Individuality and variation in gene expression patterns in human blood

Prospects for exploring the molecular-genomic foundations of therapeutic hypnosis with DNA microarrays

A new perspective on how therapeutic hypnosis and neuroscience may be integrated on the molecular-genomic level is offered as a guide for basic research and clinical applications. An update of Watson and Crick's original formulation of molecular biology is proposed to illustrate how psychosocial experiences modulate gene expression, protein synthesis, and brain plasticity during memory trace reactivation for the reorganization of neural networks that encode fear, stress, and traumatic symptoms. Examples of the scientific literature on DNA microarrays are used to explore how this new technology could integrate therapeutic hypnosis, neuroscience, and psychosocial genomics as a new foundation for mind-body medicine. Researchers and clinicians in therapeutic hypnosis need to partner with colleagues in neuroscience and molecular biology that utilize DNA microarray technology. It is recommended that hypnotic susceptibility scales of the future incorporate gene expression data to include the concept of "embodied imagination" and the "ideo-plastic faculty" on a molecular-genomic level as well as the psychological and behavioral level of ideomotor and ideosensory responses that are currently assessed.

Blood-based biomarkers for detecting mild osteoarthritis in the human knee

OBJECTIVE

This study was designed to test the utility of a blood-based approach to identify mild osteoarthritis (OA) of the knee.

METHODS

Blood samples were drawn from 161 subjects, including 85 subjects with arthroscopically diagnosed mild OA of the knee and 76 controls. Following RNA isolation, an in-house custom cDNA microarray was used to screen for differentially expressed genes. A subset of selected genes was then tested using real-time RT-PCR. Logistic regression analysis was used to evaluate linear combinations of the biomarkers and receiver operating characteristic curve analysis was used to assess the discriminatory power of the combinations.

RESULTS

Genes differentially expressed (3543 genes) between mild knee OA and control samples were identified through microarray analysis. Subsequent real-time RT-PCR verification identified six genes significantly down-regulated in mild OA: heat shock 90kDa protein 1, alpha; inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase complex-associated protein; interleukin 13 receptor, alpha 1; laminin, gamma 1; platelet factor 4 (also known as chemokine (C-X-C motif) ligand 4) and tumor necrosis factor, alpha-induced protein 6. Logistic regression analysis identified linear combinations of nine genes--the above six genes, early growth response 1; alpha glucosidase II alpha subunit; and v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian)--as discriminatory between subjects with mild OA and controls, with a sensitivity of 86% and specificity of 83% in a training set of 78 samples. The optimal biomarker combinations were then evaluated using a blind test set (67 subjects) which showed 72% sensitivity and 66% specificity.

CONCLUSIONS

Linear combinations of blood RNA biomarkers offer a substantial improvement over currently available diagnostic tools for mild OA. Blood-derived RNA biomarkers may be of significant clinical value for the diagnosis of early, asymptomatic OA of the knee.

Epigenetic predisposition to expression of TIMP1 from the human inactive X chromosome

Background

X inactivation in mammals results in the transcriptional silencing of an X chromosome in females, and this inactive X acquires many of the epigenetic features of silent chromatin. However, not all genes on the inactive X are silenced, and we have examined the TIMP1 gene, which has variable inactivation amongst females. This has allowed us to examine the features permitting expression from the otherwise silent X by comparing inactive X chromosomes with and without TIMP1 expression.

Results

Expression was generally correlated with euchromatic chromatin features, including DNA hypomethylation, nuclease sensitivity, acetylation of histone H3 and H4 and hypomethylation of H3 at lysines 9 and 27. Demethylation of the TIMP1 gene by 5-azacytidine was able to induce expression from the inactive X chromosome in somatic cell hybrids, and this expression was also accompanied by features of active chromatin. Acetylated histone H3 continued to be observed even when expression was lost in cells that naturally expressed TIMP1; while acetylation was lost upon TIMP1 silencing in cells where expression from the inactive X had been induced by demethylation. Thus ongoing acetylation of inactive X chromosomes does not seem to be simply a 'memory' of expression.

Conclusion

We propose that acetylation of H3 is an epigenetic mark that predisposes to TIMP1 expression from the inactive X chromosome in some females.

Role of the beta1-integrin subunit in the adhesion, extravasation and migration of T24 human bladder carcinoma cells

The abilities of tumor cells to extravasate from the blood vessel system and to migrate through the connective tissue are prerequisites in metastasis formation. Both processes are chiefly mediated by integrins, which mediate both cell-cell and cell-matrix interactions. We investigated the role of integrin subunits in the adhesion, extravasation and migration of the highly invasive human bladder carcinoma cell line T24. Here we show that inhibition of the beta(1)-integrin subunit using the specific beta(1)-integrin blocking antibody 4B4 significantly reduces the adhesion to HUVEC and transmigratory activity of T24 cells. The blockade of the beta(1)-integrin subunit also resulted in a significantly reduced locomotory activity of T24 cells. A detailed cell migration analysis on a single cell level revealed that blockade of the beta(1)-integrin subunit leads to an altered migration pattern of single cells but does not influence migration per se. Migration parameters such as time active, velocity and distance migrated were significantly reduced as compared to untreated control cells. Our observations strongly suggest a central role for the beta(1)-integrin subunit in forming the cell-cell and cell-matrix bonds necessary for adhesion, extravasation and migration.

Molecular signature of clinical severity in recovering patients with severe acute respiratory syndrome coronavirus (SARS-CoV)

BACKGROUND

Severe acute respiratory syndrome (SARS), a recent epidemic human disease, is caused by a novel coronavirus (SARS-CoV). First reported in Asia, SARS quickly spread worldwide through international travelling. As of July 2003, the World Health Organization reported a total of 8,437 people afflicted with SARS with a 9.6% mortality rate. Although immunopathological damages may account for the severity of respiratory distress, little is known about how the genome-wide gene expression of the host changes under the attack of SARS-CoV.

RESULTS

Based on changes in gene expression of peripheral blood, we identified 52 signature genes that accurately discriminated acute SARS patients from non-SARS controls. While a general suppression of gene expression predominated in SARS-infected blood, several genes including those involved in innate immunity, such as defensins and eosinophil-derived neurotoxin, were upregulated. Instead of employing clustering methods, we ranked the severity of recovering SARS patients by generalized associate plots (GAP) according to the expression profiles of 52 signature genes. Through this method, we discovered a smooth transition pattern of severity from normal controls to acute SARS patients. The rank of SARS severity was significantly correlated with the recovery period (in days) and with the clinical pulmonary infection score.

CONCLUSION

The use of the GAP approach has proved useful in analyzing the complexity and continuity of biological systems. The severity rank derived from the global expression profile of significantly regulated genes in patients may be useful for further elucidating the pathophysiology of their disease.

