Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches’ ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method’s rationality and feasibility when using different data from different sources.
group decision making; Vague set; water environment management; algal bloom remediation
During the past decades, there have been continuous attempts in the prediction of metabolism mediated by cytochrome P450s (CYP450s) 3A4, 2D6, and 2C9. However, it has indeed remained a huge challenge to accurately predict the metabolism of xenobiotics mediated by these enzymes. To address this issue, microsomal metabolic reaction system (MMRS)—a novel concept, which integrates information about site of metabolism (SOM) and enzyme—was introduced. By incorporating the use of multiple feature selection (FS) techniques (ChiSquared (CHI), InfoGain (IG), GainRatio (GR), Relief) and hybrid classification procedures (Kstar, Bayes (BN), K-nearest neighbours (IBK), C4.5 decision tree (J48), RandomForest (RF), Support vector machines (SVM), AdaBoostM1, Bagging), metabolism prediction models were established based on metabolism data released by Sheridan et al. Four major biotransformations, including aliphatic C-hydroxylation, aromatic C-hydroxylation, N-dealkylation and O-dealkylation, were involved. For validation, the overall accuracies of all four biotransformations exceeded 0.95. For receiver operating characteristic (ROC) analysis, each of these models gave a significant area under curve (AUC) value >0.98. In addition, an external test was performed based on dataset published previously. As a result, 87.7% of the potential SOMs were correctly identified by our four models. In summary, four MMRS-based models were established, which can be used to predict the metabolism mediated by CYP3A4, 2D6, and 2C9 with high accuracy.
CYP3A4; CYP2D6; CYP2C9; microsomal metabolic reaction system; metabolism prediction; classification; feature selection
Next-generation sequencing and the collection of genome-wide single-nucleotide polymorphisms (SNPs) allow identifying fine-scale population genetic structure and genomic regions under selection. The spotted sea bass (Lateolabrax maculatus) is a non-model species of ecological and commercial importance and widely distributed in northwestern Pacific. A total of 22 648 SNPs was discovered across the genome of L. maculatus by paired-end sequencing of restriction-site associated DNA (RAD-PE) for 30 individuals from two populations. The nucleotide diversity (π) for each population was 0.0028±0.0001 in Dandong and 0.0018±0.0001 in Beihai, respectively. Shallow but significant genetic differentiation was detected between the two populations analyzed by using both the whole data set (FST = 0.0550, P < 0.001) and the putatively neutral SNPs (FST = 0.0347, P < 0.001). However, the two populations were highly differentiated based on the putatively adaptive SNPs (FST = 0.6929, P < 0.001). Moreover, a total of 356 SNPs representing 298 unique loci were detected as outliers putatively under divergent selection by FST-based outlier tests as implemented in BAYESCAN and LOSITAN. Functional annotation of the contigs containing putatively adaptive SNPs yielded hits for 22 of 55 (40%) significant BLASTX matches. Candidate genes for local selection constituted a wide array of functions, including binding, catalytic and metabolic activities, etc. The analyses with the SNPs developed in the present study highlighted the importance of genome-wide genetic variation for inference of population structure and local adaptation in L. maculatus.
The spatial distribution of genetic diversity has been long considered as a key component of policy development for management and conservation of marine fishes. However, unraveling the population genetic structure of migratory fish species is challenging due to high potential for gene flow. Despite the shallow population differentiation revealed by putatively neutral loci, the higher genetic differentiation with panels of putatively adaptive loci could provide greater resolution for stock identification. Here, patterns of population differentiation of small yellow croaker (Larimichthys polyactis) were investigated by genotyping 15 highly polymorphic microsatellites in 337 individuals of 15 geographic populations collected from both spawning and overwintering grounds. Outlier analyses indicated that the locus Lpol03 might be under directional selection, which showed a strong homology with Grid2 gene encoding the glutamate receptor δ2 protein (GluRδ2). Based on Lpol03, two distinct clusters were identified by both STRUCTURE and PCoA analyses, suggesting that there were two overwintering aggregations of L. polyactis. A novel migration pattern was suggested for L. polyactis, which was inconsistent with results of previous studies based on historical fishing yield statistics. These results provided new perspectives on the population genetic structure and migratory routes of L. polyactis, which could have significant implications for sustainable management and utilization of this important fishery resource.
