Additional file 1 of Reverse-genetics studies of lncRNAs—what we have learnt and paths forward

Additional file 1. Review history

Additional file 1 of Common occurrence of hotspots of single strand DNA breaks at transcriptional start sites

Supplementary Material

The histogram summarizes the differential expression profiles in each ENCODE region on each chromosome

<p><b>Copyright information:</b></p><p>Taken from "Differential analysis for high density tiling microarray data"</p><p>http://www.biomedcentral.com/1471-2105/8/359</p><p>BMC Bioinformatics 2007;8():359-359.</p><p>Published online 24 Sep 2007</p><p>PMCID:PMC2231405.</p><p></p> Chromosome region specific differential expression is observed across the time-points – 30 percent change on chromosome 8 to no detectable change on chromosome 10. Globally, the highest fraction of differential expression when summarized across all transfrag is observed between 8–32 hours (53.8 percent),. The most statistically significant (FDR ≤12 percent) changes are also observed between 8–32 hours

Example: For HisH4 a certain percentage of loci manifest up-regulation, while others manifest down-regulation and yet others exhibit no differential change

<p><b>Copyright information:</b></p><p>Taken from "Differential analysis for high density tiling microarray data"</p><p>http://www.biomedcentral.com/1471-2105/8/359</p><p>BMC Bioinformatics 2007;8():359-359.</p><p>Published online 24 Sep 2007</p><p>PMCID:PMC2231405.</p><p></p> The time intervals 0–2 hr, 2–8 hr and 8–32 hr are shown in black, blue and red respectively

A representative density profile of the d-statistic for change in H3K27T histone modification between 0 and 2 hours of retinoic acid treatment for the ENCODE region on chromosome 1

<p><b>Copyright information:</b></p><p>Taken from "Differential analysis for high density tiling microarray data"</p><p>http://www.biomedcentral.com/1471-2105/8/359</p><p>BMC Bioinformatics 2007;8():359-359.</p><p>Published online 24 Sep 2007</p><p>PMCID:PMC2231405.</p><p></p> The curves of different colors illustrate differential change for the H3K27T modification in exonic (green), intronic (black) and intergenic (blue) regions. The shift into the negative territory for the d-statistic for all classes of regions suggest is a consistent downward trend for this modification between 0 and 2 hours

D-statistic versus FDR relationship at putative TREs, across the time-series (IGB view)

<p><b>Copyright information:</b></p><p>Taken from "Differential analysis for high density tiling microarray data"</p><p>http://www.biomedcentral.com/1471-2105/8/359</p><p>BMC Bioinformatics 2007;8():359-359.</p><p>Published online 24 Sep 2007</p><p>PMCID:PMC2231405.</p><p></p> Examples of enrichment fragments are observed within and upstream of the second intron of the HIC gene (pink). The upstream fragment is possibly un-annotated (UA), in so far as no RefSeq annotation is available. The top four tracks represent the HisH4 p-value graphs at 0 (red), 2 (light-blue), 8 (dark-blue) and 32 (green) hours, scaled appropriately for comparison; the subsequent tracks represent the d-statistic (top) and FDR (bottom) pair for the 0–2 (red), 2–8 (cyan) and 8–32 (blue) hour time intervals. The horizontal lines associated with the FDR data refer to the 5 percent threshold in each case

Proteomics Analysis of Co-Purifying Cellular Proteins Associated with rAAV Vectors