The quantitative genetics of transcription

Quantitative geneticists have become interested in the heritability of transcription and detection of expression quantitative trait loci (eQTLs). Linkage mapping methods have identified major-effect eQTLs for some transcripts and have shown that regulatory polymorphisms in cis and in trans affect expression. It is also clear that these mapping strategies have little power to detect polygenic factors, and some new statistical approaches are emerging that paint a more complex picture of transcriptional heritability. Several studies imply pervasive non-additivity of transcription, transgressive segregation and epistasis, and future studies will soon document the extent of genotype-environment interaction and population structure at the transcriptional level. The implications of these findings for genotype-phenotype mapping and modeling the evolution of transcription are discussed.

Sources of variation in Affymetrix microarray experiments

Background

A typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates.

Results

We performed a microarray experiment using a total of 24 Affymetrix GeneChip^® arrays. The study included 4^th mammary gland samples from eight 21-day-old Sprague Dawley CD female rats exposed to genistein (soy isoflavone). RNA samples from each rat were split to assess variation arising at labeling and hybridization steps. A general linear model was used to estimate variance components. Pearson correlations were computed to evaluate agreement between technical and biological replicates.

Conclusion

The greatest source of variation was biological variation, followed by residual error, and finally variation due to labeling when *.cel files were processed with dChip and RMA image processing algorithms. When MAS 5.0 or GCRMA-EB were used, the greatest source of variation was residual error, followed by biology and labeling. Correlations between technical replicates were consistently higher than between biological replicates.

Global approach to the diagnosis of leukemia using gene expression profiling

Accurate diagnosis and classification of leukemias are the bases for the appropriate management of patients. The diagnostic accuracy and efficiency of present methods may be improved by the use of microarrays for gene expression profiling. We analyzed gene expression profiles in 937 bone marrow and peripheral blood samples from 892 patients with all clinically relevant leukemia subtypes and from 45 nonleukemic controls by U133A and U133B GeneChip arrays. For each subgroup, differentially expressed genes were calculated. Class prediction was performed using support vector machines. Prediction accuracy was estimated by 10-fold cross-validation and was assessed for robustness in a 100-fold resampling approach using randomly chosen test sets consisting of one third of the samples. Applying the top 100 genes of each subgroup, an overall prediction accuracy of 95.1% was achieved that was confirmed by resampling (median, 93.8%; 95% confidence interval, 91.4%-95.8%). In particular, acute myeloid leukemia (AML) with t(15;17), AML with t(8;21), AML with inv(16), chronic lymphatic leukemia (CLL), and pro–B-cell acute lymphoblastic leukemia (pro–B-ALL) with t(11q23) were classified with 100% sensitivity and 100% specificity. Accordingly, cluster analysis completely separated all 13 subgroups analyzed. Gene expression profiling can predict all clinically relevant subentities of leukemia with high accuracy.

Variation in gene expression profiles of peripheral blood mononuclear cells from healthy volunteers

The normal degree of intra- and interindividual variation in gene transcription profiles of healthy human tissues has not been extensively investigated. In the study described here, microarrays were employed to analyze gene transcription in peripheral blood mononuclear cells prepared from serial blood samples that had been obtained, at weekly intervals, from apparently healthy human volunteers. Transcript levels for the majority of genes examined were found to be remarkably consistent within samples from a single donor. Conversely, marked differences were observed in samples obtained from different donors. Genes that exhibited differential expression dependent on sex, age, body mass index, and the presence of varying proportions of different leukocyte subsets were identified. These results emphasize the important contributions of genetic and environmental factors, as well as varying representation of different cell types, in determining the overall gene transcriptional profiles of human tissues. However, the study also provides evidence that, within an individual, the gene transcription profiles of sampled tissues can be comparatively stable over time.

Toxicogenomics in the pharmaceutical industry: hollow promises or real benefit?

Almost 10 years ago, microarray technology was established as a new powerful tool for large-scale analysis of gene expression. Soon thereafter the new technology was discovered by toxicologists for the purpose of deciphering the molecular events underlying toxicity, and the term "Toxicogenomics" appeared in scientific literature. Ever since, the toxicology community was fascinated by the multiplicity of sophisticated possibilities toxicogenomics seems to offer: genome-wide analysis of toxicant-induced expression profiles may provide a means for prediction of toxicity prior to classical toxicological endpoints such as histopathology or clinical chemistry. Some researchers even speculated of the classical methods being superfluous before long. It was assumed that by using toxicogenomics it would be possible to classify compounds early in drug development and consequently save animals, time, and money in pre-clinical toxicity studies. Moreover, it seemed within reach to unravel the molecular mechanisms underlying toxicity. The feasibility of bridging data derived from in vitro and in vivo systems, identifying new biomarkers, and comparing toxicological responses "across-species" was also excessively praised. After several years of intensive application of microarray technology in the field of toxicology, not only by the pharmaceutical industry, it is now time to survey its achievements and to question how many of these wishes and promises have really come true.

Gene expression and genotyping studies implicate the interleukin 7 receptor in the pathogenesis of primary progressive multiple sclerosis

Multiple sclerosis (MS) is an enigmatic disease of the central nervous system resulting in sclerotic plaques with the pathological hallmarks of demyelination and axonal damage, which can be directly or indirectly orchestrated by cells from the peripheral circulation. The majority of patients with MS follow a relapsing-remitting course in the early stages of the disease (RRMS) but most ultimately enter a secondary progressive phase (SPMS). About 10% of patients follow a primary progressive course from the onset (PPMS). We measured gene expression in whole blood of people with and without chronic progressive MS (CPMS), PPMS and SPMS, to discover genes which may be differentially expressed in peripheral blood in active disease, and so identify pathologically significant genes and pathways; and we investigated genetic differences in the promoters of dysregulated genes encoded in genomic regions associated with MS. If SPMS and PPMS were independently compared to the controls, there was little overlap in the set of most dysregulated genes. Ribosomal protein genes, whose expression is usually associated with cell proliferation and activation, were dramatically over-represented in the set of most down-regulated genes in PPMS compared to SPMS (P < 10(-4), chi(2)). The T cell proliferation gene IL7R (CD127) was also underexpressed in PPMS, but was up-regulated in SPMS compared to the controls. One interleukin 7 receptor (IL7R) promoter single nucleotide polymorphism (SNP), -504 C, was undertransmitted in PPMS trios (P = 0.05, TDT), and carriers of this allele were under-represented in PPMS cases from two independent patient cohorts (combined P = 0.006, FE). The four known IL7R promoter haplotypes were shown to have similar expression levels in healthy controls, but not in CPMS (P < 0.01, t test). These data support the hypothesis that PPMS has significant pathogenetic differences from SPMS, and that IL7R may be a useful therapeutic target in PPMS.

