BMC Medical Genomics - Latest Articles

Saliva samples are a viable alternative to blood samples as a source of DNA for high throughput genotyping

Jean Abraham — 2012-05-30T00:00:00Z

Background: The increasing trend for incorporation of biological sample collection within clinical trials requires sample collection procedures which are convenient and acceptable for both patients and clinicians. This study investigated the feasibility of using saliva-extracted DNA in comparison to blood-derived DNA, across two genotyping platforms: Applied Biosystems Taqman TM and Illumina Beadchip TM genome-wide arrays.MethodPatients were recruited from the Pharmacogenetics of Breast Cancer Chemotherapy (PGSNPS) study. Paired blood and saliva samples were collected from 79 study participants. The Oragene DNA Self-Collection kit (DNAgenotek(R)) was used to collect and extract DNA from saliva. DNA from EDTA blood samples (median volume 8 ml) was extracted by GenProbe, Livingstone, UK. DNA yields, standard measures of DNA quality, genotype call rates and genotype concordance between paired, duplicated samples were assessed. Results: Total DNA yields were lower from saliva (mean 24 ug, range 0.2-52 ug) than from blood (mean 210 ug, range 58-577 ug) and a 2-fold difference remained after adjusting for the volume of biological material collected. Protein contamination and DNA fragmentation measures were greater in saliva DNA. 78/79 saliva samples yielded sufficient DNA for use on Illumina Beadchip arrays and using Taqman assays. Four samples were randomly selected for genotyping in duplicate on the Illumina Beadchip arrays. All samples were genotyped using Taqman assays. DNA quality, as assessed by genotype call rates and genotype concordance between matched pairs of DNA was high (>97%) for each measure in both blood and saliva-derived DNA. Conclusion: We conclude that DNA from saliva and blood samples is comparable when genotyping using either Taqman assays or genome-wide chip arrays. Saliva sampling has the potential to increase participant recruitment within clinical trials, as well as reducing the resources and organisation required for multicentre sample collection.

MicroRNA profiling of a CD133+ spheroid-forming subpopulation of the OVCAR3 human ovarian cancer cell line

Eun Ji Nam — 2012-05-29T00:00:00Z

Background: Cancer stem cells (CSCs) are thought to be a source of tumor recurrence due to their stem cell-like properties. MicroRNAs (miRNAs) regulate both normal stem cells and CSCs, and dysregulation of miRNAs has an important role in tumorigenesis. Cluster of differentiation (CD) 133+and spheroid formation have been reported to be one of the main features of ovarian CSCs. Therefore, we determined the miRNA expression profile of a CD133+spheroid-forming subpopulation of the OVCAR3 human ovarian cancer cell line. Methods: Initially, we confirmed the enrichment of the OVCAR3 CD133 subpopulation by evaluating in vitro anchorage-independent growth. After obtaining a subpopulation of CD133+OVCAR3 cells with > 98% purity via cell sorting, miRNA microarray and real-time reverse transcription-polymerase chain reaction (RT-PCR) were performed to evaluate its miRNA profile. Results: We found 37 differentially expressed miRNAs in the CD133+spheroid-forming subpopulation of OVCAR3 cells, 34 of which were significantly up-regulated, including miR-205, miR-146a, miR-200a, miR-200b, and miR-3, and 3 of which were significantly downregulated, including miR-1202 and miR-1181. Conclusions: Our results indicate that dysregulation of miRNA may play a role in the stem cell-like properties of ovarian CSCs.

Molecular diagnostics for congenital hearing loss including 15 deafness genes using a next generation sequencing platform

Sarah De Keulenaer — 2012-05-18T00:00:00Z

Background: Hereditary hearing loss (HL) can originate from mutations in one of many genes involved inthe complex process of hearing. Identification of the genetic defects in patients is currentlylabor intensive and expensive. While screening with Sanger sequencing for GJB2 mutationsis common, this is not the case for the other known deafness genes (> 60). Next generationsequencing technology (NGS) has the potential to be much more cost efficient. Publishedmethods mainly use hybridization based target enrichment procedures that are time savingand efficient, but lead to loss in sensitivity. In this study we used a semi-automated PCRamplification and NGS in order to combine high sensitivity, speed and cost efficiency. Results: In this proof of concept study, we screened 15 autosomal recessive deafness genes in 5patients with congenital genetic deafness. 646 specific primer pairs for all exons and most ofthe UTR of the 15 selected genes were designed using primerXL. Using patient specificidentifiers, all amplicons were pooled and analyzed using the Roche 454 NGS technology.Three of these patients are members of families in which a region of interest has previouslybeen characterized by linkage studies. In these, we were able to identify two new mutationsin CDH23 and OTOF. For another patient, the etiology of deafness was unclear, and nocausal mutation was found. In a fifth patient, included as a positive control, we could confirma known mutation in TMC1. Conclusions: We have developed an assay that holds great promise as a tool for screening patients withfamilial autosomal recessive nonsyndromal hearing loss (ARNSHL). For the first time, anefficient, reliable and cost effective genetic test, based on PCR enrichment, for newbornswith undiagnosed deafness is available.

