Project description:This dataset was used to establish whole blood transcriptional modules (n=260) that represent groups of coordinately expressed transcripts that exhibit altered abundance within individual datasets or across multiple datasets. This modular framework was generated to reduce the dimensionality of whole blood microarray data processed on the Illumina Beadchip platform yielding data-driven transcriptional modules with biologic meaning. This series combines nine independent datasets representing a spectrum of human pathologies expected to result in changes in gene abundance related to changes in expression or cellular composition of whole blood. These nine datasets are composed of 410 individual whole blood profiles generated from patients with HIV, tuberculosis, sepsis, systemic lupus erythematosus, systemic arthritis, B-cell deficiency and liver transplant. For each dataset healthy controls are also included. Each dataset’s expression data was preprocessed independently. First, probes were discarded if they were not present in at least ten percent of the dataset’s samples. Then, the sample data for each dataset was normalized using the BeadStudio average normalization algorithm. Once normalized, the signal was scaled such that all signals less than ten were set to ten. The signal median of all of the dataset’s samples was calculated for each probe. Probes were discarded if no sample had a difference in signal from the median that was greater than or equal to thirty, or if no sample had a fold change relative to the median that was either greater than or equal to 1.5, or less than or equal to 0.67. Finally, data was transformed to the log2 of the signal divided by the mean. Each of the preprocessed datasets was clustered in parallel using Euclidean distance and the Hartigan’s K-Means clustering algorithm, a hybrid of hierarchical and K-Means clustering algorithms. The number of clusters (k) was set to thirty, chosen to provide significant power during later module extraction steps. A higher value could have been chosen for k, but was not in order to minimize possibly arbitrary cluster splitting. Taking the nine sets of thirty clusters as input, we constructed a weighted co-cluster graph, a probe by probe matrix where the value of each cell (the weight) is set to the number of times probe_i and probe_j are found in the same cluster. In this instance, the values range from zero to nine, inclusive. At this point, the goal is to extract sets of probes that are most frequently clustered together, proceeding from the most stringent requirements to the least. To accomplish this, we employ the iterative algorithm. To begin, the maximum clique threshold is initialized to the number of input cluster sets, the paraclique threshold is calculated, and a minimum seed size is chosen (we used ten). The outer loop begins by creating an unweighted graph through application of the maximum clique threshold to the weighted co-cluster graph such that a probe pair, or edge, is represented in the unweighted graph if and only if the corresponding weight in the co-cluster graph equals or exceeds this threshold. We then begin the inner loop. The first step is to isolate the largest set of probes such that all pairs of probes in the set are completely connected in the unweighted graph - that is, there is no pair of probes in the set where the weight from the initial graph is smaller than the maximum clique threshold. In graph theoretic terms, the probes form a maximum clique. If the size of the probe set is smaller than the minimum seed size, we escape from the inner loop, reduce the threshold by one, and return to the beginning of the outer loop. Otherwise, the probe set is at least as large as the minimum seed size and it becomes the seed for a module. To allow for the inevitable clustering inaccuracies, we then employ the paraclique algorithm revisiting the co-cluster graph and adding to the seed any probe that is found to cluster with at least eighty-five percent of the seed’s members a number of times equal or exceeding the paraclique threshold. This final probe set is a module. It is removed from both graphs and named in accordance with the iterations in which it was found (i.e. a module extracted in the first iteration of the outer loop and the second iteration of the inner loop is designated M1.2). The inner loop then begins again with the reduced graphs. Those modules with conserved expression across diseases (formed by transcripts that cluster together for all nine datasets) were selected in early rounds whereas modules with greater disease specificity (formed by transcripts that cluster together only in a subset of the nine datasets) were selected in later rounds.

