Replacing cRNA targets with cDNA reduces microarray cross-hybridization

What's Wrong in a Jump? Prediction and Validation of Splice Site Variants

Alternative splicing (AS) is a crucial process to enhance gene expression driving organism development. Interestingly, more than 95% of human genes undergo AS, producing multiple protein isoforms from the same transcript. Any alteration (e.g., nucleotide substitutions, insertions, and deletions) involving consensus splicing regulatory sequences in a specific gene may result in the production of aberrant and not properly working proteins. In this review, we introduce the key steps of splicing mechanism and describe all different types of genomic variants affecting this process (splicing variants in acceptor/donor sites or branch point or polypyrimidine tract, exonic, and deep intronic changes). Then, we provide an updated approach to improve splice variants detection. First, we review the main computational tools, including the recent Machine Learning-based algorithms, for the prediction of splice site variants, in order to characterize how a genomic variant interferes with splicing process. Next, we report the experimental methods to validate the predictive analyses are defined, distinguishing between methods testing RNA (transcriptomics analysis) or proteins (proteomics experiments). For both prediction and validation steps, benefits and weaknesses of each tool/procedure are accurately reported, as well as suggestions on which approaches are more suitable in diagnostic rather than in clinical research.

Genome-wide transcriptome profiling uncovers differential miRNAs and lncRNAs in ovaries of Hu sheep at different developmental stages

Ovary development is an important determinant of the procreative capacity of female animals. Here, we performed genome-wide sequencing of long non-coding RNAs (lncRNAs) and mRNAs on ovaries of 1, 3 and 8 months old Hu sheep to assess their expression profiles and roles in ovarian development. We identified 37,309 lncRNAs, 45,404 messenger RNAs (mRNAs) and 330 novel micro RNAs (miRNAs) from the transcriptomic analysis. Six thousand, seven hundred and sixteen (6716) mRNAs and 1972 lncRNAs were significantly and differentially expressed in ovaries of 1 month and 3 months old Hu sheep (H1 vs H3). These mRNAs and target genes of lncRNAs were primarily enriched in the TGF-β and PI3K-Akt signalling pathways which are closely associated with ovarian follicular development and steroid hormone biosynthesis regulation. We identified MSTRG.162061.1, MSTRG.222844.7, MSTRG.335777.1, MSTRG.334059.16, MSTRG.188947.6 and MSTRG.24344.3 as vital genes in ovary development by regulating CTNNB1, CCNA2, CDK2, CDC20, CDK1 and EGFR expressions. A total of 2903 mRNAs and 636 lncRNAs were differentially expressed in 3 and 8 months old ovaries of Hu sheep (H3 vs H8); and were predominantly enriched in PI3K-Akt, progesterone-mediated oocyte maturation, estrogen metabolism, ovulation from the ovarian follicle and oogenesis pathways. These lncRNAs were also found to regulate FGF7, PRLR, PTK2, AMH and INHBA expressions during follicular development. Our result indicates the identified genes participate in the development of the final stages of follicles and ovary development in Hu sheep.

Differential Hepatic Gene Expression Profile of Male Fathead Minnows Exposed to Daily Varying Dose of Environmental Contaminants Individually and in Mixture

Environmental contaminants are known to impair reproduction, metabolism and development in wild life and humans. To investigate the mechanisms underlying adverse effects of contaminants, fathead minnows were exposed to a number of endocrine disruptive chemicals (EDCs) including Nonylphenol (NP), bisphenol-A (BPA), Di(2-ethylhexyl) phthalate (DEHP), and a mixture of the three chemicals for 21 days, followed by determination of the liver transcriptome by expression microarrays. Pathway analysis revealed a distinct mode of action for the individual chemicals and their mixture. The results showed expression changes in over 980 genes in response to exposure to these EDC contaminants individually and in mixture. Ingenuity Pathway core and toxicity analysis were used to identify the biological processes, pathways and the top regulators affected by these compounds. A number of canonical pathways were significantly altered, including cell cycle & proliferation, lipid metabolism, inflammatory, innate immune response, stress response, and drug metabolism. We identified 18 genes that were expressed in all individual and mixed treatments. Relevant candidate genes identified from expression microarray data were verified using quantitative PCR. We were also able to identify specific genes affected by NP, BPA, and DEHP individually, but were also affected by exposure to the mixture of the contaminants. Overall the results of this study provide novel information on the adverse health impact of contaminants tested based on pathway analysis of transcriptome data. Furthermore, the results identify a number of new biomarkers that can potentially be used for screening environmental contaminants.

Sigma Factor Regulated Cellular Response in a Non-solvent Producing Clostridium beijerinckii Degenerated Strain: A Comparative Transcriptome Analysis

Clostridium beijerinckii DG-8052, derived from NCIMB 8052, cannot produce solvent or form spores, a phenomenon known as degeneration. To explore the mechanisms of degeneration at the gene level, transcriptomic profiles of the wild-type 8052 and DG-8052 strains were compared. Expression of 5168 genes comprising 98.6% of the genome was assessed. Interestingly, 548 and 702 genes were significantly up-regulated in the acidogenesis and solventogenesis phases of DG-8052, respectively, and mainly responsible for the phosphotransferase system, sugar metabolic pathways, and chemotaxis; meanwhile, 699 and 797 genes were significantly down-regulated, respectively, and mainly responsible for sporulation, oxidoreduction, and solventogenesis. The functions of some altered genes, including 286 and 333 at the acidogenesis and solventogenesis phases, respectively, remain unknown. Dysregulation of the fermentation machinery was accompanied by lower transcription levels of glycolysis rate-limiting enzymes (pfk and pyk), and higher transcription of cell chemotaxis genes (cheA, cheB, cheR, cheW, and cheY), controlled mainly by σ⁵⁴ at acidogenesis. Meanwhile, abnormal spore formation was associated with repressed spo0A, sigE, sigF, sigG, and sigK which are positively regulated by σ⁷⁰, and correspondingly inhibited expression of CoA-transferase at the solventogenesis phase. These findings indicated that morphological and physiological changes in the degenerated Clostridium strain may be related to altered expression of sigma factors, providing valuable targets for strain development of Clostridium species.

Genome-wide transcriptome analysis in the ovaries of two goats identifies differentially expressed genes related to fecundity

The goats are widely kept as livestock throughout the world. Two excellent domestic breeds in China, the Laiwu Black and Jining Grey goats, have different fecundities and prolificacies. Although the goat genome sequences have been resolved recently, little is known about the gene regulations at the transcriptional level in goat. To understand the molecular and genetic mechanisms related to the fecundities and prolificacies, we performed genome-wide sequencing of the mRNAs from two breeds of goat using the next-generation RNA-Seq technology and used functional annotation to identify pathways of interest. Digital gene expression analysis showed 338 genes were up-regulated in the Jining Grey goats and 404 were up-regulated in the Laiwu Black goats. Quantitative real-time PCR verified the reliability of the RNA-Seq data. This study suggests that multiple genes responsible for various biological functions and signaling pathways are differentially expressed in the two different goat breeds, and these genes might be involved in the regulation of goat fecundity and prolificacy. Taken together, our study provides insight into the transcriptional regulation in the ovaries of 2 species of goats that might serve as a key resource for understanding goat fecundity, prolificacy and genetic diversity between species.

Investigation of Transcriptional Gene Profiling in Normal Murine Hair Follicular Substructures Using Next-Generation Sequencing to Provide Potential Insights Into Skin Disease

Skin diseases, including hair-related diseases and neoplasia, are a major public health problem. While their prevalence is increasing, their treatment options are limited. Researchers have tried to investigate the genes and signal pathways underlying hair follicles (HFs) to develop genetically targeted therapies through microarrays, which represent an appropriate modality for the analysis of small genomes. To enable the comprehensive transcriptome analysis of large and/or complex transcriptomes, we performed RNA-seq using next-generation sequencing (NGS). We isolated interfollicular keratinocytes (IFKs), HFs, and dermal fibroblasts including dermal papilla cells (DFs-DPCs) from normal C57BL/6 murine skin, transplanted combinations of these samples into nude mice, and followed the mice over time. Sustained hair growth was supported by HFs and DFs-DPCs. We then investigated the pathways and the relevant gene ontology associated with any identified differentially expressed genes (DEGs). In addition, in the culture and flow cytometry (FCM), the HFs had a more quiescent cell cycle pattern than did the IFKs and DFs-DPCs. Therefore, the representative cell cycle-related gene expression of IFKs, HFs, and DFs-DPCs was analyzed by NGS. Our study will allow researchers to further investigate the potential interactions and signaling pathways that are active in HF-related diseases and cancer and may aid in future bioengineering applications.

