Large genomic copy number variations (CNVs) have been implicated as strong risk factors for schizophrenia. However, the rarity of these events has created challenges for the identification of further pathogenic loci, and extremely large samples are required to provide convincing replication.
To detect novel CNVs increasing susceptibility to schizophrenia, utilizing two ethnically homogeneous discovery cohorts and replication in large samples.
Genetic association study of microarray data.
DNA samples were collected at nine sites from different countries.
Two discovery cohorts were comprised of: a) 790 cases (schizophrenia and schizoaffective disorder) and 1347 controls of Ashkenazi Jewish descent; and b) 662 trios (offspring affected with schizophrenia or schizoaffective disorder) from Bulgaria. Replication datasets consisted of 12,398 cases and 17,945 controls.
Main outcome measure
Statistically increased rate of specific CNVs in cases versus controls.
One novel locus was implicated: a deletion at distal 16p11.2, which does not overlap the proximal 16p11.2 locus previously reported in schizophrenia and autism. Deletions at this locus were found in 13 out of 13,850 cases (0.094%) and in 3 out of 19,954 controls (0.015%), Fisher Exact p = 0.0014; OR = 6.25 (95%CI = 1.78 – 21.93).
Deletions at distal 16p11.2 have been previously implicated in developmental delay and obesity. The region contains nine genes, several of which are implicated in neurological diseases, regulation of body weight, and glucose homeostasis. A telomeric extension of the deletion, observed in about half the cases but no controls, potentially implicates an additional eight genes. Our findings add a new locus to the list of CNVs that increase risk to develop schizophrenia.
Transcriptomic assays that measure expression levels are widely used to study the manifestation of environmental or genetic variations in cellular processes. RNA-sequencing in particular has the potential to considerably improve such understanding because of its capacity to assay the entire transcriptome, including novel transcriptional events. However, as with earlier expression assays, analysis of RNA-sequencing data requires carefully accounting for factors that may introduce systematic, confounding variability in the expression measurements, resulting in spurious correlations. Here, we consider the problem of modeling and removing the effects of known and hidden confounding factors from RNA-sequencing data. We describe a unified residual framework that encapsulates existing approaches, and using this framework, present a novel method, HCP (Hidden Covariates with Prior). HCP uses a more informed assumption about the confounding factors, and performs as well or better than existing approaches while having a much lower computational cost. Our experiments demonstrate that accounting for known and hidden factors with appropriate models improves the quality of RNA-sequencing data in two very different tasks: detecting genetic variations that are associated with nearby expression variations (cis-eQTLs), and constructing accurate co-expression networks.
Recent meta-analyses of European ancestry subjects show strong evidence for association between smoking quantity and multiple genetic variants on chromosome 15q25. This meta-analysis extends the examination of association between distinct genes in the CHRNA5-CHRNA3-CHRNB4 region and smoking quantity to Asian and African American populations to confirm and refine specific reported associations.
Association results for a dichotomized cigarettes smoked per day (CPD) phenotype in 27 datasets (European ancestry (N=14,786), Asian (N=6,889), and African American (N=10,912) for a total of 32,587 smokers) were meta-analyzed by population and results were compared across all three populations.
We demonstrate association between smoking quantity and markers in the chromosome 15q25 region across all three populations, and narrow the region of association. Of the variants tested, only rs16969968 is associated with smoking (p < 0.01) in each of these three populations (OR=1.33, 95%C.I.=1.25–1.42, p=1.1×10−17 in meta-analysis across all population samples). Additional variants displayed a consistent signal in both European ancestry and Asian datasets, but not in African Americans.
The observed consistent association of rs16969968 with heavy smoking across multiple populations, combined with its known biological significance, suggests rs16969968 is most likely a functional variant that alters risk for heavy smoking. We interpret additional association results that differ across populations as providing evidence for additional functional variants, but we are unable to further localize the source of this association. Using the cross-population study paradigm provides valuable insights to narrow regions of interest and inform future biological experiments.
