The top canonical pathways enriched in the differentially expressed genes.

<p>(A) Top 15 canonical pathways enriched in top 10% up/down-regulated genes of 2D/CTR and 7D/CTR are shown. The –log(P value) of the enrichment of each canonical pathway was plotted. (B) LXR/RXR Activation pathway up-regulated genes are colored pink. The color of a gene reflects its fold change. The higher the fold change the deeper the color.</p

RNA-Seq Characterization of Spinal Cord Injury Transcriptome in Acute/Subacute Phases: A Resource for Understanding the Pathology at the Systems Level

<div><p>Spinal cord injury (SCI) is a devastating neurological disease without effective treatment. To generate a comprehensive view of the mechanisms involved in SCI pathology, we applied RNA-Sequencing (RNA-Seq) technology to characterize the temporal changes in global gene expression after contusive SCI in mice. We sequenced tissue samples from acute and subacute phases (2 days and 7 days after injury) and systematically characterized the transcriptomes with the goal of identifying pathways and genes critical in SCI pathology. The top enriched functional categories include “inflammation response,” “neurological disease,” “cell death and survival” and “nervous system development.” The top enriched pathways include LXR/RXR Activation and Atherosclerosis Signaling, etc. Furthermore, we developed a systems-based analysis framework in order to identify key determinants in the global gene networks of the acute and sub-acute phases. Some candidate genes that we identified have been shown to play important roles in SCI, which demonstrates the validity of our approach. There are also many genes whose functions in SCI have not been well studied and can be further investigated by future experiments. We have also incorporated pharmacogenomic information into our analyses. Among the genes identified, the ones with existing drug information can be readily tested in SCI animal models. Therefore, in this study we have described an example of how global gene profiling can be translated to identifying genes of interest for functional tests in the future and generating new hypotheses. Additionally, the RNA-Seq enables splicing isoform identification and the estimation of expression levels, thus providing useful information for increasing the specificity of drug design and reducing potential side effect. In summary, these results provide a valuable reference data resource for a better understanding of the SCI process in the acute and sub-acute phases.</p> </div

The expression of macrophage marker genes in both acute and subacute phases of SCI.

<p>Expression profile of the common macrophage marker Itgam (A), M1 specific marker CD86 (B) and M2 specific marker Arg1(C) during the time-course of SCI. P values were calculated by one-way ANOVA. GSEA analysis: differential gene expression was ranked by fold change (x-axis: 2D vs control (D), 7D vs control (E), 7D vs 2D (F)). The most up-regulated genes are shown on the left side (red), while the most up-regulated genes were shown on the right side (blue). Black bars represent the positions of the M1 vs M2 up-regulated signature genes in the ranked list. Enrichment score (ES, Y-axis) reflects the degree the genes are overrepresented. When the distribution is at random, the enrichment score is zero. Enrichment of signature genes at the top of the ranked list results in a large positive deviation of the ES from zero. NES, normalized enrichment score; FDR, false discovery rate-adjusted q value.</p

Validation using qRT-PCR.

<p>Relative expression fold change from qRT-PCR were calculated using 2^-ΔΔCt method. Error bars represent ±SD (n=3). FPKM fold change were the ratio of average FPKM between samples.</p

Identification of differentially expressed genes.

<p>(A) Detection threshold determination. False positive and negative rates for the detection of genes as a function of detection threshold used, demonstrating how a detection threshold of 0.04 FPKM was determined. A more conservative threshold 0.1 FPKM was chosen for downstream analysis (the probability that a transcript can be reliably detected is ~0.99). (B) Table and Venn diagrams show the distribution of genes that changed > 2 fold and were statistically significant (t-test p < 0.05) in 2D and 7D stages.</p

Alternative splicing analysis.

<p>(A) The annotated isoform numbers per gene (x-axis) were plotted over number of genes (y-axis). (B) Splicing isoform expression of genes Spp1 and Morfl2 in acute and subacute phases. Error bars represent ±SEM. P values for transcript NM_009263, NM_00116829 and NM_001168230 were calculated by one-way ANOVA.</p

Developing a systems based analysis framework to identify key determinants in the global gene network.

