Oral microbiome profiles: 16S rRNA pyrosequencing and microarray assay comparison

Objectives

The human oral microbiome is potentially related to diverse health conditions and high-throughput technology provides the possibility of surveying microbial community structure at high resolution. We compared two oral microbiome survey methods: broad-based microbiome identification by 16S rRNA gene sequencing and targeted characterization of microbes by custom DNA microarray.

Methods

Oral wash samples were collected from 20 individuals at Memorial Sloan-Kettering Cancer Center. 16S rRNA gene survey was performed by 454 pyrosequencing of the V3–V5 region (450 bp). Targeted identification by DNA microarray was carried out with the Human Oral Microbe Identification Microarray (HOMIM). Correlations and relative abundance were compared at phylum and genus level, between 16S rRNA sequence read ratio and HOMIM hybridization intensity.

Results

The major phyla, Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Fusobacteria were identified with high correlation by the two methods (r = 0.70∼0.86). 16S rRNA gene pyrosequencing identified 77 genera and HOMIM identified 49, with 37 genera detected by both methods; more than 98% of classified bacteria were assigned in these 37 genera. Concordance by the two assays (presence/absence) and correlations were high for common genera (Streptococcus, Veillonella, Leptotrichia, Prevotella, and Haemophilus; Correlation = 0.70–0.84).

Conclusion

Microbiome community profiles assessed by 16S rRNA pyrosequencing and HOMIM were highly correlated at the phylum level and, when comparing the more commonly detected taxa, also at the genus level. Both methods are currently suitable for high-throughput epidemiologic investigations relating identified and more common oral microbial taxa to disease risk; yet, pyrosequencing may provide a broader spectrum of taxa identification, a distinct sequence-read record, and greater detection sensitivity.     

Identification of hsa-miR-335 as a prognostic signature in gastric cancer

Background

Gastric cancer (GC) is one of the most common malignancy and primary cause of death in Chinese cancer patients. Recurrence is a major factor leading to treatment failure and low level of 5-year survival rate in GC patients following surgical resection. Therefore, identification of biomarkers with potential in predicting recurrence risk is the key problem of the prognosis in GC patients.

Patients and Methods

A total of 74 GC patients were selected for systematic analysis, consisting of 31 patients with recurrence and 43 patients without recurrence. Firstly, miRNAs microarray and bioinformatics methods were used to characterize differential expressed miRNAs from primary tumor samples. Following, we used a ROC method to select signature with best sensitivity and specificity. Finally, we validated the signature in GC samples (frozen fresh and blood samples) using quantitative PCR.

Results

We have identified 12 differential miRNAs including 7 up-regulated and 5 down-regulated miRNAs in recurrence group. Using ROC method, we further ascertained hsa-miR-335 as a signature to recognize recurrence and non-recurrence cases in the training samples. Moreover, we validated this signature using quantitative PCR method in 64 test samples with consistent result with training set. A high frequency recurrence and poor survival were observed in GC cases with high level of hsa-miR-335 (P<0.001). In addition, we evaluated that hsa-miR-335 were involved in regulating target genes in several oncogenic signal-pathways, such as p53, MAPK, TGF-β, Wnt, ERbB, mTOR, Toll-like receptor and focal adhesion.

Conclusion

Our results indicate that the hsa-miR-335 has the potential to recognize the recurrence risk and relate to the prognosis of GC patients.     

Non-Invasive Prenatal Detection of Trisomy 13 Using a Single Nucleotide Polymorphism- and Informatics-Based Approach

Purpose

To determine how a single nucleotide polymorphism (SNP)- and informatics-based non-invasive prenatal aneuploidy test performs in detecting trisomy 13.

Methods

Seventeen trisomy 13 and 51 age-matched euploid samples, randomly selected from a larger cohort, were analyzed. Cell-free DNA was isolated from maternal plasma, amplified in a single multiplex polymerase chain reaction assay that interrogated 19,488 SNPs covering chromosomes 13, 18, 21, X, and Y, and sequenced. Analysis and copy number identification involved a Bayesian-based maximum likelihood statistical method that generated chromosome- and sample-specific calculated accuracies.

