Kensuke Terai

Tissue expression of Toll-like receptors 2 and 4 in sporadic human colorectal cancer

BackgroundToll-like receptors (TLRs) play an important role in innate immunity by sensing a variety of pathogens and inducing acquired immunity. To test our hypothesis that dysregulation of innate immune responses acts to trigger carcinogenesis, we studied the expression of TLR2 and 4 in sporadic human colorectal cancer tissue.MethodsIn specimens of cancerous and noncancerous colorectal tissue obtained at surgery, mRNA expression levels of TLR2 and 4 were quantified by TaqMan real-time polymerase chain reaction and compared between the two types of tissue. To confirm TLR2 and TLR4 protein expression levels, immunohistochemical analysis was performed using the same samples.ResultsTLR2 mRNA expression was significantly higher in cancerous tissue than in noncancerous tissue, while TLR4 mRNA expression did not differ significantly. Immunohistochemical analysis revealed stronger staining for TLR2 in cancerous mucosal epithelial cells than in noncancerous tissue. Staining for TLR4 in the lamina propria of the mucosa was equally weakly positive in noncancerous tissue and cancerous tissue. This TLR-specific difference in expression suggested that such expression does not only reflect a local inflammatory response to cancer infiltration, i.e., if this was the case, both TLR2 and 4 expression would probably be up-regulated. Our results suggest that TLR2 expression might be involved in sporadic colorectal carcinogenesis, whereas TLR4 is not.

Designation of enzyme activity of glycine-N-acyltransferase family genes and depression of glycine-N-acyltransferase in human hepatocellular carcinoma

The human glycine-N-acyltransferase (hGLYAT) gene and two related-genes (GLYATL1 and GLYATL2) were isolated. Human GLYAT, GLYATL1, and GLYATL2 cDNAs were isolated and shown to encode polypeptides of 295, 302, and 294 amino acids, respectively. GLYAT catalyzes glycine-N-acyltransfer reaction with benzoyl-CoA acting as a typical aralkyl transferase, while GLYATL1 catalyzed glutamine-N-acyltransfer reaction with phenylacetyl-CoA as an arylacetyl transferase. GLYAT was shown to be expressed specifically in the liver and kidney, and the cellular localization of GLYAT protein was restricted to the mitochondria. Interestingly, labeling using highly affinity purified anti-GLYAT antibody revealed that GLYAT expression was suppressed in all hepatocellular carcinomas, but not in other liver diseases. hGLYAT repression in cancerous cells in the liver was controlled at the transcriptional level. hGLYAT is a good candidate as a novel marker of hepatocellular carcinoma and may be a key molecule in the transition between differentiation and carcinogenesis of liver cells.

Atorvastatin and pitavastatin enhance lipoprotein lipase production in L6 skeletal muscle cells through activation of adenosine monophosphate-activated protein kinase.

Pravastatin and atorvastatin increase the serum level of lipoprotein lipase (LPL) mass in vivo but do not increase LPL activity in 3T3-L1 preadipocytes in vitro. LPL is mainly produced by adipose tissue and skeletal muscle cells. Metformin enhances LPL in skeletal muscle through adenosine monophosphate-activated protein kinase (AMPK) activation but not in adipocytes. This study aimed to examine the effect of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) on LPL production and to investigate the mechanism by which statins enhance skeletal muscle cell LPL production. L6 skeletal muscle cells were incubated with pravastatin, simvastatin, atorvastatin or pitavastatin. LPL activity, protein levels and mRNA expression were measured. Atorvastatin and pitavastatin significantly increased LPL activity, protein levels and mRNA expression in L6 skeletal muscle cells at 1 μmol/L, but neither statin had an effect at 10 μmol/L. We measured AMPK to clarify the mechanism by which statins increase LPL production in skeletal muscle cells. At 1 μmol/L, both atorvastatin and pitavastatin enhanced AMPK activity, but this enhancement was abolished when AMPK signaling was blocked by compound C. The increased expressions of LPL protein and mRNA by atorvastatin and pitavastatin were reduced by compound C. In addition, mevalonic acid abolished atorvastatin- and pitavastatin-induced AMPK activation and LPL expression. These results suggest that atorvastatin and pitavastatin increase LPL activity, protein levels and LPL mRNA expression by activating AMPK in skeletal muscle cells.

