Serological protein profiling of neuroblastoma by ProteinChip SELDI-TOF technology

Serological proteins of neuroblastoma were profiled and analyzed by ProteinChip-SELDI-TOF MS technology with five types of protein chips. By comparing with normal control, a number of protein or polypeptide signals were found significantly and consistently different in their intensities (expression levels) in tumor sera. Interestingly, nine polypeptide peaks in these proteomic features can be simultaneously detected with consistent variations by more than one type of protein chips. None of the expression differences of these nine polypeptides was found in similar comparisons between healthy controls and hepatomas. Preliminary protein identification showed hints for that some of these proteomic alterations may be closely related to the tumorigenesis of neuroblastoma. These results demonstrated the potential of serological biomarker identification for neuroblastoma by ProteinChip-SELDI technology.     

Current status of protein chip development in terms of fabrication and application

Sequencing of the human genome revealed that more than 30 000 genes encode proteins comprising the human proteome. "Proteomics" can be defined as a field of research studying proteins in terms of their function, expression, structure, modification and their interaction in physiological and in pathological states. The concentration, modification and interaction of proteins in cells, plasma, and in tissues are crucial in determining the phenotype of living organisms. Although fluctuation of protein concentration is essential to maintain homeostasis, protein expression levels are also pathognomonic features. Estimating protein concentration by analyzing the quantity of mRNA in cells through conventional technologies, such as DNA chips, does not provide precise values since the half-life and translation efficacy of mRNA is variable. In addition, polypeptides undergo post-translational modification. For these reasons, novel techniques are needed to analyze multiple proteins simultaneously using protein microarrays. In the near future, protein chips may allow construction of complete relational databases for metabolic and signal transduction pathways. This article reviews the current status of technologies for fabricating protein microarrays and their applications.     

Microbial characterization of a JP-4 fuel-contaminated site using a combined lipid biomarker/polymerase chain reaction--denaturing gradient gel electrophoresis (PCR-DGGE)-based approach

The impact of pollution on soil microbial communities and subsequent bioremediation can be measured quantitatively in situ using direct, non-culture- dependent techniques. Such techniques have advantages over culture-based methods, which often account for less than 1% of the extant microbial community. In 1988, a JP-4 fuel spill contaminated the glacio-fluvial aquifer at Wurtsmith Air Force Base, Michigan, USA. In this study, lipid biomarker characterization of the bacterial and eukaryotic communities was combined with polymerase chain reaction– denaturing gradient gel electrophoresis (PCR–DGGE) analysis of the eubacterial community to evaluate correlation between contaminant (JP-4 fuel) concentration and community structure shifts. Vadose, capillary fringe and saturated zone samples were taken from cores within and up- and down-gradient from the contaminant plume. Lipid biomarker analysis indicated that samples from within the plume contained increased biomass, with large proportions of typically Gram-negative bacteria. Outside the plume, lipid profiles indicated low-biomass microbial communities compared with those within the initial spill site. 16S rDNA sequences derived from DGGE profiles from within the initial spill site suggested dominance of the eubacterial community by a limited number of phylogenetically diverse organisms. Used in tandem with pollutant quantification, these molecular techniques should facilitate significant improvements over current assessment procedures for the determination of remediation end-points.     

What is the blood concentration of extracellular vesicles? Implications for the use of extracellular vesicles as blood-borne biomarkers of cancer

Circulating biomarkers have a great potential in diagnosing cancer diseases at early stages, where curative treatment is a realistic possibility. In the recent years, using extracellular vesicles (EVs) derived from blood as biomarkers has gained widespread popularity, mainly because they are thought to be easy to isolate and carry a vast variety of biological cargos that can be analyzed for biomarker purposes. However, our current knowledge on the plasma EV concentration in normophysiological states is sparse. Here, we provide the very first mean estimate of the plasma EV concentration based on values obtained from a thorough literature review. The different estimates obtained from the literature are correlated to the isolation techniques used to obtain them, illustrating how some methodologies may over- or underestimate the plasma EV concentration. We also show that the estimated plasma EV concentration (approximately 10¹⁰ EVs per mL) defines EVs as a minority population compared to other colloidal particles of the systemic circulation, namely the lipoproteins, which are known contaminants in EV isolates and carry biomarker molecules themselves. Lastly, we introduce the possibility of regarding EVs and lipoproteins as a continuum of lipid-containing particles to which biomarker molecules can be associated. Using such a holistic approach, increased strength of plasma-derived cancer biomarkers may soon be revealed.     

