CiteULike: jyuh's Zhao

Relation of iron and red meat intake to blood pressure: cross sectional epidemiological study.

I Tzoulaki — 2008-07-27T04:59:52-00:00

BMJ (Clinical research ed.), Vol. 337 (2008)

OBJECTIVE: To investigate associations of dietary iron (total, haem, and non-haem), supplemental iron, and red meat with blood pressure. DESIGN: Cross sectional epidemiological study. SETTING: 17 population samples from Japan, China, the United Kingdom, and the United States participating in the international collaborative study on macro-/micronutrients and blood pressure (INTERMAP). PARTICIPANTS: 4680 adults aged 40-59. MAIN OUTCOME MEASURE: Average of eight blood pressure readings. RESULTS: In multiple linear regression analyses dietary total iron and non-haem iron were consistently inversely associated with blood pressure. With adjustment for potential non-dietary and dietary confounders, dietary total iron intake higher by 4.20 mg/4.2 MJ (2 SD) was associated with -1.39 mm Hg (P<0.01) lower systolic blood pressure. Dietary non-haem iron intake higher by 4.13 mg/4.2 MJ (2 SD) was associated with -1.45 mm Hg (P<0.001) lower systolic blood pressure. Differences were smaller for diastolic blood pressure. In most models haem iron intake from food was positively, non-significantly associated with blood pressure. Iron intake from combined diet and supplements yielded smaller associations than dietary iron alone. Red meat intake was directly associated with blood pressure; 102.6 g/24 h (2 SD) higher intake was associated with 1.25 mm Hg higher systolic blood pressure. Associations between red meat and blood pressure persisted after adjustment for multiple confounders. CONCLUSION: Non-haem iron has a possible role in the prevention and control of adverse blood pressure levels. An unfavourable effect of red meat on blood pressure was observed. These results need confirmation including in prospective studies, clinical trials, and from experimental evidence on possible mechanisms.

Dietary phosphorus and blood pressure: international study of macro- and micro-nutrients and blood pressure.

P Elliott — 2008-07-27T05:00:00-00:00

Hypertension, Vol. 51, No. 3. (March 2008), pp. 669-675.

Raised blood pressure is a leading cause of morbidity and mortality worldwide; improved nutritional approaches to population-wide prevention are required. Few data are available on dietary phosphorus and blood pressure and none are available on possible combined effects of phosphorus, magnesium, and calcium on blood pressure. The International Study of Macro- and Micro-Nutrients and Blood Pressure is a cross-sectional epidemiologic study of 4680 men and women ages 40 to 59 from 17 population samples in Japan, China, United Kingdom, and United States. Blood pressure was measured 8 times at 4 visits. Dietary intakes were obtained from four 24-hour recalls plus data on supplement use. Dietary phosphorus was inversely associated with blood pressure in a series of predefined multiple regression models, with the successive addition of potential confounders, both nondietary and dietary. Estimated blood pressure differences per 232 mg/1000 kcal (2 SD) of higher dietary phosphorus were -1.1 to -2.3 mm Hg systolic/-0.6 to -1.5 mm Hg diastolic (n=4680) and -1.6 to -3.5 mm Hg systolic/-0.8 to -1.8 mm Hg diastolic for 2238 "nonintervened" individuals, ie, those without special diet/nutritional supplements or diagnosis/treatment for cardiovascular disease or diabetes. Dietary calcium and magnesium, correlated with phosphorus (partial r=0.71 and r=0.68), were inversely associated with blood pressure. Blood pressures were lower by 1.9 to 4.2 mm Hg systolic/1.2 to 2.4 mm Hg diastolic for people with intakes above versus below country-specific medians for all 3 of the minerals. These results indicate the potential for increased phosphorus/mineral intake to lower blood pressure as part of the recommendations for healthier eating patterns for the prevention and control of prehypertension and hypertension.

Alignment of two-dimensional electrophoresis gels.

G Shi — 2008-07-24T09:22:16-00:00

Biochemical and biophysical research communications, Vol. 357, No. 2. (1 June 2007), pp. 427-432.

Two-dimensional electrophoresis is a major separating technique for proteins in proteomics. Alignment of gel images is critical for intra-laboratory or even more difficult inter-laboratory gel comparisons. In the paper, we propose a novel iterative closest point (ICP) method for 2D-gel electrophoresis image alignment. The paper seeks to introduce an information theoretic measure as one part of distance metric to gel image alignment. We combine intensity information of spots with geometric information of landmarks by applying information potential idea. The proposed method has been applied to both synthetic and real gel images accessible in public 2D-electrophoresis gel protein databases. The high accuracy and robustness of the algorithm indicate that it is promising for gel image alignment.

A novel method of loading samples onto mini-gels for SDS-PAGE: increased sensitivity and Western blots using sub-microgram quantities of protein.

MW Swank — 2008-07-24T08:41:31-00:00

Journal of neuroscience methods, Vol. 158, No. 2. (15 December 2006), pp. 224-233.

Commercially available mini-gels for sodium dodecyl sulphate (SDS)-PAGE and Western blotting are limited both by the number of lanes that can be loaded per gel and the minimum amount of protein per lane that must be loaded. Here we describe a method for loading protein samples onto existing commercially available mini-gels that allows loading of 50 or more lanes per gel. The enhanced sensitivity of the method allows Western blotting with sub-microgram quantities of protein. Samples are loaded onto filter paper strips mounted on a plastic backing sheet, and film-wrapped strips on a separate dummy loader interdigitate with the sample strips, creating a physical barrier to lateral diffusion. The sample loader sandwich is placed on top of the stacking gel, and is compatible with all commercially available SDS-PAGE systems. Comparison of 15-lane mini-gels with 30-lane micro-loader strips reveals up to a 10-fold increase in sensitivity with the new method. Using 50- and 66-lane micro-loaders, sub-microgram quantities of protein produce reliable and quantifiable signal by Western blotting. Manipulation of the ionic conditions within dummy loader strips provides a mechanism for enhancing lateral resolution, allowing for the possibility of further miniaturization.

Quantifying the relative importance of predictors in multiple linear regression analyses for public health studies.

YC Chao — 2008-07-22T01:53:52-00:00

Journal of occupational and environmental hygiene, Vol. 5, No. 8. (August 2008), pp. 519-529.

Multiple linear regression analysis is widely used in many scientific fields, including public health, to evaluate how an outcome or response variable is related to a set of predictors. As a result, researchers often need to assess "relative importance" of a predictor by comparing the contributions made by other individual predictors in a particular regression model. Hence, development of valid statistical methods to estimate the relative importance of a set of predictors is of great interest. In this research, the authors considered the relative importance of a predictor when defined by that portion of the squared multiple correlation explained by the contribution of each predictor in the final model of interest. Here, a number of suggested relative importance indices motivated by this definition are reviewed, including the squared zero-order correlation, squared semipartial correlation, Product Measure (i.e., Pratt's Index), General Dominance Index, and Johnson's Relative Weight. The authors compared these indices using data sets from an occupational health study in which human inhalation exposure to styrene was measured and from a laboratory animal study on risk factors for atherosclerosis, and statistical properties using bootstrap methods were examined. The analysis suggests that the General Dominance Index and Johnson's Relative Weight are preferred methods for quantifying the relative importance of predictors in a multiple linear regression model. Johnson's Relative Weight involves significantly less computational burden than the General Dominance Index when the number of predictors in the final model is large.

