Microarray analysis

Modernizing the Toolkit for Arthropod Bloodmeal Identification

Simple Summary

The ability to identify the source of vertebrate blood in mosquitoes, ticks, and other blood-feeding arthropod vectors greatly enhances our knowledge of how vector-borne pathogens are spread. The source of the bloodmeal is identified by analyzing the remnants of blood remaining in the arthropod at the time of capture, though this is often fraught with challenges. This review provides a roadmap and guide for those considering modern techniques for arthropod bloodmeal identification with a focus on progress made in the field over the past decade. We highlight genome regions that can be used to identify the vertebrate source of arthropod bloodmeals as well as technological advances made in other fields that have introduced innovative new ways to identify vertebrate meal source based on unique properties of the DNA sequence, protein signatures, or residual molecules present in the blood. Additionally, engineering progress in miniaturization has led to a number of field-deployable technologies that bring the laboratory directly to the arthropods at the site of collection. Although many of these advancements have helped to address the technical challenges of the past, the challenge of successfully analyzing degraded DNA in bloodmeals remains to be solved.

Abstract

Understanding vertebrate–vector interactions is vitally important for understanding the transmission dynamics of arthropod-vectored pathogens and depends on the ability to accurately identify the vertebrate source of blood-engorged arthropods in field collections using molecular methods. A decade ago, molecular techniques being applied to arthropod blood meal identification were thoroughly reviewed, but there have been significant advancements in the techniques and technologies available since that time. This review highlights the available diagnostic markers in mitochondrial and nuclear DNA and discusses their benefits and shortcomings for use in molecular identification assays. Advances in real-time PCR, high resolution melting analysis, digital PCR, next generation sequencing, microsphere assays, mass spectrometry, and stable isotope analysis each offer novel approaches and advantages to bloodmeal analysis that have gained traction in the field. New, field-forward technologies and platforms have also come into use that offer promising solutions for point-of-care and remote field deployment for rapid bloodmeal source identification. Some of the lessons learned over the last decade, particularly in the fields of DNA barcoding and sequence analysis, are discussed. Though many advancements have been made, technical challenges remain concerning the prevention of sample degradation both by the arthropod before the sample has been obtained and during storage. This review provides a roadmap and guide for those considering modern techniques for arthropod bloodmeal identification and reviews how advances in molecular technology over the past decade have been applied in this unique biomedical context.

MicroRNAs in adrenal tumors: relevance for pathogenesis, diagnosis, and therapy

Several lines of evidence support the relevance of microRNAs in both adrenocortical and adrenomedullary (pheochromocytomas) tumors. Significantly differentially expressed microRNAs have been described among benign and malignant adrenocortical tumors and different forms of pheochromocytomas that might affect different pathogenic pathways. MicroRNAs can be exploited as markers of malignancy or disease recurrence. Besides tissue microRNAs, novel data show that microRNAs are released in body fluids, and blood-borne microRNAs can be envisaged as minimally invasive markers of malignancy or prognosis. MicroRNAs might even serve as treatment targets that could expand the rather-limited therapeutic repertoire in the field of adrenal tumors. In this review, we present a critical synopsis of the recent observations made in the field of adrenal tumor-associated microRNAs regarding their pathogenic, diagnostic, and potential therapeutic relevance.

Comparison of eleven methods for genomic DNA extraction suitable for large-scale whole-genome genotyping and long-term DNA banking using blood samples

Over the recent years, next generation sequencing and microarray technologies have revolutionized scientific research with their applications to high-throughput analysis of biological systems. Isolation of high quantities of pure, intact, double stranded, highly concentrated, not contaminated genomic DNA is prerequisite for successful and reliable large scale genotyping analysis. High quantities of pure DNA are also required for the creation of DNA-banks. In the present study, eleven different DNA extraction procedures, including phenol-chloroform, silica and magnetic beads based extractions, were examined to ascertain their relative effectiveness for extracting DNA from ovine blood samples. The quality and quantity of the differentially extracted DNA was subsequently assessed by spectrophotometric measurements, Qubit measurements, real-time PCR amplifications and gel electrophoresis. Processing time, intensity of labor and cost for each method were also evaluated. Results revealed significant differences among the eleven procedures and only four of the methods yielded satisfactory outputs. These four methods, comprising three modified silica based commercial kits (Modified Blood, Modified Tissue, Modified Dx kits) and an in-house developed magnetic beads based protocol, were most appropriate for extracting high quality and quantity DNA suitable for large-scale microarray genotyping and also for long-term DNA storage as demonstrated by their successful application to 600 individuals.

