Biology subjects

Wilkinson, R. J.

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

26 records · Page 2Linked to original sources

bioRxiv2019

Elevated LRRK2 and α-synuclein levels in CSF of infectious meningitis patients

Neurodegenerative diseases such as Parkinsons (PD) have a complex aetiology consisting of an interplay of genetic and environmental factors. Inflammation and infection are proposed external factors that trigger disease progression. Tuberculous and cryptococcal meningitis frequently lead to long-term neurological sequelae but their association with the development of PD are unexplored. In this study, we protein profiled the CSF from 76 patients with or without infectious meningitis and found that proteins commonly associated with PD (LRRK2, tau and alpha-synuclein) were significantly elevated, establishing a link between neuroinflammation and infection. Importantly, these findings suggest that LRRK2, tau and alpha-synuclein could represent biomarkers of neuroinflammation.

immunology↗

bioRxiv2019

Mycobacterium tuberculosis cording in the cytosol of live lymphatic endothelial cells

The ability of Mycobacterium tuberculosis to form serpentine cords is intrinsically related to its virulence, but specifically how M. tuberculosis cording contributes to pathogenesis remains obscure. We show that several M. tuberculosis clinical isolates form intracellular cords in primary human lymphatic endothelial cells (hLEC) in vitro and also in the lymph nodes of patients with tuberculosis. We identified via RNA-seq a transcriptional programme in hLEC that activates cellular pro-survival and cytosolic surveillance of intracellular pathogens pathways. Consistent with this, cytosolic access of hLEC is required for intracellular M. tuberculosis cording; and cord formation is dependent on the M. tuberculosis ESX-1 type VII secretion system and the mycobacterial lipid PDIM. Finally, we show that M. tuberculosis cording is a novel size-dependent mechanism used by the pathogen to evade xenophagy in the cytosol of endothelial cells. These results provide a mechanism that explains the long-standing association between M. tuberculosis cording and virulence.

cell biology↗

bioRxiv2019

A Transcriptome Fingerprinting Assay for Clinical Immune Monitoring

BackgroundWhile our understanding of the role that the immune system plays in health and disease is growing at a rapid pace, available clinical tools to capture this complexity are lagging. We previously described the construction of a third-generation modular transcriptional repertoire derived from genome-wide transcriptional profiling of blood of 985 subjects across 16 diverse immunologic conditions, which comprises 382 distinct modules.\n\nResultsHere we describe the use of this modular repertoire framework for the development of a targeted transcriptome fingerprinting assay (TFA). The first step consisted in down-selection of the number of modules to 32, on the basis of similarities in changes in transcript abundance and functional interpretation. Next down-selection took place at the level of each of the 32 modules, with each one of them being represented by four transcripts in the final 128 gene panel. The assay was implemented on both the Fluidigm high throughput microfluidics PCR platform and the Nanostring platform, with the list of assays target probes being provided for both. Finally, we provide evidence of the versatility of this assay to assess numerous immune functions in vivo by demonstrating applications in the context of disease activity assessment in systemic lupus erythematosus and longitudinal immune monitoring during pregnancy.\n\nConclusionsThis work demonstrates the utility of data-driven network analysis applied to large-scale transcriptional profiling to identify key markers of immune responses, which can be downscaled to a rapid, inexpensive, and highly versatile assay of global immune function applicable to diverse investigations of immunopathogenesis and biomarker discovery.

immunology↗

bioRxiv2019

Identification of reduced host transcriptomic signatures for tuberculosis and digital PCR-based validation and quantification

Recently, host whole blood gene expression signatures have been identified for diagnosis of tuberculosis (TB). Absolute quantification of the concentrations of signature transcripts in blood have not been reported, but would facilitate the development of diagnostic tests.\n\nTo identify minimal transcript signatures, we applied a novel transcript selection procedure to microarray data from African adults comprising 536 patients with TB, other diseases (OD) and latent TB (LTBI), divided into training and test sets. Signatures were validated using reverse transcriptase (RT) - digital PCR (dPCR).\n\nA four-transcript signature (GBP6, TMCC1, PRDM1, ARG1) measured using RT-dPCR distinguished TB patients from those with OD (area under the curve (AUC) 93.8% (CI95% 82.2 - 100%). A three-transcript signature (FCGR1A, ZNF296, C1QB) differentiated TB from LTBI (AUC 97.3%, CI95%: 93.3 - 100%), regardless of HIV.\n\nThese signatures have been validated across platforms and across samples offering strong, quantitative support for their use as diagnostic biomarkers for TB.