Project description:The objective of this study is to: 1) Characterize the immune responsiveness to administration of non-live vaccines in three cohorts of healthy adult subjects through the analysis of blood leukocytes transcriptional profiles. 2) Validate whole blood transcriptional profiles generated from standard 3mL blood draws versus 200uL blood draws obtained by finger stick. 3) Discover potential biomarkers for immune-responsiveness to non-live vaccines. A total of 621 blood samples were collected either by venipuncture (387) or finger prick (234) from four groups of healthy adults receiving either, 2009/10 seasonal influenza or 23-valent pneumococcal vaccine or placebo (saline) injections. Subjects were recruited in 3 cohorts with 4-7 individuals per group; cohort 3 was recruited for validation of the systemic day 1 immune signature in response to seasonal influenza and pneumococcal vaccination. From each subject, peripheral blood was drawn into Tempus tubes (Applied Biosystems) or obtained by finger prick into micro capillaries and then transferred into tempus reagent to lyse blood cells and stabilize RNA before storing at -80ºC until mRNA extraction. The training set for transcriptional profiling of blood obtained by venipuncture was performed in cohort 1 which included 6 healthy adult individuals receiving seasonal influenza vaccine, 6 healthy adult individuals receiving pneumococcal vaccine, and 6 healthy adult individuals receiving placebo (saline injections). The test set for transcriptional profiling of blood obtained by venipuncture was performed in cohort 2 which included 6 healthy adult individuals receiving seasonal influenza vaccine, 6 healthy adult individuals receiving pneumococcal vaccine, and 6 healthy adult individuals receiving placebo (saline injections). The validation set for confirming systemic day 1 transcriptome immune signature in response to seasonal influenza and pneumococcal vaccination was performed in cohort 3 which included 6 healthy adult individuals receiving seasonal influenza vaccine and 4 healthy adult individuals receiving pneumococcal vaccine. The training set for transcriptional profiling of blood obtained by finger prick was performed in cohort 2 and the test set in cohort 1.

Project description:The mammalian RNA-binding protein AUF1 (AU-binding factor 1, also known as heterogeneous nuclear ribonucleoprotein D, hnRNP D) binds to numerous mRNAs and influences their post-transcriptional fate. Given that many AUF1 target mRNAs encode muscle-specific factors, we investigated the function of AUF1 in skeletal muscle differentiation. In mouse C2C12 myocytes, where AUF1 levels rise at the onset of myogenesis and remain elevated throughout myocyte differentiation into myotubes, RIP (RNP immunoprecipitation) analysis indicated that AUF1 binds prominently to Mef2c (myocyte enhancer factor 2c) mRNA, which encodes the key myogenic transcription factor Mef2c. By performing mRNA half-life measurements and polysome distribution analysis, we found that AUF1 associated with the 3M-bM-^@M-^YUTR of Mef2c mRNA and promoted Mef2c translation without affecting Mef2c mRNA stability. In addition, AUF1 promoted Mef2c gene transcription via a lesser-known role of AUF1 in transcriptional regulation. Importantly, lowering AUF1 delayed myogenesis, while ectopically restoring Mef2c expression levels partially rescued the impairment of myogenesis seen after reducing AUF1 levels. We propose that Mef2c is a key effector of the myogenesis program promoted by AUF1. Keywords: ribonucleoprotein complex; post-transcriptional gene regulation; muscle cell differentiation; myocytes; mRNA translation; mRNA stability; post-transcriptional gene regulation; transcriptome C2C12 mouse myoblasts were cultured in Dulbecco's modified Eagle's medium (DMEM; Invitrogen, Carlsbad, CA, USA) supplemented with 10% serum (Invitrogen) and antibiotics (Invitrogen). Differentiation was induced on sub-confluent cultures by replacing the growth media (GM, DMEM with 10% FBS) with differentiation media (DM, DMEM with 2% horse serum). At various time points after differentiation, cells were harvested and RNA was extracted with phenol-chloroform and either saved as input samples for microarrys or subjected to AUF1 or IgG ribonucleoprotein immunoprecipitation. C2C12 cells cultured in GM and DM were lysed in 20 mM Tris-HCl at pH 7.5, 100 mM KCl, 5 mM MgCl2, and 0.5% NP-40 for 10 min on ice and centrifuged at 15,000 M-CM-^W g for 10 min at 4M-BM-0C. The supernatants were incubated with protein-A Dynabeads beads coated with anti-AUF1 (Millipore) or with control IgG (Santa Cruz Biotechnology) antibodies for 2 hr at 4M-BM-0C. The beads were washed with NT2 buffer (50 mM Tris-HCl [pH 7.5], 150 mM NaCl, 1 mM MgCl2, 0.05% NP-40), followed by incubation with 20 units of RNase-free DNase I for 15 min at 37M-BM-0C to remove the DNA. The samples were then incubated for 15 min at 55M-BM-0C with 0.1% SDS/0.5 mg/ml Proteinase K to digest proteins. The RNA from the IP samples was extracted using phenol-chloroform, precipitated, and used along with the RNA from the input samples for cDNA microarrays.

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Candidate Vaccine Assessment in a pH1N1 Rhesus Macaque Challenge Model

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