The NCI Clinical Proteomic Tumor
Analysis Consortium (CPTAC) employed
a pair of reference xenograft proteomes for initial platform validation
and ongoing quality control of its data collection for The Cancer
Genome Atlas (TCGA) tumors. These two xenografts, representing basal
and luminal-B human breast cancer, were fractionated and analyzed
on six mass spectrometers in a total of 46 replicates divided between
iTRAQ and label-free technologies, spanning a total of 1095 LC–MS/MS
experiments. These data represent a unique opportunity to evaluate
the stability of proteomic differentiation by mass spectrometry over
many months of time for individual instruments or across instruments
running dissimilar workflows. We evaluated iTRAQ reporter ions, label-free
spectral counts, and label-free extracted ion chromatograms as strategies
for data interpretation (source code is available from http://homepages.uc.edu/~wang2x7/Research.htm). From these assessments, we found that differential genes from
a single replicate were confirmed by other replicates on the same
instrument from 61 to 93% of the time. When comparing across different
instruments and quantitative technologies, using multiple replicates,
differential genes were reproduced by other data sets from 67 to 99%
of the time. Projecting gene differences to biological pathways and
networks increased the degree of similarity. These overlaps send an
encouraging message about the maturity of technologies for proteomic
Differential proteomics; label-free; iTRAQ; quality control; xenografts; technology
The purpose of this study was to describe Chinese trends in radical surgical modalities and influential imaging and demographic factors for breast malignancies. Rates of mastectomy in the People’s Republic of China remain elevated due to diagnosis at higher stages; however, because of increased use of diagnostic imaging, improvement of biopsy methods, and patient education, rates of less invasive lumpectomy are increasing and rates of mastectomy have decreased in China.
Incidence rates of breast cancer continue to rise in the People’s Republic of China. The purpose of this study was to describe Chinese trends in radical surgical modalities and influential imaging and demographic factors for breast malignancies.
Materials and Methods.
This study was a hospital-based, multicenter, 10-year (1999–2008), retrospective study. Descriptive statistical tests were used to illustrate information regarding radical surgical trends for the treatment of breast malignancies. Chi-square tests were used to assess effect of demographic factors in addition to imaging and pathological data on the specific surgical method.
A total of 4,211 patients were enrolled in the survey. Among them, 3,335 patients with stage 0 to stage III disease undergoing mastectomy or breast-conserving surgery (BCS) were included in the final analysis. The rate of BCS increased from 1.53% in 1999 to 11.88% in 2008. The rate of mastectomy declined over this time period, from 98.47% in 1999 to 88.12% in 2008, with increasing use of diagnostic imaging methods and pathological biopsies. A significantly greater percentage of patients with office work, high education levels, unmarried status, younger age, and early pathological stages preferred BCS compared with mastectomy.
Rates of mastectomy in China remain elevated due to diagnosis at higher stages; however, because of increased use of diagnostic imaging, improvement of biopsy methods, and patient education, rates of less invasive lumpectomy are increasing and rates of mastectomy have decreased in China.
Implications for Practice:
In this study, 4,211 cases were collected from 1999 to 2008 through a multicenter retrospective study of varying geographic and socioeconomic areas to illustrate trends of surgeries in the People’s Republic of China. The correlations between demographic and tumor characteristics and among methods of surgical treatment were explored. This study shows that the rate of breast-conserving surgery (BCS) increased and the rate of mastectomy declined over this time period with increasing use of diagnostic imaging methods and pathological biopsies. Patients with office work, high education levels, unmarried status, younger age, and early pathological stages preferred BCS compared with mastectomy in China.
Breast neoplasms; Surgical; Imaging; Diagnosis
Incidence rates for breast cancer continue to rise in the People’s Republic of China. The purpose of this study was to analyze differences in characteristics of breast malignancies between China and the U.S. Chinese women were diagnosed at younger ages with higher stage and larger tumors and underwent more aggressive surgical treatment. Prospective trials should be conducted to address screening, surgical, and tumor discrepancies between China and the U.S.
Background and Objective.
Incidence of and mortality rates for breast cancer continue to rise in the People’s Republic of China. The purpose of this study was to analyze differences in characteristics of breast malignancies between China and the U.S.