The MTHFR gene polymorphism is associated with lean body mass but not fat body mass.

X Liu — 2008-06-28T23:48:07-00:00

Human genetics, Vol. 123, No. 2. (March 2008), pp. 189-196.

Along with aging, human body composition undergoes notable changes and may incur sarcopenia, obesity or osteoporosis. Sarcopenia is related to a wide series of human health problems and can be largely characterized by loss of lean body mass (LBM). Studies have showed relevance of methylenetetrahydrofolate reductase (MTHFR) with variation in LBM and fat body mass (FBM). To test if polymorphism of the MTHFR gene is underlying the pathology of sarcopenia and obesity, we concurrently tested five single nucleotide polymorphisms (SNPs) of the MTHFR gene for association with LBM, FBM and body mass index (BMI) in 405 Caucasian nuclear families comprising 1,873 individuals. After correction for multiple testing, we detected significant associations for LBM with rs2066470 (P = 0.0006), rs4846048 (P = 0.0007) and with rs3737964 (P = 0.004), as well as for BMI with rs4846048 (P = 0.009). Polymorphism of rs2066470 explains 3.67% of LBM variation in this sample. The association between BMI and rs4846048 diminished after adjusting for LBM, suggesting that the association between BMI and rs4846048 is largely due to LBM instead of the fat component. In concert, no significant associations were identified for FBM with any of the studied SNPs. The results of single-locus association analyses were corroborated by haplotype-based analyses. In summary, the MTHFR gene polymorphism is associated with LBM, suggesting that MTHFR may play an important role in LBM variation. In addition, the MTHFR gene polymorphism is not associated with FBM or obesity in this sample.

Comparison of logistic regression and linear regression in modeling percentage data.

L Zhao — 2008-06-22T12:20:28-00:00

Applied and environmental microbiology, Vol. 67, No. 5. (May 2001), pp. 2129-2135.

Percentage is widely used to describe different results in food microbiology, e.g., probability of microbial growth, percent inactivated, and percent of positive samples. Four sets of percentage data, percent-growth-positive, germination extent, probability for one cell to grow, and maximum fraction of positive tubes, were obtained from our own experiments and the literature. These data were modeled using linear and logistic regression. Five methods were used to compare the goodness of fit of the two models: percentage of predictions closer to observations, range of the differences (predicted value minus observed value), deviation of the model, linear regression between the observed and predicted values, and bias and accuracy factors. Logistic regression was a better predictor of at least 78% of the observations in all four data sets. In all cases, the deviation of logistic models was much smaller. The linear correlation between observations and logistic predictions was always stronger. Validation (accomplished using part of one data set) also demonstrated that the logistic model was more accurate in predicting new data points. Bias and accuracy factors were found to be less informative when evaluating models developed for percentage data, since neither of these indices can compare predictions at zero. Model simplification for the logistic model was demonstrated with one data set. The simplified model was as powerful in making predictions as the full linear model, and it also gave clearer insight in determining the key experimental factors.

Sumoylation regulates diverse biological processes.

J Zhao — 2008-06-17T03:17:45-00:00

Cellular and molecular life sciences : CMLS, Vol. 64, No. 23. (December 2007), pp. 3017-3033.

Ten years after its discovery, the small ubiquitin-like protein modifier (SUMO) has emerged as a key regulator of proteins. While early studies indicated that sumoylation takes place mainly in the nucleus, an increasing number of non-nuclear substrates have recently been identified, suggesting a wider stage for sumoylation in the cell. Unlike ubiquitylation, which primarily targets a substrate for degradation, sumoylation regulates a substrate's functions mainly by altering the intracellular localization, protein-protein interactions or other types of post-translational modifications. These changes in turn affect gene expression, genomic and chromosomal stability and integrity, and signal transduction. Sumoylation is counter-balanced by desumoylation, and well-balanced sumoylation is essential for normal cellular behaviors. Loss of the balance has been associated with a number of diseases. This paper reviews recent progress in the study of SUMO pathways, substrates, and cellular functions and highlights important findings that have accelerated advances in this study field and link sumoylation to human diseases.

A decision rule for sequential monitoring of clinical trials with a primary and supportive outcome.

Y Zhao — 2008-06-09T04:28:24-00:00

Clinical trials (London, England), Vol. 4, No. 2. (2007), pp. 140-153.

BACKGROUND: Many clinical trials have multiple outcomes. Formal interim monitoring guidelines that take account of multiple outcomes can be useful to Data Monitoring Committees (DMC). Previous research has focused on marginal criteria that control the overall type I error for bivariate endpoints corresponding to efficacy and safety. Usually, an 'or' decision rule is used, that is, the trial is stopped when either the safety or the efficacy endpoint crosses a boundary. PURPOSE: In many trials there is not a clear difference between what is considered a safety and an efficacy endpoint. Likewise, in some studies there is interest in more than one disease outcome and while there may be a primary efficacy endpoint, interim monitoring might also involve a measure of overall health. For these situations, an ;and' decision rule might be more appropriate; that is, the trial is stopped only when both endpoints cross a boundary. Formulation of this new decision rule at the design stage would encourage more discussion among trial investigators of monitoring guidelines and would result in improved guidelines for DMCs. METHODS: In this paper, we propose stopping guidelines for such trials with two major outcomes that control the overall type I error and stop early only if both endpoints indicate superiority for the same treatment. Trials with two treatments are considered and we develop sets of paired two-sided boundaries, permitting one endpoint to be primary and the other supportive, with or without pre-specifying which one is primary. RESULTS: The results show that the boundaries depend on the correlation between the two outcomes. The critical values increase as the correlation increases in most cases. For low to moderate correlation and before the last stage, critical values based on the O'Brien Fleming (or Pocock) error spending function that consider the correlation are lower than those which do not. LIMITATIONS: Investigators might not want to stop a trial early with the small size of the critical values that result from this decision rule for some situations. For a trial in which the treatment effect for one outcome is large, while for the other it is small, the proposed decision rule has low power to accept this treatment for its superiority on both outcomes. Also, we do not provide a separate P-value for each outcome since type I error is not controlled for individual null hypotheses. CONCLUSIONS: For trials involving two major health outcomes these stopping guidelines may be appropriate for DMCs when the trial investigators and sponsor recommend that early termination not be considered unless the findings are consistent for both outcomes.

Genetics of Kidneys in Diabetes (GoKinD) study: a genetics collection available for identifying genetic susceptibility factors for diabetic nephropathy in type 1 diabetes.

PW Mueller — 2008-05-23T15:01:48-00:00

Journal of the American Society of Nephrology : JASN, Vol. 17, No. 7. (July 2006), pp. 1782-1790.

The Genetics of Kidneys in Diabetes (GoKinD) study is an initiative that aims to identify genes that are involved in diabetic nephropathy. A large number of individuals with type 1 diabetes were screened to identify two subsets, one with clear-cut kidney disease and another with normal renal status despite long-term diabetes. Those who met additional entry criteria and consented to participate were enrolled. When possible, both parents also were enrolled to form family trios. As of November 2005, GoKinD included 3075 participants who comprise 671 case singletons, 623 control singletons, 272 case trios, and 323 control trios. Interested investigators may request the DNA collection and corresponding clinical data for GoKinD participants using the instructions and application form that are available at http://www.gokind.org/access. Participating scientists will have access to three data sets, each with distinct advantages. The set of 1294 singletons has adequate power to detect a wide range of genetic effects, even those of modest size. The set of case trios, which has adequate power to detect effects of moderate size, is not susceptible to false-positive results because of population substructure. The set of control trios is critical for excluding certain false-positive results that can occur in case trios and may be particularly useful for testing gene-environment interactions. Integration of the evidence from these three components into a single, unified analysis presents a challenge. This overview of the GoKinD study examines in detail the power of each study component and discusses analytic challenges that investigators will face in using this resource.

