Michael G. Barnes

Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms

The growth in popularity of RNA expression microarrays has been accompanied by concerns about the reliability of the data especially when comparing between different platforms. Here, we present an evaluation of the reproducibility of microarray results using two platforms, Affymetrix GeneChips and Illumina BeadArrays. The study design is based on a dilution series of two human tissues (blood and placenta), tested in duplicate on each platform. The results of a comparison between the platforms indicate very high agreement, particularly for genes which are predicted to be differentially expressed between the two tissues. Agreement was strongly correlated with the level of expression of a gene. Concordance was also improved when probes on the two platforms could be identified as being likely to target the same set of transcripts of a given gene. These results shed light on the causes or failures of agreement across microarray platforms. The set of probes we found to be most highly reproducible can be used by others to help increase confidence in analyses of other data sets using these platforms.

Subtype-specific peripheral blood gene expression profiles in recent onset juvenile idiopathic arthritis

OBJECTIVE To identify differences in peripheral blood gene expression between patients with different subclasses of juvenile idiopathic arthritis (JIA) and healthy controls in a multicenter study of patients with recent-onset JIA prior to treatment with disease-modifying antirheumatic drugs (DMARDs) or biologic agents. METHODS Peripheral blood mononuclear cells (PBMCs) from 59 healthy children and 136 patients with JIA (28 with enthesitis-related arthritis [ERA], 42 with persistent oligoarthritis, 45 with rheumatoid factor [RF]-negative polyarthritis, and 21 with systemic disease) were isolated from whole blood. Poly(A) RNA was labeled using a commercial RNA amplification and labeling system (NuGEN Ovation), and gene expression profiles were obtained using commercial expression microarrays (Affymetrix HG-U133 Plus 2.0). RESULTS A total of 9,501 differentially expressed probe sets were identified among the JIA subtypes and controls (by analysis of variance; false discovery rate 5%). Specifically, 193, 1,036, 873, and 7,595 probe sets were different in PBMCs from the controls compared with those from the ERA, persistent oligoarthritis, RF-negative polyarthritis, and systemic JIA patients, respectively. In patients with persistent oligoarthritis, RF-negative polyarthritis, and systemic JIA subtypes, up-regulation of genes associated with interleukin-10 (IL-10) signaling was prominent. A hemoglobin cluster was identified that was underexpressed in ERA patients but overexpressed in systemic JIA patients. The influence of JAK/STAT, ERK/MAPK, IL-2, and B cell receptor signaling pathways was evident in patients with persistent oligoarthritis. In systemic JIA, up-regulation of innate immune pathways, including IL-6, Toll-like receptor/IL-1 receptor, and peroxisome proliferator-activated receptor signaling, were noted, along with down-regulation of gene networks related to natural killer cells and T cells. Complement and coagulation pathways were up-regulated in systemic JIA, with a subset of these genes being differentially expressed in other subtypes as well. CONCLUSION Expression analysis identified differentially expressed genes in PBMCs obtained early in the disease from patients with different subtypes of JIA and in healthy controls, providing evidence of immunobiologic differences between these forms of childhood arthritis.

Standard Preanalytical Coding for Biospecimens: Defining the Sample PREanalytical Code

Background: Management and traceability of biospecimen preanalytical variations are necessary to provide effective and efficient interconnectivity and interoperability between Biobanks. Methods: Therefore, the International Society for Biological and Environmental Repositories Biospecimen Science Working Group developed a “Standard PREanalytical Code” (SPREC) that identifies the main preanalytical factors of clinical fluid and solid biospecimens and their simple derivatives. Results: The SPREC is easy to implement and can be integrated into Biobank quality management systems and databases. It can also be extended to nonhuman biorepository areas. Its flexibility allows integration of new novel technological developments in future versions. SPREC version 01 is presented in this article. Conclusions and Impact: Implementation of the SPREC is expected to facilitate and consolidate international multicenter biomarker identification research and biospecimen research in the clinical Biobank environment. Cancer Epidemiol Biomarkers Prev; 19(4); 1004–11. ©2010 AACR.

Standard preanalytical coding for biospecimens: review and implementation of the Sample PREanalytical Code (SPREC).

The first version of the Standard PREanalytical Code (SPREC) was developed in 2009 by the International Society for Biological and Environmental Repositories (ISBER) Biospecimen Science Working Group to facilitate documentation and communication of the most important preanalytical quality parameters of different types of biospecimens used for research. This same Working Group has now updated the SPREC to version 2.0, presented here, so that it contains more options to allow for recent technological developments. Existing elements have been fine tuned. An interface to the Biospecimen Reporting for Improved Study Quality (BRISQ) has been defined, and informatics solutions for SPREC implementation have been developed. A glossary with SPREC-related definitions has also been added.

