Fast and Accurate Shared Segment Detection and Relatedness Estimation in Un-phased Genetic Data via TRUFFLE.

Relationship estimation and segment detection between individuals is an important aspect of disease gene mapping. Existing methods are either tailored for computational efficiency or require phasing to improve accuracy. We developed TRUFFLE, a method that integrates computational techniques and statistical principles for the identification and visualization of identity-by-descent (IBD) segments using un-phased data. By skipping the haplotype phasing step and, instead, relying on a simpler region-based approach, our method is computationally efficient while maintaining inferential accuracy. In addition, an error model corrects for segment break-ups that occur as a consequence of genotyping errors. TRUFFLE can estimate relatedness for 3.1 million pairs from the 1000 Genomes Project data in a few minutes on a typical laptop computer. Consistent with expectation, we identified only three second cousin or closer pairs across different populations, while commonly used methods identified a large number of such pairs. Similarly, within populations, we identified many fewer related pairs. Compared to methods relying on phased data, TRUFFLE has comparable accuracy but is drastically faster and has fewer broken segments. We also identified specific local genomic regions that are commonly shared within populations, suggesting selection. When applied to pedigree data, we observed 99.6% accuracy in detecting 1st to 5th degree relationships. As genomic datasets become much larger, TRUFFLE can enable disease gene mapping through implicit shared haplotypes by accurate IBD segment detection.

Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients.

Seminal plasma, because of its proximity to prostate, is a promising fluid for biomarker discovery and noninvasive diagnostics. In this study, we investigated if seminal plasma proteins could increase diagnostic specificity of detecting primary prostate cancer and discriminate between high- and low-grade cancers. To select 147 most promising biomarker candidates, we combined proteins identified through five independent experimental or data mining approaches: tissue transcriptomics, seminal plasma proteomics, cell line secretomics, tissue specificity, and androgen regulation. A rigorous biomarker development pipeline based on selected reaction monitoring assays was designed to evaluate the most promising candidates. As a result, we qualified 76, and verified 19 proteins in seminal plasma of 67 negative biopsy and 152 prostate cancer patients. Verification revealed a prostate-specific, secreted and androgen-regulated protein-glutamine gamma-glutamyltransferase 4 (TGM4), which predicted prostate cancer on biopsy and outperformed age and serum Prostate-Specific Antigen (PSA). A machine-learning approach for data analysis provided improved multi-marker combinations for diagnosis and prognosis. In the independent verification set measured by an in-house immunoassay, TGM4 protein was upregulated 3.7-fold (p = 0.006) and revealed AUC = 0.66 for detecting prostate cancer on biopsy for patients with serum PSA ≥4 ng/ml and age ≥50. Very low levels of TGM4 (120 pg/ml) were detected in blood serum. Collectively, our study demonstrated rigorous evaluation of one of the remaining and not well-explored prostate-specific proteins within the medium-abundance proteome of seminal plasma. Performance of TGM4 warrants its further investigation within the distinct genomic subtypes and evaluation for the inclusion into emerging multi-biomarker panels.

sim1000G: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs.

BACKGROUND: Simulation of genetic variants data is frequently required for the evaluation of statistical methods in the fields of human and animal genetics. Although a number of high-quality genetic simulators have been developed, many of them require advanced knowledge in population genetics or in computation to be used effectively. In addition, generating simulated data in the context of family-based studies demands sophisticated methods and advanced computer programming. RESULTS: To address these issues, we propose a new user-friendly and integrated R package, sim1000G, which simulates variants in genomic regions among unrelated individuals or among families. The only input needed is a raw phased Variant Call Format (VCF) file. Haplotypes are extracted to compute linkage disequilibrium (LD) in the simulated genomic regions and for the generation of new genotype data among unrelated individuals. The covariance across variants is used to preserve the LD structure of the original population. Pedigrees of arbitrary sizes are generated by modeling recombination events with sim1000G. To illustrate the application of sim1000G, various scenarios are presented assuming unrelated individuals from a single population or two distinct populations, or alternatively for three-generation pedigree data. Sim1000G can capture allele frequency diversity, short and long-range linkage disequilibrium (LD) patterns and subtle population differences in LD structure without the need of any tuning parameters. CONCLUSION: Sim1000G fills a gap in the vast area of genetic variants simulators by its simplicity and independence from external tools. Currently, it is one of the few simulation packages completely integrated into R and able to simulate multiple genetic variants among unrelated individuals and within families. Its implementation will facilitate the application and development of computational methods for association studies with both rare and common variants.