Dual RNA sequencing reveals the expression of unique transcriptomic signatures in lipopolysaccharide-induced BV-2 microglial cells

Microglial cells become rapidly activated through interactions with pathogens, and the persistent activation of these cells is associated with various neurodegenerative diseases. Previous studies have investigated the transcriptomic signatures in microglia or macrophages using microarray technologies. However, this method has numerous restrictions, such as spatial biases, uneven probe properties, low sensitivity, and dependency on the probes spotted. To overcome this limitation and identify novel transcribed genes in response to LPS, we used RNA Sequencing (RNA-Seq) to determine the novel transcriptomic signatures in BV-2 microglial cells. Sequencing assessment and quality evaluation showed that approximately 263 and 319 genes (≥ 1.5 log₂-fold), such as cytokines and chemokines, were strongly induced after 2 and 4 h, respectively, and the induction of several genes with unknown immunological functions was also observed. Importantly, we observed that previously unidentified transcription factors (TFs) (irf1, irf7, and irf9), histone demethylases (kdm4a) and DNA methyltransferases (dnmt3l) were significantly and selectively expressed in BV-2 microglial cells. The gene expression levels, transcription start sites (TSS), isoforms, and differential promoter usage revealed a complex pattern of transcriptional and post-transcriptional gene regulation upon infection with LPS. In addition, gene ontology, molecular networks and pathway analyses identified the top significantly regulated functional classification, canonical pathways and network functions at each activation status. Moreover, we further analyzed differentially expressed genes to identify transcription factor (TF) motifs (−950 to +50 bp of the 5’ upstream promoters) and epigenetic mechanisms. Furthermore, we confirmed that the expressions of key inflammatory genes as well as pro-inflammatory mediators in the supernatants were significantly induced in LPS treated primary microglial cells. This transcriptomic analysis is the first to show a comparison of the family-wide differential expression of most known immune genes and also reveal transcription evidence of multiple gene families in BV-2 microglial cells. Collectively, these findings reveal unique transcriptomic signatures in BV-2 microglial cells required for homeostasis and effective immune responses.

Antibodies specific for nucleic acids and applications in genomic detection and clinical diagnostics

Detection of nucleic acids using antibodies is uncommon. This is in part because nucleic acids are poor immunogens and it is difficult to elicit antibodies having high affinity to each type of nucleic acid while lacking cross-reactivity to others. We describe the origins and applications of a variety of anti-nucleic acid antibodies, including ones reacting with modified nucleosides and nucleotides, single-stranded DNA, double-stranded DNA, RNA, DNA:RNA hybrids, locked-nucleic acids or peptide nucleic acid:nucleic acid hybrids. Carefully selected antibodies can be excellent reagents for detecting bacteria, viruses, small RNAs, microRNAs, R-loops, cancer cells, stem cells, apoptotic cells and so on. The detection may be sensitive, simple, rapid, specific, reproducible, quantitative and cost-effective. Current microarray and diagnostic methods that depend on cDNA or cRNA can be replaced by using antibody detection of nucleic acids. Therefore, development should be encouraged to explore new utilities and create a robust arsenal of new anti-nucleic acid antibodies.

Detecting and comparing non-coding RNAs in the high-throughput era

In recent years there has been a growing interest in the field of non-coding RNA. This surge is a direct consequence of the discovery of a huge number of new non-coding genes and of the finding that many of these transcripts are involved in key cellular functions. In this context, accurately detecting and comparing RNA sequences has become important. Aligning nucleotide sequences is a key requisite when searching for homologous genes. Accurate alignments reveal evolutionary relationships, conserved regions and more generally any biologically relevant pattern. Comparing RNA molecules is, however, a challenging task. The nucleotide alphabet is simpler and therefore less informative than that of amino-acids. Moreover for many non-coding RNAs, evolution is likely to be mostly constrained at the structural level and not at the sequence level. This results in very poor sequence conservation impeding comparison of these molecules. These difficulties define a context where new methods are urgently needed in order to exploit experimental results to their full potential. This review focuses on the comparative genomics of non-coding RNAs in the context of new sequencing technologies and especially dealing with two extremely important and timely research aspects: the development of new methods to align RNAs and the analysis of high-throughput data.

Androgen inhibits abdominal fat accumulation and negatively regulates the PCK1 gene in male chickens

Capons are male chickens whose testes have been surgically incised. Capons show a significant increase in fat accumulation compared to intact male chickens. However, while caponization leads to a significant reduction in androgen levels in roosters, little is known about the molecular mechanisms through which androgen status affects lipogenesis in avian species. Therefore, investigation of the influence of androgens on fat accumulation in the chicken will provide insights into this process. In this study, Affymetrix microarray technology was used to analyze the gene expression profiles of livers from capons and intact male chickens because the liver is the major site of lipogenesis in avian species. Through gene ontology, we found that genes involved in hepatic lipogenic biosynthesis were the most highly enriched. Interestingly, among the upregulated genes, the cytosolic form of the phosphoenolpyruvate carboxykinase (PCK1) gene showed the greatest fold change. Additionally, in conjunction with quantitative real-time PCR data, our results suggested that androgen status negatively regulated the PCK1 gene in male chickens.

Optimization of a high-throughput whole blood expression profiling methodology and its application to assess the pharmacodynamics of interferon (IFN) beta-1a or polyethylene glycol-conjugated IFN beta-1a in healthy clinical trial subjects

Background

Clinical trials offer a unique opportunity to study human disease and response to therapy in a highly controlled setting. The application of high-throughput expression profiling to peripheral blood from clinical trial subjects could facilitate the identification of transcripts that function as prognostic or diagnostic markers of disease or treatment. The paramount issue for these methods is the ability to produce robust, reproducible, and timely mRNA expression profiles from peripheral blood. Single-stranded complementary DNA (sscDNA) targets derived from whole blood exhibit improved detection of transcripts and reduced variance as compared to their complementary RNA counterparts and therefore provide a better option for interrogation of peripheral blood on oligonucleotide arrays. High-throughput microarray technologies such as the high-throughput plate array platform offer several advantages compared with slide- or cartridge-based arrays; however, manufacturer’s protocols do not support the use of sscDNA targets.

Results

We have developed a highly reproducible, high-through put, whole blood expression profiling methodology based on sscDNA and used it to analyze human brain reference RNA and universal human reference RNA samples to identify experimental conditions that most highly correlated with a gold standard quantitative polymerase chain reaction reference dataset. We then utilized the optimized method to analyze whole blood samples from healthy clinical trial subjects treated with different versions of interferon (IFN) beta-1a. Analysis of whole blood samples before and after treatment with intramuscular [IM] IFN beta-1a or polyethylene glycol-conjugated IFN (PEG-IFN) beta-1a under optimized experimental conditions demonstrated that PEG-IFN beta-1a induced a more sustained and prolonged pharmacodynamic response than unmodified IM IFN beta-1a. These results provide validation of the utility of this new methodology and suggest the potential therapeutic benefit of a sustained pharmacodynamic response to PEG-IFN beta-1a.

Conclusions

This novel microarray methodology is ideally suited for utilization in large clinical studies to identify expressed transcripts for the elucidation of disease mechanisms of action and as prognostic, diagnostic, or toxicity markers.

Cross-study homogeneity of psoriasis gene expression in skin across a large expression range

BACKGROUND

In psoriasis, only limited overlap between sets of genes identified as differentially expressed (psoriatic lesional vs. psoriatic non-lesional) was found using statistical and fold-change cut-offs. To provide a framework for utilizing prior psoriasis data sets we sought to understand the consistency of those sets.

