Connections and between-group differences in inter-network efficiencies.

<p>Connections of cortical structural network for strategic/executive control with other networks mediating language (A), mnemonic/emotional processing (B), and sensorimotor function (C) and between-group differences in inter-network efficiencies are presented in the left and right panels, respectively. Brain templates in figures demonstrate cortical parcellated regions for corresponding intrinsic cortical structural sub-network systems and inter-network connections at the sparsity threshold of 0.23. Red arrows in graphs indicate the sparsity of 0.23 that whole-brain structural networks of both control and T1DM groups included all 64 connected brain regions. Hub regions shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071304#pone-0071304-g003" target="_blank">Figure 3</a> are marked as larger white circles with the radius in proportion of the value of <i>B_i</i>. The graphs showed the differences in average inverse shortest path length of submatrix for each corresponding intrinsic cortical structural network system between the T1DM and control groups (blue line). The mean values and 95% of confidence interval of the null distribution of between-group differences in parameters obtained from 1000 permutation tests at each sparsity level were represented as gray circles and error bars, respectively. Asterisks indicate significant differences in average inverse shortest path length between the T1DM and control groups at <i>P</i><0.05. Inset graphs show average inverse shortest path length of submatrix representing inter-network efficiency, was plotted as a function of sparsity thresholds in T1DM (red line) and control (gray line) subjects. Abbreviations: T1DM, type 1 diabetes mellitus.</p

Between-group differences in global efficiency, local efficiency, and hierarchical organization of whole-brain structural networks.

<p>The graphs showed the differences in network parameters between the T1DM and control groups (blue line). The mean values and 95% of confidence interval of the null distribution of between-group differences obtained from 1000 permutation tests at each sparsity level were represented as gray circles and error bars, respectively. Asterisks indicate significant differences in parameters between the T1DM and control groups at <i>P</i><0.05. Inset graphs show the global efficiency (<i>E_glob)</i>, local efficiency (<i>E_loc</i>), and hierarchical organization (β) of whole-brain structural networks in T1DM (red line) and control (gray line) subjects as functions of sparsity thresholds. Abbreviations: T1DM, type 1 diabetes mellitus.</p

Location of hubs and inter-hub structural connections in whole-brain structural networks.

<p>Regions (brain templates of panels A for control and B T1DM subjects) in orange, green, blue, yellow, and light purple colors represent each intrinsic cortical structural sub-network system subserving strategic/executive control, language, mnemonic/emotional processing, sensorimotor, and visual functions, respectively. Figures depict hub regions and significant inter-hub structural connections of control (A) and T1DM (B) subjects at the sparsity threshold of 0.23. A given region was identified as a hub of whole-brain structural networks if its normalized betweenness-centrality (<i>B_i</i>) was greater than 1.5 <i>and</i> its degree (<i>K_i</i>) was above the network mean at the sparsity threshold of 0.23. Red circles denote the hub regions with low clustering (less than average clustering of whole-brain structural networks) indicating hubs at a higher position in the hierarchical organization. Blue circles denote the hub regions with high clustering (greater than average clustering of whole-brain structural networks) indicating hubs at a lower position in the hierarchical organization. The radius of circles is in proportion of the value of <i>B_i</i> of the region at sparsity threshold of 0.23. Abbreviations: T1DM, type 1 diabetes mellitus; R, right; L, left; SFC, superior frontal cortex; rMFC, rostral middle frontal cortex; cMFC, caudal middle frontal cortex; ICC, isthmus cingulate cortex; IFCc, inferior frontal cortex- pars opercularis; IPC, inferior parietal cortex; supM, supramarginal cortex; STC, superior temporal cortex; MTC, middle temporal cortex; rACC, rostral anterior cingulate cortex; ITC, inferior temporal cortex; Fus, fusiform cortex; postCen, postcentral cortex; preCun, precuneus cortex; Cun, cuneus cortex.</p

Group characteristics of patients with T1DM and control subjects.

a<p>Group differences were tested by independent t-tests or χ² tests appropriately.</p>b<p>Average value of HbA1C, grouped and time-weighted every 4 years for the duration of illness.</p>c<p>A severe hypoglycemic episode was defined as an event that leads to a coma, seizure, or unconsciousness according to the Diabetes Control and Complications Trial Research Group Criteria.</p><p>Abbreviations: T1DM, type 1 diabetes mellitus; SD, standard deviation; HbA1C, hemoglobin A1C; NA, not available or not applicable.</p