Results

Of the samples that passed a stringent DNA quality threshold (94.1%), the algorithm correctly identified 15/15 trisomy 13 and 49/49 euploid samples, for 320/320 correct copy number calls.

Conclusions

This informatics- and SNP-based method accurately detects trisomy 13-affected fetuses non-invasively and with high calculated accuracy.     

Transcriptomic Meta-Analysis of Multiple Sclerosis and Its Experimental Models

Background

Multiple microarray analyses of multiple sclerosis (MS) and its experimental models have been published in the last years.

Objective

Meta-analyses integrate the information from multiple studies and are suggested to be a powerful approach in detecting highly relevant and commonly affected pathways.

Data sources

ArrayExpress, Gene Expression Omnibus and PubMed databases were screened for microarray gene expression profiling studies of MS and its experimental animal models.

Study eligibility criteria

Studies comparing central nervous system (CNS) samples of diseased versus healthy individuals with n >1 per group and publically available raw data were selected.

Material and Methods

Included conditions for re-analysis of differentially expressed genes (DEGs) were MS, myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis (EAE) in rats, proteolipid protein-induced EAE in mice, Theiler’s murine encephalomyelitis virus-induced demyelinating disease (TMEV-IDD), and a transgenic tumor necrosis factor-overexpressing mouse model (TNFtg). Since solely a single MS raw data set fulfilled the inclusion criteria, a merged list containing the DEGs from two MS-studies was additionally included. Cross-study analysis was performed employing list comparisons of DEGs and alternatively Gene Set Enrichment Analysis (GSEA).

Results

The intersection of DEGs in MS, EAE, TMEV-IDD, and TNFtg contained 12 genes related to macrophage functions. The intersection of EAE, TMEV-IDD and TNFtg comprised 40 DEGs, functionally related to positive regulation of immune response. Over and above, GSEA identified substantially more differentially regulated pathways including coagulation and JAK/STAT-signaling.

Conclusion

A meta-analysis based on a simple comparison of DEGs is over-conservative. In contrast, the more experimental GSEA approach identified both, a priori anticipated as well as promising new candidate pathways.     

Rapid qualitative urinary tract infection pathogen identification by SeptiFast real-time PCR

Background

Urinary tract infections (UTI) are frequent in outpatients. Fast pathogen identification is mandatory for shortening the time of discomfort and preventing serious complications. Urine culture needs up to 48 hours until pathogen identification. Consequently, the initial antibiotic regimen is empirical.

Aim

To evaluate the feasibility of qualitative urine pathogen identification by a commercially available real-time PCR blood pathogen test (SeptiFast^®) and to compare the results with dipslide and microbiological culture.

Design of study

Pilot study with prospectively collected urine samples.

Setting

University hospital.

Methods

82 prospectively collected urine samples from 81 patients with suspected UTI were included. Dipslide urine culture was followed by microbiological pathogen identification in dipslide positive samples. In parallel, qualitative DNA based pathogen identification (SeptiFast^®) was performed in all samples.

Results

61 samples were SeptiFast^® positive, whereas 67 samples were dipslide culture positive. The inter-methodological concordance of positive and negative findings in the gram+, gram- and fungi sector was 371/410 (90%), 477/492 (97%) and 238/246 (97%), respectively. Sensitivity and specificity of the SeptiFast^® test for the detection of an infection was 0.82 and 0.60, respectively. SeptiFast^® pathogen identifications were available at least 43 hours prior to culture results.

Conclusion

The SeptiFast^® platform identified bacterial DNA in urine specimens considerably faster compared to conventional culture. For UTI diagnosis sensitivity and specificity is limited by its present qualitative setup which does not allow pathogen quantification. Future quantitative assays may hold promise for PCR based UTI pathogen identification as a supplementation of conventional culture methods.     

Salivary MicroRNAs as Promising Biomarkers for Detection of Esophageal Cancer

Background and Purpose

Tissue microRNAs (miRNAs) can detect cancers and predict prognosis. Several recent studies reported that tissue, plasma, and saliva miRNAs share similar expression profiles. In this study, we investigated the discriminatory power of salivary miRNAs (including whole saliva and saliva supernatant) for detection of esophageal cancer.