Stable Isotope Labeling with Amino Acids (SILAC)-Based Proteomics of Primary Human Kidney Cells Reveals a Novel Link between Male Sex Hormones and Impaired Energy Metabolism in Diabetic Kidney Disease.

Male sex predisposes to many kidney diseases. Considering that androgens exert deleterious effects in a variety of cell types within the kidney, we hypothesized that dihydrotestosterone (DHT) would alter the biology of the renal tubular cell by inducing changes in the proteome. We employed stable isotope labeling with amino acids (SILAC) in an indirect spike-in fashion to accurately quantify the proteome in DHT- and 17ß-estradiol (EST)-treated human proximal tubular epithelial cells (PTEC). Of the 5043 quantified proteins, 76 were differentially regulated. Biological processes related to energy metabolism were significantly enriched among DHT-regulated proteins. SILAC ratios of 3 candidates representing glycolysis, N-acetylglucosamine metabolism and fatty acid ß-oxidation, namely glucose-6-phosphate isomerase (GPI), glucosamine-6-phosphate-N-acetyltransferase 1 (GNPNAT1), and mitochondrial trifunctional protein subunit alpha (HADHA), were verified in vitro. In vivo, renal GPI and HADHA protein expression was significantly increased in males. Furthermore, male sex was associated with significantly higher GPI, GNPNAT1, and HADHA kidney protein expression in two different murine models of diabetes. Enrichment analysis revealed a link between our DHT-regulated proteins and oxidative stress within the diabetic kidney. This finding was validated in vivo, as we observed increased oxidative stress levels in control and diabetic male kidneys, compared with females. This in depth quantitative proteomics study of human primary PTEC response to sex hormone administration suggests that male sex hormone stimulation results in perturbed energy metabolism in kidney cells, and that this perturbation results in increased oxidative stress in the renal cortex. The proteome-level changes associated with androgens may play a crucial role in the development of structural and functional changes in the diseased kidney. With our findings, we propose a possible link between diabetic and non-diabetic kidney disease progression and male sex hormone levels. Data are available via ProteomeXchange (https://www.ebi.ac.uk/pride/archive/) with identifier PXD003811.

Resampling to Address the Winner's Curse in Genetic Association Analysis of Time to Event.

The "winner's curse" is a subtle and difficult problem in interpretation of genetic association, in which association estimates from large-scale gene detection studies are larger in magnitude than those from subsequent replication studies. This is practically important because use of a biased estimate from the original study will yield an underestimate of sample size requirements for replication, leaving the investigators with an underpowered study. Motivated by investigation of the genetics of type 1 diabetes complications in a longitudinal cohort of participants in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Genetics Study, we apply a bootstrap resampling method in analysis of time to nephropathy under a Cox proportional hazards model, examining 1,213 single-nucleotide polymorphisms (SNPs) in 201 candidate genes custom genotyped in 1,361 white probands. Among 15 top-ranked SNPs, bias reduction in log hazard ratio estimates ranges from 43.1% to 80.5%. In simulation studies based on the observed DCCT/EDIC genotype data, genome-wide bootstrap estimates for false-positive SNPs and for true-positive SNPs with low-to-moderate power are closer to the true values than uncorrected naïve estimates, but tend to overcorrect SNPs with high power. This bias-reduction technique is generally applicable for complex trait studies including quantitative, binary, and time-to-event traits.

Novel Biological Substrates of Human Kallikrein 7 Identified through Degradomics.