Allelic variation in gene expression in thyroid tissue

Heritable allelic variation in gene expression may contribute to sporadic and familial disease, but is relatively unexplored. Papillary thyroid cancer (PTC) is characterized by strong heritability but predisposing genes have not been detected. Here we tested the hypothesis that inherited variation in allelic expression occurs in thyroid tissue and so might contribute to disease. We studied genes with a role in thyroid function, signaling, and/or tumorigenesis (PRKAR1A, DUOX1, IP3RI, ARGBP2, TPO, TG, and PEG10). We screened thyroid tissues from controls and patients with thyroid disease and lymphoblastoid cell lines from 40 healthy individuals. We demonstrated the robustness of the technique, a fluorescent dideoxy terminator-based method distinguishing the mRNA products of alleles in individuals who are heterozygous for a polymorphism in the transcript. We confirmed that the PEG10 gene transcript was derived only from one allele because of genetic imprinting. Three other genes, DUOX1, TPO, and ARGBP2 showed allelic variation in thyroid tissue and/or lymphoblastoid cells. Benign and malignant thyroid tissue from the same patient always gave concordant results. DUOX1 variation in expression was seen in 3 of 13 patients with PTC and 6 of 27 patients with benign disease. Further studies are needed to determine the significance of these and other allelic variations.

Early detection of breast cancer based on gene-expression patterns in peripheral blood cells

Introduction

Existing methods to detect breast cancer in asymptomatic patients have limitations, and there is a need to develop more accurate and convenient methods. In this study, we investigated whether early detection of breast cancer is possible by analyzing gene-expression patterns in peripheral blood cells.

Methods

Using macroarrays and nearest-shrunken-centroid method, we analyzed the expression pattern of 1,368 genes in peripheral blood cells of 24 women with breast cancer and 32 women with no signs of this disease. The results were validated using a standard leave-one-out cross-validation approach.

Results

We identified a set of 37 genes that correctly predicted the diagnostic class in at least 82% of the samples. The majority of these genes had a decreased expression in samples from breast cancer patients, and predominantly encoded proteins implicated in ribosome production and translation control. In contrast, the expression of some defense-related genes was increased in samples from breast cancer patients.

Conclusion

The results show that a blood-based gene-expression test can be developed to detect breast cancer early in asymptomatic patients. Additional studies with a large sample size, from women both with and without the disease, are warranted to confirm or refute this finding.

Genomic abnormalities in patients with migraine and chronic migraine: preliminary blood gene expression suggests platelet abnormalities

BACKGROUND

Migraine has strong genetic and environmental components and may also be a significant contributor to chronic migraine (CM). It is hypothesized that gene expression changes in peripheral blood cells can be used to detect the interaction of these influences.

OBJECTIVE

Distinct genomic expression patterns for migraine and CM will be present. These genomic profiles will help clarify the interactions of inheritance and environment. This initial study begins to examine the feasibility of peripheral blood cell genomic analysis to assist in the understanding of the pathophysiology of migraine and CM.

METHODS

Blood samples from patients were obtained either during an acute migraine or CM. Genomic expression patterns were analyzed using Affymetrix U95A microarrays.

RESULTS

Expression patterns of 7 migraine and 15 CM patients were compared to four distinct control groups (total patients, n=56) including healthy subjects. A group of platelet genes were upregulated in both migraine and CM samples. Different gene expression patterns were also seen between migraine and CM. A group of immediate early genes including c-fos and cox-2 were expressed at higher levels in migraine, whereas specific mitochondrial genes were expressed at higher levels in CM.

CONCLUSIONS

Increased expression of platelet genes in patients with migraine and CM suggests similar underlying pathophysiology. The differences seen between migraine and CM in other genes suggest an overlapping but not identical pathophysiology. Further genomic profiling studies will help define these relationships and provide further insights into headache pathogenesis.

Variation in the hepatic gene expression in individual male Fischer rats

A new tool beginning to have wider application in toxicology studies is transcript profiling using microarrays. Microarrays provide an opportunity to directly compare transcript populations in the tissues of chemical-exposed and unexposed animals. While several studies have addressed variation between microarray platforms and between different laboratories, much less effort has been directed toward individual animal differences especially among control animals where RNA samples are usually pooled. Estimation of the variation in gene expression in tissues from untreated animals is essential for the recognition and interpretation of subtle changes associated with chemical exposure. In this study hepatic gene expression as well as standard toxicological parameters were evaluated in 24 rats receiving vehicle only in 2 independent experiments. Unsupervised clustering demonstrated some individual variation but supervised clustering suggested that differentially expressed genes were generally random. The level of hepatic gene expression under carefully controlled study conditions is less than 1.5-fold for most genes. The impact of individual animal variability on microarray data can be minimized through experimental design.

Expression analysis of psychological stress-associated genes in peripheral blood leukocytes

In this study, we have developed a microarray including 1467 cDNAs that were selected to specifically measure stress response in peripheral blood leukocytes. Venous blood was collected from 10 graduate students 2 h before and 2 or 24 h after an open presentation for their Ph.D. The mRNA levels in leukocytes were compared with those prepared 4 weeks before the presentation. Hierarchical cluster showed that distinct groups of genes uniformly changed their expression values in response to the stress. Bayesian t test identified significantly up-regulated 49 genes and down-regulated 21 genes. Most of them are categorized into cytokines, cytokine receptors, growth- or apoptosis-related molecules, and heat shock proteins, suggesting that stressful life events trigger acute responses in leukocytes. Our results suggest that gene expression profile in peripheral blood leukocytes may be a potentially useful method for the assessment of complex stress responses.

Discovery of novel biomarkers by microarray analysis of peripheral blood mononuclear cell gene expression in benzene-exposed workers

Benzene is an industrial chemical and component of gasoline that is an established cause of leukemia. To better understand the risk benzene poses, we examined the effect of benzene exposure on peripheral blood mononuclear cell (PBMC) gene expression in a population of shoe-factory workers with well-characterized occupational exposures using microarrays and real-time polymerase chain reaction (PCR). PBMC RNA was stabilized in the field and analyzed using a comprehensive human array, the U133A/B Affymetrix GeneChip set. A matched analysis of six exposed-control pairs was performed. A combination of robust multiarray analysis and ordering of genes using paired t-statistics, along with bootstrapping to control for a 5% familywise error rate, was used to identify differentially expressed genes in a global analysis. This resulted in a set of 29 known genes being identified that were highly likely to be differentially expressed. We also repeated these analyses on a smaller subset of 508 cytokine probe sets and found that the expression of 19 known cytokine genes was significantly different between the exposed and the control subjects. Six genes were selected for confirmation by real-time PCR, and of these, CXCL16, ZNF331, JUN, and PF4 were the most significantly affected by benzene exposure, a finding that was confirmed in a larger data set from 28 subjects. The altered expression was not caused by changes in the makeup of the PBMC fraction. Thus, microarray analysis along with real-time PCR confirmation reveals that altered expressions of CXCL16, ZNF331, JUN, and PF4 are potential biomarkers of benzene exposure.