Modeling the Drosophila gene cluster regulation network for muscle development

The development of accurate and reliable dynamical modeling procedures that describe the time evolution of gene expression levels is a prerequisite to understanding and controlling the transcription process. We focused on data from DNA microarray time series for 20 Drosophila genes involved in muscle development during the embryonic stage. Genes with similar expression profiles were clustered on the basis of a translation-invariant and scale-invariant distance measure. The time evolution of these clusters was modeled using coupled differential equations. Three model structures involving a transcription term and a degradation term were tested. The parameters were identified in successive steps: network construction, parameter optimization, and parameter reduction. The solutions were evaluated on the basis of the data reproduction and the number of parameters, as well as on two biology-based requirements: the robustness with respect to parameter variations and the values of the expression levels not being unrealistically large upon extrapolation in time. Various solutions were obtained that satisfied all our evaluation criteria. The regulatory networks inferred from these solutions were compared with experimental data. The best solution has half of the experimental connections, which compares favorably with previous approaches. Biasing the network toward the experimental connections led to the identification of a model that is only slightly less good on the basis of the evaluation criteria. The non-uniqueness of the solutions and the variable agreement with experimental connections were discussed in the context of the different hypotheses underlying this type of approach.

A novel multiplex polymerase chain reaction assay for profile analyses of gene expression in peripheral blood

Background

Studies have demonstrated that inflammation has a key role in the pathogenesis of atherosclerosis due to the abnormal gene expressions of multiple cytokines. We established an accurate and precise method to observe gene expression in whole blood that might provide specific diagnostic information for coronary artery disease (CAD) and other related diseases.

Methods

The fifteen selected CAD-related genes (IL1B, IL6, IL8, IFNG, MCP-1, VWF, MTHFR, SELL, TNFalpha, ubiquitin, MCSF, ICAM1, ID2, HMOX1 and LDLR) and two housekeeping genes (ACTB and GK) as internal references have been measured simultaneously with a newly developed multiplex polymerase chain reaction (multi-PCR) method. Moreover, the precision was evaluated, and a procedure for distinguishing patients from the normal population has been developed based upon analyses of peripheral blood. A total of 148 subjects were divided into group A (control group without plaques), group B (calcified plaques) and group C (non-calcified plaques, and combination group) according dual-source CT criteria. Gene expression in blood was analyzed by multi-PCR, and levels of glucose and lipids measured in 50 subjects to explore the relationship among them.

Results

The precision results of the multi-PCR system revealed within-run and between-run CV values of 3.695–12.537% and 4.405–13.405%, respectively. The profiles of cytokine gene expression in peripheral blood were set: a positive correlation between glucose and MCSF, HMOX1 or TNFalpha were found. We also found that triglyceride levels were negatively correlated with SELL gene expression in 50 subjects. Compared with controls, gene expression levels of IL1B, IL6, IL8 and MCP-1 increased significantly in group C.

Conclusions

A new multiple gene expression analysis system has been developed. The primary data suggested that gene expression was related to CAD. This system might be used for risk assessment of CVDs and other related diseases.

Robust non-linear differential equation models of gene expression evolution across Drosophila development

Background

This paper lies in the context of modeling the evolution of gene expression away from stationary states, for example in systems subject to external perturbations or during the development of an organism. We base our analysis on experimental data and proceed in a top-down approach, where we start from data on a system's transcriptome, and deduce rules and models from it without a priori knowledge. We focus here on a publicly available DNA microarray time series, representing the transcriptome of Drosophila across evolution from the embryonic to the adult stage.

Results

In the first step, genes were clustered on the basis of similarity of their expression profiles, measured by a translation-invariant and scale-invariant distance that proved appropriate for detecting transitions between development stages. Average profiles representing each cluster were computed and their time evolution was analyzed using coupled differential equations. A linear and several non-linear model structures involving a transcription and a degradation term were tested. The parameters were identified in three steps: determination of the strongest connections between genes, optimization of the parameters defining these connections, and elimination of the unnecessary parameters using various reduction schemes. Different solutions were compared on the basis of their abilities to reproduce the data, to keep realistic gene expression levels when extrapolated in time, to show the biologically expected robustness with respect to parameter variations, and to contain as few parameters as possible.