A target based approach identifies genomic predictors of breast cancer patient response to chemotherapy

Robin Hallett — 2012-05-11T00:00:00Z

Background: The efficacy of chemotherapy regimens in breast cancer patients is variable andunpredictable. Whether individual patients either achieve long-term remission or sufferrecurrence after therapy may be dictated by intrinsic properties of their breast tumorsincluding genetic lesions and consequent aberrant transcriptional programs. Global geneexpression profiling provides a powerful tool to identify such tumor-intrinsic transcriptionalprograms, whose analyses provide insight into the underlying biology of individual patienttumors. For example, multi-gene expression signatures have been identified that can predictthe likelihood of disease reccurrence, and thus guide patient prognosis. Whereas suchprognostic signatures are being introduced in the clinical setting, similar signatures thatpredict sensitivity or resistance to chemotherapy are not currently clinically available. Methods: We used gene expression profiling to identify genes that were co-expressed with genes whosetranscripts encode the protein targets of commonly used chemotherapeutic agents. Results: Here, we present target based expression indices that predict breast tumor response toanthracycline and taxane based chemotherapy. Indeed, these signatures were independentlypredictive of chemotherapy response after adjusting for standard clinic-pathological variablessuch as age, grade, and estrogen receptor status in a cohort of 488 breast cancer patientstreated with adriamycin and taxotere/taxol. Conclusions: Importantly, our findings suggest the practicality of developing target based indices thatpredict response to therapeutics, as well as highlight the possibility of using gene signaturesto guide the use of chemotherapy during treatment of breast cancer patients.

Fibroblasts from phenotypically normal palmar fascia exhibit molecular profiles highly similar to fibroblasts from active disease in Dupuytren's Contracture

Latha Satish — 2012-05-04T00:00:00Z

Background: Dupuytren's contracture (DC) is a fibroproliferative disorder characterized by the progressive development of a scar-like collagen-rich cord that affects the palmar fascia of the hand and leads to digital flexion contractures. DC is most commonly treated by surgical resection of the diseased tissue, but has a high reported recurrence rate ranging from 27% to 80%. We sought to determine if the transcriptomic profiles of fibroblasts derived from DC-affected palmar fascia, adjacent phenotypically normal palmar fascia, and non-DC palmar fascial tissues might provide mechanistic clues to understanding the puzzle of disease predisposition and recurrence in DC. Methods: To achieve this, total RNA was obtained from fibroblasts derived from primary DC-affected palmar fascia, patient-matched unaffected palmar fascia, and palmar fascia from non-DC patients undergoing carpal tunnel release (6 patients in each group). These cells were grown on a type-1 collagen substrate (to better mimic their in vivo environments). Microarray analyses were subsequently performed using Illumina BeadChip arrays to compare the transcriptomic profiles of these three cell populations. Data were analyzed using Significance Analysis of Microarrays (SAM v3.02), hierarchical clustering, concordance mapping and Venn diagram. Results: We found that the transcriptomic profiles of DC-disease fibroblasts and fibroblasts from unaffected fascia of DC patients exhibited a much greater overlap than fibroblasts derived from the palmar fascia of patients undergoing carpal tunnel release. Quantitative real time RT-PCR confirmed the differential expression of select genes validating the microarray data analyses. These data are consistent with the hypothesis that predisposition and recurrence in DC may stem, at least in part, from intrinsic similarities in the basal gene expression of diseased and phenotypically unaffected palmar fascia fibroblasts. These data also demonstrate that a collagen-rich environment differentially alters gene expression in these cells. In addition, Ingenuity pathway analysis of the specific biological pathways that differentiate DC-derived cells from carpal tunnel-derived cells has identified the potential involvement of microRNAs in this fibroproliferative disorder. Conclusions: These data show that the transcriptomic profiles of DC-disease fibroblasts and fibroblasts from unaffected palmar fascia in DC patients are highly similar, and differ significantly from the transcriptomic profiles of fibroblasts from the palmar fascia of patients undergoing carpal tunnel release.

