Expression of <i>SLC45A3-ELK4</i> and <i>CTCF</i>, and CTCF binding to the two insulators in different cell lines.

<p>Hela (cervical cancer cells), 293T (SV40 transformed embryonic kidney cells), HCT116 (colon cancer cells), LNCaP, and RWPE-1 (prostate cells). (A) <i>SLC45A3-ELK4</i> expression level was measured by qRT-PCR, normalized against <i>GAPDH</i>, and further normalized against the level in LNCaP. (B) <i>CTCF</i> expression level measured by qRT-PCR, normalized against <i>GAPDH</i>, and further normalized against the level in LNCaP. (C) Binding of CTCF to the two insulators measured by ChIP and qPCR. IgG was used as control for the CTCF antibody.</p

The most common social pathologigal traits of perpetrators of traffic accidents and their causes (based on the probes into their psyche in the book Fatal meetings by K. Havlík)

The aim of this thesis is to clear up and acquiant with psychological profile of failing drivers. There are dozer of drivers failing in these days, although we can suppose that we are not involved in this topic. But contrariwise, we are a part of this problematics as well, we could not be direct offenders of accidents or drivers with socially-pathological phenomenons, but we can be thein victims as well. That is why it is really important to pay attention to this problematics and to be interested in it and try to prevent it. So the aim of the thesis was to find out, what are the most usual phenomenonsof offenders of accidents, what are the causes of these phenomenons, where and in which situations are they springing up thein beginnings, that leads us to accidents afterwards. In kontent of thesis is analysis of risk factors in road traffic as well. The last charter is focused on preventive steps leading to limitation of accidents. The book by Karel Havlík "Osudová střetnutí" was a part of there search complex just as an interview with traffic psychologist. I have dividend this interview into eight questions, that were fulfilling theoretical problematics in the first part. I have found out by processing of the interview and by processing of the book "Osudová střetnutí" that usual socially-pathological phenomenons were the phenomenons, that have aggressive features in thein elements, from which it is springing up much more. On the contrary people with personality disorders are usually offenders of accidents as well. I have reached the conclusion that each human might chase anything in himself and in his behaving either in attitude to the others, but it is needed a lot of effort and persistence

AR and CTCF do not colocalize, or interact in LNCaP cells.

<p>(A) Re-ChIP by CTCF, AR antibodies, or control IgG. Insulator1, 2 and, ARE1, 2, and 3 were tested. (B) Co-immunoprecipitation by CTCF antibody, and western blotting by AR, CTCF, or GAPDH. R-RWPE-1, L-LNCaP, R1881-synthetic androgen.</p

Expression of <i>SLC45A3-ELK4</i> and <i>CTCF</i>, and CTCF binding to the two insulators in 11 clinical prostate cancer samples.

<p>(A) <i>SLC45A3-ELK4</i> expression level was measured by qRT-PCR, normalized against <i>GAPDH</i>, and further normalized against the level in C1. (B) <i>CTCF</i> expression level measured by qRT-PCR, normalized against <i>GAPDH</i>, and further normalized against the level in C1. (C) Binding of CTCF to the two insulators measured by ChIP and qPCR. IgG was used as control for the CTCF antibody.</p

Discovery of CTCF-Sensitive Cis-Spliced Fusion RNAs between Adjacent Genes in Human Prostate Cells

<div><p>Genes or their encoded products are not expected to mingle with each other unless in some disease situations. In cancer, a frequent mechanism that can produce gene fusions is chromosomal rearrangement. However, recent discoveries of RNA trans-splicing and cis-splicing between adjacent genes (cis-SAGe) support for other mechanisms in generating fusion RNAs. In our transcriptome analyses of 28 prostate normal and cancer samples, 30% fusion RNAs on average are the transcripts that contain exons belonging to same-strand neighboring genes. These fusion RNAs may be the products of cis-SAGe, which was previously thought to be rare. To validate this finding and to better understand the phenomenon, we used LNCaP, a prostate cell line as a model, and identified 16 additional cis-SAGe events by silencing transcription factor CTCF and paired-end RNA sequencing. About half of the fusions are expressed at a significant level compared to their parental genes. Silencing one of the in-frame fusions resulted in reduced cell motility. Most out-of-frame fusions are likely to function as non-coding RNAs. The majority of the 16 fusions are also detected in other prostate cell lines, as well as in the 14 clinical prostate normal and cancer pairs. By studying the features associated with these fusions, we developed a set of rules: 1) the parental genes are same-strand-neighboring genes; 2) the distance between the genes is within 30kb; 3) the 5′ genes are actively transcribing; and 4) the chimeras tend to have the second-to-last exon in the 5′ genes joined to the second exon in the 3′ genes. We then randomly selected 20 neighboring genes in the genome, and detected four fusion events using these rules in prostate cancer and non-cancerous cells. These results suggest that splicing between neighboring gene transcripts is a rather frequent phenomenon, and it is not a feature unique to cancer cells.</p></div

Procedures to further identify cis-SAGe fusions.