Factors contributing to discrepancies in population monitoring studies

A review of the scientific literature on population monitoring studies (on non-accidentally exposed populations) frequently show that many of these studies using similarly exposed populations and the same laboratory techniques do not produce consistent results. To illustrate the problem, a brief review of studies using well validated techniques (chromosome aberrations and hprt gene mutation) to elucidate genotoxic effects of cigarette smoking is presented. Although many factors can contribute to the generation of discrepant results, two obvious factors are small sample sizes and inadequate experimental data. In addition, a new factor on genetic susceptibility should be considered in population studies whenever appropriate. The new factor is based on recent data showing the influence of polymorphic metabolizing genes on response to environmental mutagens towards biological effects and disease outcome. The common ones include the cytochrome P450 and the glutathione S-transferase genes. The inclusion of susceptibility factors in population monitoring may revolutionize the approach for health risk assessment and for environmental regulations.     

Gene-centric characteristics of genome-wide association studies

Background

The high-throughput genotyping chips have contributed greatly to genome-wide association (GWA) studies to identify novel disease susceptibility single nucleotide polymorphisms (SNPs). The high-density chips are designed using two different SNP selection approaches, the direct gene-centric approach, and the indirect quasi-random SNPs or linkage disequilibrium (LD)-based tagSNPs approaches. Although all these approaches can provide high genome coverage and ascertain variants in genes, it is not clear to which extent these approaches could capture the common genic variants. It is also important to characterize and compare the differences between these approaches.

Methodology/Principal Findings

In our study, by using both the Phase II HapMap data and the disease variants extracted from OMIM, a gene-centric evaluation was first performed to evaluate the ability of the approaches in capturing the disease variants in Caucasian population. Then the distribution patterns of SNPs were also characterized in genic regions, evolutionarily conserved introns and nongenic regions, ontologies and pathways. The results show that, no mater which SNP selection approach is used, the current high-density SNP chips provide very high coverage in genic regions and can capture most of known common disease variants under HapMap frame. The results also show that the differences between the direct and the indirect approaches are relatively small. Both have similar SNP distribution patterns in these gene-centric characteristics.

Conclusions/Significance

This study suggests that the indirect approaches not only have the advantage of high coverage but also are useful for studies focusing on various functional SNPs either in genes or in the conserved regions that the direct approach supports. The study and the annotation of characteristics will be helpful for designing and analyzing GWA studies that aim to identify genetic risk factors involved in common diseases, especially variants in genes and conserved regions.     

Characterization of DNA chips by nanogold staining

DNA microarray is an important tool in biomedical research. Up to now, there are no chips that can allow both quality analysis and hybridization using the same chip. It is risky to draw conclusions from results of different chips if there is no knowledge of the quality of the chips before hybridization. In this article, we report a colorimetric method to do quality control on an array. The quality analysis of probe spots can be obtained by using gold nanoparticles with positive charges to label DNA through electrostatic attraction. The probe spots can also be detected by a simple personal computer scanner. Gold nanoparticles deposited on a glass surface can be dissolved in bromine-bromide solution. The same microarray treated with gold particles staining and destaining can still be used for hybridization with nearly the same efficiency. This approach makes quality control of a microarray chip feasible and should be a valuable tool for biomarker discovery in the future.     

Partial summary measures of the predictiveness curve

In the evaluation of a biomarker for risk prediction, one can assess the performance of the biomarker in the population of interest by displaying the predictiveness curve. In conjunction with an assessment of the classification accuracy of a biomarker, the predictiveness curve is an important tool for assessing the usefulness of a risk prediction model. Inference for a single biomarker or for multiple biomarkers can be performed using summary measures of the predictiveness curve. We propose two partial summary measures, the partial total gain and the partial proportion of explained variation, that summarize the predictiveness curve over a restricted range of risk. The methods we describe can be used to compare two biomarkers when there are existing thresholds for risk stratification. We describe inferential tools for one and two samples that are shown to have adequate power in a simulation study. The methods are illustrated by assessing the accuracy of a risk score for predicting the onset of Alzheimer's disease.     

