Genome-wide detection of allelic gene expression in hepatocellular carcinoma cells using a human exome SNP chip

Allelic variations in gene expression influence many biological responses and cause phenotypic variations in humans. In this study, Illumina Human Exome BeadChips containing more than 240,000 single nucleotide polymorphisms (SNPs) were used to identify changes in allelic gene expression in hepatocellular carcinoma cells following lipopolysaccharide (LPS) stimulation. We found 17 monoallelically expressed genes, 58 allelic imbalanced genes, and 7 genes showing allele substitution. In addition, we also detected 33 differentially expressed genes following LPS treatment in vitro using these human exome SNP chips. However, alterations in allelic gene expression following LPS treatment were detected in only three genes (MLXIPL, TNC, and MX2), which were observed in one cell line sample only, indicating that changes in allelic gene expression following LPS stimulation of liver cells are rare events. Among a total of 75 genes showing allelic expression in hepatocellular carcinoma cells, either monoallelic or imbalanced, 43 genes (57.33%) had expression quantitative trait loci (eQTL) data, indicating that high-density exome SNP chips are useful and reliable for studying allelic gene expression. Furthermore, most genes showing allelic expression were regulated by cis-acting mechanisms and were also significantly associated with several human diseases. Overall, our study provides a better understanding of allele-specific gene expression in hepatocellular carcinoma cells with and without LPS stimulation and potential clues for the cause of human disease due to alterations in allelic gene expression.     

Direct inference of SNP heterozygosity rates and resolution of LOH detection

Single nucleotide polymorphisms (SNPs) have been increasingly utilized to investigate somatic genetic abnormalities in premalignancy and cancer. LOH is a common alteration observed during cancer development, and SNP assays have been used to identify LOH at specific chromosomal regions. The design of such studies requires consideration of the resolution for detecting LOH throughout the genome and identification of the number and location of SNPs required to detect genetic alterations in specific genomic regions. Our study evaluated SNP distribution patterns and used probability models, Monte Carlo simulation, and real human subject genotype data to investigate the relationships between the number of SNPs, SNP HET rates, and the sensitivity (resolution) for detecting LOH. We report that variances of SNP heterozygosity rate in dbSNP are high for a large proportion of SNPs. Two statistical methods proposed for directly inferring SNP heterozygosity rates require much smaller sample sizes (intermediate sizes) and are feasible for practical use in SNP selection or verification. Using HapMap data, we showed that a region of LOH greater than 200 kb can be reliably detected, with losses smaller than 50 kb having a substantially lower detection probability when using all SNPs currently in the HapMap database. Higher densities of SNPs may exist in certain local chromosomal regions that provide some opportunities for reliably detecting LOH of segment sizes smaller than 50 kb. These results suggest that the interpretation of the results from genome-wide scans for LOH using commercial arrays need to consider the relationships among inter-SNP distance, detection probability, and sample size for a specific study. New experimental designs for LOH studies would also benefit from considering the power of detection and sample sizes required to accomplish the proposed aims.     

Genome-wide two-locus epistasis scans in prostate cancer using two European populations

Approximately 40 single nucleotide polymorphisms (SNPs) that are associated with prostate cancer (PCa) risk have been identified through genome-wide association studies (GWAS). However, these GWAS-identified PCa risk-associated SNPs can explain only a small proportion of heritability (~13%) of PCa risk. Gene-gene interaction is speculated to be one of the major factors contributing to the so-called missing heritability. To evaluate the gene-gene interaction and PCa risk, we performed a two-stage genome-wide gene-gene interaction scan using a novel statistical approach named "Boolean Operation-based Screening and Testing". In the first stage, we exhaustively evaluated all pairs of SNP-SNP interactions for ~500,000 SNPs in 1,176 PCa cases and 1,101 control subjects from the National Cancer Institute Cancer Genetic Markers of Susceptibility (CGEMS) study. No SNP-SNP interaction reached a genome-wide significant level of 4.4E-13. The second stage of the study involved evaluation of the top 1,325 pairs of SNP-SNP interactions (P(interaction) <1.0E-08) implicated in CGEMS in another GWAS population of 1,964 PCa cases from the Johns Hopkins Hospital (JHH) and 3,172 control subjects from the Illumina iControl database. Sixteen pairs of SNP-SNP interactions were significant in the JHH population at a P(interaction) cutoff of 0.01. However, none of the 16 pairs of SNP-SNP interactions were significant after adjusting for multiple tests. The current study represents one of the first attempts to explore the high-dimensional etiology of PCa on a genome-wide scale. Our results suggested a list of SNP-SNP interactions that can be followed in other replication studies.     