Data from 384,262 breast cancer patients registered in the U.S. Surveillance, Epidemiology, and End Results (SEER) program from 2000 to 2010 were compared with 4,211 Chinese breast cancer patients registered in a Chinese database from 1999 to 2008. Outcomes included age, race, histology, tumor and node staging, laterality, surgical treatment method, and reconstruction. The Pearson chi-square and Fisher’s exact tests were used to compare rates.
Infiltrating ductal carcinoma was the most common type of malignancy in the U.S. and China. The mean number of positive lymph nodes was higher in China (2.59 vs. 1.31, p < .001). Stage at diagnosis was higher in China (stage IIA vs. I, p < .001). Mean size of tumor at diagnosis was higher in China (32.63 vs. 21.57 mm). Mean age at diagnosis was lower in China (48.28 vs. 61.29 years, p < .001). Moreover, 2.0% of U.S. women underwent radical mastectomy compared with 12.5% in China, and 0.02% in China underwent reconstructive surgery.
Chinese women were diagnosed at younger ages with higher stage and larger tumors and underwent more aggressive surgical treatment. Prospective trials should be conducted to address screening, surgical, and tumor discrepancies between China and the U.S.
Implications for Practice:
Breast cancer patients in China are diagnosed at later stages than those in America, which might contribute to different clinical management and lower 5-year survival rate. This phenomenon suggests that an earlier detection and treatment program should be widely implemented in China. By comparing the characteristics of Chinese and Chinese-American patients, we found significant differences in tumor size, lymph nodes metastasis, and age at diagnosis. These consequences indicated that patients with similar genetic backgrounds may have different prognoses due to the influence of environment and social economic determinates.
Breast cancer; China; Disparities
Proteomics analysis is important for characterizing tissues to gain biological and pathological insights, which could lead to the identification of disease-associated proteins for disease diagnostics or targeted therapy. However, tissues are commonly embedded in optimal cutting temperature compound (OCT) or they are formalin-fixed and paraffin-embedded (FFPE) in order to maintain tissue morphology for histology evaluation. Although several tissue proteomics analyses have been performed on FFPE tissues using advanced mass spectrometry technologies, high throughput proteomic analysis of OCT-embedded tissues has been difficult due to the interference of OCT in the mass spectrometry analysis. In addition, molecules other than proteins present in tissues further complicate tissue proteomic analysis. Herein, we report the development of a method using Chemical Immobilization of Proteins for Peptide Extraction (CIPPE). In this method, proteins are chemically immobilized onto a solid support; interferences from tissues and OCT embedding are removed by extensive washing of proteins conjugated on the solid support. Peptides are then released from the solid phase by proteolysis, enabling mass spectrometry analysis. This method was first validated by eliminating OCT interference from a standard protein, human serum albumin, where all the unique peaks contributed by OCT contamination were eradicated. Finally, this method was applied for the proteomic analysis of frozen and OCT-embedded tissues using iTRAQ labeling and 2D liquid chromatography tandem mass spectrometry. The data showed reproducible extraction and quantitation of 10,284 proteins from 3,996 protein groups and a minimal impact of OCT embedding on the analysis of the global proteome of the stored tissue samples.
OCT; proteomics; protein chemical immobilization; polymer contamination; tissues
Summary: We have developed an integrated molecular network learning method, within a well-grounded mathematical framework, to construct differential dependency networks with significant rewiring. This knowledge-fused differential dependency networks (KDDN) method, implemented as a Java Cytoscape app, can be used to optimally integrate prior biological knowledge with measured data to simultaneously construct both common and differential networks, to quantitatively assign model parameters and significant rewiring p-values and to provide user-friendly graphical results. The KDDN algorithm is computationally efficient and provides users with parallel computing capability using ubiquitous multi-core machines. We demonstrate the performance of KDDN on various simulations and real gene expression datasets, and further compare the results with those obtained by the most relevant peer methods. The acquired biologically plausible results provide new insights into network rewiring as a mechanistic principle and illustrate KDDN’s ability to detect them efficiently and correctly. Although the principal application here involves microarray gene expressions, our methodology can be readily applied to other types of quantitative molecular profiling data.