<div><p>Recombinant adeno-associated vectors (rAAV) are commonly purified by either chromatography or equilibrium CsCl gradient. Nevertheless, even after purification various cellular proteins often associate with rAAV vector capsids. Such co-purifying cellular proteins may raise concern about safety of gene therapy. Here we report identification and characterization of the co-purifying cellular protein in the vector preparations by using a combination of two proteomics approaches, GeLC-MS (gel electrophoresis liquid chromatography-mass spectrometry) and 2DE (two-dimensional gel electrophoresis). Most prominent bands revealed by Coomassie Blue staining were mostly similar to the AAV capsid proteins. Posttranslational modifications of capsid proteins were detected by the proteomics analysis. A total of 13 cellular proteins were identified in the rAAV vectors purified by two rounds of cesium chloride gradient centrifugation, including 9 by the GeLC-MS analysis and 4 by the 2DE analysis. Selected cellular proteins were verified by western blot. Furthermore, the cellular proteins could be consistently found associated with different AAV serotypes and carrying different transgenes. Yet, the proteins were not integral components of the viral capsis since a stringent washing procedure by column purification could remove them. These co-purified proteins in AAV vector preparations may have a role in various stages of the AAV life cycle.</p></div

Table1_Complex Age- and Cancer-Related Changes in Human Blood Transcriptome—Implications for Pan-Cancer Diagnostics.XLSX

Early cancer detection is the key to a positive clinical outcome. While a number of early diagnostics methods exist in clinics today, they tend to be invasive and limited to a few cancer types. Thus, a clear need exists for non-invasive diagnostics methods that can be used to detect the presence of cancer of any type. Liquid biopsy based on analysis of molecular components of peripheral blood has shown significant promise in such pan-cancer diagnostics; however, existing methods based on this approach require improvements, especially in sensitivity of early-stage cancer detection. The improvement would likely require diagnostics assays based on multiple different types of biomarkers and, thus, calls for identification of novel types of cancer-related biomarkers that can be used in liquid biopsy. Whole-blood transcriptome, especially its non-coding component, represents an obvious yet under-explored biomarker for pan-cancer detection. In this study, we show that whole transcriptome analysis using RNA-seq could indeed serve as a viable biomarker for pan-cancer detection. Furthermore, a class of long non-coding (lnc) RNAs, very long intergenic non-coding (vlinc) RNAs, demonstrated superior performance compared with protein-coding mRNAs. Finally, we show that age and presence of non-blood cancers change transcriptome in similar, yet not identical, directions and explore implications of this observation for pan-cancer diagnostics.</p

Detection of protein SET in different fractions of the AAV2-dsEGFP preparation.

<p>The vector was produced by triple plasmid transfection and purified by two rounds of cesium chloride ultra-centrifugation. The fractions with different densities were collected after the second round of ultra-centrifugation. Protein form 1×10¹⁰ viral particles was resolved on 10% SDS/PAGE. The full AAV vector particles have buoyant densities in CsCl from 1.41 to 1.45 g/cm³ while empty particles have the density of 1.32 g/cm³. A, silver staining; and B, western blot.</p

Table4_Complex Age- and Cancer-Related Changes in Human Blood Transcriptome—Implications for Pan-Cancer Diagnostics.XLSX

Early cancer detection is the key to a positive clinical outcome. While a number of early diagnostics methods exist in clinics today, they tend to be invasive and limited to a few cancer types. Thus, a clear need exists for non-invasive diagnostics methods that can be used to detect the presence of cancer of any type. Liquid biopsy based on analysis of molecular components of peripheral blood has shown significant promise in such pan-cancer diagnostics; however, existing methods based on this approach require improvements, especially in sensitivity of early-stage cancer detection. The improvement would likely require diagnostics assays based on multiple different types of biomarkers and, thus, calls for identification of novel types of cancer-related biomarkers that can be used in liquid biopsy. Whole-blood transcriptome, especially its non-coding component, represents an obvious yet under-explored biomarker for pan-cancer detection. In this study, we show that whole transcriptome analysis using RNA-seq could indeed serve as a viable biomarker for pan-cancer detection. Furthermore, a class of long non-coding (lnc) RNAs, very long intergenic non-coding (vlinc) RNAs, demonstrated superior performance compared with protein-coding mRNAs. Finally, we show that age and presence of non-blood cancers change transcriptome in similar, yet not identical, directions and explore implications of this observation for pan-cancer diagnostics.</p