Genetics and genomics: gene expression microarrays

Transcriptome-wide analysis of intracranial artery in patients with moyamoya disease showing upregulation of immune response, and downregulation of oxidative phosphorylation and DNA repair

OBJECTIVE

Moyamoya disease (MMD) is a rare cerebrovascular disease characterized by progressive occlusion of the internal carotid artery and the secondary formation of collateral vessels. Patients with MMD have ischemic attacks or intracranial bleeding, but the disease pathophysiology remains unknown. In this study, the authors aimed to identify a gene expression profile specific to the intracranial artery in MMD.

METHODS

This was a single-center, prospectively sampled, retrospective cohort study. Microsamples of the middle cerebral artery (MCA) were collected from patients with MMD (n = 11) and from control patients (n = 9). Using microarray techniques, transcriptome-wide analysis was performed.

RESULTS

Comparison of MCA gene expression between patients with MMD and control patients detected 62 and 26 genes whose expression was significantly (p < 0.001 and fold change > 2) up- or downregulated, respectively, in the MCA of MMD. Gene set enrichment analysis of genes expressed in the MCA of patients with MMD revealed positive correlations with genes involved in antigen processing and presentation, the dendritic cell pathway, cytokine pathway, and interleukin-12 pathway, and negative correlations with genes involved in oxidative phosphorylation and DNA repair. Microarray analysis was validated by quantitative polymerase chain reaction.

CONCLUSIONS

Transcriptome-wide analysis showed upregulation of genes for immune responses and downregulation of genes for DNA repair and oxidative phosphorylation within the intracranial artery of patients with MMD. These findings may represent clues to the pathophysiology of MMD.

Study Design and qPCR Data Analysis Guidelines for Reliable Circulating miRNA Biomarker Experiments: A Review

BACKGROUND

In the past decade, the search for circulating microRNA (miRNA) biomarkers has yielded numerous associations between miRNAs and different types of disease. However, many of these relations could not be replicated in subsequent studies under similar experimental conditions. Although this lack of replicability may be explained by the variation in experimental design and analysis methods, guidelines on the most appropriate design and analysis methods to study circulating miRNAs are scarce.

CONTENT

miRNA biomarker experiments generally consist of a discovery phase and a validation phase. In the discovery phase, typically hundreds of miRNAs are measured in parallel to identify candidate biomarkers. Because of the costs of such high-throughput experiments, the number of individuals included in those studies is often too small, which can easily lead to false positives and false negatives. In the validation phase, a small number of identified biomarker candidates are measured in a large cohort of cases and controls, generally by quantitative PCR (qPCR). Although qPCR is a sensitive method to measure miRNAs in the circulation, experimental design and qPCR data analysis remain challenging. Omitting some crucial steps in the design and analysis of the qPCR experiment or performing them incorrectly can cause serious biases, ultimately leading to false conclusions.

SUMMARY

In this review, we aim to expose and discuss the most common sources of interstudy variation in miRNA research from a methodological point of view and to provide guidelines on how to perform these steps correctly to increase replicability of studies on circulating miRNAs.

Small sample sizes in high-throughput miRNA screens: A common pitfall for the identification of miRNA biomarkers

Since the discovery of microRNAs (miRNAs), circulating miRNAs have been proposed as biomarkers for disease. Consequently, many groups have tried to identify circulating miRNA biomarkers for various types of diseases including cardiovascular disease and cancer. However, the replicability of these experiments has been disappointingly low. In order to identify circulating miRNA candidate biomarkers, in general, first an unbiased high-throughput screen is performed in which a large number of miRNAs is detected and quantified in the circulation. Because these are costly experiments, many of such studies have been performed using a low number of study subjects (small sample size). Due to lack of power in small sample size experiments, true effects are often missed and many of the detected effects are wrong. Therefore, it is important to have a good estimate of the appropriate sample size for a miRNA high-throughput screen. In this review, we discuss the effects of small sample sizes in high-throughput screens for circulating miRNAs. Using data from a miRNA high-throughput experiment on isolated monocytes, we illustrate that the implementation of power calculations in a high-throughput miRNA discovery experiment will avoid unnecessarily large and expensive experiments, while still having enough power to be able to detect clinically important differences.