Conclusions

We showed that the linear model did very well in reproducing the data with few parameters, but was not sufficiently robust and yielded unrealistic values upon extrapolation in time. In contrast, the non-linear models all reached the latter two objectives, but some were unable to reproduce the data. A family of non-linear models, constructed from the exponential of linear combinations of expression levels, reached all the objectives. It defined networks with a mean number of connections equal to two, when restricted to the embryonic time series, and equal to five for the full time series. These networks were compared with experimental data about gene-transcription factor and protein-protein interactions. The non-uniqueness of the solutions was discussed in the context of plasticity and cluster versus single-gene networks.

Evolving methods for single nucleotide polymorphism detection: Factor V Leiden mutation detection

BACKGROUND

The many techniques used to diagnose the Factor V Leiden (FVL) mutation, the most common hereditary hypercoagulation disorder in Eurasians, and the most frequently requested genetic test reflect the evolving strategies in protein and DNA diagnosis.

METHODS

Here, molecular methods to diagnose the FVL mutation are discussed.

RESULTS

Protein-based detection assays include the conventional functional activated protein C resistance coagulation test and the recently reported antibody-mediated sensor detection; and DNA-based assays include approaches that use electrophoretic fractionation e.g., restriction fragment length polymorphism, denaturing gradient gel electrophoresis, and single-stranded conformational PCR analysis, DNA hybridization (e.g., microarrays), DNA polymerase-based assays, e.g., extension reactions, fluorescence polarization template-directed dye-terminator incorporation, PCR assays (e.g., amplification-refractory mutation system, melting curve analysis using real-time quantitative PCR, and helicase-dependent amplification), DNA sequencing (e.g., direct sequencing, pyrosequencing), cleavase-based Invader assay and ligase-based assays (e.g., oligonucleotide ligation assay and ligase-mediated rolling circle amplification).

CONCLUSION

The method chosen by a laboratory to diagnose FVL not only depends on the available technical expertise and equipment, but also the type, variety, and extent of other genetic disorders being diagnosed.

Nonlinear dependence in the discovery of differentially expressed genes

Microarray data are used to determine which genes are active in response to a changing cell environment. Genes are “discovered” when they are significantly differentially expressed in the microarray data collected under the differing conditions. In one prevalent approach, all genes are assumed to satisfy a null hypothesis, ℍ₀, of no difference in expression. A false discovery (type 1 error) occurs when ℍ₀ is incorrectly rejected. The quality of a detection algorithm is assessed by estimating its number of false discoveries, 𝔉. Work involving the second-moment modeling of the z-value histogram (representing gene expression differentials) has shown significantly deleterious effects of intergene expression correlation on the estimate of 𝔉. This paper suggests that nonlinear dependencies could likewise be important. With an applied emphasis, this paper extends the “moment framework” by including third-moment skewness corrections in an estimator of 𝔉. This estimator combines observed correlation (corrected for sampling fluctuations) with the information from easily identifiable null cases. Nonlinear-dependence modeling reduces the estimation error relative to that of linear estimation. Third-moment calculations involve empirical densities of 3 × 3 covariance matrices estimated using very few samples. The principle of entropy maximization is employed to connect estimated moments to 𝔉 inference. Model results are tested with BRCA and HIV data sets and with carefully constructed simulations.

Gene expression changes in steroid-induced IOP elevation in bovine trabecular meshwork

PURPOSE

To determine whether gene expression changes occur in the trabecular meshwork (TM) of cow eyes with steroid-induced intraocular pressure (IOP) elevation.

METHODS

Adult female Braford cows (n = 4) were subjected to uniocular prednisolone acetate treatment for 6 weeks. IOP was monitored with an applanation tonometer. At the conclusion of the experiment, animals were euthanized, eyes were enucleated, and the TM was dissected and stored in an aqueous nontoxic tissue storage reagent. RNA was extracted and subjected to microarray analysis using commercial oligonucleotide bovine arrays. Some of the genes differentially expressed between control and experimental eyes were confirmed by quantitative RT-PCR and some of the respective proteins were studied by immunoblotting.

