Assessing the utility of SELDI-TOF and model averaging for serum proteomic biomarker discovery

The progress and potential of proteomic biomarkers for type 1 diabetes in children

INTRODUCTION

Although it is possible to identify the genetic risk for type 1 diabetes (T1D), it is not possible to predict who will develop the disease. New biomarkers are needed that would help understand the mechanisms of disease onset and when to administer targeted therapies and interventions. Areas covered: An overview is presented of international study efforts towards understanding the cause of T1D, including the collection of several extensive temporal sample series that follow the development of T1D in at risk children. The results of the proteomics analysis of these materials are presented, which have included bodily fluids, such as serum or plasma and urine, as well as tissue samples from the pancreas. Expert commentary: Promising recent reports have indicated detection of early proteomic changes in the serum of patients prior to diagnosis, potentially providing new measures for risk assessment. Similarly, there has been evidence that post-translational modification (PTM) may result in the recognition of islet cell proteins as autoantigens; modified proteins could thus be used as targets for immunomodulation to overcome the threat of the autoimmune response.

Luminex and other multiplex high throughput technologies for the identification of, and host response to, environmental triggers of type 1 diabetes

Complex interactions between a series of environmental factors and genes result in progression to clinical type 1 diabetes in genetically susceptible individuals. Despite several decades of research in the area, these interactions remain poorly understood. Several studies have yielded associations of certain foods, infections, and immunizations with the onset and progression of diabetes autoimmunity, but most findings are still inconclusive. Environmental triggers are difficult to identify mainly due to (i) large number and complex nature of environmental exposures, including bacteria, viruses, dietary factors, and environmental pollutants, (ii) reliance on low throughput technology, (iii) less efforts in quantifying host response, (iv) long silent period between the exposure and clinical onset of T1D which may lead to loss of the exposure fingerprints, and (v) limited sample sets. Recent development in multiplex technologies has enabled systematic evaluation of different classes of molecules or macroparticles in a high throughput manner. However, the use of multiplex assays in type 1 diabetes research is limited to cytokine assays. In this review, we will discuss the potential use of multiplex high throughput technologies in identification of environmental triggers and host response in type 1 diabetes.

Predicting early intrahepatic recurrence of hepatocellular carcinoma after microwave ablation using SELDI-TOF proteomic signature

BACKGROUND/AIMS

Despite great progress in the treatment of hepatocellular carcinoma (HCC) over the last-decade, intrahepatic recurrence is still the most frequent serious adverse event after all the treatments including microwave ablation. This study aimed to predict early recurrence of HCC after microwave ablation using serum proteomic signature.

METHODS

After curative microwave ablation of HCC, 86 patients were followed-up for 1 year. Serum samples were collected before microwave ablation. The mass spectra of proteins were generated using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Serum samples from 50 patients were randomly selected as a training set and for biomarkers discovery and model development. The remaining serum samples were categorized for validation of the algorithm.

RESULTS

According to preablation serum protein profiling obtained from the 50 HCC samples in the training set, nine significant differentially-expressed proteins were detected in the serum samples between recurrent and non-recurrent patients. Decision classification tree combined with three candidate proteins with m/z values of 7787, 6858 and 6646 was produced using Biomarker Patterns Software with sensitivity of 85.7% and specificity of 88.9% in the training set. When the SELDI marker pattern was tested with the blinded testing set, it yielded a sensitivity of 80.0%, a specificity of 88.5% and a positive predictive value of 86.1%.

CONCLUSIONS

Differentially-expressed protein peaks in preablation serum screened by SELDI are associated with prognosis of HCC. The decision classification tree is a potential tool in predicting early intrahepatic recurrence in HCC patients after microwave ablation.