Gene expression profiling of peripheral blood mononuclear cells from children with active hemophagocytic lymphohistiocytosis

Familial hemophagocytic lymphohistiocytosis (FHL) is a rare, genetically heterogeneous autosomal recessive immune disorder that results when the critical regulatory pathways that mediate immune defense mechanisms and the natural termination of immune/inflammatory responses are disrupted or overwhelmed. To advance the understanding of FHL, we performed gene expression profiling of peripheral blood mononuclear cells from 11 children with untreated FHL. Total RNA was isolated and gene expression levels were determined using microarray analysis. Comparisons between patients with FHL and normal pediatric controls (n = 30) identified 915 down-regulated and 550 up-regulated genes with more than or equal to 2.5-fold difference in expression (P ≤ .05). The expression of genes associated with natural killer cell functions, innate and adaptive immune responses, proapoptotic proteins, and B- and T-cell differentiation were down-regulated in patients with FHL. Genes associated with the canonical pathways of interleukin-6 (IL-6), IL-10 IL-1, IL-8, TREM1, LXR/RXR activation, and PPAR signaling and genes encoding of antiapoptotic proteins were overexpressed in patients with FHL. This first study of genome-wide expression profiling in children with FHL demonstrates the complexity of gene expression patterns, which underlie the immunobiology of FHL.

Gene expression signatures in polyarticular juvenile idiopathic arthritis demonstrate disease heterogeneity and offer a molecular classification of disease subsets.

OBJECTIVE To determine whether peripheral blood mononuclear cells (PBMCs) from children with recent-onset polyarticular juvenile idiopathic arthritis (JIA) exhibit biologically or clinically informative gene expression signatures. METHODS Peripheral blood samples were obtained from 59 healthy children and 61 children with polyarticular JIA prior to treatment with second-line medications, such as methotrexate or biologic agents. RNA was extracted from isolated mononuclear cells, fluorescence labeled, and hybridized to commercial gene expression microarrays (Affymetrix HG-U133 Plus 2.0). Data were analyzed using analysis of variance at a 5% false discovery rate threshold after robust multichip analysis preprocessing and distance-weighted discrimination normalization. RESULTS Initial analysis revealed 873 probe sets for genes that were differentially expressed between polyarticular JIA patients and healthy controls. Hierarchical clustering of these probe sets distinguished 3 subgroups within the polyarticular JIA group. Prototypical patients within each subgroup were identified and used to define subgroup-specific gene expression signatures. One of these signatures was associated with monocyte markers, another with transforming growth factor beta-inducible genes, and a third with immediate early genes. Correlation of gene expression signatures with clinical and biologic features of JIA subgroups suggested relevance to aspects of disease activity and supported the division of polyarticular JIA into distinct subsets. CONCLUSION Gene expression signatures in PBMCs from patients with recent-onset polyarticular JIA reflect discrete disease processes and offer a molecular classification of disease.

Biologic similarities based on age at onset in oligoarticular and polyarticular subtypes of juvenile idiopathic arthritis.

OBJECTIVE To explore biologic correlates to age at onset in patients with juvenile idiopathic arthritis (JIA) using peripheral blood mononuclear cell (PBMC) gene expression analysis. METHODS PBMCs were isolated from 56 healthy controls and 104 patients with recent-onset JIA (39 with persistent oligoarticular JIA, 45 with rheumatoid factor-negative polyarticular JIA, and 20 with systemic JIA). RNA was amplified and labeled using NuGEN Ovation, and gene expression was assessed with Affymetrix HG-U133 Plus 2.0 GeneChips. RESULTS A total of 832 probe sets revealed gene expression differences (false discovery rate 5%) in PBMCs from children with oligoarticular JIA whose disease began before age 6 years (early-onset disease) compared with those whose disease began at or after age 6 years (late-onset disease). In patients with early-onset disease, there was greater expression of genes related to B cells and less expression of genes related to cells of the myeloid lineage. Support vector machine analyses identified samples from patients with early- or late-onset oligoarticular JIA (with 97% accuracy) or from patients with early- or late-onset polyarticular JIA (with 89% accuracy), but not from patients with systemic JIA or healthy controls. Principal components analysis showed that age at onset was the major classifier of samples from patients with oligoarticular JIA and patients with polyarticular JIA. CONCLUSION PBMC gene expression analysis reveals biologic differences between patients with early-and late-onset JIA, independent of classification based on the number of joints involved. These data suggest that age at onset may be an important parameter to consider in JIA classification. Furthermore, pathologic mechanisms may vary with age at onset, and understanding these processes may lead to improved treatment of JIA.