A High-Dimensional, Deep-Sequencing Study of Lung Adenocarcinoma in Female Never-Smokers

<div><h3>Background</h3><p>Deep sequencing techniques provide a remarkable opportunity for comprehensive understanding of tumorigenesis at the molecular level. As omics studies become popular, integrative approaches need to be developed to move from a simple cataloguing of mutations and changes in gene expression to dissecting the molecular nature of carcinogenesis at the systemic level and understanding the complex networks that lead to cancer development.</p> <h3>Results</h3><p>Here, we describe a high-throughput, multi-dimensional sequencing study of primary lung adenocarcinoma tumors and adjacent normal tissues of six Korean female never-smoker patients. Our data encompass results from exome-seq, RNA-seq, small RNA-seq, and MeDIP-seq. We identified and validated novel genetic aberrations, including 47 somatic mutations and 19 fusion transcripts. One of the fusions involves the <em>c-RET</em> gene, which was recently reported to form fusion genes that may function as drivers of carcinogenesis in lung cancer patients. We also characterized gene expression profiles, which we integrated with genomic aberrations and gene regulations into functional networks. The most prominent gene network module that emerged indicates that disturbances in G2/M transition and mitotic progression are causally linked to tumorigenesis in these patients. Also, results from the analysis strongly suggest that several novel microRNA-target interactions represent key regulatory elements of the gene network.</p> <h3>Conclusions</h3><p>Our study not only provides an overview of the alterations occurring in lung adenocarcinoma at multiple levels from genome to transcriptome and epigenome, but also offers a model for integrative genomics analysis and proposes potential target pathways for the control of lung adenocarcinoma.</p> </div

Circos plot of somatic mutations, copy number variations, transcriptome expression, and structural variations.

<p>From inside to out, structural variations (purple and orange), copy number variations (gain in dark red, loss in dark blue, mRNA expression (up in gold, down in olive), differentially expressed microRNAs (up in red, down in green), DNA methylation with sky-blue background (up in dark orange, down in chartreuse), somatic mutations with a gene symbols, and chromosomal cytobands.</p

Differential expression of microRNAs.

<p>Fold change versus expression level is shown in the MA-plot of DEmiRs and anti-correlated microRNAs. MicroRNAs from the same genomic locus are shown with the same color and symbol (e.g., 96, 182, 183). MicroRNAs inversely correlated with DEGs are indicated with a black circle. Fold changes in log₂ (tumor/normal) and expression magnitude in ½log₂ (tumor × normal) are the average values over six patients. Inset figures show subsets of microRNA-centric relationships with targets potentially involved in carcinogenesis. Relevant microRNAs are indicated by background orange and blue ovals within the plot. Only the validated targets are shown for simplicity. Changes in expression levels are indicated via node color.</p

<i>MARK4-ERCC2</i> fusion transcript.

<p>(a) Allignment of sequence reads of fusion transcripts. The extent of the assembled fusion transcript appears at the top and reads are shows below it. The vertical line indicates the fusion point. The sequence to the left matches the 3′ end of exon 7 of <i>MARK4</i>, and the sequence to the right matches the 5′ end of exon 18 of <i>ERCC2</i>. (b) cDNA samples taken from tumor (T) and adjacent normal (N) tissue of patient 3 were used to confirm the presence of the <i>MARK4-ERCC2</i> fusion transcript by RT-PCR only in the tumor sample. ACTB was used as the internal control. (c) Schematic diagram of the predicted fusion protein along with domains having a defined function. The fusion protein is predicted to contain a part of the <i>MARK4</i> kinase domain and most of the C-terminal helicase domain of <i>ERCC2</i>. (d) Array-CGH profiles are shown for the <i>MARK4-ERCC2</i> intrachromosomal fusion. Note that the copy number variation is seen only in the tumor tissue but in not normal tissue. Vertical lines represent fusion points.</p

NSCLC pathway modeling for female never-smokers.

<p>The pathway information was obtained from an Ingenuity Pathway Analysis (IPA) using the 66 network module genes as an input list. The resulting genes were grouped into five functional categories as suggested by IPA. Validated and predicted microRNA-target relations are shown in solid and dotted lines, respectively. Changes in expression levels are indicated via node color (red for up-regulation and blue for down-regulation). For <i>c-RET</i> and <i>PTK2</i>, the+symbol was used to indicate that they are involved in gene fusion event.</p