Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus

Abbas, Alexander R.; Wolslegel, Kristen; Seshasayee, Dhaya; Modrušan, Zora; Clark, Hilary

doi:10.1371/journal.pone.0006098

Cited by 397 publications

(525 citation statements)

References 32 publications

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“…Samples were prepared with the RNeasy Mini Kit (QIAGEN), and microarray studies were carried out following standard protocols as previously described (17). Data were analyzed by the bioconductor packages with R software.…”

Section: Microarray and Data Analysismentioning

confidence: 99%

Androgen Deprivation Causes Epithelial–Mesenchymal Transition in the Prostate: Implications for Androgen-Deprivation Therapy

Sun¹,

Wang²,

Leong³

et al. 2012

Cancer Research

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318

315

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Androgen deprivation is currently a standard-of-care, first-line therapy for prostate cancer in the United States. Although this regimen effectively regresses androgen-dependent disease, relapse often occurs in an androgenindependent manner and is associated with poor prognosis. Such castration-resistant prostate cancer represents a major clinical challenge, and the mechanisms underlying castration resistance are not fully understood. Epithelial-mesenchymal transition (EMT) is a key developmental process and has also been implicated in cancer metastasis and therapeutic resistance in recent years. However, the factors contributing to EMT in human cancers remain unclear. Here, we show that both normal mouse prostate tissue and human LuCaP35 prostate tumor explants display an EMT as well as increased stem cell-like features following androgen deprivation. Importantly, we observed similar changes in mesenchymal features in prostate tumors from patients treated with androgen-deprivation therapy. In addition, we have delineated a feedback loop involving the androgen receptor and the Zeb1 transcription factor that seems to mediate this transition. In summary, we show for the first time that androgen deprivation induces EMT in both normal prostate and prostate cancer, revealing a potentially important consequence of a standard-of-care treatment for prostate cancer. This finding could have significant implications for second-line treatment strategies in this clinical setting. Cancer Res; 72(2); 527-36. Ó2011 AACR.

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Section: Microarray and Data Analysismentioning

confidence: 99%

Androgen Deprivation Causes Epithelial–Mesenchymal Transition in the Prostate: Implications for Androgen-Deprivation Therapy

Sun¹,

Wang²,

Leong³

et al. 2012

Cancer Research

Self Cite

318

315

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“…Raw data were normalized with the variance stabilizing transformation method (Huber et al, 2002) using the 'justvsn' function from the vsn package in R. Relative gene expression values for HP1BP3 and TTC9B were extracted from the normalized data set for subsequent analysis. We quantified the relative proportions of CD8-T, CD4-T, B cell, monocyte, and granulocyte proportions using the CellMix package in R (Gaujoux and Seoighe, 2013) based on reference data in 42 probes available in both GSE45603 and those available in the reference data set generated by Abbas et al (2009).…”

Section: Gene Expression Datamentioning

confidence: 99%

Replication of Epigenetic Postpartum Depression Biomarkers and Variation with Hormone Levels

Osborne

Clive

Kimmel

et al. 2015

Neuropsychopharmacol

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DNA methylation variation at HP1BP3 and TTC9B is modified by estrogen exposure in the rodent hippocampus and was previously shown to be prospectively predictive of postpartum depression (PPD) when modeled in antenatal blood. The objective of this study was to replicate the predictive efficacy of the previously established model in women with and without a previous psychiatric diagnosis and to understand the effects of changing hormone levels on PPD biomarker loci. Using a statistical model trained on DNA methylation data from N = 51 high-risk women, we prospectively predicted PPD status in an independent N = 51 women using first trimester antenatal gene expression levels of HP1BP3 and TTC9B, with an area under the receiver operator characteristic curve (AUC) of 0.81 (95% CI: 0.69-0.92, po5 × 10 − 4 ). Modeling DNA methylation of these genes in N = 240 women without a previous psychiatric diagnosis resulted in a cross-sectional prediction of PPD status with an AUC of 0.81 (95% CI: 0.68-0.93, p = 0.01). TTC9B and HP1BP3 DNA methylation at early antenatal time points showed moderate evidence for association to the change in estradiol and allopregnanolone over the course of pregnancy, suggesting that epigenetic variation at these loci may be important for mediating hormonal sensitivity. In addition both loci showed PPD-specific trajectories with age, possibly mediated by age-associated hormonal changes. The data add to the growing body of evidence suggesting that PPD is mediated by differential gene expression and epigenetic sensitivity to pregnancy hormones and that modeling proxies of this sensitivity enable accurate prediction of PPD.

