<Emphasis Type="Italic">Lactobacillus paracasei</Emphasis> strain 06TCa19 suppresses inflammatory chemokine induced by <Emphasis Type="Italic">Helicobacter pylori</Emphasis> in human gastric epithelial cells

Helicobacter (H.) pylori infection is an important risk factor for gastric cancer that causes gastric inflammation. Inflammatory chemokines such as interleukin (IL)-8 and regulated on activation normal T cell expressed and secreted (RANTES) are elevated in the gastric mucosa by H. pylori. This study aimed to investigate the effects of Lactobacillus paracasei strain 06TCa19, a probiotic strain, on IL-8 and RANTES expression and production induced by H. pylori using human gastric epithelial cell lines. Strain 06TCa19 was shown to suppress H. pylori-mediated elevation of gene expression related to these chemokines in MKN45 cells. The strain also suppressed the increase in IL-8 and RANTES products induced by H. pylori in AGS cells as well as in MKN45 cells. In MKN45 cells inoculated with H. pylori, strain 06TCa19 was shown to downregulate the activation of NF-κB and p38 MAPK signaling pathways. Additionally, the level of the CagA virulence protein of H. pylori in the MKN45 cells and the number of viable H. pylori adhering to MKN45 cells decreased with the addition of strain 06TCa19. Moreover, the strain 06TCa19 notably increased lactic acid in the supernatant of MKN45 cells. Thus, lactic acid released from strain 06TCa19 might have inhibited the adhesion of H. pylori to MKN45 cells and prevented the insertion of H. pylori CagA into the cells, and elevation of IL-8 and RANTES genes and proteins might be suppressed by downregulating the NF-κB and p38 MAPK pathways. Therefore, use of strain 06TCa19 may prevent H. pylori-associated gastric inflammation.     

3,3'-Diindolylmethane, a cruciferous vegetable derived synthetic anti-proliferative compound in thyroid disease

Considerable epidemiological evidence exists to link thyroid disease with differing patterns of dietary consumption, in particular, cruciferous vegetables. We have been studying the anti-thyroid cancer (TCa) activity of indole-3-carbinol (I3C) found in cruciferous vegetables and its acid catalyzed dimer, 3,3'-diindolylmethane (DIM). There are no studies as yet to elucidate the effect of these compounds on the altered proliferative patterns in goiter or thyroid neoplasia. In this study, we tested the anti-proliferative effects of I3C and DIM on four different thyroid cancer cell lines representative of papillary (B-CPAP and 8505-C) and follicular carcinoma of the thyroid (CGTH-W-1 and ML-1), and primary human goiter cells. Cell survival and IC50 values for I3C and DIM were calculated by the XTT assay and cell cycle distribution analysis was done by flow cytometry. DIM was found to be a better anti-proliferative agent than I3C in both papillary and follicular TCa resulting in a greater cytotoxic effect at a concentration over three fold lower than predicted by the molar ratio of DIM and I3C. The anti-proliferative activity of DIM in follicular TCa was mediated by a G1 arrest followed by induction of apoptosis. DIM also inhibited the growth of primary goiter cells by 70% compared to untreated controls. Contrary to traditional belief that cruciferous vegetables are "goitrogenic", DIM has anti-proliferative effects in glandular thyroid proliferative disease. Our preclinical studies provide a strong rationale for the clinical exploration of DIM as an adjuvant to surgery in thyroid proliferative disease.     

喀斯特森林土壤养分的空间异质性及其对树种分布的影响

以贵州省茂兰国家级自然保护区喀斯特峰丛坡面中原生性常绿落叶阔叶混交林为研究对象, 以建立的100 m × 100 m样地的群落学调查数据和基于网格取样的土壤养分数据为基础, 采用半方差函数、Kriging空间插值和典范对应分析(canonical correspondence analysis, CCA)等方法分析了喀斯特森林土壤养分的空间异质性特征及其对树种分布的影响。结果表明: 喀斯特峰丛坡面土壤养分的变异系数为10%-80%, 变异程度中等。各土壤养分指标均具有良好的空间自相关性, 其中全磷(TP)、全钾(TK)、全镁(TMg)和pH值呈强烈的空间自相关, 而有机质(OM)、全钙(TCa)、速效磷(AP)和速效钾(AK)为中等程度的空间自相关; TCa的空间变异尺度最小, OM、TP和AK的空间变异尺度较大。土壤TK、TP、TCa、TMg、AP和pH值等随着海拔高度的增加和岩石裸露率的降低而逐渐减少, OM则随着海拔高度的增加而趋于增加, 这表明喀斯特地形因子是造成土壤养分空间变异的重要因素。CCA分析表明, 土壤养分的空间变异性显著影响到群落中树种的组成与空间分布, 其中TK、TMg、pH值、TCa和OM的影响最为明显, 体现了不同植物在土壤资源利用上的生态位分化, 这有助于喀斯特森林群落物种多样性与稳定性的维持。     

