[Show abstract][Hide abstract] ABSTRACT: In the new pathologic classification of lung adenocarcinoma proposed by IASLC/ATS/ERS in 2011, lepidic type adenocarcinomas are constituted by three subtypes; adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and lepidic predominant invasive adenocarcinoma (LPIA). Although these subtypes are speculated to show sequential progression from preinvasive lesion to invasive lung cancer, changes of protein expressions during these processes have not been fully studied yet. This study aims to glimpse a proteomic view of the early lepidic type lung adenocarcinomas.
A total of nine formalin-fixed and paraffin-embedded (FFPE) lepidic type lung adenocarcinoma tissues were selected from our archives, three tissues each in AIS, MIA and LPIA. The tumor and peripheral non-tumor cells in these FFPE tissues were collected with laser microdissection (LMD). Using liquid chromatography-tandem mass spectrometry (MS/MS), protein compositions were compared with respect to the peptide separation profiles among tumors collected from three types of tissues, AIS, MIA and LPIA. Proteins identified were semi-quantified by spectral counting-based or identification-based protein-based approach, and statistical evaluation was performed by pairwise G-tests.
A total of 840 proteins were identified. Spectral counting-based semi-quantitative comparisons of all identified proteins through AIS to LPIA have revealed that the protein expression profile of LPIA was significantly differentiated from other subtypes. 70 proteins including HPX, CTTN, CDH1, EGFR, MUC1 were found as LPIA-type marker candidates, 15 protein candidates for MIA-type marker included CRABP2, LMO7, and NPEP, and 26 protein candidates for AIS-type marker included LTA4H and SOD2. The STRING gene set enrichment resulted from the protein-protein interaction (PPI) network analysis suggested that AIS was rather associated with pathways of focal adhesion, adherens junction, tight junction, that MIA had a strong association predominantly with pathways of proteoglycans in cancer and with PI3K-Akt. In contrast, LPIA was associated broadly with numerous tumor-progression pathways including ErbB, Ras, Rap1 and HIF-1 signalings.
The proteomic profiles obtained in this study demonstrated the technical feasibility to elucidate protein candidates differentially expressed in FFPE tissues of LPIA. Our results may provide candidates of disease-oriented proteins which may be related to mechanisms of the early-stage progression of lung adenocarcinoma.
Clinical and Translational Medicine 12/2015; 4(1):64. DOI:10.1186/s40169-015-0064-3
[Show abstract][Hide abstract] ABSTRACT: Selective PPARα modulators (SPPARMα) are under development for use as next-generation lipid lowering drugs. In the current study, to predict the pharmacological and toxicological effects of a novel SPPARMα K-877, comprehensive transcriptome analyses of K-877-treated primary human hepatocytes and mouse liver tissue were carried out.
Total RNA was extracted from the K-877 treated primary human hepatocytes and mouse liver and adopted to the transcriptome analysis. Using a cluster analysis, commonly and species specifically regulated genes were identified. Also, the profile of genes regulated by K-877 and fenofibrate were compared to examine the influence of different SPPARMα on the liver gene expression.
Consequently, a cell-based transactivation assay showed that K-877 activates PPARα with much greater potency and selectivity than fenofibric acid, the active metabolite of clinically used fenofibrate. K-877 upregulates the expression of several fatty acid β-oxidative genes in human hepatocytes and the mouse liver. Almost all genes up- or downregulated by K-877 treatment in the mouse liver were also regulated by fenofibrate treatment. In contrast, the K-877-regulated genes in the mouse liver were not affected by K-877 treatment in the Ppara-null mouse liver. Depending on the species, the peroxisomal biogenesis-related gene expression was robustly induced in the K-877-treated mouse liver, but not human hepatocytes, thus suggesting that the clinical dose of K-877 may not induce peroxisome proliferation or liver toxicity in humans. Notably, K-877 significantly induces the expression of clinically beneficial target genes (VLDLR, FGF21, ABCA1, MBL2, ENPEP) in human hepatocytes.
These results indicate that changes in the gene expression induced by K-877 treatment are mainly mediated through PPARα activation. K-877 regulates the hepatic gene expression as a SPPARMα and thus may improve dyslipidemia as well as metabolic disorders, such as metabolic syndrome and type 2 diabetes, without untoward side effects.
Journal of atherosclerosis and thrombosis 06/2015; 22(8). DOI:10.5551/jat.28720 · 2.73 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: ROBO1 is a membrane protein that contributes to tumor metastasis and angiogenesis. We previously reported that 90Y-labeled anti-ROBO1 monoclonal antibody (90Y-anti-ROBO1 IgG) showed an antitumor effect against ROBO1-positive tumors. In this study, we performed a biodistribution study and radioimmunotherapy (RIT) against ROBO1-positive small cell lung cancer (SCLC) models.
