LitInspector: literature and signal transduction pathway mining in PubMed abstracts

LitInspector is a literature search tool providing gene and signal transduction pathway mining within NCBI's PubMed database. The automatic gene recognition and color coding increases the readability of abstracts and significantly speeds up literature research. A main challenge in gene recognition is the resolution of homonyms and rejection of identical abbreviations used in a ‘non-gene’ context. LitInspector uses automatically generated and manually refined filtering lists for this purpose. The quality of the LitInspector results was assessed with a published dataset of 181 PubMed sentences. LitInspector achieved a precision of 96.8%, a recall of 86.6% and an F-measure of 91.4%. To further demonstrate the homonym resolution qualities, LitInspector was compared to three other literature search tools using some challenging examples. The homonym MIZ-1 (gene IDs 7709 and 9063) was correctly resolved in 87% of the abstracts by LitInspector, whereas the other tools achieved recognition rates between 35% and 67%. The LitInspector signal transduction pathway mining is based on a manually curated database of pathway names (e.g. wingless type), pathway components (e.g. WNT1, FZD1), and general pathway keywords (e.g. signaling cascade). The performance was checked for 10 randomly selected genes. Eighty-two per cent of the 38 predicted pathway associations were correct. LitInspector is freely available at http://www.litinspector.org/

RNA-sequencing reveals that STRN, ZNF484 and WNK1 add to the value of mitochondrial MT-COI and COX10 as markers of unstable coronary artery disease

Markers in monocytes, precursors of macrophages, which are related to CAD, are largely unknown. Therefore, we aimed to identify genes in monocytes predictive of a new ischemic event in patients with CAD and/or discriminate between stable CAD and acute coronary syndrome. We included 66 patients with stable CAD, of which 24 developed a new ischemic event, and 19 patients with ACS. Circulating CD14+ monocytes were isolated with magnetic beads. RNA sequencing analysis in monocytes of patients with (n = 13) versus without (n = 11) ischemic event at follow-up and in patients with ACS (n = 12) was validated with qPCR (n = 85). MT-COI, STRN and COX10 predicted new ischemic events in CAD patients (power for separation at 1% error rate of 0.97, 0.90 and 0.77 respectively). Low MT-COI and high STRN were also related to shorter time between blood sampling and event. COX10 and ZNF484 together with MT-COI, STRN and WNK1 separated ACS completely from stable CAD patients. RNA expressions in monocytes of MT-COI, COX10, STRN, WNK1 and ZNF484 were independent of cholesterol lowering and antiplatelet treatment. They were independent of troponin T, a marker of myocardial injury. But, COX10 and ZNF484 in human plaques correlated to plaque markers of M1 macrophage polarization, reflecting vascular injury. Expression of MT-COI, COX10, STRN and WNK1, but not that of ZNF484, PBMCs paired with that in monocytes. The prospective study of relation of MT-COI, COX10, STRN, WNK1 and ZNF484 with unstable CAD is warranted

Transcriptional Regulation of Rod Photoreceptor Homeostasis Revealed by In Vivo NRL Targetome Analysis

A stringent control of homeostasis is critical for functional maintenance and survival of neurons. In the mammalian retina, the basic motif leucine zipper transcription factor NRL determines rod versus cone photoreceptor cell fate and activates the expression of many rod-specific genes. Here, we report an integrated analysis of NRL-centered gene regulatory network by coupling chromatin immunoprecipitation followed by high-throughput sequencing (ChIP–Seq) data from Illumina and ABI platforms with global expression profiling and in vivo knockdown studies. We identified approximately 300 direct NRL target genes. Of these, 22 NRL targets are associated with human retinal dystrophies, whereas 95 mapped to regions of as yet uncloned retinal disease loci. In silico analysis of NRL ChIP–Seq peak sequences revealed an enrichment of distinct sets of transcription factor binding sites. Specifically, we discovered that genes involved in photoreceptor function include binding sites for both NRL and homeodomain protein CRX. Evaluation of 26 ChIP–Seq regions validated their enhancer functions in reporter assays. In vivo knockdown of 16 NRL target genes resulted in death or abnormal morphology of rod photoreceptors, suggesting their importance in maintaining retinal function. We also identified histone demethylase Kdm5b as a novel secondary node in NRL transcriptional hierarchy. Exon array analysis of flow-sorted photoreceptors in which Kdm5b was knocked down by shRNA indicated its role in regulating rod-expressed genes. Our studies identify candidate genes for retinal dystrophies, define cis-regulatory module(s) for photoreceptor-expressed genes and provide a framework for decoding transcriptional regulatory networks that dictate rod homeostasis