Semiparametric transformation models for multiple continuous biomarkers in ROC analysis

Recent technological advances continue to provide noninvasive and more accurate biomarkers for evaluating disease status. One standard tool for assessing the accuracy of diagnostic tests is the receiver operating characteristic (ROC) curve. Few statistical methods exist to accommodate multiple continuous‐scale biomarkers in the framework of ROC analysis. In this paper, we propose a method to integrate continuous‐scale biomarkers to optimize classification accuracy. Specifically, we develop semiparametric transformation models for multiple biomarkers. We assume that unknown and marker‐specific transformations of biomarkers follow a multivariate normal distribution. Our models accommodate biomarkers subject to limits of detection and account for the dependence among biomarkers by including a subject‐specific random effect. We also propose a diagnostic measure using an optimal linear combination of the transformed biomarkers. Our diagnostic rule does not depend on any monotone transformation of biomarkers and is not sensitive to extreme biomarker values. Nonparametric maximum likelihood estimation (NPMLE) is used for inference. We show that the parameter estimators are asymptotically normal and efficient. We illustrate our semiparametric approach using data from the Endometriosis, Natural History, Diagnosis, and Outcomes (ENDO) study.     

DNA microarrays and likelihood ratio bioinformatic methods: discovery of human melanocyte biomarkers

In this article, some of the advantages and limitations of DNA microarray technologies for gene expression profiling are summarized. As a model experiment, DermArray DNA microarrays were utilized to identify potential biomarkers of cultured normal human melanocytes in two different experimental comparisons. In the first case, melanocyte RNA was compared with vastly dissimilar non-melanocytic RNA samples of normal skin keratinocytes and fibroblasts. In the second case, melanocyte RNA was compared with a primary cutaneous melanoma line (MS7) and a metastatic melanoma cell line (SKMel-28). The alternative approaches provide dramatically different lists of 'normal melanocyte' biomarkers. The most robust biomarkers were identified using principal component analysis bioinformatic methods related to likelihood ratios. Only three of 25 robust biomarkers in the melanocyte-proximal study (i.e. melanocytes vs. melanoma cells) were coincidentally identified in the melanocyte-distal study (i.e. melanocytes vs. non-melanocytic cells). Selected up-regulated biomarkers of melanocytes (i.e. TRP-1, melan-A/MART-1, silver/Pmel17, and nidogen-2) were validated by qRT-PCR. Some of the melanocytic biomarkers identified here may be useful in molecular diagnostics, as potential molecular targets for drug discovery, and for understanding the biochemistry of melanocytic cells.     

Protein expression profiling of breast cancer cells by dissociable antibody microarray (DAMA) staining

Dissociable antibody microarray (DAMA) staining is a technology that combines protein microarrays with traditional immunostaining techniques. It can simultaneously determine the expression and subcellular location of hundreds of proteins in cultured cells and tissue samples. We developed this technology and demonstrated its application in identifying potential biomarkers for breast cancer. We compared the expression profiles of 312 proteins among three normal breast cell lines and seven breast cancer cell lines and identified 10 differentially expressed proteins by the data analysis program DAMAPEP (DAMA protein expression profiling). Among those proteins, RAIDD, Rb p107, Rb p130, SRF, and Tyk2 were confirmed by Western blot and statistical analysis to have higher expression levels in breast cancer cells than in normal breast cells. These proteins could be potential biomarkers for the diagnosis of breast cancer.     

Biomarkers of human skin cells identified using DermArray DNA arrays and new bioinformatics methods

Biomarker genes of human skin-derived cells were identified by new simple bioinformatic methods and DNA microarray analysis utilizing in vitro cultures of normal neonatal human epidermal keratinocytes, melanocytes, and dermal fibroblasts. A survey of 4405 human cDNAs was performed using DermArray DNA microarrays. Biomarkers were rank ordered by "likelihood ratio" algorithms and stringent selection criteria that have general applicability for analyzing a minimum of three RNA samples. Signature biomarker genes (up-regulated in one cell type) and anti-signature biomarker genes (down-regulated in one cell type) were determined for the three major skin cell types. Many of the signature genes are known biomarkers for these cell types. In addition, 17 signature genes were identified as ESTs, and 22 anti-signature biomarkers were discovered. Quantitative RT-PCR was used to verify nine signature biomarker genes. A total of 158 biomarkers of normal human skin cells were identified, many of which may be valuable in diagnostic applications and as molecular targets for drug discovery and therapeutic intervention.     

