A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus - PubMed (original) (raw)

. 2008 Jul 18;29(1):150-64.

doi: 10.1016/j.immuni.2008.05.012.

Charles Quinn, Jing Shen, Pinakeen Patel, Casey Glaser, Nicole Baldwin, Dorothee Stichweh, Derek Blankenship, Lei Li, Indira Munagala, Lynda Bennett, Florence Allantaz, Asuncion Mejias, Monica Ardura, Ellen Kaizer, Laurence Monnet, Windy Allman, Henry Randall, Diane Johnson, Aimee Lanier, Marilynn Punaro, Knut M Wittkowski, Perrin White, Joseph Fay, Goran Klintmalm, Octavio Ramilo, A Karolina Palucka, Jacques Banchereau, Virginia Pascual

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A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus

Damien Chaussabel et al. Immunity. 2008.

Abstract

The analysis of patient blood transcriptional profiles offers a means to investigate the immunological mechanisms relevant to human diseases on a genome-wide scale. In addition, such studies provide a basis for the discovery of clinically relevant biomarker signatures. We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. These transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus. Thus, this work describes the implementation and application of a methodology designed to support systems-scale analysis of the human immune system in translational research settings.

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Figures

Figure 1

Figure 1. Analysis of patient blood leukocyte transcriptional profiles

A) Module-level analysis: Gene expression from patients with acute S. pneumoniae infection and respective healthy volunteer PBMCs were compared (p<0.05, Mann-Whitney U test) in modules M1.3, M1.5, M1.8 and M3.2. Pie charts indicate the proportion of genes significantly changed for each module. Graphs represent transcriptional profiles of genes that were significantly changed. Each line shows levels of expression (y-axis) of a single transcript across multiple conditions (samples, x-axis). Expression is normalized to the median expression value of the control group. Results obtained for the 28 PBMC transcriptional modules are displayed on a grid. Coordinates indicate module IDs (e.g. M2.8 is row M2, column 8). Spots indicate proportion of genes significantly changed for each module in patient with S. pneumoniae infection as compared to healthy controls. Red: overexpressed, Blue: underexpressed. B) Disease Fingerprints: Three additional datasets were similarly processed. Profiles were obtained from patients with Systemic Lupus Erythematosus, Liver transplant recipients under pharamacological immunosuppression infection and patients with metastatic melanoma. Functional interpretation is indicated on a grid by a color code. Detailed functional module descriptions are in Table 1.

Figure 2

Figure 2. Module-based biomarker selection strategy

Modules are used as a starting point for the generation of biomarker signatures and progressive reduction of the dimension of microarray data: A) Mapping global transcriptional changes using a modular framework identified 11 modules for which at least 15% of the transcripts are significantly changed between controls and SLE. B) Transcriptional vectors were generated by averaging the normalized expression values of differentially expressed transcripts for each one of the 11 modules selected. C) Composite expression values are plotted as vectors on a “spider graph”. Each line represents a patient profile. Multivariate scores can be generated to recapitulate the changes registered by several transcriptional vectors and monitor changes in an individual patient over time.

Figure 3

Figure 3. SLE transcriptional vectors

A) Composite transcriptional vectors identified from a pediatric SLE patient population sampled prior to the initiation of therapy. Each line on the radar plot represents a patient profile (logarithmic scale). Values are normalized per-gene using the median expression value of healthy. The thicker line represents the average normalized expression profile for this group of patients. Profiles generated for healthy volunteers B) and an independent cohort of pediatric SLE patients under treatment C). Averaged normalized expression profiles for treated (green) and untreated (orange) SLE patients cohorts D). Patient profiles were plotted on the same vectors on the basis of clinical activity (SLEDAI), regardless of treatment. E) Patients with low disease activity (SLEDAI from 0 to 6). F) Patients with high disease activity (SLEDAI from 14 to 28).

Figure 4

Figure 4. SLE transcriptional vectors correlate with disease activity

A) Expression profiles of genes forming vectors V1.7SLE, V2.2SLE, V2.4SLE, V2.8SLE and V3.1SLE correlating with a clinical SLE disease activity index (SLEDAI). Graphs represent expression level of individual transcripts forming each of the vectors in 12 healthy individuals and 21 untreated pediatric SLE patients. Average expression values across transcripts forming each vector are shown on the graph in yellow. Correlations between averaged vector expression values and SLEDAI are shown below (Spearman correlation). B) Multivariate scores were obtained for 22 untreated pediatric SLE patients by linear regression analysis of multivariate transcriptional U-scores (y axis) for vector V1.7SLE, V2.2SLE, V2.4SLE, V2.8SLE, V3.1 SLE, and SLEDAI (x axis). The light shaded area indicates the 95% limits confidence limits for individual predicted values. The dark shaded area indicates the 95% limits confidence limits for the slope and intercept. C) The same analysis applied to 31 pediatric SLE patients receiving different combinations of therapy.

Figure 5

Figure 5. Longitudinal disease monitoring with a multivariate disease activity score

SLEDAI index (blue, right y axis) and transcriptional U-scores (red, left y axis) of pediatric patients (identified by an SLE ID) over time (x axis). Time elapsed between sampling is indicated in months.

Figure 6

Figure 6. Cross-microarray platform comparison

PBMC samples from healthy donors and liver transplant recipient analyzed on two different microarray platforms: Affymetrix U133A&B GeneChips and Illumina Sentrix Human Ref8 BeadChips. The same source of total RNA was used to independently prepare biotin-labeled cRNA targets. Expression is normalized to the median of measurements obtained across all samples. Averaged expression values of the genes in each module are shown for both Affymetrix and Illumina platforms.

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References

    1. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403:503–511. - PubMed
    1. Allantaz F, Chaussabel D, Stichweh D, Bennett L, Allman W, Mejias A, Ardura M, Chung W, Wise C, Palucka K, et al. Blood leukocyte microarrays to diagnose systemic onset juvenile idiopathic arthritis and follow the response to IL-1 blockade. J Exp Med. 2007;204:2131–2144. - PMC - PubMed
    1. Bae SC, Koh HK, Chang DK, Kim MH, Park JK, Kim SY. Reliability and validity of systemic lupus activity measure-revised (SLAM-R) for measuring clinical disease activity in systemic lupus erythematosus. Lupus. 2001;10:405–409. - PubMed
    1. Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA, Espe KJ, Shark KB, Grande WJ, Hughes KM, Kapur V, et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci U S A. 2003;100:2610–2615. - PMC - PubMed
    1. Bammler T, Beyer RP, Bhattacharya S, Boorman GA, Boyles A, Bradford BU, Bumgarner RE, Bushel PR, Chaturvedi K, Choi D, et al. Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods. 2005;2:351–356. - PubMed

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