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“…Our model is based on G = 2;131 genes found in all these datasets and in the coronal ABA. The model proposed to estimate the brain-wide density profiles of cell types can be compared with the deconvolution techniques (27) in the context of microarray data and cellular types in the blood, but the brain-wide nature of the ABA allows us to interpret the results in terms of the region specificity of cell types. …”

mentioning

confidence: 99%

Cell-type–based model explaining coexpression patterns of genes in the brain

Grange

Bohland

Okaty

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

125

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Spatial patterns of gene expression in the vertebrate brain are not independent, as pairs of genes can exhibit complex patterns of coexpression. Two genes may be similarly expressed in one region, but differentially expressed in other regions. These correlations have been studied quantitatively, particularly for the Allen Atlas of the adult mouse brain, but their biological meaning remains obscure. We propose a simple model of the coexpression patterns in terms of spatial distributions of underlying cell types and establish its plausibility using independently measured cell-typespecific transcriptomes. The model allows us to predict the spatial distribution of cell types in the mouse brain.neuroscience | bioinformatics | neuroanatomy B rain-wide and genome-wide maps of gene expression are now available (1, 2), due to the development of high-throughput neuroanatomical methods (3-6). This has enabled analysis of the spatial correlation structure of gene expression (7-12). In the Allen Brain Atlas (ABA) of the adult mouse, the brain is divided up into cubic voxels of size 200 μm. The expression energies of up to 20,000 genes in the adult C57BL/6J mouse are given by automatic processing of in situ hybridized (ISH) brain sections, coregistered to the Allen Reference Atlas (ARA) (13). Coexpression of two genes in a voxel may arise from two sources: ðiÞ both genes are expressed within the same cell type or ðiiÞ the two genes are expressed in two different cell types, both present in the voxel. These two possibilities cannot be disentangled solely on the basis of the ABA. Ideally, gene expression profiles should be experimentally obtained for each cell type in the brain, and indeed such transcriptome profiles are now available (14-21). This cell-based approach to the study of gene expression is complementary to the gene-based approach of the ABA. In ref. 22, the data of ref. 19 were used to extract neuron-specific genes, astrocyte-specific genes, and oligodendrocyte-specific genes, which resulted in three combinations of brain-wide maps from the ABA, whose clustering showed anatomical signatures of major brain subdivisions (see also ref. 23 for estimates of neuron-specific and oligodendrocyte-specific expression patterns, both in the mouse and in the human brain). The present paper goes beyond the broad classification of cell types into three classes and attempts to estimate density profiles of every cell type known by its transcriptome profile. To study the genes collectively we use a voxel-by-gene data matrix E, corresponding to V = 49;742 cubic voxels, and 3,041 genes, as in refs. 24-26. The entry Eðv; gÞ is the expression energy of the gene labeled g in the voxel labeled v [a measure representing the level of mRNA in situ hybridization (1, 10)]. We combine the ABA with cell-type-specific transcriptome profiles, to gain biological understanding of the coexpression patterns of the genes. Our model is based on G = 2;131 genes found in all these datasets and in the coronal ABA. The model proposed to estimate the brain-w...

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Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus

Cited by 397 publications

References 32 publications

Androgen Deprivation Causes Epithelial–Mesenchymal Transition in the Prostate: Implications for Androgen-Deprivation Therapy

Androgen Deprivation Causes Epithelial–Mesenchymal Transition in the Prostate: Implications for Androgen-Deprivation Therapy

Replication of Epigenetic Postpartum Depression Biomarkers and Variation with Hormone Levels

Cell-type–based model explaining coexpression patterns of genes in the brain

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