smoking; genetics; meta-analysis; cross-population
Genome-wide association studies (GWAS) of body mass index (BMI) using large samples have yielded approximately a dozen robustly associated variants and implicated additional loci. Individually these variants have small effects and in aggregate explain a small proportion of the variance. As a result, replication attempts have limited power to achieve genome-wide significance, even with several thousand subjects. Since there is strong prior evidence for genetic influence on BMI for specific variants, alternative approaches to replication can be applied. Instead of testing individual loci sequentially, a genetic risk sum score (GRSS) summarizing the total number of risk alleles can be tested. In the current study, GRSS comprising 56 top variants catalogued from two large meta-analyses was tested for association with BMI in the Molecular Genetics of Schizophrenia controls (2,653 European-Americans, 973 African-Americans). After accounting for covariates known to influence BMI (ancestry, sex, age), GRSS was highly associated with BMI (p value = 3.19E−06) although explained a limited amount of the variance (0.66%). However, area under receiver operator criteria curve (AUC) estimates indicated that the GRSS and covariates significantly predicted overweight and obesity classification with maximum discriminative ability for predicting class III obesity (AUC = 0.697). The relative contributions of the individual loci to GRSS were examined post hoc and the results were not due to a few highly significant variants, but rather the result of numerous variants of small effect. This study provides evidence of the utility of a GRSS as an alternative approach to replication of common polygenic variation in complex traits.
Recent studies suggest that variation in complex disorders (e.g., schizophrenia) is explained by a large number of genetic variants with small effect size (Odds Ratio∼1.05–1.1). The statistical power to detect these genetic variants in Genome Wide Association (GWA) studies with large numbers of cases and controls (∼15,000) is still low. As it will be difficult to further increase sample size, we decided to explore an alternative method for analyzing GWA data in a study of schizophrenia, dramatically reducing the number of statistical tests. The underlying hypothesis was that at least some of the genetic variants related to a common outcome are collocated in segments of chromosomes at a wider scale than single genes. Our approach was therefore to study the association between relatively large segments of DNA and disease status. An association test was performed for each SNP and the number of nominally significant tests in a segment was counted. We then performed a permutation-based binomial test to determine whether this region contained significantly more nominally significant SNPs than expected under the null hypothesis of no association, taking linkage into account. Genome Wide Association data of three independent schizophrenia case/control cohorts with European ancestry (Dutch, German, and US) using segments of DNA with variable length (2 to 32 Mbp) was analyzed. Using this approach we identified a region at chromosome 5q23.3-q31.3 (128–160 Mbp) that was significantly enriched with nominally associated SNPs in three independent case-control samples. We conclude that considering relatively wide segments of chromosomes may reveal reliable relationships between the genome and schizophrenia, suggesting novel methodological possibilities as well as raising theoretical questions.
While genetic influences on Alcohol Dependence (AD) are substantial, progress in the identification of individual genetic variants that impact on risk has been difficult.
We performed a genome-wide association study on 3,169 alcohol consuming subjects from the population-based Molecular Genetics of Schizophrenia (MGS2) control sample. Subjects were asked 7 questions about symptoms of AD which were analyzed by confirmatory factor analysis. Genotyping was performed using the Affymetrix 6.0 array. Three sets of analyses were conducted separately for European American (EA, n=2,357) and African-American (AA, n=812) subjects: individual SNPs, candidate genes and enriched pathways using Gene Ontology (GO) categories.
The symptoms of AD formed a highly coherent single factor. No SNP approached genome-wide significance. In the EA sample, the most significant intragenic SNP was in KCNMA1, the human homolog of the slo-1 gene in C. Elegans. Genes with clusters of significant SNPs included AKAP9, PIGG and KCNMA1. In the AA sample, the most significant intragenic SNP was CEACAM6 and genes showing empirically significant SNPs included KCNQ5, SLC35B4 and MGLL. In the candidate gene based analyses, the most significant findings were with ADH1C, NFKB1 and ANKK1 in the EA sample, and ADH5, POMC, and CHRM2 in the AA sample. The ALIGATOR program identified a significant excess of associated SNPs within and near genes in a substantial number of GO categories over a range of statistical stringencies in both the EA and AA sample.
While we cannot be highly confident about any single result from these analyses, a number of findings were suggestive and worthy of follow-up. Although quite large samples will be needed to obtain requisite power, the study of AD symptoms in general population samples is a viable complement to case-control studies in identifying genetic risk variants for AD.
alcohol dependence; genome-wide association study; gene ontology; control
Autozygosity occurs when two chromosomal segments that are identical from a common ancestor are inherited from each parent. This occurs at high rates in the offspring of mates who are closely related (inbreeding), but also occurs at lower levels among the offspring of distantly related mates. Here, we use runs of homozygosity in genome-wide SNP data to estimate the proportion of the autosome that exists in autozygous tracts in 9,388 cases with schizophrenia and 12,456 controls. We estimate that the odds of schizophrenia increase by ∼17% for every 1% increase in genome-wide autozygosity. This association is not due to one or a few regions, but results from many autozygous segments spread throughout the genome, and is consistent with a role for multiple recessive or partially recessive alleles in the etiology of schizophrenia. Such a bias towards recessivity suggests that alleles that increase the risk of schizophrenia have been selected against over evolutionary time.