The use of allelic expression differences to ascertain functional polymorphisms acting in cis: analysis of MMP1 transcripts in normal lung tissue

Summary Aberrant expression of matrix metalloproteinase 1 (MMP1) has been implicated in a number of pathological conditions of the lung. In vitro results and analysis of tumours and cell lines suggest that an insertion/deletion polymorphism at position -1607 in the promoter of the gene can influence expression levels. However, whether this polymorphism is associated with differences in expression in normal lung tissue remains to be established. Polymorphisms affecting expression in cis will lead to alleles with different expression levels and will result in unequal expression of both alleles in heterozygous individuals (allelic expression imbalance, AEI). This can be detected using a transcribed marker. Here we follow a new approach and use AEI to ascertain that the -1607 polymorphism is associated with allelic expression differences of MMP1 in normal lung tissue. This approach could be used to map the sites associated with inter-individual expression differences in other genes. This is of particular interest since such sites allow prediction of expression levels, and can be used to test whether genetically determined differences in expression influence inter-individual differences of a phenotype of interest, such as disease predisposition.

Using a microfluidic device for 1 microl DNA microarray hybridization in 500 s

This work describes a novel and simple modification of the current microarray format. It reduces the sample/reagent volume to 1 μl and the hybridization time to 500 s. Both 20mer and 80mer oligonucleotide probes and singly labeled 20mer and 80mer targets, representative of the T-cell acute lymphocytic leukemia 1 (TAL1) gene, have been used to elucidate the performance of this hybridization approach. In this format, called shuttle hybridization, a conventional flat glass DNA microarray is integrated with a PMMA microfluidic chip to reduce the sample and reagent consumption to 1/100 of that associated with the conventional format. A serpentine microtrench is designed and fabricated on a PMMA chip using a widely available CO₂ laser scriber. The trench spacing is compatible with the inter-spot distance in standard microarrays. The microtrench chip and microarray chip are easily aligned and assembled manually so that the microarray is integrated with a microfluidic channel. Discrete sample plugs are employed in the microchannel for hybridization. Flowing through the microchannel with alternating depths and widths scrambles continuous sample plug into discrete short plugs. These plugs are shuttled back and forth along the channel, sweeping over microarray probes while re-circulation mixing occurs inside the plugs. Integrating the microarrays into the microfluidic channel reduces the DNA–DNA hybridization time from 18 h to 500 s. Additionally, the enhancement of DNA hybridization reaction by the microfluidic device is investigated by determining the coefficient of variation (CV), the growth rate of the hybridization signal and the ability to discriminate single-base mismatch. Detection limit of 19 amol was obtained for shuttle hybridization. A 1 μl target was used to hybridize with an array that can hold 5000 probes.

Modeling of SEB-induced host gene expression to correlate in vitro to in vivo responses

Detection of exposure to biological threat agents has relied on ever more sensitive methods for pathogen identification, but that usually requires pathogen proliferation to dangerous, near untreatable levels. Recent events have demonstrated that assessing exposure to a biological threat agent well in advance of onset of illness or at various stages post-exposure is invaluable among the diagnostic options. There is an urgent need for better diagnostic tools that will be sensitive, rapid, and unambiguous. Since human clinical cases of illness induced by biothreat agents are, fortunately, rare, use of animal models that closely mimic the human illness is the only in vivo option. Such studies can be very difficult and expensive; therefore, maximizing the information obtained from in vitro exposures to peripheral blood mononuclear cells (PBMCs) provide an opportunity to investigate dose/time variability in host responses. In our quest to study staphylococcal enterotoxin B (SEB) induced host gene expression patterns, we addressed two core issues using microarray analysis and predictive modeling. Our first objective was to determine gene expression patterns in human PBMCs exposed to SEB in vitro. Second, we compared the in vitro data with host responses gene expression patterns in vivo using PBMCs from an animal model of SEB intoxication that closely replicates the progression of illness in humans. We used cDNA microarrays to study global gene expression patterns in piglets intoxicated with SEB. We applied a supervised learning method for class prediction based on the k-nearest neighbor algorithm for the data obtained in piglets exposed to SEB in vivo against a training data set. This data set included gene expression profiles derived from in vitro exposures to eight different pathogens (Bacillus anthracis, Yersinia pestis, Brucella melitensis, SEB, cholera toxin, Clostridium botulinum toxin A, Venezuelan equine encephalitis, and Dengue-2) in PBMCs. We found that despite differences in gene expression profiles between in vitro and in vivo systems, there exists a subset of genes that show correlations between in vitro and in vivo exposures, which can be used as a predictor of exposure to SEB in vivo.

Antigen-specific gene expression profiles of peripheral blood mononuclear cells do not reflect those of T-lymphocyte subsets

Advances in microarray technology have allowed for the monitoring of thousands of genes simultaneously. This technology is of particular interest to immunologists studying infectious diseases, because it provides tremendous potential for investigating host-pathogen interactions at the level of immune gene expression. To date, many studies have focused either on cell lines, where the physiological relevance is questionable, or on mixed cell populations, where the contributions of individual subpopulations are unknown. In the present study, we perform an intrasubject comparison of antigen-stimulated immune gene expression profiles between a mixed population of peripheral blood mononuclear cells (PBMC) and the two predominant cell types found in PBMC, CD4+ and CD8+ T lymphocytes. We show that the microarray profiles of CD4+ and CD8+ T lymphocytes differ from each other as well as from that of the mixed cell population. The independence of the gene expression profiles of different cell types is demonstrated with a ubiquitous antigen (Candida albicans) as well as with a disease-specific antigen (human immunodeficiency virus p24). This study has important implications for microarray studies of host immunity and underscores the importance of profiling the expression of specific cell types.

[Overview: application of DNA chips to clinical immunology]

DNA microarrays or DNA chips are rapidly evolved technologies. Hybridization of labeled cDNA or cRNA to DNA probes on the solid phases enables analyses of transcriptome of particular cells. Storage and analysis of microarray data are also important factors. Several studies have been published on the gene expression in the immune cells during immune responses and immune mediated diseases.