SNP microarray analysis for genome-wide detection of crossover regions

There is a great deal of interest in understanding the non-random distribution of recombination events over the human genome, because it has important implications for using linkage disequilibrium (LD) to identify human disease genes. So far, only a few recombination hotspots in the human genome have been characterised and the identification of new crossover hotspots will contribute to a better understanding of the mechanisms that govern their formation and distribution. This study shows that high-density single nucleotide polymorphism (SNP) arrays, together with the presented analysis method, are an appropriate tool for generating a whole-genome recombination pattern and for detecting new crossover regions with enhanced recombination frequency. Based on the genotype data of 16 members of a Caucasian three-generation family, we identified 825 crossover regions. The average recombination frequency of females and males was 0.77 and 0.56 cM/Mb, respectively. We detected 24 crossover regions showing elevated recombination activity, which comprised known hotspots, like the MHC II region, confirming the non-random distribution of recombination events along the genome. Interestingly, 29.2% of the identified crossover hotspot regions overlapped with regions flanked by segmental duplications published by Bailey et al. (Science 297:1003–1007, 2002) suggesting that segmental duplications and crossover hotspot regions are mechanistically linked. By extrapolating the results of the present study, we conclude that it might be feasible, at least in part, to estimate to what extent the block-like pattern of LD exactly relies on the genome-wide crossover pattern using the next generation high-density SNP microarrays.Electronic Supplementary Material Supplementary material is available for this article at     

一种新的基于单碱基延伸的SNP芯片技术

单核苷酸多态性(Single nucleotide polymorphism, SNP)是人类基因组中最常见的一种变异, 与疾病易感性、药物代谢等有着密切的关系。已经建立了多种SNP检测技术并得到了应用。单碱基延伸(Single base extension, SBE)是常用的SNP分型技术之一。文章建立了SBE结合Zip-code芯片技术对SNP进行分型, 为个体化用药及临床诊断芯片的研究与开发提供技术和方法。     

Glycopeptide microarray for autoantibody detection in cancer

Evaluation of: Pedersen JW, Blixt O, Bennett EP et al. Seromic profiling of colorectal cancer patients with novel glycopeptide microarray. Int. J. Cancer 128(8), 1860–1871 (2011).

Autoantibodies to cancer-associated antigens hold promise as sensitive biomarkers for cancer detection. Based on this hypothesis, and knowing that O-glycans on proteins constitute a source of possible epitopes recognized by autoantibodies, Pedersen and colleagues have generated a glycopeptide array displaying a comprehensive library of glycopeptides and glycoproteins derived from human mucins. The profiling of sera immunoreactivity of colon cancer patients allowed the identification of cancer-associated autoantibodies to various mucin (MUC)1 and MUC4 glycopeptides carrying aberrant glycosylation. This article provides evidence for the value of glycopeptides displaying cancer-associated glycans in diagnostic applications, and opens new avenues for the expansion to other protein glycoforms, as well as to further applications of such a microarray strategy for other post-translational modifications of proteins in the search for cancer biomarker.     

Glycopeptide microarray for autoantibody detection in cancer

Evaluation of: Pedersen JW, Blixt O, Bennett EP et al. Seromic profiling of colorectal cancer patients with novel glycopeptide microarray. Int. J. Cancer 128(8), 1860-1871 (2011). Autoantibodies to cancer-associated antigens hold promise as sensitive biomarkers for cancer detection. Based on this hypothesis, and knowing that O-glycans on proteins constitute a source of possible epitopes recognized by autoantibodies, Pedersen and colleagues have generated a glycopeptide array displaying a comprehensive library of glycopeptides and glycoproteins derived from human mucins. The profiling of sera immunoreactivity of colon cancer patients allowed the identification of cancer-associated autoantibodies to various mucin (MUC)1 and MUC4 glycopeptides carrying aberrant glycosylation. This article provides evidence for the value of glycopeptides displaying cancer-associated glycans in diagnostic applications, and opens new avenues for the expansion to other protein glycoforms, as well as to further applications of such a microarray strategy for other post-translational modifications of proteins in the search for cancer biomarker.     