How large a training set is needed to develop a classifier for microarray data?

KK Dobbin — 2008-04-14T15:45:58-00:00

Clinical cancer research : an official journal of the American Association for Cancer Research, Vol. 14, No. 1. (1 January 2008), pp. 108-114.

PURPOSE: A common goal of gene expression microarray studies is the development of a classifier that can be used to divide patients into groups with different prognoses, or with different expected responses to a therapy. These types of classifiers are developed on a training set, which is the set of samples used to train a classifier. The question of how many samples are needed in the training set to produce a good classifier from high-dimensional microarray data is challenging. EXPERIMENTAL DESIGN: We present a model-based approach to determining the sample size required to adequately train a classifier. RESULTS: It is shown that sample size can be determined from three quantities: standardized fold change, class prevalence, and number of genes or features on the arrays. Numerous examples and important experimental design issues are discussed. The method is adapted to address ex post facto determination of whether the size of a training set used to develop a classifier was adequate. An interactive web site for performing the sample size calculations is provided. CONCLUSION: We showed that sample size calculations for classifier development from high-dimensional microarray data are feasible, discussed numerous important considerations, and presented examples.

Building pathway clusters from Random Forests classification using class votes

Herbert Pang — 2008-02-07T08:34:55-00:00

BMC Bioinformatics, Vol. 9, No. 1. (2008)

BACKGROUND:Recent years have seen the development of various pathway-based methods for the analysis of microarray gene expression data. These approaches have the potential to bring biological insights into microarray studies. A variety of methods have been proposed to construct networks using gene expression data. Because individual pathways do not act in isolation, it is important to understand how different pathways coordinate to perform cellular functions. However, there are no published methods describing how to build pathway clusters that are closely related to traits of interest.RESULTS:We propose to build pathway clusters from pathway-based classification methods. The proposed methods allow researchers to identify clusters of pathways sharing similar functions. These pathways may or may not share genes. As an illustration, our approach is applied to three human breast cancer microarray data sets. We found that our methods yielded consistent and interpretable results for these three data sets. We further investigated one of the pathway clusters found using PubMatrix. We found that informative genes in the pathway clusters do have more publications with keywords, like estrogen receptor, compared with informative genes in other top pathways. In addition, using the shortest path analysis in GeneGo's MetaCore and Human Protein Reference Database, we were able to identify the links which connect the pathways without shared genes within the pathway cluster. CONCLUSIONS:Our proposed pathway clustering methods allow bioinformaticians and biologists to investigate how informative genes within pathways are related to each other and understand possible crosstalk between pathways in a cluster. Therefore, building pathway clusters may lead to a better understanding of molecular mechanisms affecting a trait of interest, and help generate further biological hypotheses from gene expression data.

Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments.

H Zhao — 2008-05-16T08:44:23-00:00

BMC bioinformatics, Vol. 9 Suppl 1 (2008)

BACKGROUND: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular to solve this type of problems. These models show good performance in accommodating noise, variability and low replication of microarray data. However, the correlation between different fluorescent signals measured from a gene spot is ignored, which can diversely affect the data analysis step. In fact, the intensities of the two signals are significantly correlated across samples. The larger the log-transformed intensities are, the smaller the correlation is. RESULTS: Motivated by the complicated error relations in microarray data, we propose a multivariate hierarchical Bayesian framework for data analysis in the replicated microarray experiments. Gene expression data are modelled by a multivariate normal distribution, parameterized by the corresponding mean vectors and covariance matrixes with a conjugate prior distribution. Within the Bayesian framework, a generalized likelihood ratio test (GLRT) is also developed to infer the gene expression patterns. Simulation studies show that the proposed approach presents better operating characteristics and lower false discovery rate (FDR) than existing methods, especially when the correlation coefficient is large. The approach is illustrated with two examples of microarray analysis. The proposed method successfully detects significant genes closely related to the experimental states, which are verified by the biological information. CONCLUSIONS: The multivariate Bayesian model, compatible with the dependence between mean and variance in the univariate Bayesian model, relaxes the constant coefficient of variation assumption between measurements by adding a covariance structure. This model improves the identification of differentially expressed genes significantly since the Bayesian model fit well with the microarray data.

Protein interaction predictions from diverse sources.

Y Liu — 2008-05-16T13:03:09-00:00

Drug discovery today, Vol. 13, No. 9-10. (May 2008), pp. 409-416.

Protein-protein interactions play an important role in many cellular processes. The availability of a comprehensive and accurate list of protein interactions can facilitate drug target discovery. Recent advances in high-throughput experimental technologies have generated enormous amounts of data and provided valuable resources for studying protein interactions. However, these technologies suffer from high error rates because of their inherent limitations. Therefore, computational approaches capable of incorporating multiple data sources are needed to fully take advantage of the rapid accumulation of data. In this review, we focus on the computational methods that integrate multiple data sources by combining direct measurements on protein interactions from diverse organisms, and by integrating different types of indirect information from various genomic and proteomic approaches.

Krüppel-like factor 8 induces epithelial to mesenchymal transition and epithelial cell invasion.

X Wang — 2008-05-16T02:12:05-00:00

Cancer research, Vol. 67, No. 15. (1 August 2007), pp. 7184-7193.

Tumor invasion and metastasis are the main causes of death from cancer. Epithelial to mesenchymal transition (EMT) is a determining step for a cancer cell to progress from a noninvasive to invasive state. Krüppel-like factor 8 (KLF8) plays a key role in oncogenic transformation and is highly overexpressed in several types of invasive human cancer, including breast cancer. To understand the role of KLF8 in regulating the progression of human breast cancer, we first established stable expression of KLF8 in an immortalized normal human breast epithelial cell line. We found that KLF8 strongly induced EMT and enhanced motility and invasiveness in the cells, by analyzing changes in cell morphology and epithelial and mesenchymal marker proteins, and using cell migration and Matrigel invasion assays. Chromatin immunoprecipitations (ChIP), oligonucleotide precipitations, and promoter-reporter assays showed that KLF8 directly bound and repressed the promoter of E-cadherin independent of E boxes in the promoter and Snail expression. Aberrant elevation of KLF8 expression is highly correlated with the decrease in E-cadherin expression in the invasive human breast cancer. Blocking KLF8 expression by RNA interference restored E-cadherin expression in the cancer cells and strongly inhibited the cell invasiveness. This work identifies KLF8 as a novel EMT-regulating transcription factor that opens a new avenue in EMT research and suggests an important role for KLF8 in human breast cancer invasion and metastasis.

A highly sensitive and reproducible HCV RNA hybridization method valuable for antiviral drug discovery.