Global gene expression as a function of germline genetic variation

Common, functional, germline genetic polymorphisms have been associated with clinical cancer outcomes. Little attention has been paid to the potential phenotypic consequences of germline genetic variation on downstream genes. We determined the germline status of 16 well-characterized functional polymorphisms in 126 children with newly diagnosed acute lymphoblastic leukemia (ALL). We assessed whether global gene expression profiles of diagnostic ALL blasts from the same patients differed by these germline polymorphic genotypes. Gene expression values were adjusted for ALL-subtype-specific patterns. Of the 16 loci, only the UGT1A1 promoter repeat polymorphism [A(TA)nTAA] (UGT1A1*28) and GSTM1 deletion were significant predictors of global gene expression in a supervised approach, which divided patients based on their germline genotypes [UGT1A1: 124 probe sets, false discovery rate (FDR)=13%, P< or =0.0031; GSTM1: 112 probe sets, FDR=42.5%, P< or =0.0084]. Genes whose expression distinguished the UGT1A1 (TA) 7/7 genotype from the other UGT1A1 genotypes included HDAC1, RELA and SLC2A1; those that distinguished the GSTM1 null genotype from non-null genotype included NBS1 and PRKR. In an unsupervised approach, the gene expression profiles using the entire array delineated two major clusters of patients. The only germline genotype frequency that differed between the two clusters was UGT1A1 (P=0.002; Fisher's exact test). Although their expression is limited to specific tissues, both GSTM1 and UGT1A1 are involved in the conjugation (and thus transport, excretion and lipophilicity) of a broad range of endobiotics and xenobiotics, which could plausibly have consequences for gene expression in different tissues.

Use of 'Omic' technologies to study humans exposed to benzene

'Omic' technologies include genomics, transcriptomics (gene expression profiling), proteomics and metabolomics. We are utilizing these new technologies in an effort to develop novel biomarkers of exposure, susceptibility and response to benzene. Advances in genomics allow one to study hundreds to thousands of single nucleotide polymorphisms simultaneously on small quantities of DNA using array-based technologies. We are currently utilizing these technologies to examine genetic variation in pathways relating to biotransformation, DNA repair, folate metabolism and immune response with the goal of finding biomarkers of susceptibility to benzene hematotoxicity. Transcriptomics is used to measure the full complement of activated genes, mRNAs or transcripts in a particular tissue at a particular time typically using microarray technology. We have applied microarrays to the study of global gene expression in the peripheral blood cells of benzene-exposed workers. More than 100 genes were identified as being potentially differentially expressed, with genes related to apoptosis and immune function being the most significantly affected. Initial studies employing proteomics have also shown that several proteins are altered in the serum of exposed compared to control subjects and these proteins are potential biomarkers of benzene exposure. Omic technologies therefore have significant potential in generating novel biomarkers of exposure, susceptibility and response to benzene.

Expression profiling: toward an application in sepsis diagnostics

Sepsis is a common and serious health problem whereby improvements in diagnosis are crucial in increasing survival rates. To test whether profiling transcription is applicable to sepsis diagnosis, we analyzed whole blood using a microarray containing probes for 340 genes relevant to inflammation. The patient's gene expression pattern was highly homogenous, resulting in 69% of differentially expressed genes. With a positive predictive value of 98%, a list of 50 differentially expressed genes was compiled, and randomly chosen transcripts were confirmed by PCR. Here, we present the first evidence that microarrays can identify typical gene expression profiles in the blood of patients with severe sepsis. Regardless of the heterogeneity of the patients, we observed a striking correlation between the conventional diagnostic classification and our approach. The unity of responses suggests that the principle of this multiparameter approach can be adapted to early stage sepsis diagnosis.

Microarray analysis identifies an aberrant expression of apoptosis and DNA damage-regulatory genes in multiple sclerosis

To clarify the molecular mechanisms underlying multiple sclerosis (MS)-promoting autoimmune process, we have investigated a comprehensive gene expression profile of T cell and non-T cell fractions of peripheral blood mononuclear cells (PBMC) isolated from 72 MS patients and 22 age- and sex-matched healthy control (CN) subjects by using a cDNA microarray. Among 1258 genes examined, 173 genes in T cells and 50 genes in non-T cells were expressed differentially between MS and CN groups. Downregulated genes greatly outnumbered upregulated genes in MS. More than 80% of the top 30 most significant genes were categorized into apoptosis signaling-related genes of both proapoptotic and antiapoptotic classes. They included upregulation in MS of orphan nuclear receptor Nurr1 (NR4A2), receptor-interacting serine/threonine kinase 2 (RIPK2), and silencer of death domains (SODD), and downregulation in MS of TNF-related apoptosis-inducing ligand (TRAIL), B-cell CLL/lymphoma 2 (BCL2), and death-associated protein 6 (DAXX). Furthermore, a set of the genes involved in DNA repair, replication, and chromatin remodeling was downregulated in MS. These results suggest that MS lymphocytes show a complex pattern of gene regulation that represents a counterbalance between promoting and preventing apoptosis and DNA damage of lymphocytes.

The use of real time rtPCR to quantify inflammatory mediator expression in leukocytes from patients with severe sepsis

Real-time reverse transcriptase polymerase chain reaction (RT rtPCR) was used to quantify the pattern of inflammatory mediator mRNA expression in circulating leukocytes from adult patients diagnosed with severe sepsis. We analysed 29 blood samples from 26 severely septic patients with different septic sources and eight samples from eight healthy adult volunteers. RT rtPCR was used to quantify mRNA expression of 21 different inflammatory mediators in peripheral leukocytes. The median variability in gene expression in the sepsis patients was 10.5 times greater than the variability of the healthy comparison group. We found a significant change in the regulation for the following genes: C5aR (20-fold, P < 0.001), IL-8 (29-fold, P < 0.001), MMP9 (72-fold, P < 0.001), HSP70 (2.4-fold, P = 0.02), and RIP2 (1.8-fold, P < 0.04) were up-regulated. Conversely the median expression of IFNgamma, and IL-6 were zero (P < 0.001), and mtHSP (0.4-fold, P = 0.02) was significantly down-regulated. Using linear discriminant analysis, IFNgamma, IL-12, and TLR4 were correlated to a negative outcome. Different septic sources (peritonitis, burn, pneumonia and musculo-skeletal infections) resulted in significantly different mRNA patterns. The RT rtPCR is a useful tool to monitor the immune response in septic patients. We found a very high variability in inflammatory mediator expression among septic patients compared to healthy volunteers. This suggests that any future immune-modulatory therapy may need to be individualized to the patient's requirements as monitored by RT rtPCR. Different sources of sepsis may result in markedly different activation patterns.

