An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation.

BACKGROUND: A proper balance between different T helper (Th) cell subsets is necessary for normal functioning of the adaptive immune system. Revealing key genes and pathways driving the differentiation to distinct Th cell lineages provides important insight into underlying molecular mechanisms and new opportunities for modulating the immune response. Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior. RESULTS: We studied transcriptional dynamics of human umbilical cord blood T helper cells cultured in absence and presence of cytokines promoting Th1 or Th2 differentiation. To identify genes that exhibit distinct lineage commitment dynamics and are specific for initiating differentiation to different Th cell subsets, we developed a novel computational methodology (LIGAP) allowing integrative analysis and visualization of multiple lineages over whole time-course profiles. Applying LIGAP to time-course data from multiple Th cell lineages, we identified and experimentally validated several differentially regulated Th cell subset specific genes as well as reciprocally regulated genes. Combining differentially regulated transcriptional profiles with transcription factor binding site and pathway information, we identified previously known and new putative transcriptional mechanisms involved in Th cell subset differentiation. All differentially regulated genes among the lineages together with an implementation of LIGAP are provided as an open-source resource. CONCLUSIONS: The LIGAP method is widely applicable to quantify differential time-course dynamics of many types of datasets and generalizes to any number of conditions. It summarizes all the time-course measurements together with the associated uncertainty for visualization and manual assessment purposes. Here we identified novel human Th subset specific transcripts as well as regulatory mechanisms important for the initiation of the Th cell subset differentiation.

Troponin-specific autoantibody interference in different cardiac troponin I assay configurations.

BACKGROUND: Autoantibodies to cardiac troponins (cTnAAb) can interfere with the measurement of cardiac troponin I (cTnI) by immunoassays. The aim of this study was to explore the degree of cTnAAb interference in different cTnI assay configurations. METHODS: Ternary troponin complex was added into samples (serum or plasma, n = 132, 68% cTnAAb positive) from individuals without known cardiac conditions. The recovery of cTnI was then measured with 6 investigational cTnI assays (2, 3, or 4 antibodies per assay). Three of these assays were then selected for further comparison by use of samples (plasma, n = 210, 33% cTnAAb positive) from non-ST-elevation acute coronary syndrome patients in the FRISC-II (FRagmin/Fast Revascularisation during InStability in Coronary artery disease) cohort. Finally, these results were compared to those obtained with 3 commercial cTnI assays. RESULTS: Analytical recoveries varied widely among the 6 investigational assays. Notably the low recoveries (median 9%) of the midfragment-targeting reference assay were normalized (median 103%) with the use of the 4-antibody assay construct (3 capture, 1 tracer antibody) with only 1 antibody against a midfragment epitope. Reduced analytical recoveries correlated closely with measured autoantibody amounts. cTnI concentrations from cTnAAb-positive patient samples determined with 3 investigational assays confirmed the reduced concentrations expected from the low analytical recoveries. The results from the commercial cTnI assays with antibody selections representative for contemporary assay constructs revealed a similar underestimation (up to 20-fold) of cTnI in cTnAAb-positive samples. CONCLUSIONS: A novel cTnI assay deviating from the conventional IFCC-recommended midfragment approach substantially improves cTnI detection in samples containing cTnAAbs.

Autoantibodies to cardiac troponin associate with higher initial concentrations and longer release of troponin I in acute coronary syndrome patients.

BACKGROUND: Cardiac troponin (cTn) is an established marker of myocardial infarction. Pronounced heterogeneity and the minute amounts released into the circulation constitute significant challenges for cTn detection. Recently, autoantibody formation to cTn was shown to be common and to interfere with immunoassay performance. In this study, we investigated cTn autoantibodies and cardiac troponin I (cTnI) in acute coronary syndrome (ACS) patients over a 1-year period after the index event. METHODS: We used a second-generation cTnI assay designed to reduce the interference of cTn autoantibodies. The assay for cTn autoantibodies used 2 anti-cTnI antibodies to capture the ternary cTnI-complex, enabling unrestricted binding of the autoantibodies, which were detected with a labeled antihuman IgG antibody. We analyzed serum samples from 81 non-ST-elevation ACS patients taken at admission and after 1 week and 3 and 12 months. RESULTS: We found 14 cTn autoantibody-positive patients (21%) among the 67 cTnI-positive and none among the 14 cTnI-negative patients. Nine were autoantibody-positive at admission, and 5 became positive at 1 week. Autoantibody signals significantly increased in the 1-week and 3-month samples. At all time points, cTnI was significantly increased in the autoantibody-positive group relative to the negative group. Persistent cTnI elevations at 3 and 12 months were seen in the patients already autoantibody positive at admission. CONCLUSIONS: During ACS, patients with cTn autoantibodies have higher cTnI release and therefore larger myocardial damage than patients without autoantibodies. Their cTnI release also lasts longer, at least months. The possible prognostic impact of these observations must be evaluated in larger clinical cohorts.