Metabolic profiling in human lung injuries by high-resolution nuclear magnetic resonance spectroscopy of bronchoalveolar lavage fluid (BALF)

We present a method for identifying biomarkers in human lung injury. The method is based on high-resolution nuclear magnetic resonance (NMR) spectroscopy applied to bronchoalveolar lavage fluid (BALF) collected from lungs of critically ill patients. This biological fluid can be obtained by bronchoscopic and non-bronchoscopic methods. The type of lung injury in acute respiratory failure presenting as acute lung injury (ALI) and its severe form, acute respiratory distress syndrome (ARDS), continues to challenge critical care physicians. We characterize different metabolites in BAL fluid by non-bronchoscopic method (mBALF) for better diagnosis and understanding of ALI/ARDS by NMR spectroscopy. NMR spectra of mBALF collected from 30 patients (9 controls, 10 ARDS and 11 ALI) were analyzed for the identification of biomarkers. Statistical methods such as principal components analysis and partial least square discriminant analysis were carried out on ¹H NMR spectrum of mBALF to identify biomarker responsible for separation among different lung injuries classes (ALI and ARDS) and normal lungs. The corresponding correlation of biomarkers with metabolic cycle has given insight into metabolism of lung injuries in critically ill patients. Our study shows statistically significant differentiation of various metabolites concentration in mBALF collected from lungs of ALI, ARDS and healthy control patients, making NMR spectroscopy as a possible new method of characterizing human lung injury.     

Quantitative proteomic profiling of pancreatic cancer juice

Pancreatic juice is an exceptionally rich source of cancer-specific proteins shed from cancerous ductal cells into the pancreatic juice. Quantitative proteomic analysis of the proteins specific to pancreatic cancer juice has not previously been reported. We used isotope-code affinity tag (ICAT) technology and MS/MS to perform quantitative protein profiling of pancreatic juice from pancreatic cancer patients and normal controls. ICAT technology coupled with MS/MS allows the systematic study of the proteome and measures the protein abundance in pancreatic juice with the potential for development of biomarkers. A total of 105 proteins were identified and quantified in the pancreatic juice from a pancreatic cancer patient, of which 30 proteins showed abundance changes of at least twofold in pancreatic cancer juice compared to normal controls. Many of these proteins have been externally validated. This is the first comprehensive study of the pancreatic juice proteome by quantitative global protein profiling, and the study reveals numerous proteins that are shown for the first time to be associated with pancreatic cancer, providing candidates for diagnostic biomarkers. One of the identified proteins, insulin-like growth factor binding protein-2 was further validated by Western blotting to be elevated in pancreatic cancer juice and overexpressed in pancreatic cancer tissue.     

Network Topologies Decoding Cervical Cancer

According to the GLOBOCAN statistics, cervical cancer is one of the leading causes of death among women worldwide. It is found to be gradually increasing in the younger population, specifically in the developing countries. We analyzed the protein-protein interaction networks of the uterine cervix cells for the normal and disease states. It was found that the disease network was less random than the normal one, providing an insight into the change in complexity of the underlying network in disease state. The study also portrayed that, the disease state has faster signal processing as the diameter of the underlying network was very close to its corresponding random control. This may be a reason for the normal cells to change into malignant state. Further, the analysis revealed VEGFA and IL-6 proteins as the distinctly high degree nodes in the disease network, which are known to manifest a major contribution in promoting cervical cancer. Our analysis, being time proficient and cost effective, provides a direction for developing novel drugs, therapeutic targets and biomarkers by identifying specific interaction patterns, that have structural importance.     

Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry

An efficient means of identifying protein biomarkers is essential to proper cancer management. A well-characterized proteome resource holds special promise for the discovery of novel biomarkers. However, quantification of the differences between physiological conditions together with deep down profiling has become increasingly challenging in proteomics. Here, we perform expression profiling of the colorectal cancer (CRC) proteome by stable isotope labeling and mass spectrometry. Quantitative analysis included performing mTRAQ and cICAT labeling in a pooled sample of three microsatellite stable (MSS) type CRC tissues and a pooled sample of their matched normal tissues. We identified and quantified a total of 3688 proteins. Among them, 1487 proteins were expressed differentially between normal and cancer tissues by higher than 2-fold; 1009 proteins showed increased expression in cancer tissue, whereas 478 proteins showed decreased expression. Bioinformatic analysis revealed that our data were largely consistent with known CRC relevant signaling pathways, such as the Wnt/β-catenin, caveolar-mediated endocytosis, and RAN signaling pathways. Mitochondrial dysfunction, known as the Waburg hypothesis, was also confirmed. Therefore, our data showing alterations in the proteomic profile of CRC constitutes a useful resource that may provide insights into tumor progression with later goal of identifying biologically and clinically relevant marker proteins. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

Systematic discovery of ectopic pregnancy serum biomarkers using 3-D protein profiling coupled with label-free quantitation

Ectopic pregnancy (EP) and normal intrauterine pregnancy (IUP) serum proteomes were quantitatively compared to systematically identify candidate biomarkers. A 3-D biomarker discovery strategy consisting of abundant protein immunodepletion, SDS gels, LC-MS/MS, and label-free quantitation of MS signal intensities identified 70 candidate biomarkers with differences between groups greater than 2.5-fold. Further statistical analyses of peptide quantities were used to select the most promising 12 biomarkers for further study, which included known EP biomarkers, novel EP biomarkers (ADAM12 and ISM2), and five specific isoforms of the pregnancy specific beta-1-glycoprotein family. Technical replicates showed good reproducibility and protein intensities from the label-free discovery analysis compared favorably with reported abundance levels of several known reference serum proteins over at least 3 orders of magnitude. Similarly, relative abundances of candidate biomarkers from the label-free discovery analysis were consistent with relative abundances from pilot validation assays performed for five of the 12 most promising biomarkers using label-free multiple reaction monitoring of both the patient serum pools used for discovery and the individual samples that constituted these pools. These results demonstrate robust, reproducible, in-depth 3-D serum proteome discovery, and subsequent pilot-scale validation studies can be achieved readily using label-free quantitation strategies.     

Evaluation of an integrated strategy for proteomic profiling of skeletal muscle

Proteomic analysis of skeletal muscle presents particular challenges when trying to identify valid biomarkers of phenotypic change in small biopsies from genetically diverse human subjects. Currently, two-dimensional (2-D) gel electrophoresis and mass spectrometry are the chosen analytical strategies but 2-D gels are not appropriate for analyzing proteins less than 11 kDa, they can suffer from problems of reproducibility and in routine use are not a viable high-throughput technique. We have evaluated an integrated proteomic strategy employing Ciphergen ProteinChip arrays, one-dimensional polyacrylamide gel electrophoresis and mass spectrometry. Protein fingerprints characteristic of fast and slow contracting muscles from normal and kyphoscoliosis (ky) mutant mice were obtained from Ciphergen protein arrays. Eight statistically validated protein biomarkers have so far been identified capable of discriminating fast from slow muscle. Five of these showed further differential expression in ky versus normal BDL soleus muscles. Several biomarkers have been formally identified, and were myosin light chain isoforms shown previously to be expressed differentially by fast versus slow skeletal muscles. This integrated experimental approach using a model mouse muscle system shows the potential of Ciphergen protein array technology for proteomic analysis of small proteins in small muscle samples and its applicability for phenotypic characterization of skeletal muscle in general.     

Rapid determination of serological cytokine biomarkers for hepatitis B virus-related hepatocellular carcinoma using antibody microarrays

Hepatocellular carcinoma (HCC) is one of the most frequent tumors worldwide with an increasing incidence. The exploration of biomarkers for HCC is one of the main aims for improving the efficacy of diagnosis and treatment. The microarray technology provides a high-throughput platform for parallel exploration of biomarkers for clinics. In this study, we used antibody microarrays to screen the novel cytokine biomarkers of hepatitis B virus (HBV)-related HCC. Cytokine-secreting patterns in sera were determined from 109 cases including 43 HBV-related HCC patients, 33 chronic hepatitis B patients, and 33 normal controls by RayBio Biotin label-based human antibody array. The correlation analysis was performed with conventional clinical diagnostic biomarkers, including serum alanine aminotransferase, alpha-fetoprotein (AFP) and hepatitis B surface antigen. Our results showed that in HBV-related HCC group, which had the highest percentage of AFP positive (>20 ng/ml) ratio, six cytokines were found differentially expressed in HCC patients (P < 0.05), compared with either normal controls or chronic hepatitis B group. Two macrophage-related cytokines, macrophage-derived chemokine (MDC) and macrophage-stimulating protein α (MSPα), displayed significant difference in the HCC group. Furthermore, an HCC diagnostic model for prediction was constructed, by which the combination of MDC and MSPα together with AFP had improved the diagnostic sensitivity from 60% (AFP alone) to 73.2% with similar specificity. Our results suggested that MDC and MSPα screened by antibody microarrays might serve as novel cytokines biomarkers for potential auxiliary diagnosis of HBV-related HCC.     