Gene expression profiling using RNA extracted from whole blood: technologies and clinical applications

Gene expression profiling promises to provide an insight into normal biologic and pathologic processes with the hope of predicting disease outcome or indicating individualized courses of therapy. The entire process for gene expression profiling from clinical samples consists of sample collection, transport and storage of clinical sample, isolation of nucleic acids, enzymatic modification of nucleic acids, detection and data analysis. All steps exert an influence on the quality, accuracy and reliability of the final result. A standardization of the entire process from sample collection to nucleic acid analysis is therefore required in order to achieve reliable gene expression results. After providing a general overview of technologies for gene expression profiling and respective caveats, this review will focus on clinical applications of blood sample profiling.

Comparative primate genomics: the year of the chimpanzee

This is the year of the chimpanzee genome. Chimpanzee chromosome 22 has been sequenced and soon will be followed by the whole genome, and thousands of chimpanzee cDNA sequences are available for comparative analysis. Not only does this genomic information allow us to identify human-specific changes in particular genes that are potentially under selection, but also to understand molecular evolutionary dynamics characterizing the two most closely related mammalian genomes sequenced so far. Studies comparing gene expression in chimpanzees and other closely related primates reveal significant species differences in brain, liver and fibroblasts. New empirical data, in combination with models of speciation, are giving insight into how humans and chimpanzees speciated.

Application of genome-wide expression analysis to human health and disease

The application of genome-wide expression analysis to a large-scale, multicentered program in critically ill patients poses a number of theoretical and technical challenges. We describe here an analytical and organizational approach to a systematic evaluation of the variance associated with genome-wide expression analysis specifically tailored to study human disease. We analyzed sources of variance in genome-wide expression analyses performed with commercial oligonucleotide arrays. In addition, variance in gene expression in human blood leukocytes caused by repeated sampling in the same subject, among different healthy subjects, among different leukocyte subpopulations, and the effect of traumatic injury, were also explored. We report that analytical variance caused by sample processing was acceptably small. Blood leukocyte gene expression in the same individual over a 24-h period was remarkably constant. In contrast, genome-wide expression varied significantly among different subjects and leukocyte subpopulations. Expectedly, traumatic injury induced dramatic changes in apparent gene expression that were greater in magnitude than the analytical noise and interindividual variance. We demonstrate that the development of a nation-wide program for gene expression analysis with careful attention to analytical details can reduce the variance in the clinical setting to a level where patterns of gene expression are informative among different healthy human subjects, and can be studied with confidence in human disease.

Gene Expression Profile of Papillary Thyroid Cancer: Sources of Variability and Diagnostic Implications

The study looked for an optimal set of genes differentiating between papillary thyroid cancer (PTC) and normal thyroid tissue and assessed the sources of variability in gene expression profiles. The analysis was done by oligonucleotide microarrays (GeneChip HG-U133A) in 50 tissue samples taken intraoperatively from 33 patients (23 PTC patients and 10 patients with other thyroid disease). In the initial group of 16 PTC and 16 normal samples, we assessed the sources of variability in the gene expression profile by singular value decomposition which specified three major patterns of variability. The first and the most distinct mode grouped transcripts differentiating between tumor and normal tissues. Two consecutive modes contained a large proportion of immunity-related genes. To generate a multigene classifier for tumor-normal difference, we used support vector machines-based technique (recursive feature replacement). It included the following 19 genes: DPP4, GJB3, ST14, SERPINA1, LRP4, MET, EVA1, SPUVE, LGALS3, HBB, MKRN2, MRC2, IGSF1, KIAA0830, RXRG, P4HA2, CDH3, IL13RA1, and MTMR4, and correctly discriminated 17 of 18 additional PTC/normal thyroid samples and all 16 samples published in a previous microarray study. Selected novel genes (LRP4, EVA1, TMPRSS4, QPCT, and SLC34A2) were confirmed by Q-PCR. Our results prove that the gene expression signal of PTC is easily detectable even when cancer cells do not prevail over tumor stroma. We indicate and separate the confounding variability related to the immune response. Finally, we propose a potent molecular classifier able to discriminate between PTC and nonmalignant thyroid in more than 90% of investigated samples.

Optimizing RNA extraction yield from whole blood for microarray gene expression analysis

OBJECTIVES

Microarray analysis of gene expression profiles of blood leukocytes has many potential clinical and research applications.

DESIGN AND METHODS

We used the PAXgene Blood RNA System to prepare RNA from the whole blood of normal volunteers using two incubation times followed by gene expression profiling using the Affymetrix HU133A GeneChip.

CONCLUSIONS

Longer incubation gave a significantly higher RNA yield and samples that were satisfactory for microarray analysis, with excellent pairwise correlations between replicates.

Gene expression profile of human bone marrow stromal cells determined by restriction fragment differential display analysis

Using an in vitro osteogenic culture system, we carried out a restriction fragment differential display (RFDD-PCR) to identify genes expressed by these cells in their undifferentiated stage and not expressed, or expressed at a lower level, in a closely related but distinct cell type: bone marrow stromal cells (BMSC)-derived osteoblasts (BDO). Forty-seven candidate regulated genes, selected by RFDD, were analyzed by RT-PCR analysis in three cell clones and in primary cultures from seven different donors. A subset of three genes were confirmed as upregulated in BMSC relative to BDO in every primary culture and cloned population examined: betaIG-h3, IGFbp3, and LOXL2. Their differential expression was confirmed by Northern analysis and the corresponding proteins were detected by immunolocalization in BMSC.

Evidence for widespread degradation of gene control regions in hominid genomes

Although sequences containing regulatory elements located close to protein-coding genes are often only weakly conserved during evolution, comparisons of rodent genomes have implied that these sequences are subject to some selective constraints. Evolutionary conservation is particularly apparent upstream of coding sequences and in first introns, regions that are enriched for regulatory elements. By comparing the human and chimpanzee genomes, we show here that there is almost no evidence for conservation in these regions in hominids. Furthermore, we show that gene expression is diverging more rapidly in hominids than in murids per unit of neutral sequence divergence. By combining data on polymorphism levels in human noncoding DNA and the corresponding human–chimpanzee divergence, we show that the proportion of adaptive substitutions in these regions in hominids is very low. It therefore seems likely that the lack of conservation and increased rate of gene expression divergence are caused by a reduction in the effectiveness of natural selection against deleterious mutations because of the low effective population sizes of hominids. This has resulted in the accumulation of a large number of deleterious mutations in sequences containing gene control elements and hence a widespread degradation of the genome during the evolution of humans and chimpanzees.

