ImmuCo: a database of gene co-expression in immune cells

Self Cite

Immune cells are highly plastic in both gene expression and cell phenotype. We have established a method of gene expressional plasticity and virtual sorting to evaluate immune cell subpopulations and their characteristic genes in human CD4 T cells. In this study, we continued to investigate the informatics mechanism on the effectiveness of virtual sorting. We found that virtual sorting had an overall positive correlation to the Pearson correlation in the identification of positively correlated genes. However, owing to nonlinear biological anticorrelation, virtual sorting showed a distinctive advantage for anticorrelated genes, suggesting an important role in the identification of negative regulators. In addition, based on virtual sorting results, we identified two basic gene sets among highly plastic genes, i.e., highly plastic cell cycle-associated molecules and highly plastic immune and defense response-associated molecules. Genes within each set tended to be positively connected, but genes between two sets were often anticorrelated. Further analysis revealed preferential transcription factor binding motifs existed between highly plastic cell cycle-associated molecules and highly plastic immune and defense response-associated molecules. Our results strongly suggested predetermined regulation, which was called an immune cell internal phenotype, should exist and could be mined by virtual sorting analysis. This provided efficient functional clues to study immune cell phenotypes and their regulation. Moreover, the current substantial virtual sorting results in both CD4 T cells and B cells provide a useful resource for big-data-driven experimental studies and knowledge discoveries.

Section: Virtual Sorting Has a Distinctive Advantage In Identificatiomentioning

confidence: 99%

“…All data (generated by Affymetrix human U133 plus 2.0 arrays) were from the Gene Expression Omnibus (GEO) database and downloaded according to the method previously described (10,12). Healthy human CD4 + T cells (470 GEO samples, or 470 arrays and measurements) were taken directly from our recent study (11).…”

Section: Dataset and Analysismentioning

confidence: 99%

Virtual Sorting Has a Distinctive Advantage in Identification of Anticorrelated Genes and Further Negative Regulators of Immune Cell Subpopulations

Wang

Han

2017

Self Cite

“…To study coregulation of ISG15/IL-10/IFNG pathways in different cell types ex vivo, we next examined transcriptome datasets of purified major human leukocyte subsets (ImmuCo, ImmuSort) (28,29). Expression levels of ISG15 and IL-10 are positively correlated in total PBMCs, purified monocytes, and macrophages, but not neutrophils and T cells (Fig.…”

Section: Cd14 + Cells Are the Main Producers Of Isg15-induced Il-10mentioning

confidence: 99%

ISG15-Induced IL-10 Is a Novel Anti-Inflammatory Myeloid Axis Disrupted during Active Tuberculosis

Santos

Weyenbergh

Delgobo

et al. 2018

IFN-stimulated gene 15 (ISG15) deficiency in humans leads to severe IFNopathies and mycobacterial disease, the latter being previously attributed to its extracellular cytokine-like activity. In this study, we demonstrate a novel role for secreted ISG15 as an IL-10 inducer, unique to primary human monocytes. A balanced ISG15-induced monocyte/IL-10 versus lymphoid/IFN-γ expression, correlating with p38 MAPK and PI3K signaling, was found using targeted in vitro and ex vivo systems analysis of human transcriptomic datasets. The specificity and MAPK/PI3K-dependence of ISG15-induced monocyte IL-10 production was confirmed in vitro using CRISPR/Cas9 knockout and pharmacological inhibitors. Moreover, this ISG15/IL-10 axis was amplified in leprosy but disrupted in human active tuberculosis (TB) patients. Importantly, ISG15 strongly correlated with inflammation and disease severity during active TB, suggesting its potential use as a biomarker, awaiting clinical validation. In conclusion, this study identifies a novel anti-inflammatory ISG15/IL-10 myeloid axis that is disrupted in active TB.

“…These big data imply that a great deal of knowledge will be discovered through data mining. Previously, we used immunologically relevant transcriptome data from microarrays to analyze gene coexpression (15), and we evaluated marker molecules based on gene plasticity (GPL) (14). GPL is the degree of change in the expression of a gene that happens in response to various environmental or genetic influences.…”

Section: Any Phenotypes and Immune Cell Subpopulations Have Been Idmentioning

confidence: 99%

“…The expressional correlation analysis and present-present (PP) rate calculation were described previously by using the ImmuCo data set (15). The Pearson's r was calculated by using the signal values in human CD4 + T cells.…”

Section: Correlation Analysismentioning

confidence: 99%

Electronic Sorting of Immune Cell Subpopulations Based on Highly Plastic Genes

Wang

Han

2016

Self Cite

Immune cells are highly heterogeneous and plastic with regard to gene expression and cell phenotype. In this study, we categorized genes into those with low and high gene plasticity, and those categories revealed different functions and applications. We proposed that highly plastic genes could be suited for the labeling of immune cell subpopulations; thus, novel immune cell subpopulations could be identified by gene plasticity analysis. For this purpose, we systematically analyzed highly plastic genes in human and mouse immune cells. In total, 1,379 human and 883 mouse genes were identified as being extremely plastic. We also expanded our previous immunoinformatic method, electronic sorting, which surveys big data to perform virtual analysis. This approach used correlation analysis and took dosage changes into account, which allowed us to identify the differentially expressed genes. A test with human CD4+ T cells supported the method’s feasibility, effectiveness, and predictability. For example, with the use of human nonregulatory T cells, we found that FOXP3hiCD4+ T cells were highly expressive of certain known molecules, such as CD25 and CTLA4, and that this process of investigation did not require isolating or inducing these immune cells in vitro. Therefore, the sorting process helped us to discover the potential signature genes or marker molecules and to conduct functional evaluations for immune cell subpopulations. Finally, in human CD4+ T cells, 747 potential immune cell subpopulations and their candidate signature genes were identified, which provides a useful resource for big data–driven knowledge discoveries.