Materials and Methods

By Agilent microarray, six deregulated miRNAs from whole saliva samples from seven patients with esophageal cancer and three healthy controls were selected. The six selected miRNAs were subjected to validation of their expression levels by RT-qPCR using both whole saliva and saliva supernatant samples from an independent set of 39 patients with esophageal cancer and 19 healthy controls.

Results

Six miRNAs (miR-10b*, miR-144, miR-21, miR-451, miR-486-5p, and miR-634) were identified as targets by Agilent microarray. After validation by RT-qPCR, miR-10b*, miR-144, and miR-451 in whole saliva and miR-10b*, miR-144, miR-21, and miR-451 in saliva supernatant were significantly upregulated in patients, with sensitivities of 89.7, 92.3, 84.6, 79.5, 43.6, 89.7, and 51.3% and specificities of 57.9, 47.4, 57.9%, 57.9, 89.5, 47.4, and 84.2%, respectively.

Conclusions

We found distinctive miRNAs for esophageal cancer in both whole saliva and saliva supernatant. These miRNAs possess discriminatory power for detection of esophageal cancer. Because saliva collection is noninvasive and convenient, salivary miRNAs show great promise as biomarkers for detection of esophageal cancer in areas at high risk.     

GWAS Meets Microarray: Are the Results of Genome-Wide Association Studies and Gene-Expression Profiling Consistent? Prostate Cancer as an Example

Background

Genome-wide association studies (GWASs) and global profiling of gene expression (microarrays) are two major technological breakthroughs that allow hypothesis-free identification of candidate genes associated with tumorigenesis. It is not obvious whether there is a consistency between the candidate genes identified by GWAS (GWAS genes) and those identified by profiling gene expression (microarray genes).

Methodology/Principal Findings

We used the Cancer Genetic Markers Susceptibility database to retrieve single nucleotide polymorphisms from candidate genes for prostate cancer. In addition, we conducted a large meta-analysis of gene expression data in normal prostate and prostate tumor tissue. We identified 13,905 genes that were interrogated by both GWASs and microarrays. On the basis of P values from GWASs, we selected 1,649 most significantly associated genes for functional annotation by the Database for Annotation, Visualization and Integrated Discovery. We also conducted functional annotation analysis using same number of the top genes identified in the meta-analysis of the gene expression data. We found that genes involved in cell adhesion were overrepresented among both the GWAS and microarray genes.

Conclusions/Significance

We conclude that the results of these analyses suggest that combining GWAS and microarray data would be a more effective approach than analyzing individual datasets and can help to refine the identification of candidate genes and functions associated with tumor development.     

Bayesian neural networks for detecting epistasis in genetic association studies

Background

Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions.

Results

A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships.

Conclusions

The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0368-0) contains supplementary material, which is available to authorized users.     

Layered Signaling Regulatory Networks Analysis of Gene Expression Involved in Malignant Tumorigenesis of Non-Resolving Ulcerative Colitis via Integration of Cross-Study Microarray Profiles

Background

Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups.

Methodology/Principal Findings

In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks.

Results

Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one.

Conclusions

The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions.     

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation

Background

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study.

Results

Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this “gold-standard” comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues.

Conclusions

Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-649) contains supplementary material, which is available to authorized users.     

Fluorescence-Based Methods for Detecting Caries Lesions: Systematic Review,Meta-Analysis and Sources of Heterogeneity

Background

Fluorescence-based methods have been proposed to aid caries lesion detection. Summarizing and analysing findings of studies about fluorescence-based methods could clarify their real benefits.

Objective

We aimed to perform a comprehensive systematic review and meta-analysis to evaluate the accuracy of fluorescence-based methods in detecting caries lesions.

Data Source

Two independent reviewers searched PubMed, Embase and Scopus through June 2012 to identify papers/articles published. Other sources were checked to identify non-published literature.