A comparison of hominid and rodent lineages reveals that the gene control regions of hominids are not conserved and are accumulating mutations, suggesting widespread degradation of the hominid genome

Assessing natural variations in gene expression in humans by comparing with monozygotic twins using microarrays

Quantitative variation in gene expression in humans is the outcome of various factors, including differences in genetic background, gender, age, and environment. However, the extent of the influence of these factors on gene expression is not clear. We attempted to address this issue by carrying out gene expression profiling in blood leukocytes with 13 individuals (including 5 pairs of monozygotic twins) on 10,000 genes using HG-U95Av2 oligonucleotide microarrays. The proportion of differentially expressed genes between monozygotic twins was low (up to 1.76%). Most of the variations belonged to the least variable category. These genes, exhibiting "random variations," did not show clear preference to any functional class, although "signaling and communication" and "immune and related functions" generally topped the list. The extent of variation in gene expression increased in comparisons between unrelated individuals (up to 14.13%). Most of the genes (89%) exhibiting random variations in twins also varied in expression in unrelated individuals. As with twins, signaling and communication topped the list, and substantial variations were observed in all three categories: least variable, moderately variable, and most variable. An important outcome of this study was that the housekeeping genes were nearly insensitive to random variations but appeared to be more susceptible to genetic differences. However, the highly expressed housekeeping genes exhibited low variation and appeared to be insensitive to all known factors. Gene expression profiling in monozygotic twins can provide useful data for the assessment of natural variation in gene expression in humans.

Assessment of human stress and depression by DNA microarray analysis

Precise assessment of stress is an imminent issue to deal with stress-related social, medical and psychological problems. Psychological stress is known to stimulate the neuroendocrine, sympathetic nervous, and immune systems. By analyzing mRNA expression levels in leukocytes, which express receptors for hormones, neurotransmitters, growth factors, cytokines, and other stress related signals, levels of stress may be adequately measured. In a series of studies, our group has developed a cDNA microarray specifically designed to measure the mRNA levels of stress-related genes in peripheral blood leukocytes. This microarray enabled us to sensitively detect the response to psychological stress. In addition, our preliminary study suggests that the array could differentiate patients with depression from sex- and age-matched control subjects.

Comparison of different isolation techniques prior gene expression profiling of blood derived cells: impact on physiological responses, on overall expression and the role of different cell types

Owing to its clinical accessibility, peripheral blood is probably the best source for the assessment of differences or changes in gene expression associated with disease or drug response and therapy. Gene expression patterns in peripheral blood cells greatly depend on temporal and interindividual variations. However, technical aspects of blood sampling, isolation of cellular components, RNA isolation techniques and clinical aspects such as time to analysis and temperature during processing have been suggested to affect gene expression patterns. We therefore assessed gene expression patterns in peripheral blood from 29 healthy individuals by using Affymetrix microarrays. When RNA isolation was delayed for 20-24 h-a typical situation in clinical studies-gene signatures related to hypoxia were observed, and downregulation of genes associated with metabolism, cell cycle or apoptosis became dominant preventing the assessment of gene signatures of interindividual variation. Similarly, gene expression patterns were strongly dependent on choice of cell and RNA isolation and preparation techniques. We conclude that for large clinical studies, it is crucial to reduce maximally the time to RNA isolation. Furthermore, prior to study initiation, the cell type of interest should already be defined. Our data therefore will help to optimize clinical studies applying gene expression analysis of peripheral blood to exploit drug responses and to better understand changes associated with disease.

Getting the right cells to the array: Gene expression microarray analysis of cell mixtures and sorted cells

BACKGROUND

Most biological samples are cell mixtures. Some basic questions are still unanswered about analyzing these heterogeneous samples using gene expression microarray technology (MAT). How meaningful is a cell mixture's overall gene expression profile (GEP)? Is it necessary to purify the cells of interest before microarray analysis, and how much purity is needed? How much does the purification itself distort the GEP, and how well can the GEP of a small cell subset be recovered?

METHODS

Model cell mixtures with different cell ratios were analyzed by both spotted and Affymetrix MAT. GEP distortion during cell purification and GEPs of purified cells were studied. CD34+ cord blood cells were purified and analyzed by MAT.

RESULTS

GEPs for mixed cell populations were found to mirror the cell ratios in the mixture. Over 75% pure samples were indistinguishable from pure cells by their overall GEP. Cell purification preserved the GEP. The GEPs of small cell subsets could be accurately recovered by cell sorting both from model cell mixtures and from cord blood.

CONCLUSIONS

Purification of small cell subsets from a mixture prior to MAT is necessary for meaningful results. Even completely hidden GEPs of small cell subpopulations can be recovered by cell sorting.

Molecular genetics and genomics of heart failure

Heart failure is a major disease burden worldwide, and its incidence continues to increase as premature deaths from other cardiovascular conditions decline. Although the overall molecular portrait of this multifactorial disease remains incomplete, molecular and genetic studies have implicated, in recent decades, various pathways and genes that participate in the pathophysiology of heart failure. Here, we highlight the current understanding of the molecular and genetic basis of heart failure and show how recently developed genomic tools are providing a new perspective on this complex disease.

Genetic inheritance of gene expression in human cell lines

Combining genetic inheritance information, for both molecular profiles and complex traits, is a promising strategy not only for detecting quantitative trait loci (QTLs) for complex traits but for understanding which genes, pathways, and biological processes are also under the influence of a given QTL. As a primary step in determining the feasibility of such an approach in humans, we present the largest survey to date, to our knowledge, of the heritability of gene-expression traits in segregating human populations. In particular, we measured expression for 23,499 genes in lymphoblastoid cell lines for members of 15 Centre d'Etude du Polymorphisme Humain (CEPH) families. Of the total set of genes, 2,340 were found to be expressed, of which 31% had significant heritability when a false-discovery rate of 0.05 was used. QTLs were detected for 33 genes on the basis of at least one P value <.000005. Of these, 13 genes possessed a QTL within 5 Mb of their physical location. Hierarchical clustering was performed on the basis of both Pearson correlation of gene expression and genetic correlation. Both reflected biologically relevant activity taking place in the lymphoblastoid cell lines, with greater coherency represented in Kyoto Encyclopedia of Genes and Genomes database (KEGG) pathways than in Gene Ontology database pathways. However, more pathway coherence was observed in KEGG pathways when clustering was based on genetic correlation than when clustering was based on Pearson correlation. As more expression data in segregating populations are generated, viewing clusters or networks based on genetic correlation measures and shared QTLs will offer potentially novel insights into the relationship among genes that may underlie complex traits.