Kallikrein-related peptidases (KLKs) are a group of serine proteases widely expressed in various tissues and involved in a wide range of physiological and pathological processes. Although our understanding of the pathophysiological roles of most KLKs has blossomed in recent years, identification of the direct endogenous substrates of human KLKs remains an unmet objective. In this study we employed a degradomics approach to systemically investigate the endogenous substrates of KLK7 in an effort to understand the molecular pathways underlying KLK7 action in skin. We identified several previously known as well as novel protein substrates. Our most promising candidates were further validated with the use of targeted quantitative proteomics (selected reaction monitoring methods) and in vitro recombinant protein digestion assays. Our study revealed midkine, CYR61, and tenascin-C as endogenous substrates for KLK7. Interestingly, some of these substrates (e.g. midkine) were prone to proteolytic cleavage only by KLK7 (and not by other skin-associated KLKs), whereas others (e.g. CYR61 and tenascin-C) could be digested by several KLKs. Furthermore, using melanoma cell line, we show that KLK7-mediated cleavage of midkine results in an overall reduction in the pro-proliferative and pro-migratory effect of midkine. An inverse relation between KLK7 and midkine is also observed in human melanoma tissues. In summary, our degradomics approach revealed three novel endogenous substrates for KLK7, which may shed more light on the pathobiological roles of KLK7 in human skin. Similar substrate screening approaches could be applied for the discovery of biological substrates of other protease.

Quantification of angiotensin II-regulated proteins in urine of patients with polycystic and other chronic kidney diseases by selected reaction monitoring.

BACKGROUND: Angiotensin-II (Ang II) mediates progression of autosomal-dominant polycystic kidney disease (ADPKD) and other chronic kidney diseases (CKD). However, markers of kidney Ang II activity are lacking. We previously defined 83 Ang II-regulated proteins in vitro, which reflected kidney Ang II activity in vivo. METHODS: In this study, we developed selected reaction monitoring (SRM) assays for quantification of Ang II-regulated proteins in urine of ADPKD and CKD patients. We demonstrated that 47 of 83 Ang II-regulated transcripts were differentially expressed in cystic compared to normal kidney tissue. We then developed SRM assays for 18 Ang II-regulated proteins overexpressed in cysts and/or secreted in urine. Methods that yielded CV ≤ 6 % for control proteins, and recovery ~100 % were selected. Heavy-labeled peptides corresponding to 13 identified Ang II-regulated peptides were spiked into urine samples of 17 ADPKD patients, 9 patients with CKD predicted to have high kidney Ang II activity and 11 healthy subjects. Samples were then digested and analyzed on triple-quadrupole mass spectrometer in duplicates. RESLUTS: Calibration curves demonstrated linearity (R(2) > 0.99) and within-run CVs < 9 % in the concentration range of 7/13 peptides. Peptide concentrations were normalized by urine creatinine. Deamidated peptide forms were monitored, and accounted for <15 % of the final concentrations. Urine excretion rates of proteins BST1, LAMB2, LYPA1, RHOB and TSP1 were significantly different (p < 0.05, one-way ANOVA) between patients with CKD, those with ADPKD and healthy controls. Urine protein excretion rates were highest in CKD patients and lowest in ADPKD patients. Univariate analysis demonstrated significant association between urine protein excretion rates of most proteins and disease group (p < 0.05, ANOVA) as well as sex (p < 0.05, unpaired t test). Multivariate analysis across protein concentration, age and sex demonstrated good separation between ADPKD and CKD patients. CONCLUSIONS: We have optimized methods for quantification of Ang II-regulated proteins, and we demonstrated that they reflected differences in underlying kidney disease in this pilot study. High urine excretion of Ang II-regulated proteins in CKD patients likely reflects high kidney Ang II activity. Low excretion in ADPKD appears related to lack of communication between cysts and tubules. Future studies will determine whether urine excretion rate of Ang II-regulated proteins correlates with kidney Ang II activity in larger cohorts of chronic kidney disease patients.

The relationship between sperm viability and DNA fragmentation rates.