Study Eligibility Criteria, Participants and Diagnostic Methods

The eligibility criteria were studies that: (1) have assessed the accuracy of fluorescence-based methods of detecting caries lesions on occlusal, approximal or smooth surfaces, in both primary or permanent human teeth, in the laboratory or clinical setting; (2) have used a reference standard; and (3) have reported sufficient data relating to the sample size and the accuracy of methods.

Study Appraisal and Synthesis Methods

A diagnostic 2×2 table was extracted from included studies to calculate the pooled sensitivity, specificity and overall accuracy parameters (Diagnostic Odds Ratio and Summary Receiver-Operating curve). The analyses were performed separately for each method and different characteristics of the studies. The quality of the studies and heterogeneity were also evaluated.

Results

Seventy five studies met the inclusion criteria from the 434 articles initially identified. The search of the grey or non-published literature did not identify any further studies. In general, the analysis demonstrated that the fluorescence-based method tend to have similar accuracy for all types of teeth, dental surfaces or settings. There was a trend of better performance of fluorescence methods in detecting more advanced caries lesions. We also observed moderate to high heterogeneity and evidenced publication bias.

Conclusions

Fluorescence-based devices have similar overall performance; however, better accuracy in detecting more advanced caries lesions has been observed.     

Informatics enhanced SNP microarray analysis of 30 miscarriage samples compared to routine cytogenetics

Purpose

The metaphase karyotype is often used as a diagnostic tool in the setting of early miscarriage; however this technique has several limitations. We evaluate a new technique for karyotyping that uses single nucleotide polymorphism microarrays (SNP). This technique was compared in a blinded, prospective fashion, to the traditional metaphase karyotype.

Methods

Patients undergoing dilation and curettage for first trimester miscarriage between February and August 2010 were enrolled. Samples of chorionic villi were equally divided and sent for microarray testing in parallel with routine cytogenetic testing.

Results

Thirty samples were analyzed, with only four discordant results. Discordant results occurred when the entire genome was duplicated or when a balanced rearrangement was present. Cytogenetic karyotyping took an average of 29 days while microarray-based karytoyping took an average of 12 days.

Conclusions

Molecular karyotyping of POC after missed abortion using SNP microarray analysis allows for the ability to detect maternal cell contamination and provides rapid results with good concordance to standard cytogenetic analysis.     

Atrial fibrillation and delayed gastric emptying

Background

Atrial fibrillation and delayed gastric emptying (DGE) are common after pancreaticoduodenectomy. Our aim was to investigate a potential relationship between atrial fibrillation and DGE, which we defined as failure to tolerate a regular diet by the 7^th postoperative day.

Methods

We performed a retrospective chart review of 249 patients who underwent pancreaticoduodenectomy at our institution between 2000 and 2009. Data was analyzed with Fisher exact test for categorical variables and Mann-Whitney U or unpaired T-test for continuous variables.

Results

Approximately 5% of the 249 patients included in the analysis experienced at least one episode of postoperative atrial fibrillation. Median age of patients with atrial fibrillation was 74 years, compared with 66 years in patients without atrial fibrillation (p = 0.0005). Patients with atrial fibrillation were more likely to have a history of atrial fibrillation (p = 0.03). 92% of the patients with atrial fibrillation suffered from DGE, compared to 46% of patients without atrial fibrillation (p = 0.0007). This association held true when controlling for age.

Conclusion

Patients with postoperative atrial fibrillation are more likely to experience delayed gastric emptying. Interventions to manage delayed gastric function might be prudent in patients at high risk for postoperative atrial fibrillation.     

A signature inferred from Drosophila mitotic genes predicts survival of breast cancer patients

Introduction

The classification of breast cancer patients into risk groups provides a powerful tool for the identification of patients who will benefit from aggressive systemic therapy. The analysis of microarray data has generated several gene expression signatures that improve diagnosis and allow risk assessment. There is also evidence that cell proliferation-related genes have a high predictive power within these signatures.

Methods

We thus constructed a gene expression signature (the DM signature) using the human orthologues of 108 Drosophila melanogaster genes required for either the maintenance of chromosome integrity (36 genes) or mitotic division (72 genes).

