Comparative transcriptomic analysis of whole blood mycobacterial growth assays and tuberculosis patients’ blood RNA profiles

In vitro whole blood infection models are used for elucidating the immune response to Mycobacterium tuberculosis (Mtb). They exhibit commonalities but also differences, to the in vivo blood transcriptional response during natural human Mtb disease. Here, we present a description of concordant and discordant components of the immune response in blood, quantified through transcriptional profiling in an in vitro whole blood infection model compared to whole blood from patients with tuberculosis disease. We identified concordantly and discordantly expressed gene modules and performed in silico cell deconvolution. A high degree of concordance of gene expression between both adult and paediatric in vivo-in vitro tuberculosis infection was identified. Concordance in paediatric in vivo vs in vitro comparison is largely characterised by immune suppression, while in adults the comparison is marked by concordant immune activation, particularly that of inflammation, chemokine, and interferon signalling. Discordance between in vitro and in vivo increases over time and is driven by T-cell regulation and monocyte-related gene expression, likely due to apoptotic depletion of monocytes and increasing relative fraction of longer-lived cell types, such as T and B cells. Our approach facilitates a more informed use of the whole blood in vitro model, while also accounting for its limitations

Bioinformatics: living on the edge

A report on the 11th European Conference on Computational Biology (ECCB), Basel, Switzerland, September 9-12, 2012

Genome-wide host RNA signatures of infectious diseases: discovery and clinical translation

The use of whole blood gene expression to derive diagnostic biomarkers capable of distinguishing between phenotypically similar diseases holds great promise but remains a challenge. Differential gene expression analysis is used to identify the key genes that undergo changes in expression relative to healthy individuals, as well as to patients with other diseases. These key genes can act as diagnostic, prognostic and predictive markers of disease. Gene expression 'signatures' in the blood hold potential to be used for the diagnosis of infectious diseases, where current diagnostics are unreliable, ineffective or of limited potential. For diagnostic tests based on RNA signatures to be useful clinically, the first step is to identify the minimum set of gene transcripts that accurately identify the disease in question. The second requirement is rapid and cost effective detection of the gene expression levels. Whilst signatures have been described for a number of infectious diseases, 'clinic-ready' technologies for RNA detection from clinical samples are limited, though existing methods such as reverse transcription-polymerase chain reaction (RT-PCR) are likely to be superseded by a number of emerging technologies, which may form the basis of the translation of gene expression signatures into routine diagnostic tests for a range of disease states

Transcriptomic Profiling in Childhood H1N1/09 Influenza Reveals Reduced Expression of Protein Synthesis Genes

We compared the blood RNA transcriptome of children hospitalized with influenza A H1N1/09, respiratory syncytial virus (RSV) or bacterial infection, and healthy controls. Compared to controls, H1N1/09 patients showed increased expression of inflammatory pathway genes and reduced expression of adaptive immune pathway genes. This was validated on an independent cohort. The most significant function distinguishing H1N1/09 patients from controls was protein synthesis, with reduced gene expression. Reduced expression of protein synthesis genes also characterized the H1N1/09 expression profile compared to children with RSV and bacterial infection, suggesting that this is a key component of the pathophysiological response in children hospitalized with H1N1/09 infection

Detection of tuberculosis in HIV-infected and-uninfected African adults using whole blood RNA expression signatures: a case-control study

Background: A major impediment to tuberculosis control in Africa is the difficulty in diagnosing active tuberculosis (TB), particularly in the context of HIV infection. We hypothesized that a unique host blood RNA transcriptional signature would distinguish TB from other diseases (OD) in HIV-infected and -uninfected patients, and that this could be the basis of a simple diagnostic test. Methods and Findings: Adult case-control cohorts were established in South Africa and Malawi of HIV-infected or -uninfected individuals consisting of 584 patients with either TB (confirmed by culture of Mycobacterium tuberculosis [M.TB] from sputum or tissue sample in a patient under investigation for TB), OD (i.e., TB was considered in the differential diagnosis but then excluded), or healthy individuals with latent TB infection (LTBI). Individuals were randomized into training (80%) and test (20%) cohorts. Blood transcriptional profiles were assessed and minimal sets of significantly differentially expressed transcripts distinguishing TB from LTBI and OD were identified in the training cohort. A 27 transcript signature distinguished TB from LTBI and a 44 transcript signature distinguished TB from OD. To evaluate our signatures, we used a novel computational method to calculate a disease risk score (DRS) for each patient. The classification based on this score was first evaluated in the test cohort, and then validated in an independent publically available dataset (GSE19491). In our test cohort, the DRS classified TB from LTBI (sensitivity 95%, 95% CI [87–100]; specificity 90%, 95% CI [80–97]) and TB from OD (sensitivity 93%, 95% CI [83–100]; specificity 88%, 95% CI [74–97]). In the independent validation cohort, TB patients were distinguished both from LTBI individuals (sensitivity 95%, 95% CI [85–100]; specificity 94%, 95% CI [84–100]) and OD patients (sensitivity 100%, 95% CI [100–100]; specificity 96%, 95% CI [93–100]). Limitations of our study include the use of only culture confirmed TB patients, and the potential that TB may have been misdiagnosed in a small proportion of OD patients despite the extensive clinical investigation used to assign each patient to their diagnostic group. Conclusions: In our study, blood transcriptional signatures distinguished TB from other conditions prevalent in HIV-infected and -uninfected African adults. Our DRS, based on these signatures, could be developed as a test for TB suitable for use in HIV endemic countries. Further evaluation of the performance of the signatures and DRS in prospective populations of patients with symptoms consistent with TB will be needed to define their clinical value under operational conditions