BACKGROUND: In humans, sperm DNA fragmentation rates have been correlated with sperm viability rates. Reduced sperm viability is associated with high sperm DNA fragmentation, while conversely high sperm viability is associated with low rates of sperm DNA fragmentation. Both elevated DNA fragmentation rates and poor viability are correlated with impaired male fertility, with a DNA fragmentation rate of >30% indicating subfertility. We postulated that in some men, the sperm viability assay could predict the sperm DNA fragmentation rates. This in turn could reduce the need for sperm DNA fragmentation assay testing, simplifying the infertility investigation and saving money for infertile couples. METHODS: All men having semen analyses with both viability and DNA fragmentation testing were identified via a prospectively collected database. Viability was measured by eosin-nigrosin assay. DNA fragmentation was measured using the sperm chromosome structure assay. The relationship between DNA fragmentation and viability was assessed using Pearson's correlation coefficient. RESULTS: From 2008-2013, 3049 semen analyses had both viability and DNA fragmentation testing. A strong inverse relationship was seen between sperm viability and DNA fragmentation rates, with r=-0.83. If viability was ≤50% (n=301) then DNA fragmentation was ≥ 30% for 95% of the samples. If viability was ≥75% (n=1736), then the DNA fragmentation was ≤30% for 95% of the patients. Sperm viability correlates strongly with DNA fragmentation rates. CONCLUSIONS: In men with high levels of sperm viability≥75%, or low levels of sperm viability≤ 30%, DFI testing may be not be routinely necessary. Given that DNA fragmentation testing is substantially more expensive than vitality testing, this may represent a valuable cost-saving measure for couples undergoing a fertility evaluation.

Laminin, gamma 2 (LAMC2): a promising new putative pancreatic cancer biomarker identified by proteomic analysis of pancreatic adenocarcinoma tissues.

In pancreatic cancer, the incidence and mortality curves coincide. One major reason for this high mortality rate in pancreatic ductal adenocarcinoma (PDAC) patients is the dearth of effective diagnostic, prognostic, and disease-monitoring biomarkers. Unfortunately, existing tumor markers, as well as current imaging modalities, are not sufficiently sensitive and/or specific for early-stage diagnosis. There is, therefore, an urgent need for improved serum markers of the disease. Herein, we performed Orbitrap® mass spectrometry proteomic analysis of four PDAC tissues and their adjacent benign tissues and identified a total of 2190 nonredundant proteins. Sixteen promising candidates were selected for further scrutiny using a systematic scoring algorithm. Our preliminary serum verification of the top four candidates (DSP, LAMC2, GP73, and DSG2) in 20 patients diagnosed with pancreatic cancer and 20 with benign pancreatic cysts, showed a significant (p < 0.05) elevation of LAMC2 in pancreatic cancer serum. Extensive validation of LAMC2 in healthy, benign, and PDAC sera from geographically diverse cohorts (n = 425) (Japan, Europe, and USA) demonstrated a significant increase in levels in early-stage PDAC compared with benign diseases. The sensitivity of LAMC2 was comparable to CA19.9 in all data sets, with an AUC value greater than 0.85 in discriminating healthy patients from early-stage PDAC patients. LAMC2 exhibited diagnostic complementarity with CA19.9 by showing significant (p < 0.001 in two out of three cohorts) elevation in PDAC patients with clinically low CA19.9 levels.

Integrating meta-analysis of microarray data and targeted proteomics for biomarker identification: application in breast cancer.

The development of signature biomarkers has gained considerable attention in the past decade. Although the most well-known examples of biomarker panels stem from gene expression studies, proteomic panels are becoming more relevant, with the advent of targeted mass spectrometry-based methodologies. At the same time, the development of multigene prognostic classifiers for early stage breast cancer patients has resulted in a wealth of publicly available gene expression data from thousands of breast cancer specimens. In the present study, we integrated transcriptome and proteome-based platforms to identify genes and proteins related to patient survival. Candidate biomarker proteins have been identified in a previously generated breast cancer tissue extract proteome. A mass-spectrometry-based assay was then developed for the simultaneous quantification of these 20 proteins in breast cancer tissue extracts. We quantified the relative expression levels of the 20 potential biomarkers in a cohort of 96 tissue samples from patients with early stage breast cancer. We identified two proteins, KPNA2 and CDK1, which showed potential to discriminate between estrogen receptor positive patients of high and low risk of disease recurrence. The role of these proteins in breast cancer prognosis warrants further investigation.

Determination of an angiotensin II-regulated proteome in primary human kidney cells by stable isotope labeling of amino acids in cell culture (SILAC).

