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- Repsilber, Dirk, 1971-, et al.
(författare)
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Biomarker discovery in heterogeneous tissue samples : taking the in-silico deconfounding approach
- 2010
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Ingår i: BMC Bioinformatics. - London, UK : BioMed Central. - 1471-2105. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Background: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.Results: Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.Conclusions: The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.
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| 2. |
- Upton, Caryn M., et al.
(författare)
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Safety and efficacy of BCG re-vaccination in relation to COVID-19 morbidity in healthcare workers : A double- blind, randomised, controlled, phase 3 trial
- 2022
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Ingår i: eClinicalMedicine. - : Elsevier. - 2589-5370. ; 48
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Tidskriftsartikel (refereegranskat)abstract
- Background BCG vaccination prevents severe childhood tuberculosis (TB) and was introduced in South Africa in the 1950s. It is hypothesised that BCG trains the innate immune system by inducing epigenetic and functional reprogramming, thus providing non-specific protection from respiratory tract infections. We evaluated BCG for reduction of morbidity and mortality due to COVID-19 in healthcare workers in South Africa. Methods This randomised, double-blind, placebo-controlled trial recruited healthcare workers at three facilities in the Western Cape, South Africa, unless unwell, pregnant, breastfeeding, immunocompromised, hypersensitivity to BCG, or undergoing experimental COVID-19 treatment. Participants received BCG or saline intradermally (1:1) and were contacted once every 4 weeks for 1 year. COVID-19 testing was guided by symptoms. Hospitalisation, COVID-19, and respiratory tract infections were assessed with Cox proportional hazard modelling and time-to-event analyses, and event severity with post hoc Markovian analysis. This study is registered with ClinicalTrials.gov, NCT04379336. Findings Between May 4 and Oct 23, 2020, we enrolled 1000 healthcare workers with a median age of 39 years (IQR 30-49), 70.4% were female, 16.5% nurses, 14.4% medical doctors, 48.5% had latent TB, and 15.3% had evidence of prior SARS-CoV-2 exposure. Hospitalisation due to COVID-19 occurred in 15 participants (1.5%); ten (66.7%) in the BCG group and five (33.3%) in the placebo group, hazard ratio (HR) 2.0 (95% CI 0.69-5.9, p= 0.20), indicating no statistically significant protection. Similarly, BCG had no statistically significant effect on COVID-19 (p= 0.63, HR = 1.08, 95% CI 0.82-1.42). Two participants (0.2%) died from COVID-19 and two (0.2%) from other reasons, all in the placebo group. Interpretation BCG did not protect healthcare workers from SARS-CoV-2 infection or related severe COVID-19 disease and hospitalisation.
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| 3. |
- Weiner, January, 3rd, et al.
(författare)
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Biomarkers of inflammation, immunosuppression and stress with active disease are revealed by metabolomic profiling of tuberculosis patients
- 2012
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Ingår i: PLOS ONE. - San Fransisco, USA : Public Library Science. - 1932-6203. ; 7:7, s. e40221-
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Tidskriftsartikel (refereegranskat)abstract
- Although tuberculosis (TB) causes more deaths than any other pathogen, most infected individuals harbor the pathogen without signs of disease. We explored the metabolome of >400 small molecules in serum of uninfected individuals, latently infected healthy individuals and patients with active TB. We identified changes in amino acid, lipid and nucleotide metabolism pathways, providing evidence for anti-inflammatory metabolomic changes in TB. Metabolic profiles indicate increased activity of indoleamine 2,3 dioxygenase 1 (IDO1), decreased phospholipase activity, increased abundance of adenosine metabolism products, as well as indicators of fibrotic lesions in active disease as compared to latent infection. Consistent with our predictions, we experimentally demonstrate TB-induced IDO1 activity. Furthermore, we demonstrate a link between metabolic profiles and cytokine signaling. Finally, we show that 20 metabolites are sufficient for robust discrimination of TB patients from healthy individuals. Our results provide specific insights into the biology of TB and pave the way for the rational development of metabolic biomarkers for TB.
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