Investigation of serum protein profiles in scrapie infected sheep by means of SELDI-TOF-MS and multivariate data analysis

Background

Classical scrapie in sheep is a fatal neurodegenerative disease associated with the conversion PrP^C to PrP^Sc. Much is known about genetic susceptibility, uptake and dissemination of PrP^Sc in the body, but many aspects of prion diseases are still unknown. Different proteomic techniques have been used during the last decade to investigate differences in protein profiles between affected animals and healthy controls. We have investigated the protein profiles in serum of sheep with scrapie and healthy controls by SELDI-TOF-MS and LC-MS/MS. Latent Variable methods such as Principal Component Analysis, Partial Least Squares-Discriminant Analysis and Target Projection methods were used to describe the MS data.

Results

The serum proteomic profiles showed variable differences between the groups both throughout the incubation period and at the clinical end stage of scrapie. At the end stage, the target projection model separated the two groups with a sensitivity of 97.8%, and serum amyloid A was identified as one of the protein peaks that differed significantly between the groups.

Conclusions

At the clinical end stage of classical scrapie, ten SELDI peaks significantly discriminated the scrapie group from the healthy controls. During the non-clinical incubation period, individual SELDI peaks were differently expressed between the groups at different time points. Investigations of differences in -omic profiles can contribute to new insights into the underlying disease processes and pathways, and advance our understanding of prion diseases, but comparison and validation across laboratories is difficult and challenging.

Gender dimorphism in regulation of plasma proteins in streptozotocin-induced diabetic rats

In the present study, we examined differentially regulated plasma proteins between healthy control and streptozotocin (STZ)-induced male and female diabetic rats by 2DE-based proteomic analysis. Animal experiments revealed that significantly lower plasma insulin levels were observed in female diabetic rats, consequently resulting in higher blood glucose levels in female diabetic rats. Importantly, plasma levels of sex hormones were significantly altered in a gender-dependent manner before and after STZ treatment. Results of the animal experiment indicated the existence of sexual dimorphism in the regulation of plasma proteins between healthy control and diabetic rats. Plasma proteome analysis enabled us to identify a total of 38 proteins showing sexual dimorphic regulation patterns. In addition, for the first time, we identified several differentially regulated plasma proteins between healthy control and diabetic rats, including apolipoprotein E, fetuin B, α-1-acid glycoprotein, β-2-glycoprotein 1, 3-hydroxyanthranilate 3,4-dioxygenase, and serum amyloid P-component. To the best of our knowledge, this is the first proteomic approach to address sexual dimorphism in diabetic animals. These proteomic data on gender-dimorphic regulation of plasma proteins provide valuable information that can be used for evidence-based gender-specific clinical treatment of diabetes.

Proteome-base biomarkers in diabetes mellitus: progress on biofluids' protein profiling using mass spectrometry

The worldwide number of individuals suffering from diabetes mellitus (DM) has been projected to rise from 171 million in 2000 to 366 million in 2030. Identification of specific biomarkers for prediction and monitoring of DM is needed not only for the adequate screening diagnosis but also to assist the design of interventions to prevent or delay progression of this pathology and its attendant complications. Proteomic methods based on MS hold special promise for the identification of novel biomarkers that might form the foundation for new clinical tests, but to date, their contribution has been somehow unfruitful. Indeed, from more than 300 proteins found differently modulated in body fluids from diabetic patients, approximately 50 were validated with other approaches like ELISA or Western blotting and the clinical trials are being initiated to employ biofluids' proteomics (specifically urinary proteomics) in clinical decision. This review provides an overview of MS-based applications in the identification of potential biomarkers for DM, emphasizing the methodological challenges involved.

