Target genes and corresponding factors in networks 1.

<p>This table lists the target genes and factors that regulate them. The regression coefficients are listed on the right side.</p

Regulatory networks found by our model.

<p>The first column is differentially expressed isoform groups in our cases, the second and third columns are the Transcription factors and splicing factors predicted by our regression model. Some cells are blank, which means no corresponding factors for that co-expressed group.</p

Names and description of splicing factors used in our model.

<p>This table contains 22 splicing factors which are selected to predict the expression levels of differentially expressed isoforms. This table lists their names and some related references. Most of these details are from SpliceAid.</p

Flowchart of proposed method for constructing regulatory networks.

<p>Flowchart of proposed method for constructing regulatory networks: (A). Process raw RNA-seq data, find out deferentially expressed isoforms using Tophat and Cufflinks and cluster these isoforms to get gene cluster that may be regulated by same TFs and SFs. (B). Construct two dataset, promote region data (PRD) and exon-intron data (EID), for mining the interaction strength of the TF-isoform interactions and SF-isoform interaction. (C). Use interaction strength to predict the expression levels of isoforms in a co-expressed group. (D). Link model-selected TFs and SFs with their target genes.</p

Pseudocode of LARS algorithm.

<p>Pseudocode of LARS algorithm.</p

Results of enrichment analysis using GO database.

<p>Three networks enriched in some GO biological processes. This table lists the details and the P values.</p

Systematically Studying Kinase Inhibitor Induced Signaling Network Signatures by Integrating Both Therapeutic and Side Effects

<div><p>Substantial effort in recent years has been devoted to analyzing data based large-scale biological networks, which provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or compounds. In this work, we proposed a novel strategy to investigate kinase inhibitor induced pathway signatures by integrating multiplex data in Library of Integrated Network-based Cellular Signatures (LINCS), e.g. KINOMEscan data and cell proliferation/mitosis imaging data. Using this strategy, we first established a PC9 cell line specific pathway model to investigate the pathway signatures in PC9 cell line when perturbed by a small molecule kinase inhibitor GW843682. This specific pathway revealed the role of PI3K/AKT in modulating the cell proliferation process and the absence of two anti-proliferation links, which indicated a potential mechanism of abnormal expansion in PC9 cell number. Incorporating the pathway model for side effects on primary human hepatocytes, it was used to screen 27 kinase inhibitors in LINCS database and PF02341066, known as Crizotinib, was finally suggested with an optimal concentration 4.6 uM to suppress PC9 cancer cell expansion while avoiding severe damage to primary human hepatocytes. Drug combination analysis revealed that the synergistic effect region can be predicted straightforwardly based on a threshold which is an inherent property of each kinase inhibitor. Furthermore, this integration strategy can be easily extended to other specific cell lines to be a powerful tool for drug screen before clinical trials.</p></div

Expression ratio of SRSF11’s three isoforms (A), motifs in SRSF11’s isoforms and classical SR proteins (B), RT-PCR results (C) and protein Expression of SRSF11.

<p>(A). Expression ratio of SRSF11’s three isoforms in seven disease sample and control: uc009wbj.1 (light green), uc001deu.2 (light blue) and uc001.dev.3 (light red). They have almost the same total expression levels but very different ratios in MDS (four ) and control (average of five controls), which means the splicing patterns of SRSF11 are switched. (B). This figure demonstrates motifs in SRSF11’s isoforms and classical SR proteins. Different motifs have different bio-function. (C). Three isoforms that are over-expressed in our disease samples are picked up for RT-PCR validation. They are isoforms of three splicing factor, one isoform (uc001deu.2, refseq ID: NM_001190987) of SRSF11, one isoform (uc001xlp.3, refseq ID: NM_006925) of SRSF5 and one isoform (uc003jun.2, refseq ID:NM_080743) of SRSF12. Validation demonstrated that their expression levels in MDS disease are higher than in control. (D). Isofrom uc001deu.2 is translated into protein Q05519 and Q05519 is highly expressed in blood disease according to the Model Organism Protein Expression Database (MOPED); COPD: Chronic obstructive pulmonary disease.</p

Results of enrichment analysis using KEGG database.

<p>This table lists top enriched KEGG pathways and corresponding networks number.</p

A comparison of the experimental and simulated data after BHQ880 treatment.

<p>A comparison of the experimental and simulated data after BHQ880 treatment.</p