<p>(A) Network constructed in 7D stage using top 10% up-regulated genes. Gene TNF (tumor necrosis factor) was highlighted with its connected edges (in blue). (B) The workflow of a systems based analysis framework in identifying potentially important genes.</p

Comprehensive Identification of Long Non-coding RNAs in Purified Cell Types from the Brain Reveals Functional LncRNA in OPC Fate Determination

<div><p>Long non-coding RNAs (lncRNAs) (> 200 bp) play crucial roles in transcriptional regulation during numerous biological processes. However, it is challenging to comprehensively identify lncRNAs, because they are often expressed at low levels and with more cell-type specificity than are protein-coding genes. In the present study, we performed <i>ab initio</i> transcriptome reconstruction using eight purified cell populations from mouse cortex and detected more than 5000 lncRNAs. Predicting the functions of lncRNAs using cell-type specific data revealed their potential functional roles in Central Nervous System (CNS) development. We performed motif searches in ENCODE DNase I digital footprint data and Mouse ENCODE promoters to infer transcription factor (TF) occupancy. By integrating TF binding and cell-type specific transcriptomic data, we constructed a novel framework that is useful for systematically identifying lncRNAs that are potentially essential for brain cell fate determination. Based on this integrative analysis, we identified lncRNAs that are regulated during Oligodendrocyte Precursor Cell (OPC) differentiation from Neural Stem Cells (NSCs) and that are likely to be involved in oligodendrogenesis. The top candidate, <i>lnc-OPC</i>, shows highly specific expression in OPCs and remarkable sequence conservation among placental mammals. Interestingly, <i>lnc-OPC</i> is significantly up-regulated in glial progenitors from experimental autoimmune encephalomyelitis (EAE) mouse models compared to wild-type mice. OLIG2-binding sites in the upstream regulatory region of <i>lnc-OPC</i> were identified by ChIP (chromatin immunoprecipitation)-Sequencing and validated by luciferase assays. Loss-of-function experiments confirmed that <i>lnc-OPC</i> plays a functional role in OPC genesis. Overall, our results substantiated the role of lncRNA in OPC fate determination and provided an unprecedented data source for future functional investigations in CNS cell types. We present our datasets and analysis results <i>via</i> the interactive genome browser at our laboratory website that is freely accessible to the research community. This is the first lncRNA expression database of collective populations of glia, vascular cells, and neurons. We anticipate that these studies will advance the knowledge of this major class of non-coding genes and their potential roles in neurological development and diseases.</p></div

Overview of study diagram and evaluation of lncRNA expression.

<p>(A) Schematic of the data integration and experiment validation. (B) Box plots illustrate expression level distributions of lncRNAs detected in Astrocytes, Neurons, and OPC cells (Red: lncRNAs that are also detected in cortex tissue samples; Blue: lncRNAs that are not detected in cortex tissue samples. Any lncRNAs with expression level of FPKM > 5 were excluded to allow the plot to be presented at a suitable scale. (C) Venn diagram shows lncRNAs detected in purified cell samples (red) or tissue samples (blue). (D) Cell-type specificity of the expression patterns of lncRNAs. Shown are the distributions (represented as a density curve) of specificity scores calculated for each gene across cell types, for coding genes (blue) and lncRNAs (red). The specificity score was calculated with a previously proposed index, which varies from 0 for housekeeping genes to 1 for cell-type specific genes.</p

Comparative genomics analysis of <i>lnc-OPC</i> revealed its evolutionary history.

<p>(A) Signal tracks of the expression of two TEs at the <i>lnc-OPC</i> locus in ESCs. RepeatMask annotation (bottom) shows two MERVL located in the last intron of <i>lnc-OPC</i>. The height of the bar above the repeat elements corresponds to (1 –%divergence). (B) The structure of <i>lnc-OPC</i> is shown. (C) Conservation displayed as Phylip score. (D) Pairwise alignments of mouse-rat and mouse-human <i>lnc</i>-OPC. (E) Multiple alignments of conservation of <i>lnc-OPC</i> in 60 vertebrates shown in the UCSC browser.</p