Inbreeding occurs when genetic relatives have offspring. Because all humans are related to one another, even if very distantly, all people are inbred to various degrees. From a genetic standpoint, it is well known that inbreeding increases the risk that a child will have a rare recessive genetic disease, but there is also increasing interest in understanding whether inbreeding is a risk factor for more common, complex disorders such as schizophrenia. In this investigation, we used single-nucleotide polymorphism data to quantify the degree to which 9,388 schizophrenia cases and 12,456 controls were inbred, and we tested the hypothesis that people whose genome shows higher evidence of being inbred are at higher risk of having schizophrenia. We estimate that the odds of schizophrenia increase by ∼17% for every 1% increase in inbreeding. This finding is consistent with a role for multiple recessive or partially recessive alleles in the etiology of schizophrenia, and it suggests that genetic variants that increase the risk of schizophrenia have been selected against over evolutionary time.
Individuals with schizophrenia tend to be heavy smokers and are at high risk for tobacco dependence. However, the nature of the comorbidity is not entirely clear. We previously reported evidence for association of schizophrenia with SNPs and SNP haplotypes in a region of chromosome 5q containing the SPEC2, PDZ-GEF2 and ACSL6 genes. In this current study, analysis of the control subjects of the Molecular Genetics of Schizophrenia (MGS) sample showed similar pattern of association with number of cigarettes smoked per day (numCIG) for the same region. To further test if this locus is associated with tobacco smoking as measured by numCIG and FTND, we conducted replication and meta-analysis in 12 independent samples (n>16,000) for two markers in ACSL6 reported in our previous schizophrenia study. In the meta-analysis of the replication samples, we found that rs667437 and rs477084 were significantly associated with numCIG (p = 0.00038 and 0.00136 respectively) but not with FTND scores. We then used in vitro and in vivo techniques to test if nicotine exposure influences the expression of ACSL6 in brain. Primary cortical culture studies showed that chronic (5-day) exposure to nicotine stimulated ACSL6 mRNA expression. Fourteen days of nicotine administration via osmotic mini pump also increased ACSL6 protein levels in the prefrontal cortex and hippocampus of mice. These increases were suppressed by injection of the nicotinic receptor antagonist mecamylamine, suggesting that elevated expression of ACSL6 requires nicotinic receptor activation. These findings suggest that variations in the ACSL6 gene may contribute to the quantity of cigarettes smoked. The independent associations of this locus with schizophrenia and with numCIG in non-schizophrenic subjects suggest that this locus may be a common liability to both conditions.
Recent genome-wide association studies have associated polymorphisms in the gene CACNA1C, which codes for Cav1.2, with a bipolar disorder and depression diagnosis.
The behaviors of wild type and Cacna1c heterozygous mice of both sexes were evaluated in a number of tests. Based upon sex differences in our mouse data, we assessed a gene x sex interaction for diagnosis of mood disorders in human subjects. Data from the NIMH-BP Consortium and the GenRED Consortium were examined utilizing a combined dataset that included 2,021 mood disorder cases (1,223 females) and 1,840 controls (837 females).
In both male and female mice, Cacna1c haploinsufficiency is associated with lower exploratory behavior, decreased response to amphetamine, and antidepressant-like behavior in the forced swim and tail suspension tests. Female, but not male, heterozygous mice displayed decreased risk-taking behavior or increased anxiety in multiple tests, greater attenuation of amphetamine-induced hyperlocomotion, decreased development of learned helplessness, and a decreased acoustic startle response indicating a sex-specific role of Cacna1c. In humans, sex-specific genetic association was seen for two intronic single nucleotide polymorphisms (SNPs), rs2370419 and rs2470411, in CACNA1C, with effects in females (OR=1.64, 1.32), but not in males (OR=0.82, 0.86). The interactions by sex were significant after correction for testing 190 SNPs (P=1.4 x 10−4, 2.1 x 10−4; Pcorrected=0.03, 0.04), and were consistent across two large data sets.
Our preclinical results support a role for CACNA1C in mood disorder pathophysiology, and the combination of human genetic and preclinical data support an interaction between sex and genotype.
