Increasing consistency of disease biomarker prediction across datasets

Microarray studies with human subjects often have limited sample sizes which hampers the ability to detect reliable biomarkers associated with disease and motivates the need to aggregate data across studies. However, human gene expression measurements may be influenced by many non-random factors such as genetics, sample preparations, and tissue heterogeneity. These factors can contribute to a lack of agreement among related studies, limiting the utility of their aggregation. We show that it is feasible to carry out an automatic correction of individual datasets to reduce the effect of such 'latent variables' (without prior knowledge of the variables) in such a way that datasets addressing the same condition show better agreement once each is corrected. We build our approach on the method of surrogate variable analysis but we demonstrate that the original algorithm is unsuitable for the analysis of human tissue samples that are mixtures of different cell types. We propose a modification to SVA that is crucial to obtaining the improvement in agreement that we observe. We develop our method on a compendium of multiple sclerosis data and verify it on an independent compendium of Parkinson's disease datasets. In both cases, we show that our method is able to improve agreement across varying study designs, platforms, and tissues. This approach has the potential for wide applicability to any field where lack of inter-study agreement has been a concern. © 2014 Chikina, Sealfon

Gene Expression Profile and Functionality of ESC-Derived Lin-ckit+Sca-1+ Cells Are Distinct from Lin-ckit+Sca-1+ Cells Isolated from Fetal Liver or Bone Marrow

In vitro bioreactor-based cultures are being extensively investigated for large-scale production of differentiated cells from embryonic stem cells (ESCs). However, it is unclear whether in vitro ESC-derived progenitors have similar gene expression profiles and functionalities as their in vivo counterparts. This is crucial in establishing the validity of ESC-derived cells as replacements for adult-isolated cells for clinical therapies. In this study, we compared the gene expression profiles of Lin-ckit+Sca-1+ (LKS) cells generated in vitro from mouse ESCs using either static or bioreactor-based cultures, with that of native LKS cells isolated from mouse fetal liver (FL) or bone marrow (BM). We found that in vitro-generated LKS cells were more similar to FL- than to BM LKS cells in gene expression. Further, when compared to cells derived from bioreactor cultures, static culture-derived LKS cells showed fewer differentially expressed genes relative to both in vivo LKS populations. Overall, the expression of hematopoietic genes was lower in ESC-derived LKS cells compared to cells from BM and FL, while the levels of non-hematopoietic genes were up-regulated. In order to determine if these molecular profiles correlated with functionality, we evaluated ESC-derived LKS cells for in vitro hematopoietic-differentiation and colony formation (CFU assay). Although static culture-generated cells failed to form any colonies, they did differentiate into CD11c+ and B220+ cells indicating some hematopoietic potential. In contrast, bioreactor-derived LKS cells, when differentiated under the same conditions failed to produce any B220+ or CD11c+ cells and did not form colonies, indicating that these cells are not hematopoietic progenitors. We conclude that in vitro culture conditions significantly affect the transcriptome and functionality of ESC-derived LKS cells and although in vitro differentiated LKS cells were lineage negative and expressed both ckit and Sca-1, these cells, especially those obtained from dynamic cultures, are significantly different from native cells of the same phenotype.This work was partially supported through National Institutes of Health grant number EB005026 to KR and PWT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received for this study.Biomedical Engineerin

Transcription factor motifs associated with anterior insula gene-expression underlying mood disorder phenotypes

ABSTRACTBackgroundMood disorders represent a major cause of morbidity and mortality worldwide but the brain-related molecular pathophysiology in mood disorders remains largely undefined.MethodsBecause the anterior insula is reduced in volume in patients with mood disorders, RNA was extracted from postmortem mood disorder samples and compared with unaffected control samples for RNA-sequencing identification of differentially expressed genes (DEGs) in a) bipolar disorder (BD; n=37) versus (vs.) controls (n=33), and b) major depressive disorder (MDD n=30) vs controls, and c) low vs. high Axis-I comorbidity (a measure of cumulative psychiatric disease burden). Given the regulatory role of transcription factors (TFs) in gene expression via specific-DNA-binding domains (motifs), we used JASPAR TF binding database to identify TF-motifs.ResultsWe found that DEGs in BD vs. controls, MDD vs. controls, and high vs. low Axis-I comorbidity were associated with TF-motifs that are known to regulate expression of toll-like receptor genes, cellular homeostatic-control genes, and genes involved in embryonic, cellular/organ and brain development.DiscussionRobust imaging-guided transcriptomics (i.e., using meta-analytic imaging results to guide independent post-mortem dissection for RNA-sequencing) was applied by targeting the gray matter volume reduction in the anterior insula in mood disorders, to guide independent postmortem identification of TF motifs regulating DEG. TF motifs were identified for immune, cellular, embryonic and neurodevelopmental processes.ConclusionOur findings of TF-motifs that regulate the expression of immune, cellular homeostatic-control, and developmental genes provides novel information about the hierarchical relationship between gene regulatory networks, the TFs that control them, and proximate underlying neuroanatomical phenotypes in mood disorders.</jats:sec