Segmentation-based detection of allelic imbalance and loss-of-heterozygosity in cancer cells using whole genome SNP arrays

We present a strategy for detection of loss-of-heterozygosity and allelic imbalance in cancer cells from whole genome single nucleotide polymorphism genotyping data. Using a dilution series of a tumor cell line mixed with its paired normal cell line and data generated on Affymetrix and Illumina platforms, including paired tumor-normal samples and tumors characterized by fluorescent in situ hybridization, we demonstrate a high sensitivity and specificity of the strategy for detecting both minute and gross allelic imbalances in heterogeneous tumor samples.     

High-throughput SNP detection using nano-scale engineered biomagnetite

A semi-automated system for the large-scale detection of single nucleotide polymorphisms (SNPs) has been developed based on allele-specific oligonucleotide hybridization and thermal dissociation curve analysis using nano-scale engineered biomagnetite (bacterial magnetic particles; BacMPs). For reliable detection in large numbers of samples, several conditions for the capture of target DNA on nano-sized BacMPs and the denaturation of double-stranded DNA were optimized. The most efficient target DNA capture was observed using short PCR amplicons (69 bp). Captured DNAs were denatured using 50 mM NaOH. With these optimizations, large-scale SNP detection was performed on 822 samples of the transforming growth factor (TGF)-beta1 gene, which is rich in both GC content and repetitive sequences. High reliability for the semi-automated BacMP-based SNP detection system was confirmed following comparison to traditional sequencing-based methods.     

Immunohistological detection of BRAF25 in human prostate tumor and cancer specimens

BRAF25 is an alternatively spliced protein of BRAF35 (see associated paper). We have mapped the BRAF25 gene to chromosome sub-band 19p13.3, a region where loss of chromosomal heterozygosity has been reported in about 50% of ovarian cancers. Because of the high incidence of genetic links of prostate cancer to breast and ovarian cancers, we investigated the BRAF25 expression in the prostate specimens. Immunohistochemical analysis using antibodies specific for BRAF25 revealed a strong immunostaining in sections of the benign prostatic hyperplasia (BPH). The staining was concentrated on the nuclei of cells facing the lumen of prostatic glands, even though the sporadic nuclei of cells in stromas were also stained. However, the expression of BRAF25 was dramatically reduced in intermediate prostate cancer and absent in advanced prostate cancer. Preincubation of the antibody with the immunizing peptide abolished immunostaining in BPH specimens. Therefore, the expression of BRAF25 was gradually lost in prostate cancer.     

Genome-wide SNP discovery in mungbean by Illumina HiSeq

Mungbean [Vigna radiata (L.) Wilczek], a self-pollinated diploid plant with 2n = 22 chromosomes, is an important legume crop with a high-quality amino acid profile. Sequence variation at the whole-genome level was examined by comparing two mungbean cultivars, Sunhwanokdu and Gyeonggijaerae 5, using Illumina HiSeq sequencing data. More than 40 billion bp from both mungbean cultivars were sequenced to a depth of 72×. After de novo assembly of Sunhwanokdu contigs by ABySS 1.3.2 (N50 = 9,958 bp), those longer than 10 kb were aligned with Gyeonggijaerae 5 reads using the Burrows–Wheeler Aligner. SAMTools was used for retrieving single nucleotide polymorphisms (SNPs) between Sunhwanokdu and Gyeonggijaerae 5, defining the lowest and highest depths as 5 and 100, respectively, and the sequence quality as 100. Of the 305,504 single-base changes identified, 40,503 SNPs were considered heterozygous in Gyeonggijaerae 5. Among the remaining 265,001 SNPs, 65.9 % (174,579 cases) were transitions and 34.1 % (90,422 cases) were transversions. For SNP validation, a total of 42 SNPs were chosen among Sunhwanokdu contigs longer than 10 kb and sharing at least 80 % sequence identity with common bean expressed sequence tags as determined with est2genome. Using seven mungbean cultivars from various origins in addition to Sunhwanokdu and Gyeonggijaerae 5, most of the SNPs identified by bioinformatics tools were confirmed by Sanger sequencing. These genome-wide SNP markers could enrich the current molecular resources and might be of value for the construction of a mungbean genetic map and the investigation of genetic diversity.     