Availability: Source code and compiled package are freely available for download at http://apps.cytoscape.org/apps/kddn
Supplementary data are available at Bioinformatics online.
Conduction abnormalities can lead to dyssynchronous contraction, which significantly worsens morbidity and mortality of heart failure. Cardiac resynchronization therapy (CRT) can reverse ventricular remodeling and improve cardiac function. Although the underlying molecular changes are unknown, the use of a canine model of dyssynchrony heart failure (DHF) and CRT has shown that there are global changes across the cardiac proteome. This study determines changes in serum glycoprotein concentration from DHF and CRT compared to normal. We hypothesize that CRT invokes protective or advantageous pathways that can be reflected in the circulating proteome. A two prong discovery approaches were carried out on pooled normal, DHF and CRT samples composed of individual canine serum to determine the overall protein concentration and the N-linked glycosites of circulating glycoproteins. The level of the glycoproteins was altered in DHF and CRT compared to control sera, with 63 glycopeptides substantially increased in DHF and/or CRT. Among the 32 elevated glycosite-containing peptides in DHF, 13 glycopeptides were reverted to normal level after CRT therapy. We further verify the changes of glycopeptides using label-free LC-MS from individual canine serum. Circulating glycoproteins such as alpha-fetoprotein, alpha-2-macroglobulin, galectin-3-binding protein, collectin-10 show association to failing heart and CRT treatment model.
Cardiac resynchronization therapy; Dyssynchronous heart failure; Glycoprotein; MS; SPEG
Aberrant protein glycosylation is known to be associated with the development of cancers. The aberrant glycans are produced by the combined actions of changed glycosylation enzymes, substrates and transporters in glycosylation synthesis pathways in cancer cells. To identify glycosylation enzymes associated with aggressive prostate cancer (PCa), we analyzed the difference in the expression of glycosyltransferase genes between aggressive and non-aggressive PCa. Three candidate genes encoding glycosyltransferases that were elevated in aggressive PCa were subsequently selected. The expression of the three candidates was then further evaluated in androgen-dependent (LNCaP) and androgen-independent (PC3) PCa cell lines. We found that the protein expression of one of the glycosyltransferases, α (1,6) fucosyltransferase (FUT8), was only detected in PC3 cells, but not in LNCaP cells. We further showed that FUT8 protein expression was elevated in metastatic PCa tissues compared to normal prostate tissues. In addition, using tissue microarrays, we found that FUT8 overexpression was statistically associated with PCa with a high Gleason score. Using PC3 and LNCaP cells as models, we found that FUT8 overexpression in LNCaP cells increased PCa cell migration, while loss of FUT8 in PC3 cells decreased cell motility. Our results suggest that FUT8 may be associated with aggressive PCa and thus is potentially useful for its prognosis.
aggressive prostate cancer; α (1,6) fucosyltransferase
Due to the proved clinical efficacy, Shuang-Huang-Lian (SHL) has
developed a variety of dosage forms. However, the in-depth
research on targets and pharmacological mechanisms of SHL
preparations was scarce. In the presented study, the
bioinformatics approaches were adopted to integrate relevant data
and biological information. As a result, a PPI network was built
and the common topological parameters were characterized. The
results suggested that the PPI network of SHL exhibited a
scale-free property and modular architecture. The drug target
network of SHL was structured with 21 functional modules.
According to certain modules and pharmacological effects
distribution, an antitumor effect and potential drug targets were
predicted. A biological network which contained 26 subnetworks was
constructed to elucidate the antipneumonia mechanism of SHL. We
also extracted the subnetwork to explicitly display the pathway
where one effective component acts on the pneumonia related
targets. In conclusions, a bioinformatics approach was established
for exploring the drug targets, pharmacological activity
distribution, effective components of SHL, and its mechanism of
antipneumonia. Above all, we identified the effective components
and disclosed the mechanism of SHL from the view of system.
This retrospective study evaluated the role of adjuvant radiotherapy (AR) after surgery in patients with uterine sarcoma and analyzed the prognostic factors of local-regional failure-free survival (LRFFS) and overall survival (OS).