<p><b>A</b>, cis-SAGe fusions should not have interstitial deletions between fused intergenic exons. Shown is the sequencing data of GSM947411 for the <i>MFGE8-HAPLN3</i> fusion as one example. <b>B</b>, we required the cis-SAGe fusions to contain CTCF bindings in the intergenic region between two parental genes. Shown is CTCF ChIP-seq data on kidney, LNCaP, and lung in USCS genome browser for the <i>MFGE8-HAPLN3</i> fusion as one example. <b>C</b>, the relative expression of 16 fusions by quantitative RT-PCR in si- and siCTCF treated LNCaP cells. <b>D</b>, reverse transcription using antisense primers annealing to the first exons of 3′ parental genes, and PCR of the intergenic transcripts for the 16 fusions. “+RT”, with reverse transcriptase; “-RT”, no reverse transcriptase. <b>E,</b> ratios of fusion FKPM versus parental gene FKPM were plotted following an order from the highest to the lowest.</p

Identification of novel cis-SAGe candidate events.

<p><b>A,</b> configuration of most common cis-SAGe events based on the 16 fusions. Bars represent exons and lines represent introns and intergenic regions. Arrows represent primers used to detect novel cis-SAGe events. <b>B,</b> Sanger sequencing of four novel fusions detected in LNCaP cells. <b>C,</b> RT-PCR of the same four fusions in LHS, RWPE-1 and PC3 cells.</p

High percentage of fusion RNAs involving neighboring genes, and strategy for identification novel cis-SAGe chimeric RNAs.

<p><b>A,</b> fusion RNAs categorized into INTRACHR-SS-0GAP, INTRACHR-OTHER and INTERCHR. Percentages of each category in individual tumor (upper) and matched normal (lower) prostate samples were plotted. <b>B,</b> correlation of the percentage of INTRACHR-SS-0GAP fusions in matched tumor and normal cases. Peason R = 0.6. <b>C,</b> CTCF knockdown induced chimeric <i>SLC45A3-ELK4</i> RNA expression. LNCaP cells were transfected with either si—or siCTCF. <i>CTCF</i> and <i>SLC45A3-ELK4</i> expression were monitored by qRT-PCR. Transcript amount was normalized to internal control GAPDH. The level of these transcripts was set to 1 in si- transfected cells. <b>D</b>, experimental flow for identification and validation of cis-SAGe events. Quality of RAW sequencing data was checked using FastQC. Paired reads were mapped to both human genome and transcriptome to identify chimeric RNAs using SOAPfuse software. Three groups of chimeric RNAs, classified by genomic features between two parental parts, were validated by RT-PCR and Sanger sequencing. Five additional steps were then applied to remove potential non-neighboring fusions, or fusions resulting from interstitial deletion, and to identify cis-SAGe events.</p

Detection of cis-SAGe chimeric mRNA in prostate cell lines and clinical tissues.

<p><b>A</b>, distribution of 16 fusion RNAs in nuclear vs. cytoplasmic fractions. Traditional protein-coding gene <i>GAPDH</i> and a known long non-coding RNA MALAT1 were used as controls. The protein-coding potential of each fusion is marked below. N: not affecting protein coding, applies to fusions where junction sites fall in the UTR. O: out-of-frame, applies to fusions where the reading frame of the 5′ gene is different from that of the 3′ gene. I: in-frame, the reading frame of 5′ gene is the same as that of 3′ gene. <b>B,</b> qRT-PCR for <i>ADCK4-NUMBL</i> and the parental genes. LNCaP cells were transfected with siRNAs targeting the fusion RNA (si-AN1 and si-AN2). Levels of various transcripts were normalized to that in si-negative control (si-). <b>C,</b> cell motility was measured by wound healing assay. Cells were transfected with siRNAs targeting <i>ADCK4-NUMBL</i> and si-negative control. The changes of the wound size were normalized to that in the si-negative control group (n>3, p<0.0001) <b>D,</b> detection of the 16 cis-SAGe chimeras in RWPE-1 (benign prostate cell), LNCaP, and PC-3 cells by RT-PCR and followed by agarose electrophoresis. GAPDH as internal control. <b>E</b>, summary of the 16 cis-SAGe chimeric RNAs in 14 clinical prostate cancer and normal tissues. STAR software was used to align the chimeras onto the clinical RNA-seq data; samtools and IGV were used to map spanning reads across the fusion junction. Black indicates the absence of a fusion, while red indicates the detection of a fusion.</p

Landscape of software-identified chimeric RNAs.

<p><b>A</b>, Venn gram showing the number of fusions in si- and siCTCF groups. <b>B,</b> Circos plot depicting chimeric RNAs discovered across the genome. Ring: chromosomes. Within the ring, lines denote the chimeric RNAs connecting two parental genes. <b>C</b>, putative chimeric RNAs were categorized into INTERCHR, INTRACHR-SS-0GAP, and INTRACHR-OTHER.</p