Gene set enrichment analysis identifies LIF as a negative regulator of human Th2 cell differentiation

EBF1-Correlated Long Non-coding RNA Transcript Levels in 3rd Trimester Maternal Blood and Risk of Spontaneous Preterm Birth

Biomarkers associated with spontaneous preterm birth (sPTB) before labor onset could aid in prediction, triage, and stratification for testing interventions. In this study we examined maternal blood EBF1-correlated long non-coding RNAs (lncRNAs) in relation to sPTB. We retrieved all lncRNA transcripts from a public gene expression dataset (GSE59491) derived from maternal blood in trimesters 2 and 3 from a Canadian cohort with a matched set of sPTB (n = 51) and term births (n = 106). LncRNA transcripts differentially expressed (limma moderated t-tests) in sPTB vs. term were tested for correlations (Pearson) with EBF1 mRNA levels in the same blood samples. Using logistic regression, EBF1-correlated lncRNAs were divided into tertiles and assessed in relation to odds of sPTB. Two lncRNA transcripts in the 3rd trimester maternal blood were differentially expressed between sPTB and term births (all p < 0.001 and FDR < 0.250) and positively and negatively correlated with EBF1 mRNA levels. They were as follows: (1) LINC00094 r = 0.196 (95% CI: 0.039 to 0.344), p = 0.015, and BH adjusted p = 0.022 and (2) LINC00870 r = - 0.303 (95% CI: - 0.441 to - 0.152), p < 0.001, and BH adjusted p < 0.001. As compared with term births, sPTBs were more likely to be in the highest tertile of LINC00870 (odds ratio (OR) = 4.08 (95% CI 1.60, 10.40), p = 0.003) and the lowest tertile of LINC00094 (OR = 5.16 (95% CI 1.96, 13.61), p < 0.001). Two sPTB-associated EBF1-correlated lncRNAs (LINC00870 and LINC00094) had multiple potential enhancers containing EBF1 binding site(s). Our current findings, along with previous reports linking EBF1 and sPTB, motivate additional research on the EBF1 gene-related gene expression and regulation in relation to sPTB within other cohorts and within laboratory-based models.

EBF1 Gene mRNA Levels in Maternal Blood and Spontaneous Preterm Birth

Genetic variants of six genes (EBF1, EEFSEC, AGTR2, WNT4, ADCY5, and RAP2C) have been linked recently to gestational duration and/or spontaneous preterm birth (sPTB). Our goal was to examine sPTB in relation to maternal blood mRNA levels of these genes. We used a public gene expression dataset (GSE59491) derived from maternal blood in trimesters 2 and 3 that included women with sPTB (n = 51) and term births (n = 106) matched for maternal age, race/ethnicity, pre-pregnancy body mass index, smoking during pregnancy, and parity. T tests were used to examine mRNA mean differences (sPTB vs term) within and across trimesters, and logistic regression models with mRNA quartiles were applied to assess associations between candidate gene mRNA levels and sPTB. Based on these analyses, one significant candidate gene was used in a Gene Set Enrichment Analysis (GSEA) to identify related gene sets. These gene sets were then compared with the ones previously linked to sPTB in the same samples. Our results indicated that among women in the lowest quartile of EBF1 mRNA in the 2nd or 3rd trimester, the odds ratio for sPTB was 2.86 (95%CI 1.08, 7.58) (p = 0.0349, false discovery rate (FDR) = 0.18) and 4.43 (95%CI 1.57, 12.50) (p = 0.0049, FDR = 0.06), respectively. No other candidate gene mRNAs were significantly associated with sPTB. In GSEA, 24 downregulated gene sets were correlated with 2nd trimester low EBF1 mRNA and part of previous sPTB-associated gene sets. In conclusion, mRNA levels of EBF1 in maternal blood may be useful in detecting increased risk of sPTB as early as 2nd trimester. The potential underlying mechanism might involve maternal-fetal immune and cell cycle/apoptosis pathways.

PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology

Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug-gene, miRNA-gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/.

Meta-analysis approaches to combine multiple gene set enrichment studies

In the field of gene set enrichment analysis (GSEA), meta-analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta-Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from component studies and (2) using enrichment scores whose distributions depend on the set sizes. In this paper, we adapt meta-analysis approaches recently developed for genome-wide association studies, which are based on fixed effect and random effects (RE) models, to integrate multiple GSEA studies. We further develop a mixed strategy via adaptive testing for choosing RE versus FE models to achieve greater statistical efficiency as well as flexibility. In addition, a size-adjusted enrichment score based on a one-sided Kolmogorov-Smirnov statistic is proposed to formally account for varying set sizes when testing multiple gene sets. Our methods tend to have much better performance than the MAPE methods and can be applied to both discrete and continuous phenotypes. Specifically, the performance of the adaptive testing method seems to be the most stable in general situations.

Integrative gene set enrichment analysis utilizing isoform-specific expression

Gene set enrichment analysis (GSEA) aims at identifying essential pathways, or more generally, sets of biologically related genes that are involved in complex human diseases. In the past, many studies have shown that GSEA is a very useful bioinformatics tool that plays critical roles in the innovation of disease prevention and intervention strategies. Despite its tremendous success, it is striking that conclusions of GSEA drawn from isolated studies are often sparse, and different studies may lead to inconsistent and sometimes contradictory results. Further, in the wake of next generation sequencing technologies, it has been made possible to measure genome-wide isoform-specific expression levels, calling for innovations that can utilize the unprecedented resolution. Currently, enormous amounts of data have been created from various RNA-seq experiments. All these give rise to a pressing need for developing integrative methods that allow for explicit utilization of isoform-specific expression, to combine multiple enrichment studies, in order to enhance the power, reproducibility, and interpretability of the analysis. We develop and evaluate integrative GSEA methods, based on two-stage procedures, which, for the first time, allow statistically efficient use of isoform-specific expression from multiple RNA-seq experiments. Through simulation and real data analysis, we show that our methods can greatly improve the performance in identifying essential gene sets compared to existing methods that can only use gene-level expression.

Electronic Sorting of Immune Cell Subpopulations Based on Highly Plastic Genes

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 FOXP3(hi)CD4(+) 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.

Effector T cell differentiation: are master regulators of effector T cells still the masters?

Effector CD4 T cell lineages have been implicated as potent inducers of autoimmune diseases. Tbet, Gata3 and Rorgt are master transcriptional regulators of Th1, Th2 and Th17 lineages respectively and promote the distinct expression of signature cytokines. Significant progress has been made in understanding the transcriptional network that drives CD4 T cell differentiation, revealing novel points of regulation mediated by transcription factors, cell surface receptors, cytokines and chemokines. Epigenetic modifications and metabolic mediators define the transcriptional landscape in which master transcription factors operate and collaborate with a network of transcriptional modifiers to guide lineage specification, plasticity and function.