Yongsen Zhao — 2008-05-13T05:34:14-00:00

Journal of virological methods (6 May 2008)

Real-time RT-PCR and Northern blot are employed for the measurement of HCV RNA but suffer from multiple purification steps, high cost, and relatively large variability. In this study, a hybridization method for HCV RNA detection is described. This method does not need RNA purification, and is sensitive enough to detect HCV RNA present in replicon cellular lysates harvested from a single well of a 96-well plate. Fixation of RNA by UV cross-linking is crucial for this sensitivity. A linear relationship exists between hybridization signal and cell density ranging from 10(5) to as few as 300 cells per well. The signal-to-background ratio is greater than 40 and the Z factor is above 0.7. Using several known anti-HCV agents, dose-response curves and EC(50) values generated from hybridization were similar to those obtained from a luciferase assay. This method has been successfully applied to replicons of different HCV subtypes and hepatitis B virus in our laboratory. In summary, this hybridization assay is sensitive, highly reproducible, easy to handle, and a valuable tool for antiviral drug discovery.

Protein function prediction with high-throughput data.

Xing-Ming Zhao — 2008-05-10T08:52:29-00:00

Amino acids (22 April 2008)

Protein function prediction is one of the main challenges in post-genomic era. The availability of large amounts of high-throughput data provides an alternative approach to handling this problem from the computational viewpoint. In this review, we provide a comprehensive description of the computational methods that are currently applicable to protein function prediction, especially from the perspective of machine learning. Machine learning techniques can generally be classified as supervised learning, semi-supervised learning and unsupervised learning. By classifying the existing computational methods for protein annotation into these three groups, we are able to present a comprehensive framework on protein annotation based on machine learning techniques. In addition to describing recently developed theoretical methodologies, we also cover representative databases and software tools that are widely utilized in the prediction of protein function.

Targeted pre-mRNA modification for gene silencing and regulation

Xinliang Zhao — 2007-12-29T05:58:20-00:00

Nature Methods, Vol. 5, No. 1. (09 December 2007), pp. 95-100.

Treatment with the xanthine oxidase inhibitor febuxostat lowers uric acid and alleviates systemic and glomerular hypertension in experimental hyperuricaemia.

LG Sánchez-Lozada — 2008-05-08T03:57:27-00:00

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, Vol. 23, No. 4. (April 2008), pp. 1179-1185.

BACKGROUND: Experimentally-induced hyperuricaemia [due to inhibition of uricase with oxonic acid (OA)] in rats causes hypertension and renal alterations which can be prevented by lowering uric acid (UA) with allopurinol. Febuxostat (Fx), an investigational, nonpurine and selective xanthine oxidase inhibitor, is a more effective UA-lowering agent than allopurinol. We therefore tested the hypothesis that Fx might be useful in treating hyperuricemia-induced hypertension and renal damage. METHODS: Four groups of male rats were studied: OA (750 mg/kg by daily gavage) was given for 8 weeks and Fx (5-6 mg/kg/day in drinking water; OA+Fx: n = 10) or placebo (OA+P: n = 11) were administered for 4 weeks beginning at 4 weeks after initiation of the study. Two groups of normal (N) rats were studied as controls (N+P and N+Fx: n = 10/group). Systolic blood pressure (SBP) and fasting plasma UA were measured in all animals at baseline and at 4 and 8 weeks. Glomerular haemodynamics by micropuncture techniques were determined at 8 weeks followed by histological evaluation of glomerular and afferent arteriole morphologies. RESULTS: In OA-induced hyperuricaemic rats, Fx lowered UA and ameliorated systemic and glomerular hypertension as well as mesangial matrix expansion and the development of preglomerular arteriolar disease as indicated by a reduction of the arteriolar area and media-to-lumen ratio. In normal rats, Fx tended to lower UA and had no effect on blood pressure, renal hemodynamics and afferent arteriole morphology. CONCLUSION: These results suggest that Fx merits further evaluation for the treatment of hypertension and renal alterations induced by hyperuricaemia.

Protein production and purification.

2008-02-01T14:36:35-00:00

Nat Methods, Vol. 5, No. 2. (February 2008), pp. 135-146.

In selecting a method to produce a recombinant protein, a researcher is faced with a bewildering array of choices as to where to start. To facilitate decision-making, we describe a consensus 'what to try first' strategy based on our collective analysis of the expression and purification of over 10,000 different proteins. This review presents methods that could be applied at the outset of any project, a prioritized list of alternate strategies and a list of pitfalls that trip many new investigators.

Cross talk among Smad, MAPK, and integrin signaling pathways enhances adventitial fibroblast functions activated by transforming growth factor-beta1 and inhibited by Gax.

P Liu — 2008-04-27T01:31:42-00:00

Arteriosclerosis, thrombosis, and vascular biology, Vol. 28, No. 4. (April 2008), pp. 725-731.

OBJECTIVE: We investigated whether Smad, mitogen-activated protein kinase (MAPK), and integrin signaling pathways cross-talk to enhance adventitial fibroblast (AF) bioactivity, which was activated by transforming growth factor (TGF)-beta1 and inhibited by Gax. METHODS AND RESULTS: Cultured AFs were stimulated with Ad-Gax, TGF-beta1, and siRNA-Gax. Assays for AFs viabilities demonstrated that TGF-beta1 and siRNA-Gax enhanced AFs proliferative, migratory, and adherent abilities, whereas Gax counteracted TGF-beta1-activated actions. Flow cytometry revealed that TGF-beta1 and siRNA-Gax increased S phase cells; however, Gax decreased AFs in the S phase and increased those in the G0-G1 and apoptotic phases. RT-PCR, Western blotting, and immunocytochemistry showed that TGF-beta1 and siRNA-Gax upregulated the expression of cytokines in Smad, MAPK, and integrin signaling pathways, and downregulated that of p15, p16, and p21. Conversely, Gax induced downregulation of these cytokines and upregulation of p15, p16, and p21. Thus, these signaling pathways cross-talk to enhance AF bioactivity; Gax effectively counteracts TGF-beta1 effects, blocks the cross-talk of these pathways, inhibits AF functions, and increases AF apoptosis. CONCLUSIONS: Our findings indicate that cross-talk among Smad, MAPK, and integrin signaling pathways may account mainly for the mechanism of AF functions. Gax is a promising therapeutic gene for dissecting the signaling pathways controlling AF bioactivities.

Genome-wide approaches to studying chromatin modifications

Dustin Schones — 2008-02-18T13:07:54-00:00

Nat Rev Genet, Vol. 9, No. 3. (March 2008), pp. 179-191.

Regression analysis of mean quality-adjusted lifetime with censored data.

H Wang — 2008-04-11T04:22:33-00:00

Biostatistics (Oxford, England), Vol. 8, No. 2. (April 2007), pp. 368-382.

In clinical trials of chronic diseases such as acquired immunodeficiency syndrome, cancer, or cardiovascular diseases, the concept of quality-adjusted lifetime (QAL) has received more and more attention. In this paper, we consider the problem of how the covariates affect the mean QAL when the data are subject to right censoring. We allow a very general form for the mean model as a function of covariates. Using the idea of inverse probability weighting, we first construct a simple weighted estimating equation for the parameters in our mean model. We then find the form of the most efficient estimating equation, which yields the most efficient estimator for the regression parameters. Since the most efficient estimator depends on the distribution of the health history processes, and thus cannot be estimated nonparametrically, we consider different approaches for improving the efficiency of the simple weighted estimating equation using observed data. The applicability of these methods is demonstrated by both simulation experiments and a data example from a breast cancer clinical trial study.

Quantification of variability and uncertainty for censored data sets and application to air toxic emission factors.