Novel biomarkers in type 1 diabetes

Biomarkers are useful tools for research into type 1 diabetes (T1D) for a number of purposes, including elucidation of disease pathogenesis, risk prediction, and therapeutic monitoring. Susceptibility genes and islet autoantibodies are currently the most useful biomarkers for T1D risk prediction. However, these markers do not fully meet the needs of scientists and physicians for several reasons. First, improvement of the specificity and sensitivity is still desirable to achieve better positive predictive values. Second, autoantibodies appear relatively late in the disease process, thus limiting their value in early disease prediction. Third, the currently available biomarkers are not useful for assessing therapeutic outcomes because some are not involved in the disease process (autoantibodies) and others do not change during disease progression (susceptibility genes). Therefore, considerable effort has been devoted to the discovery of novel T1D biomarkers in the last three decades. The advent of high-throughput technologies for genetic, transcriptomic, and proteomic studies has allowed genome-wide examinations of genetic polymorphisms, global gene changes, and protein expression changes in T1D patients and prediabetic subjects. These large-scale studies resulted in the discovery of a large number of susceptibility genes and changes in gene and protein expression. While these studies have provided a number of novel biomarker candidates, their clinical benefits remain to be evaluated in prospective studies, and no new "star biomarker" has been identified until now. Previous studies suggest that significant improvements in study design and analytical methodologies have to be made to identify clinically relevant biomarkers. In this review, we discuss progress, opportunities, challenges, and future directions in the development of T1D biomarkers, mainly by focusing on the genetic, transcriptomic, and proteomic aspects.

An estrogen-responsive plasma protein expression signature in Atlantic cod (Gadus morhua) revealed by SELDI-TOF MS

Compound-specific protein expression signatures (PESs) can be revealed by proteomic techniques. The SELDI-TOF MS approach is advantageous due to its simplicity and high-throughput capacity, however, there are concerns regarding the reproducibility of this method. The aim of this study was to define an estrogen-responsive PES in plasma of Atlantic cod (Gadus morhua) using the SELDI-TOF MS technique. Protein expression analysis of male cod exposed to 17β-estradiol (E₂) showed that 27 plasma peaks were differentially expressed following exposure. The reproducibility of this result was evaluated by reanalyzing the samples six months later, and a significant change in expression was confirmed for 13 of the 27 peaks detected in the first analysis. The performance of the reproducible E₂-responsive PES, constituting these 13 peaks, was then tested on samples from juvenile cod exposed to 4-nonylphenol, North Sea oil, or North Sea oil spiked with alkylphenols. Principal component analysis revealed that nonylphenol-exposed cod could be separated from unexposed cod based on the E₂-responsive PES, indicating that the PES can be used to assess estrogenic exposure of both juvenile and adult specimens of cod. A targeted antibody-assisted SELDI-TOF MS approach was carried out in an attempt to identify the E₂-responsive peaks. Results indicated that 2 peaks were fragments of the well-known biomarkers VTG and/or ZRP. In this study, the SELDI-TOF MS technology has shown its potential for defining compound-specific PESs in fish. Nevertheless, thorough validation of reproducibility, specificity and sensitivity of a PES is required before it can be applied in environmental monitoring.

Advances and challenges in biomarker development for type 1 diabetes prediction and prevention using omic technologies

IMPORTANCE OF THE FIELD: Biomarkers are essential for the identification of high risk children as well as monitoring of prevention outcomes for type 1 diabetes (T1D). AREAS COVERED IN THIS REVIEW: This review discusses progress, opportunities and challenges in biomarker discovery and validation using high throughput genomic, transcriptomic and proteomic technologies. The authors also suggest potential solutions to deal with the current challenges. WHAT THE READER WILL GAIN: Readers will gain an overview of the current status on T1D biomarkers, an integrated review of three omic technologies, their applications and limitations for biomarker discovery and validation, and a critical discussion of the major issues encountered in biomarker development. TAKE HOME MESSAGE: Better biomarkers are still urgently needed for T1D prediction and prevention. The high throughput omic technologies offer great opportunities but also face significant challenges that have to be solved before their potential for biomarker development is fully realized.