An Extended Method of Higher-order Local Autocorrelation Feature Extraction for Classification of Histopathological Images

In histopathological diagnosis, a clinical pathologist discriminates between normal tissues and cancerous tissues. However, recently, the shortage of clinical pathologists is posing increasing burdens on meeting the demands for such diagnoses, and this is becoming a serious social problem. Currently, it is necessary to develop new medical technologies to help reduce their burdens. Therefore, as a diagnostic support technology, this paper describes an extended method of HLAC feature extraction for classification of histopathological images into normal and anomaly. The proposed method can automatically classify cancerous images as anomaly by using an extended geometric invariant HLAC features with rotation- and reflection-invariant properties from three-level histopathological images, which are segmented into nucleus, cytoplasm and background. In conducted experiments, we demonstrate a reduction in the rate of not only false-negative errors but also of false-positive errors, where a normal image is falsely classified as an image with an anomaly that is suspected as being cancerous.

Histopathological Diagnostic Support Technology Using Higher-Order Local Autocorrelation Features

This paper proposes a technology for histopathological diagnostic support that utilizes the correlation-based features of histopathological tissues. In histopathological diagnosis, a clinical pathologist conducts a diagnosis of normal tissues and cancerous tissues. However, recently, the shortage of clinical pathologists is posing increasing burdens to meet the demands for such diagnoses, and this is causing serious social problems. In order to overcome this problem, we propose a technology of histopathological diagnostic support that uses higher-order local autocorrelation (HLAC) features. The proposed method can automatically screen tissue that is believed to be normal tissue to detect cancerous tissue as well as tissue that is suspected of being cancerous to detect abnormalities. Consequently, we can reduce the burden on clinical pathologists, allowing them to concentrate on diagnosing cancer.

Heregulin-induced cell migration is promoted by aryl hydrocarbon receptor in HER2-overexpressing breast cancer cells

ABSTRACT HER2 overexpression accounts for approximately 15–20% of all breast cancers. We have shown that HER2 overexpression leads to elevated expression of the aryl hydrocarbon receptor (AhR) in breast cancer cells. In this study, firstly, we showed that AhR expression was up‐regulated by treatment with the HER3 ligand heregulin (HRG) in HER2‐overexpressing breast cancer cell lines. Induction of AhR was mediated by transcriptional activation of the region of AhR promoter corresponding to − 190 to − 100 bp. In addition, HRG treatment elicited nuclear translocation of AhR. To investigate the role of AhR in HRG‐HER2/HER3 signaling in HER2‐overexpressing cells, we established AhR knockout (KO) HER2‐overexpressing cells to perform wound‐healing assays. HRG‐induced cell migration was markedly attenuated by AhR KO. HRG‐induced cell migration was associated with increased expression of the inflammatory cytokines interleukin (IL)‐6 and IL‐8 in wild type cells, but not in AhR KO cells. These results elucidate that AhR is an important factor for the malignancy in HER2 overexpressing breast cancers. HighlightsHeregulin signaling up‐regulates AhR expression at the transcriptional level in HER2‐overexpressing breast cancer cells.Heregulin signaling induces nuclear translocation of AhR.Knockout of the AhR decreases cell migration by heregulin signaling in HER2‐overexpressing breast cancer cells.