Patients and methods
A study of a total of 182 patients with uterine sarcoma was conducted between June 1994 and October 2014. Adjuvant radiotherapy was defined as postoperative external beam radiation to the pelvis (30–50 Gray/10–25 fractions at five fractions/week). The primary end point was LRFFS, and the secondary end point was OS. Kaplan–Meier curves were compared using the log-rank test. Cox regression analyses were used to determine prognosticators for LRFFS and OS.
The median follow-up time of all patients was 75 months, with a 5-year LRFFS of 62.1%. The 2-year and 5-year LRFFS rates were longer for those who received AR than for those who did not receive AR (83.4% vs 70.3%; 78% vs 55.3%; P=0.013). The 5-year OS of all patients was 56.2%, and no significant differences were observed in the 2-year and 5-year OS rates between these two groups (82.7% vs 71.4%; 64.1% vs 51.7%; P=0.067). Importantly, in patients with leiomyosarcoma, the 2-year and 5-year LRFFS and OS rates were longer for those who received AR than for those who did not receive AR (P=0.04 and P=0.02 for the 2-year and 5-year LRFFS, respectively).
Patients with uterine sarcoma who were treated with AR after surgery demonstrated an improved LRFFS compared with those who were treated with surgery alone, especially those patients with leiomyosarcoma. Therefore, the role of personalized adjuvant radiation for patients with uterine sarcoma still requires further discussion.
uterine neoplasm; radiation; local-regional failure-free survival; overall survival
Accumulating evidence indicates a potential role of adventitial vasa vasorum (VV) dysfunction in the pathophysiology of restenosis. However, characterization of VV vascularization in restenotic arteries with primary lesions is still missing. In this study, we quantitatively evaluated the response of adventitial VV to vascular injury resulting from balloon angioplasty in diseased arteries.
Primary atherosclerotic-like lesions were induced by the placement of an absorbable thread surrounding the carotid artery of New Zealand rabbits. Four weeks following double-injury induced that was induced by secondary balloon dilation, three-dimensional patterns of adventitial VV were reconstructed; the number, density, and endothelial surface of VV were quantified using micro-computed tomography. Histology and immunohistochemistry were performed in order to examine the development of intimal hyperplasia.
Results from our study suggest that double injured arteries have a greater number of VV, increased luminal surface, and an elevation in the intima/media ratio (I/M), along with an accumulation of macrophages and smooth muscle cells in the intima, as compared to sham or single injury arteries. I/M and the number of VV were positively correlated (R2 = 0.82, P < 0.001).
Extensive adventitial VV neovascularization occurs in injured arteries after balloon angioplasty, which is associated with intimal hyperplasia. Quantitative assessment of adventitial VV response may provide insight into the basic biological process of postangioplasty restenosis.
Angioplasty; Micro-computed Tomography; Restenosis; Vasa Vasorum
bioinformatics; statistical model; network biology; systems biology; network models; biological databases; network analysis
RhoA/ROCK signaling plays an important role in diabetic nephropathy, and ROCK inhibitor fasudil exerts nephroprotection in experimental diabetic nephropathy. In this study we investigated the molecular mechanisms underlying the protective actions of fasudil in a rat model of diabetic nephropathy.
Streptozotocin (STZ)-induced diabetic rats, to which fasudil or a positive control drug enalapril were orally administered for 8 months. Metabolic parameters and blood pressure were assessed during the treatments. After the rats were euthanized, kidney samples were collected for histological and molecular biological studies. VEGF, VEGFR1, VEGFR2 and fibronectin expression, and Src and caveolin-1 phosphorylation in the kidneys were assessed using RT-PCR, Western blot and immunohistochemistry assays. The association between VEGFR2 and caveolin-1 was analyzed with immunoprecipitation.
Chronic administration of fasudil (30 and 100 mg·kg−1·d−1) or enalapril (10 mg/kg, bid) significantly attenuated the glomerular sclerosis and albuminuria in the diabetic rats. Furthermore, fasudil treatment prevented the upregulation of VEGF, VEGFR1, VEGFR2 and fibronectin, and the increased association between VEGFR2 and caveolin-1 in the renal cortices, and partially blocked Src activation and caveolin-1 phosphorylation on tyrosine 14 in the kidneys, whereas enalapril treatment had no effects on the VEGFR2/Src/caveolin-1 signaling pathway.