Angiotensin II (AngII), the major effector of the renin-angiotensin system, mediates kidney disease progression by signaling through the AT-1 receptor (AT-1R), but there are no specific measures of renal AngII activity. Accordingly, we sought to define an AngII-regulated proteome in primary human proximal tubular cells (PTEC) to identify potential AngII activity markers in the kidney. We utilized stable isotope labeling with amino acids (SILAC) in PTECs to compare proteomes of AngII-treated and control cells. Of the 4618 quantified proteins, 83 were differentially regulated. SILAC ratios for 18 candidates were confirmed by a different mass spectrometry technique called selected reaction monitoring. Both SILAC and selected reaction monitoring revealed heme oxygenase-1 (HO-1) as the most significantly up-regulated protein in response to AngII stimulation. AngII-dependent regulation of the HO-1 gene and protein was further verified in PTECs. To extend these in vitro observations, we overlaid a network of significantly enriched gene ontology terms from our AngII-regulated proteins with a dataset of differentially expressed kidney genes from AngII-treated wild type mice and AT-1R knock-out mice. Five gene ontology terms were enriched in both datasets and included HO-1. Furthermore, HO-1 kidney expression and urinary excretion were reduced in AngII-treated mice with PTEC-specific AT-1R deletion compared with AngII-treated wild-type mice, thus confirming AT-1R-mediated regulation of HO-1. Our in vitro approach identified novel molecular markers of AngII activity, and the animal studies demonstrated that these markers are relevant in vivo. These interesting proteins hold promise as specific markers of renal AngII activity in patients and in experimental models.

Mining for single nucleotide variants (SNVs) at the kallikrein locus with predicted functional consequences.

Kallikreins (KLKs) are a group of 15 serine proteases encoded by the KLK locus on chromosome 19. Certain single nucleotide variants (SNVs) within the KLK locus have been linked to human disease. Next-generation sequencing of large human cohorts enables reexamination of genomic variation at the KLK locus. We aimed to identify all KLK-related SNVs and examine their impact on gene regulation and function. To this end, we mined KLK SNVs across Ensembl and Exome Variant Server, with exome-sequencing data from 6503 individuals. PolyPhen-2-based prediction of damaging SNVs and population frequencies of these SNVs were examined. Damaging SNVs were plotted on protein sequence and structure. We identified 4866 SNVs, the largest number of KLK-related SNVs reported. Fourteen percent of noncoding SNVs overlapped with transcription factor binding sites. We identified 602 missense coding SNVs, among which 148 were predicted to be damaging. Nine missense SNVs were common (>1% frequency) and displayed significantly different frequencies between European-American and African-American populations. SNVs predicted to be damaging appeared to alter tertiary structure of KLK1 and KLK6. Similarly, these missense SNVs may affect KLK function, resulting in disease phenotypes. Our study represents a mine of information for those studying KLK-related SNVs and their associations with diseases.

False biomarker discovery due to reactivity of a commercial ELISA for CUZD1 with cancer antigen CA125.

BACKGROUND: By using proteomics and bioinformatics, we have previously identified a group of highly pancreas-specific proteins as candidate pancreatic ductal adenocarcinoma (PDAC) biomarkers. With the use of commercially available ELISAs, the performance of some of these candidates was initially evaluated in a relatively small serum cohort (n = 100 samples). This phase revealed that CUB and zona pellucida-like domains protein 1 (CUZD1) may represent a new, promising PDAC biomarker. METHODS: We performed detailed experiments to investigate the specificity of the commercial CUZD1 ELISA assay. CUZD1 was expressed in house in both bacteria and yeast expression systems. Recombinant CUZD1 and biological samples containing CUZD1, as well as commercial CUZD1 ELISA standards, were analyzed by Western blot, size exclusion HPLC, and mass spectrometry (LC-MS Orbitrap). RESULTS: We confirmed that instead of CUZD1, the commercial assay is recognizing a nonhomologous, known cancer antigen [cancer antigen 125 (CA125)]. CONCLUSIONS: We conclude that poor characterization of commercial ELISA assays is a factor that could lead to false biomarker discovery. To our knowledge, this is the first report documenting that a commercial ELISA marketed for one analyte (CUZD1) may, in fact, recognize a different, nonhomologous antigen (CA125).

Quantitative analysis of energy metabolic pathways in MCF-7 breast cancer cells by selected reaction monitoring assay.