METHODOLOGY/PRINCIPAL FINDINGS

Microarray expression profiling and qRT-PCR were used to characterize gene expression in PP and PN skin from psoriasis patients. cDNA (three new data sets) and cRNA hybridization (four existing data sets) data were compared using a common analysis pipeline. Agreement between data sets was assessed using varying qualitative and quantitative cut-offs to generate a DEG list in a source data set and then using other data sets to validate the list. Concordance increased from 67% across all probe sets to over 99% across more than 10,000 probe sets when statistical filters were employed. The fold-change behavior of individual genes tended to be consistent across the multiple data sets. We found that genes with <2-fold change values were quantitatively reproducible between pairs of data-sets. In a subset of transcripts with a role in inflammation changes detected by microarray were confirmed by qRT-PCR with high concordance. For transcripts with both PN and PP levels within the microarray dynamic range, microarray and qRT-PCR were quantitatively reproducible, including minimal fold-changes in IL13, TNFSF11, and TNFRSF11B and genes with >10-fold changes in either direction such as CHRM3, IL12B and IFNG.

CONCLUSIONS/SIGNIFICANCE

Gene expression changes in psoriatic lesions were consistent across different studies, despite differences in patient selection, sample handling, and microarray platforms but between-study comparisons showed stronger agreement within than between platforms. We could use cut-offs as low as log10(ratio) = 0.1 (fold-change = 1.26), generating larger gene lists that validate on independent data sets. The reproducibility of PP signatures across data sets suggests that different sample sets can be productively compared.

Deep RNA sequencing reveals novel cardiac transcriptomic signatures for physiological and pathological hypertrophy

Although both physiological hypertrophy (PHH) and pathological hypertrophy (PAH) of the heart have similar morphological appearances, only PAH leads to fatal heart failure. In the present study, we used RNA sequencing (RNA-Seq) to determine the transcriptomic signatures for both PHH and PAH. Approximately 13-20 million reads were obtained for both models, among which PAH showed more differentially expressed genes (DEGs) (2,041) than PHH (245). The expression of 417 genes was barely detectable in the normal heart but was suddenly activated in PAH. Among them, Foxm1 and Plk1 are of particular interest, since Ingenuity Pathway Analysis (IPA) using DEGs and upstream motif analysis showed that they are essential hub proteins that regulate the expression of downstream proteins associated with PAH. Meanwhile, 52 genes related to collagen, chemokines, and actin showed opposite expression patterns between PHH and PAH. MAZ-binding motifs were enriched in the upstream region of the participating genes. Alternative splicing (AS) of exon variants was also examined using RNA-Seq data for PAH and PHH. We found 317 and 196 exon inclusions and exon exclusions, respectively, for PAH, and 242 and 172 exon inclusions and exclusions, respectively for PHH. The AS pattern was mostly related to gains or losses of domains, changes in activity, and localization of the encoded proteins. The splicing variants of 8 genes (i.e., Fhl1, Rcan1, Ndrg2, Synpo, Ttll1, Cxxc5, Egfl7, and Tmpo) were experimentally confirmed. Multilateral pathway analysis showed that the patterns of quantitative (DEG) and qualitative (AS) changes differ depending on the type of pathway in PAH and PHH. One of the most significant changes in PHH is the severe downregulation of autoimmune pathways accompanied by significant AS. These findings revealed the unique transcriptomic signatures of PAH and PHH and also provided a more comprehensive understanding at both the quantitative and qualitative levels.

A Wnt-bmp feedback circuit controls intertissue signaling dynamics in tooth organogenesis

Many vertebrate organs form through the sequential and reciprocal exchange of signaling molecules between juxtaposed epithelial and mesenchymal tissues. We undertook a systems biology approach that combined the generation and analysis of large-scale spatiotemporal gene expression data with mouse genetic experiments to gain insight into the mechanisms that control epithelial-mesenchymal signaling interactions in the developing mouse molar tooth. We showed that the shift in instructive signaling potential from dental epithelium to dental mesenchyme was accompanied by temporally coordinated genome-wide changes in gene expression in both compartments. To identify the mechanism responsible, we developed a probabilistic technique that integrates regulatory evidence from gene expression data and from the literature to reconstruct a gene regulatory network for the epithelial and mesenchymal compartments in early tooth development. By integrating these epithelial and mesenchymal gene regulatory networks through the action of diffusible extracellular signaling molecules, we identified a key epithelial-mesenchymal intertissue Wnt-Bmp (bone morphogenetic protein) feedback circuit. We then validated this circuit in vivo with compound genetic mutations in mice that disrupted this circuit. Moreover, mathematical modeling demonstrated that the structure of the circuit accounted for the observed reciprocal signaling dynamics. Thus, we have identified a critical signaling circuit that controls the coordinated genome-wide expression changes and reciprocal signaling molecule dynamics that occur in interacting epithelial and mesenchymal compartments during organogenesis.

Genetical genomics for evolutionary studies

Genetical genomics combines acquired high-throughput genomic data with genetic analysis. In this chapter, we discuss the application of genetical genomics for evolutionary studies, where new high-throughput molecular technologies are combined with mapping quantitative trait loci (QTL) on the genome in segregating populations.The recent explosion of high-throughput data--measuring thousands of proteins and metabolites, deep sequencing, chromatin, and methyl-DNA immunoprecipitation--allows the study of the genetic variation underlying quantitative phenotypes, together termed xQTL. At the same time, mining information is not getting easier. To deal with the sheer amount of information, powerful statistical tools are needed to analyze multidimensional relationships. In the context of evolutionary computational biology, a well-designed experiment may help dissect a complex evolutionary trait using proven statistical methods for associating phenotypical variation with genomic locations.Evolutionary expression QTL (eQTL) studies of the last years focus on gene expression adaptations, mapping the gene expression landscape, and, tentatively, eQTL networks. Here, we discuss the possibility of introducing an evolutionary prior, in the form of gene families displaying evidence of positive selection, and using that in the context of an eQTL experiment for elucidating host-pathogen protein-protein interactions. Through the example of an experimental design, we discuss the choice of xQTL platform, analysis methods, and scope of results. The resulting eQTL can be matched, resulting in putative interacting genes and their regulators. In addition, a prior may help distinguish QTL causality from reactivity, or independence of traits, by creating QTL networks.

DLK1-DIO3 genomic imprinted microRNA cluster at 14q32.2 defines a stemlike subtype of hepatocellular carcinoma associated with poor survival

Hepatocellular carcinoma (HCC) is a heterogeneous and highly aggressive malignancy, for which there are no effective cures. Identification of a malignant stemlike subtype of HCC may offer patients with a dismal prognosis a potential targeted therapy using c-MET and Wnt pathway inhibitors. MicroRNAs (miRNAs) show promise as diagnostic and prognostic tools for cancer detection and stratification. Using a TRE-c-Met-driven transgenic HCC mouse model, we identified a cluster of 23 miRNAs that is encoded within the Dlk1-Gtl2 imprinted region on chromosome 12qF1 overexpressed in all of the isolated liver tumors. Interestingly, this region is conserved among mammalian species and maps to the human DLK1-DIO3 region on chromosome 14q32.2. We thus examined the expression of the DLK1-DIO3 miRNA cluster in a cohort of 97 hepatitis B virus-associated HCC patients and identified a subgroup (n = 18) of patients showing strong coordinate overexpression of miRNAs in this cluster but not in other cancer types (breast, lung, kidney, stomach, and colon) that were tested. Expression levels of imprinted gene transcripts from neighboring loci in this 14q32.2 region and from a subset of other imprinted sites were concomitantly elevated in human HCC. Interestingly, overexpression of the DLK1-DIO3 miRNA cluster was positively correlated with HCC stem cell markers (CD133, CD90, EpCAM, Nestin) and associated with a high level of serum α-fetoprotein, a conventional biomarker for liver cancer, and poor survival rate in HCC patients. In conclusion, our findings suggest that coordinate up-regulation of the DLK1-DIO3 miRNA cluster at 14q32.2 may define a novel molecular (stem cell-like) subtype of HCC associated with poor prognosis.