RESULTS

IOP began to increase after 3 weeks of treatment, reaching a peak 2 weeks later. IOP differences between corticosteroid-treated and fellow control eyes were 6 ± 1 mm Hg (mean ± SD) at the conclusion of the study. Microarray analysis revealed that expression of 258 genes was upregulated, whereas expression of 187 genes was downregulated in the TM of eyes with steroid-induced IOP elevation. Genes identified to be differentially expressed include genes coding for cytoskeletal proteins, enzymes, growth and transcription factors, as well as extracellular matrix proteins and immune response proteins. A number of relevant gene networks were detected by bioinformatic analysis.

CONCLUSIONS

Steroid-induced IOP elevation alters gene expression in the bovine TM. Identification of genes with changing expression in this model of open-angle glaucoma may help elucidate the primary changes occurring at the molecular level in this condition.

Retinal gene expression changes related to IOP exposure and axonal loss in DBA/2J mice

PURPOSE

To determine the effects of cumulative IOP exposure and axonal damage on retinal gene expression in DBA/2 mice.

METHODS

DBA/2J, DBA/2J(pe) (pearl), and C57BL/6 mice from 3 to 12 months of age were used. IOP was measured with a rebound tonometer, and optic nerve (ON) damage was determined by grading of ON sections. Retinal RNA was subjected to microarray analysis. Comparisons explored the effects of cumulative IOP exposure (cIOPx) as well as ON damage (ONd) in the DBA/2J animals compared with that in the C57BL/6 and pearl mice. RT-PCR was performed to confirm some of the genes and bioinformatic analysis to identify affected gene networks.

RESULTS

Microarrays revealed that an increasing number of genes were up- or downregulated in 9- and 12-month DBA/2J mice with various degrees of ONd. A smaller number of genes were expressed differentially between eyes with different cIOPx at the same age, from 6 months on. Expression of 1385 and 1133 genes differed between DBA/2J animals and C57BL/6 or pearl mice, respectively, and some them were confirmed by RT-PCR. Bioinformatics analysis identified functional gene networks, including members of the complement system, that appeared to be related to cIOPx, ONd, or both.

CONCLUSIONS

Gene expression changes occur in retinas of DBA/2 mice with various amounts of cIOPx as well as ONd. Genes involved, code for proteins with diverse cellular functions and include among others the complement system. cIOPx and ONd affect common as well as unique gene sets.

Microarray analysis of embryonic stem cells and differentiated embryoid bodies

By altering the cellular microenvironment and culture media composition, embryonic stem cells (ESCs) can be induced to differentiate in vitro into somatic cell types from the three primitive germ layers. ESC differentiation is regulated by an intricate series of signaling events that result in their transcriptional reprogramming, asymmetric cell division, and differentiation. Using various pharmacological agents and/or genetic manipulations, one can drive and enrich ESC differentiation to specific cell lineages. Identifying the transcriptional fingerprint during ESC differentiation could yield novel targets for genetic or pharmacological regulation to increase the yield of desirable cell types. We discuss here how to culture undifferentiated mouse ESCs (E14 line from 129/Ola) and generate embryoid bodies (EBs). We also discuss in detail how to prepare Affymetrix samples, how to hybridize and scan arrays, and how to quality control data and generate signal values and permutation based P-values.

Diagnostic microarrays in hematologic oncology: applications of high- and low-density arrays

Microarrays have become important tools for high-throughput analysis of gene expression, chromosome aberrations, and gene mutations in cancer cells. In addition to high-density experimental microarrays, low-density, gel-based biochip technology represents a versatile platform for translation of research into clinical practice. Gel-based microarrays (biochips) consist of nanoliter gel drops on a hydrophobic surface with different immobilized biopolymers (primarily nucleic acids and proteins). Because of the high immobilization capacity of the gel, such biochips have a high probe concentration and high levels of fluorescence signals after hybridization, which allow the use of simple, portable detection systems. The notable accuracy of the analysis is reached as a result of the high level of discrimination between positive and negative gel-bound probes. Different applications of biochips in the field of hematologic oncology include analysis of chromosomal translocations in leukemias, diagnostics of T-cell lymphomas, and pharmacogenetics.