Fasudil exerts protective actions in STZ-induced diabetic nephropathy by blocking the VEGFR2/Src/caveolin-1 signaling pathway and fibronectin upregulation. Thus, VEGFR2 may be a potential therapeutic target for the treatment of diabetic nephropathy.
diabetic nephropathy; glomerular sclerosis; RhoA/ROCK signaling; fasudil; enalapril; VEGF; Src; caveolin-1
Purpose: 18F-FLT-PET imaging was proposed as a tool for measuring in vivo tumor cell proliferation and detecting sub-volumes to propose escalation in radiotherapy. The aim of this study was to validate whether high FLT uptake areas in 18F-FLT PET/CT are coincident with tumor cell proliferation distribution indicated by Ki-67 staining in non-small cell lung cancer, thus provide theoretical support for the application of dose painting guided by 18F-FLT PET/CT. Materials and methods: Twelve treatment naive patients with biopsy proven NSCLC underwent 18F-FLT PET/CT scans followed by lobectomy were enrolled. The surgical specimen was dissected into 4-7 μm sections at approximately 4-mm intervals. The best slice was sort out to complete Ki-67 staining. Maximum Ki-67 labelling Index and SUVmax of the corresponding PET image was calculated. The correlation between Ki-67 Labelling Index and SUVmax of FLT was determined using Spearman Correlation analysis. High uptake areas and high proliferating areas were delineated on the two images, respectively, and their location was compared. Results: The maximal SUV was 3.26 ± 0.97 (1.96-5.05), maximal Ki-67 labeling index was 49% ± 27.56% (5%-90%). Statistical analysis didn’t reveal a significant correlation between them (r = -0.157, P = 0.627, > 0.05). 9 patients can contour high proliferating area on Ki-67 staining slice, and eight can contour the high uptake areas. In 4 patients, we can observe a generally close distribution of high uptake areas and high proliferating areas, in one patient, both the uptake level and proliferation status was low, while the others didn’t not find a significant co-localization. Conclusion: Noninvasive 18F-FLT PET assessing the proliferative status may be a valuable aid to guide dose painting in NSCLC, but it needs to be confirmed further.
18F-FLT PET; pathological spatial validation; non-small-cell lung cancer
Current data regarding the association between the methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism and the risk of developing gastric cancer are insufficient to draw definite conclusions. Therefore, the present meta-analysis was conducted to achieve a more precise estimation of the association. MEDLINE, EMBASE and Wanfang database searches resulted in the identification of 28 eligible studies describing 5,757 cases and 8,501 controls. The strength of the association between the MTHFR C677T polymorphism and gastric cancer risk were evaluated using crude odds ratios (ORs), with 95% confidence intervals (CIs). The pooled ORs were determined using homozygous (TT vs. CC), heterozygous (CT vs. CC), dominant (TT+CT vs. CC) and recessive (TT vs. CC+CT) models. When all studies were pooled into the meta-analysis, significant associations were identified between the MTHFR C677T polymorphism and the risk of gastric cancer (homozygous model: OR, 1.39; 95% CI, 1.20–1.62; heterozygous model: OR, 1.18; 95% CI, 1.05–1.32; dominant model: OR, 1.23; 95% CI, 1.10–1.38; recessive model: OR, 1.26; 95% CI, 1.12–1.42). Stratification of the data by ethnicity identified a statistically significantly elevated risk of gastric cancer in Asian MTHFR C677T polymorphism populations (homozygous model: OR, 1.64; 95% CI, 1.43–1.90; heterozygous model: OR, 1.30; 95% CI, 1.16–1.45; dominant model: OR, 1.39; 95% CI, 1.25–1.54; recessive model: OR, 1.41; 95% CI, 1.25–1.51), but not in Caucasian populations (homozygous model: OR, 1.15; 95% CI, 0.89–1.48; heterozygous model: OR, 1.03; 95% CI, 0.84–1.25; dominant model: OR, 1.05; 95% CI, 0.86–1.28; recessive model: OR, 1.09; 95% CI, 0.91–1.31). Following adjustment for heterogeneity, the current meta-analysis demonstrated that the MTHFR C677T polymorphism was not associated with the risk of gastric cancer in Caucasian individuals. Furthermore, no evidence of publication bias was observed. Thus, the current meta-analysis indicates that the MTHFR C677T allele may be a low-penetrant risk factor for the development of gastric cancer in Asian populations.