Mass spectrometry-based expression profiling of clinical prostate cancer

The maturation of MS technologies has provided a rich opportunity to interrogate protein expression patterns in normal and disease states by applying expression protein profiling methods. Major goals of this research strategy include the identification of protein biomarkers that demarcate normal and disease populations, and the identification of therapeutic biomarkers for the treatment of diseases such as cancer (Celis, J. E., and Gromov, P. (2003) Proteomics in translational cancer research: Toward an integrated approach. Cancer Cell 3, 9-151). Prostate cancer is one disease that would greatly benefit from implementing MS-based expression profiling methods because of the need to stratify the disease based on molecular markers. In this review, we will summarize the current MS-based methods to identify and validate biomarkers in human prostate cancer. Lastly, we propose a reverse proteomic approach implementing a quantitative MS research strategy to identify and quantify biomarkers implicated in prostate cancer development. With this approach, the absolute levels of prostate cancer biomarkers will be identified and quantified in normal and diseased samples by measuring the levels of native peptide biomarkers in relation to a chemically identical but isotopically labeled reference peptide. Ultimately, a centralized prostate cancer peptide biomarker expression database could function as a repository for the identification, quantification, and validation of protein biomarker(s) during prostate cancer progression in men.     

肾透明细胞癌预后相关miRNAs的生物信息学分析

目的寻找可作为肾透明细胞癌（ccRCC）生物标志物的miRNA,以及ccRCC与正常组织间miRNA差异表达情况。 方法利用TCGA数据库下载ccRCC中miRNA表达数据,分析肿瘤与正常组织间差异表达miRNA。使用Kaplan-Meier曲线对患者进行生存分析,筛选出表达情况与临床预后相关的miRNA。通过生物信息学对miRNA的靶基因进行预测,然后运用FunRich软件和ClueGO对靶基因进行GO和KEGG富集分析。 结果通过TCGA数据库分析发现,ccRCC较正常组织差异表达miRNA共54个,其中上调33个,下调21个。通过生存分析发现hsa-miR-21和hsa-miR-155与患者预后相关,P≤0.05。进一步通过Perl软件在Targetscan、miRDB、miRTarBase、miRPath这四个数据库中预测miRNA靶基因并将结果取交集,共发现129个靶基因。GO和KEGG分析结果表明,这些靶基因主要与转录因子活性、信号转导以及FoxO、TNF等信号通路密切相关。 结论通过生物信息学分析发现了ccRCC与正常组织的差异表达miRNA;其中hsa-miR-21和hsa-miR-155与患者总体生存率相关,并通过调控靶基因参与相关的信号通路进而影响ccRCC的发生发展进程,提示hsa-miR-21和hsa-miR-155可能是ccRCC潜在的生物标志物。     

Identification of the Plasma Metabolomics as Early Diagnostic Markers between Biliary Atresia and Neonatal Hepatitis Syndrome

Early detection is the most effective way to improve the clinical outcome of biliary atresia (BA). Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve early diagnosis. The aim of this study is to find the potential biomarkers to distinguish BA from neonatal hepatitis syndrome (NHS) by using a metabolomics method. We comprehensively analyzed the serum metabolites in a total of 124 blood samples from patients with BA or neonatal hepatitis syndrome (NHS) and from normal individuals using advanced metabolomic approaches, and found that the levels of glutarylcarnitine (C5DC) significantly increased in the BA group while the levels of threonine (Thr) significantly rose in the NHS group comparing with the other groups. The levels of glutamic acid (Glu) in the BA group were significantly elevated compared to those in the NHS group, but still lower than the hyperbilirubinemia and normal controls. The levels of propionyl carnitine (C3), isovaleryl carnitine (C5) and glutamine (Gln) were reduced in the BA group compared to those in the NHS group, but still higher than the hyperbilirubinemia and normal controls. This study demonstrates the possibility of metabolomics as non-invasive biomarkers for the early detection of BA and also provides new insight into pathophysiologic mechanisms for BA.