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.

Identifying Cryptic Relationships.

Cryptic relationships such as first-degree relatives often appear in studies that collect population samples, including genome-wide association studies (GWAS) and next-generation sequencing (NGS) analyses. Cryptic relatedness not only increases type 1 error rate of association tests but also affects other analytical aspects of GWAS and NGS such as population stratification via principal component analysis. Here, we discuss three effective methods, as implemented in PREST, PLINK, and KING, to detect and correct for the problem of cryptic relatedness using high-throughput SNP data collected from GWAS and NGS experiments. We provide the analytical and practical details involved using three application examples.

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.

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.

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.

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.

PREST-plus identifies pedigree errors and cryptic relatedness in the GAW18 sample using genome-wide SNP data.

Pedigree errors and cryptic relatedness often appear in families or population samples collected for genetic studies. If not identified, these issues can lead to either increased false negatives or false positives in both linkage and association analyses. To identify pedigree errors and cryptic relatedness among individuals from the 20 San Antonio Family Studies (SAFS) families and cryptic relatedness among the 157 putatively unrelated individuals, we apply PREST-plus to the genome-wide single-nucleotide polymorphism (SNP) data and analyze estimated identity-by-descent (IBD) distributions for all pairs of genotyped individuals. Based on the given pedigrees alone, PREST-plus identifies the following putative pairs: 1091 full-sib, 162 half-sib, 360 grandparent-grandchild, 2269 avuncular, 2717 first cousin, 402 half-avuncular, 559 half-first cousin, 2 half-sib+first cousin, 957 parent-offspring and 440,546 unrelated. Using the genotype data, PREST-plus detects 7 mis-specified relative pairs, with their IBD estimates clearly deviating from the null expectations, and it identifies 4 cryptic related pairs involving 7 individuals from 6 families.

Validation of biomarkers that complement CA19.9 in detecting early pancreatic cancer.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is a significant cause of cancer mortality. Carbohydrate antigen 19.9 (CA19.9), the only tumor marker available to detect and monitor PDAC, is not sufficiently sensitive and specific to consistently differentiate early cancer from benign disease. In this study, we aimed to validate recently discovered serum protein biomarkers for the early detection of PDAC and ultimately develop a biomarker panel that could discriminate PDAC from other benign disease better than the existing marker CA19.9. PATIENTS AND METHODS: We performed a retrospective blinded evaluation of 400 serum samples collected from individuals recruited on a consecutive basis. The sample population consisted of 250 individuals with PDAC at various stages, 130 individuals with benign conditions and 20 healthy individuals. The serum levels of each biomarker were determined by ELISAs or automated immunoassay. RESULTS: By randomly splitting matched samples into a training (n = 186) and validation (n = 214) set, we were able to develop and validate a biomarker panel consisting of CA19.9, CA125, and LAMC2 that significantly improved the performance of CA19.9 alone. Improved discrimination was observed in the validation set between all PDAC and benign conditions (AUCCA19.9 = 0.80 vs. AUCCA19.9+CA125+LAMC2 = 0.87; P < 0.005) as well as between early-stage PDAC and benign conditions (AUCCA19.9 = 0.69 vs. AUCCA19.9+CA125+LAMC2 = 0.76; P < 0.05) and between early-stage PDAC and chronic pancreatitis (CP; AUCCA19.9 = 0.59 vs. AUCCA19.9+CA125+LAMC2 = 0.74; P < 0.05). CONCLUSIONS: The data demonstrate that a serum protein biomarker panel consisting of CA125, CA19.9, and LAMC2 is able to significantly improve upon the performance of CA19.9 alone in detecting PDAC.

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.

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.

In-depth proteomic delineation of the colorectal cancer exoproteome: Mechanistic insight and identification of potential biomarkers.