CACNA1C; bipolar disorder; major depression; Cav1.2; animal model; gender; sex differences
Schizophrenia, a devastating psychiatric disorder, has a prevalence of 0.5–1%, with high heritability (80–85%) and complex transmission.1 Recent studies implicate rare, large, high-penetrance copy number variants (CNVs) in some cases2, but it is not known what genes or biological mechanisms underlie susceptibility. Here we show that schizophrenia is significantly associated with single nucleotide polymorphisms (SNPs) in the extended Major Histocompatibility Complex (MHC) region on chromosome 6. We carried out a genome-wide association study (GWAS) of common SNPs in the Molecular Genetics of Schizophrenia (MGS) case-control sample, and then a meta-analysis of data from the MGS, International Schizophrenia Consortium (ISC) and SGENE datasets. No MGS finding achieved genome-wide statistical significance. In the meta-analysis of European-ancestry subjects (8,008 cases, 19,077 controls), significant association with schizophrenia was observed in a region of linkage disequilibrium on chromosome 6p22.1 (P = 9.54 × 10−9). This region includes a histone gene cluster and several immunity-related genes, possibly implicating etiologic mechanisms involving chromatin modification, transcriptional regulation, auto-immunity and/or infection. These results demonstrate that common schizophrenia susceptibility alleles can be detected. The characterization of these signals will suggest important directions for research on susceptibility mechanisms.
When testing large numbers of null hypotheses, one needs to assess the evidence against the global null hypothesis that none of the hypotheses is false. Such evidence typically is based on the test statistic of the largest magnitude, whose statistical significance is evaluated by permuting the sample units to simulate its null distribution. Efron (2007) has noted that correlation among the test statistics can induce substantial interstudy variation in the shapes of their histograms, which may cause misleading tail counts. Here, we show that permutation-based estimates of the overall significance level also can be misleading when the test statistics are correlated. We propose that such estimates be conditioned on a simple measure of the spread of the observed histogram, and we provide a method for obtaining conditional significance levels. We justify this conditioning using the conditionality principle described by Cox and Hinkley (1974). Application of the method to gene expression data illustrates the circumstances when conditional significance levels are needed.
Conditional p-value; Gene expression data; Genome-wide association data; Multiple testing; Overall p-value
We reported genome-wide significant linkage on chromosome 15q25.3–26.2 to recurrent early-onset major depressive disorder (MDD-RE). Here we present initial linkage-disequilibrium (LD) fine-mapping of this signal and sequence analysis of NTRK3 (neurotrophic receptor kinase-3), a biologically plausible candidate gene.
In 300 pedigrees informative for family-based association, 1195 individuals were genotyped for 795 SNPs. We resequenced 21 exons and seven highly conserved NTRK3 regions in 176 MDD-RE cases to test for an excess of rare functional variants, and in 176 controls for case-control analysis of common variants.
LD mapping showed nominally significant association in nine genes–NTRK3, FLJ12484, RHCG, DKFZp547K1113, VPS33B, SV2B, SLCO3A1, RGMA and MCTP2–with MDD-RE. In NTRK3, five SNPs had nominally significant p-values (0.035–0.001). Sequence analysis revealed 35 variants (24 novel, including nine rare exonic); the number of rare variants did not exceed chance expectation. Case-control analysis of 13 common variants showed modest nominal association of MDD-RE with rs4887379, rs6496463 and rs3825882 (p = 0.008, 0.048, and 0.034), which were in partial LD with four of five associated SNPs from the family-based experiment.
Common variants in NTRK3 or one of the other genes identified might play a role in MDD-RE. However, much larger studies will be required for full evaluation of this region.
NTRK3; TRKC; Neurotrophin; tag SNPs; Association; Major Depression
The study of chronicity in the course of major depression has been complicated by varying definitions of this illness feature. Because familial clustering is one component of diagnostic validity we compared family clustering of chronicity as defined in the DSM-IV to that of chronicity determined by an assessment of lifetime course of depressive illness.
In 1750 affected subjects from 652 families recruited for a genetic study of recurrent, early-onset depression, we applied several definitions of chronicity. Odds ratios were determined for the likelihood of chronicity in a proband predicting chronicity in an affected relative.
