BloodGen3Module: blood transcriptional module repertoire analysis and visualization using R

Rinchai, Darawan; Roelands, Jessica; Toufiq, Mohammed; Hendrickx, Wouter; Altman, Matthew C.; Bedognetti, Davide; Chaussabel, Damien

doi:10.1093/bioinformatics/btab121

Cited by 27 publications

(33 citation statements)

References 38 publications

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“…To further characterize patient cluster variability at a molecular level, we then used the blood transcriptome modular repertoire recently established on an expended range of disease and pathological states. The latter includes 382 transcriptome modules based on genes co-expression patterns across 16 diseases and 985 unique transcriptome profiles 9 . Again, no aggregate was found differentially expressed in C2 confirming the healthy-like profile of these patients, whereas an up-regulated IFN signature dominated in C1, C3, and C4 (Fig.…”

Section: Resultsmentioning

confidence: 99%

“… Each heatmap, achieved with BloodGen3Module R package 9 , represents one of the most significant patterns differentiating the four clusters of 304 pSS patients (C1: 101, C2: 77, C3: 88, and C4: 38) compared to 330 healthy volunteers (HV). These patterns correspond to modules associated with IFN, neutrophils, inflammation, cytokines/chemokines, protein synthesis, erythrocytes, monocytes, B cells and T cells.…”

Section: Resultsmentioning

confidence: 99%

“…Enrichment analysis was performed by applying a two-tailed Fisher-exact test 61 against different sources of gene modules or pathways: (i) 3 strongly upregulated IFN-annotated modules from 10 (M1.2, M3.4, and M5.12) determined from peripheral blood transcriptomic data with for each a distinct activation threshold, (ii) genes preferentially induced by IFNα or IFNγ identified by 10 , (iii) canonical pathway from Ingenuity Pathway Analysis (IPA, Release Date: 2020-06-01), (iv) repertoire recently established on an expended range of disease and pathological states (382 transcriptome modules based on genes co-expression patterns across 16 diseases and 985 unique transcriptome profiles) by 9 .…”

Section: Methodsmentioning

confidence: 99%

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A new molecular classification to drive precision treatment strategies in primary Sjögren’s syndrome

et al. 2021

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There is currently no approved treatment for primary Sjögren’s syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren’s syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A new molecular classification to drive precision treatment strategies in primary Sjögren’s syndrome

et al. 2021

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“…Other groups proposed a modular approach to group DEGs into groups according to their pathway or function [ 3 ]. There are 382 modules while each has a dozen of genes, and software was used to interpret change of TA inside modules [ 25 ]. Nonetheless, interpretation of fingerprint profile output from these 382 modules is not easy.…”

Section: Discussionmentioning

confidence: 99%

Direct Measurement of B Lymphocyte Gene Expression Biomarkers in Peripheral Blood Transcriptomics Enables Early Prediction of Vaccine Seroconversion

Huang

Liu

Leung

et al. 2021

Genes

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Peripheral blood transcriptome is a highly promising area for biomarker development. However, transcript abundances (TA) in these cell mixture samples are confounded by proportions of the component leukocyte subpopulations. This poses a challenge to clinical applications, as the cell of origin of any change in TA is not known without prior cell separation procedure. We developed a framework to develop a cell-type informative TA biomarkers which enable determination of TA of a single cell-type (B lymphocytes) directly in cell mixture samples of peripheral blood (e.g., peripheral blood mononuclear cells, PBMC) without the need for subpopulation separation. It is applicable to a panel of genes called B cell informative genes. Then a ratio of two B cell informative genes (a target gene and a stably expressed reference gene) obtained in PBMC was used as a new biomarker to represent the target gene expression in purified B lymphocytes. This approach, which eliminates the tedious procedure of cell separation and directly determines TA of a leukocyte subpopulation in peripheral blood samples, is called the Direct LS-TA method. This method is applied to gene expression datasets collected in influenza vaccination trials as early predictive biomarkers of seroconversion. By using TNFRSF17 or TXNDC5 as the target genes and TNFRSF13C or FCRLA as the reference genes, the Direct LS-TA B cell biomarkers were determined directly in the PBMC transcriptome data and were highly correlated with TA of the corresponding target genes in purified B lymphocytes. Vaccination responders had almost a 2-fold higher Direct LS-TA biomarker level of TNFRSF17 (log 2 SMD = 0.84, 95% CI = 0.47–1.21) on day 7 after vaccination. The sensitivity of these Direct LS-TA biomarkers in the prediction of seroconversion was greater than 0.7 and area-under curves (AUC) were over 0.8 in many datasets. In this paper, we report a straightforward approach to directly estimate B lymphocyte gene expression in PBMC, which could be used in a routine clinical setting. Moreover, the method enables the practice of precision medicine in the prediction of vaccination response. More importantly, seroconversion could now be predicted as early as day 7. As the acquired immunology pathway is common to vaccination against influenza and COVID-19, these biomarkers could also be useful to predict seroconversion for the new COVID-19 vaccines.

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“…Gene set enrichment analysis (GSEA) (Subramanian et al, 2005) was performed with fast GSEA (Korotkevich et al, 2021) using the fgseaMultilevel function on genes ranked bylog 10 (p value)*sign(log 2 fold-change), with significance and fold-change values obtained from the DGE analyses. Minimum gene set size was set to 20 and gene sets used were low-annotation blood transcription modules (BTMs) (Li et al, 2014), high-annotation BTMs (Kazmin et al, 2017), BloodGen3 modules (Rinchai et al, 2021), or modules derived from the Monaco et al RNA-seq dataset (Monaco et al, 2019). For the Monaco modules, a gene was included within a cell type-specific module if its expression z-score (scaled across all cell types) was greater than 1.75 for that cell type.…”

Section: Methods Detailsmentioning

confidence: 99%

Innate immune activation restricts priming and protective efficacy of the radiation-attenuated PfSPZ malaria vaccine

Senkpeil

Bhardwaj

Little

et al. 2021

Preprint

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Baseline innate immune signatures can influence protective immunity following vaccination. Here, we used systems transcriptional analysis to assess the molecular mechanisms underlying differential immunogenicity and protective efficacy results of a clinical trial of the radiation-attenuated whole sporozoite PfSPZ Vaccine in African infants. Innate immune activation and myeloid signatures at pre-vaccination baseline correlated with protection from Plasmodium falciparum infection in placebo controls, while the same signatures predicted susceptibility to infection among infants who received the highest and most protective dose of the PfSPZ Vaccine. Machine learning identified monocytes and an antigen presentation signature as pre-vaccination features predictive of malaria infection after highest-dose PfSPZ vaccination. Consistent with these human data, innate stimulation in vivo conferred protection against malaria infection in mice while diminishing the CD8+ T cell response to radiation-attenuated sporozoites. These data establish a dichotomous role of innate stimulation for malaria protection and induction of protective immunity of whole-sporozoite malaria vaccines.

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BloodGen3Module: blood transcriptional module repertoire analysis and visualization using R

Cited by 27 publications

References 38 publications

A new molecular classification to drive precision treatment strategies in primary Sjögren’s syndrome

A new molecular classification to drive precision treatment strategies in primary Sjögren’s syndrome

Direct Measurement of B Lymphocyte Gene Expression Biomarkers in Peripheral Blood Transcriptomics Enables Early Prediction of Vaccine Seroconversion

Innate immune activation restricts priming and protective efficacy of the radiation-attenuated PfSPZ malaria vaccine

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