Optimization of prostate cancer cell detection using multiplex tyramide signal amplification

Approximately 29 000 men die of prostate cancer (PCa) each year in the United States, and 90% to 100% of them are due to incurable bone metastasis. It is difficult to determine (1) when PCa disseminates in the natural history of the disease; (2) where cancer cell disseminates before becoming overt metastatic lesions; and (3) which tumors are aggressive and which are indolent. Tumor tissue and liquid (blood and bone marrow) biopsies provide important information to answer these questions, but significant limitations exist for immunostaining strategies that assess protein expression in these tissues. Classic immunohistochemistry (IHC) assays can typically assess expression of one or two proteins per tissue section. We have developed a novel immunofluorescence staining protocol to detect a panel of seven proteins on PCa tissue from primary tumor biopsies and metastatic lesion autopsy tissue, as well as cancer cells from liquid biopsies. We used a tyramide-based system to amplify the true signal and optimized the protocol to reduce background signal, thereby boosting the signal-to-noise ratio. Any protein-specific antibody in this protocol can be exchanged for a different validated antibody. This protocol therefore, represents a highly informative and flexible assay that can be used to provide important information about cancer tissue for the purpose of improving detection, diagnosis, and treatment.     

Antibody microarray profiling of human prostate cancer sera: antibody screening and identification of potential biomarkers

We developed a practical strategy for serum protein profiling using antibody microarrays and applied the method to the identification of potential biomarkers in prostate cancer serum. Protein abundances from 33 prostate cancer and 20 control serum samples were compared to abundances from a common reference pool using a two-color fluorescence assay. Robotically spotted microarrays containing 184 unique antibodies were prepared on two different substrates: polyacrylamide based hydrogels on glass and poly-1-lysine coated glass with a photoreactive cross-linking layer. The hydrogel substrate yielded an average six-fold higher signal-to-noise ratio than the other substrate, and detection of protein binding was possible from a greater number of antibodies using the hydrogels. A statistical filter based on the correlation of data from "reverse-labeled" experiment sets accurately predicted the agreement between the microarray measurements and enzyme-linked immunosorbent assay measurements, showing that this parameter can serve to screen for antibodies that are functional on microarrays. Having defined a set of reliable microarray measurements, we identified five proteins (von Willebrand Factor, immunoglobulinM, Alpha1-antichymotrypsin, Villin and immunoglobulinG) that had significantly different levels between the prostate cancer samples and the controls. These developments enable the immediate use of high-density antibody and protein microarrays in biomarker discovery studies.     

Differential coexpression analysis using microarray data and its application to human cancer

MOTIVATION: Microarrays have been used to identify differential expression of individual genes or cluster genes that are coexpressed over various conditions. However, alteration in coexpression relationships has not been studied. Here we introduce a model for finding differential coexpression from microarrays and test its biological validity with respect to cancer. RESULTS: We collected 10 published gene expression datasets from cancers of 13 different tissues and constructed 2 distinct coexpression networks: a tumor network and normal network. Comparison of the two networks showed that cancer affected many coexpression relationships. Functional changes such as alteration in energy metabolism, promotion of cell growth and enhanced immune activity were accompanied with coexpression changes. Coregulation of collagen genes that may control invasion and metastatic spread of tumor cells was also found. Cluster analysis in the tumor network identified groups of highly interconnected genes related to ribosomal protein synthesis, the cell cycle and antigen presentation. Metallothionein expression was also found to be clustered, which may play a role in apoptosis control in tumor cells. Our results show that this model would serve as a novel method for analyzing microarrays beyond the specific implications for cancer.