Fabrication of titanium based MALDI bacterial chips for rapid, sensitive and direct analysis of pathogenic bacteria

For the first time, we report the fabrication of a titanium bacterial chip for MALDI-MS produced from a simple, cost effective and rapid heat treatment process. This bacterial chip can be reused many times and is highly versatile. These bacterial chips serve dual roles: (1) They can be applied as MALDI-MS target plates for direct and highly sensitive bacterial analysis. (2) They can be used as bacterial sensors for direct analysis of the captured bacteria using MALDI-MS. The sensitivity of these chips when used as bacterial sensors is <10(3)cfu/mL. The lowest detectable concentration for direct MALDI-MS analysis was found to be 10(4)cfu/mL. The results were further justified by using standard plate counting method combined with Tukey-Kramer statistical analysis and fluorescence imaging followed by image processing for fluorescence quantification using ImageJ software to substantiate the MALDI-MS results.     

Nanotechnologic biosensor ellipsometry and biomarker pattern analysis in the evaluation of atherosclerotic risk profile

A proteoheparan sulfate coated, hydrophobic silica surface serves as lipoprotein receptor at which the Ca(2+)-driven arteriosclerotic nanoplaque formation can be pursued by laser-based ellipsometry. Any lipoprotein from human blood can be very sensitively tested for its atherogenic properties. From the same blood sample, it is possible to determine the concentration and activity of a series of interacting biomarker molecules which, through a pattern analysis, allow to assess the state of health with respect to cardiovascular diseases. These two interlinked and complementary biosensors make a prospective cardio-cerebro-vascular risk stratification feasible, especially the sequelae of an underlying arteriosclerotic disease. Based on these diagnostic tools, an optimized therapy decision for the patient can be taken and the necessary preventive measures for the still healthy person.     

Gene functional similarity search tool (GFSST)

Background  

With the completion of the genome sequences of human, mouse, and other species and the advent of high throughput functional genomic research technologies such as biomicroarray chips, more and more genes and their products have been discovered and their functions have begun to be understood. Increasing amounts of data about genes, gene products and their functions have been stored in databases. To facilitate selection of candidate genes for gene-disease research, genetic association studies, biomarker and drug target selection, and animal models of human diseases, it is essential to have search engines that can retrieve genes by their functions from proteome databases. In recent years, the development of Gene Ontology (GO) has established structured, controlled vocabularies describing gene functions, which makes it possible to develop novel tools to search genes by functional similarity.     

分离差异表达基因的方法

了解不同细胞或同类细胞在不同发育阶段、不同生理状态下的基因表达状况,可以为研究生命活动过程提供重要信息。以差别筛选,扣除杂交等基本方法为出发点,研究基因表达差异的方法不断完善,先后出现了DDRT-PCR,RDA,SSH,cDNA微阵列(基因芯片)等技术。这里着重对这些方法的优缺点及改进进行了论述和评介,并对技术的发展趋势进行了分析。     

Cardiovascular risk assessment of dyslipidemic children: analysis of biomarkers to identify monogenic dyslipidemia

The distinction between a monogenic dyslipidemia and a polygenic/environmental dyslipidemia is important for the cardiovascular risk assessment, counseling, and treatment of these patients. The present work aims to perform the cardiovascular risk assessment of dyslipidemic children to identify useful biomarkers for clinical criteria improvement in clinical settings. Main cardiovascular risk factors were analyzed in a cohort of 237 unrelated children with clinical diagnosis of familial hypercholesterolemia (FH). About 40% carried at least two cardiovascular risk factors and 37.6% had FH, presenting mutations in LDLR and APOB. FH children showed significant elevated atherogenic markers and lower concentration of antiatherogenic particles. Children without a molecular diagnosis of FH had higher levels of TGs, apoC2, apoC3, and higher frequency of BMI and overweight/obesity, suggesting that environmental factors can be the underlying cause of their hypercholesterolem≥ia. An apoB/apoA1 ratio ≥0.68 was identified as the best biomarker (area under the curve = 0.835) to differentiate FH from other dyslipidemias. The inclusion in clinical criteria of a higher cut-off point for LDL cholesterol or an apoB/apoA1 ratio ≥0.68 optimized the criteria sensitivity and specificity. The correct identification, at an early age, of all children at-risk is of great importance so that specific interventions can be implemented. apoB/apoA1 can improve the identification of FH patients.     