Proteomic technologies for the discovery of type 1 diabetes biomarkers

In this review, we discuss several important issues concerning the discovery of protein biomarkers for complex human diseases, with a focus on type 1 diabetes. Serum or plasma is the first choice of specimen due to its richness in biological information and relatively easy availability. It is a challenging task to comprehensively characterize the serum/plasma proteome because of the large dynamic range of protein concentration. Therefore, sample pretreatment is required in order to explore the low- to medium-abundance proteins contained in serum/plasma. In this regard, enrichment of low-abundance proteins using random hexapeptide library beads has distinct advantages over the traditional immune-depletion methods, including higher efficiency, higher binding capacity, and lower cost. In-depth mining of serum/plasma proteome using different separation techniques have also been evaluated and are discussed in this review. Overall, the shotgun proteomics-multidimensional separation of digested peptides followed by mass spectrometry analysis--is highly efficient and therefore has become a preferred method for protein biomarker discovery.

Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data

Background

Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.

Results

We propose a method based on statistical moments to reduce feature dimensions. After refining and t-testing, SELDI-TOF data are divided into several intervals. Four statistical moments (mean, variance, skewness and kurtosis) are calculated for each interval and are used as representative variables. The high dimensionality of the data can thus be rapidly reduced. To improve efficiency and classification performance, the data are further used in kernel PLS models. The method achieved average sensitivity of 0.9950, specificity of 0.9916, accuracy of 0.9935 and a correlation coefficient of 0.9869 for 100 five-fold cross validations. Furthermore, only one control was misclassified in leave-one-out cross validation.

Conclusion

The proposed method is suitable for analyzing high-throughput proteomics data.

On-plate-selective enrichment of glycopeptides using boronic acid-modified gold nanoparticles for direct MALDI-QIT-TOF MS analysis

In this study, an on-plate-selective enrichment method is developed for fast and efficient glycopeptide investigation. Gold nanoparticles were first spotted and sintered on a stainless-steel plate, then modified with 4-mercaptophenylboronic acid to provide porous substrate with large specific surface and dual functions. These spots were used to selectively capture glycopeptides from peptide mixtures and the captured target peptides could be analyzed by MALDI-MS simply by deposition of 2,5-dihydroxybenzoic acid matrix. Horseradish peroxidase was employed as a standard glycoprotein to investigate the enrichment efficiency. In this way, the enrichment, washing and detection steps can all be fulfilled on a single MALDI target plate. The relatively small sample amount needed, low detection limit and rapid selective enrichment have made this on-plate strategy promising for online enrichment of glycopeptides, which could be applied in high-throughput proteome research.

SELDI-TOF as a method for biomarker discovery in the urine of aristolochic-acid-treated mice

Aristolochic acids (AAs) present in Aristolochia plants are substances responsible for Chinese herbs nephropathy. Recently, strong indications have also been presented, which dietary poisoning with AA is responsible for endemic (Balkan) nephropathy (EN), an enigmatic renal disease that affects rural population living in some countries in Southeastern Europe. A mouse model was applied to follow the effects of two forms of AA, AAI and AAII. SDS-PAGE and SELDI-TOF mass spectrometry with normal phase chips were used to evaluate changes in the urine of treated animals. These two methods are demonstrated to be comparable. The use of SELDI-TOF MS for rapid analysis of a large number of samples and the combination of this method with nano-LC-ESI MS/MS for protein identification were demonstrated. Biomarker discovery after analysis of large cohort of EN patients will be the final aim of these investigations.

Sevelamer restores bone volume and improves bone microarchitecture and strength in aged ovariectomized rats