Molecular cloning, expression, and stress response of the estrogen-related receptor gene (AccERR) from Apis cerana cerana

Estrogen-related receptor (ERR), which belongs to the nuclear receptor superfamily, has been implicated in diverse physiological processes involving the estrogen signaling pathway. However, little information is available on ERR in Apis cerana cerana. In this report, we isolated the ERR gene and investigated its involvement in antioxidant defense. Quantitative real-time polymerase chain reaction (qPCR) revealed that the highest mRNA expression occurred in eggs during different developmental stages. The expression levels of AccERR were highest in the muscle, followed by the rectum. The predicted transcription factor binding sites in the promoter of AccERR suggested that AccERR potentially functions in early development and in environmental stress responses. The expression of AccERR was induced by cold (4 °C), heat (42 °C), ultraviolet light (UV), HgCl2, and various types of pesticides (phoxim, deltamethrin, triadimefon, and cyhalothrin). Western blot was used to measure the expression levels of AccERR protein. These data suggested that AccERR might play a vital role in abiotic stress responses.

Differential co-expression network centrality and machine learning feature selection for identifying susceptibility hubs in networks with scale-free structure

Background

Biological insights into group differences, such as disease status, have been achieved through differential co-expression analysis of microarray data. Additional understanding of group differences may be achieved by integrating the connectivity structure of the differential co-expression network and per-gene differential expression between phenotypic groups. Such a global differential co-expression network strategy may increase sensitivity to detect gene-gene interactions (or expression epistasis) that may act as candidates for rewiring susceptibility co-expression networks.

Methods

We test two methods for inferring Genetic Association Interaction Networks (GAIN) incorporating both differential co-expression effects and differential expression effects: a generalized linear model (GLM) regression method with interaction effects (reGAIN) and a Fisher test method for correlation differences (dcGAIN). We rank the importance of each gene with complete interaction network centrality (CINC), which integrates each gene’s differential co-expression effects in the GAIN model along with each gene’s individual differential expression measure. We compare these methods with statistical learning methods Relief-F, Random Forests and Lasso. We also develop a mixture model and permutation approach for determining significant importance score thresholds for network centralities, Relief-F and Random Forest. We introduce a novel simulation strategy that generates microarray case–control data with embedded differential co-expression networks and underlying correlation structure based on scale-free or Erdos-Renyi (ER) random networks.

Results

Using the network simulation strategy, we find that Relief-F and reGAIN provide the best balance between detecting interactions and main effects, plus reGAIN has the ability to adjust for covariates and model quantitative traits. The dcGAIN approach performs best at finding differential co-expression effects by design but worst for main effects, and it does not adjust for covariates and is limited to dichotomous outcomes. When the underlying network is scale free instead of ER, all interaction network methods have greater power to find differential co-expression effects. We apply these methods to a public microarray study of the differential immune response to influenza vaccine, and we identify effects that suggest a role in influenza vaccine immune response for genes from the PI3K family, which includes genes with known immunodeficiency function, and KLRG1, which is a known marker of senescence.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-015-0040-x) contains supplementary material, which is available to authorized users.

Phenotypic spectrum of heart failure with preserved ejection fraction

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome, with several underlying etiologic and pathophysiologic factors. The heterogeneity of the HFpEF syndrome may explain why (1) diagnosing and treating HFpEF is so challenging and (2) clinical trials in HFpEF have failed thus far. Here we describe 4 ways of categorizing HFpEF based on pathophysiology, clinical/etiologic subtype, type of clinical presentation, and quantitative phenomics (phenomapping analysis). Regardless of the classification method used, improved phenotypic characterization of HFpEF, and matching targeted therapies with specific HFpEF subtypes, will be a critical step towards improving outcomes in this increasingly prevalent syndrome.

Association of distance from a transplant center with access to waitlist placement, receipt of liver transplantation, and survival among US veterans

IMPORTANCE

Centralization of specialized health care services such as organ transplantation and bariatric surgery is advocated to improve quality, increase efficiency, and reduce cost. The effect of increased travel on access and outcomes from these services is not fully understood.

OBJECTIVE

To evaluate the association between distance from a Veterans Affairs (VA) transplant center (VATC) and access to being waitlisted for liver transplantation, actually having a liver transplant, and mortality.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective study of veterans meeting liver transplantation eligibility criteria from January 1, 2003, until December 31, 2010, using data from the Veterans Health Administration's integrated, national, electronic medical record linked to Organ Procurement and Transplantation Network data.

MAIN OUTCOMES AND MEASURES

The primary outcome was being waitlisted for transplantation at a VATC. Secondary outcomes included being waitlisted at any transplant center, undergoing a transplantation, and survival.

