Dataset Information

System immunology-based identification of blood transcriptional modules correlating to antibody responses in sheep.

ABSTRACT: Inactivated vaccines lack immunogenicity and therefore require potent adjuvants. To understand the in vivo effects of adjuvants, we used a system immunology-based analysis of ovine blood transcriptional modules (BTMs) to dissect innate immune responses relating to either antibody or haptoglobin levels. Using inactivated foot-and-mouth disease virus as an antigen, we compared non-adjuvanted to liposomal-formulated vaccines complemented or not with TLR4 and TLR7 ligands. Early after vaccination, BTM relating to myeloid cells, innate immune responses, dendritic cells, and antigen presentation correlated positively, whereas BTM relating to T and natural killer cells, as well as cell cycle correlated negatively with antibody responses. Interestingly, similar BTM also correlated with haptoglobin, but in a reversed manner, indicating that acute systemic inflammation is not beneficial for early antibody responses. Analysis of vaccine-dependent BTM modulation showed that liposomal formulations induced similar responses to those correlating to antibody levels. Surprisingly, the addition of the TLR ligands appeared to reduce early immunological perturbations and mediated anti-inflammatory effects, despite promoting antibody responses. When pre-vaccination BTM were analyzed, we found that high vaccine responders expressed higher levels of many BTM relating to cell cycle, antigen-presenting cells, and innate responses as compared with low responders. In conclusion, we have transferred human BTM to sheep and identified early vaccine-induced responses associated with antibody levels or unwanted inflammation in a heterogeneous and small group of animals. Such readouts are applicable to other veterinary species and very useful to identify efficient vaccine adjuvants, their mechanism of action, and factors related to low responders.

SUBMITTER: Braun RO

PROVIDER: S-EPMC6170373 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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System immunology-based identification of blood transcriptional modules correlating to antibody responses in sheep.

Braun Roman Othmar RO Brunner Livia L Wyler Kurt K Auray Gaël G García-Nicolás Obdulio O Python Sylvie S Zumkehr Beatrice B Gaschen Véronique V Stoffel Michael Hubert MH Collin Nicolas N Barnier-Quer Christophe C Bruggmann Rémy R Summerfield Artur A

NPJ vaccines 20181003

Inactivated vaccines lack immunogenicity and therefore require potent adjuvants. To understand the in vivo effects of adjuvants, we used a system immunology-based analysis of ovine blood transcriptional modules (BTMs) to dissect innate immune responses relating to either antibody or haptoglobin levels. Using inactivated foot-and-mouth disease virus as an antigen, we compared non-adjuvanted to liposomal-formulated vaccines complemented or not with TLR4 and TLR7 ligands. Early after vaccination, B ...[more]

PMID: 30302283

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Project description:BackgroundThe heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA.MethodsWe downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and 'WGCNA' package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by 'glmnet' package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model.ResultsA total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the 'lightyellow' module for NPM1 mutation, the 'saddlebrown' module for RUNX1 mutation, the 'lightgreen' module for TP53 mutation). ORA revealed that the 'lightyellow' module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The 'saddlebrown' module was enriched in immune response process. And the 'lightgreen' module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743-0.79).ConclusionsWe established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

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.

Dataset Information

System immunology-based identification of blood transcriptional modules correlating to antibody responses in sheep.

Publications

System immunology-based identification of blood transcriptional modules correlating to antibody responses in sheep.

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