Subfraction analysis of circulating lipoproteins in a patient with Tangier disease due to a novel ABCA1 mutation

Tangier disease, characterized by low or absent high-density lipoprotein (HDL), is a rare hereditary lipid storage disorder associated with frequent, but not obligatory, severe premature atherosclerosis due to disturbed reverse cholesterol transport from tissues. The reasons for the heterogeneity in atherogenicity in certain dyslipidemias have not been fully elucidated. Here, using high-performance liquid chromatography with a gel filtration column (HPLC-GFC), we have studied the lipoprotein profile of a 17-year old male patient with Tangier disease who to date has not developed manifest coronary atherosclerosis. The patient was shown to be homozygous for a novel mutation (Leu1097Pro) in the central cytoplasmic region of ATP-binding cassette transporter A1 (ABCA1). Serum total and HDL-cholesterol levels were 59mg/dl and 2mg/dl, respectively. Lipoprotein electrophoretic analyses on agarose and polyacrylamide gels showed the presence of massively abnormal lipoproteins. Further analysis by HPLC-GFC identified significant amounts of lipoproteins in low-density lipoprotein (LDL) subfractions. The lipoprotein particles found in the peak subfraction were smaller than normal LDL, were rich in triglycerides, but poor in cholesterol and phospholipids. These findings in an adolescent Tangier patient suggest that patients in whom these triglyceride-rich, cholesterol- and phospholipid-poor LDL-type particles accumulate over time, would experience an increased propensity for developing atherosclerosis.

Levels of soluble LR11/SorLA are highly increased in the bile of patients with biliary tract and pancreatic cancers.

BACKGROUND The utility of molecules derived from cancer cells as biomarkers of the pathological status in biliary tract and pancreatic cancers is still limited. Soluble LDL receptor relative with 11 ligand-binding repeats (sLR11), a molecule released from immature cells, has been shown to be a circulating biomarker for early stage hematological malignancies. METHODS We have evaluated the pathological significance of bile sLR11 levels in 147 samples from 72 patients with biliary tract cancer (BTC), pancreatic cancer (PC), or benign diseases. RESULTS The bile sLR11 levels in the cancer patients were significantly increased compared with those in patients without cancer, independent of cytological detection of cancer cells in bile. The average bile sLR11 levels in cancer patients were significantly higher than in those with benign diseases, while levels of bile carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) were not different. LR11 protein was found to be highly expressed in the BTC and PC cells. The LR11 transcript levels in cholangiocarcinoma and pancreatic cancer cell lines were sharply induced during proliferation and significantly increased under hypoxic conditions. CONCLUSIONS Therefore, sLR11 levels in bile may be indicative of cancer cell conditions and may serve as potential novel biomarker in patients with BTC and PC.

Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks

Deep learning using convolutional neural networks (CNNs) is a distinguished tool for many image classification tasks. Due to its outstanding robustness and generalization, it is also expected to play a key role to facilitate advanced computer-aided diagnosis (CAD) for pathology images. However, the shortage of well-annotated pathology image data for training deep neural networks has become a major issue at present because of the high-cost annotation upon pathologists professional observation. Faced with this problem, transfer learning techniques are generally used to reinforcing the capacity of deep neural networks. In order to further boost the performance of the state-of-the-art deep neural networks and alleviate insufficiency of well-annotated data, this paper presents a novel stepwise fine-tuning-based deep learning scheme for gastric pathology image classification and establishes a new type of target-correlative intermediate datasets. Our proposed scheme is deemed capable of making the deep neural network imitating the pathologists perception manner and of acquiring pathology-related knowledge in advance, but with very limited extra cost in data annotation. The experiments are conducted with both well-annotated gastric pathology data and the proposed target-correlative intermediate data on several state-of-the-art deep neural networks. The results congruously demonstrate the feasibility and superiority of our proposed scheme for boosting the classification performance.

Computational cancer detection of pathological images based on an optimization method for color-index local auto-correlation feature extraction

Aiming to lessen the burdens of the pathologist with efficient diagnosis assistance, this paper proposes a cancer detection method for pathological images utilizing color features based on color-index local auto-correlations (CILAC), applied to color-indexed images to utilize co-occurrence information about indexed pixels. Moreover, a method for the automatic optimization of feature extraction is also proposed. Based on a database including both benign and cancerous pathological images, experimental results show enhanced performance compared to prior research, which demonstrate the effectiveness of the proposed cancer detection method.