Y Zhao — 2008-04-11T04:30:51-00:00

Risk analysis : an official publication of the Society for Risk Analysis, Vol. 24, No. 4. (August 2004), pp. 1019-1034.

Many environmental data sets, such as for air toxic emission factors, contain several values reported only as below detection limit. Such data sets are referred to as "censored." Typical approaches to dealing with the censored data sets include replacing censored values with arbitrary values of zero, one-half of the detection limit, or the detection limit. Here, an approach to quantification of the variability and uncertainty of censored data sets is demonstrated. Empirical bootstrap simulation is used to simulate censored bootstrap samples from the original data. Maximum likelihood estimation (MLE) is used to fit parametric probability distributions to each bootstrap sample, thereby specifying alternative estimates of the unknown population distribution of the censored data sets. Sampling distributions for uncertainty in statistics such as the mean, median, and percentile are calculated. The robustness of the method was tested by application to different degrees of censoring, sample sizes, coefficients of variation, and numbers of detection limits. Lognormal, gamma, and Weibull distributions were evaluated. The reliability of using this method to estimate the mean is evaluated by averaging the best estimated means of 20 cases for small sample size of 20. The confidence intervals for distribution percentiles estimated with bootstrap/MLE method compared favorably to results obtained with the nonparametric Kaplan-Meier method. The bootstrap/MLE method is illustrated via an application to an empirical air toxic emission factor data set.

A DNA Damage-Induced p53 Serine 392 Kinase Complex Contains CK2, hSpt16, and SSRP1

David Keller — 2008-04-10T16:10:25-00:00

Molecular Cell, Vol. 7, No. 2. (February 2001), pp. 283-292.

Phosphorylation of the human p53 protein at Ser-392 has been shown to be responsive to UV but not [gamma] irradiation. Here we describe identification and purification of a mammalian UV-activated protein kinase complex that phosphorylates Ser-392 of p53 in vitro. This kinase complex contains casein kinase 2 (CK2) and the chromatin transcriptional elongation factor FACT (a heterodimer of hSpt16 and SSRP1). In vitro studies show that FACT alters the specificity of CK2 in the complex such that it selectively phosphorylates p53 over other substrates including casein. In addition, phosphorylation by the kinase complex enhances p53 activity. These results thus provide a potential mechanism for p53 activation by UV irradiation.

Phosphatidylinositol 3-kinase/protein kinase B pathway stabilizes DNA methyltransferase I protein and maintains DNA methylation.

L Sun — 2008-04-05T01:06:50-00:00

Cellular signalling, Vol. 19, No. 11. (November 2007), pp. 2255-2263.

DNA methylation, which affects gene expression and chromatin stability, is catalyzed by DNA methyltransferases (DNMTs) of which DNMT1 possesses most abundant activity. PI3K/PKB pathway is an important pathway involved in cell proliferation, viability, and metabolism and often disrupted in cancer. Here we investigated the impact of PKB on DNMT1 and DNA methylation. Positive correlation between PKB-Ser473-phosphorylation and DNMT1 protein level in 17 human cell lines (p<0.01) and in 27 human bladder cancer tissues (p<0.05) was found. With activator, inhibitor, siRNA and constitutively active or dominant-negative plasmids of PKB, we found that PKB increased the protein level of DNMT1 without coordinate mRNA change, which was specific rather than due to cell-cycle change. PKB enhanced DNMT1 protein stability independent of de novo synthesis of any protein, which was attributed to down-regulation of N-terminal-120-amino-acids-dependent DNMT1 degradation via ubiquitin-proteasome pathway. Gsk3beta inhibitor rescued the decrease of DNMT1 by PKB inhibition, suggesting that Gsk3beta mediated the stabilization of DNMT1 by PKB. Then role of PKB regulating DNMT1 was investigated. Inhibition of PKB caused observable DNA hypomethylation and chromatin decondensation and DNMT1 overexpression partially reversed cell growth inhibition by PKB inhibition. In conclusion, our results suggested that PKB enhanced DNMT1 stability and maintained DNA methylation and chromatin structure, which might contribute to cancer cell growth.

Practical guidelines for assessing power and false discovery rate for a fixed sample size in microarray experiments.

Tiejun Tong — 2008-03-31T03:38:14-00:00

Stat Med (12 March 2008)

One major goal in microarray studies is to identify genes having different expression levels across different classes/conditions. In order to achieve this goal, a study needs to have an adequate sample size to ensure the desired power. Owing to the importance of this topic, a number of approaches to sample size calculation have been developed. However, due to the cost and/or experimental difficulties in obtaining sufficient biological materials, it might be difficult to attain the required sample size. In this article, we address more practical questions for assessing power and false discovery rate (FDR) for a fixed sample size. The relationships between power, sample size and FDR are explored. We also conduct simulations and a real data study to evaluate the proposed findings. Copyright (c) 2008 John Wiley & Sons, Ltd.

PIP: a database of potential intron polymorphism markers.

Long Yang — 2007-06-06T11:39:14-00:00

Bioinformatics (1 June 2007)

MOTIVATION: With the recent progress made in large-scale plant functional genome sequencing projects, a great amount of EST (express sequence tag) data is becoming available. With the help of complete genomic sequence information of model plants (rice and Arabidopsis), it is possible to predict the joints between adjacent exons after splicing (or termed 'intron positions' for short) in homologous ESTs of other plants. This would allow developing potential intron polymorphism (PIP) markers in these plants by designing primers in exons flanking the target intron. RESULTS: We have extracted a total of 57,658 PIP markers in 59 plant species and created a web-based database platform named PIP to provide detailed information of these PIP markers and homologous relationships among PIP markers from different species. The platform also provides a function of online designing of PIP markers based on cDNA/EST sequences submitted by users. With evaluations performed in silico, we have found that the intron position prediction is highly reliable and the polymorphism level of PIP markers is high enough for practical need. AVAILABILITY: http://ibi.zju.edu.cn/pgl/pip/.

Sample Size Needed to Detect Gene-Gene Interactions Using Linkage Analysis

Wang — 2007-10-08T16:18:52-00:00

Annals of Human Genetics, Vol. 71, No. 6. (November 2007), pp. 828-842.

Association of a Functional Cytochrome P450 4F2 Haplotype with Urinary 20-HETE and Hypertension.

Hong Liu — 2008-03-25T09:08:07-00:00

J Am Soc Nephrol (30 January 2008)

Cytochrome P450 4F2 (CYP4F2) catalyzes the omega-hydroxylation of arachidonic acid to 20-hydroxyeicosatetraenoic acid (20-HETE), a natriuretic and vasoactive eicosanoid that participates in the development of hypertension. The relationship among CYP4F2 genetic variants in the regulatory region, formation of renal 20-HETE, and hypertension is unknown. Here are reported seven genetic variants around the CYP4F2 intronic regulatory region. Four of these variants made up two common haplotypes, Hap I (c.-91T/c.-48G/c.-13T/c.+34T) and Hap II (c.-91C/c.-48C/c.-13C/c.+34G). Hap I included a major functional variant, c.-91T-->C, which was identified by reporter assay and electrophoretic mobility shift assay. Transfected into HEK293 cells, the Hap I construct showed a trend toward higher basal transcriptional activity and exhibited significantly greater LPS-stimulated activity than Hap II; these findings were the result of different NF-kappaB binding affinity between the two constructs. In vivo, a case-control study demonstrated that homozygosity for Hap I doubled the risk for hypertension in a Chinese population, even after adjustment for risk factors including age, gender, and body mass index. This association was confirmed in a family-based association study. In addition, Hap I was associated with elevated urinary 20-HETE. These results indicate that a functional variant of the CYP4F2 regulatory region, which increases the binding affinity of NF-kappaB, increases the risk for hypertension, likely by modulating the production of 20-HETE.