molecular biology↗

bioRxiv2019

A Novel Repertoire of Blood Transcriptome Modules Based on Co-expression Patterns Across Sixteen Disease and Physiological States

As the capacity for generating large scale data continues to grow the ability to extract meaningful biological knowledge from it remains a limitation. Here we describe the development of a new fixed repertoire of transcriptional modules. It is meant to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome profiling data. It is supported by customized resources, which include analysis workflows, fingerprint grid plots data visualizations, interactive web applications providing access to a vast number of module-specific functional profiling reports, reference transcriptional profiles and give users the ability to visualize of changes in transcript abundance across the modular repertoire at different granularity levels. A use case focusing on a set of six modules comprising interferon-inducible genes is also provided. Altogether we hope that this resource will also serve as a framework for improving over time our collective understanding of the immunobiology underlying blood transcriptome profiling data.

immunology↗

bioRxiv2018

Drug susceptibility testing and mortality in patients treated for tuberculosis in high-burden countries

BackgroundDrug resistance and HIV co-infection are challenges for the global control of tuberculosis.\n\nMethodsWe collected Mycobacterium tuberculosis isolates from adult patients in Cote dIvoire, Democratic Republic of the Congo, Kenya, Nigeria, South Africa, Peru, and Thailand, stratified by HIV status and tuberculosis drug resistance. Molecular or phenotypic drug susceptibility testing (DST) was done locally and at the Swiss tuberculosis reference laboratory. We examined mortality during treatment according to DST results and treatment adequacy in logistic regression models adjusting for sex, age, sputum microscopy and HIV status.\n\nFindings634 tuberculosis patients were included; median age was 33.2 years, 239 (37.7%) were female, 272 (42.9%) HIV-positive and 69 (10.9%) patients died. Based on the reference laboratory DST, 394 (62.2%) strains were pan-susceptible, 45 (7.1%) mono-resistant, 163 (25.7%) multidrug-resistant (MDR-TB), and 30 (4.7%) had pre-extensive or extensive drug resistance (pre-XDR/XDR-TB). Results of reference and local laboratories were discordant in 121 (19.1%) cases, corresponding to a sensitivity of 84.3% and a specificity of 90.8%. In patients with drug-resistant tuberculosis, discordant results were associated with increased mortality (risk ratio 1.81; 95% CI 1.07-3.07). In logistic regression, compared to adequately treated patients with pan-susceptible strains, the adjusted odds ratio for death was 4.23 (95% CI 2.16-8.29) for adequately treated patients with drug-resistant strains and 21.54 (95% CI 3.36-138.1) for inadequately treated patients with drug-resistant strains. HIV status was not associated with mortality.\n\nInterpretationUsing a reference laboratory standard, inaccurate DST leading to inappropriate treatment of drug-resistant tuberculosis, but not HIV infection, contributed to mortality.

epidemiology↗

bioRxiv2018

ANIMA: Association Network Integration for Multiscale Analysis

Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of datapoints on single samples has become routine but relating big data datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publically available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular signatures of disease states or apply modular analysis frameworks to the data. Here we show, using a network-based data integration method using clinical phenotype and microarray data as inputs, that we can reconstruct multiple features (or endophenotypes) of disease states at various scales of organization, from transcript abundance patterns of individual genes through co-expression patterns of groups of genes to patterns of cellular behavior in whole blood samples, both in single experiments as well as in a meta-analysis of multiple datasets.

systems biology↗

bioRxiv2017

A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection

Whole blood transcriptional signatures distinguishing active tuberculosis patients from asymptomatic latently infected individuals exist. Consensus has not been achieved regarding the optimal reduced gene sets as diagnostic biomarkers that also achieve discrimination from other diseases. Here we show a blood transcriptional signature of active tuberculosis using RNA-Seq, confirming microarray results, that discriminates active tuberculosis from latently infected and healthy individuals, validating this signature in an independent cohort. Using an advanced modular approach, we utilise information from the entire transcriptome, which includes over-abundance of type I interferon-inducible genes and under-abundance of IFNG and TBX21, to develop a signature that discriminates active tuberculosis patients from latently infected individuals, or those with acute viral and bacterial infections. We suggest methods targeting gene selection across multiple discriminant modules can improve development of diagnostic biomarkers with improved performance. Finally, utilising the modular approach we demonstrate dynamic heterogeneity in a longitudinal study of recent tuberculosis contacts.

immunology↗