Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy

Rationale: Globally there are approximately 9 million new active tuberculosis cases and 1.4 million deaths annually . Effective antituberculosis treatment monitoring is difficult as there are no existing biomarkers of poor adherence or inadequate treatment earlier than 2 months after treatment initiation. Inadequate treatment leads to worsening disease, disease transmission and drug resistance. Objectives To determine if blood transcriptional signatures change in response to antituberculosis treatment and could act as early biomarkers of a successful response. METHODS: Blood transcriptional profiles of untreated active tuberculosis patients in South Africa were analysed before, during (2 weeks and 2 months), at the end of (6 months) and after (12 months) antituberculosis treatment, and compared to individuals with latent tuberculosis. An active-tuberculosis transcriptional signature and a specific treatment-response transcriptional signature were derived. The specific treatment response transcriptional signature was tested in two independent cohorts. Two quantitative scoring algorithms were applied to measure the changes in the transcriptional response. The most significantly represented pathways were determined using Ingenuity Pathway Analysis. RESULTS: An active tuberculosis 664-transcript signature and a treatment specific 320-transcript signature significantly diminished after 2 weeks of treatment in all cohorts, and continued to diminish until 6 months. The transcriptional response to treatment could be individually measured in each patient. CONCLUSIONS: Significant changes in the transcriptional signatures measured by blood tests were readily detectable just 2 weeks after treatment initiation. These findings suggest that blood transcriptional signatures could be used as early surrogate biomarkers of successful treatment response

Host Immune Transcriptional Profiles Reflect the Variability in Clinical Disease Manifestations in Patients with Staphylococcus aureus Infections

Staphylococcus aureus infections are associated with diverse clinical manifestations leading to significant morbidity and mortality. To define the role of the host response in the clinical manifestations of the disease, we characterized whole blood transcriptional profiles of children hospitalized with community-acquired S. aureus infection and phenotyped the bacterial strains isolated. The overall transcriptional response to S. aureus infection was characterized by over-expression of innate immunity and hematopoiesis related genes and under-expression of genes related to adaptive immunity. We assessed individual profiles using modular fingerprints combined with the molecular distance to health (MDTH), a numerical score of transcriptional perturbation as compared to healthy controls. We observed significant heterogeneity in the host signatures and MDTH, as they were influenced by the type of clinical presentation, the extent of bacterial dissemination, and time of blood sampling in the course of the infection, but not by the bacterial isolate. System analysis approaches provide a new understanding of disease pathogenesis and the relation/interaction between host response and clinical disease manifestations

IFN priming is necessary but not sufficient to turn on a migratory dendritic cell program in lupus monocytes.

Blood monocytes from children with systemic lupus erythematosus (SLE) behave similar to dendritic cells (DCs), and SLE serum induces healthy monocytes to differentiate into DCs in a type I IFN-dependent manner. In this study, we found that these monocytes display significant transcriptional changes, including a prominent IFN signature, compared with healthy controls. Few of those changes, however, explain DC function. Exposure to allogeneic T cells in vitro reprograms SLE monocytes to acquire DC phenotype and function, and this correlates with both IFN-inducible (IP10) and proinflammatory cytokine (IL-1β and IL6) expression. Furthermore, we found that both IFN and SLE serum induce the upregulation of CCR7 transcription in these cells. CCR7 protein expression, however, requires a second signal provided by TLR agonists such as LPS. Thus, SLE serum primes a subset of monocytes to readily (\u3c24 \u3eh) respond to TLR agonists and acquire migratory DC properties. Our findings might explain how microbial infections exacerbate lupus. J Immunol 2014 Jun 15; 192(12):5586-98

Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients.

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by loss of tolerance to nucleic acids and highly diverse clinical manifestations. To assess its molecular heterogeneity, we longitudinally profiled the blood transcriptome of 158 pediatric patients. Using mixed models accounting for repeated measurements, demographics, treatment, disease activity (DA), and nephritis class, we confirmed a prevalent IFN signature and identified a plasmablast signature as the most robust biomarker of DA. We detected gradual enrichment of neutrophil transcripts during progression to active nephritis and distinct signatures in response to treatment in different nephritis subclasses. Importantly, personalized immunomonitoring uncovered individual correlates of disease activity that enabled patient stratification into seven groups, supported by patient genotypes. Our study uncovers the molecular heterogeneity of SLE and provides an explanation for the failure of clinical trials. This approach may improve trial design and implementation of tailored therapies in genetically and clinically complex autoimmune diseases. PAPERCLIP. Cell 2016 Apr 21; 165(4):551-65

Individual patient’s transcriptional response occurred at a variable rate.

<p>320 gene list, differentially expressed genes derived from comparing the untreated expression profiles and their corresponding end of treatment (6 months) expression profiles in the South Africa 2011 Active TB Training Set. <b>(A)</b> Heatmap of South Africa 2011 cohort Active TB Training Set, normalised to the median of all transcripts, shows hierarchical clustered transcripts differentiating over time per individual. <b>(B)</b> Each patient’s temporal molecular response diminishes in the Active TB Training Set cohort.</p

Specific module subsets correlate with laboratory results.

<p><b>A.</b> Heatmap representing correlation (Spearman R) between module percent expression in columns and continuous laboratory parameters in rows. Hierarchical clustering (Euclidian distance) was applied in both dimensions. <b>B.</b> Connection network representing correlation between molecular nodes (modules) in blue and clinical nodes (laboratory parameters) in green. Spearman R correlation greater than 0.3 are represented.</p

Demographic and laboratory characteristics of patients and healthy controls in training and test sets.

<p>Median values [min-max range]; AA = African-American, C = Caucasian, H = Hispanic, O = Other; M = Male, F = Female; WBC = White Blood Count; RBC = Red Blood Cell count; MCV = Mean Corpuscular Volume; MCH = Mean Corpuscular Hemoglobin; MCHC = Mean Corpuscular Hemoglobin Concentration; RDW = Red blood cell Distribution Width; MPV = Mean Platelet Volume; ESR = Erythrocyte Sedimentation Rate; CRP = C-reactive Protein;</p>*<p>only 1 sample.</p><p>Statistics: for categorical variables, Fisher's exact test is used; for continuous variables, Mann-Whitney test is used.</p

Change in treatment specific signature is validated in an independent UK cohort.

<p>320 gene list derived from the differentially expressed genes between the untreated and 6 month treated samples in the treated South Africa 2011 cohort. <b>(A)</b> Heatmap of the treated UK 2011 Cohort, normalised to the median of all transcripts, shows diminution of the treatment specific transcriptional signature in the UK cohort in response to successful anti-TB treatment. <b>(B)</b> Temporal molecular response shows significant changes in response at 2 weeks in the UK cohort (linear mixed models, bars represent mean & 95% confidence intervals, *** = p<0.001, ** = p<0.01, * = p<0.05). <b>(C)</b> A diminished response can be seen in each patient by their temporal molecular response.</p