Association between delta-aminolevulinic acid dehydratase (ALAD) polymorphism and blood lead levels: a meta-regression analysis.

Y Zhao — 2008-03-25T04:45:33-00:00

J Toxicol Environ Health A, Vol. 70, No. 23. (December 2007), pp. 1986-1994.

A meta-regression analysis was undertaken to evaluate the association between delta-aminolevulinic acid dehydratase (ALAD) genotypes and blood lead levels obtained from data published in various journals. In total, 15 studies were included in the final analysis. Both fixed effects and random effects models were used to undertake the pooled analysis. Using a fixed effects model, pooled estimates of mean differences of various ALAD genotypes was significant at 0.61 microg/dl. Using a random effects model, the pooled estimate was also significant at 1.51 microg/dl. Data indicated that certain ALAD genotypes may affect the susceptibility of humans to lead.

A multiplexed protein kinase assay.

MD Shults — 2008-03-18T05:03:37-00:00

Chembiochem, Vol. 8, No. 8. (25 May 2007), pp. 933-942.

We report a novel protein kinase assay designed for high-throughput detection of one or many kinases in a complex mixture. A solution-phase phosphorylation reaction is performed on 900 different peptide substrates, each covalently linked to an oligonucleotide tag. After incubation, phosphoserine, phosphothreonine, and phosphotyrosine are chemically labeled, and the substrates are hybridized to a microarray with oligonucleotides complementary to the tags to read out the phosphorylation state of each peptide. Because protein kinases act on more than one peptide sequence, each kinase can be characterized by a unique signature of phosphorylation activity on multiple substrates. Using this method, we determined signatures for 26 purified kinases and demonstrated that enzyme mixtures can be screened for activity and selectivity of inhibition.

Discrete-time survival models with long-term survivors.

X Zhao — 2008-03-16T00:50:06-00:00

Stat Med, Vol. 27, No. 8. (15 April 2008), pp. 1261-1281.

Discrete-time survival data typically possess three features: discreteness, ties, and concomitant information, which require appropriate discrete-time models to analyze. In this paper, we first review some existing discrete-time survival models and then extend them to discrete-time cure survival models, which account for the presence of long-term survivors (cured individuals). The maximum likelihood estimation as well as approximate partial likelihood approaches are used to estimate the model parameters. Simulation results are shown to support the suitability of such models for discrete-time survival data with long-term survivors. An example of applications on a set of bladder tumor recurrence data is also presented. Copyright (c) 2007 John Wiley & Sons, Ltd.

Aptamer biosensor for protein detection using gold nanoparticles.

W Wang — 2008-03-13T16:26:34-00:00

Anal Biochem, Vol. 373, No. 2. (15 February 2008), pp. 213-219.

Combining gold nanoparticles (GNPs) as fluorescence quencher and aptamer as probe, we have developed protein biosensors by using DNA-modified GNPs. We examined how the experimental design, such as the type of interaction between DNA strands and GNPs, temperature, and microenvironment of aptamer, influences the recognition ability of the biosensor. Under our experimental conditions, the recognition of protein by the complex of dye-labeled DNA hybridized with aptamer that is immobilized on GNPs (Ap-Im-GNPs) shows the best character in protein detection.

Ultrasensitive assays for proteins.

H Zhang — 2008-03-08T16:10:31-00:00

Analyst, Vol. 132, No. 8. (August 2007), pp. 724-737.

Proteins are essential components of organisms and are involved in a wide range of biological functions. There are increasing demands for ultra-sensitive protein detection, because many important protein biomarkers are present at ultra-low levels, especially during the early stages of disease. Measuring proteins at low levels is also crucial for investigations of the protein synthesis and functions in biological systems. In this review, we summarize the recent developments of novel technology enabling ultrasensitive protein detection. We focus on two groups of techniques that involve either polymerase amplification of affinity DNA probes or signal amplification by the use of nano-/micro-materials. The polymerase-based amplification of affinity DNA probes indirectly improves the sensitivity of protein detection by increasing the number of detection molecules. The use of nano-/micro-materials conjugated to affinity probes enhances the measurement signals by using the unique electrical, optical, and catalytic properties of these novel materials. This review describes the basic principles, performances, applications, merits, and limitations of these techniques.

MethyCancer: the database of human DNA methylation and cancer.

X He — 2008-03-07T07:52:35-00:00

Nucleic Acids Res, Vol. 36, No. Database issue. (January 2008)

Cancer is ranked as one of the top killers in all human diseases and continues to have a devastating effect on the population around the globe. Current research efforts are aiming to accelerate our understanding of the molecular basis of cancer and develop effective means for cancer diagnostics, treatment and prognosis. An altered pattern of epigenetic modifications, most importantly DNA methylation events, plays a critical role in tumorigenesis through regulating oncogene activation, tumor suppressor gene silencing and chromosomal instability. To study interplay of DNA methylation, gene expression and cancer, we developed a publicly accessible database for human DNA Methylation and Cancer (MethyCancer, http://methycancer.genomics.org.cn). MethyCancer hosts both highly integrated data of DNA methylation, cancer-related gene, mutation and cancer information from public resources, and the CpG Island (CGI) clones derived from our large-scale sequencing. Interconnections between different data types were analyzed and presented. Furthermore, a powerful search tool is developed to provide user-friendly access to all the data and data connections. A graphical MethyView shows DNA methylation in context of genomics and genetics data facilitating the research in cancer to understand genetic and epigenetic mechanisms that make dramatic changes in gene expression of tumor cells.

Regulation of calcium-sensitive tyrosine kinase Pyk2 by angiotensin II in endothelial cells. Roles of Yes tyrosine kinase and tyrosine phosphatase SHP-2.

H Tang — 2008-03-05T09:39:38-00:00

J Biol Chem, Vol. 275, No. 12. (24 March 2000), pp. 8389-8396.

Calcium-sensitive tyrosine kinase Pyk2 has been implicated in the regulation of ion channels, cellular adhesion, and mitogenic and hypertrophic reactions. In this study, we have investigated the regulation of Pyk2 by angiotensin II (Ang II) in pulmonary vein endothelial cells. We found that the Ang II-induced tyrosine phosphorylation of Pyk2, which requires the activity of Src family kinase, was specifically regulated by the Src family kinase member, Yes kinase. Moreover, we identified for the first time the constitutive association of Pyk2 with an Src homology 2 (SH2) domain-containing tyrosine phosphatase SHP-2. SHP-2 interacts with Pyk2 through a region other than its SH2 domains. Pyk2 can be dephosphorylated in vitro in SHP-2 immunoprecipitates and in intact cells expressing an NH(2) terminus-truncated form of SHP-2, which lacks the two SH2 domains but has an enhanced phosphatase activity. Ang II activates the endogenous SHP-2. Finally, the SHP-2-mediated dephosphorylation of Pyk2 correlates with the negative effect of SHP-2 on the Ang II-induced activation of extracellular signal-regulated kinase and c-Jun NH(2)-terminal kinase. Thus, the balance of Pyk2 tyrosine phosphorylation in response to Ang II is controlled by Yes kinase and by a tyrosine phosphatase SHP-2 in endothelial cells.