Identification of genes with a correlation between copy number and expression in gastric cancer

Lei Cheng — 2012-05-04T00:00:00Z

Background: To elucidate gene expression associated with copy number changes, we performed a genome-wide copy number and expression microarray analysis of 25 pairs of gastric tissues. Methods: We applied laser capture microdissection (LCM) to obtain samples for microarray experiments and profiled DNA copy number and gene expression using 244K CGH Microarray and Human Exon 1.0 ST Microarray. Results: Obviously, gain at 8q was detected at the highest frequency (70%) and 20q at the second (63%). We also identified molecular genetic divergences for different TNM-stages or histological subtypes of gastric cancers. Interestingly, the C20orf11 amplification and gain at 20q13.33 almost separated moderately differentiated (MD) gastric cancers from poorly differentiated (PD) type. A set of 163 genes showing the correlations between gene copy number and expression was selected and the identified genes were able to discriminate matched adjacent noncancerous samples from gastric cancer samples in an unsupervised two-way hierarchical clustering. Quantitative RT-PCR analysis for 4 genes (C20orf11, XPO5, PUF60, and PLOD3) of the 163 genes validated the microarray results. Notably, some candidate genes (MCM4 and YWHAZ) and its adjacent genes such as PRKDC, UBE2V2, ANKRD46, ZNF706, and GRHL2, were concordantly deregulated by genomic aberrations. Conclusions: Taken together, our results reveal diverse chromosomal region alterations for different TNM-stages or histological subtypes of gastric cancers, which is helpful in researching clinicopathological classification, and highlight several interesting genes as potential biomarkers for gastric cancer.

Novel biomarker combination improves the diagnosis of serious bacterial infections in Malawian children

Adam Irwin — 2012-05-04T00:00:00Z

Background: High throughput technologies offer insight into disease processes and heightens opportunities for improved diagnostics. Using transcriptomic analyses, we aimed to discover and to evaluate the clinical validity of a combination of reliable and functionally important biomarkers of serious bacterial infection (SBI). Methods: We identified three previously reported biomarkers of infection (neutrophil gelatinase-associated lipocalin (NGAL), granulysin and resistin) and measured gene expression using quantitative real-time PCR. Protein products related to the three transcripts were measured by immunoassays. Results: Relative gene expression values of NGAL and resistin were significantly increased, and expression of granulysin significantly decreased in cases compared to controls. Plasma concentrations of NGAL and resistin were significantly increased in children with confirmed SBI compared to children with no detectable bacterial infection (NBI), and to controls (287 versus 128 versus 62 ng/ml and 195 versus 90 versus 18 ng/ml, respectively, p < 0.05). Plasma protein concentrations of NGAL and resistin were significantly increased in non-survivors compared to survivors (306 versus 211 and 214 versus 150 ng/ml, p = 0.02). The respective areas under the curve (AUC) for NGAL, resistin and procalcitonin in predicting SBI were 0.79, 0.80 and 0.86, whilst a combination of NGAL, resistin and procalcitonin achieved an AUC of 0.90. Conclusions: We have demonstrated a unique combination of diagnostic biomarkers of SBI using transcriptomics, and demonstrated translational concordance with the corresponding protein. The addition of NGAL and resistin protein measurement to procalcitonin significantly improved the diagnosis of SBI.

Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs

S Krithika — 2012-05-01T00:00:00Z

Background: We explored the imputation performance of the program IMPUTE in an admixed samplefrom Mexico City. The following issues were evaluated: (a) the impact of different referencepanels (HapMap vs. 1000 Genomes) on imputation; (b) potential differences in imputationperformance between single-step vs. two-step (phasing and imputation) approaches; (c) theeffect of different INFO score thresholds on imputation performance and (d) imputationperformance in common vs. rare markers. Methods: The sample from Mexico City comprised 1,310 individuals genotyped with the Affymetrix5.0 array. We randomly masked 5% of the markers directly genotyped on chromosome 12 (n= 1,046) and compared the imputed genotypes with the microarray genotype calls. Imputationwas carried out with the program IMPUTE. The concordance rates between the imputed andobserved genotypes were used as a measure of imputation accuracy and the proportion ofnon-missing genotypes as a measure of imputation efficacy. Results: The single-step imputation approach produced slightly higher concordance rates than thetwo-step strategy (99.1% vs. 98.4% when using the HapMap phase II combined panel), but atthe expense of a lower proportion of non-missing genotypes (85.5% vs. 90.1%). The 1,000Genomes reference sample produced similar concordance rates to the HapMap phase II panel(98.4% for both datasets, using the two-step strategy). However, the 1000 Genomes referencesample increased substantially the proportion of non-missing genotypes (94.7% vs. 90.1%).Rare variants (<1%) had lower imputation accuracy and efficacy than common markers. Conclusions: The program IMPUTE had an excellent imputation performance for common alleles in anadmixed sample from Mexico City, which has primarily Native American (62%) andEuropean (33%) contributions. Genotype concordances were higher than 98.4% using all theimputation strategies, in spite of the fact that no Native American samples are present in theHapMap and 1000 Genomes reference panels. The best balance of imputation accuracy andefficiency was obtained with the 1,000 Genomes panel. Rare variants were not capturedeffectively by any of the available panels, emphasizing the need to be cautious in theinterpretation of association results for imputed rare variants.

Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer

Nicole Lavender — 2012-04-30T00:00:00Z

Background: Molecular and epidemiological evidence demonstrate that altered gene expression and single nucleotide polymorphisms in the apoptotic pathway are linked to many cancers. Yet, few studies emphasize the interaction of variant apoptotic genes and their joint modifying effects on prostate cancer (PCA) outcomes. An exhaustive assessment of all the possible two-, three- and four-way gene-gene interactions is computationally burdensome. This statistical conundrum stems from the prohibitive amount of data needed to account for multiple hypothesis testing. Methods: To address this issue, we systematically prioritized and evaluated individual effects and complex interactions among 172 apoptotic SNPs in relation to PCA risk and aggressive disease (i.e., Gleason score ≥ 7 and tumor stages III/IV). Single and joint modifying effects on PCA outcomes among European-American men were analyzed using statistical epistasis networks coupled with multi-factor dimensionality reduction (SEN-guided MDR). The case-control study design included 1,175 incident PCA cases and 1,111 controls from the prostate, lung, colo-rectal, and ovarian (PLCO) cancer screening trial. Moreover, a subset analysis of PCA cases consisted of 688 aggressive and 488 non-aggressive PCA cases. SNP profiles were obtained using the NCI Cancer Genetic Markers of Susceptibility (CGEMS) data portal. Main effects were assessed using logistic regression (LR) models. Prior to modeling interactions, SEN was used to pre-process our genetic data. SEN used network science to reduce our analysis from > 36 million to < 13,000 SNP interactions. Interactions were visualized, evaluated, and validated using entropy-based MDR. All parametric and non-parametric models were adjusted for age, family history of PCA, and multiple hypothesis testing. Results: Following LR modeling, eleven and thirteen sequence variants were associated with PCA risk and aggressive disease, respectively. However, none of these markers remained significant after we adjusted for multiple comparisons. Nevertheless, we detected a modest synergistic interaction between AKT3 rs2125230-PRKCQ rs571715 and disease aggressiveness using SEN-guided MDR (p = 0.011). Conclusions: In summary, entropy-based SEN-guided MDR facilitated the logical prioritization and evaluation of apoptotic SNPs in relation to aggressive PCA. The suggestive interaction between AKT3-PRKCQ and aggressive PCA requires further validation using independent observational studies.

DNA methylation differences at growth related genes correlate with birth weight: a molecular signature linked to developmental origins of adult disease?

Nahid Turan — 2012-04-12T00:00:00Z

Background: Infant birth weight is a complex quantitative trait associated with both neonatal and long-term health outcomes. Numerous studies have been published in which candidate genes (IGF1, IGF2, IGF2R, IGF binding proteins, PHLDA2 and PLAGL1) have been associated with birth weight, but these studies are difficult to reproduce in man and large cohort studies are needed due to the large inter individual variance in transcription levels. Also, very little of the trait variance is explained. We decided to identify additional candidates without regard for what is known about the genes. We hypothesize that DNA methylation differences between individuals can serve as markers of gene "expression potential" at growth related genes throughout development and that these differences may correlate with birth weight better than single time point measures of gene expression. Methods: We performed DNA methylation and transcript profiling on cord blood and placenta from newborns. We then used novel computational approaches to identify genes correlated with birth weight. Results: We identified 23 genes whose methylation levels explain 70-87% of the variance in birth weight. Six of these (ANGPT4, APOE, CDK2, GRB10, OSBPL5 and REG1B) are associated with growth phenotypes in human or mouse models. Gene expression profiling explained a much smaller fraction of variance in birth weight than did DNA methylation. We further show that two genes, the transcriptional repressor MSX1 and the growth factor receptor adaptor protein GRB10, are correlated with transcriptional control of at least seven genes reported to be involved in fetal or placental growth, suggesting that we have identified important networks in growth control. GRB10 methylation is also correlated with genes involved in reactive oxygen species signaling, stress signaling and oxygen sensing and more recent data implicate GRB10 in insulin signaling. Conclusions: Single time point measurements of gene expression may reflect many factors unrelated to birth weight, while inter-individual differences in DNA methylation may represent a "molecular fossil record" of differences in birth weight-related gene expression. Finding these "unexpected" pathways may tell us something about the long-term association between low birth weight and adult disease, as well as which genes may be susceptible to environmental effects. These findings increase our understanding of the molecular mechanisms involved in human development and disease progression.