Global transcriptomic profiling using small volumes of whole blood: a cost-effective method for translational genomic biomarker identification in small animals

Blood is an ideal tissue for the identification of novel genomic biomarkers for toxicity or efficacy. However, using blood for transcriptomic profiling presents significant technical challenges due to the transcriptomic changes induced by ex vivo handling and the interference of highly abundant globin mRNA. Most whole blood RNA stabilization and isolation methods also require significant volumes of blood, limiting their effective use in small animal species, such as rodents. To overcome these challenges, a QIAzol-based RNA stabilization and isolation method (QSI) was developed to isolate sufficient amounts of high quality total RNA from 25 to 500 μL of rat whole blood. The method was compared to the standard PAXgene Blood RNA System using blood collected from rats exposed to saline or lipopolysaccharide (LPS). The QSI method yielded an average of 54 ng total RNA per μL of rat whole blood with an average RNA Integrity Number (RIN) of 9, a performance comparable with the standard PAXgene method. Total RNA samples were further processed using the NuGEN Ovation Whole Blood Solution system and cDNA was hybridized to Affymetrix Rat Genome 230 2.0 Arrays. The microarray QC parameters using RNA isolated with the QSI method were within the acceptable range for microarray analysis. The transcriptomic profiles were highly correlated with those using RNA isolated with the PAXgene method and were consistent with expected LPS-induced inflammatory responses. The present study demonstrated that the QSI method coupled with NuGEN Ovation Whole Blood Solution system is cost-effective and particularly suitable for transcriptomic profiling of minimal volumes of whole blood, typical of those obtained with small animal species.

microRNA-122 as a regulator of mitochondrial metabolic gene network in hepatocellular carcinoma

A moderate loss of miR-122 function correlates with up-regulation of seed-matched genes and down-regulation of mitochondrially localized genes in both human hepatocellular carcinoma and in normal mice treated with anti-miR-122 antagomir.

Putative direct targets up-regulated with loss of miR-122 and secondary targets down-regulated with loss of miR-122 are conserved between human beings and mice and are rapidly regulated in vitro in response to miR-122 over- and under-expression.

Loss of miR-122 secondary target expression in either tumorous or adjacent non-tumorous tissue predicts poor survival of heptatocellular carcinoma patients.

Hepatocellular carcinoma (HCC) is one of the most aggressive human malignancies, common in Asia, Africa, and in areas with endemic infections of hepatitis-B or -C viruses (HBV or HCV) (But et al, 2008). Globally, the 5-year survival rate of HCC is <5% and about 600 000 HCC patients die each year. The high mortality associated with this disease is mainly attributed to the failure to diagnose HCC patients at an early stage and a lack of effective therapies for patients with advanced stage HCC. Understanding the relationships between phenotypic and molecular changes in HCC is, therefore, of paramount importance for the development of improved HCC diagnosis and treatment methods.

In this study, we examined mRNA and microRNA (miRNA)-expression profiles of tumor and adjacent non-tumor liver tissue from HCC patients. The patient population was selected from a region of endemic HBV infection, and HBV infection appears to contribute to the etiology of HCC in these patients. A total of 96 HCC patients were included in the study, of which about 88% tested positive for HBV antigen; patients testing positive for HCV antigen were excluded. Among the 220 miRNAs profiled, miR-122 was the most highly expressed miRNA in liver, and its expression was decreased almost two-fold in HCC tissue relative to adjacent non-tumor tissue, confirming earlier observations (Lagos-Quintana et al, 2002; Kutay et al, 2006; Budhu et al, 2008).

Over 1000 transcripts were correlated and over 1000 transcripts were anti-correlated with miR-122 expression. Consistent with the idea that transcripts anti-correlated with miR-122 are potential miR-122 targets, the most highly anti-correlated transcripts were highly enriched for the presence of the miR-122 central seed hexamer, CACTCC, in the 3′UTR. Although the complete set of negatively correlated genes was enriched for cell-cycle genes, the subset of seed-matched genes had no significant KEGG Pathway annotation, suggesting that miR-122 is unlikely to directly regulate the cell cycle in these patients. In contrast, transcripts positively correlated with miR-122 were not enriched for 3′UTR seed matches to miR-122. Interestingly, these 1042 transcripts were enriched for genes coding for mitochondrially localized proteins and for metabolic functions.

To analyze the impact of loss of miR-122 in vivo, silencing of miR-122 was performed by antisense inhibition (anti-miR-122) in wild-type mice (Figure 3). As with the genes negatively correlated with miR-122 in HCC patients, no significant biological annotation was associated with the seed-matched genes up-regulated by anti-miR-122 in mouse livers. The most significantly enriched biological annotation for anti-miR-122 down-regulated genes, as for positively correlated genes in HCC, was mitochondrial localization; the down-regulated mitochondrial genes were enriched for metabolic functions. Putative direct and downstream targets with orthologs on both the human and mouse microarrays showed significant overlap for regulations in the same direction. These overlaps defined sets of putative miR-122 primary and secondary targets. The results were further extended in the analysis of a separate dataset from 180 HCC, 40 cirrhotic, and 6 normal liver tissue samples (Figure 4), showing anti-correlation of proposed primary and secondary targets in non-healthy tissues.

To validate the direct correlation between miR-122 and some of the primary and secondary targets, we determined the expression of putative targets after transfection of miR-122 mimetic into PLC/PRF/5 HCC cells, including the putative direct targets SMARCD1 and MAP3K3 (MEKK3), a target described in the literature, CAT-1 (SLC7A1), and three putative secondary targets, PPARGC1A (PGC-1α) and succinate dehydrogenase subunits A and B. As expected, the putative direct targets showed reduced expression, whereas the putative secondary target genes showed increased expression in cells over-expressing miR-122 (Figure 4).

Functional classification of genes using the total ancestry method (Yu et al, 2007) identified PPARGC1A (PGC-1α) as the most connected secondary target. PPARGC1A has been proposed to function as a master regulator of mitochondrial biogenesis (Ventura-Clapier et al, 2008), suggesting that loss of PPARGC1A expression may contribute to the loss of mitochondrial gene expression correlated with loss of miR-122 expression. To further validate the link of miR-122 and PGC-1α protein, we transfected PLC/PRF/5 cells with miR-122-expression vector, and observed an increase in PGC-1α protein levels. Importantly, transfection of both miR-122 mimetic and miR-122-expression vector significantly reduced the lactate content of PLC/PRF/5 cells, whereas anti-miR-122 treatment increased lactate production. Together, the data support the function of miR-122 in mitochondrial metabolic functions.

Patient survival was not directly associated with miR-122-expression levels. However, miR-122 secondary targets were expressed at significantly higher levels in both tumor and adjacent non-tumor tissues among survivors as compared with deceased patients, providing supporting evidence for the potential relevance of loss of miR-122 function in HCC patient morbidity and mortality.

Overall, our findings reveal potentially new biological functions for miR-122 in liver physiology. We observed decreased expression of miR-122, a liver-specific miRNA, in HBV-associated HCC, and loss of miR-122 seemed to correlate with the decrease of mitochondrion-related metabolic pathway gene expression in HCC and in non-tumor liver tissues, a result that is consistent with the outcome of treatment of mice with anti-miR-122 and is of prognostic significance for HCC patients. Further investigation will be conducted to dissect the regulatory function of miR-122 on mitochondrial metabolism in HCC and to test whether increasing miR-122 expression can improve mitochondrial function in liver and perhaps in liver tumor tissues. Moreover, these results support the idea that primary targets of a given miRNA may be distributed over a variety of functional categories while resulting in a coordinated secondary response, potentially through synergistic action (Linsley et al, 2007).

Tumorigenesis involves multistep genetic alterations. To elucidate the microRNA (miRNA)–gene interaction network in carcinogenesis, we examined their genome-wide expression profiles in 96 pairs of tumor/non-tumor tissues from hepatocellular carcinoma (HCC). Comprehensive analysis of the coordinate expression of miRNAs and mRNAs reveals that miR-122 is under-expressed in HCC and that increased expression of miR-122 seed-matched genes leads to a loss of mitochondrial metabolic function. Furthermore, the miR-122 secondary targets, which decrease in expression, are good prognostic markers for HCC. Transcriptome profiling data from additional 180 HCC and 40 liver cirrhotic patients in the same cohort were used to confirm the anti-correlation of miR-122 primary and secondary target gene sets. The HCC findings can be recapitulated in mouse liver by silencing miR-122 with antagomir treatment followed by gene-expression microarray analysis. In vitro miR-122 data further provided a direct link between induction of miR-122-controlled genes and impairment of mitochondrial metabolism. In conclusion, miR-122 regulates mitochondrial metabolism and its loss may be detrimental to sustaining critical liver function and contribute to morbidity and mortality of liver cancer patients.