RESULTS

From 2003-2010, 50,637 veterans were classified as potentially eligible for transplant; 2895 (6%) were waitlisted and 1418 of those were waitlisted (49%) at 1 of the 5 VATCs. Of 3417 veterans receiving care at a VA hospital located within 100 miles from a VATC, 244 (7.1%) were waitlisted at a VATC and 372 (10.9%) at any transplant center (VATC and non-VATCs). Of 47,219 veterans receiving care at a VA hospital located more than 100 miles from a VATC, 1174 (2.5%) were waitlisted at a VATC and 2523 (5.3%) at any transplant center (VATC and non-VATCs). In multivariable models, increasing distance to closest VATC was associated with significantly lower odds of being waitlisted at a VATC (odds ratio [OR], 0.91 [95% CI, 0.89-0.93] for each doubling in distance) or any transplant center (OR, 0.94 [95% CI, 0.92-0.96] for each doubling in distance). For example, a veteran living 25 miles from a VATC would have a 7.4% (95% CI, 6.6%-8.1%) adjusted probability of being waitlisted, whereas a veteran 100 miles from a VATC would have a 6.2% (95% CI, 5.7%-6.6%) adjusted probability. In adjusted models, increasing distance from a VATC was associated with significantly lower transplantation rates (subhazard ratio, 0.97; 95% CI, 0.95-0.98 for each doubling in distance). There was significantly increased mortality among waitlisted veterans from the time of first hepatic decompensation event in multivariable survival models (hazard ratio, 1.03; 95% CI, 1.01-1.04 for each doubling in distance). For example, a waitlisted veteran living 25 miles from a VATC would have a 62.9% (95% CI, 59.1%-66.1%) 5-year adjusted probability of survival from first hepatic decompensation event compared with a 59.8% (95% CI, 56.3%-63.1%) 5-year adjusted probability of survival for a veteran living 100 miles from a VATC.

CONCLUSIONS AND RELEVANCE

Among VA patients meeting eligibility criteria for liver transplantation, greater distance from a VATC or any transplant center was associated with lower likelihood of being waitlisted, receiving a liver transplant, and greater likelihood of death. The relationship between these findings and centralizing specialized care deserves further investigation.

Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes

Background

Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets.

Results

A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups.

Conclusions

The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease.

The gene vitellogenin affects microRNA regulation in honey bee (Apis mellifera) fat body and brain

In honey bees, Vitellogenin (Vg) is hypothesized to be a major factor affecting hormone signaling, food-related behavior, immunity, stress resistance and lifespan. Likewise microRNAs play important roles in posttranscriptional gene regulation and affect many biological processes. The action of microRNAs and Vg are known to intersect in the context of reproduction; however, the role of these associations on social behavior is unknown. The phenotypic effects of Vg knockdown are best established and studied in the forager stage of workers. Thus, we exploited the well-established RNA interference (RNAi) protocol for Vg knockdown to investigate its downstream effects on microRNA population in honey bee foragers' brain and fat body tissue. To identify microRNAs that are differentially expressed between tissues in control and knockdown foragers, we used µParaflo® microfluidic oligonucleotide microRNA microarrays. Our results show 76 and 74 microRNAs were expressed in the brain of control and knockdown foragers whereas 66 and 69 microRNAs were expressed in the fat body of control and knockdown foragers respectively. Target prediction identified potential seed matches for a differentially expressed subset of microRNAs affected by Vg knockdown. These candidate genes are involved in a broad range of biological processes including insulin signaling, juvenile hormone (JH) and ecdysteroid signaling previously shown to affect foraging behavior. Thus, here we demonstrate a causal link between the Vg knockdown forager phenotype and variation in the abundance of microRNAs in different tissues with possible consequences for regulation of foraging behavior.