Sevelamer hydrochloride, a noncalcium phosphate binder, has been shown to reduce coronary artery and aortic calcification, and to improve trabecular bone mineral density in hemodialysis patients with chronic kidney disease. Here, we examined whether sevelamer given orally for 12 wk with normal food could restore bone volume (BV) and strength in aged ovariectomized (OVX) rats starting at 4 wk after OVX. Dual-energy x-ray absorptiometry, microcomputerized tomography, and bone histomorphometry analyses showed that OVX animals receiving sevelamer had increased trabecular BV (51%), trabecular number (43%), trabecular thickness (9%), cortical thickness (16%), mineral apposition rate (103%), bone formation rate (25%), and enhanced cortical and trabecular bone mechanical strength as compared with OVX rats. Sevelamer decreased collagen C telopeptide, increased osteocalcin levels, and decreased phosphate and magnesium levels without affecting calcium levels in the blood. Although sevelamer was not absorbed systemically, it stimulated osteoblast differentiation in BM-derived mesenchymal stem cell cultures, as evaluated by alkaline phosphatase positive colony-forming units, and inhibited recombinant human soluble receptor activator of nuclear factor-kappaB ligand-induced osteoclast differentiation, as evaluated by tartrate-resistant acid phosphatase positive cells in bone mineral-hematopoietic stem cell cultures. Surface enhanced laser desorption/ionization time-of-flight mass spectrometry analysis revealed that 69 proteins were differently expressed after OVX, of which 30% (20 of 69) were reversed to sham activity after sevelamer intake. PTH, fibroblast growth factor-23, and cytokine profile in serum were not significantly changed. Together, these results suggest that sevelamer in food increases the BV and improves biomechanical properties of bone in OVX rats.

Differential expression of serum proteomic spectra in rat model of pancreatic intraepithelial neoplasia and dimethylbenzanthracene-induced pancreatic carcinoma

AIM: To investigate relationship between differential expression of serum proteomic spectra in rat model of pancreatic intraepithelial neoplasia (PanIN) and dimethylbenzanthracene-induced pancreatic carcinoma (PC) using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) technology.

METHODS: Forty male SD rats were implanted with DMBA into the pancreas to induce rat model of PanIN and PC. Histopathology was evaluated according to PanIN classification system. And normal control group of twenty-six male SD rats was established. The serum protein spectra were detected using IMAC-Cu²⁺ proteinchip and SELDI-TOF MS. The data were analyzed using Biomarker Wizard 3.0 Software of Ciphergen Biosystem Co.

RESULTS: DMBA was implanted into pancreas of rats in PC group (n = 11) and PanIN group (n = 18). Compared with the normal control group, there were significant differences (P < 0.001) of 30 protein peaks in PanIN and PC of which 19 protein peaks were up-regulated and 11 down-regulated. The expression of 9 protein peaks, with a ratio of mass to charge (M/Z) of 5835.2, 4087.3, 4786.5, 4800.5, 3932.2, 5765.9, 5924.8, 5001.9, 3913.7 gradually increased from normal to PanIN and PC group, and 4 protein peaks with a M/Z ratio of 1096.9, 1478.9, 8572.9, 1007.1 gradually decreased.

CONCLUSION: Serum proteomic spectra were differentially expressed in rat model of PanIN and PC. Identification and function of these differentially expressed proteins necessitate further investigation.

Serum biomarker profiling by solid-phase extraction with particle-embedded micro tips and matrix-assisted laser desorption/ionization mass spectrometry

One of the main challenges in high-throughput serum profiling by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is the development of proteome fractionation approaches that allow the acquisition of reproducible profiles with a maximum number of spectral features and minimum interferences from biological matrices. This study evaluates a new class of solid-phase extraction (SPE) pipette tips embedded with different chromatographic media for fractionation of model protein digests and serum samples. The materials embedded include strong anion exchange (SAX), weak cation exchange (WCX), C18, C8, C4, immobilized metal affinity chromatography (IMAC) and zirconium dioxide particles. Simple and rapid serum proteome profiling protocols based on these SPE micro tips are described and tested using a variety of MALDI matrices. We show that different types of particle-embedded SPE micro tips provide complementary information in terms of the spectral features detected for beta-casein digests and control human serum samples. The effect of different sample pretreatments, such as serum dilution and ultrafiltration using molecular weight cut-off membranes, and the reproducibility observed for replicate experiments, are also evaluated. The results demonstrate the usefulness of these simple SPE tips combined with offline MALDI-TOF MS for obtaining information-rich serum profiles, resulting in a robust, versatile and reproducible open-source platform for serum biomarker discovery.