Host transcriptional response to TB preventive therapy differentiates two sub-groups of IGRA-positive individuals.

We hypothesised that individuals with immunological sensitisation to Mycobacterium tuberculosis (Mtb), conventionally regarded as evidence of latent tuberculosis infection (LTBI), would demonstrate binary responses to preventive therapy (PT), reflecting the differential immunological consequences of the sterilisation of viable infection in those with active Mtb infection versus no Mtb killing in those who did not harbour viable bacilli. We investigated longitudinal whole blood transcriptional profile responses to PT of Interferon gamma release assay (IGRA)-positive tuberculosis contacts and IGRA-negative, tuberculosis-unexposed controls. Longitudinal unsupervised clustering analysis with a subset of 474 most variable genes in antigen-stimulated blood separated the IGRA-positive participants into two distinct subgroups, one of which clustered with the IGRA-negative controls. 117 probes were differentially expressed over time between the two cluster groups, many of them associated with immunological pathways important in mycobacterial control. We contend that the differential host RNA response reflects lack of Mtb viability in the group that clustered with the IGRA-negative unexposed controls, and Mtb viability in the group (1/3 of IGRA-positives) that clustered away. Gene expression patterns in the blood of IGRA-positive individuals emerging during the course of PT, which reflect Mtb viability, could have major implications in the identification of risk of progression, treatment stratification and biomarker development

Diagnosis of childhood tuberculosis and host RNA expression in Africa

Improved diagnostic tests for tuberculosis in children are needed. We hypothesized that transcriptional signatures of host blood could be used to distinguish tuberculosis from other diseases in African children who either were or were not infected with the human immunodeficiency virus (HIV

Diagnosis of Kawasaki disease using a minimal whole blood gene expression signature

Importance There is no diagnostic test for Kawasaki disease (KD). Diagnosis is based on clinical features shared with other febrile conditions, frequently resulting in delayed or missed treatment and an increased risk of coronary artery aneurysms. Objective To identify a whole blood gene expression signature that distinguishes children with KD in the first week of illness from other febrile conditions. Design Case-control discovery study groups comprising training, test, and validation groups of children with KD or comparator febrile illness. Setting Hospitals in the UK, Spain, Netherlands and USA. Participants The training and test discovery group comprised 404 children with infectious and inflammatory conditions (78 KD, 84 other inflammatory diseases, 242 bacterial or viral infections) and 55 healthy controls. The independent validation group included 130 febrile children and 102 KD patients, including 72 in the first 7 days of illness. Exposures Whole blood gene expression was evaluated using microarrays, and minimal transcript sets distinguishing KD were identified using a novel variable selection method (Parallel Deterministic Model Search). Main outcomes and measures The ability of transcript signatures - implemented as Disease Risk Scores - to discriminate KD cases from controls, was assessed by Area Under the Curve (AUC), sensitivity, and specificity at the optimal cut-point according to Youden’s index. Results A 13-transcript signature identified in the discovery training set distinguished KD from other infectious and inflammatory conditions in the discovery test set with AUC, sensitivity, and specificity (95% confidence intervals (CI)) of 96.2% (92.5-99.9), 81.7% (60.0-94.8), and 92.1% (84.0-97.0), respectively. In the validation set, the signature distinguished KD from febrile controls with AUC, sensitivity, and specificity (95% CI) of 94.6% (91.3-98.0), 85.9% (76.8-92.6), and 89.1% (83.0-93.7) respectively. The signature was applied to clinically defined categories of Definite, Highly Probable and Possible KD resulting in AUCs of 98.1%, 96.3% and 70.0% respectively, mirroring clinical certainty. Conclusions and relevance A 13-transcript blood gene expression signature distinguished KD from other febrile conditions. Diagnostic accuracy increased with certainty of clinical diagnosis. A test incorporating the 13-transcript Disease Risk Score may enable earlier diagnosis and treatment of KD, and reduce inappropriate treatment in those with other diagnoses