To investigate the quantitative response of energy metabolic pathways in human MCF-7 breast cancer cells to hypoxia, glucose deprivation, and estradiol stimulation, we developed a targeted proteomics assay for accurate quantification of protein expression in glycolysis/gluconeogenesis, TCA cycle, and pentose phosphate pathways. Cell growth conditions were selected to roughly mimic the exposure of cells in the cancer tissue to the intermittent hypoxia, glucose deprivation, and hormonal stimulation. Targeted proteomics assay allowed for reproducible quantification of 76 proteins in four different growth conditions after 24 and 48 h of perturbation. Differential expression of a number of control and metabolic pathway proteins in response to the change of growth conditions was found. Elevated expression of the majority of glycolytic enzymes was observed in hypoxia. Cancer cells, as opposed to near-normal MCF-10A cells, exhibited significantly increased expression of key energy metabolic pathway enzymes (FBP1, IDH2, and G6PD) that are known to redirect cellular metabolism and increase carbon flux through the pentose phosphate pathway. Our quantitative proteomic protocol is based on a mass spectrometry-compatible acid-labile detergent and is described in detail. Optimized parameters of a multiplex selected reaction monitoring (SRM) assay for 76 proteins, 134 proteotypic peptides, and 401 transitions are included and can be downloaded and used with any SRM-compatible mass spectrometer. The presented workflow is an integrated tool for hypothesis-driven studies of mammalian cells as well as functional studies of proteins, and can greatly complement experimental methods in systems biology, metabolic engineering, and metabolic transformation of cancer cells.

Coupling proteomics and transcriptomics in the quest of subtype-specific proteins in breast cancer.

Breast-cancer subtypes present with distinct clinical characteristics. Therefore, characterization of subtype-specific proteins may augment the development of targeted therapies and prognostic biomarkers. To address this issue, MS-based secretome analysis of eight breast cancer cell lines, corresponding to the three main breast cancer subtypes was performed. More than 5200 non-redundant proteins were identified with 23, four, and four proteins identified uniquely in basal, HER2-neu-amplified, and luminal breast cancer cells, respectively. An in silico mRNA analysis using publicly available breast cancer tissue microarray data was carried out as a preliminary verification step. In particular, the expression profiles of 15 out of 28 proteins included in the microarray (from a total of 31 in our subtype-specific signature) showed significant correlation with estrogen receptor (ER) expression. A MS-based analysis of breast cancer tissues was undertaken to verify the results at the proteome level. Eighteen out of 31 proteins were quantified in the proteomes of ER-positive and ER-negative breast cancer tissues. Survival analysis using microarray data was performed to examine the prognostic potential of these selected candidates. Three proteins correlated with ER status at both mRNA and protein levels: ABAT, PDZK1, and PTX3, with the former showing significant prognostic potential.

Quantitative proteomic analysis of amniocytes reveals potentially dysregulated molecular networks in Down syndrome.

BACKGROUND: Down syndrome (DS), caused by an extra copy of chromosome 21, affects 1 in 750 live births and is characterized by cognitive impairment and a constellation of congenital defects. Currently, little is known about the molecular pathogenesis and no direct genotype-phenotype relationship has yet been confirmed. Since DS amniocytes are expected to have a distinct biological behaviour compared to normal amniocytes, we hypothesize that relative quantification of proteins produced from trisomy and euploid (chromosomally normal) amniocytes will reveal dysregulated molecular pathways. RESULTS: Chromosomally normal- and Trisomy 21-amniocytes were quantitatively analyzed by using Stable Isotope Labeling of Amino acids in Cell culture and tandem mass spectrometry. A total of 4919 unique proteins were identified from the supernatant and cell lysate proteome. More specifically, 4548 unique proteins were identified from the lysate, and 91% of these proteins were quantified based on MS/MS spectra ratios of peptides containing isotope-labeled amino acids. A total of 904 proteins showed significant differential expression and were involved in 25 molecular pathways, each containing a minimum of 16 proteins. Sixty of these proteins consistently showed aberrant expression from trisomy 21 affected amniocytes, indicating their potential role in DS pathogenesis. Nine proteins were analyzed with a multiplex selected reaction monitoring assay in an independent set of Trisomy 21-amniocyte samples and two of them (SOD1 and NES) showed a consistent differential expression. CONCLUSIONS: The most extensive proteome of amniocytes and amniotic fluid has been generated and differentially expressed proteins from amniocytes with Trisomy 21 revealed molecular pathways that seem to be most significantly affected by the presence of an extra copy of chromosome 21.