2-amino-nonyl-6-methoxyl-tetralin muriate activity against Candida albicans augments endogenous reactive oxygen species production --a microarray analysis study

Candida infections have become an increasingly significant problem, mainly because of the widespread nature of Candida and drug resistance. There is an urgent need to develop new classes of drugs for the treatment of opportunistic Candida infections, especially in medically complex patients. Previous studies have confirmed that 2-amino-nonyl-6-methoxyl-tetralin muriate (10b) possesses powerful antifungal activity in vitro against Candia albicans. To clarify the underlying action mechanism, an oligonucleotide microarray study was performed in C. albicans SC5314 without and with 10b treatment. The analytical results showed that energy metabolism-related genes, including glycolysis-related genes (PFK1, CDC19 and HXK2), fermentation-related genes (PDC11, ALD5 and ADH1) and respiratory electron transport chain-related genes (CBP3, COR1 and QCR8), were downregulated significantly. Functional analysis revealed that 10b treatment increased the generation of endogenous reactive oxygen species, and decreased mitochondrial membrane potential, ubiquinone-cytochrome c reductase (complex III) activity and intracellular ATP levels in C. albicans SC5314. Also, addition of the antioxidant ascorbic acid reduced the antifungal activity of 10b significantly. These results suggest that mitochondrial aerobic respiration shift and endogenous reactive oxygen species augmentation might contribute to the antifungal activity of 10b against C. albicans. This information may prove to be useful for the development of new strategies to treat Candida infections.

Accurate expression profiling of very small cell populations

BACKGROUND

Expression profiling, the measurement of all transcripts of a cell or tissue type, is currently the most comprehensive method to describe their physiological states. Given that accurate profiling methods currently available require RNA amounts found in thousands to millions of cells, many fields of biology working with specialized cell types cannot use these techniques because available cell numbers are limited. Currently available alternative methods for expression profiling from nanograms of RNA or from very small cell populations lack a broad validation of results to provide accurate information about the measured transcripts.

METHODS AND FINDINGS

We provide evidence that currently available methods for expression profiling of very small cell populations are prone to technical noise and therefore cannot be used efficiently as discovery tools. Furthermore, we present Pico Profiling, a new expression profiling method from as few as ten cells, and we show that this approach is as informative as standard techniques from thousands to millions of cells. The central component of Pico Profiling is Whole Transcriptome Amplification (WTA), which generates expression profiles that are highly comparable to those produced by others, at different times, by standard protocols or by Real-time PCR. We provide a complete workflow from RNA isolation to analysis of expression profiles.

CONCLUSIONS

Pico Profiling, as presented here, allows generating an accurate expression profile from cell populations as small as ten cells.

cDNA targets improve whole blood gene expression profiling and enhance detection of pharmocodynamic biomarkers: a quantitative platform analysis

Background

Genome-wide gene expression profiling of whole blood is an attractive method for discovery of biomarkers due to its non-invasiveness, simple clinical site processing and rich biological content. Except for a few successes, this technology has not yet matured enough to reach its full potential of identifying biomarkers useful for clinical prognostic and diagnostic applications or in monitoring patient response to therapeutic intervention. A variety of technical problems have hampered efforts to utilize this technology for identification of biomarkers. One significant hurdle has been the high and variable concentrations of globin transcripts in whole blood total RNA potentially resulting in non-specific probe binding and high background. In this study, we investigated and quantified the power of three whole blood profiling approaches to detect meaningful biological expression patterns.

Methods

To compare and quantify the impact of different mitigation technologies, we used a globin transcript spike-in strategy to synthetically generate a globin-induced signature and then mitigate it with the three different technologies. Biological differences, in globin transcript spiked samples, were modeled by supplementing with either 1% of liver or 1% brain total RNA. In order to demonstrate the biological utility of a robust globin artifact mitigation strategy in biomarker discovery, we treated whole blood ex vivo with suberoylanilide hydroxamic acid (SAHA) and compared the overlap between the obtained signatures and signatures of a known biomarker derived from SAHA-treated cell lines and PBMCs of SAHA-treated patients.

Results

We found cDNA hybridization targets detect at least 20 times more specific differentially expressed signatures (2597) between 1% liver and 1% brain in globin-supplemented samples than the PNA (117) or no treatment (97) method at FDR = 10% and p-value < 3x10-3. In addition, we found that the ex vivo derived gene expression profile was highly concordant with that of the previously identified SAHA pharmacodynamic biomarkers.

Conclusions

We conclude that an amplification method for gene expression profiling employing cDNA targets effectively mitigates the negative impact on data of abundant globin transcripts and greatly improves the ability to identify relevant gene expression based pharmacodynamic biomarkers from whole blood.

High density gene expression microarrays and gene ontology analysis for identifying processes in implanted tissue engineering constructs

The in vivo performance of tissue-engineered constructs is often based on generally accepted read-out parameters, like (immuno)histology. In this study, high-density gene expression microarrays and gene ontology (GO) analysis were used as a read-out tool to identify the biological processes occurring after implantation of an acellular collagen-based skin construct using a rat full-thickness wound model. A freely-available program (DAVID) was used to identify up/downregulated biological processes (GO-terms) and results were compared to wound healing/regeneration without a construct. The entire process from RNA isolation to biological interpretation is explained step-by-step. Conventional (immuno)histology was used to validate the biological processes identified and indicate that microarray analysis may provide a valuable, fast and unbiased tool to evaluate the in vivo performance of tissue-engineered constructs. However, challenges remain e.g. with regards to the development of specific GO-terms and annotation of the (rat) genome.

A novel method of amplification of FFPET-derived RNA enables accurate disease classification with microarrays

A new method for amplification and labeling of RNA is assessed that permits gene expression microarray analysis of formalin-fixed paraffin-embedded tissue (FFPET) samples. Valid biological data were obtained using gene expression microarrays of diffuse large B-cell lymphoma (DLBCL) FFPET samples. We examined 59 matched DLBCL patient samples, FFPET, and fresh/frozen. The samples contained both prognostic subgroups of DLBCL: germinal center B-cell (GCB) and activated B-cell (ABC). Fresh/frozen (FF) samples were amplified by both the traditional Eberwine oligo-dT method and a new method described herein. The matching FFPET samples were also amplified using the new method. Here we detail the comparison of results from all three datasets of matched samples. An established classification model built from previous data accurately classified these new samples. This new method provides a useful technology advance for microarray analysis of FFPET archival samples.

An optimized RNA amplification method for prokaryotic expression profiling analysis

DNA microarray technology has been extensively used for gene expression analysis of both eukaryotic and prokaryotic organisms. For eukaryotic gene expression profiling, the poly(A)-based reverse transcription of messenger RNA (mRNA) followed by T7 RNA polymerase-based in vitro transcription is generally required to produce enough RNA targets for hybridization with the microarray chips. However, the same method cannot be directly applied to prokaryotic mRNAs due to the lack of poly(A) sequences at the 3' ends. Conventional methods usually require large amounts of starting RNAs and lead to high background noise. Recently developed amplification methods enable smaller amounts of prokaryotic RNA to be used from samples with species-specific primers, oligo(dT) primers, or random primers. In this study, three target preparation methods, including the direct labeling, polyadenylation-involved oligo-dT priming, and random priming amplification (respectively referred to as DL, PAOD, and RPA hereafter) were evaluated through expression profiling of a heat shock model of Escherichia coli. The PAOD method was found to be more sensitive and more specific in differential gene expression measurements than either DL and RPA, even when the E. coli RNA was only a small proportion of the simulated eukaryotic host RNA. The results suggest that PAOD is the preferred target preparation method for prokaryotic transcriptome.