gastric cancer; case-control; meta-analysis; methylenetetrahydrofolate reductase; polymorphism
Ever growing “omics” data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical measurements such as in vivo imaging. Since signaling or regulatory networks are dynamic and context-specific, systematic efforts to characterize such structural alterations must effectively distinguish significant network rewiring from random background fluctuations. Here we introduced a novel integration of network biology and imaging to study cancer phenotypes and responses to treatments at the molecular systems level. Specifically, Differential Dependence Network (DDN) analysis was used to detect statistically significant topological rewiring in molecular networks between two phenotypic conditions, and in vivo Magnetic Resonance Imaging (MRI) was used to more accurately define phenotypic sample groups for such differential analysis. We applied DDN to analyze two distinct phenotypic groups of breast cancer and study how genomic instability affects the molecular network topologies in high-grade ovarian cancer. Further, FDA-approved arsenic trioxide (ATO) and the ND2-SmoA1 mouse model of Medulloblastoma (MB) were used to extend our analyses of combined MRI and Reverse Phase Protein Microarray (RPMA) data to assess tumor responses to ATO and to uncover the complexity of therapeutic molecular biology.
Network biology; MRI; differential network; cancer biology
Protein glycosylation has long been recognized as one of the most common post-translational modifications. Most membrane proteins and extracellular proteins are N-linked glycosylated and they account for the majority of current clinical diagnostic markers or therapeutic targets. Quantitative proteomic analysis of detectable N-linked glycoproteins from cells or tissues using mass spectrometry has the potential to provide biological basis for disease development and identify disease associated glycoproteins. However, the information of low abundance but important peptides is lost due to the lack of MS/MS fragmentation or low quality of MS/MS spectra for low abundant peptides. Here, we show the feasibility of formerly N-glycopeptide identification and quantification at MS1 level using genomic N-glycosite prediction (GenoGlyco) coupled with stable isotopic labeling and accurate mass matching. The GenoGlyco Analyzer software uses accurate precursor masses of detected N-deglycopeptide peaks to match them to N-linked deglycopeptides which are predicted from genes expressed in the cells. This method results in more robust glycopeptide identification compared to MS/MS based identification. Our results showed that over three times the quantity of N-deglycopeptide assignments from the same mass spectrometry data could be produced in ovarian cancer cell lines compared to a MS/MS fragmentation method. Furthermore, the method was also applied to N-deglycopeptide analysis of ovarian tumors using the identified deglycopeptides from the two ovarian cell lines as heavy standards. We show that the described method has a great potential in the analysis of detectable N-glycoproteins from cells and tissues.
glycosylation; prediction; genome-wide; SILAC; accurate mass matching; ovarian cancer; mass spectrometry
Endometrial regenerative cells (ERCs) are mesenchymal-like stem cells that can be non-invasively obtained from menstrual blood and are easily grown /generated at a large scale without tumorigenesis. We previously reported that ERCs exhibit unique immunoregulatory properties in vitro, however their immunosuppressive potential in protecting the colon from colitis has not been investigated. The present study was undertaken to determine the efficacy of ERCs in mediating immunomodulatory functions against colitis.
Colitis was induced by 4% dextran-sulfate-sodium (DSS, in drinking water) in BALB/c mice for 7 days. ERCs were cultured from healthy female menstrual blood, and injected (1 million/mouse/day, i.v.) into mice on days 2, 5, and 8 following colitis induction. Colonic and splenic tissues were collected on day 14 post-DSS-induction. Clinical signs, disease activity index (DAI), pathological and immunohistological changes, cytokine profiles and cell populations were evaluated.
DSS-induced mice in untreated group developed severe colitis, characterized by body-weight loss, bloody stool, diarrhea, mucosal ulceration and colon shortening, as well as pathological changes of intra-colon cell infiltrations of neutrophils and Mac-1 positive cells. Notably, ERCs attenuated colitis with significantly reduced DAI, decreased levels of intra-colon IL-2 and TNF-α, but increased expressions of IL-4 and IL-10. Compared with those of untreated colitis mice, splenic dendritic cells isolated from ERC-treated mice exhibited significantly decreased MHC-II expression. ERC-treated mice also demonstrated much less CD3+CD25+ active T cell and CD3+CD8+ T cell population and significantly higher level of CD4+CD25+Foxp3+ Treg cells.