Semiquantitative proteomic analysis of human hippocampal tissues from Alzheimer's disease and age-matched control brains.

BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia affecting people over 65 years of age. The hallmarks of AD are the extracellular deposits known as amyloid ß plaques and the intracellular neurofibrillary tangles, both of which are the principal players involved in synaptic loss and neuronal cell death. Tau protein and Aß fragment 1-42 have been investigated so far in cerebrospinal fluid as a potential AD biomarkers. However, an urgent need to identify novel biomarkers which will capture disease in the early stages and with better specificity remains. High-throughput proteomic and pathway analysis of hippocampal tissue provides a valuable source of disease-related proteins and biomarker candidates, since it represents one of the earliest affected brain regions in AD. RESULTS: In this study 2954 proteins were identified (with at least 2 peptides for 1203 proteins) from both control and AD brain tissues. Overall, 204 proteins were exclusively detected in AD and 600 proteins in control samples. Comparing AD and control exclusive proteins with cerebrospinal fluid (CSF) literature-based proteome, 40 out of 204 AD related proteins and 106 out of 600 control related proteins were also present in CSF. As most of these proteins were extracellular/secretory origin, we consider them as a potential source of candidate biomarkers that need to be further studied and verified in CSF samples. CONCLUSIONS: Our semiquantitative proteomic analysis provides one of the largest human hippocampal proteome databases. The lists of AD and control related proteins represent a panel of proteins potentially involved in AD pathogenesis and could also serve as prospective AD diagnostic biomarkers.

Validation of four candidate pancreatic cancer serological biomarkers that improve the performance of CA19.9.

BACKGROUND: The identification of new serum biomarkers with high sensitivity and specificity is an important priority in pancreatic cancer research. Through an extensive proteomics analysis of pancreatic cancer cell lines and pancreatic juice, we previously generated a list of candidate pancreatic cancer biomarkers. The present study details further validation of four of our previously identified candidates: regenerating islet-derived 1 beta (REG1B), syncollin (SYCN), anterior gradient homolog 2 protein (AGR2), and lysyl oxidase-like 2 (LOXL2). METHODS: The candidate biomarkers were validated using enzyme-linked immunosorbent assays in two sample sets of serum/plasma comprising a total of 432 samples (Sample Set A: pancreatic ductal adenocarcinoma (PDAC, n = 100), healthy (n = 92); Sample Set B: PDAC (n = 82), benign (n = 41), disease-free (n = 47), other cancers (n = 70)). Biomarker performance in distinguishing PDAC from each control group was assessed individually in the two sample sets. Subsequently, multiparametric modeling was applied to assess the ability of all possible two and three marker panels in distinguishing PDAC from disease-free controls. The models were generated using sample set B, and then validated in Sample Set A. RESULTS: Individually, all markers were significantly elevated in PDAC compared to healthy controls in at least one sample set (p ≤ 0.01). SYCN, REG1B and AGR2 were also significantly elevated in PDAC compared to benign controls (p ≤ 0.01), and AGR2 was significantly elevated in PDAC compared to other cancers (p < 0.01). CA19.9 was also assessed. Individually, CA19.9 showed the greatest area under the curve (AUC) in receiver operating characteristic (ROC) analysis when compared to the tested candidates; however when analyzed in combination, three panels (CA19.9 + REG1B (AUC of 0.88), CA19.9 + SYCN + REG1B (AUC of 0.87) and CA19.9 + AGR2 + REG1B (AUC of 0.87)) showed an AUC that was significantly greater (p < 0.05) than that of CA19.9 alone (AUC of 0.82). In a comparison of early-stage (Stage I-II) PDAC to disease free controls, the combination of SYCN + REG1B + CA19.9 showed the greatest AUC in both sample sets, (AUC of 0.87 and 0.92 in Sets A and B, respectively). CONCLUSIONS: Additional serum biomarkers, particularly SYCN and REG1B, when combined with CA19.9, show promise as improved diagnostic indicators of pancreatic cancer, which therefore warrants further validation.