Transcriptional response of Candida albicans biofilms following exposure to 2-amino-nonyl-6-methoxyl-tetralin muriate

AIM

To identify changes in the gene expression profile of Candida albicans (C albicans) biofilms following exposed to 2-amino-nonyl-6-methoxyl-tetralin muriate(10b) and clarify the mechanism of 10b against C albicans biofilms.

METHODS

Anti-biofilm activity of 10b was assessed by tetrazolium (XTT) reduction assay and the action mechanism against biofilms was investigated by cDNA microarray analysis and real-time RT-PCR assay.

RESULTS

Ten differentially expressed genes were directly linked to biofilm formation and filamentous or hyphal growth (eg, NRG1, ECE1 and CSA1). Decreased gene expression was involved in glycolysis (eg, HXK2 and PFK1) and antioxidant defense (eg, SOD5), while increased gene expression was associated with enzymes that specifically hydrolyzed beta-1,3 glucan (XOG1), and with lipid, fatty acid and sterol metabolism (eg, SLD1, ERG6 and ERG2). Functional analysis indicated that addition of anti-oxidant ascorbic acid reduced inhibitory efficiency of 10b on mature biofilm.

CONCLUSION

Inhibition of 10b on biofilm formation possibly depends on impairing the ability of C albicans to change its morphology via altering the expression of biofilm formation genes. Mitochondrial aerobic respiration shift and endogenous ROS augmentation might be a major contribution to reduce mature biofilm metabolic activity. The data may be useful for the development of new strategies to reduce the incidence of device-associated infections.

Evaluation of the QIAsymphony SP Workstation for Magnetic Particle—Based Nucleic Acid Purification from Different Sample Types for Demanding Downstream Applications

We evaluated automated nucleic acid (NA) extraction from a variety of different biological specimens using the QIAsymphony SP instrument. QIAsymphony DNA kits were used for DNA purification from human blood and from diverse human and animal tissue specimens. RNA was isolated from human blood stabilized in PAXgene Blood RNA tubes with the QIAsymphony PAXgene Blood RNA kit, and from human colon and bladder carcinoma biopsies using the QIAsymphony RNA kit. Photometric measurement, gel electrophoresis, and LabChip analysis on an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, California) showed that the purified NAs were highly pure and intact, and that excellent yields were obtained. The DNA purified from blood and tissues performed well in single nucleotide polymorphism (SNP) array analysis, shown by call rates for the Affymetrix Genome-Wide Human 6.0 SNP arrays of > 99%. No significant differences were observed when array results of DNA purified either with magnetic particle technology or silica membrane technology were compared. The quality of the DNA allowed accurate allelic discrimination by TaqMan SNP PCR. Gene expression analyses of purified RNA either by “Human Endogenous Control Panel” TaqMan low-density array or on Affymetrix HG UI33 plus 2.0 GeneChips revealed high concordance between manually purified samples and those extracted on the QIAsymphony SP.

Correlation of mRNA and protein in complex biological samples

The correlation between mRNA and protein abundances in the cell has been reported to be notoriously poor. Recent technological advances in the quantitative analysis of mRNA and protein species in complex samples allow the detailed analysis of this pathway at the center of biological systems. We give an overview of available methods for the identification and quantification of free and ribosome-bound mRNA, protein abundances and individual protein turnover rates. We review available literature on the correlation of mRNA and protein abundances and discuss biological and technical parameters influencing the correlation of these central biological molecules.

An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays

Background

Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches.

Results

In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA) will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay.

Conclusions

By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at http://www.laurenzi.net.

Optimization of the BLASTN substitution matrix for prediction of non-specific DNA microarray hybridization

DNA microarray measurements are susceptible to error caused by non-specific hybridization between a probe and a target (cross-hybridization), or between two targets (bulk-hybridization). Search algorithms such as BLASTN can quickly identify potentially hybridizing sequences. We set out to improve BLASTN accuracy by modifying the substitution matrix and gap penalties. We generated gene expression microarray data for samples in which 1 or 10% of the target mass was an exogenous spike of known sequence. We found that the 10% spike induced 2-fold intensity changes in 3% of the probes, two-third of which were decreases in intensity likely caused by bulk-hybridization. These changes were correlated with similarity between the spike and probe sequences. Interestingly, even very weak similarities tended to induce a change in probe intensity with the 10% spike. Using this data, we optimized the BLASTN substitution matrix to more accurately identify probes susceptible to non-specific hybridization with the spike. Relative to the default substitution matrix, the optimized matrix features a decreased score for A–T base pairs relative to G–C base pairs, resulting in a 5–15% increase in area under the ROC curve for identifying affected probes. This optimized matrix may be useful in the design of microarray probes, and in other BLASTN-based searches for hybridization partners.

Nonspecific hybridization scaling of microarray expression estimates: a physicochemical approach for chip-to-chip normalization

The problem of inferring accurate quantitative estimates of transcript abundances from gene expression microarray data is addressed. Particular attention is paid to correcting chip-to-chip variations arising mainly as a result of unwanted nonspecific background hybridization to give transcript abundances measured in a common scale. This study verifies and generalizes a model of the mutual dependence between nonspecific background hybridization and the sensitivity of the specific signal using an approach based on the physical chemistry of surface hybridization. We have analyzed GeneChip oligonucleotide microarray data taken from a set of five benchmark experiments including dilution, Latin Square, and "Golden spike" designs. Our analysis concentrates on the important effect of changes in the unwanted nonspecific background inherent in the technology due to changes in total RNA target concentration and/or composition. We find that incremental changes in nonspecific background entail opposite sign incremental changes in the effective specific binding constant. This effect, which we refer to as the "up-down" effect, results from the subtle interplay of competing interactions between the probes and specific and nonspecific targets at the chip surface and in bulk solution. We propose special rules for proper normalization of expression values considering the specifics of the up-down effect. Particularly for normalization one has to level the expression values of invariant expressed probes. Existing heuristic normalization techniques which do not exclude absent probes, level intensities instead of expression values, and/or use low variance criteria for identifying invariant sets of probes lead to biased results. Strengths and pitfalls of selected normalization methods are discussed. We also find that the extent of the up-down effect is modified if RNA targets are replaced by DNA targets, in that microarray sensitivity and specificity are improved via a decrease in nonspecific background, which effectively amplifies specific binding. The results emphasize the importance of physicochemical approaches for improving heuristic normalization algorithms to proceed toward quantitative microarray data analysis.

Detection and subtyping of influenza A virus based on a short oligonucleotide microarray

We report the design and characterization of a microarray with 46 short virus-specific oligonucleotides for detecting influenza A virus of 5 subtypes: H1N1, H1N2, H3N2, H5N1, and H9N2. A unique combination of 3 specific modifications was introduced into the microarray assay: (1) short probes of 19 to 27 nucleotides, (2) simple amplification of full-length hemagglutinin and neuraminidase cDNAs with universal primers, and (3) Klenow-mediated labeling and further amplification of the samples before hybridization. The assay correctly and specifically detected and subtyped 11 different influenza A isolates from human, avian, and swine species representing the 5 subtypes. When tested with 225 clinical samples, 20 were detected to be positive using our microarray-based assay, whereas only 10 were positive by the conventional culture method. The entire analysis was completed within 7 h. Thus, these modifications result in a specific, sensitive, and rapid microarray assay and may be used for constructing microarrays for the detection of all influenza subtypes and strains.

Gaining insight into the response logic of Saccharomyces cerevisiae to heat shock by combining expression profiles with metabolic pathways

Extensive alteration of gene expression and metabolic remodeling enable the budding yeast Saccharomyces cerevisiae to ensure cellular homeostasis and adaptation to heat shock. The response logic of the cells to heat shock is still not entirely clear. In this study, we combined the expression profiles with metabolic pathways to investigate the logical relations between heat shock response metabolic pathways. The results showed that the heat-stressed S. cerevisiae cell accumulated trehalose and glycogen, which protect cellular proteins against denaturation, and modulate its phospholipid structure to sustain stability of the cell wall. The TCA cycle was enhanced, and the heat shock-induced turnover of amino acids and nucleotides served to meet the extra energy requirement due to heat-induced protein metabolism and modification. The enhanced respiration led to oxidative stress, and subsequently induced the aldehyde detoxification system. These results indicated that new insight into the response logic of S. cerevisiae to heat shock can be gained by integrating expression profiles and the logical relations between heat shock response metabolic pathways.

