Chromosomal hotspots for HPV integration.

<p>Depicted are integration sites located within the cytogenetic bands 3q28 (A), 4q13.3 (B), 8q24.21 (C), 13q22.1 (D) and 17q21.2 (E). Light blue arrows: genes affected by HPV integration; red arrows: HPV fusion transcripts described in this work; grey arrows: HPV fusion transcripts described by Kraus et al. 2008; green arrows: fragile site; dark blue arrows: microRNAs.</p

Summary of all viral-cellular fusion transcripts analysed.

1<p>Common and rare fragile sites located at a distance of up to 5 Mb adjacent to the integration locus. Rare fragile sites are shown in italics.</p>‡<p>n.a.: not applicable, because fusion transcript is in antisense orientation.</p

Genes affected by HPV integration at least twice in individual tumors.

‡<p>n.s.: not specified; n.a.: not applicable, because fusion transcript is in antisense orientation; ^†integration sites without reference result from this work.</p>1<p>Two fusion transcripts were found in T2107; ²refers to sequenced integration sites; ³TP73L alias TP63; ⁴<i>sweeker</i> in Kraus 2008 is no longer listed in any database; ⁵VMP1 alias TMEM49; ⁶DKFZP566I133 alias TMEM49.</p

Relative enrichment of HPV integration sites within regions marked as DNase hypersensitive across cell types.

<p>The table shows the cell types with the ten highest and ten lowest enrichment values. A table of all 147 analyzed cells is provided at the supplementary Galaxy Page. The enrichment values are normalized in the same way as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119566#pone.0119566.t001" target="_blank">Table 1</a> (see text).</p><p>* Epidermal keratinocytes are highly specialized epithelial cells.</p><p>Relative enrichment of HPV integration sites within regions marked as DNase hypersensitive across cell types.</p

Enrichment of HPV integration sites inside regions marked by H3K4me3 in different cell types.

<p>Enrichment for a given cell type is calculated based on overlap between H3K4me3 chip-seq peaks for the cell type and HPV integration sites including 10kb flanks in both directions. Enrichment values are further normalized as described in Methods.</p

Sequence homologies between the HPV16 genome and the two adjoining genes <i>TP63</i> and <i>LEPREL1</i>.

<p>Three homologous stretches of up to 50 nucleotides are shown. The number of exact nucleotide matches is given in brackets (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039632#pone.0039632.s001" target="_blank">Sequences S1</a>). The DNA loop between the two homologies located on <i>LEPREL1</i> comprises 102.689 nucleotides; the second loop 269.968 nucleotides. Grey dots refer to the approximate location of the corresponding viral-cellular fusion transcripts detected in tumors D3829, T2107 and T4335 (from left to right).</p

Medical imaging training with eye movement modeling examples: A randomized controlled study

To determine whether ultrasound training in which the expert’s eye movements are superimposed to the underlying ultrasound video (eye movement modeling examples; EMMEs) leads to better learner outcomes than traditional eye movement-free instructions. 106 undergraduate medical students were randomized in two groups; 51 students in the EMME group watched 5-min ultrasound examination videos combined with the eye movements of an expert performing the task. The identical videos without the eye movements were shown to 55 students in the control group. Performance and behavioral parameters were compared prepost interventional using ANOVAs. Additionally, cognitive load, and prior knowledge in anatomy were surveyed. After training, the EMME group identified more sonoanatomical structures correctly, and completed the tasks faster than the control group. This effect was partly mediated by a reduction of extraneous cognitive load. Participants with greater prior anatomical knowledge benefited the most from the EMME training. Displaying experts’ eye movements in medical imaging training appears to be an effective way to foster medical interpretation skills of undergraduate medical students. One underlying mechanism might be that practicing with eye movements reduces cognitive load and helps learners activate their prior knowledge.</p

Relative enrichment of HPV integration sites within regions marked by H3K4me3 across cell types.

<p>All four mucosal cell types in the dataset are among the top ten cells. Enrichment values are normalized so that a value of 1 corresponds to the average across all cell types (details provided in text). P-values are computed conservatively based on the relative enrichment, and are not adjusted for multiple testing.</p><p>Relative enrichment of HPV integration sites within regions marked by H3K4me3 across cell types.</p

Similarity of H3K4me3 segments between different mucosal cell types.

<p>Approximately one fifth of H3K4me3 segment coverage is unique to one cell type, showing strong similarity between different mucosal cell types.</p><p>Similarity of H3K4me3 segments between different mucosal cell types.</p

Multiplex Identification of Human Papillomavirus 16 DNA Integration Sites in Cervical Carcinomas

<div><p>Cervical cancer is caused by high-risk human papillomaviruses (HPV), in more than half of the worldwide cases by HPV16. Viral DNA integration into the host genome is a frequent mutation in cervical carcinogenesis. Because integration occurs into different genomic locations, it creates unique viral-cellular DNA junctions in every single case. This singularity complicates the precise identification of HPV integration sites enormously. We report here the development of a novel multiplex strategy for sequence determination of HPV16 DNA integration sites. It includes DNA fragmentation and adapter tagging, PCR enrichment of the HPV16 early region, Illumina next-generation sequencing, data processing, and validation of candidate integration sites by junction-PCR. This strategy was performed with 51 cervical cancer samples (47 primary tumors and 4 cell lines). Altogether 75 HPV16 integration sites (3′-junctions) were identified and assigned to the individual samples. By comparing the DNA junctions with the presence of viral oncogene fusion transcripts, 44 tumors could be classified into four groups: Tumors with one transcriptionally active HPV16 integrate (n = 12), tumors with transcribed and silent DNA junctions (n = 8), tumors carrying episomal HPV16 DNA (n = 10), and tumors with one to six DNA junctions, but without fusion transcripts (n = 14). The 3′-breakpoints of integrated HPV16 DNA show a statistically significant (p<0.05) preferential distribution within the early region segment upstream of the major splice acceptor underscoring the importance of deregulated viral oncogene expression for carcinogenesis. Half of the mapped HPV16 integration sites target cellular genes pointing to a direct influence of HPV integration on host genes (insertional mutagenesis). In summary, the multiplex strategy for HPV16 integration site determination worked very efficiently. It will open new avenues for comprehensive mapping of HPV integration sites and for the possible use of HPV integration sites as individualized biomarkers after cancer treatment of patients for the early diagnosis of residual and recurrent disease.</p></div