Plasma proteome changes in subjects with Type 2 diabetes mellitus with a low or high early insulin response

Circulating proteins contribute to the pathogenesis of T2DM (Type 2 diabetes mellitus) in various ways. The aim of the present study was to investigate variations in plasma protein levels in subjects with T2DM and differences in β-cell function, characterized by the EIR (early insulin response), and to compare these protein levels with those observed in individuals with NGT (normal glucose tolerance). Ten subjects with NGT+high EIR, ten with T2DM+high EIR, and ten with T2DM+low EIR were selected from the community-based ULSAM (Uppsala Longitudinal Study of Adult Men) cohort. Plasma protein profiling was performed using SELDI-TOF (surface-enhanced laser-desorption ionization–time-of-flight) MS. In total, nine plasma proteins differed between the three study groups (P<0.05, as determined by ANOVA). The levels of two forms of transthyretin, haemoglobin α-chain and haemoglobin β-chain were decreased in plasma from subjects with T2DM compared with subjects with NGT, irrespective of the EIR of the subjects. Apolipoprotein H was decreased in plasma from individuals with T2DM+high EIR compared with subjects with NGT. Four additional unidentified plasma proteins also varied in different ways between the experimental groups. In conclusion, the proteins detected in the present study may be related to the development of β-cell dysfunction.

Serum protein patterns in newly diagnosed type 2 diabetes mellitus--influence of diabetic environment and family history of diabetes

BACKGROUND

Individuals with normal glucose tolerance (NGT) and type 2 diabetes mellitus (T2DM) represent heterogeneous groups with differences in beta-cell function and genetic background. The aim of the present study was to compare serum protein profiles of NGT and T2DM individuals and determine the influence of the genetic background versus diabetic environment on differentially displayed proteins.

METHODS

Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to compare serum protein profiles of NGT persons and T2DM patients. All participants were from the Stockholm Diabetes Prevention Program (SDPP) cohort. They were selected to have high or low beta-cell function (HOMA-beta) and family history of type 2 diabetes (FHD) or not.

RESULTS

Eight proteins were found to be elevated and five lowered (p<0.05) in serum of T2DM patients. In a second comparison, the NGT and T2DM groups were divided into persons with FHD and low HOMA-beta and those without FHD and high HOMA-beta. Three proteins were rediscovered and interpreted to be different due to genetic background. Two of these were identified as apolipoprotein C3 (apoC3) and albumin. Ten proteins were interpreted to be not related to FHD, and one of these was identified as transthyretin.

CONCLUSIONS

Using the SELDI-technique, serum protein profiles of NGT and T2DM persons with differences in beta-cell function and FHD were compared. The diabetic environment had a major influence on most of these proteins, while FHD was an important factor for apoC3 and albumin.

Application of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry for early detection of pancreatic cancer

Pancreatic cancer is a devastating and lethal disease. Early detection continues to be a serious, unsolved problem. However, proteomics is emerging as a powerful new tool for the diagnosis of pancreatic cancer. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a new technique that allows for rapid high-throughput screening of protein expression in clinical samples. The progress and challenges in applying SELDI-TOF MS to protein biomarker discovery in pancreatic cancer are reviewed in this paper.

Serum proteomic profiling of dementia with Lewy bodies: diagnostic potential of SELDI-TOF MS analysis

Dementia with Lewy bodies (DLB) is the second most common senile degenerative dementia after Alzheimer's disease (AD). The presentation of overlapping symptoms between these two disorders leads to difficulties in the determination of clinical entities. Serum samples were subjected to surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) analysis in order to identify a diagnostic marker for DLB. Four putative protein peaks (m/z 3,883, 4,964, 7,761 and 10,534) were differentially expressed in DLB patients compared to AD patients and control subjects. Receiver operating characteristics (ROC) analysis of a multivariate logistic model of the combination of three peaks (m/z 3,883, 7,761 and 10,534) exhibited the highest discriminatory ability of DLB subjects from non-DLB subjects with a sensitivity of 83.3%, a specificity of 95.8%, a positive predictive value of 90.9% and a negative predictive value of 92.0%. SELDI-TOF MS profiling, therefore, has revealed a serum signature with high diagnostic potential for DLB.