Glycoproteomic identification of potential glycoprotein biomarkers in ovarian cancer proximal fluids.

BACKGROUND: Ovarian cancer is the leading cause of death among all gynecological disorders. Aberrant glycosylation, or more specifically, increased sialylation of proteins has been observed in ovarian cancer. Several sialyltransferase genes have been shown to be up-regulated at both the mRNA and protein levels in a number of cancers, including that of the ovary. ST6GAL1 (ß-galactosamide α2,6-sialyltranferase 1) gene expression has previously been shown to be upregulated in ovarian cancers of all major subtypes. METHODS: We have identified the sialome (i.e., sialic acid containing glycoproteins) of biological fluids from ovarian cancer patients and ovarian cancer cell lines utilizing tandem mass spectrometry as a potential pool of novel biomarker candidates. The sialoglycopeptides from four ovarian cancer cell lines, pooled ascites (n=13) and ovarian cyst (n=14) fluids from ovarian cancer patients were enriched utilizing affinity to agarose-immobilized Elderberry lectin (Sambucus nigra agglutinin) and magnetic hydrazide beads folowing periodate-mediated oxidation of sialic acids. Benign ovarian cyst (n=10) and peritoneal effusion (n=20) fluids were analyzed in the same fashion to serve as controls. PNGase F deglycosylated peptides were identified using electrospray ionization-LTQ Orbitrap tandem mass spectrometry. RESULTS: In all of the samples analyzed in the glycoproteomic portion of the study, we have identified 579 glycosylation sites on 333 proteins. Of these, 13 were exclusively identified in biological fluids from ovarian cancer patients, and another eight were common to these fluids and the ovarian cancer cell line supernatants. CONCLUSIONS: The proteins identified in the present study could form the basis for future studies examining and quantifying their sialylation status as biomarkers of ovarian cancer.

A genome-wide linkage study of mammographic density, a risk factor for breast cancer.

INTRODUCTION: Mammographic breast density is a highly heritable (h2 > 0.6) and strong risk factor for breast cancer. We conducted a genome-wide linkage study to identify loci influencing mammographic breast density (MD). METHODS: Epidemiological data were assembled on 1,415 families from the Australia, Northern California and Ontario sites of the Breast Cancer Family Registry, and additional families recruited in Australia and Ontario. Families consisted of sister pairs with age-matched mammograms and data on factors known to influence MD. Single nucleotide polymorphism (SNP) genotyping was performed on 3,952 individuals using the Illumina Infinium 6K linkage panel. RESULTS: Using a variance components method, genome-wide linkage analysis was performed using quantitative traits obtained by adjusting MD measurements for known covariates. Our primary trait was formed by fitting a linear model to the square root of the percentage of the breast area that was dense (PMD), adjusting for age at mammogram, number of live births, menopausal status, weight, height, weight squared, and menopausal hormone therapy. The maximum logarithm of odds (LOD) score from the genome-wide scan was on chromosome 7p14.1-p13 (LOD = 2.69; 63.5 cM) for covariate-adjusted PMD, with a 1-LOD interval spanning 8.6 cM. A similar signal was seen for the covariate adjusted area of the breast that was dense (DA) phenotype. Simulations showed that the complete sample had adequate power to detect LOD scores of 3 or 3.5 for a locus accounting for 20% of phenotypic variance. A modest peak initially seen on chromosome 7q32.3-q34 increased in strength when only the 513 families with at least two sisters below 50 years of age were included in the analysis (LOD 3.2; 140.7 cM, 1-LOD interval spanning 9.6 cM). In a subgroup analysis, we also found a LOD score of 3.3 for DA phenotype on chromosome 12.11.22-q13.11 (60.8 cM, 1-LOD interval spanning 9.3 cM), overlapping a region identified in a previous study. CONCLUSIONS: The suggestive peaks and the larger linkage signal seen in the subset of pedigrees with younger participants highlight regions of interest for further study to identify genes that determine MD, with the goal of understanding mammographic density and its involvement in susceptibility to breast cancer.