Multiplexed protein profiling on antibody-based microarrays by rolling circle amplification

Multiplexed immunoassays on antibody-based protein microarrays are an attractive solution for analyzing biological responses in normal and diseased states. Recently, the feasibility and utility of these assays has been established as concerns about specificity and sensitivity are being overcome by careful quality control and amplification technologies such as rolling circle amplification (RCA). RCA-amplified protein chips can now profile up to 150 proteins in various substrates including serum, plasma, and supernatants with high sensitivity, broad dynamic range and good reproducibility. Diagnostic utility of RCA-amplified protein chips has been shown for multiplexed allergen testing. When allied with multivariate statistical analysis, RCA protein chips have the potential to identify multiplexed biomarker classifiers for disease diagnosis and drug response.     

ModelPROBE: model driven soil probing, site assessment and evaluation

Conventional techniques for site characterisation are time consuming, cost intensive, and often do not support decision making with regard to sustainable remediation. Therefore, new techniques for step by step site characterization with smart feed back loops are necessary that are able to support a future “soil framework directive”. Advanced geophysical site characterization techniques combined with new types of vegetation analysis will be developed. Based on these non-invasive surveys, the extension of sources, contamination levels (THP, BTEX, PAH, CHC, explosives, heavy metals and radio nuclides) and soil heterogeneities will be localized first. Hot spots will then be investigated by new direct push probing systems integrated with geophysical and hydrogeological methods and combined with chemical and isotopic contaminant analysis for source localization and identification (environmental forensics). The actually occurring bioprocesses, such as contaminant degradation or sorption and mobilization processes, will be assessed using biosensors, in situ microcosms, and stable isotope and biomarker analysis. These new techniques and tools will be evaluated against best practice of conventional methods. The ModelPROBE project (EU-FP7 collaborative project) gives the opportunity to test, optimize and demonstrate the proposed approach at fully equipped and characterized European brownfield reference sites in Germany, Italy, Norway and the Czech Republic. Integrated statistical analysis and modelling at different stages, the step by step approach will result in an improved view of soil and subsurface contamination and will provide a sound basis for a cost-effective risk assessment and decision in the choice of the most appropriate sustainable remediation strategy.     

Mutant p21ras in vinyl chloride exposed workers

The production of mutations in cellular oncogenes such as ras is involved in the development of many human cancers. These mutations result in the expression of mutant forms of the encoded p21 protein which can potentially serve as a biomarker for this carcinogenic process. Workers exposed to vinyl chloride (VC) who are at risk for the development of the sentinel neoplasm angiosarcoma of the liver (ASL) represent a model population for the study of such a mutant p21ras biomarker, since VC is known to cause a specific ras mutation in ASL. In order to determine the relationship between VC exposure and this biomarker, serum samples from a cohort of 225 French VC workers and 111 age-sex-race-smoking-drinking matched unexposed controls were examined for the presence of mutant p21ras by immunoblotting with a mouse monoclonal antibody specific for the mutant protein. Stratifying the exposed workers by degree of VC exposure in estimated ppm-years by quartiles yielded a statistically significant trend for increasing odds ratio for sero-positivity of the p21ras biomarker with increasing exposure. These results suggest that this serum biomarker is related to VC exposure and may be an early indicator of carcinogenic risk in exposed individuals.     

Knowledge-based gene expression classification via matrix factorization

MOTIVATION: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. RESULTS: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.     

Mutant p21ras in vinyl chloride exposed workers

The production of mutations in cellular oncogenes such as ras is involved in the development of many human cancers. These mutations result in the expression of mutant forms of the encoded p21 protein which can potentially serve as a biomarker for this carcinogenic process. Workers exposed to vinyl chloride (VC) who are at risk for the development of the sentinel neoplasm angiosarcoma of the liver (ASL) represent a model population for the study of such a mutant p21ras biomarker, since VC is known to cause a specific ras mutation in ASL. In order to determine the relationship between VC exposure and this biomarker, serum samples from a cohort of 225 French VC workers and 111 age-sex-race-smoking-drinking matched unexposed controls were examined for the presence of mutant p21ras by immunoblotting with a mouse monoclonal antibody specific for the mutant protein. Stratifying the exposed workers by degree of VC exposure in estimated ppm-years by quartiles yielded a statistically significant trend for increasing odds ratio for sero-positivity of the p21ras biomarker with increasing exposure. These results suggest that this serum biomarker is related to VC exposure and may be an early indicator of carcinogenic risk in exposed individuals.