There was greater family clustering of chronicity as determined by assessment of lifetime course (OR=2.54) than by DSM–IV defined chronic major depressive episode (MDE) (OR=1.93) or dysthymic disorder (OR = 1.76). In families with probands who had preadolescent onset of MDD, familiality was increased by all definitions, with a much larger increase observed for chronicity by lifetime course (ORs were 6.14 for lifetime chronicity, 2.43 for chronic MDE, and 3.42 for comorbid dysthymic disorder), Agreement between these definitions of chronicity was only fair.
The data used to determine chronicity were collected retrospectively and not blindly to relatives’ status, and assessment of lifetime course was based on global clinical impressions gathered during a semi-structured diagnostic interview. Also, it can be difficult to determine whether individuals with recurrent major depressive episodes who frequently experience long periods of low grade depressive symptoms meet the strict timing requirements of DSM–IV dysthymic disorder.
An assessment of lifetime symptom course identifies a more familial, and thus possibly a more valid, type of chronic depression than the current DSM-IV categories which are defined in terms of particular cross-sectional features of illness.
Depression; Chronic; Familial aggregation; Diagnosis
Morley et al. (Nature 2004, 430:743–747) detected significant linkages to the expression levels of 142 genes (of 3554) at a reported threshold of genome-wide p = 0.001 (LOD ≈ 5.3), using 14 three-generation Centre d'Etude du Polymorphisme Humain pedigrees. Most of the linkages (77%) were trans, i.e., more than 5 Mb from the expressed gene. However, the analysis did not account for the expected anti-conservative effect of the skewed distribution of score- or regression-based statistics in large sibships, or for the possible variance distortion due to correlations among tests. Therefore, we re-analyzed their data, using a robust score statistic for the entire pedigrees and correcting the p-values for skewness. We found that a LOD of 5.3 had a skewness-corrected genome-wide p-value of 0.016 instead of 0.001 (a result that we confirmed using simulation), with around 50 expected false positives. We then further corrected for correlation among the (skew-corrected) p-values by using Efron's method for obtaining the empirical null distribution. Setting a threshold of FDR = 10% (Z = 6.4, LOD = 8.9), we detected linkage for the expression levels of 22 genes, 19 of which are cis. Limiting the analysis to cis regions, linkage was detected to the expression levels of 46 genes with 4.6 expected false positives (FDR = 10%).
A bootstrap method for point-based detection of candidate biomarker peaks has been developed from pattern classifiers. Point-based detection methods are advantageous in comparison to peak-based methods. Peak determination and selection is problematic when spectral peaks are not baseline resolved or on a varying baseline. The benefit of point-based detection is that peaks can be globally determined from the characteristic features of the entire data set (i.e., subsets of candidate points) as opposed to the traditional method of selecting peaks from individual spectra and then combining the peak list into a data set. The point-based method is demonstrated to be more effective and efficient using a synthetic data set when compared to using Mahalanobis distance for feature selection. In addition, probabilities that characterize the uniqueness of the peaks are determined.
This method was applied for detecting peaks that characterize age-specific patterns of protein expression of developing and adult mouse cerebella from matrix assisted laser desorption/ionization (MALDI) mass spectrometry (MS) data. The mice comprised three age groups; 42 adults, 19 14-day old pups, and 16 7-day old pups. Three sequential spectra were obtained from each tissue section to yield 126, 57 and 48 spectra for adult, 14-day old pup, and 7-day old pup spectra, respectively. Each spectrum comprised 71,879 mass measurements in a range of 3.5-50 kDa. A previous study revealed that 846 unique peaks were detected that were consistent for 50% of the mice in each age group1.
A fuzzy rule-building expert system (FuRES) was applied to investigate the correlation of age with features in the MS data. FuRES detected two outlier pup-14 spectra. Prediction was evaluated using 100 bootstrap samples of 2 Latin-partitions (i.e., 50:50 split between training and prediction set) of the mice. The spectra without the outliers yielded classification rates of 99.1±0.1%, 90.1±0.8%, and 97.0±0.6% for adults, 14-day old pups, and 7-day old pups, respectively. At a 95% level of significance, 100 bootstrap samples disclosed 35 adult and 21 pup distinguishing peaks for separating adults from pups; and 8 14-day old and 15 7-day old predictive peaks for separating 14-day old pup from 7-day old pup spectra. A compressed matrix comprising 40,393 points that were outside the 95% confidence intervals of one of the two FuRES discriminants was evaluated and the classification improved significantly for all classes. When peaks that satisfied a quality criterion were integrated, the 55 integrated peak areas furnished significantly improved classification for all classes: the selected peak areas furnished classification rates of 100%, 97.3±0.6%, and 97.4±0.3% for adult, 14-day old pups, and 7-day old pups using 100 bootstrap Latin partitions evaluations with the predictions averaged. When the bootstrap size was increased to 1000 samples, the results were not significantly affected. The FuRES predictions were consistent with those obtained by discriminant partial least squares (DPLS) classifications.