Evaluation of candidate reference genes for normalization of quantitative RT-PCR in soybean tissues under various abiotic stress conditions

Quantitative RT-PCR can be a very sensitive and powerful technique for measuring differential gene expression. Changes in gene expression induced by abiotic stresses are complex and multifaceted, which make determining stably expressed genes for data normalization difficult. To identify the most suitable reference genes for abiotic stress studies in soybean, 13 candidate genes collected from literature were evaluated for stability of expression under dehydration, high salinity, cold and ABA (abscisic acid) treatments using delta CT and geNorm approaches. Validation of reference genes indicated that the best reference genes are tissue- and stress-dependent. With respect to dehydration treatment, the Fbox/ABC, Fbox/60s gene pairs were found to have the highest expression stability in the root and shoot tissues of soybean seedlings, respectively. Fbox and 60s genes are the most suitable reference genes across dehydrated root and shoot tissues. Under salt stress the ELF1b/IDE and Fbox/ELF1b are the most stably expressed gene pairs in roots and shoots, respectively, while 60s/Fbox is the best gene pair in both tissues. For studying cold stress in roots or shoots, IDE/60s and Fbox/Act27 are good reference gene pairs, respectively. With regard to gene expression analysis under ABA treatment in either roots, shoots or across these tissues, 60s/ELF1b, ELF1b/Fbox and 60s/ELF1b are the most suitable reference genes, respectively. The expression of ELF1b/60s, 60s/Fbox and 60s/Fbox genes was most stable in roots, shoots and both tissues, respectively, under various stresses studied. Among the genes tested, 60s was found to be the best reference gene in different tissues and under various stress conditions. The highly ranked reference genes identified from this study were proved to be capable of detecting subtle differences in expression rates that otherwise would be missed if a less stable reference gene was used.

Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides

Shotgun proteomics via mass spectrometry (MS) is a powerful technology for biomarker discovery that has the potential to lead to noninvasive disease screening mechanisms. Successful application of MS-based proteomics technologies for biomarker discovery requires accurate expectations of bias, reproducibility, variance, and the true detectable differences in platforms chosen for analyses. Characterization of the variability inherent in MS assays is vital and should affect interpretation of measurements of observed differences in biological samples. Here we describe observed biases, variance structure, and the ability to detect known differences in spike-in data sets for which true relative abundance among defined samples were known and were subsequently measured with the iTRAQ technology on two MS platforms. Global biases were observed within these data sets. Measured variability was a function of mean abundance. Fold changes were biased toward the null and variance of a fold change was a function of protein mass and abundance. The information presented herein will be valuable for experimental design and analysis of the resulting data.

A distinct expression of various gene subsets in CD34+ cells from patients with early and advanced myelodysplastic syndrome

Gene expression profiles of CD34+ cells were compared between 51 MDS patients and 7 controls. The most up-regulated genes in patients included HBG2, HBG1, CYBRD1, HSPA1B, ANGPT, and MYC, while 13 genes related to B-lymphopoiesis showed down-regulation. We observed in advanced MDS patients decreased expression of genes involved in cell cycle control, DNA repair and increased expression of proto-oncogenes, angiogenic and anti-apoptic genes. The results suggest that increased cell proliferation and resistance to apoptosis together with a loss of cell cycle control, damaged DNA repair and altered immune response may play an important role in malignant clone expansion in MDS.

Differential expression profiling between atypical teratoid/rhabdoid and medulloblastoma tumor in vitro and in vivo using microarray analysis

OBJECTIVES

Atypical teratoid/rhabdoid tumor (AT/RT) and medulloblastoma (MB) are the most malignant primary brain tumors in early childhood. AT/RT is frequently misdiagnosed as primitive neuroectodermal tumor/medulloblastoma. The biological features and clinical outcomes of AT/RT and MB are extremely different. In this study, we used microarray as a platform to distinguish these two tumors with the definitive diagnostic marker as well as the profiling of expression genes.

METHODS

In order to clarify the pathogenesis and find the biological markers for AT/RT, we established a derivative AT/RT primary cell culture. The differential profiling between AT/RT and MB were analyzed by using microarray method.

RESULTS

With the use of the microarray method, we demonstrated that 15 genes were significantly changed (at least 5-fold in upregulation and 1/5-fold in downregulation) between AT/RT and MB in tissues and cell lines. The quantitative reverse transcription-polymerase chain reaction analyses further confirmed that mRNA expression levels of SERPINI1 and osteopontin were highly expressed in AT/RT cells and tissues than those in MB. Importantly, our microarray result suggested that AT/RT presents the stemness-like pattern and expression profiling of embryonic stem cells as well as high mRNA expressions of Oct-4, Nanog, Sox-2, and c-Myc.

CONCLUSIONS

Our study demonstrated the differential gene expression profiling between AT/RT and MB. Based on the microarray findings, AT/RTs present embryonic stem-like gene recapitulation and further provide novel insights into their underlying biology.