Characterizing multiple exogenous and endogenous small RNA populations in parallel with subfemtomolar sensitivity using a streptavidin gel-shift assay

Here we present a simple and inexpensive gel-shift assay for the detection and quantification of small RNAs. The assay is at least 5-10 times more sensitive than a conventional Northern, and is highly scalable. Total RNA is first size purified to enrich the desired size range, phosphatase treated, and then radiolabeled to high specific activity using polynucleotide kinase. The resulting RNA stock is then hybridized to an excess of biotinylated DNA probe oligonucleotide, prior to mixing with streptavidin and loading on a native gel. The amount of supershifted material was proportional to the amount of labeled target RNA in the sample. We applied this method to verify sequencing data originally obtained from a four-point comparison study on the effect of endogenous expression of HC-Pro on Y-satellite/cucumber mosaic virus infection in tobacco plants. The results of the streptavidin gel-shift assay were consistent with the concentrations of small RNA infected plants inferred by our original cloning data, and rapidly provided information about the relative concentration of a number of viral and endogenous small RNAs. Further straightforward improvements to this simple methodology might be expected to improve the methods sensitivity by as much as another 10-fold.

Gene expression profiling of whole blood: comparison of target preparation methods for accurate and reproducible microarray analysis

Background

Peripheral blood is an accessible and informative source of transcriptomal information for many human disease and pharmacogenomic studies. While there can be significant advantages to analyzing RNA isolated from whole blood, particularly in clinical studies, the preparation of samples for microarray analysis is complicated by the need to minimize artifacts associated with highly abundant globin RNA transcripts. The impact of globin RNA transcripts on expression profiling data can potentially be reduced by using RNA preparation and labeling methods that remove or block globin RNA during the microarray assay. We compared four different methods for preparing microarray hybridization targets from human whole blood collected in PAXGene tubes. Three of the methods utilized the Affymetrix one-cycle cDNA synthesis/in vitro transcription protocol but varied treatment of input RNA as follows: i. no treatment; ii. treatment with GLOBINclear; or iii. treatment with globin PNA oligos. In the fourth method cDNA targets were prepared with the Ovation amplification and labeling system.

Results

We find that microarray targets generated with labeling methods that reduce globin mRNA levels or minimize the impact of globin transcripts during hybridization detect more transcripts in the microarray assay compared with the standard Affymetrix method. Comparison of microarray results with quantitative PCR analysis of a panel of genes from the NF-kappa B pathway shows good correlation of transcript measurements produced with all four target preparation methods, although method-specific differences in overall correlation were observed. The impact of freezing blood collected in PAXGene tubes on data reproducibility was also examined. Expression profiles show little or no difference when RNA is extracted from either fresh or frozen blood samples.

Conclusion

RNA preparation and labeling methods designed to reduce the impact of globin mRNA transcripts can significantly improve the sensitivity of the DNA microarray expression profiling assay for whole blood samples. While blockage of globin transcripts during first strand cDNA synthesis with globin PNAs resulted in the best overall performance in this study, we conclude that selection of a protocol for expression profiling studies in blood should depend on several factors, including implementation requirements of the method and study design. RNA isolated from either freshly collected or frozen blood samples stored in PAXGene tubes can be used without altering gene expression profiles.

Expression Profiling Using Affymetrix GeneChip Microarrays

The approximately 25,000 genes in mammalian genomes can be transcribed at different levels. Measurements of gene expression for ten thousands of genes in parallel give the most comprehensive picture of steady-state levels of transcripts and is used in basic and applied research. Microarrays are the most frequently used technology for genome-wide expression profiling; from the various available microarray platforms, Affymetrix GeneChips are most frequently used for expression profiling and over 3,000 scientific publications describe results of this technology. In medical research, expression profiling by microarrays holds great promises for better understanding of diseases, identification of new therapeutic targets, and subclassification of diseases to identify individualized treatment strategies.

Calibration of microarray gene-expression data

Calibration of microarray measurements aims at removing systematic biases from the probe-level data to get expression estimates that linearly correlate with the transcript abundance in the studied samples. The improvement of calibration methods is an essential prerequisite for estimating absolute expression levels, which, in turn, are required for quantitative analyses of transcriptional regulation, for example, in the context of gene profiling of diseases. We address hybridization on microarrays as a reaction process in a complex environment and express the measured intensities as a function of the input quantities of the experiment. Popular calibration methods such as MAS5, dChip, RMA, gcRMA, vsn, and PLIER are briefly reviewed and assessed in light of the hybridization model and of previous benchmark studies. We present our hook method, a new calibration approach that is based on a graphical summary of the actual hybridization characteristics of a particular microarray. Although single-chip related, hook performs as well as the multi-chip-related gcRMA, presently one of the best state-of-the-art methods for estimating expression values. The hook method, in addition, provides a set of chip summary characteristics that evaluate the performance of a given hybridization. The algorithm of the method is briefly described and its performance is exemplified.

Cross-hybridization modeling on Affymetrix exon arrays

Motivation: Microarray designs have become increasingly probe-rich, enabling targeting of specific features, such as individual exons or single nucleotide polymorphisms. These arrays have the potential to achieve quantitative high-throughput estimates of transcript abundances, but currently these estimates are affected by biases due to cross-hybridization, in which probes hybridize to off-target transcripts.

Results: To study cross-hybridization, we map Affymetrix exon array probes to a set of annotated mRNA transcripts, allowing a small number of mismatches or insertion/deletions between the two sequences. Based on a systematic study of the degree to which probes with a given match type to a transcript are affected by cross-hybridization, we developed a strategy to correct for cross-hybridization biases of gene-level expression estimates. Comparison with Solexa ultra high-throughput sequencing data demonstrates that correction for cross-hybridization leads to a significant improve-ment of gene expression estimates.

Availability: We provide mappings between human and mouse exon array probes and off-target transcripts and provide software extending the GeneBASE program for generating gene-level expression estimates including the cross-hybridization correction http://biogibbs.stanford.edu/~kkapur/GeneBase/.

Contact:whwong@stanford.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

"Hook"-calibration of GeneChip-microarrays: chip characteristics and expression measures

Background

Microarray experiments rely on several critical steps that may introduce biases and uncertainty in downstream analyses. These steps include mRNA sample extraction, amplification and labelling, hybridization, and scanning causing chip-specific systematic variations on the raw intensity level. Also the chosen array-type and the up-to-dateness of the genomic information probed on the chip affect the quality of the expression measures. In the accompanying publication we presented theory and algorithm of the so-called hook method which aims at correcting expression data for systematic biases using a series of new chip characteristics.

Results

In this publication we summarize the essential chip characteristics provided by this method, analyze special benchmark experiments to estimate transcript related expression measures and illustrate the potency of the method to detect and to quantify the quality of a particular hybridization. It is shown that our single-chip approach provides expression measures responding linearly on changes of the transcript concentration over three orders of magnitude. In addition, the method calculates a detection call judging the relation between the signal and the detection limit of the particular measurement. The performance of the method in the context of different chip generations and probe set assignments is illustrated. The hook method characterizes the RNA-quality in terms of the 3'/5'-amplification bias and the sample-specific calling rate. We show that the proper judgement of these effects requires the disentanglement of non-specific and specific hybridization which, otherwise, can lead to misinterpretations of expression changes. The consequences of modifying probe/target interactions by either changing the labelling protocol or by substituting RNA by DNA targets are demonstrated.

Conclusion

The single-chip based hook-method provides accurate expression estimates and chip-summary characteristics using the natural metrics given by the hybridization reaction with the potency to develop new standards for microarray quality control and calibration.