Natural variation in cardiac metabolism and gene expression in Fundulus heteroclitus

Individual variation in gene expression is important for evolutionary adaptation and susceptibility to diseases and pathologies. In this study, we address the functional importance of this variation by comparing cardiac metabolism to patterns of mRNA expression using microarrays. There is extensive variation in both cardiac metabolism and the expression of metabolic genes among individuals of the teleost fish Fundulus heteroclitus from natural outbred populations raised in a common environment: metabolism differed among individuals by a factor of more than 2, and expression levels of 94% of genes were significantly different (P < 0.01) between individuals in a population. This unexpectedly high variation in metabolic gene expression explains much of the variation in metabolism, suggesting that it is biologically relevant. The patterns of gene expression that are most important in explaining cardiac metabolism differ between groups of individuals. Apparently, the variation in metabolism seems to be related to different patterns of gene expression in the different groups of individuals. The magnitude of differences in gene expression in these groups is not important; large changes in expression have no greater predictive value than small changes. These data suggest that variation in physiological performance is related to the subtle variation in gene expression and that this relationship differs among individuals.

Primary biliary cirrhosis: does X mark the spot?

Primary biliary cirrhosis (PBC) is an autoimmune disease of unknown etiology leading to progressive destruction of intrahepatic bile duct, with cholestasis, cirrhosis, and eventually liver failure. Epidemiological data indicate that environmental factors trigger autoimmunity in genetically susceptible individuals, although no definitive association of PBC with specific genes has been found. Further, no convincing explanation has been provided for the strong female predominance observed in the prevalence of PBC. However, we recently suggested that the enhanced monosomy X in peripheral white blood cells, and particularly in lymphocytes, of affected women might play a role in the induction of PBC. Such observations appear independent from the degree of cholestasis and specific for PBC. In this review we discuss the implications of these findings and their immunological implications.

In silico study of transcriptome genetic variation in outbred populations

Dissecting the genetic architecture of regulatory elements on a genome-wide basis is now technically feasible. The potential medical and genetical implications of this kind of experiment being very large, it is paramount to assess the reliability and repeatability of the results. This is especially relevant in outbred populations, such as humans, where the genetic architecture is necessarily more complex than in crosses between inbred lines. Here we simulated a chromosome-wide SNP association study using real human microarray data. Our model predicted, as observed, a highly significant clustering of quantitative trait loci (QTL) for gene expression. Importantly, the estimates of QTL positions were often unstable, and a decrease in the number of individuals of 16% resulted in a loss of power of approximately 30% and a large shift in the position estimate in approximately 30-40% of the remaining significant QTL. We also found that the analysis of two repeated measures of the same mRNA can also result in two QTL that are located far apart. The intrinsic difficulties of analyzing outbred populations should not be underestimated. We anticipate that (many) conflicting results may be collected in the future if whole-genome association studies for mRNA levels are carried out in outbred populations.

Genetic variations in peripheral blood mononuclear cells in piglets used as an animal model for staphylococcal enterotoxin exposures

We have used piglets as an animal model for studying the toxic effects of staphylococcal enterotoxins (SEs). Piglets are easy to handle, easy to carry out vital measurements, inexpensive, and more importantly, express remarkably similar pathological symptoms and responses to SE intoxication as humans at comparable doses. Microarray analyses are used to study the effect of many infections on gene expression profile in peripheral blood mononuclear cells. This high throughput application offers detailed depiction of alteration at the molecular levels. When using high throughput gene expression analysis, there is a high possibility of finding genes that vary normally in the tissues under study. It is necessary to verify genes that are normally differentially expressed between piglets. To evaluate the normal physiological variation in gene expression in vivo in piglets, we used cDNA microarray to measure gene expression levels in peripheral blood mononuclear cells from 10 normal Yorkshire piglets. We used analysis of variance to determine genes that showed statistically significant variations across piglets. Out of 1185 genes, 19 (1.6%) genes revealed statistically significant variance between RNA samples. Some of these varying genes are involved in stress response, immune response, and transcription. This study facilitates the characterization of gene expression base line needed for meaningful interpretation of microarray data.

Differential-display PCR of peripheral blood for biomarker discovery in chronic fatigue syndrome

We used differential-display PCR of peripheral blood mononuclear cells (PBMCs) to search for candidate biomarkers for chronic fatigue syndrome (CFS). PBMCs were collected from a subject with CFS and an age- and sex-matched control before and 24 h after exercise. RNA expression profiles were generated using 46 primer combinations, and the similarity between the individuals was striking. Differentially expressed bands were excised, reamplified, and sequenced, yielding 95 nonredundant sequences, of which 50 matched to known gene transcripts, 38 matched to genes with unknown functions, and 7 had no similarity to any database entry. Most (86%) of the differences between the two subjects were present at baseline. Differential expression of ten genes was verified by real-time reverse-transcription PCR: five (cystatin F, MHC class II, platelet factor 4, fetal brain expressed sequence tag, and perforin) were downregulated, and the remaining five genes (cathepsin B, DNA polymerase epsilon4, novel EST PBMC191MSt, heparanase precursor, and ORF2/L1 element) were upregulated in the subject with CFS. Many of these genes have known functions in defense and immunity, thus supporting prior suggestions of immune dysregulation in the pathogenesis of CFS. Differential-display PCR is a powerful tool for identification of candidate biomarkers. Investigation of these markers in samples from well-designed epidemiological studies of CFS will be required to determine the validity of these candidate biomarkers. The real-time reverse-transcription PCR assays that we developed for assay of these biomarkers will facilitate high-throughput testing of these additional samples.

Amplification protocols introduce systematic but reproducible errors into gene expression studies

The desire to perform microarray experiments with small starting amounts of RNA has led to the development of a variety of protocols for preparing and amplifying mRNA. This has consequences not only for the standardization of experimental design, but also for reproducibility and comparability between experiments. Here we investigate the differences between the Affymetrix standard and small sample protocols and address the data analysis issues that arise when comparing samples and experiments that have been processed in different ways. We show that data generated on the same platform using different protocols are not directly comparable. Further, protocols introduce systematic biases that can be largely accounted for by using the correct data analysis techniques.

Exploration of neuroendocrine and immune gene expression in peripheral blood mononuclear cells

As pathways of communication between the nervous, endocrine, and immune systems are identified, the importance of the interplay of these systems for health and well-being is increasingly recognized. In this study, we created a comprehensive database of 1622 genes likely to be involved in synthetic, biochemical, and regulatory psycho-neuroendocrine-immune (PNI) pathways. Expression of 1058 of these genes was detected in the peripheral blood by querying both a peripheral blood-specific expressed sequence tag (EST) database and a peripheral blood database generated from microarray evaluation of 30,000 genes. Several neural and endocrine genes were expressed in the peripheral blood including hormone receptors, a hormone-responsive transcription factor, and neurotransmitter receptors. These findings document the expression of nervous and endocrine genes in the peripheral blood that have previously only been characterized in the respective system tissues, and indicate that the blood is a rich source of information that should help in deciphering the communication between the mind and the body.