Whole genome microarray analysis, from neonatal blood cards

Background

Neonatal blood, obtained from a heel stick and stored dry on paper cards, has been the standard for birth defects screening for 50 years. Such dried blood samples are used, primarily, for analysis of small-molecule analytes. More recently, the DNA complement of such dried blood cards has been used for targeted genetic testing, such as for single nucleotide polymorphism in cystic fibrosis. Expansion of such testing to include polygenic traits, and perhaps whole genome scanning, has been discussed as a formal possibility. However, until now the amount of DNA that might be obtained from such dried blood cards has been limiting, due to inefficient DNA recovery technology.

Results

A new technology is employed for efficient DNA release from a standard neonatal blood card. Using standard Guthrie cards, stored an average of ten years post-collection, about 1/40^th of the air-dried neonatal blood specimen (two 3 mm punches) was processed to obtain DNA that was sufficient in mass and quality for direct use in microarray-based whole genome scanning. Using that same DNA release technology, it is also shown that approximately 1/250^th of the original purified DNA (about 1 ng) could be subjected to whole genome amplification, thus yielding an additional microgram of amplified DNA product. That amplified DNA product was then used in microarray analysis and yielded statistical concordance of 99% or greater to the primary, unamplified DNA sample.

Conclusion

Together, these data suggest that DNA obtained from less than 10% of a standard neonatal blood specimen, stored dry for several years on a Guthrie card, can support a program of genome-wide neonatal genetic testing.

Modeling the temporal evolution of the Drosophila gene expression from DNA microarray time series

The time evolution of gene expression across the developmental stages of the host organism can be inferred from appropriate DNA microarray time series. Modeling this evolution aims eventually at improving the understanding and prediction of the complex phenomena that are the basis of life. We focus on the embryonic-to-adult development phases of Drosophila melanogaster, and chose to model the expression network with the help of a system of differential equations with constant coefficients, which are nonlinear in the transcript concentrations but linear in their logarithms. To reduce the dimensionality of the problem, genes having similar expression profiles are grouped into 17 clusters. We show that a simple linear model is able to reproduce the experimental data with very good precision, owing to the large number of parameters that represent the connections between the clusters. Remarkably, the parameter reduction allowed elimination of up to 80-85% of these connections while keeping fairly good precision. This result supports the low-connectivity hypothesis of gene expression networks, with about three connections per cluster, without introducing a priori hypotheses. The core of the network shows a few gene clusters with negative self-regulation, and some highly connected clusters involving proteins with crucial functions.

Analytical validation of the GeXP analyzer and design of a workflow for cancer-biomarker discovery using multiplexed gene-expression profiling

The GeXP genetic analysis system allows for multiplexed detection and quantitation of up to 35 genes in 192 samples in a single analysis. The analytical procedure includes modified reverse transcription and PCR amplification, followed by capillary electrophoretic separation. RNA material from multiple sample types can be used, including blood, cell lines, and tissue material. This instrumentation has a lower limit of detection of <2 ng with a dynamic range greater than two orders of magnitude, as tested in this validation. Precision experiments demonstrate a within-run coefficient-of-variation (CV) = 11.1% at 25 ng and 12.9% at 12.5 ng total RNA for the complete workflow and CV = 4.8% at 25 ng and 6.7% at 12.5 ng levels for the GeXP analysis alone. The between-run precision for the entire workflow was determined to be 25% at 25 ng. We have devised an optimized protocol and use it to successfully identify a gene expression signature capable of discriminating prostate tumor and non-tumor tissue samples. We used a combination of multiplex gene panels to interrogate ~70 genes in our primary screen. Our results demonstrate that a subset of these genes can be used to separate normal and tumor prostate tissue samples. This protocol using the GeXP analyzer allows for a high-throughput, robust, and reproducible assessment of multiplexed gene expression analysis, and can be used for biomarker discovery to compare different sample groups. With a dynamic linear range and satisfactory precision, this technology holds promise for rapidly identifying gene expression signatures from multiplexed reactions of up to 35 genes in large numbers of samples with limited amounts of starting material.

Board-invited review: the biology and regulation of preadipocytes and adipocytes in meat animals

The quality and value of the carcass in domestic meat animals are reflected in its protein and fat content. Preadipocytes and adipocytes are important in establishing the overall fatness of a carcass, as well as being the main contributors to the marbling component needed for consumer preference of meat products. Although some fat accumulation is essential, any excess fat that is deposited into adipose depots other than the marbling fraction is energetically unfavorable and reduces efficiency of production. Hence, this review is focused on current knowledge about the biology and regulation of the important cells of adipose tissue: preadipocytes and adipocytes.