomics.ca/rtstar/rtstar.html. Technical support is provided through ldomic@uottawa.ca

bioinformatics↗

bioRxiv2021

Immunological factors, but not clinical features, predict visceral leishmaniasis relapse in patients co-infected with HIV

Visceral leishmaniasis (VL) has emerged as a clinically important opportunistic infection in HIV patients, as VL/HIV co-infected patients suffer from frequent VL relapse. Here, we followed cohorts of VL patients with or without HIV co-infections in Ethiopia and collected detailed clinical and immunological data during 12 months of follow-up. By the end of the study 78.1% of VL/HIV patients, but none of the VL only patients, had relapsed. Despite clinically defined cure, VL/HIV patients maintained high parasite loads, low BMI, hepatosplenomegaly and pancytopenia throughout follow-up. During detailed immunological study throughout the follow-up period, we identified three markers associated with VL relapse: i) failure to restore antigen-specific production of IFN{gamma}, ii) persistently low CD4+ T cell counts, and iii) high expression of PD1 on CD4+ T cells. We show that these three markers combine well in predicting VL relapse, and that all three measurements are needed for optimal predictive power. These three immunological markers can be measured in primary hospital settings in Ethiopia and can predict VL relapse after anti-leishmanial therapy. The use of our prediction model has the potential to improve disease management and patient care.

immunology↗

bioRxiv2021

BATL: Bayesian annotations for targeted lipidomics

MotivationBioinformatic tools capable of annotating, rapidly and reproducibly, large, targeted lipidomic datasets are limited. Specifically, few programs enable high-throughput peak assessment of liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) data acquired in either selected or multiple reaction monitoring (SRM and MRM) modes. ResultsWe present here Bayesian Annotations for Targeted Lipidomics (BATL), a Gaussian naive Bayes classifier for targeted lipidomics that annotates peak identities according to eight features related to retention time, intensity, and peak shape. Lipid identification is achieved by modelling distributions of these eight input features across biological conditions and maximizing the joint posterior probabilities of all peak identities at a given transition. When applied to sphingolipid and glycerophosphocholine SRM datasets, we demonstrate over 95% of all peaks are rapidly and correctly identified. Availability and implementationBATL software is freely accessible online at https://complimet.ca/batl/ and is compatible with Safari, Firefox, Chrome and Edge. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