Analysis of apoptosis by cytometry using TUNEL assay

Zbigniew Darzynkiewicz — 2008-03-03T03:25:08-00:00

Methods, Vol. 44, No. 3. (March 2008), pp. 250-254.

Activation of endonucleases that cleave chromosomal DNA preferentially at internucleosomal sections is a hallmark of apoptosis. DNA fragmentation revealed by the presence of a multitude of DNA strand breaks, therefore, is considered to be the gold standard for identification apoptotic cells. Several variants of the methodology that is based on fluorochrome-labeling of 3'-OH termini of DNA strand breaks in situ with the use of exogenous terminal deoxynucleotidyl transferase (TdT), commonly defined as the TUNEL assay, have been developed by us. This Chapter describes the variant based on strand breaks labeling with Br-dUTP that is subsequently detected immunocytochemically with Br-dUAb. Compared with other TUNEL variants the Br-dU-labeling assay offers the greatest sensitivity in detecting DNA breaks. Described also are modifications of the protocol that allow one to use other than Br-dUTP fluorochrome-tagged deoxynucleotides to label DNA breaks. Concurrent staining of DNA with propidium or 4',6-diamidino-2-phenylindole (DAPI) and multiparameter analysis of cells by flow- or laser scanning cytometry enables one to correlate induction of apoptosis with the cell cycle phase.

Weighted rank regression for clustered data analysis.

YG Wang — 2008-03-01T09:42:42-00:00

Biometrics, Vol. 64, No. 1. (March 2008), pp. 39-45.

We consider ranked-based regression models for clustered data analysis. A weighted Wilcoxon rank method is proposed to take account of within-cluster correlations and varying cluster sizes. The asymptotic normality of the resulting estimators is established. A method to estimate covariance of the estimators is also given, which can bypass estimation of the density function. Simulation studies are carried out to compare different estimators for a number of scenarios on the correlation structure, presence/absence of outliers and different correlation values. The proposed methods appear to perform well, in particular, the one incorporating the correlation in the weighting achieves the highest efficiency and robustness against misspecification of correlation structure and outliers. A real example is provided for illustration.

A hierarchical and modular approach to the discovery of robust associations in genome-wide association studies from pooled DNA samples

Paola Sebastiani — 2008-01-15T03:46:26-00:00

BMC Genetics, Vol. 9 (14 January 2008), 6.

Optimal Two-Stage Design for Case-Control Association Analysis Incorporating Genotyping Errors.

Y Zuo — 2008-02-27T15:59:42-00:00

Ann Hum Genet (23 January 2008)

Two-stage design is a cost effective approach for identifying disease genes in genetic studies and it has received much attention recently. In general, there are two types of two-stage designs that differ on the methods and samples used to measure allele frequencies in the first stage: (1) Individual genotyping is used in the first stage; (2) DNA pooling is used in the first stage. In this paper, we focus on the latter. Zuo et al. (2006) investigated statistical power of such a design, among other things, but the cost of the study was not taken into account. The purpose of this paper is to study the optimal design under the given overall cost. We investigate how to allocate the resources to the two stages. Note that in addition to the measurement errors associated with DNA pooling, genotyping errors are also unavoidable with individual genotyping. Therefore, we discuss the optimal design combining genotyping errors associated with individual genotyping. The joint statistical distributions of test statistics in the first and second stages are derived. For a fixed cost, our results show that the optimal design requires no additional samples in the second stage but only that the samples in the first stage be re-used. When the second stage uses an entirely independent sample, however, the optimal design under a given cost depends on the population allele frequency and allele frequency difference between the case and control groups. For the current genotyping costs, we can roughly allocate 1/3 to 1/2 of the total sample size to the first stage for screening.

Highly cost-efficient genome-wide association studies using DNA pools and dense SNP arrays.

Stuart Macgregor — 2008-02-27T15:57:09-00:00

Nucleic Acids Res (14 February 2008)

Genome-wide association (GWA) studies to map genes for complex traits are powerful yet costly. DNA-pooling strategies have the potential to dramatically reduce the cost of GWA studies. Pooling using Affymetrix arrays has been proposed and used but the efficiency of these arrays has not been quantified. We compared and contrasted Affymetrix Genechip HindIII and Illumina HumanHap300 arrays on the same DNA pools and showed that the HumanHap300 arrays are substantially more efficient. In terms of effective sample size, HumanHap300-based pooling extracts >80% of the information available with individual genotyping (IG). In contrast, Genechip HindIII-based pooling only extracts approximately 30% of the available information. With HumanHap300 arrays concordance with IG data is excellent. Guidance is given on best study design and it is shown that even after taking into account pooling error, one stage scans can be performed for >100-fold reduced cost compared with IG. With appropriately designed two stage studies, IG can provide confirmation of pooling results whilst still providing approximately 20-fold reduction in total cost compared with IG-based alternatives. The large cost savings with Illumina HumanHap300-based pooling imply that future studies need only be limited by the availability of samples and not cost.

Retrospective analysis of main and interaction effects in genetic association studies of human complex traits

Qihua Tan — 2007-10-16T08:21:18-00:00

BMC Genetics, Vol. 8 (16 October 2007), 70.

Functional protein expression from a DNA based wheat germ cell-free system.

Kate Zhao — 2008-02-25T03:21:27-00:00

J Struct Funct Genomics (23 November 2007)

Wheat germ based eukaryotic cell-free systems have been shown to be applicable for both functional and structural analyses of proteins. However, the existing methods might require specialized instrumentation and/or a separate mRNA synthesis step. We have developed a DNA based, highly productive, coupled transcription/translation wheat germ cell-free system that incorporates the normally separate mRNA synthesis step and does not require specialized instrumentation. Using a small-volume batch reaction with fluorescence labeling, DNA templates predicted to encode proteins could be quickly screened for their ability to direct the expression of proteins of the appropriate size. Protein yield can be increased as much as 2 to 4-fold in this system using a dialysis reaction, reaching approximately 200-440 mug/ml in 10-20 h. Furthermore, enzyme activities can be assayed directly in the extract without further purification. Simple purification with affinity tags can be achieved in one-step and with minor modifications, efficient SeMet and [U-(15)N] labeling of >95% can be accomplished in this system. Thus, this efficient cell-free expression system can facilitate both functional and structural proteomics.

HiRes--a tool for comprehensive assessment and interpretation of metabolomic data.

Q Zhao — 2008-02-24T01:46:54-00:00

Bioinformatics, Vol. 22, No. 20. (15 October 2006), pp. 2562-2564.

The increasing role of metabolomics in system biology is driving the development of tools for comprehensive analysis of high-resolution NMR spectral datasets. This task is quite challenging since unlike the datasets resulting from other 'omics', a substantial preprocessing of the data is needed to allow successful identification of spectral patterns associated with relevant biological variability. HiRes is a unique stand-alone software tool that combines standard NMR spectral processing functionalities with techniques for multi-spectral dataset analysis, such as principal component analysis and non-negative matrix factorization. In addition, HiRes contains extensive abilities for data cleansing, such as baseline correction, solvent peak suppression, removal of frequency shifts owing to experimental conditions as well as auxiliary information management. Integration of these components together with multivariate analytical procedures makes HiRes very capable of addressing the challenges for assessment and interpretation of large metabolomic datasets, greatly simplifying this otherwise lengthy and difficult process and assuring optimal information retrieval. AVAILABILITY: HiRes is freely available for research purposes at http://hatch.cpmc.columbia.edu/highresmrs.html

High prevalence of chronic kidney disease in population-based patients diagnosed with type 2 diabetes in downtown Shanghai.