Fuzzy Rule-building Expert System (FuRES); Matrix Assisted Laser Desorption/Ionization (MALDI); Mass Spectrometry (MS); Latin-partitions; Mouse (Mus musculus domesticus); Cerebellum Tissue, Biomarker
Although large-scale genetic association studies involving hundreds to thousands of SNPs have become feasible, the associated cost is substantial. Even with the increased efficiency introduced by the use of tagSNPs, researchers are often seeking ways to maximize resource utilization given a set of SNP-based gene-mapping goals. We have developed a web server named QuickSNP in order to provide cost-effective selection of SNPs, and to fill in some of the gaps in existing SNP selection tools. One useful feature of QuickSNP is the option to select only gene-centric SNPs from a chromosomal region in an automated fashion. Other useful features include automated selection of coding non-synonymous SNPs, SNP filtering based on inter-SNP distances and information regarding the availability of genotyping assays for SNPs and whether they are present on whole genome chips. The program produces user-friendly summary tables and results, and a link to a UCSC Genome Browser track illustrating the position of the selected tagSNPs in relation to genes and other genomic features. We hope the unique combination of features of this server will be useful for researchers aiming to select markers for their genotyping studies. The server is freely available and can be accessed at the URL http://bioinformoodics.jhmi.edu/quickSNP.pl.
Dense SNP maps can be highly informative for linkage studies. But when parental genotypes are missing, multipoint linkage scores can be inflated in regions with substantial marker-marker linkage disequilibrium (LD). Such regions were observed in the Affymetrix SNP genotypes for the Genetic Analysis Workshop 14 (GAW14) Collaborative Study on the Genetics of Alcoholism (COGA) dataset, providing an opportunity to test a novel simulation strategy for studying this problem. First, an inheritance vector (with or without linkage present) is simulated for each replicate, i.e., locations of recombinations and transmission of parental chromosomes are determined for each meiosis. Then, two sets of founder haplotypes are superimposed onto the inheritance vector: one set that is inferred from the actual data and which contains the pattern of LD; and one set created by randomly selecting parental alleles based on the known allele frequencies, with no correlation (LD) between markers. Applying this strategy to a map of 176 SNPs (66 Mb of chromosome 7) for 100 replicates of 116 sibling pairs, significant inflation of multipoint linkage scores was observed in regions of high LD when parental genotypes were set to missing, with no linkage present. Similar inflation was observed in analyses of the COGA data for these affected sib pairs with parental genotypes set to missing, but not after reducing the marker map until r2 between any pair of markers was ≤ 0.05. Additional simulation studies of affected sib pairs assuming uniform LD throughout a marker map demonstrated inflation of significance levels at r2 values greater than 0.05. When genotypes are available only from two affected siblings in many families in a sample, trimming SNP maps to limit r2 to 0–0.05 for all marker pairs will prevent inflation of linkage scores without sacrificing substantial linkage information. Simulation studies on the observed pedigree structures and map can also be used to determine the effect of LD on a particular study.
T1/ST2 is an orphan receptor of unknown function that is expressed on the surface of murine T helper cell type 2 (Th2), but not Th1 effector cells. In vitro blockade of T1/ST2 signaling with an immunoglobulin (Ig) fusion protein suppresses both differentiation to and activation of Th2, but not Th1 effector populations. In a nascent Th2-dominated response, anti-T1/ST2 monoclonal antibody (mAb) inhibited eosinophil infiltration, interleukin 5 secretion, and IgE production. To determine if these effects were mediated by a direct effect on Th2 cells, we next used a murine adoptive transfer model of Th1- and Th2-mediated lung mucosal immune responses. Administration of either T1/ST2 mAb or T1/ST2-Ig abrogated Th2 cytokine production in vivo and the induction of an eosinophilic inflammatory response, but failed to modify Th1-mediated inflammation. Taken together, our data demonstrate an important role of T1/ST2 in Th2-mediated inflammatory responses and suggest that T1/ST2 may prove to be a novel target for the selective suppression of Th2 immune responses.
inflammation; eosinophil; asthma; cytokines; immunoglobulin superfamily