Dataset Information

Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers.

ABSTRACT:

Rationale

New approaches to define factors underlying the immunopathogenesis of pulmonary diseases including sarcoidosis and tuberculosis are needed to develop new treatments and biomarkers. Comparing the blood transcriptional response of tuberculosis to other similar pulmonary diseases will advance knowledge of disease pathways and help distinguish diseases with similar clinical presentations.

Objectives

To determine the factors underlying the immunopathogenesis of the granulomatous diseases, sarcoidosis and tuberculosis, by comparing the blood transcriptional responses in these and other pulmonary diseases.

Methods

We compared whole blood genome-wide transcriptional profiles in pulmonary sarcoidosis, pulmonary tuberculosis, to community acquired pneumonia and primary lung cancer and healthy controls, before and after treatment, and in purified leucocyte populations.

Measurements and main results

An Interferon-inducible neutrophil-driven blood transcriptional signature was present in both sarcoidosis and tuberculosis, with a higher abundance and expression in tuberculosis. Heterogeneity of the sarcoidosis signature correlated significantly with disease activity. Transcriptional profiles in pneumonia and lung cancer revealed an over-abundance of inflammatory transcripts. After successful treatment the transcriptional activity in tuberculosis and pneumonia patients was significantly reduced. However the glucocorticoid-responsive sarcoidosis patients showed a significant increase in transcriptional activity. 144-blood transcripts were able to distinguish tuberculosis from other lung diseases and controls.

Conclusions

Tuberculosis and sarcoidosis revealed similar blood transcriptional profiles, dominated by interferon-inducible transcripts, while pneumonia and lung cancer showed distinct signatures, dominated by inflammatory genes. There were also significant differences between tuberculosis and sarcoidosis in the degree of their transcriptional activity, the heterogeneity of their profiles and their transcriptional response to treatment.

SUBMITTER: Bloom CI

PROVIDER: S-EPMC3734176 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers.

Bloom Chloe I CI Graham Christine M CM Berry Matthew P R MP Rozakeas Fotini F Redford Paul S PS Wang Yuanyuan Y Xu Zhaohui Z Wilkinson Katalin A KA Wilkinson Robert J RJ Kendrick Yvonne Y Devouassoux Gilles G Ferry Tristan T Miyara Makoto M Bouvry Diane D Valeyre Dominique D Gorochov Guy G Blankenship Derek D Saadatian Mitra M Vanhems Phillip P Beynon Huw H Vancheeswaran Rama R Wickremasinghe Melissa M Chaussabel Damien D Banchereau Jacques J Pascual Virginia V Ho Ling-Pei LP Lipman Marc M O'Garra Anne A

PloS one 20130805 8

<h4>Rationale</h4>New approaches to define factors underlying the immunopathogenesis of pulmonary diseases including sarcoidosis and tuberculosis are needed to develop new treatments and biomarkers. Comparing the blood transcriptional response of tuberculosis to other similar pulmonary diseases will advance knowledge of disease pathways and help distinguish diseases with similar clinical presentations.<h4>Objectives</h4>To determine the factors underlying the immunopathogenesis of the granulomat ...[more]

PMID: 23940611

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Project description:Purpose: Pulmonary tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), has long been associated with the development of lung adenocarcinoma (LUAD) and sarcoidosis. These chronic lung diseases share histopathological similarities that challenge differential diagnosis. Methods: Human lung tissues were used for total RNA extraction with RNeasy Plus Mini Kit (Qiagen) according to the manufacturer’s protocol. RNA purity was assessed using a Nanodrop ND-2000 (Thermo Scientific), and each RNA sample had an A260:A280 ratio above 1.8 and an A260:A230 ratio above 2.0. RNA integrity was evaluated using the Agilent 2200 TapeStation (Agilent Technologies), and each sample had the RNA Integrity Number (RIN) above 7.0. The ribosomal RNAs (rRNAs) were removed using Ribo-Zero rRNA Removal Kits (Illumina). RNA libraries were then constructed by NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs) and evaluated using the Agilent 2200 TapeStation and Qubit® 2.0 (Life Technologies). Sequencing was performed by RiboBio Co., Ltd. on an Illumina HiSeq 3000 machine with paired-end 150-bp reads. The adaptors and low quality bases assessed using FASTQC 0.11.3 were trimmed by Trimmomatic 0.39 using the following options: TRAILING:20, MINLEN:235 and CROP:235. Trimmed reads were then aligned to the ensembl 79 (GRCh38.p2) reference genome using STAR 2.4.2a. FeatureCounts 1.6.2 was subsequently employed to convert aligned short reads into read counts for each sample. Genes with less than ten counts in two or more samples were removed. The data were then analyzed using R 3.4.4 and DEseq2 1.18.1. Differentially expressed genes of each disease group were identified using Wald statistics test, with fold-change > 2 and Benjamini–Hochberg (BH)-adjusted P < 0.1 as compared to NC group. Results: These findings provide novel pathogenic links and molecular markers for better understanding and differential diagnosis of pulmonary TB, LUAD and sarcoidosis.

Project description:BackgroundBlood transcriptomics can be used for confirmation of tuberculosis diagnosis or sputumless triage, and a comparison of their practical diagnostic accuracy is needed to assess their usefulness. In this study, we investigated potential biomarkers to improve our understanding of the pathogenesis of active pulmonary tuberculosis (PTB) using bioinformatics methods.MethodsDifferentially expressed genes (DEGs) were analyzed between PTB and healthy controls (HCs) based on two microarray datasets. Pathways and functional annotation of DEGs were identified and ten hub genes were selected. They were further analyzed and selected, then verified with an independent sample set. Finally, their diagnostic power was further evaluated between PTB and HCs or other diseases.Results62 DEGs mostly related to type I IFN pathway, IFN-γ-mediated pathway, etc. in GO term and immune process, and especially RIG-I-like receptor pathway were acquired. Among them, OAS1, IFIT1 and IFIT3 were upregulated and were the main risk factors for predicting PTB, with adjusted risk ratios of 1.36, 3.10, and 1.32, respectively. These results further verified that peripheral blood mRNA expression levels of OAS1, IFIT1 and IFIT3 were significantly higher in PTB patients than HCs (all P < 0.01). The performance of a combination of these three genes (three-gene set) had exceeded that of all pairwise combinations of them in discriminating TB from HCs, with mean AUC reaching as high as 0.975 with a sensitivity of 94.4% and a specificity of 100%. The good discernibility capacity was evaluated d via 7 independent datasets with an AUC of 0.902, as well as mean sensitivity of 87.9% and mean specificity of 90.2%. In regards to discriminating PTB from other diseases (i.e., initially considered to be possible TB, but rejected in differential diagnosis), the three-gene set equally exhibited an overall strong ability to separate PTB from other diseases with an AUC of 0.999 (sensitivity: 99.0%; specificity: 100%) in the training set, and 0.974 with a sensitivity of 96.4% and a specificity of 98.6% in the test set.ConclusionThe described commonalities and unique signatures in the blood profiles of PTB and the other control samples have considerable implications for PTB biosignature design and future diagnosis, and provide insights into the biological processes underlying PTB.

Dataset Information

Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers.

Rationale

Objectives

Methods

Measurements and main results

Conclusions

Publications

Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers.

Similar Datasets

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