B Lu — 2008-02-24T01:12:41-00:00

J Diabetes Complications, Vol. 22, No. 2. (r 2008), pp. 96-103.

OBJECTIVE: This study aimed to evaluate the prevalence of chronic kidney disease (CKD) and the risk factors associated with CKD among Chinese patients diagnosed with type 2 diabetes aged over 30 in downtown Shanghai and to assess the relationship between CKD and diabetic retinopathy (DR). METHODS: We investigated 1039 Chinese patients diagnosed with type 2 diabetes aged over 30 by randomized cluster sampling in downtown Shanghai, and 1009 patients in this study were analyzed based on data integrity. Body measurements including height, weight, waist circumference and hip circumference, resting blood pressure, fasting blood measures, and urinary albumin-to-creatinine ratio (ACR), as well as the digitally stored fundus images, were investigated. Glomerular filtration rate (GFR) was estimated using the Cockcroft-Gault equation. The prevalence of CKD was calculated, and the risk factors associated with CKD were evaluated using stepwise logistic regression. The relationship between CKD and DR was evaluated using Spearman correlation and the chi-square test. RESULTS: The following were the results found in this study: (a) The prevalence rate of CKD (Stages 1-5) was 63.9% in Chinese patients diagnosed with type 2 diabetes, 8.8% in those with CKD Stage 1, 22.3% in those with CKD Stage 2, and 32.8% in those with CKD Stages 3-5 (GFR<60 ml/min/1.73 m(2)). The prevalence of CKD increased with age. (b) CKD patients were older and had higher duration of diabetes, systolic blood pressure, urea nitrogen, uric acid, creatinine, and ACR of the first urine than those without CKD. (c) Male patients had a higher percentage of CKD Stages 3-5, and female patients had a higher percentage of CKD Stages 1-2. (d) CKD was significantly associated with duration of diabetes, older age, systolic blood pressure, and serum urea nitrogen based on logistic regression analysis. (e) Of the patients without CKD, 15.6% had DR, and of those with CKD, 27.6% had DR. The decrease in GFR was significantly correlated with DR after controlling for sex, age, and albuminuria staging. CONCLUSION: The high prevalence of CKD observed in Chinese patients diagnosed with type 2 diabetes aged over 30 in downtown Shanghai was similar to that in Western patients, and the cause of CKD is likely to be any of the following: type 2 diabetes, IgA nephropathy, hypertension, or any combination of these. The screening program for GFR in type 2 diabetic patients should be performed even on those with normoalbuminuria. The decrease in GFR might predict the occurrence of DR among patients diagnosed with type 2 diabetes.

High prevalence of albuminuria in population-based patients diagnosed with type 2 diabetes in the Shanghai downtown.

B Lu — 2008-02-24T01:07:34-00:00

Diabetes Res Clin Pract, Vol. 75, No. 2. (February 2007), pp. 184-192.

OBJECTIVE: The prevalence of albuminuria and the risk factors associated with albuminuria were evaluated among the Chinese patients diagnosed with type 2 diabetes aged over 30 in the Shanghai downtown. We also evaluated the variability of urinary albumin-to-creatinine ratio (ACR) among the three measurements and the relationship between diabetic retinopathy (DR) and albuminuria. METHODS: The 1039 Chinese patients diagnosed with type 2 diabetes aged over 30 were investigated by randomized cluster sampling in the Shanghai downtown and 1018 patients were analyzed in this study. Body mass measurements including height, weight, waist circumference and hip circumference, resting blood pressure, fasting blood measures, urinary ACR and the digitally stored fundus images were investigated. The prevalence of albuminuria was calculated and the risk factors associated with albuminuria were evaluated by stepwise logistic regression. The concordance of urinary ACR was evaluated by observed agreement. The relationship between albuminuria and DR was also evaluated. RESULTS: (1) The mean age of all patients was 66.10+/-11.54 years and the duration of diabetes was 7.89+/-7.16 years. (2) The prevalence of albuminuria was 49.6% among the Chinese patients diagnosed with type 2 diabetes aged over 30 in the Shanghai downtown, 41.4% with microalbuminuria and 8.2% with macroalbuminuria. (3) Microalbuminuria was significantly associated with systolic blood pressure, gender and waist circumference. Macroalbuminuria was significantly associated with systolic blood pressure and duration of diabetes. (4) Observed agreement among the three urinary ACR measurement for albuminuria staging was 73.3% (first versus second), 64.5% (first versus third) and 77.5% (second versus third). Observed agreement in the albuminuria staging between the single urinary ACR measurement and all three urinary ACR measurements was 85.8% (first versus all three), 87.6% (second versus all three) and 81.9% (third versus all three). (5) The percentage of DR in the macroalbuminuric group (59.2%) was significantly higher than that in the normalbuminuria group (16.1%) and microalbuminuria group (24.6%). (6) The macroalbuminuric patients with DR had significantly increased fasting blood glucose and HbA1c compared with the macroalbuminuric patients without DR. CONCLUSION: The prevalence of microalbuminuria observed in the Chinese patients diagnosed with type 2 diabetes aged over 30 in the Shanghai downtown reached up to 41.4% though the observations in our study might be representative of the diabetic patients of the Shanghai downtown. We agreed that at least two of the three urinary collections were done in a 3- to 6-month period because of the day-to-day variability in albumin excretion. The percentage of DR among the patients with macroalbuminuria was 59.2%, and the macroalbuminuric patients with the significantly high plasma glucose and DR were prone to diagnose DN.

Practical Approach for the Identification and Isomer Elucidation of Biomarkers Detected in a Metabonomic Study for the Discovery of Individuals at Risk for Diabetes by Integrating the Chromatographic and Mass Spectrometric Information.

Jing Chen — 2008-02-22T01:58:51-00:00

Anal Chem, Vol. 80, No. 4. (15 February 2008), pp. 1280-1289.

Sensitive and high-resolution chromatographic-driven metabonomomics studies experienced major growth with the aid of new analytical technologies and bioinformatics software packages. Hence, data collections by LC-MS and data analyses by multivariate statistical methods are by far the most straightforward steps, and the detection of biomarker candidates can easily be achieved. However, the unequivocal identification of the detected metabolite candidates, including isomer elucidation, is still a crux of current metabonomics studies. Here we present a comprehensive analytical strategy for the elucidation of the molecular structure of metabolite biomarkers detected in a metabonomics study, exemplified analyzing spot urine of a cohort of healthy, insulin sensitive subjects and clinically well characterized prediabetic, insulin resistant individuals. An integrated approach of LC-MS fingerprinting, multivariate statistic analysis, LC-MSn experiments, micro preparation, FTICR-MS, GC retention index, database search, and generation of an isotope labeled standard was applied. Overall, we could demonstrate the efficiency of our analytical approach by the unambiguous elucidation of the molecular structure of an isomeric biomarker candidate detected in a complex human biofluid. The proposed strategy is a powerful new analytical tool, which will allow the definite identification of physiologically important molecules in metabonomics studies from basic biochemistry to clinical biomarker discovery.