Screening of Serum Biomarkers and Establishment of a Decision Tree in Silica-Exposed Populations by Surface-Enhanced Laser Desorption Ionization Time-of-Fly Mass Spectrometry

OBJECTIVE

To screen for serum biomarkers of silicosis and to study their roles using surface-enhanced laser desorption ionization time-of-fly mass spectrometry (SELDI-TOF-MS) techniques.

METHODS

The serum protein/peptide profile on the CM10 proteinchip was acquired using SELDI-TOF-MS from workers classified by the Chinese national diagnostic standard for pneumoconiosis. Discriminant analysis was performed to establish a decision tree using protein/peptide peaks.

RESULTS

Protein/peptide peaks changed much more in silica-exposed populations than they did in the control. Discriminant analysis using Wilks' lambda method could give high sensitivity and specificity in distinguishing the silica-exposed population from the control; the decision tree could give 100% sensitivity and 95% specificity in distinguishing the silicosis phase among the control group.

CONCLUSIONS

Special proteins/peptides may change in silica-exposed workers and these changes may be used to distinguish silica-exposed populations from the control with the proper discriminant analytical method.

A mixture model approach to the tests of concordance and discordance between two large-scale experiments with two-sample groups

MOTIVATION

Due to advances in experimental technologies, such as microarray, mass spectrometry and nuclear magnetic resonance, it is feasible to obtain large-scale data sets, in which measurements for a large number of features can be simultaneously collected. However, the sample sizes of these data sets are usually small due to their relatively high costs, which leads to the issue of concordance among different data sets collected for the same study: features should have consistent behavior in different data sets. There is a lack of rigorous statistical methods for evaluating this concordance or discordance.

METHODS

Based on a three-component normal-mixture model, we propose two likelihood ratio tests for evaluating the concordance and discordance between two large-scale data sets with two sample groups. The parameter estimation is achieved through the expectation-maximization (E-M) algorithm. A normal-distribution-quantile-based method is used for data transformation.

RESULTS

To evaluate the proposed tests, we conducted some simulation studies, which suggested their satisfactory performances. As applications, the proposed tests were applied to three SELDI-MS data sets with replicates. One data set has replicates from different platforms and the other two have replicates from the same platform. We found that data generated by SELDI-MS showed satisfactory concordance between replicates from the same platform but unsatisfactory concordance between replicates from different platforms.

AVAILABILITY

The R codes are freely available at http://home.gwu.edu/~ylai/research/Concordance.

Serum proteomic profiling associated with immune system impaired by stress using ProteinChip technology

We set out to use proteomic profiling as a means of defining serum peptides that are indicative of a stress response related to an impact on the immune system. Two types of mouse models with the immune response impaired under stress (restraint stress and thermal stress) comprised the stressed groups (n = 10 in each group), while mice without stress represented the control group (n = 10). ProteinChip technology (surface-enhanced laser desorption/ionization time-of-flight mass spectrometry) was used to investigate the serum proteomic profiles. Histopathological examination of spleens from the stressed and control groups was performed to select appropriate individuals. Compared with the spleens of normal mice, those of mice subjected to restraint and thermal stress had decreased white pulp and lymphoid nodules, and their distribution and structure changed irregularly. Pathological changes occurred in all individuals in both stressed groups. The m/z values of the protein peaks ranged from 1,500 to 50,000 daltons and were mainly in the 2,000- to 20,000-dalton range. One hundred and fifty protein peaks were detected in the three groups. Four downregulated universal stress protein peaks with m/z of 4,389, 5,341, 5,526, and 6,252 were finally selected. Unexpectedly, no upregulated universal stress protein peaks were detected. These results suggested that impairment of the immune system results from inhibition of growth-promoting factors associated with the immune system. Identified protein peaks may be biomarkers of the impaired immune system under stress.