Results

The DM signature has minimal overlap with the extant signatures and is highly predictive of survival in 5 large breast cancer datasets. In addition, we show that the DM signature outperforms many widely used breast cancer signatures in predictive power, and performs comparably to other proliferation-based signatures. For most genes of the DM signature, an increased expression is negatively correlated with patient survival. The genes that provide the highest contribution to the predictive power of the DM signature are those involved in cytokinesis.

Conclusion

This finding highlights cytokinesis as an important marker in breast cancer prognosis and as a possible target for antimitotic therapies.     

All-Food-Seq (AFS): a quantifiable screen for species in biological samples by deep DNA sequencing

Background

DNA-based methods like PCR efficiently identify and quantify the taxon composition of complex biological materials, but are limited to detecting species targeted by the choice of the primer assay. We show here how untargeted deep sequencing of foodstuff total genomic DNA, followed by bioinformatic analysis of sequence reads, facilitates highly accurate identification of species from all kingdoms of life, at the same time enabling quantitative measurement of the main ingredients and detection of unanticipated food components.

Results

Sequence data simulation and real-case Illumina sequencing of DNA from reference sausages composed of mammalian (pig, cow, horse, sheep) and avian (chicken, turkey) species are able to quantify material correctly at the 1% discrimination level via a read counting approach. An additional metagenomic step facilitates identification of traces from animal, plant and microbial DNA including unexpected species, which is prospectively important for the detection of allergens and pathogens.

Conclusions

Our data suggest that deep sequencing of total genomic DNA from samples of heterogeneous taxon composition promises to be a valuable screening tool for reference species identification and quantification in biosurveillance applications like food testing, potentially alleviating some of the problems in taxon representation and quantification associated with targeted PCR-based approaches.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-639) contains supplementary material, which is available to authorized users.     

Expression Variation: Its Relevance to Emergence of Chronic Disease and to Therapy

Background

Stochastic fluctuations in the protein turnover underlie the random emergence of neural precursor cells from initially homogenous cell population. If stochastic alteration of the levels in signal transduction networks is sufficient to spontaneously alter a phenotype, can it cause a sporadic chronic disease as well – including cancer?

Methods

Expression in >80 disease-free tissue environments was measured using Affymetrix microarray platform comprising 54675 probe-sets. Steps were taken to suppress the technical noise inherent to microarray experiment. Next, the integrated expression and expression variability data were aligned with the mechanistic data covering major human chronic diseases.

Results

Measured as class average, variability of expression of disease associated genes measured in health was higher than variability of random genes for all chronic pathologies. Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes. Same held for magnitude of gene expression. The genes known to participate in multiple chronic disorders demonstrated the highest variability. Disease-related gene categories displayed on average more intricate regulation of biological function vs random reference, were enriched in adaptive and transient functions as well as positive feedback relationships.

Conclusions

A possible causative link can be suggested between normal (healthy) state gene expression variation and inception of major human pathologies, including cancer. Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization. The results of the study suggest the need to advance personalized therapy development.     

Evaluation of Biochip System in Determining Isoniazid and Rifampicin Resistances of Mycobacterium Tuberculosis in Sputum Samples

Objective

To evaluate a biochip system in determining isoniazid and rifampicin resistances of Mycobacterium tuberculosis in sputum samples in a Chinese population.

Methods

We assembled 907 sputum smeared positive specimens of tuberculosis patients in total. Each sample would be separated into two parts for culture and biochip assay simultaneously. And those cultured positive and having full drug resistance results would be used as reference. The McNemar χ² test was adopted for evaluating the paired 2×2 table.

Results

Compared with drug sensitivity test, the agreement rates of the two methods in detecting rifampicin and isoniazid resistances were 93.37% and 94.49%, respectively. The sensitivity and specificity of biochip in detecting isoniazid were 74.31% and 96.92%, respectively. Meanwhile, the sensitivity and specificity for rifampicin were 79.76% and 96.53%, respectively. For multi-drug resistance, the sensitivity and specificity were 64.62% and 97.75%, respectively.

Conclusions

The biochip system is a rapid and accurate method for drug resistant tuberculosis diagnosis using sputum samples directly, especially for rifampicin resistance detection.