BrkA protein of Bordetella pertussis inhibits the classical pathway of complement after C1 deposition

ABSTRACT Bordetella pertussis produces a 73-kDa protein,BrkA (Bordetella resistance to killing), which inhibits the bactericidal activity of complement. In this study we characterized the step in the complement cascade where BrkA acts, using three strains: a wild-type strain, a strain containing an insertional disruption of brkA, and a strain containing two copies of the brkA locus. Following incubation with 10% human serum, killing was greatest for the BrkA mutant, followed by that for the wild-type strain, while the strain with two copies ofbrkA was the most resistant. Complement activation was monitored by enzyme-linked immunosorbent assay (ELISA) or Western blotting. ELISAs for SC5b-9, the soluble membrane attack complex, showed that production of SC5b-9 was greatest with thebrkA mutant, less with the wild type, and least with the strain containing two copies of brkA. Deposition of complement proteins on the bacteria was monitored by Western blotting. A decrease in deposition on the bacteria of C4, C3, and C9 corresponded with decreased complement sensitivity. Deposition of C1, however, was not affected by the presence of BrkA. These studies show that BrkA inhibits the classical pathway of complement activation and prevents accumulation of deposited C4.

Genome-wide association analysis of juvenile idiopathic arthritis identifies a new susceptibility locus at chromosomal region 3q13

OBJECTIVE In a genome-wide association study of Caucasian patients with juvenile idiopathic arthritis (JIA), we have previously described findings limited to autoimmunity loci shared by JIA and other diseases. The present study was undertaken to identify novel JIA-predisposing loci using genome-wide approaches. METHODS The discovery cohort consisted of Caucasian JIA cases (n = 814) and local controls (n = 658) genotyped on the Affymetrix Genome-Wide SNP 6.0 Array, along with 2,400 out-of-study controls. In a replication study, we genotyped 10 single-nucleotide polymorphisms (SNPs) in 1,744 cases and 7,010 controls from the US and Europe. RESULTS Analysis within the discovery cohort provided evidence of associations at 3q13 within C3orf1 and near CD80 (rs4688011) (odds ratio [OR] 1.37, P = 1.88 × 10(-6) ) and at 10q21 near JMJD1C (rs647989 [OR 1.59, P = 6.1 × 10(-8) ], rs12411988 [OR 1.57, P = 1.16 × 10(-7) ], and rs10995450 [OR 1.31, P = 6.74 × 10(-5) ]). Meta-analysis provided further evidence of association for these 4 SNPs (P = 3.6 × 10(-7) for rs4688011, P = 4.33 × 10(-5) for rs6479891, P = 2.71 × 10(-5) for rs12411988, and P = 5.39 × 10(-5) for rs10995450). Gene expression data on 68 JIA cases and 23 local controls showed cis expression quantitative trait locus associations for C3orf1 SNP rs4688011 (P = 0.024 or P = 0.034, depending on the probe set) and JMJD1C SNPs rs6479891 and rs12411988 (P = 0.01 or P = 0.04, depending on the probe set and P = 0.008, respectively). Using a variance component liability model, it was estimated that common SNP variation accounts for approximately one-third of JIA susceptibility. CONCLUSION Genetic association results and correlated gene expression findings provide evidence of JIA association at 3q13 and suggest novel genes as plausible candidates in disease pathology.

Immature cell populations and an erythropoiesis gene-expression signature in systemic juvenile idiopathic arthritis: implications for pathogenesis

IntroductionPrevious observations suggest that active systemic juvenile idiopathic arthritis (sJIA) is associated with a prominent erythropoiesis gene-expression signature. The aim of this study was to determine the association of this signature with peripheral blood mononuclear cell (PBMC) subpopulations and its specificity for sJIA as compared with related conditions.MethodsThe 199 patients with JIA (23 sJIA and 176 non-sJIA) and 38 controls were studied. PBMCs were isolated and analyzed for multiple surface antigens with flow cytometry and for gene-expression profiles. The proportions of different PBMC subpopulations were compared among sJIA, non-sJIA patients, and controls and subsequently correlated with the strength of the erythropoiesis signature. Additional gene-expression data from patients with familial hemophagocytic lymphohistiocytosis (FHLH) and from a published sJIA cohort were analyzed to determine whether the erythropoiesis signature was present.ResultsPatients with sJIA had significantly increased proportions of immature cell populations, including CD34+ cells, correlating highly with the strength of the erythropoiesis signature. The erythropoiesis signature strongly overlapped with the gene-expression pattern in purified immature erythroid precursors. The expansion of immature cells was most prominently seen in patients with sJIA and anemia, even in the absence of reticulocytosis. Patients with non-sJIA and anemia did not exhibit the erythropoiesis signature. The erythropoiesis signature was found to be prominent in patients with FHLH and in a published cohort of patients with active sJIA, but not in patients with inactive sJIA.ConclusionsAn erythropoiesis signature in active sJIA is associated with the expansion of CD34+ cells, also is seen in some patients with FHLH and infection, and may be an indicator of ineffective erythropoiesis and hemophagocytosis due to hypercytokinemia.