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

RNA-sequencing reveals that STRN, ZNF484 and WNK1 add to the value of mitochondrial MT-COI and COX10 as markers of unstable coronary artery disease

Markers in monocytes, precursors of macrophages, which are related to CAD, are largely unknown. Therefore, we aimed to identify genes in monocytes predictive of a new ischemic event in patients with CAD and/or discriminate between stable CAD and acute coronary syndrome. We included 66 patients with stable CAD, of which 24 developed a new ischemic event, and 19 patients with ACS. Circulating CD14+ monocytes were isolated with magnetic beads. RNA sequencing analysis in monocytes of patients with (n = 13) versus without (n = 11) ischemic event at follow-up and in patients with ACS (n = 12) was validated with qPCR (n = 85). MT-COI, STRN and COX10 predicted new ischemic events in CAD patients (power for separation at 1% error rate of 0.97, 0.90 and 0.77 respectively). Low MT-COI and high STRN were also related to shorter time between blood sampling and event. COX10 and ZNF484 together with MT-COI, STRN and WNK1 separated ACS completely from stable CAD patients. RNA expressions in monocytes of MT-COI, COX10, STRN, WNK1 and ZNF484 were independent of cholesterol lowering and antiplatelet treatment. They were independent of troponin T, a marker of myocardial injury. But, COX10 and ZNF484 in human plaques correlated to plaque markers of M1 macrophage polarization, reflecting vascular injury. Expression of MT-COI, COX10, STRN and WNK1, but not that of ZNF484, PBMCs paired with that in monocytes. The prospective study of relation of MT-COI, COX10, STRN, WNK1 and ZNF484 with unstable CAD is warranted.status: publishe

Identification of NRL target genes and co-regulatory transcription factors by ChIP–Seq and transcriptional profiling.

<p>(A) Identification of direct NRL transcriptional target genes by ChIP–Seq and transcription profiling. ABI ChIP–Seq peaks and Illumina ChIP–Seq peaks were assigned to the nearest genes (ABI and Illumina). Transcriptional profiling of flow-sorted photoreceptors of WT or <i>Nrl</i>^−/− mice was generated using microarrays. Up: genes up-regulated in <i>Nrl</i>^−/− photoreceptors. Down: genes down-regulated in <i>Nrl</i>^−/− photoreceptors. (B) Correlation of NRL ChIP–Seq peaks to the promoters of its target genes. The number of correlation (y-axis) was plotted to the distance (x-axis) of ABI ChIP–Seq peaks (green graph) or Illumina ChIP–Seq peaks (blue graph) to the promoters of the genes that were down-regulated (top) or up-regulated (bottom) in the <i>Nrl</i>^−/− photoreceptors. (C) TF enrichment and TF binding site (TFBS) positional bias analysis. NRL ChIP–Seq peak regions were analyzed for TF enrichment using Genomatix RegionMiner. The positional bias of TFBS (P) was calculated and plotted as −log(P) (y-axis) to the distance of TFBS to the peak center (x-axis). Positions where TFBS are overrepresented appear as peaks in these plots. * significantly enriched. (D) Correlation of NRL ChIP–Seq peaks with CRX ChIP–Seq peaks. The number of correlation (y-axis) was plotted to the distance of NRL ChIP–Seq peaks to CRX ChIP–Seq peaks (x-axis). The Venn diagram (inset) calculated the percentage of NRL ChIP–Seq peaks (Illumina and ABI) within 500 bp of CRX peaks: 65% of Illumina peaks and 48% of ABI peaks are within 500 bp of CRX peaks.</p

Comparison of ChIP–Seq peaks by Illumina and ABI.

<p>ChIP–Seq libraries were prepared according to manufacture's instructions and sequenced by Illumina 1G Genome Analyzer or ABI/SOLiD platform. Uniquely mapped reads: reads mapped to unique genomic locations.</p

Genome-wide Occupancy of NRL revealed by ChIP–Seq using Illumina and ABI/SOLiD sequencing platforms.