Systemic mining of cancer exoproteome/secretome has emerged as a pivotal strategy for delineation of molecular pathways with mechanistic importance in cancer development, as well as the discovery of diagnostic/prognostic biomarkers. Although major advances in diagnostic and therapeutic management of colorectal cancer have been underscored in the last decade, this cancer still remains the second leading cause of cancer-related deaths in the developed world. Despite previous studies on deciphering the colorectal cancer exoproteome, such studies lack adequate depth and robustness due to technological limitations. Here, using a well-established LC-MS/MS method on an LTQ-Orbitrap mass spectrometer, we extensively delineated the exoproteome of 12 colon cancer cell lines. In total, 2979 non-redundant proteins were identified with a minimum of two peptides, of which ~62% were extracellular or cell membrane-bound, based on prediction software. To further characterize this dataset and identify clinical opportunities, first, we investigated overrepresented molecular concepts of interest via enrichment map analysis and second, we demonstrated translational importance of certain proteins, such as olfactomedin-4 and kallikrein-related peptidases-6 and -10, by investigating their expression levels in patient tissues and/or fluids. Overall, the present study details a comprehensive colorectal cancer exoproteome dataset, and may be used as future platform for biomarker discovery, and hypothesis-generating studies. BIOLOGICAL SIGNIFICANCE: This article represents one of the most extensive and comprehensive proteomic datasets regarding the secreted/extracellular proteome of colorectal cancer cell lines. The reported datasets may form a platform for a plethora of future, discovery-based or hypothesis-generating studies, attempting to either delineate putative cancer biomarkers for CRC, or elucidate questions of mechanistic importance (e.g. investigation of deregulated pathways for CRC progression).

Phenotypic differences in mosaic Klinefelter patients as compared with non-mosaic Klinefelter patients.

OBJECTIVE: To determine whether men with Klinefelter syndrome (KS) have the same phenotype as men with mosaic KS. DESIGN: Subject identification via prospectively collected database. SETTING: Male infertility specialty clinic. PATIENT(S): Men undergoing a fertility evaluation from 2005 to 2012 at a single male infertility specialty clinic and having a karyotype demonstrating KS (mosaic or non-mosaic). INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Testicular size, and semen and hormone parameters, genetic evaluation, and signs of testosterone (T) deficiency using validated questionnaires. RESULT(S): Of 86 men identified with KS, 6 (6.7%) were mosaic KS, and 80 (93.3%) were non-mosaic KS. Men with mosaic KS had lower baseline luteinizing hormone (LH) levels (10.31 IU/L ± 5.52 vs. 19.89 IU/L ± 6.93), lower estradiol levels (58.71 ± 31.10 pmol/L vs. 108.57 ± 43.45 pmol/L), larger mean testicular volumes (11 ± 7.3 mL vs. 6.35 ± 3.69 mL), and a higher mean total sperm count (4.43 ± 9.86 M/mL vs. 0.18 ± 1.17 M/mL). A higher proportion of men with mosaic KS had sperm in the ejaculate: 3 (50%) of 6 versus 3 (3.75%) of 80. The Sexual Health Inventory for Men (SHIM) and Androgen Deficiency in the Aging Male (ADAM) questionnaire scores were not different between groups. CONCLUSION(S): Men with mosaic KS seem to be more well androgenized than their non-mosaic KS counterparts, both with respect to hormones and sperm in the ejaculate.

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).

Differential diagnosis of azoospermia with proteomic biomarkers ECM1 and TEX101 quantified in seminal plasma.

Male fertility problems range from diminished production of sperm, or oligozoospermia, to nonmeasurable levels of sperm in semen, or azoospermia, which is diagnosed in nearly 2% of men in the general population. Testicular biopsy is the only definitive diagnostic method to distinguish between obstructive (OA) and nonobstructive (NOA) azoospermia and to identify the NOA subtypes of hypospermatogenesis, maturation arrest and Sertoli cell-only syndrome. We measured by selected reaction monitoring assay 18 biomarker candidates in 119 seminal plasma samples from men with normal spermatogenesis and azoospermia, and identified two proteins, epididymis-expressed ECM1 and testis-expressed TEX101, which differentiated OA and NOA with high specificities and sensitivities. The performance of ECM1 was confirmed by enzyme-linked immunosorbent assay. On the basis of a cutoff level of 2.3 µg/ml derived from the current data, we could distinguish OA from normal spermatogenesis with 100% specificity, and OA from NOA with 73% specificity, at 100% sensitivity. Immunohistochemistry and an immunoenrichment mass spectrometry-based assay revealed the differential expression of TEX101 in distinct NOA subtypes. TEX101 semen concentrations differentiated Sertoli cell-only syndrome from the other categories of NOA. As a result, we propose a simple two-biomarker decision tree for the differential diagnosis of OA and NOA and, in addition, for the differentiation of NOA subtypes. Clinical assays for ECM1 and TEX101 have the potential to replace most of the diagnostic testicular biopsies and facilitate the prediction of outcome of sperm retrieval procedures, thus increasing the reliability and success of assisted reproduction techniques.

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.

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.

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.