This study demonstrated novel anti-inflammatory and immunosuppressive effects of ERCs in attenuating colitis in mice, and suggested that the unique features of ERCs make them a promising therapeutic tool for the treatment of ulcerative colitis.
Endometrial regenerative cells; Colitis; Mice
Mass spectrometry based glycoproteomics has become a major means of identifying and characterizing previously N-linked glycan attached loci (glycosites). In the bottom-up approach, several factors which include but not limited to sample preparation, mass spectrometry analyses, and protein sequence database searches result in previously N-linked peptide spectrum matches (PSMs) of varying lengths. Given that multiple PSM scan map to a glycosite, we reason that identified PSMs are varying length peptide species of a unique set of glycosites. Because associated spectra of these PSMs are typically summed separately, true glycosite associated spectra counts are lost or complicated. Also, these varying length peptide species complicate protein inference as smaller sized peptide sequences are more likely to map to more proteins than larger sized peptides or actual glycosite sequences. Here, we present XGlycScan. XGlycScan maps varying length peptide species to glycosites to facilitate an accurate quantification of glycosite associated spectra counts. We observed that this reduced the variability in reported identifications of mass spectrometry technical replicates of our sample dataset. We also observed that mapping identified peptides to glycosites provided an assessment of search-engine identification. Inherently, XGlycScan reported glycosites reduce the complexity in protein inference. We implemented XGlycScan in the platform independent Java programing language and have made it available as open source. XGlycScan's source code is freely available at https://bitbucket.org/paiyetan/xglycscan/src and its compiled binaries and documentation can be freely downloaded at https://bitbucket.org/paiyetan/xglycscan/downloads. The graphical user interface version can also be found at https://bitbucket.org/paiyetan/xglycscangui/src and https://bitbucket.org/paiyetan/xglycscangui/downloads respectively.
Bioinformatics; Peptide; Glycopeptide; Glycosite; Protein identification; Proteomics; Quality assessment
Proteome Discoverer is one of many tools used for protein database search and peptide to spectrum assignment in mass spectrometry-based proteomics. However, the inadequacy of conversion tools makes it challenging to compare and integrate its results to those of other analytical tools. Here we present M2Lite, an open-source, light-weight, easily pluggable and fast conversion tool. M2Lite converts proteome discoverer derived MSF files to the proteomics community defined standard – the mzIdentML file format. M2Lite’s source code is available as open-source at https://bitbucket.org/paiyetan/m2lite/src and its compiled binaries and documentation can be freely downloaded at https://bitbucket.org/paiyetan/m2lite/downloads.
Bioinformatics; Peptide identification; Proteome Discoverer; MSF; Proteomics; mzIdentML; pepXML; IDPicker
Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context-specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. While differential networks cannot be constructed by existing knowledge alone, novel incorporation of prior knowledge into data-driven approaches can improve the robustness and biological relevance of network inference. However, the major unresolved roadblocks include: big solution space but a small sample size; highly complex networks; imperfect prior knowledge; missing significance assessment; and heuristic structural parameter learning.
To address these challenges, we formulated the inference of differential dependency networks that incorporate both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to “random” knowledge. Based on that scheme, we developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results. The open-source R software package and the experimental data are freely available at http://www.cbil.ece.vt.edu/software.htm.
Experiments on both synthetic and real data demonstrate the effectiveness of the knowledge-fused differential dependency network in revealing the statistically significant rewiring in biological networks. The method efficiently leverages data-driven evidence and existing biological knowledge while remaining robust to the false positive edges in the prior knowledge. The identified network rewiring events are supported by previous studies in the literature and also provide new mechanistic insight into the biological systems. We expect the knowledge-fused differential dependency network analysis, together with the open-source R package, to be an important and useful bioinformatics tool in biological network analyses.
Biological networks; Probabilistic graphical models; Differential dependency network; Network rewiring; Network analysis; Systems biology; Knowledge incorporation; Convex optimization