<p>(A) Analysis workflow. Raw sequence reads from Illumina or ABI/SOLiD were mapped to the mouse genome (NCBI build 37) using the Genomatix Mining Station (GMS) and the reads mapped to unique genomic locations (uniquely mapped reads) were used for further analyses. ChIP–Seq peaks were called using NGS Analyzer (Genomatix) or MACS (Zhang et al., 2008), and the common peaks were used for further analyses. The NRL ChIP–Seq peaks were compared to the CRX ChIP–Seq peaks for overlapping using GenomeInspector (Genomatix) software. The ChIP–Seq peaks were assigned to the nearest gene. Transcription profile analyses of flow-sorted photoreceptors of WT and <i>Nrl</i>^−/− were performed using ChipInsepector program (Genomatix) and 1.5 fold expression change was used as a criterion for NRL target genes. TF motif enrichment analyses were performed on the NRL ChIP–Seq peak regions that were associated with NRL target genes. Comparison was made between CRX-overlapping and non CRX-overlapping NRL ChIP–Seq peaks. Gene regulatory network was constructed based on TF enrichment analysis. (B) Correlation of ChIP–Seq peaks by Illumina and ABI. The number of correlations (y-axis) was plotted to the distance of ABI ChIP–Seq peaks to Illumina ChIP–Seq peaks (x-axis). The Venn diagram (inset) calculated the percentage of ABI and Illumina peaks within 500 bp of each other: 88% of Illumina peaks are within 500 bp of ABI peaks and 49% of ABI peaks are within 500 bp of Illumina peaks. (C) Correlation of ChIP–Seq peaks to promoters. The number of correlation (y-axis) was plotted to the distance of ABI ChIP–Seq peaks (green graph) or Illumina ChIP–Seq peaks (blue graph) to the transcription start site (TSS) (x-axis). The Venn diagram (inset) calculated the percentage of ABI (75%) or Illumina (72%) peaks within 10,000 bp from the TSS. (D) Genomic distribution of NRL ChIP–Seq peaks relative to the nearest annotated genes. Promoters and exons account for 2.3% and 5.4% of the mouse genome, respectively.</p

Validation of NRL binding to corresponding peak regions by ChIP–qPCR.

<p>ChIP-qPCR was performed to validate NRL binding to 26 ChIP–Seq peak regions (left panel), and 5 non-peak regions (right panel) served as negative controls. The amount of ChIP DNA was measured by qPCR in triplicates using primers flanking the regions of interest. Normal IgG served as an antibody control when ChIP was performed using WT retinas (white bars). White bars (NRL Ab/IgG) represent fold change (FC) of qPCR signals comparing NRL ChIP DNA to the IgG control ChIP DNA. A separate set of ChIP assays was performed using NRL antibody to compare signals from WT retina to signals from <i>Nrl</i>^−/− retina (tissue control). Black bars (WT/<i>Nrl</i>^−/−) represent fold increase (Fc) of qPCR signals comparing NRL ChIP DNA from wild type C57BL/6 mouse retina to NRL ChIP DNA from <i>Nrl^−/</i>⁻ mouse retina. The ChIP-qPCR assays were performed twice. The representative results were shown as mean ± SD. P<0.01 for all by Student's t test.</p

Rescue of shRNA–knockdown with shRNA–resistant dcDNA.

<p>CD-1 mice were transfected at P0 with Ub-GFP and shRNA against <i>Gapdh</i>, <i>Lman1 or Wisp1</i> by sub-retinal injection and <i>in vivo</i> electroporation. shRNA-resistant degenerate cDNA (dcDNA) was co-injected together with shRNA against <i>Gapdh</i>, <i>Lman1 or Wisp1</i> for rescue experiments. Retinas were harvested at P20 and examined for GFP fluorescence (green), Rho immuno-reactivity (red) and DAPI staining (blue). Three biological replicate retinas were collected and imaged. (A, C). Scale bar: 20 µM. (B, D) Higher magnification images of (A, C). OS: outer segment. OONL: outer portion of the outer nuclear layer. IONL: inner portion of the outer nuclear layer. Scale bar: 10 µM. GFP positive (+) cells were counted in sections of retina electroporated with shRNA targeting <i>Gapdh</i> or NRL target genes (E, H). Distribution of electroporated cell bodies in the retina (F). Fraction of GFP positive cells in the retinal outer nuclear layer is counted. OONL, outer region of outer nuclear layer; IONL, inner region of outer nuclear layer. Average outer segment (OS) lengths of electroporated cells were measured (G). Data are represented as mean ± SD. (E–H) *P<0.01, **P<0.001 by Student's t test (n = 3 electroporated retinas).</p