For the biodistribution study, 111In-labeled anti-ROBO1 monoclonal antibody (111In-anti-ROBO1 IgG) was injected into ROBO1-positive SCLC xenograft mice via the tail vein. To evaluate antitumor effects, an RIT study was performed, and SCLC xenograft mice were treated with 90Y-anti-ROBO1 IgG. Tumor volume and body weight were periodically measured throughout the experiments. The tumors and organs of mice were then collected, and a pathological analysis was carried out.
As a result of the biodistribution study, we observed tumor uptake of 111In-anti-ROBO1 IgG. The liver, kidney, spleen, and lung showed comparably high accumulation of 111In-labeled anti-ROBO1. In the RIT study, 90Y-anti-ROBO1 IgG significantly reduced tumor volume compared with baseline. Pathological analyses of tumors revealed coagulation necrosis and fatal degeneration of tumor cells, significant reduction in the number of Ki-67-positive cells, and an increase in the number of apoptotic cells. A transient reduction of hematopoietic cells was observed in the spleen, sternum, and femur.
These results suggest that RIT with 90Y-anti-ROBO1 IgG is a promising treatment for ROBO1-positive SCLC.
PLoS ONE 05/2015; 10(5):e0125468. DOI:10.1371/journal.pone.0125468 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Histone 3 lysine 9 (H3K9) demethylase JMJD1A regulates b-adrenergic-induced systemic metabolism and body weight control. Here we show that JMJD1A is phosphorylated at S265 by protein kinase A (PKA), and this is pivotal to activate the b1-adrenergic receptor gene (Adrb1) and downstream targets including Ucp1 in brown adipocytes (BATs). Phosphorylation of JMJD1A by PKA increases its interaction with the SWI/SNF nucleosome remodelling complex and DNA-bound PPARg. This complex confers b-adrenergic-induced rapid JMJD1A recruitment to target sites and facilitates long-range chromatin interactions and target gene activation. This rapid gene induction is dependent on S265 phosphorylation but not on demethylation activity. Our results show that JMJD1A has two important roles in regulating hormone-stimulated chromatin dynamics that modulate thermogenesis in BATs. In one role, JMJD1A is recruited to target sites and functions as a cAMP-responsive scaffold that facilitates long-range chromatin interactions, and in the second role, JMJD1A demethylates H3K9 di-methylation.
[Show abstract][Hide abstract] ABSTRACT: In this study, we propose a supercomputer-assisted drug design approach involving all-atom molecular dynamics (MD)-based binding free energy prediction after the traditional design/selection step. Because this prediction is more accurate than the empirical binding affinity scoring of the traditional approach, the compounds selected by the MD-based prediction should be better drug candidates. In this study, we discuss the applicability of the new approach using two examples. Although the MD-based binding free energy prediction has a huge computational cost, it is feasible with the latest 10 petaflop-scale computer. The supercomputer-assisted drug design approach also involves two important feedback procedures: The first feedback is generated from the MD-based binding free energy prediction step to the drug design step. While the experimental feedback usually provides binding affinities of tens of compounds at one time, the supercomputer allows us to simultaneously obtain the binding free energies of hundreds of compounds. Because the number of calculated binding free energies is sufficiently large, the compounds can be classified into different categories whose properties will aid in the design of the next generation of drug candidates. The second feedback, which occurs from the experiments to the MD simulations, is important to validate the simulation parameters. To demonstrate this, we compare the binding free energies calculated with various force fields to the experimental ones. The results indicate that the prediction will not be very successful, if we use an inaccurate force field. By improving/validating such simulation parameters, the next prediction can be made more accurate.
[Show abstract][Hide abstract] ABSTRACT: The Tokyo Medical University Hospital in Japan and the Lund University hospital in Sweden have recently initiated a research program with the objective to impact on patient treatment by clinical disease stage characterization (phenotyping), utilizing proteomics sequencing platforms. By sharing clinical experiences, patient treatment principles, and biobank strategies, our respective clinical teams in Japan and Sweden will aid in the development of predictive and drug related protein biomarkers.
Data from joint lung cancer studies are presented where protein expression from Neuro- Endocrine lung cancer (LCNEC) phenotype patients can be separated from Small cell- (SCLC) and Large Cell lung cancer (LCC) patients by deep sequencing and spectral counting analysis. LCNEC, a subtype of large cell carcinoma (LCC), is characterized by neuroendocrine differentiation that small cell lung carcinoma (SCLC) shares. Pre-therapeutic histological distinction between LCNEC and SCLC has so far been problematic, leading to adverse clinical outcome. An establishment of protein targets characteristic of LCNEC is quite helpful for decision of optimal therapeutic strategy by diagnosing individual patients. Proteoform annotation and clinical biobanking is part of the HUPO initiative (http://www.hupo.org) within chromosome 10 and chromosome 19 consortia.
Clinical and Translational Medicine 12/2014; 3(1):61. DOI:10.1186/s40169-014-0038-x