Genes and environment - striking the fine balance between sophisticated biomonitoring and true functional environmental genomics

This article provides an overview how the application of the gene profiling (mainly via microarray technology) can be used in different organisms to address issues of environmental importance. Only recently, environmental sciences, including ecotoxicology, and molecular biology have started to mutually fertilize each other. This conceptual blend has enabled the identification of the interaction between molecular events and whole animal and population responses. Likewise, striking the fine balance between biomonitoring and functional environmental genomics will allow legislative and administrative measures to be based on a more robust platform. The application of DNA microarrays to ecotoxicogenomics links ecotoxicological effects of exposure with expression profiles of several thousand genes. The gene expression profiles are altered during toxicity, as either a direct or indirect result of toxicant exposure and the comparison of numerous specific expression profiles facilitates the differentiation between intoxication and true responses to environmental stressors. Furthermore, the application of microarrays provides the means to identify complex pathways and strategies that an exposed organism applies in response to environmental stressors. This review will present evidence that the widespread phenomenon of hormesis has a genetic basis that goes beyond an adaptive response. Some more practical advantages emerge: the toxicological assessment of complex mixtures, such as effluents or sediments, as well as drugs seems feasible, especially when classical ecotoxicological tests have failed. The review of available information demonstrates the advantages of microarray application to environmental issues spanning from bacteria, over algae and spermatophytes, to invertebrates (nematode Caenorhabditis elegans, crustacea Daphnia spp., earthworms), and various fish species. Microarrays have also highlighted why populations of a given species respond differently to similar contaminations. Furthermore, this review points at inherent limits of microarrays which may not yet have been properly addressed, namely epigenetics, which may explain heritable variation observed in natural population that cannot be explained by differences in the DNA sequence. Finally, the review will address promising future molecular biological developments which may supersede the microarray technique.

At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana

Gene expression maps for model organisms, including Arabidopsis thaliana, have typically been created using gene-centric expression arrays. Here, we describe a comprehensive expression atlas, Arabidopsis thaliana Tiling Array Express (At-TAX), which is based on whole-genome tiling arrays. We demonstrate that tiling arrays are accurate tools for gene expression analysis and identified more than 1,000 unannotated transcribed regions. Visualizations of gene expression estimates, transcribed regions, and tiling probe measurements are accessible online at the At-TAX homepage.

Exon and junction microarrays detect widespread mouse strain- and sex-bias expression differences

Background

Studies have shown that genetic and sex differences strongly influence gene expression in mice. Given the diversity and complexity of transcripts produced by alternative splicing, we sought to use microarrays to establish the extent of variation found in mouse strains and genders. Here, we surveyed the effect of strain and sex on liver gene and exon expression using male and female mice from three different inbred strains.

Results

71 liver RNA samples from three mouse strains – DBA/2J, C57BL/6J and C3H/HeJ – were profiled using a custom-designed microarray monitoring exon and exon-junction expression of 1,020 genes representing 9,406 exons. Gene expression was calculated via two different methods, using the 3'-most exon probe ("3' gene expression profiling") and using all probes associated with the gene ("whole-transcript gene expression profiling"), while exon expression was determined using exon probes and flanking junction probes that spanned across the neighboring exons ("exon expression profiling"). Widespread strain and sex influences were detected using a two-way Analysis of Variance (ANOVA) regardless of the profiling method used. However, over 90% of the genes identified in 3' gene expression profiling or whole transcript profiling were identified in exon profiling, along with 75% and 38% more genes, respectively, showing evidence of differential isoform expression. Overall, 55% and 32% of genes, respectively, exhibited strain- and sex-bias differential gene or exon expression.

Conclusion

Exon expression profiling identifies significantly more variation than both 3' gene expression profiling and whole-transcript gene expression profiling. A large percentage of genes that are not differentially expressed at the gene level demonstrate exon expression variation suggesting an influence of strain and sex on alternative splicing and a need to profile expression changes at sub-gene resolution.

Mapping and quantifying mammalian transcriptomes by RNA-Seq

We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41-52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3' untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 x 10(5) distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.

Affymetrix Whole-Transcript Human Gene 1.0 ST array is highly concordant with standard 3′ expression arrays

The recently released Affymetrix Human Gene 1.0 ST array has two major differences compared with standard 3′ based arrays: (i) it interrogates the entire mRNA transcript, and (ii) it uses DNA targets. To assess the impact of these differences on array performance, we performed a series of comparative hybridizations between the Human Gene 1.0 ST and the Affymetrix HG-U133 Plus 2.0 and the Illumina HumanRef-8 BeadChip arrays. Additionally, both RNA and DNA targets were hybridized on HG-U133 Plus 2.0 arrays. The results show that the overall reproducibility of the Gene 1.0 ST array is best. When looking only at the high intensity probes, the reproducibility of the Gene 1.0 ST array and the Illumina BeadChip array is equally good. Concordance of array results was assessed using different inter-platform mappings. Agreements are best between the two labeling protocols using HG-U133 Plus 2.0 array. The Gene 1.0 ST array is most concordant with the HG-U133 array hybridized with cDNA targets. This may reflect the impact of the target type. Overall, the high degree of correspondence provides strong evidence for the reliability of the Gene 1.0 ST array.

A dual-tag microarray platform for high-performance nucleic acid and protein analyses

DNA microarrays serve to monitor a wide range of molecular events, but emerging applications like measurements of weakly expressed genes or of proteins and their interaction patterns will require enhanced performance to improve specificity of detection and dynamic range. To further extend the utility of DNA microarray-based approaches we present a high-performance tag microarray procedure that enables probe-based analysis of as little as 100 target cDNA molecules, and with a linear dynamic range close to 10(5). Furthermore, the protocol radically decreases the risk of cross-hybridization on microarrays compared to current approaches, and it also allows for quantification by single-molecule analysis and real-time on-chip monitoring of rolling-circle amplification. We provide proof of concept for microarray-based measurement of both mRNA molecules and of proteins, converted to tag DNA sequences by padlock and proximity probe ligation, respectively.

Microarray-based gene expression profiles in multiple tissues of the domesticated silkworm, Bombyx mori

Using a genome-wide oligonucleotide microarray, gene expression was surveyed in multiple silkworm tissues on day 3 of the fifth instar, providing a new resource for annotating the silkworm genome.

We designed and constructed a genome-wide microarray with 22,987 70-mer oligonucleotides covering the presently known and predicted genes in the silkworm genome, and surveyed the gene expression in multiple silkworm tissues on day 3 of the fifth instar. Clusters of tissue-prevalent and tissue-specific genes and genes that are differentially expressed in different tissues were identified, and they reflect well major tissue-specific functions on the molecular level. The data presented in this study provide a new resource for annotating the silkworm genome.

Complementary RNA amplification methods enhance microarray identification of transcripts expressed in the C. elegans nervous system

Background

DNA microarrays provide a powerful method for global analysis of gene expression. The application of this technology to specific cell types and tissues, however, is typically limited by small amounts of available mRNA, thereby necessitating amplification. Here we compare microarray results obtained with two different methods of RNA amplification to profile gene expression in the C. elegans larval nervous system.

Results

We used the mRNA-tagging strategy to isolate transcripts specifically from C. elegans larval neurons. The WT-Ovation Pico System (WT-Pico) was used to amplify 2 ng of pan-neural RNA to produce labeled cDNA for microarray analysis. These WT-Pico-derived data were compared to microarray results obtained with a labeled aRNA target generated by two rounds of In Vitro Transcription (IVT) of 25 ng of pan-neural RNA. WT-Pico results in a higher fraction of present calls than IVT, a finding consistent with the proposal that DNA-DNA hybridization results in lower mismatch signals than the RNA-DNA heteroduplexes produced by IVT amplification. Microarray data sets from these samples were compared to a reference profile of all larval cells to identify transcripts with elevated expression in neurons. These results were validated by the high proportion of known neuron-expressed genes detected in these profiles and by promoter-GFP constructs for previously uncharacterized genes in these data sets. Together, the IVT and WT-Pico methods identified 2,173 unique neuron-enriched transcripts. Only about half of these transcripts (1,044), however, are detected as enriched by both IVT and WT-Pico amplification.

Conclusion

We show that two different methods of RNA amplification, IVT and WT-Pico, produce valid microarray profiles of gene expression in the C. elegans larval nervous system with a low rate of false positives. However, our results also show that each method of RNA amplification detects a unique subset of bona fide neural-enriched transcripts and thus a wider array of authentic neural genes are identified by the combination of these data sets than by the microarray profiles obtained with either method of RNA amplification alone. With its relative ease of implementation and greater sensitivity, WT-Pico is the preferred method of amplification for cases in which sample RNA is limiting.